WorldWideScience

Sample records for big sagebrush communities

  1. Seed bank and big sagebrush plant community composition in a range margin for big sagebrush

    Science.gov (United States)

    Martyn, Trace E.; Bradford, John B.; Schlaepfer, Daniel R.; Burke, Ingrid C.; Laurenroth, William K.

    2016-01-01

    The potential influence of seed bank composition on range shifts of species due to climate change is unclear. Seed banks can provide a means of both species persistence in an area and local range expansion in the case of increasing habitat suitability, as may occur under future climate change. However, a mismatch between the seed bank and the established plant community may represent an obstacle to persistence and expansion. In big sagebrush (Artemisia tridentata) plant communities in Montana, USA, we compared the seed bank to the established plant community. There was less than a 20% similarity in the relative abundance of species between the established plant community and the seed bank. This difference was primarily driven by an overrepresentation of native annual forbs and an underrepresentation of big sagebrush in the seed bank compared to the established plant community. Even though we expect an increase in habitat suitability for big sagebrush under future climate conditions at our sites, the current mismatch between the plant community and the seed bank could impede big sagebrush range expansion into increasingly suitable habitat in the future.

  2. Is fire exclusion in mountain big sagebrush communities prudent? Soil nutrient, plant diversity, and arthropod response to burning

    Science.gov (United States)

    Fire has largely been excluded from many mountain big sagebrush (Artemisia tridentata Nutt. ssp. vaseyana (Rydb.) Beetle) communities. Land and wildlife managers are especially reluctant to reintroduce fire in mountain big sagebrush plant communities, especially those communities without significan...

  3. Investigating Seed Longevity of Big Sagebrush (Artemisia tridentata)

    Science.gov (United States)

    Wijayratne, Upekala C.; Pyke, David A.

    2009-01-01

    The Intermountain West is dominated by big sagebrush communities (Artemisia tridentata subspecies) that provide habitat and forage for wildlife, prevent erosion, and are economically important to recreation and livestock industries. The two most prominent subspecies of big sagebrush in this region are Wyoming big sagebrush (A. t. ssp. wyomingensis) and mountain big sagebrush (A. t. ssp. vaseyana). Increased understanding of seed bank dynamics will assist with sustainable management and persistence of sagebrush communities. For example, mountain big sagebrush may be subjected to shorter fire return intervals and prescribed fire is a tool used often to rejuvenate stands and reduce tree (Juniperus sp. or Pinus sp.) encroachment into these communities. A persistent seed bank for mountain big sagebrush would be advantageous under these circumstances. Laboratory germination trials indicate that seed dormancy in big sagebrush may be habitat-specific, with collections from colder sites being more dormant. Our objective was to investigate seed longevity of both subspecies by evaluating viability of seeds in the field with a seed retrieval experiment and sampling for seeds in situ. We chose six study sites for each subspecies. These sites were dispersed across eastern Oregon, southern Idaho, northwestern Utah, and eastern Nevada. Ninety-six polyester mesh bags, each containing 100 seeds of a subspecies, were placed at each site during November 2006. Seed bags were placed in three locations: (1) at the soil surface above litter, (2) on the soil surface beneath litter, and (3) 3 cm below the soil surface to determine whether dormancy is affected by continued darkness or environmental conditions. Subsets of seeds were examined in April and November in both 2007 and 2008 to determine seed viability dynamics. Seed bank samples were taken at each site, separated into litter and soil fractions, and assessed for number of germinable seeds in a greenhouse. Community composition data

  4. Effects of using winter grazing as a fuel treatment on Wyoming big sagebrush plant communities

    Science.gov (United States)

    More frequent wildfires and incidences of mega-fires have increased the pressure for fuel treatments in sagebrush (Artemisia) communities. Winter grazing has been one of many fuel treatments proposed for Wyoming big sagebrush (A. tridentata Nutt. subsp. wyomingensis Beetle and A. Young) communitie...

  5. Attempting to restore herbaceous understories in Wyoming big sagebrush communities with mowing and seeding

    Science.gov (United States)

    Shrub steppe communities with depleted perennial herbaceous understories need to be restored to increase resilience, provide quality wildlife habitat, and improve ecosystem function. Mowing has been applied to Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle &Young) steppe...

  6. Woody fuels reduction in Wyoming big sagebrush communities

    Science.gov (United States)

    Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young) ecosystems historically have been subject to disturbances that reduce or remove shrubs primarily by fire, although insect outbreaks and disease have also been important. Depending on site productivity, fire return in...

  7. Challenges of establishing big sgebrush (Artemisia tridentata) in rangeland restoration: effects of herbicide, mowing, whole-community seeding, and sagebrush seed sources

    Science.gov (United States)

    Brabec, Martha M.; Germino, Matthew J.; Shinneman, Douglas J.; Pilliod, David S.; McIlroy, Susan K.; Arkle, Robert S.

    2015-01-01

    The loss of big sagebrush (Artemisia tridentata Nutt.) on sites disturbed by fire has motivated restoration seeding and planting efforts. However, the resulting sagebrush establishment is often lower than desired, especially in dry areas. Sagebrush establishment may be increased by addressing factors such as seed source and condition or management of the plant community. We assessed initial establishment of seeded sagebrush and four populations of small outplants (from different geographies, climates, and cytotypes) and small sagebrush outplants in an early seral community where mowing, herbicide, and seeding of other native plants had been experimentally applied. No emergence of seeded sagebrush was detected. Mowing the site before planting seedlings led to greater initial survival probabilities for sagebrush outplants, except where seeding also occurred, and these effects were related to corresponding changes in bare soil exposure. Initial survival probabilities were > 30% greater for the local population of big sagebrush relative to populations imported to the site from typical seed transfer distances of ~320–800 km. Overcoming the high first-year mortality of outplanted or seeded sagebrush is one of the most challenging aspects of postfire restoration and rehabilitation, and further evaluation of the impacts of herb treatments and sagebrush seed sources across different site types and years is needed.

  8. Natural regeneration processes in big sagebrush (Artemisia tridentata)

    Science.gov (United States)

    Schlaepfer, Daniel R.; Lauenroth, William K.; Bradford, John B.

    2014-01-01

    Big sagebrush, Artemisia tridentata Nuttall (Asteraceae), is the dominant plant species of large portions of semiarid western North America. However, much of historical big sagebrush vegetation has been removed or modified. Thus, regeneration is recognized as an important component for land management. Limited knowledge about key regeneration processes, however, represents an obstacle to identifying successful management practices and to gaining greater insight into the consequences of increasing disturbance frequency and global change. Therefore, our objective is to synthesize knowledge about natural big sagebrush regeneration. We identified and characterized the controls of big sagebrush seed production, germination, and establishment. The largest knowledge gaps and associated research needs include quiescence and dormancy of embryos and seedlings; variation in seed production and germination percentages; wet-thermal time model of germination; responses to frost events (including freezing/thawing of soils), CO2 concentration, and nutrients in combination with water availability; suitability of microsite vs. site conditions; competitive ability as well as seedling growth responses; and differences among subspecies and ecoregions. Potential impacts of climate change on big sagebrush regeneration could include that temperature increases may not have a large direct influence on regeneration due to the broad temperature optimum for regeneration, whereas indirect effects could include selection for populations with less stringent seed dormancy. Drier conditions will have direct negative effects on germination and seedling survival and could also lead to lighter seeds, which lowers germination success further. The short seed dispersal distance of big sagebrush may limit its tracking of suitable climate; whereas, the low competitive ability of big sagebrush seedlings may limit successful competition with species that track climate. An improved understanding of the

  9. Simulated big sagebrush regeneration supports predicted changes at the trailing and leading edges of distribution shifts

    Science.gov (United States)

    Schlaepfer, Daniel R.; Taylor, Kyle A.; Pennington, Victoria E.; Nelson, Kellen N.; Martin, Trace E.; Rottler, Caitlin M.; Lauenroth, William K.; Bradford, John B.

    2015-01-01

    Many semi-arid plant communities in western North America are dominated by big sagebrush. These ecosystems are being reduced in extent and quality due to economic development, invasive species, and climate change. These pervasive modifications have generated concern about the long-term viability of sagebrush habitat and sagebrush-obligate wildlife species (notably greater sage-grouse), highlighting the need for better understanding of the future big sagebrush distribution, particularly at the species' range margins. These leading and trailing edges of potential climate-driven sagebrush distribution shifts are likely to be areas most sensitive to climate change. We used a process-based regeneration model for big sagebrush, which simulates potential germination and seedling survival in response to climatic and edaphic conditions and tested expectations about current and future regeneration responses at trailing and leading edges that were previously identified using traditional species distribution models. Our results confirmed expectations of increased probability of regeneration at the leading edge and decreased probability of regeneration at the trailing edge below current levels. Our simulations indicated that soil water dynamics at the leading edge became more similar to the typical seasonal ecohydrological conditions observed within the current range of big sagebrush ecosystems. At the trailing edge, an increased winter and spring dryness represented a departure from conditions typically supportive of big sagebrush. Our results highlighted that minimum and maximum daily temperatures as well as soil water recharge and summer dry periods are important constraints for big sagebrush regeneration. Overall, our results confirmed previous predictions, i.e., we see consistent changes in areas identified as trailing and leading edges; however, we also identified potential local refugia within the trailing edge, mostly at sites at higher elevation. Decreasing

  10. Modeling regeneration responses of big sagebrush (Artemisia tridentata) to abiotic conditions

    Science.gov (United States)

    Schlaepfer, Daniel R.; Lauenroth, William K.; Bradford, John B.

    2014-01-01

    Ecosystems dominated by big sagebrush, Artemisia tridentata Nuttall (Asteraceae), which are the most widespread ecosystems in semiarid western North America, have been affected by land use practices and invasive species. Loss of big sagebrush and the decline of associated species, such as greater sage-grouse, are a concern to land managers and conservationists. However, big sagebrush regeneration remains difficult to achieve by restoration and reclamation efforts and there is no regeneration simulation model available. We present here the first process-based, daily time-step, simulation model to predict yearly big sagebrush regeneration including relevant germination and seedling responses to abiotic factors. We estimated values, uncertainty, and importance of 27 model parameters using a total of 1435 site-years of observation. Our model explained 74% of variability of number of years with successful regeneration at 46 sites. It also achieved 60% overall accuracy predicting yearly regeneration success/failure. Our results identify specific future research needed to improve our understanding of big sagebrush regeneration, including data at the subspecies level and improved parameter estimates for start of seed dispersal, modified wet thermal-time model of germination, and soil water potential influences. We found that relationships between big sagebrush regeneration and climate conditions were site specific, varying across the distribution of big sagebrush. This indicates that statistical models based on climate are unsuitable for understanding range-wide regeneration patterns or for assessing the potential consequences of changing climate on sagebrush regeneration and underscores the value of this process-based model. We used our model to predict potential regeneration across the range of sagebrush ecosystems in the western United States, which confirmed that seedling survival is a limiting factor, whereas germination is not. Our results also suggested that modeled

  11. Effect of fungicides on Wyoming big sagebrush seed germination

    Science.gov (United States)

    Robert D. Cox; Lance H. Kosberg; Nancy L. Shaw; Stuart P. Hardegree

    2011-01-01

    Germination tests of Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young [Asteraceae]) seeds often exhibit fungal contamination, but the use of fungicides should be avoided because fungicides may artificially inhibit germination. We tested the effect of seed-applied fungicides on germination of Wyoming big sagebrush at 2 different...

  12. Banking Wyoming big sagebrush seeds

    Science.gov (United States)

    Robert P. Karrfalt; Nancy Shaw

    2013-01-01

    Five commercially produced seed lots of Wyoming big sagebrush (Artemisia tridentata Nutt. var. wyomingensis (Beetle & Young) S.L. Welsh [Asteraceae]) were stored under various conditions for 5 y. Purity, moisture content as measured by equilibrium relative humidity, and storage temperature were all important factors to successful seed storage. Our results indicate...

  13. Influence of container size on Wyoming big sagebrush seedling morphology and cold hardiness

    Science.gov (United States)

    Kayla R. Herriman; Anthony S. Davis; R. Kasten Dumroese

    2009-01-01

    Wyoming big sagebrush (Artemisia tridentata) is a key component of sagebrush steppe ecosystems and is a dominant shrub throughout the western United States. Our objective was to identify the effect of container size on plant morphology of Wyoming big sagebrush. We used three different stocktypes (45/340 ml [20 in3], 60/250 ml [15 in3], 112/105 ml [6....

  14. Spatial variability in cost and success of revegetation in a Wyoming big sagebrush community.

    Science.gov (United States)

    Boyd, Chad S; Davies, Kirk W

    2012-09-01

    The ecological integrity of the Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle and A. Young) alliance is being severely interrupted by post-fire invasion of non-native annual grasses. To curtail this invasion, successful post-fire revegetation of perennial grasses is required. Environmental factors impacting post-fire restoration success vary across space within the Wyoming big sagebrush alliance; however, most restorative management practices are applied uniformly. Our objectives were to define probability of revegetation success over space using relevant soil-related environmental factors, use this information to model cost of successful revegetation and compare the importance of vegetation competition and soil factors to revegetation success. We studied a burned Wyoming big sagebrush landscape in southeast Oregon that was reseeded with perennial grasses. We collected soil and vegetation data at plots spaced at 30 m intervals along a 1.5 km transect in the first two years post-burn. Plots were classified as successful (>5 seedlings/m(2)) or unsuccessful based on density of seeded species. Using logistic regression we found that abundance of competing vegetation correctly predicted revegetation success on 51 % of plots, and soil-related variables correctly predicted revegetation performance on 82.4 % of plots. Revegetation estimates varied from $167.06 to $43,033.94/ha across the 1.5 km transect based on probability of success, but were more homogenous at larger scales. Our experimental protocol provides managers with a technique to identify important environmental drivers of restoration success and this process will be of value for spatially allocating logistical and capital expenditures in a variable restoration environment.

  15. Conversion of sagebrush shrublands to exotic annual grasslands negatively impacts small mammal communities

    Science.gov (United States)

    Ostoja, S.M.; Schupp, E.W.

    2009-01-01

    Aim The exotic annual cheatgrass (Bromus tectorum) is fast replacing sagebrush (Artemisia tridentata) communities throughout the Great Basin Desert and nearby regions in the Western United States, impacting native plant communities and altering fire regimes, which contributes to the long-term persistence of this weedy species. The effect of this conversion on native faunal communities remains largely unexamined. We assess the impact of conversion from native perennial to exotic annual plant communities on desert rodent communities. Location Wyoming big sagebrush shrublands and nearby sites previously converted to cheatgrass-dominated annual grasslands in the Great Basin Desert, Utah, USA. Methods At two sites in Tooele County, Utah, USA, we investigated with Sherman live trapping whether intact sagebrush vegetation and nearby converted Bromus tectorum-dominated vegetation differed in rodent abundance, diversity and community composition. Results Rodent abundance and species richness were considerably greater in sagebrush plots than in cheatgrass-dominated plots. Nine species were captured in sagebrush plots; five of these were also trapped in cheatgrass plots, all at lower abundances than in the sagebrush. In contrast, cheatgrass-dominated plots had no species that were not found in sagebrush. In addition, the site that had been converted to cheatgrass longer had lower abundances of rodents than the site more recently converted to cheatgrass-dominated plots. Despite large differences in abundances and species richness, Simpson's D diversity and Shannon-Wiener diversity and Brillouin evenness indices did not differ between sagebrush and cheatgrass-dominated plots. Main conclusions This survey of rodent communities in native sagebrush and in converted cheatgrass-dominated vegetation suggests that the abundances and community composition of rodents may be shifting, potentially at the larger spatial scale of the entire Great Basin, where cheatgrass continues to invade

  16. Restoration of mountain big sagebrush steppe following prescribed burning to control western juniper.

    Science.gov (United States)

    Davies, K W; Bates, J D; Madsen, M D; Nafus, A M

    2014-05-01

    Western juniper (Juniperus occidentalis ssp. occidentalis Hook) encroachment into mountain big sagebrush (Artemisia tridentata spp. vaseyana (Rydb.) Beetle) steppe has reduced livestock forage production, increased erosion risk, and degraded sagebrush-associated wildlife habitat. Western juniper has been successfully controlled with partial cutting followed by prescribed burning the next fall, but the herbaceous understory and sagebrush may be slow to recover. We evaluated the effectiveness of seeding perennial herbaceous vegetation and sagebrush at five sites where juniper was controlled by partially cutting and prescribed burning. Treatments tested at each site included an unseeded control, herbaceous seed mix (aerially seeded), and the herbaceous seed mix plus sagebrush seed. In the third year post-treatment, perennial grass cover and density were twice as high in plots receiving the herbaceous seed mix compared to the control plots. Sagebrush cover and density in the sagebrush seeded plots were between 74- and 290-fold and 62- and 155-fold greater than the other treatments. By the third year after treatment, sagebrush cover was as high as 12 % in the sagebrush seeded plots and between 0 % and 0.4 % where it was not seeded. These results indicate that aerial seeding perennial herbaceous vegetation can accelerate the recovery of perennial grasses which likely stabilize the site. Our results also suggest that seeding mountain big sagebrush after prescribed burning encroaching juniper can rapidly recover sagebrush cover and density. In areas where sagebrush habitat is limited, seeding sagebrush after juniper control may increase sagebrush habitat and decrease the risks to sagebrush-associated species.

  17. Wyoming big sagebrush: Efforts towards development of target plants for restoration

    Science.gov (United States)

    Kayla R. Herriman

    2009-01-01

    Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis) is a dominant shrub throughout much of the interior western United States. It is a key component of sagebrush steppe ecosystems, which have been degraded due to European settlement, improper land use, and changing fire regimes resulting from the invasion of exotic...

  18. Restoring big sagebrush after controlling encroaching western juniper with fire: aspect and subspecies effects

    Science.gov (United States)

    The need for restoration of shrubs is increasingly recognized around the world. In the western USA, restoration of mountain big sagebrush (Artemisia tridentata Nutt. ssp. vaseyana (Rydb.) Beetle) after controlling encroaching conifers is a priority to improve sagebrush-associated wildlife habitat. ...

  19. Investigation of Great Basin big sagebrush and black greasewood as biogeochemical indicators of uranium mineralization. Final report. National Uranium Resource Evaluation

    International Nuclear Information System (INIS)

    Diebold, F.E.; McGrath, S.

    1982-11-01

    The effects of varying phosphate concentrations in natural aqueous systems upon the uptake of uranium by big sagebrush (Artemesia tridentata subsp. tridentata) and black greasewood (Sarcobatus vermiculatus (Hook) Torr.) were investigated. Two separate growth experiments with five drip-flow hyroponic units were used and plant seedlings were grown for 60 days in solutions of varying phosphate and uranium concentrations. Successful growth experiments were obtained only for big sagebrush; black greasewood did not sustain sufficient growth. The phosphate concentration of the water did affect the uptake of uranium by the big sagebrush, and this effect is most pronounced in the region of higher concentrations of uranium in the water. The ratio of the concentration of uranium in the plant to that in the water was observed to decrease with increasing uranium concentration in solution. This is indicative of an absorption barrier in the plants. The field data shows that big sagebrush responds to uranium concentrations in the soil water and not the groundwater. The manifestation of these results is that the use of big sagebrush as a biogeochemical indicator of uranium is not recommended. Since the concentration of phosphate must also be knwon in the water supplying the uranium to the plant, one should analyze this natural aqueous phase as a hydrochemical indicator rather than the big sagebrush

  20. Container configuration influences western larch and big sagebrush seedling development

    Science.gov (United States)

    Matthew Mehdi. Aghai

    2012-01-01

    Big sagebrush (Artemisia tridentata Nutt.), a woody shrub, and western larch (Larix occidentalis Nutt.), a deciduous conifer, are among many western North American species that have suffered a decline in presence and natural regeneration across their native ranges. These species are economically, ecologically, and intrinsically valuable, therefore many current...

  1. Big sagebrush (Artemisia tridentata) in a shifting climate context: Assessment of seedling responses to climate

    Science.gov (United States)

    Martha A. Brabec

    2014-01-01

    The loss of big sagebrush (Artemisia tridentata) throughout the Great Basin Desert has motivated efforts to restore it because of fire and other disturbance effects on sagebrush-dependent wildlife and ecosystem function. Initial establishment is the first challenge to restoration, and appropriateness of seeds, climate, and weather variability are factors that may...

  2. Sagebrush, greater sage-grouse, and the occurrence and importance of forbs

    Science.gov (United States)

    Pennington, Victoria E.; Schlaepfer, Daniel R.; Beck, Jeffrey L.; Bradford, John B.; Palmquist, Kyle A.; Lauenroth, William K.

    2016-01-01

    Big sagebrush (Artemisia tridentata Nutt.) ecosystems provide habitat for sagebrush-obligate wildlife species such as the Greater Sage-Grouse (Centrocercus urophasianus). The understory of big sagebrush plant communities is composed of grasses and forbs that are important sources of cover and food for wildlife. The grass component is well described in the literature, but the composition, abundance, and habitat role of forbs in these communities is largely unknown. Our objective was to synthesize information about forbs and their importance to Greater Sage-Grouse diets and habitats, how rangeland management practices affect forbs, and how forbs respond to changes in temperature and precipitation. We also sought to identify research gaps and needs concerning forbs in big sagebrush plant communities. We searched for relevant literature including journal articles and state and federal agency reports. Our results indicated that in the spring and summer, Greater Sage-Grouse diets consist of forbs (particularly species in the Asteraceae family), arthropods, and lesser amounts of sagebrush. The diets transition to sagebrush in fall and winter. Forbs provide cover for Greater Sage-Grouse individuals at their lekking, nesting, and brood-rearing sites, and the species has a positive relationship with arthropod presence. The effect of grazing on native forbs may be compounded by invasion of nonnative species and differs depending on grazing intensity. The effect of fire on forbs varies greatly and may depend on time elapsed since burning. In addition, chemical and mechanical treatments affect annual and perennial forbs differently. Temperature and precipitation influence forb phenology, biomass, and abundance differently among species. Our review identified several uncertainties and research needs about forbs in big sagebrush ecosystems. First, in many cases the literature about forbs is reported only at the genus or functional type level. Second, information about forb

  3. Narrow hybrid zone between two subspecies of big sagebrush (Artemisia tridentata: Asteraceae): XI. Plant-insect interactions in reciprocal transplant gardens

    Science.gov (United States)

    John H. Graham; E. Durant McArthur; D. Carl Freeman

    2001-01-01

    Basin big sagebrush (Artemisia tridentata ssp. tridentata) and mountain big sagebrush (A. t. ssp. vaseyana) hybridize in a narrow zone near Salt Creek, Utah. Reciprocal transplant experiments in this hybrid zone demonstrate that hybrids are more fit than either parental subspecies, but only in the hybrid zone. Do hybrids experience greater, or lesser, use by...

  4. Mountain big sagebrush age distribution and relationships on the northern Yellowstone Winter Range

    Science.gov (United States)

    Carl L. Wambolt; Trista L. Hoffman

    2001-01-01

    This study was conducted within the Gardiner Basin, an especially critical wintering area for native ungulates utilizing the Northern Yellowstone Winter Range. Mountain big sagebrush plants on 33 sites were classified as large (≥22 cm canopy cover), small (

  5. Big sagebrush in pinyon-juniper woodlands: Using forest inventory and analysis data as a management tool for quantifying and monitoring mule deer habitat

    Science.gov (United States)

    Chris Witt; Paul L. Patterson

    2011-01-01

    We used Interior West Forest Inventory and Analysis (IW-FIA) data to identify conditions where pinyon-juniper woodlands provide security cover, thermal cover, and suitable amounts of big sagebrush (Artemisia tridentata spp.) forage to mule deer in Utah. Roughly one quarter of Utah's pinyon-juniper woodlands had a big sagebrush component in their understory....

  6. Seasonal soil CO2 flux under big sagebrush (Artemisia tridentata Nutt.)

    Science.gov (United States)

    Michael C. Amacher; Cheryl L. Mackowiak

    2011-01-01

    Soil respiration is a major contributor to atmospheric CO2, but accurate landscape-scale estimates of soil CO2 flux for many ecosystems including shrublands have yet to be established. We began a project to measure, with high spatial and temporal resolution, soil CO2 flux in a stand (11 x 25 m area) of big sagebrush (Artemisia tridentata Nutt.) at the Logan, Utah,...

  7. Short-term regeneration dynamics of Wyoming big sagebrush at two sites in northern Utah

    Science.gov (United States)

    The herbicide tebuthiuron has been used historically to control cover of Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis - complete taxonomic designation), a widespread shrub across the western United States, with the intent of increasing herbaceous plant cover. Although the tebuthiur...

  8. Attempting to restore mountain big sagebrush (Artemisia tridentata ssp. vaseyana) four years after fire

    Science.gov (United States)

    Restoration of shrubs is increasingly needed throughout the world because of altered fire regimes, anthropogenic disturbance, and over-utilization. The native shrub mountain big sagebrush (Artemisia tridentata Nutt. ssp. vaseyana (Rydb.) Beetle) is a restoration priority in western North America be...

  9. The response of big sagebrush (Artemisia tridentata) to interannual climate variation changes across its range.

    Science.gov (United States)

    Kleinhesselink, Andrew R; Adler, Peter B

    2018-05-01

    Understanding how annual climate variation affects population growth rates across a species' range may help us anticipate the effects of climate change on species distribution and abundance. We predict that populations in warmer or wetter parts of a species' range should respond negatively to periods of above average temperature or precipitation, respectively, whereas populations in colder or drier areas should respond positively to periods of above average temperature or precipitation. To test this, we estimated the population sensitivity of a common shrub species, big sagebrush (Artemisia tridentata), to annual climate variation across its range. Our analysis includes 8,175 observations of year-to-year change in sagebrush cover or production from 131 monitoring sites in western North America. We coupled these observations with seasonal weather data for each site and analyzed the effects of spring through fall temperatures and fall through spring accumulated precipitation on annual changes in sagebrush abundance. Sensitivity to annual temperature variation supported our hypothesis: years with above average temperatures were beneficial to sagebrush in colder locations and detrimental to sagebrush in hotter locations. In contrast, sensitivity to precipitation did not change significantly across the distribution of sagebrush. This pattern of responses suggests that regional abundance of this species may be more limited by temperature than by precipitation. We also found important differences in how the ecologically distinct subspecies of sagebrush responded to the effects of precipitation and temperature. Our model predicts that a short-term temperature increase could produce an increase in sagebrush cover at the cold edge of its range and a decrease in cover at the warm edge of its range. This prediction is qualitatively consistent with predictions from species distribution models for sagebrush based on spatial occurrence data, but it provides new mechanistic

  10. Outplanting Wyoming big sagebrush following wldfire: stock performance and economics

    Science.gov (United States)

    Dettweiler-Robinson, Eva; Bakker, Jonathan D.; Evans, James R.; Newsome, Heidi; Davies, G. Matt; Wirth, Troy A.; Pyke, David A.; Easterly, Richard T.; Salstrom, Debra; Dunwiddle, Peter W.

    2013-01-01

    Finding ecologically and economically effective ways to establish matrix species is often critical for restoration success. Wyoming big sagebrush (Artemisia tridentata subsp. wyomingensis) historically dominated large areas of western North America, but has been extirpated from many areas by large wildfires; its re-establishment in these areas often requires active management. We evaluated the performance (survival, health) and economic costs of container and bare-root stock based on operational plantings of more than 1.5 million seedlings across 2 200 ha, and compared our plantings with 30 other plantings in which sagebrush survival was tracked for up to 5 yr. Plantings occurred between 2001 and 2007, and included 12 combinations of stock type, planting amendment, and planting year.We monitored 10 500 plants for up to 8 yr after planting. Survival to Year 3 averaged 21% and was higher for container stock (30%) than bare-root stock (17%). Survival did not differ among container stock plantings, whereas survival of bare-root stock was sometimes enhanced by a hydrogel dip before planting, but not by

  11. Historical fire regimes, reconstructed from land-survey data, led to complexity and fluctuation in sagebrush landscapes.

    Science.gov (United States)

    Bukowski, Beth E; Baker, William L

    2013-04-01

    Sagebrush landscapes provide habitat for Sage-Grouse and other sagebrush obligates, yet historical fire regimes and the structure of historical sagebrush landscapes are poorly known, hampering ecological restoration and management. To remedy this, General Land Office Survey (GLO) survey notes were used to reconstruct over two million hectares of historical vegetation for four sagebrush-dominated (Artemisia spp.) study areas in the western United States. Reconstructed vegetation was analyzed for fire indicators used to identify historical fires and reconstruct historical fire regimes. Historical fire-size distributions were inverse-J shaped, and one fire > 100 000 ha was identified. Historical fire rotations were estimated at 171-342 years for Wyoming big sagebrush (A. tridentata ssp. wyomingensis) and 137-217 years for mountain big sagebrush (A. tridentata ssp. vaseyana). Historical fire and patch sizes were significantly larger in Wyoming big sagebrush than mountain big sagebrush, and historical fire rotations were significantly longer in Wyoming big sagebrush than mountain big sagebrush. Historical fire rotations in Wyoming were longer than those in other study areas. Fine-scale mosaics of burned and unburned area and larger unburned inclusions within fire perimeters were less common than in modern fires. Historical sagebrush landscapes were dominated by large, contiguous areas of sagebrush, though large grass-dominated areas and finer-scale mosaics of grass and sagebrush were also present in smaller amounts. Variation in sagebrush density was a common source of patchiness, and areas classified as "dense" made up 24.5% of total sagebrush area, compared to 16.3% for "scattered" sagebrush. Results suggest significant differences in historical and modern fire regimes. Modern fire rotations in Wyoming big sagebrush are shorter than historical fire rotations. Results also suggest that historical sagebrush landscapes would have fluctuated, because of infrequent

  12. Selecting sagebrush seed sources for restoration in a variable climate: ecophysiological variation among genotypes

    Science.gov (United States)

    Germino, Matthew J.

    2012-01-01

    Big sagebrush (Artemisia tridentata) communities dominate a large fraction of the United States and provide critical habitat for a number of wildlife species of concern. Loss of big sagebrush due to fire followed by poor restoration success continues to reduce ecological potential of this ecosystem type, particularly in the Great Basin. Choice of appropriate seed sources for restoration efforts is currently unguided due to knowledge gaps on genetic variation and local adaptation as they relate to a changing landscape. We are assessing ecophysiological responses of big sagebrush to climate variation, comparing plants that germinated from ~20 geographically distinct populations of each of the three subspecies of big sagebrush. Seedlings were previously planted into common gardens by US Forest Service collaborators Drs. B. Richardson and N. Shaw, (USFS Rocky Mountain Research Station, Provo, Utah and Boise, Idaho) as part of the Great Basin Native Plant Selection and Increase Project. Seed sources spanned all states in the conterminous Western United States. Germination, establishment, growth and ecophysiological responses are being linked to genomics and foliar palatability. New information is being produced to aid choice of appropriate seed sources by Bureau of Land Management and USFS field offices when they are planning seed acquisitions for emergency post-fire rehabilitation projects while considering climate variability and wildlife needs.

  13. Do container volume, site preparation, and field fertilization affect restoration potential of Wyoming big sagebrush?

    Science.gov (United States)

    Kayla R. Herriman; Anthony S. Davis; Kent G. Apostol; Olga. A. Kildisheva; Amy L. Ross-Davis; Kas Dumroese

    2016-01-01

    Land management practices, invasive species expansion, and changes in the fire regime greatly impact the distribution of native plants in natural areas. Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis), a keystone species in the Great Basin, has seen a 50% reduction in its distribution. For many dryland species, reestablishment efforts have...

  14. Big and black sagebrush landscapes [Chapter 5

    Science.gov (United States)

    Stanley G. Kitchen; E. Durant McArthur

    2007-01-01

    Perhaps no plant evokes a common vision of the semi-arid landscapes of western North America as do the sagebrushes. A collective term, sagebrush is applied to shrubby members of the mostly herbaceous genus, Artemisia L. More precisely, the moniker is usually restricted to members of subgenus Tridentatae, a collection of some 20 woody taxa endemic to North America (...

  15. Variation in sagebrush communities historically seeded with crested wheatgrass in the eastern great basin

    Science.gov (United States)

    Although crested wheatgrass (CWG; Agropyron cristatum [L.] Gaertn.) has been one of the most commonly seeded exotic species in the western United States, long-term successional trajectories of seeded sites are poorly characterized, especially for big sagebrush (Artemisia tridentana Nutt.) ecosystems...

  16. Reclamation after oil and gas development does not speed up succession or plant community recovery in big sagebrush ecosystems in Wyoming

    Science.gov (United States)

    Rottler, Caitlin M.; Burke, Ingrid C.; Palmquist, Kyle A.; Bradford, John B.; Lauenroth, William K.

    2018-01-01

    Article for intended outlet: Restoration Ecology. Abstract: Reclamation is an application of treatment(s) following a disturbance to promote succession and accelerate the return of target conditions. Previous studies have framed reclamation in the context of succession by studying its effectiveness in re-establishing late-successional plant communities. Re-establishment of these plant communities is especially important and potentially challenging in regions such as drylands and shrub steppe ecosystems where succession proceeds slowly. Dryland shrub steppe ecosystems are frequently associated with areas rich in fossil-fuel energy sources, and as such the need for effective reclamation after disturbance from fossil-fuel-related energy development is great. Past research in this field has focused primarily on coal mines; few researchers have studied reclamation after oil and gas development. To address this research gap and to better understand the effect of reclamation on rates of succession in dryland shrub steppe ecosystems, we sampled oil and gas wellpads and adjacent undisturbed big sagebrush plant communities in Wyoming, USA and quantified the extent of recovery for major functional groups on reclaimed and unreclaimed (recovered via natural succession) wellpads relative to the undisturbed plant community. Reclamation increased the rate of recovery for all forb and grass species as a group and for perennial grasses, but did not affect other functional groups. Rather, analyses comparing recovery to environmental variables and time since wellpad abandonment showed that recovery of other groups were affected primarily by soil texture and time since wellpad abandonment. This is consistent with studies in other ecosystems where reclamation has been implemented, suggesting that reclamation may not help re-establish late-successional plant communities more quickly than they would re-establish naturally.

  17. Sagebrush ecosystems: current status and trends.

    Science.gov (United States)

    Beever, E.A.; Connelly, J.W.; Knick, S.T.; Schroeder, M.A.; Stiver, S. J.

    2004-01-01

    The sagebrush (Artemisia spp.) biome has changed since settlement by Europeans. The current distribution, composition and dynamics, and disturbance regimes of sagebrush ecosystems have been altered by interactions among disturbance, land use, and invasion of exotic plants. In this chapter, we present the dominant factors that have influenced habitats across the sagebrush biome. Using a large-scale analysis, we identified regional changes and patterns in “natural disturbance”, invasive exotic species, and influences of land use in sagebrush systems. Number of fires and total area burned has increased since 1980 across much of the sagebrush biome. Juniper (Juniperus spp.) and pinyon (Pinus spp.) woodlands have expanded into sagebrush habitats at higher elevations. Cheatgrass (Bromus tectorum), an exotic annual grass, has invaded much of lower elevation, more xeric sagebrush landscapes across the western portion of the biome. Consequently, synergistic feedbacks between habitats and disturbance (natural and human-caused) have altered disturbance regimes, plant community dynamics and contributed to loss of sagebrush habitats and change in plant communities. Habitat conversion to agriculture has occurred in the highly productive regions of the sagebrush biome and influenced up to 56% of the Conservation Assessment area. Similarly, urban areas, and road, railroad, and powerline networks fragment habitats, facilitate predator movements, and provide corridors for spread of exotic species across the entire sagebrush biome. Livestock grazing has altered sagebrush habitats; the effects of overgrazing combined with drought on plant communities in the late 1880s and early 1900s still influences current habitats. Management of livestock grazing has influenced sagebrush ecosystems by habitat treatments to increase forage and reduce sagebrush and other plant species unpalatable to livestock. Fences, roads, and water developments to manage livestock movements have further

  18. Uptake and kinetics of 226Ra, 210Pb and 210Po in big sagebrush

    International Nuclear Information System (INIS)

    Simon, S.L.

    1985-01-01

    Root uptake of 226 Ra, 210 Pb and 210 Po by mature sagebrush was studied using a soil injection method for spiking the soil with minimal root disturbance. The main objective was to measure vegetation concentrations and determine concentration ratios (CR's) due to root uptake as a function of time in mature big sagebrush. Concentration ratios obtained in mature vegetation and in steady-state situations may be valuable in assessing the impact of uranium mining and milling. The vegetation was sampled approximately every 3 months for a 2 year period. Significant levels of activity were detected in the vegetation beginning at the first sampling (81 days after soil injection for 226 Ra, 28 days for 210 Pb and 210 Po). There was an exponential decrease in concentration to an apparent steady-state value. Mean values (geometric) of the data pooled over the second year period indicated that the steady-state CR's for 226 Ra, 210 Pb and 210 Po, as determined in mature sagebrush, were 0.04, 0.009, and 0.08, respectively. A three compartment mathematical model was formulated to help understand mechanisms of plant uptake and to predict, if possible, the concentration of 226 Ra, 210 Pb and 210 Po in vegetation as a function of time after soil spiking. A numerical solution was determined by 'calibrating' the general model solution with constants determined from regressions of concentrations in vegetation, soil leaching and leaf leaching data. Validation of the model is currently not possible because of an absence of similar time-dependent uptake studies. 168 refs., 19 figs., 18 tabs

  19. The economics of fuel management: Wildfire, invasive plants, and the dynamics of sagebrush rangelands in the western United States

    Science.gov (United States)

    Michael H. Taylor; Kimberly Rollins; Mimako Kobayashi; Robin J. Tausch

    2013-01-01

    In this article we develop a simulation model to evaluate the economic efficiency of fuel treatments and apply it to two sagebrush ecosystems in the Great Basin of the western United States: the Wyoming Sagebrush Steppe and Mountain Big Sagebrush ecosystems. These ecosystems face the two most prominent concerns in sagebrush ecosystems relative to wildfire: annual grass...

  20. Evaluating a seed technology for sagebrush restoration across an elevation gradient: support for bet hedging

    Science.gov (United States)

    Big sagebrush (Artemisia tridentata Nutt.) restoration is needed across vast areas, especially after large wildfires, to restore important ecosystem services. Sagebrush restoration success is inconsistent with a high rate of seeding failures, particularly at lower elevations. Seed enhancement tech...

  1. Legacy effects of no-analogue disturbances alter plant community diversity and composition in semi-arid sagebrush steppe

    Science.gov (United States)

    Ripplinger, Julie; Franklin, Janet; Edwards, Thomas C.

    2015-01-01

    Questions(i) What role does the type of managed disturbance play in structuring sagebrush steppe plant communities? (ii) How does the composition of post-disturbance plant communities change with time since disturbance? (iii) Does plant community diversity change over time following managed disturbance?LocationField study within the sagebrush steppe ecosystem. Rich County, Utah, USA.MethodsWe developed a chronosequence spanning up to 50 yrs post-treatment to study sagebrush steppe vegetation dynamics. Direct ordination was used to examine plant community composition by managed disturbance type and time since disturbance, and factorial analysis of covariance was used to examine diversity dynamics following disturbance. Indicator species values were calculated in order to identify characteristic species for each disturbance type.ResultsPlant communities experienced a shift toward distinct community composition for each of the three managed disturbance types, and gave no indication of returning to untreated community composition or diversity. Small post-disturbance increases in the number of non-native grass species were observed in the treatments relative to reference, with native forb species making the largest contribution to altered composition. On fire- and chemically-treated sites the proportional native forb species richness increased over time since disturbance, while the proportional contribution of non-native forbs to total species richness decreased. For all three treatment types, native grasses contributed less on average to total richness than on reference sites, while non-native grasses made up a higher proportion of total richness.ConclusionsCommon shrubland management techniques have legacy effects on the composition and diversity of sagebrush steppe plant communities, and no-analogue disturbances, such as chemical or mechanical treatments, have more pronounced legacy effects than treatments similar to natural disturbance regimes (fire). This study

  2. Consequences of pre-inoculation with native arbuscular mycorrhizae on root colonization and survival of Artemisia tridentata ssp. wyomingensis (Wyoming big sagebrush) seedlings after transplanting

    Science.gov (United States)

    Bill Eugene Davidson

    2015-01-01

    Inoculation of seedlings with arbuscular mycorrhizal fungi (AMF) is a common practice aimed at improving seedling establishment. The success of this practice largely depends on the ability of the inoculum to multiply and colonize the growing root system after transplanting. These events were investigated in Artemisia tridentata ssp. wyomingensis (Wyoming big sagebrush...

  3. Multiscale sagebrush rangeland habitat modeling in the Gunnison Basin of Colorado

    Science.gov (United States)

    Homer, Collin G.; Aldridge, Cameron L.; Meyer, Debra K.; Schell, Spencer J.

    2013-01-01

    North American sagebrush-steppe ecosystems have decreased by about 50 percent since European settlement. As a result, sagebrush-steppe dependent species, such as the Gunnison sage-grouse, have experienced drastic range contractions and population declines. Coordinated ecosystem-wide research, integrated with monitoring and management activities, is needed to help maintain existing sagebrush habitats; however, products that accurately model and map sagebrush habitats in detail over the Gunnison Basin in Colorado are still unavailable. The goal of this project is to provide a rigorous large-area sagebrush habitat classification and inventory with statistically validated products and estimates of precision across the Gunnison Basin. This research employs a combination of methods, including (1) modeling sagebrush rangeland as a series of independent objective components that can be combined and customized by any user at multiple spatial scales; (2) collecting ground measured plot data on 2.4-meter QuickBird satellite imagery in the same season the imagery is acquired; (3) modeling of ground measured data on 2.4-meter imagery to maximize subsequent extrapolation; (4) acquiring multiple seasons (spring, summer, and fall) of Landsat Thematic Mapper imagery (30-meter) for optimal modeling; (5) using regression tree classification technology that optimizes data mining of multiple image dates, ratios, and bands with ancillary data to extrapolate ground training data to coarser resolution Landsat Thematic Mapper; and 6) employing accuracy assessment of model predictions to enable users to understand their dependencies. Results include the prediction of four primary components including percent bare ground, percent herbaceous, percent shrub, and percent litter, and four secondary components including percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata wyomingensis), and shrub height (centimeters

  4. Gold and other metals in big sagebrush (Artemisia tridentata Nutt.) as an exploration tool, Gold Run District, Humboldt County, Nevada

    Science.gov (United States)

    Erdman, J.A.; Cookro, T.M.; O'Leary, R. M.; Harms, T.F.

    1988-01-01

    Big sagebrush - a cold-desert species that dominates the terrain over large parts of western United States - was sampled along several traverses that crossed thermally metamorphosed limestone, phyllitic shale, and schist of the Middle and Upper Cambrian Preble Formation that host skarn-, disseminated gold and silver-, and hot springs gold-type mineral occurrences. Patterns of detectable levels of gold (8 to 28 ppb or ng g-1) in ash of new growth were consistent with areas affected by known or suspected gold mineralization. Soils collected along one of the traverses where a selenium-indicator plant was common contained no gold above background levels of 2ppb, but were consistently high in As, Sb, and Zn, and several samples were unusually high in Se (maximum 11 ppm or ??g g-1). Sagebrush along this traverse contained Li at levels above norms for this species. We also found a puzzling geochemical anomaly at a site basinward from active hot springs along a range-front fault scarp. Sagebrush at this site contained a trace of gold and an unusually high concentration of Cd (13 ppm) and the soil had anomalous concentrations of Cd and Bi (3.2 and 6 ppm, respectively). The source of this anomaly could be either metal-rich waters from an irrigation ditch or leakage along a buried fault. Despite the limited nature of the study, we conclude that gold in sagebrush could be a cost-effective guide to drilling locations in areas where the geology seems favorable for disseminated and vein precious metals. ?? 1988.

  5. Characterization of a sagebrush (Artemisia tridentata ssp. wyomingensis) die-off on the Handford Site

    International Nuclear Information System (INIS)

    Cardenas, A.; Lewinsohn, J.; Auger, C.; Downs, J.L.; Cadwell, L.L.; Burrows, R.

    1997-09-01

    The Hanford Site contains one of the few remaining contiguous areas of shrub-steppe habitat left in Washington State. This habitat is home to many native plant and wildlife species, some of which are threatened with extinction or are unique to the Site. The importance of the Hanford Site increases as other lands surrounding the Site are developed, and these native species and habitats are lost. Stands of Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis) on the Site are a particularly important component of shrub-steppe habitat, because a number of wildlife require big sagebrush for food and cover. Since 1993, researchers and field biologists have made anecdotal observations of dying and declining sagebrush in stands of shrubs near the 100 Areas. This study was initiated to delineate and document the general boundary where sagebrush stands appear to be declining. We mapped the areal extent of the die-off using a global positioning system and found that the central portion of the die-off encompasses 280 hectares. Shrub stand defoliation was estimated to be near or greater than 80% in this area. The remainder of the die-off area exhibits varying mixtures of completely defoliated, partially defoliated, and healthy-looking stands. Declining sagebrush stands comprise a total of 1776 hectares

  6. Characterization of a sagebrush (Artemisia tridentata ssp. wyomingensis) die-off on the Handford Site

    Energy Technology Data Exchange (ETDEWEB)

    Cardenas, A.; Lewinsohn, J.; Auger, C.; Downs, J.L.; Cadwell, L.L.; Burrows, R.

    1997-09-01

    The Hanford Site contains one of the few remaining contiguous areas of shrub-steppe habitat left in Washington State. This habitat is home to many native plant and wildlife species, some of which are threatened with extinction or are unique to the Site. The importance of the Hanford Site increases as other lands surrounding the Site are developed, and these native species and habitats are lost. Stands of Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis) on the Site are a particularly important component of shrub-steppe habitat, because a number of wildlife require big sagebrush for food and cover. Since 1993, researchers and field biologists have made anecdotal observations of dying and declining sagebrush in stands of shrubs near the 100 Areas. This study was initiated to delineate and document the general boundary where sagebrush stands appear to be declining. We mapped the areal extent of the die-off using a global positioning system and found that the central portion of the die-off encompasses 280 hectares. Shrub stand defoliation was estimated to be near or greater than 80% in this area. The remainder of the die-off area exhibits varying mixtures of completely defoliated, partially defoliated, and healthy-looking stands. Declining sagebrush stands comprise a total of 1776 hectares.

  7. Resilience and resistance of sagebrush ecosystems: implications for state and transition models and management treatments

    Science.gov (United States)

    Chambers, Jeanne C.; Miller, Richard F.; Board, David I.; Pyke, David A.; Roundy, Bruce A.; Grace, James B.; Schupp, Eugene W.; Tausch, Robin J.

    2014-01-01

    In sagebrush ecosystems invasion of annual exotics and expansion of piñon (Pinus monophylla Torr. and Frem.) and juniper (Juniperus occidentalis Hook., J. osteosperma [Torr.] Little) are altering fire regimes and resulting in large-scale ecosystem transformations. Management treatments aim to increase resilience to disturbance and enhance resistance to invasive species by reducing woody fuels and increasing native perennial herbaceous species. We used Sagebrush Steppe Treatment Evaluation Project data to test predictions on effects of fire vs. mechanical treatments on resilience and resistance for three site types exhibiting cheatgrass (Bromus tectorum L.) invasion and/or piñon and juniper expansion: 1) warm and dry Wyoming big sagebrush (WY shrub); 2) warm and moist Wyoming big sagebrush (WY PJ); and 3) cool and moist mountain big sagebrush (Mtn PJ). Warm and dry (mesic/aridic) WY shrub sites had lower resilience to fire (less shrub recruitment and native perennial herbaceous response) than cooler and moister (frigid/xeric) WY PJ and Mtn PJ sites. Warm (mesic) WY Shrub and WY PJ sites had lower resistance to annual exotics than cool (frigid to cool frigid) Mtn PJ sites. In WY shrub, fire and sagebrush mowing had similar effects on shrub cover and, thus, on perennial native herbaceous and exotic cover. In WY PJ and Mtn PJ, effects were greater for fire than cut-and-leave treatments and with high tree cover in general because most woody vegetation was removed increasing resources for other functional groups. In WY shrub, about 20% pretreatment perennial native herb cover was necessary to prevent increases in exotics after treatment. Cooler and moister WY PJ and especially Mtn PJ were more resistant to annual exotics, but perennial native herb cover was still required for site recovery. We use our results to develop state and transition models that illustrate how resilience and resistance influence vegetation dynamics and management options.

  8. Multiscale sagebrush rangeland habitat modeling in southwest Wyoming

    Science.gov (United States)

    Homer, Collin G.; Aldridge, Cameron L.; Meyer, Debra K.; Coan, Michael J.; Bowen, Zachary H.

    2009-01-01

    four secondary targets included percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata wyomingensis), and sagebrush height (centimeters). Results were validated by an independent accuracy assessment with root mean square error (RMSE) values ranging from 6.38 percent for bare ground to 2.99 percent for sagebrush at the QuickBird scale and RMSE values ranging from 12.07 percent for bare ground to 6.34 percent for sagebrush at the full Landsat scale. Subsequent project phases are now in progress, with plans to deliver products that improve accuracies of existing components, model new components, complete models over larger areas, track changes over time (from 1988 to 2007), and ultimately model wildlife population trends against these changes. We believe these results offer significant improvement in sagebrush rangeland quantification at multiple scales and offer users products that have been rigorously validated.

  9. Models for predicting fuel consumption in sagebrush-dominated ecosystems

    Science.gov (United States)

    Clinton S. Wright

    2013-01-01

    Fuel consumption predictions are necessary to accurately estimate or model fire effects, including pollutant emissions during wildland fires. Fuel and environmental measurements on a series of operational prescribed fires were used to develop empirical models for predicting fuel consumption in big sagebrush (Artemisia tridentate Nutt.) ecosystems....

  10. Conserving and restoring habitat for Greater Sage-Grouse and other sagebrush-obligate wildlife: The crucial link of forbs and sagebrush diversity

    Science.gov (United States)

    Kas Dumroese; Tara Luna; Bryce A. Richardson; Francis F. Kilkenny; Justin B. Runyon

    2015-01-01

    In the western US, Greater Sage-Grouse (Centrocercus urophasianus Bonaparte [Phasianidae]) have become an indicator species of the overall health of the sagebrush (Artemisia L. [Asteraceae]) dominated communities that support a rich diversity of flora and fauna. This species has an integral association with sagebrush, its understory forbs and grasses, and the...

  11. Genetic and environmental effects on seed weight in subspecies of big sagebrush: Applications for restoration

    Science.gov (United States)

    Bryce A. Richardson; Hector G. Ortiz; Stephanie L. Carlson; Deidre M. Jaeger; Nancy L. Shaw

    2015-01-01

    The sagebrush steppe is a patchwork of species and subspecies occupying distinct environmental niches across the intermountain regions of western North America. These ecosystems face degradation from disturbances and exotic weeds. Using sagebrush seed that is matched to its appropriate niche is a critical component to successful restoration, improving habitat for the...

  12. Protocols for sagebrush seed processing and seedling production at the Lucky Peak Nursery

    Science.gov (United States)

    Clark D. Fleege

    2010-01-01

    This paper presents the production protocols currently practiced at the USDA Forest Service Lucky Peak Nursery (Boise, ID) for seed processing and bareroot and container seedling production for three subspecies of big sagebrush (Artemisia tridentata).

  13. Mid-latitude shrub steppe plant communities: climate change consequences for soil water resources.

    Science.gov (United States)

    Palmquist, Kyle A; Schlaepfer, Daniel R; Bradford, John B; Lauenroth, William K

    2016-09-01

    In the coming century, climate change is projected to impact precipitation and temperature regimes worldwide, with especially large effects in drylands. We use big sagebrush ecosystems as a model dryland ecosystem to explore the impacts of altered climate on ecohydrology and the implications of those changes for big sagebrush plant communities using output from 10 Global Circulation Models (GCMs) for two representative concentration pathways (RCPs). We ask: (1) What is the magnitude of variability in future temperature and precipitation regimes among GCMs and RCPs for big sagebrush ecosystems, and (2) How will altered climate and uncertainty in climate forecasts influence key aspects of big sagebrush water balance? We explored these questions across 1980-2010, 2030-2060, and 2070-2100 to determine how changes in water balance might develop through the 21st century. We assessed ecohydrological variables at 898 sagebrush sites across the western US using a process-based soil water model, SOILWAT, to model all components of daily water balance using site-specific vegetation parameters and site-specific soil properties for multiple soil layers. Our modeling approach allowed for changes in vegetation based on climate. Temperature increased across all GCMs and RCPs, whereas changes in precipitation were more variable across GCMs. Winter and spring precipitation was predicted to increase in the future (7% by 2030-2060, 12% by 2070-2100), resulting in slight increases in soil water potential (SWP) in winter. Despite wetter winter soil conditions, SWP decreased in late spring and summer due to increased evapotranspiration (6% by 2030-2060, 10% by 2070-2100) and groundwater recharge (26% and 30% increase by 2030-2060 and 2070-2100). Thus, despite increased precipitation in the cold season, soils may dry out earlier in the year, resulting in potentially longer, drier summer conditions. If winter precipitation cannot offset drier summer conditions in the future, we expect big

  14. Mid-latitude shrub steppe plant communities: Climate change consequences for soil water resources

    Science.gov (United States)

    Palmquist, Kyle A.; Schlaepfer, Daniel R.; Bradford, John B.; Lauenroth, Willliam K.

    2016-01-01

    In the coming century, climate change is projected to impact precipitation and temperature regimes worldwide, with especially large effects in drylands. We use big sagebrush ecosystems as a model dryland ecosystem to explore the impacts of altered climate on ecohydrology and the implications of those changes for big sagebrush plant communities using output from 10 Global Circulation Models (GCMs) for two representative concentration pathways (RCPs). We ask: 1) What is the magnitude of variability in future temperature and precipitation regimes among GCMs and RCPs for big sagebrush ecosystems and 2) How will altered climate and uncertainty in climate forecasts influence key aspects of big sagebrush water balance? We explored these questions across 1980-2010, 2030-2060, and 2070-2100 to determine how changes in water balance might develop through the 21st century. We assessed ecohydrological variables at 898 sagebrush sites across the western US using a process-based soil water model, SOILWAT to model all components of daily water balance using site-specific vegetation parameters and site-specific soil properties for multiple soil layers. Our modeling approach allowed for changes in vegetation based on climate. Temperature increased across all GCMs and RCPs, while changes in precipitation were more variable across GCMs. Winter and spring precipitation was predicted to increase in the future (7% by 2030-2060, 12% by 2070-2100), resulting in slight increases in soil water potential (SWP) in winter. Despite wetter winter soil conditions, SWP decreased in late spring and summer due to increased evapotranspiration (6% by 2030-2060, 10% by 2070-2100) and groundwater recharge (26% and 30% increase by 2030-2060 and 2070-2100). Thus, despite increased precipitation in the cold season, soils may dry out earlier in the year, resulting in potentially longer drier summer conditions. If winter precipitation cannot offset drier summer conditions in the future, we expect big

  15. Scales of snow depth variability in high elevation rangeland sagebrush

    Science.gov (United States)

    Tedesche, Molly E.; Fassnacht, Steven R.; Meiman, Paul J.

    2017-09-01

    In high elevation semi-arid rangelands, sagebrush and other shrubs can affect transport and deposition of wind-blown snow, enabling the formation of snowdrifts. Datasets from three field experiments were used to investigate the scales of spatial variability of snow depth around big mountain sagebrush ( Artemisia tridentata Nutt.) at a high elevation plateau rangeland in North Park, Colorado, during the winters of 2002, 2003, and 2008. Data were collected at multiple resolutions (0.05 to 25 m) and extents (2 to 1000 m). Finer scale data were collected specifically for this study to examine the correlation between snow depth, sagebrush microtopography, the ground surface, and the snow surface, as well as the temporal consistency of snow depth patterns. Variograms were used to identify the spatial structure and the Moran's I statistic was used to determine the spatial correlation. Results show some temporal consistency in snow depth at several scales. Plot scale snow depth variability is partly a function of the nature of individual shrubs, as there is some correlation between the spatial structure of snow depth and sagebrush, as well as between the ground and snow depth. The optimal sampling resolution appears to be 25-cm, but over a large area, this would require a multitude of samples, and thus a random stratified approach is recommended with a fine measurement resolution of 5-cm.

  16. Common raven occurrence in relation to energy transmission line corridors transiting human-altered sagebrush steppe

    Science.gov (United States)

    Coates, Peter S.; Howe, Kristy B.; Casazza, Michael L.; Delehanty, David J.

    2014-01-01

    Energy-related infrastructure and other human enterprises within sagebrush steppe of the American West often results in changes that promote common raven (Corvus corax; hereafter, raven) populations. Ravens, a generalist predator capable of behavioral innovation, present a threat to many species of conservation concern. We evaluate the effects of detailed features of an altered landscape on the probability of raven occurrence using extensive raven survey (n= 1045) and mapping data from southern Idaho, USA. We found nonlinear relationships between raven occurrence and distances to transmission lines, roads, and facilities. Most importantly, raven occurrence was greater with presence of transmission lines up to 2.2 km from the corridor.We further explain variation in raven occurrence along anthropogenic features based on the amount of non-native vegetation and cover type edge, such that ravens select fragmented sagebrush stands with patchy, exotic vegetative introgression. Raven occurrence also increased with greater length of edge formed by the contact of big sagebrush (Artemisia tridentate spp.) with non-native vegetation cover types. In consideration of increasing alteration of sagebrush steppe, these findings will be useful for planning energy transmission corridor placement and other management activities where conservation of sagebrush obligate species is a priority.

  17. Success of seeding native compared with introduced perennial vegetation for revegetating medusahead-invaded sagebrush rangeland

    Science.gov (United States)

    Millions of hectares of Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle &Young) rangeland have been invaded by medusahead (Taeniatherum caput-medusae [L.] Nevski), an exotic annual grass that degrades wildlife habitat, reduces forage production, and decreases biodiversity....

  18. W-519 Sagebrush Mitigation Project FY-2004 Final Review and Status

    Energy Technology Data Exchange (ETDEWEB)

    Durham, Robin E.; Sackschewsky, Michael R.

    2004-09-30

    This report summarizes activities conducted as mitigation for loss of sagebrush-steppe habitats due to Project W-519, the construction of the infrastructure for the Tank Waste Remediation System Vitrification Plant. The focus of this report is to provide a review and final status of mitigation actions performed through FY2004. Data collected since FY1999 have been included where appropriate. The Mitigation Action Plan (MAP) for Project W-519 prescribed three general actions to be performed as mitigation for the disturbance of approximately 40 ha (100 acres) of mature sagebrush-steppe habitat. These actions included: (1) transplanting approximately 130,000 sagebrush seedlings on the Fitzner-Eberhardt Arid Lands Ecology Reserve (ALE); (2) rectification of the new transmission line corridor via seeding with native grasses and sagebrush; and (3) research on native plant species with a goal of increasing species diversity in future mitigation or restoration actions. Nearly 130,000 Wyoming big sagebrush seedlings where planted on ALE during FY2000 and FY2001. About 39,000 of those seedlings were burned during the 24-Command Fire of June 2000. The surviving and subsequent replanting has resulted in about 91,000 seedlings that were planted across four general areas on ALE. A 50% survival rate at any monitoring period was defined as the performance standard in the MAP for this project. Data collected in 2004 indicate that of the over 5000 monitored plants, 51.1% are still alive, and of those the majority are thriving and blooming. These results support the potential for natural recruitment and the ultimate goal of wildlife habitat replacement. Thus, the basic performance standard for sagebrush survival within the habitat compensation planting has been met. Monitoring activities conducted in 2004 indicate considerable variation in seedling survival depending on the type of plant material, site conditions, and to a lesser extent, treatments performed at the time of planting

  19. W-519 Sagebrush Mitigation Project FY-2004 Final Review and Status

    International Nuclear Information System (INIS)

    Durham, Robin E.; Sackschewsky, Michael R.

    2004-01-01

    This report/SUMmarizes activities conducted as mitigation for loss of sagebrush-steppe habitats due to Project W-519, the construction of the infrastructure for the Tank Waste Remediation System Vitrification Plant. The focus of this report is to provide a review and final status of mitigation actions performed through FY2004. Data collected since FY1999 have been included where appropriate. The Mitigation Action Plan (MAP) for Project W-519 prescribed three general actions to be performed as mitigation for the disturbance of approximately 40 ha (100 acres) of mature sagebrush-steppe habitat. These actions included: (1) transplanting approximately 130,000 sagebrush seedlings on the Fitzner-Eberhardt Arid Lands Ecology Reserve (ALE); (2) rectification of the new transmission line corridor via seeding with native grasses and sagebrush; and (3) research on native plant species with a goal of increasing species diversity in future mitigation or restoration actions. Nearly 130,000 Wyoming big sagebrush seedlings where planted on ALE during FY2000 and FY2001. About 39,000 of those seedlings were burned during the 24-Command Fire of June 2000. The surviving and subsequent replanting has resulted in about 91,000 seedlings that were planted across four general areas on ALE. A 50% survival rate at any monitoring period was defined as the performance standard in the MAP for this project. Data collected in 2004 indicate that of the over 5000 monitored plants, 51.1% are still alive, and of those the majority are thriving and blooming. These results support the potential for natural recruitment and the ultimate goal of wildlife habitat replacement. Thus, the basic performance standard for sagebrush survival within the habitat compensation planting has been met. Monitoring activities conducted in 2004 indicate considerable variation in seedling survival depending on the type of plant material, site conditions, and to a lesser extent, treatments performed at the time of planting

  20. Genotype, soil type, and locale effects on reciprocal transplant vigor, endophyte growth, and microbial functional diversity of a narrow sagebrush hybrid zone in Salt Creek Canyon, Utah

    Science.gov (United States)

    Miglia, K.J.; McArthur, E.D.; Redman, R.S.; Rodriguez, R.J.; Zak, J.C.; Freeman, D.C.

    2007-01-01

    When addressing the nature of ecological adaptation and environmental factors limiting population ranges and contributing to speciation, it is important to consider not only the plant's genotype and its response to the environment, but also any close interactions that it has with other organisms, specifically, symbiotic microorganisms. To investigate this, soils and seedlings were reciprocally transplanted into common gardens of the big sagebrush hybrid zone in Salt Creek Canyon, Utah, to determine location and edaphic effects on the fitness of parental and hybrid plants. Endophytic symbionts and functional microbial diversity of indigenous and transplanted soils and sagebrush plants were also examined. Strong selection occurred against the parental genotypes in the middle hybrid zone garden in middle hybrid zone soil; F1 hybrids had the highest fitness under these conditions. Neither of the parental genotypes had superior fitness in their indigenous soils and habitats; rather F1 hybrids with the nonindigenous maternal parent were superiorly fit. Significant garden-by-soil type interactions indicate adaptation of both plant and soil microorganisms to their indigenous soils and habitats, most notably in the middle hybrid zone garden in middle hybrid zone soil. Contrasting performances of F1 hybrids suggest asymmetrical gene flow with mountain, rather than basin, big sagebrush acting as the maternal parent. We showed that the microbial community impacted the performance of parental and hybrid plants in different soils, likely limiting the ranges of the different genotypes.

  1. Bumble bee (Hymenoptera: Apidae) community structure on two sagebrush steppe sites in southern Idaho

    Science.gov (United States)

    Stephen P. Cook; Sara M. Birch; Frank W. Merickel; Carrie Caselton Lowe; Deborah Page-Dumroese

    2011-01-01

    Although sagebrush, Artemisia spp., does not require an insect pollinator, there are several native species of bumble bees, Bombus spp. (Hymenoptera: Apidae), that are present in sagebrush steppe ecosystems where they act as pollinators for various forbs and shrubs. These native pollinators contribute to plant productivity and reproduction. We captured 12 species of...

  2. History of fire and Douglas-fir establishment in a savanna and sagebrush-grassland mosaic, southwestern Montana, USA

    Science.gov (United States)

    Emily K. Heyerdahl; Richard F. Miller; Russell A. Parsons

    2006-01-01

    Over the past century, trees have encroached into grass- and shrublands across western North America. These include Douglas-fir trees (Pseudotsuga menziesii (Mirb.) Franco var. glauca (Beissn.) Franco) encroaching into mountain big sagebrush Nutt. ssp. vaseyana (Rydb.) Beetle) from stable islands of savanna in...

  3. Data Report: Meteorological and Evapotranspiration Data from Sagebrush and Pinyon Pine/Juniper Communities at Pahute Mesa, Nevada National Security Site, 2011-2012

    Energy Technology Data Exchange (ETDEWEB)

    Jasoni, Richard L [DRI; Larsen, Jessica D [DRI; Lyles, Brad F. [DRI; Healey, John M [DRI; Cooper, Clay A [DRI; Hershey, Ronald L [DRI; Lefebre, Karen J [DRI

    2013-04-01

    Pahute Mesa is a groundwater recharge area at the Nevada National Security Site. Because underground nuclear testing was conducted at Pahute Mesa, groundwater recharge may transport radionuclides from underground test sites downward to the water table; the amount of groundwater recharge is also an important component of contaminant transport models. To estimate the amount of groundwater recharge at Pahute Mesa, an INFIL3.0 recharge-runoff model is being developed. Two eddy covariance (EC) stations were installed on Pahute Mesa to estimate evapotranspiration (ET) to support the groundwater recharge modeling project. This data report describes the methods that were used to estimate ET and collect meteorological data. Evapotranspiration was estimated for two predominant plant communities on Pahute Mesa; one site was located in a sagebrush plant community, the other site in a pinyon pine/juniper community. Annual ET was estimated to be 310±13.9 mm for the sagebrush site and 347±15.9 mm for the pinyon pine/juniper site (March 26, 2011 to March 26, 2012). Annual precipitation measured with unheated tipping bucket rain gauges was 179 mm at the sagebrush site and 159 mm at the pinyon pine/juniper site. Annual precipitation measured with bulk precipitation gauges was 222 mm at the sagebrush site and 227 mm at the pinyon pine/juniper site (March 21, 2011 to March 28, 2012). A comparison of tipping bucket versus bulk precipitation data showed that total precipitation measured by the tipping bucket rain gauges was 17 to 20 percent lower than the bulk precipitation gauges. These differences were most likely the result of the unheated tipping bucket precipitation gauges not measuring frozen precipitation as accurately as the bulk precipitation gauges. In this one-year study, ET exceeded precipitation at both study sites because estimates of ET included precipitation that fell during the winter of 2010-2011 prior to EC instrumentation and the precipitation gauges started

  4. Vegetation responses to sagebrush-reduction treatments measured by satellites

    Science.gov (United States)

    Johnston, Aaron; Beever, Erik; Merkle, Jerod A.; Chong, Geneva W.

    2018-01-01

    Time series of vegetative indices derived from satellite imagery constitute tools to measure ecological effects of natural and management-induced disturbances to ecosystems. Over the past century, sagebrush-reduction treatments have been applied widely throughout western North America to increase herbaceous vegetation for livestock and wildlife. We used indices from satellite imagery to 1) quantify effects of prescribed-fire, herbicide, and mechanical treatments on vegetative cover, productivity, and phenology, and 2) describe how vegetation changed over time following these treatments. We hypothesized that treatments would increase herbaceous cover and accordingly shift phenologies towards those typical of grass-dominated systems. We expected prescribed burns would lead to the greatest and most-prolonged effects on vegetative cover and phenology, followed by herbicide and mechanical treatments. Treatments appeared to increase herbaceous cover and productivity, which coincided with signs of earlier senescence − signals expected of grass-dominated systems, relative to sagebrush-dominated systems. Spatial heterogeneity for most phenometrics was lower in treated areas relative to controls, which suggested treatment-induced homogenization of vegetative communities. Phenometrics that explain spring migrations of ungulates mostly were unaffected by sagebrush treatments. Fire had the strongest effect on vegetative cover, and yielded the least evidence for sagebrush recovery. Overall, treatment effects were small relative to those reported from field-based studies for reasons most likely related to sagebrush recovery, treatment specification, and untreated patches within mosaicked treatment applications. Treatment effects were also small relative to inter-annual variation in phenology and productivity that was explained by temperature, snowpack, and growing-season precipitation. Our results indicated that cumulative NDVI, late-season phenometrics, and spatial

  5. The Sagebrush Steppe Treatment Evaluation Project (SageSTEP): a test of state-and-transition theory

    Science.gov (United States)

    James D. McIver; Mark Brunson; Steve C. Bunting; Jeanne Chambers; Nora Devoe; Paul Doescher; James Grace; Dale Johnson; Steve Knick; Richard Miller; Mike Pellant; Fred Pierson; David Pyke; Kim Rollins; Bruce Roundy; Eugene Schupp; Robin Tausch; David Turner

    2010-01-01

    The Sagebrush Steppe Treatment Evaluation Project (SageSTEP) is a comprehensive, integrated, long-term study that evaluates the ecological effects of fire and fire surrogate treatments designed to reduce fuel and to restore sagebrush (Artemisia spp.) communities of the Great Basin and surrounding areas. SageSTEP has several features that make it ideal for testing...

  6. Curating Big Data Made Simple: Perspectives from Scientific Communities.

    Science.gov (United States)

    Sowe, Sulayman K; Zettsu, Koji

    2014-03-01

    The digital universe is exponentially producing an unprecedented volume of data that has brought benefits as well as fundamental challenges for enterprises and scientific communities alike. This trend is inherently exciting for the development and deployment of cloud platforms to support scientific communities curating big data. The excitement stems from the fact that scientists can now access and extract value from the big data corpus, establish relationships between bits and pieces of information from many types of data, and collaborate with a diverse community of researchers from various domains. However, despite these perceived benefits, to date, little attention is focused on the people or communities who are both beneficiaries and, at the same time, producers of big data. The technical challenges posed by big data are as big as understanding the dynamics of communities working with big data, whether scientific or otherwise. Furthermore, the big data era also means that big data platforms for data-intensive research must be designed in such a way that research scientists can easily search and find data for their research, upload and download datasets for onsite/offsite use, perform computations and analysis, share their findings and research experience, and seamlessly collaborate with their colleagues. In this article, we present the architecture and design of a cloud platform that meets some of these requirements, and a big data curation model that describes how a community of earth and environmental scientists is using the platform to curate data. Motivation for developing the platform, lessons learnt in overcoming some challenges associated with supporting scientists to curate big data, and future research directions are also presented.

  7. Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: laying a foundation for monitoring

    Science.gov (United States)

    Homer, Collin G.; Aldridge, Cameron L.; Meyer, Debra K.; Schell, Spencer J.

    2012-01-01

    agebrush ecosystems in North America have experienced extensive degradation since European settlement. Further degradation continues from exotic invasive plants, altered fire frequency, intensive grazing practices, oil and gas development, and climate change – adding urgency to the need for ecosystem-wide understanding. Remote sensing is often identified as a key information source to facilitate ecosystem-wide characterization, monitoring, and analysis; however, approaches that characterize sagebrush with sufficient and accurate local detail across large enough areas to support this paradigm are unavailable. We describe the development of a new remote sensing sagebrush characterization approach for the state of Wyoming, U.S.A. This approach integrates 2.4 m QuickBird, 30 m Landsat TM, and 56 m AWiFS imagery into the characterization of four primary continuous field components including percent bare ground, percent herbaceous cover, percent litter, and percent shrub, and four secondary components including percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata Wyomingensis), and shrub height using a regression tree. According to an independent accuracy assessment, primary component root mean square error (RMSE) values ranged from 4.90 to 10.16 for 2.4 m QuickBird, 6.01 to 15.54 for 30 m Landsat, and 6.97 to 16.14 for 56 m AWiFS. Shrub and herbaceous components outperformed the current data standard called LANDFIRE, with a shrub RMSE value of 6.04 versus 12.64 and a herbaceous component RMSE value of 12.89 versus 14.63. This approach offers new advancements in sagebrush characterization from remote sensing and provides a foundation to quantitatively monitor these components into the future.

  8. Effectiveness of prescribed fire to re-establish sagebrush vegetation and ecohydrologic function on woodland-encroached sagebrush steppe, Great Basin, USA

    Science.gov (United States)

    Williams, C. J.; Pierson, F. B.; Kormos, P.; Al-Hamdan, O. Z.; Nouwakpo, S.; Weltz, M.; Vega, S.; Lindsay, K.

    2017-12-01

    Range expansion of pinyon (Pinus spp.) and juniper (Juniperus spp.) conifers into sagebrush steppe (Artemisia spp.) communities has imperiled a vast domain in the western US. Encroachment of sagebrush ecosystems by pinyon and juniper conifers has negative ramifications to ecosystem structure and function and delivery of goods and services. Scientists, land management agencies, and private land owners throughout the western US are challenged with selecting from a suite of options to reduce pinyon and juniper woody fuels and re-establish sagebrush steppe structure and function. This study evaluated the effectiveness of prescribed fire to re-establish sagebrush vegetation and ecohydrologic function over a 9 yr period. Nine years post-fire hydrologic and erosion responses reflect the combination of pre-fire site conditions, perennial grass recruitment, delayed litter cover, and inherent site characteristics. Burning initially increased bare ground, runoff, and erosion for well-vegetated areas underneath tree and shrub canopies, but had minimal impact on hydrology and erosion for degraded interspaces between plants. The degraded interspaces were primarily bare ground and exhibited high runoff and erosion rates prior to burning. Initial fire effects persisted for two years, but increased productivity of grasses improved hydrologic function of interspaces over the full 9 yr period. At the hillslope scale, grass recruitment in the intercanopy between trees reduced runoff from rainsplash, sheetflow, and concentrated overland flow at one site, but did not reduce the high levels of runoff and erosion from a more degraded site. In areas formerly occupied by trees (tree zones), burning increased invasive annual grass cover due to fire removal of limited native perennial plants and competition for resources. The invasive annual grass cover had no net effect on runoff and erosion from tree zones however. Runoff and erosion increased in tree zones at the more degraded site due to

  9. Prescribed Fire Effects on Runoff, Erosion, and Soil Water Repellency on Steeply-Sloped Sagebrush Rangeland over a Five Year Period

    Science.gov (United States)

    Williams, C. J.; Pierson, F. B.; Al-Hamdan, O. Z.

    2014-12-01

    Fire is an inherent component of sagebrush steppe rangelands in western North America and can dramatically affect runoff and erosion processes. Post-fire flooding and erosion events pose substantial threats to proximal resources, property, and human life. Yet, prescribed fire can serve as a tool to manage vegetation and fuels on sagebrush rangelands and to reduce the potential for large catastrophic fires and mass erosion events. The impact of burning on event hydrologic and erosion responses is strongly related to the degree to which burning alters vegetation, ground cover, and surface soils and the intensity and duration of precipitation. Fire impacts on hydrologic and erosion response may be intensified or reduced by inherent site characteristics such as topography and soil properties. Parameterization of these diverse conditions in predictive tools is often limited by a lack of data and/or understanding for the domain of interest. Furthermore, hydrologic and erosion functioning change as vegetation and ground cover recover in the years following burning and few studies track these changes over time. In this study, we evaluated the impacts of prescribed fire on vegetation, ground cover, soil water repellency, and hydrologic and erosion responses 1, 2, and 5 yr following burning of a mountain big sagebrush community on steep hillslopes with fine-textured soils. The study site is within the Reynolds Creek Experimental Watershed, southwestern Idaho, USA. Vegetation, ground cover, and soil properties were measured over plot scales of 0.5 m2 to 9 m2. Rainfall simulations (0.5 m2) were used to assess the impacts of fire on soil water repellency, infiltration, runoff generation, and splash-sheet erosion. Overland flow experiments (9 m2) were used to assess the effects of fire-reduced ground cover on concentrated-flow runoff and erosion processes. The study results provide insight regarding fire impacts on runoff, erosion, and soil water repellency in the immediate and

  10. Insect community responses to climate and weather across elevation gradients in the Sagebrush Steppe, eastern Oregon

    Science.gov (United States)

    Pilliod, David S.; Rohde, Ashley T.

    2016-11-17

    Executive SummaryIn this study, the U.S. Geological Survey investigated the use of insects as bioindicators of climate change in sagebrush steppe shrublands and grasslands in the Upper Columbia Basin. The research was conducted in the Stinkingwater and Pueblo mountain ranges in eastern Oregon on lands administered by the Bureau of Land Management.We used a “space-for-time” sampling design that related insect communities to climate and weather along elevation gradients. We analyzed our insect dataset at three levels of organization: (1) whole-community, (2) feeding guilds (detritivores, herbivores, nectarivores, parasites, and predators), and (3) orders within nectarivores (i.e., pollinators). We captured 59,517 insects from 176 families and 10 orders at the Pueblo Mountains study area and 112,305 insects from 185 families and 11 orders at the Stinkingwater Mountains study area in 2012 and 2013. Of all the individuals captured at the Stinkingwater Mountains study area, 77,688 were from the family Cecidomyiidae (Diptera, gall gnats).We found that the composition of insect communities was associated with variability in long-term (30-yr) temperature and interannual fluctuations in temperature. We found that captures of certain fly, bee, moth, and butterfly pollinators were more strongly associated with some climate and vegetation variables than others. We found that timing of emergence, as measured by first detection of families, was associated with elevation. When analyzed by feeding guilds, we found that all guilds emerged later at high elevations except for detritivores, which emerged earlier at high elevations. The abundance of most taxa varied through time, mostly in response to temperature and precipitation. Of the pollinators, bees (particularly, Halictidae and Megachilidae) peaked in abundance in late June and early July, whereas butterflies and moths peaked in August. Flies peaked in abundance in July.Overall, our interpretation of these patterns is that

  11. Selection of anthropogenic features and vegetation characteristics by nesting Common Ravens in the sagebrush ecosystem

    Science.gov (United States)

    Howe, Kristy B.; Coates, Peter S.; Delehanty, David J.

    2014-01-01

    Common Raven (Corvus corax) numbers and distribution are increasing throughout the sagebrush steppe, influencing avian communities in complex ways. Anthropogenic structures are thought to increase raven populations by providing food and nesting subsidies, which is cause for concern because ravens are important nest predators of sensitive species, including Greater Sage-Grouse (Centrocercus urophasianus). During 2007–2009, we located raven nests in southeastern Idaho and conducted a resource selection analysis. We measured variables at multiple spatial scales for 72 unique nest locations, including landscape-level vegetation characteristics and anthropogenic structures. Using generalized linear mixed models and an information-theoretic approach, we found a 31% decrease in the odds of nesting by ravens for every 1 km increase in distance away from a transmission line. Furthermore, a 100-m increase in distance away from the edge of two different land cover types decreased the odds of nesting by 20%, and an increase in the amount of edge by 1 km within an area of 102.1 ha centered on the nest increased the odds of nesting by 49%. A post hoc analysis revealed that ravens were most likely to nest near edges of adjoining big sagebrush (Artemisia tridentata) and land cover types that were associated with direct human disturbance or fire. These findings contribute to our understanding of raven expansion into rural environments and could be used to make better-informed conservation decisions, especially in the face of increasing renewable energy development.

  12. Simulating the Dependence of Sagebrush Steppe Vegetation on Redistributed Snow in a Semi-Arid Watershed.

    Science.gov (United States)

    Soderquist, B.; Kavanagh, K.; Link, T. E.; Strand, E. K.; Seyfried, M. S.

    2014-12-01

    In mountainous regions across the western USA, the composition of aspen (Populus tremuloides) and sagebrush steppe plant communities is often closely related to heterogeneous soil moisture subsidies resulting from redistributed snow. With decades of climate and precipitation data across elevational and precipitation gradients, the Reynolds Creek Experimental Watershed (RCEW) and critical zone observatory (CZO) in southwest Idaho provides a unique opportunity to study the relationship between vegetation types and redistributed snow. Within the RCEW, the total amount of precipitation has remained unchanged over the past 50 years, however the percentage of the precipitation falling as snow has declined by approximately 4% per decade at mid-elevation sites. As shifts in precipitation phase continue, future trends in vegetation composition and net primary productivity (NPP) of different plant functional types remains a critical question. We hypothesize that redistribution of snow may supplement drought sensitive species like aspen more so than drought tolerant species like mountain big sagebrush (Artemisia tridentata spp. vaseyana). To assess the importance of snowdrift subsidies on sagebrush steppe vegetation, NPP of aspen, shrub, and grass species was simulated at three sites using the biogeochemical process model BIOME-BGC. Each site is located directly downslope from snowdrifts providing soil moisture inputs to aspen stands and neighboring vegetation. Drifts vary in size with the largest containing up to four times the snow water equivalent (SWE) of a uniform precipitation layer. Precipitation inputs used by BIOME-BGC were modified to represent the redistribution of snow and simulations were run using daily climate data from 1985-2013. Simulated NPP of annual grasses at each site was not responsive to subsidies from drifting snow. However, at the driest site, aspen and shrub annual NPP was increased by as much as 44 and 30%, respectively, with the redistribution of

  13. Carbon dioxide effluxes and their environmental controls in sagebrush steppe ecosystems along an elevation gradient in the Reynolds Creek Critical Zone Observatory

    Science.gov (United States)

    Lohse, K. A.; Fellows, A.; Flerchinger, G. N.; Seyfried, M. S.

    2017-12-01

    The spatial and temporal variation of carbon dioxide effluxes and their environmental controls are poorly constrained in cold shrub steppe ecosystems. The objectives of this study were to 1) analyze environmental parameters in determining soil CO2 efflux, 2) assess the level of agreement between manual chambers and force diffusion (FD) soil CO2 efflux chambers, when both measurements are extrapolated across the growing season, and lastly to compare respiration fluxes to modeled ecosystem respiration fluxes. We installed FD chambers at four sites co-located with eddy covariance (EC) towers and soil moisture and temperature sensors along an elevation gradient in the Reynolds Creek Critical Zone Observatory in SW Idaho. FD chamber fluxes were collected continuously at 15-minute intervals. We sampled soil CO2 efflux with manual chambers at plant and interplant spaces in five plots at each site biweekly to monthly during the growing season. The sites included a Wyoming big sagebrush site, a low sagebrush site, a post-fire mountain big sagebrush site, and a mountain big sagebrush site located at elevations of 1425, 1680, 1808 and 2111 m. Climate variation followed the montane elevation gradient; mean annual precipitation (MAP) at the sites is 290, 337, 425, and 795 mm, respectively, and mean annual temperature is 8.9, 8.4, 6.1, 5.4°C. Automated force diffusion chambers detected large differences in carbon dioxide pulse dynamics along the elevation gradient. Growing season carbon dioxide fluxes were 3 times higher at the 425 mm MAP site compared than the lowest elevation sites at 290 and 337 MAP sites and >1.5 higher than the 795 mm MAP site over the same period. Manual fluxes showed similar seasonal patterns as FD chamber fluxes but often higher and greater spatial variability in fluxes than FD chamber fluxes. Plant and interplant flux differences were surprisingly similar, especially at higher elevations. Soil respiration ranged from 0.2-0.48 of ecosystem respiration

  14. Evaluating winter/spring seeding of a native perennial bunchgrass in the sagebrush steppe

    Science.gov (United States)

    Sagebrush (Artemisia tridentata Nutt.) plant communities in the US Great Basin region are being severely impacted by increasingly frequent wildfires in association with the expansion of exotic annual grasses. Maintenance of native perennial bunchgrasses is key to controlling annual grass expansion,...

  15. Ecological influence and pathways of land use in sagebrush

    Science.gov (United States)

    Knick, Steven T.; Hanser, Steven E.; Miller, Richard F.; Pyke, David A.; Wisdom, Michael J.; Finn, Sean P.; Rinkes, E. Thomas; Henny, Charles J.; Knick, Steven T.; Connelly, John W.

    2011-01-01

    Land use in sagebrush (Artemisia spp.) landscapes influences all sage-grouse (Centrocer-cus spp.) populations in western North America. Croplands and the network of irrigation canals cover 230,000 km2 and indirectly influence up to 77% of the Sage-Grouse Conservation Area and 73% of sagebrush land cover by subsidizing synanthropic predators on sage-grouse. Urbanization and the demands of human population growth have created an extensive network of con-necting infrastructure that is expanding its influence on sagebrush landscapes. Over 2,500 km2 are now covered by interstate highways and paved roads; when secondary roads are included, 15% of the Sage-Grouse Conservation Area and 5% of existing sagebrush habitats are 2.5 km from roads. Density of secondary roads often exceeds 5 km/km2, resulting in widespread motorized access for recreation, creating extensive travel corridors for management actions and resource development, subsidizing predators adapted to human presence, and facilitating spread of exotic or invasive plants. Sagebrush lands also are being used for their wilderness and recreation values, including off highway vehicle use. Approximately 12,000,000 animal use months (AUM amount of forage to support one livestock unit per month) are permitted for grazing livestock on public lands in the western states. Direct effects of grazing on sage-grouse populations or sagebrush landscapes are not possible to assess from current data. However, management of lands grazed by livestock has influenced sagebrush ecosystems by vegetation treatments to increase forage and reduce sagebrush and other plant species unpalatable to livestock. Fences (2 km/km2 in some regions), roads, and water developments to manage livestock movements further modify the landscape. Oil and gas development influences 8% of the sagebrush habitats with the highest intensities occurring in the eastern range of sage-grouse; 20% of the sagebrush distribution is indirectly influenced in the Great

  16. 2004 annual progress report: Stratton Sagebrush Hydrology Study Area: Establishment of a long-term research site in a high-elevation sagebrush steppe

    Science.gov (United States)

    Schoenecker, Kate; Lange, Bob; Calton, Mike

    2005-01-01

    In 2004 the U.S. Geological Survey, Fort Collins Science Center (FORT) and the Bureau of Land Management (BLM), Rawlins Field Office (RFO), began a cooperative effort to reestablish the Stratton Sagebrush Hydrology Study Area (Stratton) as a research location, with the goal of making it a site for long-term research on sagebrush (Artemisia spp.) ecology. No other long-term research sites in high-elevation sagebrush habitat currently exist, and the Stratton area, with its 30+ year history of research and baseline data, was a logical location to restart investigations aimed at answering pertinent and timely questions about sagebrush ecology and sagebrush-obligate species. During the first year of the study, USGS scientists conducted an in-depth literature search to locate publications from research conducted at Stratton. We contacted previous researchers to acquire literature and unpublished reports of work conducted at Stratton. Collated papers and published manuscripts were presented in an annotated bibliography (Burgess and Schoenecker, 2004).

  17. Crop type influences edge effects on the reproduction of songbirds in sagebrush habitat near agriculture

    Directory of Open Access Journals (Sweden)

    Elly C. Knight

    2014-06-01

    Full Text Available Extensive fragmentation of the sagebrush shrubsteppe of western North America could be contributing to observed population declines of songbirds in sagebrush habitat. We examined whether habitat fragmentation impacts the reproduction of songbirds in sagebrush edge habitat near agriculture, and if potential impacts vary depending on the adjacent crop type. Specifically, we evaluated whether nest abundance and nest survival varied between orchard edge habitat, vineyard edge habitat, and interior habitat. We then examined whether the local nest predator community and vegetation could explain the differences detected. We detected fewer nests in edge than interior habitat. Nest abundance per songbird was also lower in edge than interior habitat, although only adjacent to vineyards. Nest predation was more frequent in orchard edge habitat than vineyard edge or interior habitat. Predators identified with nest cameras were primarily snakes, however, reduced nest survival in orchard edge habitat was not explained by differences in the abundance of snakes or any other predator species identified. Information theoretic analysis of daily survival rates showed that greater study plot shrub cover and lower grass height at nests were partially responsible for the lower rate of predation-specific daily nest survival rate (PDSR observed in orchard edge habitat, but additional factors are likely important. Results of this study suggest that different crop types have different edge effects on songbirds nesting in sagebrush shrubsteppe, and that these reproductive edge effects may contribute to observed declines of these species. Habitat managers should avoid the creation of new orchard-sagebrush habitat edges to avoid further impacts on already declining songbird populations.

  18. A synopsis of short-term response to alternative restoration treatments in sagebrush-steppe: the SageSTEP project

    Science.gov (United States)

    McIver, James; Brunson, Mark; Bunting, Steve; Chambers, Jeanne; Doescher, Paul; Grace, James; Hulet, April; Johnson, Dale; Knick, Steven T.; Miller, Richard; Pellant, Mike; Pierson, Fred; Pyke, David; Rau, Benjamin; Rollins, Kim; Roundy, Bruce; Schupp, Eugene; Tausch, Robin; Williams, Jason

    2014-01-01

    The Sagebrush Steppe Treatment Evaluation Project (SageSTEP) is an integrated long-term study that evaluates ecological effects of alternative treatments designed to reduce woody fuels and to stimulate the herbaceous understory of sagebrush steppe communities of the Intermountain West. This synopsis summarizes results through 3 yr posttreatment. Woody vegetation reduction by prescribed fire, mechanical treatments, or herbicides initiated a cascade of effects, beginning with increased availability of nitrogen and soil water, followed by increased growth of herbaceous vegetation. Response of butterflies and magnitudes of runoff and erosion closely followed herbaceous vegetation recovery. Effects on shrubs, biological soil crust, tree cover, surface woody fuel loads, and sagebrush-obligate bird communities will take longer to be fully expressed. In the short term, cool wet sites were more resilient than warm dry sites, and resistance was mostly dependent on pretreatment herbaceous cover. At least 10 yr of posttreatment time will likely be necessary to determine outcomes for most sites. Mechanical treatments did not serve as surrogates for prescribed fire in how each influenced the fuel bed, the soil, erosion, and sage-obligate bird communities. Woody vegetation reduction by any means resulted in increased availability of soil water, higher herbaceous cover, and greater butterfly numbers. We identified several trade-offs (desirable outcomes for some variables, undesirable for others), involving most components of the study system. Trade-offs are inevitable when managing complex natural systems, and they underline the importance of asking questions about the whole system when developing management objectives. Substantial spatial and temporal heterogeneity in sagebrush steppe ecosystems emphasizes the point that there will rarely be a “recipe” for choosing management actions on any specific area. Use of a consistent evaluation process linked to monitoring may be the

  19. Using Big (and Critical) Data to Unmask Inequities in Community Colleges

    Science.gov (United States)

    Rios-Aguilar, Cecilia

    2014-01-01

    This chapter presents various definitions of big data and examines some of the assumptions regarding the value and power of big data, especially as it relates to issues of equity in community colleges. Finally, this chapter ends with a discussion of the opportunities and challenges of using big data, critically, for institutional researchers.

  20. Sagebrush-ungulate relationships on the Northern Yellowstone Winter Range

    Science.gov (United States)

    Carl L. Wambolt

    2005-01-01

    Sagebrush (Artemisia) taxa have historically been the landscape dominants over much of the Northern Yellowstone Winter Range (NYWR). Their importance to the unnaturally large ungulate populations on the NYWR throughout the twentieth century has been recognized since the 1920s. Sagebrush-herbivore ecology has been the focus of research on the NYWR for...

  1. Response of bird community structure to habitat management in piñon-juniper woodland-sagebrush ecotones

    Science.gov (United States)

    Knick, Steven T.; Hanser, Steven E.; Grace, James B.; Hollenbeck, Jeff P.; Leu, Matthias

    2017-01-01

    Piñon (Pinus spp.) and juniper (Juniperus spp.) woodlands have been expanding their range across the intermountain western United States into landscapes dominated by sagebrush (Artemisia spp.) shrublands. Management actions using prescribed fire and mechanical cutting to reduce woodland cover and control expansion provided opportunities to understand how environmental structure and changes due to these treatments influence bird communities in piñon-juniper systems. We surveyed 43 species of birds and measured vegetation for 1–3 years prior to treatment and 6–7 years post-treatment at 13 locations across Oregon, California, Idaho, Nevada, and Utah. We used structural equation modeling to develop and statistically test our conceptual model that the current bird assembly at a site is structured primarily by the previous bird community with additional drivers from current and surrounding habitat conditions as well as external regional bird dynamics. Treatment reduced woodland cover by >5% at 80 of 378 survey sites. However, habitat change achieved by treatment was highly variable because actual disturbance differed widely in extent and intensity. Biological inertia in the bird community was the strongest single driver; 72% of the variation in the bird assemblage was explained by the community that existed seven years earlier. Greater net reduction in woodlands resulted in slight shifts in the bird community to one having ecotone or shrubland affinities. However, the overall influence of woodland changes from treatment were relatively small and were buffered by other extrinsic factors. Regional bird dynamics did not significantly influence the structure of local bird communities at our sites. Our results suggest that bird communities in piñon-juniper woodlands can be highly stable when management treatments are conducted in areas with more advanced woodland development and at the level of disturbance measured in our study.

  2. 76 FR 62087 - Draft Conservation Plan and Draft Environmental Assessment; Dunes Sagebrush Lizard, Texas

    Science.gov (United States)

    2011-10-06

    ...] Draft Conservation Plan and Draft Environmental Assessment; Dunes Sagebrush Lizard, Texas AGENCY: Fish... draft Texas Conservation Plan for the Dunes Sagebrush Lizard (TCP). The draft TCP will function as a... the Applicant for the dunes sagebrush lizard (Sceloporus arenicolus) throughout its range in Texas...

  3. Comparison of radionuclide levels in soil, sagebrush, plant litter, cryptogams, and small mammals

    International Nuclear Information System (INIS)

    Landeen, D.S.

    1994-09-01

    Soil, sagebrush, plant litter, cryptogam, and small mammal samples were collected and analyzed for cesium-137, strontium-90, plutonium-238, plutonium 239/240, technetium-99, and iodine-129 from 1981 to 1986 at the US Department of Energy Hanford Site in southeastern Washington State as part of site characterization and environmental monitoring activities. Samples were collected on the 200 Areas Plateau, downwind from ongoing waste management activities. Plant litter, cryptogams, and small mammals are media that are not routinely utilized in monitoring or characterization efforts for determination of radionuclide concentrations. Studies at Hanford, other US Department of Energy sites, and in eastern Europe have indicated that plant litter and cryptogams may serve as effective ''natural'' monitors of air quality. Plant litter in this study consists of fallen leaves from sagebrush and ''cryptogams'' describes that portion of the soil crust composed of mosses, lichens, algae, and fungi. Comparisons of cesium-137 and strontium-90 concentrations in the soil, sagebrush, litter, and cryptogams revealed significantly higher (p<0.05) levels in plant litter and cryptogams. Technetium-99 values were the highest in sagebrush and litter. Plutonium-238 and 239/40 and iodine-129 concentrations never exceeded 0.8 pCi/gm in all media. No evidence of any significant amounts of any radionuclides being incorporated into the small mammal community was discovered. The data indicate that plant litter and cryptogams may be better, indicators of environmental quality than soil or vegetation samples. Augmenting a monitoring program with samples of litter and cryptogams may provide a more accurate representation of radionuclide environmental uptake and/or contamination levels in surrounding ecosystems. The results of this study may be applied directly to other radioecological monitoring conducted at other nuclear sites and to the monitoring of other pollutants

  4. 76 FR 19304 - Endangered and Threatened Wildlife and Plants; Endangered Status for Dunes Sagebrush Lizard

    Science.gov (United States)

    2011-04-07

    ... for Dunes Sagebrush Lizard AGENCY: Fish and Wildlife Service, Interior. ACTION: Proposed rule... list the dunes sagebrush lizard (Sceloporus arenicolus) under the Endangered Species Act of 1973, as... dunes sagebrush lizard (Sceloporus arenicolus) that was published in the Federal Register on December 14...

  5. U.S. Geological Survey sage-grouse and sagebrush ecosystem research annual report for 2017

    Science.gov (United States)

    Hanser, Steven E.

    2017-09-08

    The sagebrush (Artemisia spp.) ecosystem extends across a large portion of the Western United States, and the greater sage-grouse (Centrocercus urophasianus) is one of the iconic species of this ecosystem. Greater sage-grouse populations occur in 11 States and are dependent on relatively large expanses of sagebrush-dominated habitat. Sage-grouse populations have been experiencing long-term declines owing to multiple stressors, including interactions among fire, exotic plant invasions, and human land uses, which have resulted in significant loss, fragmentation, and degradation of landscapes once dominated by sagebrush. In addition to the sage-grouse, over 350 species of plants and animals are dependent on the sagebrush ecosystem.Increasing knowledge about how these species and the sagebrush ecosystem respond to these stressors and to management actions can inform and improve strategies to maintain existing areas of intact sagebrush and restore degraded landscapes. The U.S. Geological Survey (USGS) has a broad research program focused on providing the science needed to inform these strate-gies and to help land and resource managers at the Federal, State, Tribal, and local levels as they work towards sustainable sage-grouse populations and restored landscapes for the broad range of uses critical to stakeholders in the Western United States.USGS science has provided a foundation for major land and resource management decisions including those that precluded the need to list the greater sage-grouse under the Endangered Species Act. The USGS is continuing to build on that foundation to inform science-based decisions to help support local economies and the continued conservation, management, and restoration of the sagebrush ecosystem.This report contains descriptions of USGS sage-grouse and sagebrush ecosystem research projects that are ongoing or were active during 2017 and is organized into five thematic areas: Fire, Invasive Species, Restoration, Sagebrush and Sage

  6. Effects of land cover and regional climate variations on long-term spatiotemporal changes in sagebrush ecosystems

    Science.gov (United States)

    Xian, George Z.; Homer, Collin G.; Aldridge, Cameron L.

    2012-01-01

    This research investigated the effects of climate and land cover change on variation in sagebrush ecosystems. We combined information of multi-year sagebrush distribution derived from multitemporal remote sensing imagery and climate data to study the variation patterns of sagebrush ecosystems under different potential disturbances. We found that less than 40% of sagebrush ecosystem changes involved abrupt changes directly caused by landscape transformations and over 60% of the variations involved gradual changes directly related to climatic perturbations. The primary increases in bare ground and declines in sagebrush vegetation abundance were significantly correlated with the 1996-2006 decreasing trend in annual precipitation.

  7. Greater sage-grouse as an umbrella species for sagebrush-associated vertebrates.

    Science.gov (United States)

    Mary M. Rowland; Michael J. Wisdom; Lowell Suring; Cara W. Meinke

    2006-01-01

    Widespread degradation of the sagebrush ecosystem in the western United States, including the invasion of cheatgrass, has prompted resource managers to consider a variety of approaches to restore and conserve habitats for sagebrush-associated species. One such approach involves the use of greater sage-grouse, a species of prominent conservation interest, as an umbrella...

  8. A conservation paradox in the Great Basin—Altering sagebrush landscapes with fuel breaks to reduce habitat loss from wildfire

    Science.gov (United States)

    Shinneman, Douglas J.; Aldridge, Cameron L.; Coates, Peter S.; Germino, Matthew J.; Pilliod, David S.; Vaillant, Nicole M.

    2018-03-15

    to ultimately reduce a greater threat of their destruction from wildfire. However, there is relatively little published science that directly addresses the ability of fuel breaks to influence fire behavior in dryland landscapes or that addresses the potential ecological effects of the construction and maintenance of fuel breaks on sagebrush ecosystems and associated wildlife species.This report is intended to provide an initial assessment of both the potential effectiveness of fuel breaks and their ecological costs and benefits. To provide this assessment, we examined prior studies on fuel breaks and other scientific evidence to address three crucial questions: (1) How effective are fuel breaks in reducing or slowing the spread of wildfire in arid and semi-arid shrubland ecosystems? (2) How do fuel breaks affect sagebrush plant communities? (3) What are the effects of fuel breaks on the greater sage-grouse, other sagebrush obligates, and sagebrush-associated wildlife species? We also provide an overview of recent federal policies and management directives aimed at protecting remaining sagebrush and greater sage-grouse habitat; describe the fuel conditions, fire behavior, and fire trends in the Great Basin; and suggest how scientific inquiry and management actions can improve our understanding of fuel breaks and their effects in sagebrush landscapes.

  9. The effect of herbaceous species removal, fire and cheatgrass (Bromus tectorum) on soil water availability in sagebrush steppe

    Science.gov (United States)

    Alison Whittaker; Bruce Roundy; Jeanne Chambers; Susan Meyer; Robert Blank; Stanley Kitchen; John Korfmacher

    2008-01-01

    Over the past several decades, cheatgrass (Bromus tectorum) has been continually expanding in the sagebrush steppe ecosystem. There has been very little research that examines why cheatgrass is able to invade these communities. To determine the effects of herbaceous vegetation removal and fire on available water for cheatgrass invasion, as well as...

  10. Remote sensing of sagebrush canopy nitrogen

    Science.gov (United States)

    Mitchell, Jessica J.; Glenn, Nancy F.; Sankey, Temuulen T.; Derryberry, DeWayne R.; Germino, Matthew J.

    2012-01-01

    This paper presents a combination of techniques suitable for remotely sensing foliar Nitrogen (N) in semiarid shrublands – a capability that would significantly improve our limited understanding of vegetation functionality in dryland ecosystems. The ability to estimate foliar N distributions across arid and semi-arid environments could help answer process-driven questions related to topics such as controls on canopy photosynthesis, the influence of N on carbon cycling behavior, nutrient pulse dynamics, and post-fire recovery. Our study determined that further exploration into estimating sagebrush canopy N concentrations from an airborne platform is warranted, despite remote sensing challenges inherent to open canopy systems. Hyperspectral data transformed using standard derivative analysis were capable of quantifying sagebrush canopy N concentrations using partial least squares (PLS) regression with an R2 value of 0.72 and an R2 predicted value of 0.42 (n = 35). Subsetting the dataset to minimize the influence of bare ground (n = 19) increased R2 to 0.95 (R2 predicted = 0.56). Ground-based estimates of canopy N using leaf mass per unit area measurements (LMA) yielded consistently better model fits than ground-based estimates of canopy N using cover and height measurements. The LMA approach is likely a method that could be extended to other semiarid shrublands. Overall, the results of this study are encouraging for future landscape scale N estimates and represent an important step in addressing the confounding influence of bare ground, which we found to be a major influence on predictions of sagebrush canopy N from an airborne platform.

  11. Development of Competency-Based Articulated Automotive Program. Big Bend Community College and Area High Schools. Final Report.

    Science.gov (United States)

    Buche, Fred; Cox, Charles

    A competency-based automotive mechanics curriculum was developed at Big Bend Community College (Washington) in order to provide the basis for an advanced placement procedure for high school graduates and experienced adults through a competency assessment. In order to create the curriculum, Big Bend Community College automotive mechanics…

  12. Ecohydrology of adjacent sagebrush and lodgepole pine ecosystems: the consequences of climate change and disturbance

    Science.gov (United States)

    Bradford, John B.; Schlaepfer, Daniel R.; Lauenroth, William K.

    2014-01-01

    Sagebrush steppe and lodgepole pine forests are two of the most widespread vegetation types in the western United States and they play crucial roles in the hydrologic cycle of these water-limited regions. We used a process-based ecosystem water model to characterize the potential impact of climate change and disturbance (wildfire and beetle mortality) on water cycling in adjacent sagebrush and lodgepole pine ecosystems. Despite similar climatic and topographic conditions between these ecosystems at the sites examined, lodgepole pine, and sagebrush exhibited consistent differences in water balance, notably more evaporation and drier summer soils in the sagebrush and greater transpiration and less water yield in lodgepole pine. Canopy disturbances (either fire or beetle) have dramatic impacts on water balance and availability: reducing transpiration while increasing evaporation and water yield. Results suggest that climate change may reduce snowpack, increase evaporation and transpiration, and lengthen the duration of dry soil conditions in the summer, but may have uncertain effects on drainage. Changes in the distribution of sagebrush and lodgepole pine ecosystems as a consequence of climate change and/or altered disturbance regimes will likely alter ecosystem water balance.

  13. Fungal and bacterial contributions to nitrogen cycling in cheatgrass-invaded and uninvaded native sagebrush soils of the western USA

    Science.gov (United States)

    DeCrappeo, Nicole; DeLorenze, Elizabeth J.; Giguere, Andrew T; Pyke, David A.; Bottomley, Peter J.

    2017-01-01

    AimThere is interest in determining how cheatgrass (Bromus tectorum L.) modifies N cycling in sagebrush (Artemisia tridentata Nutt.) soils of the western USA.MethodsTo gain insight into the roles of fungi and bacteria in N cycling of cheatgrass-invaded and uninvaded sagebrush soils, the fungal protein synthesis inhibitor, cycloheximide (CHX), and the bacteriocidal compound, bronopol (BRO) were combined with a 15NH4+ isotope pool dilution approach.ResultsCHX reduced gross N mineralization to the same rate in both sagebrush and cheatgrass soils indicating a role for fungi in N mineralization in both soil types. In cheatgrass soils BRO completely inhibited gross N mineralization, whereas, in sagebrush soils a BRO-resistant gross N mineralization rate was detected that was slower than CHX sensitive gross N mineralization, suggesting that the microbial drivers of gross N mineralization were different in sagebrush and cheatgrass soils. Net N mineralization was stimulated to a higher rate in sagebrush than in cheatgrass soils by CHX, implying that a CHX inhibited N sink was larger in the former than the latter soils. Initial gross NH4+ consumption rates were reduced significantly by both CHX and BRO in both soil types, yet, consumption rates recovered significantly between 24 and 48 h in CHX-treated sagebrush soils. The recovery of NH4+ consumption in sagebrush soils corresponded with an increase in the rate of net nitrification.ConclusionsThese results suggest that cheatgrass invasion of sagebrush soils of the northern Great Basin reduces the capacity of the fungal N consumption sink, enhances the capacity of a CHX resistant N sink and alters the contributions of bacteria and fungi to gross N mineralization.

  14. Creation a Geo Big Data Outreach and Training Collaboratory for Wildfire Community

    Science.gov (United States)

    Altintas, I.; Sale, J.; Block, J.; Cowart, C.; Crawl, D.

    2015-12-01

    A major challenge for the geoscience community is the training and education of current and next generation big data geoscientists. In wildfire research, there are an increasing number of tools, middleware and techniques to use for data science related to wildfires. The necessary computing infrastructures are often within reach and most of the software tools for big data are freely available. But what has been lacking is a transparent platform and training program to produce data science experts who can use these integrated tools effectively. Scientists well versed to take advantage of big data technologies in geoscience applications is of critical importance to the future of research and knowledge advancement. To address this critical need, we are developing learning modules to teach process-based thinking to capture the value of end-to-end systems of reusable blocks of knowledge and integrate the tools and technologies used in big data analysis in an intuitive manner. WIFIRE is an end-to-end cyberinfrastructure for dynamic data-driven simulation, prediction and visualization of wildfire behavior.To this end, we are openly extending an environment we have built for "big data training" (biobigdata.ucsd.edu) to similar MOOC-based approaches to the wildfire community. We are building an environment that includes training modules for distributed platforms and systems, Big Data concepts, and scalable workflow tools, along with other basics of data science including data management, reproducibility and sharing of results. We also plan to provide teaching modules with analytical and dynamic data-driven wildfire behavior modeling case studies which address the needs not only of standards-based K-12 science education but also the needs of a well-educated and informed citizenry.Another part our outreach mission is to educate our community on all aspects of wildfire research. One of the most successful ways of accomplishing this is through high school and undergraduate

  15. Restoration handbook for sagebrush steppe ecosystems with emphasis on greater sage-grouse habitat—Part 1. Concepts for understanding and applying restoration

    Science.gov (United States)

    Pyke, David A.; Chambers, Jeanne C.; Pellant, Mike; Knick, Steven T.; Miller, Richard F.; Beck, Jeffrey L.; Doescher, Paul S.; Schupp, Eugene W.; Roundy, Bruce A.; Brunson, Mark; McIver, James D.

    2015-10-26

    Sagebrush steppe ecosystems in the United States currently occur on only about one-half of their historical land area because of changes in land use, urban growth, and degradation of land, including invasions of non-native plants. The existence of many animal species depends on the existence of sagebrush steppe habitat. The greater sage-grouse (Centrocercus urophasianus) is a landscape-dependent bird that requires intact habitat and combinations of sagebrush and perennial grasses to exist. In addition, other sagebrush-obligate animals also have similar requirements and restoration of landscapes for greater sage-grouse also will benefit these animals. Once sagebrush lands are degraded, they may require restoration actions to make those lands viable habitat for supporting sagebrushobligate animals. This restoration handbook is the first in a three-part series on restoration of sagebrush ecosystems. In Part 1, we discuss concepts surrounding landscape and restoration ecology of sagebrush ecosystems and greater sage-grouse that habitat managers and restoration practitioners need to know to make informed decisions regarding where and how to restore specific areas. We will describe the plant dynamics of sagebrush steppe ecosystems and their responses to major disturbances, fire, and defoliation. We will introduce the concepts of ecosystem resilience to disturbances and resistance to invasions of annual grasses within sagebrush steppe. An introduction to soils and ecological site information will provide insights into the specific plants that can be restored in a location. Soil temperature and moisture regimes are described as a tool for determining resilience and resistance and the potential for various restoration actions. Greater sage-grouse are considered landscape birds that require large areas of intact sagebrush steppe; therefore, we describe concepts of landscape ecology that aid our decisions regarding habitat restoration. We provide a brief overview of

  16. Benchmarking Big Data Systems and the BigData Top100 List.

    Science.gov (United States)

    Baru, Chaitanya; Bhandarkar, Milind; Nambiar, Raghunath; Poess, Meikel; Rabl, Tilmann

    2013-03-01

    "Big data" has become a major force of innovation across enterprises of all sizes. New platforms with increasingly more features for managing big datasets are being announced almost on a weekly basis. Yet, there is currently a lack of any means of comparability among such platforms. While the performance of traditional database systems is well understood and measured by long-established institutions such as the Transaction Processing Performance Council (TCP), there is neither a clear definition of the performance of big data systems nor a generally agreed upon metric for comparing these systems. In this article, we describe a community-based effort for defining a big data benchmark. Over the past year, a Big Data Benchmarking Community has become established in order to fill this void. The effort focuses on defining an end-to-end application-layer benchmark for measuring the performance of big data applications, with the ability to easily adapt the benchmark specification to evolving challenges in the big data space. This article describes the efforts that have been undertaken thus far toward the definition of a BigData Top100 List. While highlighting the major technical as well as organizational challenges, through this article, we also solicit community input into this process.

  17. Restoration handbook for sagebrush steppe ecosystems with emphasis on greater sage-grouse habitat - Part 1

    Science.gov (United States)

    David A. Pyke; Jeanne C. Chambers; Mike Pellant; Steven T. Knick; Richard F. Miller; Jeffrey L. Beck; Paul S. Doescher; Eugene W. Schupp; Bruce A. Roundy; Mark Brunson; James D. McIver

    2015-01-01

    Sagebrush steppe ecosystems in the United States currently occur on only about one-half of their historical land area because of changes in land use, urban growth, and degradation of land, including invasions of non-native plants. The existence of many animal species depends on the existence of sagebrush steppe habitat. The greater sage-grouse (Centrocercus...

  18. Land use and habitat conditions across the southwestern Wyoming sagebrush steppe: development impacts, management effectiveness and the distribution of invasive plants

    Science.gov (United States)

    Manier, Daniel J.; Aldridge, Cameron L.; Anderson, Patrick; Chong, Geneva; Homer, Collin G.; O'Donnell, Michael S.; Schell, Spencer

    2011-01-01

    For the past several years, USGS has taken a multi-faceted approach to investigating the condition and trends in sagebrush steppe ecosystems. This recent effort builds upon decades of work in semi-arid ecosystems providing a specific, applied focus on the cumulative impacts of expanding human activities across these landscapes. Here, we discuss several on-going projects contributing to these efforts: (1) mapping and monitoring the distribution and condition of shrub steppe communities with local detail at a regional scale, (2) assessing the relationships between specific, land-use features (for example, roads, transmission lines, industrial pads) and invasive plants, including their potential (environmentally defined) distribution across the region, and (3) monitoring the effects of habitat treatments on the ecosystem, including wildlife use and invasive plant abundance. This research is focused on the northern sagebrush steppe, primarily in Wyoming, but also extending into Montana, Colorado, Utah and Idaho. The study area includes a range of sagebrush types (including, Artemisia tridentata ssp. tridentata, Artemisia tridentata ssp. wyomingensis, Artemisia tridentata ssp. vaseyana, Artemisia nova) and other semi-arid shrubland types (for example, Sarcobatus vermiculatus, Atriplex confertifolia, Atriplex gardneri), impacted by extensive interface between steppe ecosystems and industrial energy activities resulting in a revealing multiple-variable analysis. We use a combination of remote sensing (AWiFS (1 Any reference to platforms, data sources, equipment, software, patented or trade-marked methods is for information purposes only. It does not represent endorsement of the U.S.D.I., U.S.G.S. or the authors), Landsat and Quickbird platforms), Geographic Information System (GIS) design and data management, and field-based, replicated sampling to generate multiple scales of data representing the distribution of shrub communities for the habitat inventory. Invasive plant

  19. The role of symbiotic nitrogen fixation in nitrogen availability, competition and plant invasion into the sagebrush steppe

    Science.gov (United States)

    Erin M. Goergen

    2009-01-01

    In the semi-arid sagebrush steppe of the Northeastern Sierra Nevada, resources are both spatially and temporally variable, arguably making resource availability a primary factor determining invasion success. N fixing plant species, primarily native legumes, are often relatively abundant in sagebrush steppe and can contribute to ecosystem nitrogen budgets. ...

  20. The community-driven BiG CZ software system for integration and analysis of bio- and geoscience data in the critical zone

    Science.gov (United States)

    Aufdenkampe, A. K.; Mayorga, E.; Horsburgh, J. S.; Lehnert, K. A.; Zaslavsky, I.; Valentine, D. W., Jr.; Richard, S. M.; Cheetham, R.; Meyer, F.; Henry, C.; Berg-Cross, G.; Packman, A. I.; Aronson, E. L.

    2014-12-01

    Here we present the prototypes of a new scientific software system designed around the new Observations Data Model version 2.0 (ODM2, https://github.com/UCHIC/ODM2) to substantially enhance integration of biological and Geological (BiG) data for Critical Zone (CZ) science. The CZ science community takes as its charge the effort to integrate theory, models and data from the multitude of disciplines collectively studying processes on the Earth's surface. The central scientific challenge of the CZ science community is to develop a "grand unifying theory" of the critical zone through a theory-model-data fusion approach, for which the key missing need is a cyberinfrastructure for seamless 4D visual exploration of the integrated knowledge (data, model outputs and interpolations) from all the bio and geoscience disciplines relevant to critical zone structure and function, similar to today's ability to easily explore historical satellite imagery and photographs of the earth's surface using Google Earth. This project takes the first "BiG" steps toward answering that need. The overall goal of this project is to co-develop with the CZ science and broader community, including natural resource managers and stakeholders, a web-based integration and visualization environment for joint analysis of cross-scale bio and geoscience processes in the critical zone (BiG CZ), spanning experimental and observational designs. We will: (1) Engage the CZ and broader community to co-develop and deploy the BiG CZ software stack; (2) Develop the BiG CZ Portal web application for intuitive, high-performance map-based discovery, visualization, access and publication of data by scientists, resource managers, educators and the general public; (3) Develop the BiG CZ Toolbox to enable cyber-savvy CZ scientists to access BiG CZ Application Programming Interfaces (APIs); and (4) Develop the BiG CZ Central software stack to bridge data systems developed for multiple critical zone domains into a single

  1. BigDataBench: a Big Data Benchmark Suite from Internet Services

    OpenAIRE

    Wang, Lei; Zhan, Jianfeng; Luo, Chunjie; Zhu, Yuqing; Yang, Qiang; He, Yongqiang; Gao, Wanling; Jia, Zhen; Shi, Yingjie; Zhang, Shujie; Zheng, Chen; Lu, Gang; Zhan, Kent; Li, Xiaona; Qiu, Bizhu

    2014-01-01

    As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data systems, big data benchmarks must include diversity of data and workloads. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purpo...

  2. Abiotic and biotic influences on Bromus tectoreum invasion and Artemisia tridentata recovery after fire

    Science.gov (United States)

    Lea Condon; Peter J. Weisberg; Jeanne C. Chambers

    2011-01-01

    Native sagebrush ecosystems in the Great Basin (western USA) are often invaded following fire by exotic Bromus tectorum (cheatgrass), a highly flammable annual grass. Once B. tectorum is established, higher fire frequencies can lead to local extirpation of Artemisia tridentata ssp. vaseyana (mountain big sagebrush) and have cascading effects on sagebrush ecosystems and...

  3. Discussing the Life of the Others: Doing Ethnography in the Brazilian Big Brother Fan Community

    Directory of Open Access Journals (Sweden)

    Bruno Campanella

    2009-05-01

    Full Text Available The aim of this paper is to present some of the initial results of ethnographic research conducted in early 2008 with the online fan community of the Brazilian Big Brother (BBB. After a brief introduction to some of the challenges faced by ethnographic work on television audiences in the last couple of decades, the current piece will explore some of the main characteristics constituting this new social space. Beyond the gossiping, and the more immediate talks about behaviors and game strategies of the Big Brother housemates, these forums sometimes trigger exchanges about Brazilian society at large, and the role of television broadcasting in general. Nonetheless, a closer inspection reveals how the debates found in the community are themselves permeated by the participants' struggle for status.

  4. Assessing long-term variations in sagebrush habitat: characterization of spatial extents and distribution patterns using multi-temporal satellite remote-sensing data

    Science.gov (United States)

    Xian, George; Homer, Collin G.; Aldridge, Cameron L.

    2012-01-01

    An approach that can generate sagebrush habitat change estimates for monitoring large-area sagebrush ecosystems has been developed and tested in southwestern Wyoming, USA. This prototype method uses a satellite-based image change detection algorithm and regression models to estimate sub-pixel percentage cover for five sagebrush habitat components: bare ground, herbaceous, litter, sagebrush and shrub. Landsat images from three different months in 1988, 1996 and 2006 were selected to identify potential landscape change during these time periods using change vector (CV) analysis incorporated with an image normalization algorithm. Regression tree (RT) models were used to estimate percentage cover for five components on all change areas identified in 1988 and 1996, using unchanged 2006 baseline data as training for both estimates. Over the entire study area (24 950 km2), a net increase of 98.83 km2, or 0.7%, for bare ground was measured between 1988 and 2006. Over the same period, the other four components had net losses of 20.17 km2, or 0.6%, for herbaceous vegetation; 30.16 km2, or 0.7%, for litter; 32.81 km2, or 1.5%, for sagebrush; and 33.34 km2, or 1.2%, for shrubs. The overall accuracy for shrub vegetation change between 1988 and 2006 was 89.56%. Change patterns within sagebrush habitat components differ spatially and quantitatively from each other, potentially indicating unique responses by these components to disturbances imposed upon them.

  5. Bird-habitat relationships in interior Columbia Basin shrubsteppe

    Science.gov (United States)

    Earnst, S.L.; Holmes, A.L.

    2012-01-01

    Vegetation structure is considered an important habitat feature structuring avian communities. In the sagebrush biome, both remotely-sensed and field-acquired measures of big sagebrush (Artemisia tridentata) cover have proven valuable in understanding avian abundance. Differences in structure between the exotic annual cheatgrass (Bromus tectorum) and native bunchgrasses are also expected to be important. We used avian abundance data from 318 point count stations, coupled with field vegetation measurements and a detailed vegetation map, to model abundance for four shrub- and four grassland-associated avian species in southeastern Washington shrubsteppe. Specifically, we ask whether species distinguish between bunchgrass and cheatgrass, and whether mapped, categorical cover types adequately explain species' abundance or whether fine-grained, field-measured differences in vegetation cover are also important. Results indicate that mapped cover types alone can be useful for predicting patterns of distribution and abundance within the sagebrush biome for several avian species (five of eight studied here). However, field-measured sagebrush cover was a strong positive predictor for Sage Sparrow (Amphispiza belli), the only sagebrush obligate in this study, and a strong negative predictor for two grassland associates, Horned Lark (Eremophila alpestris) and Grasshopper Sparrow (Ammodramus savannarum). Likewise, shrub associates did not differ in abundance in sagebrush with a cheatgrass vs. bunchgrass understory, but grassland associates were more common in either bunchgrass (Horned Lark and Grasshopper Sparrow) or cheatgrass grasslands (Long-billed Curlew, Numenius americanus), or tended to use sagebrush-cheatgrass less than sagebrush-bunchgrass (Horned Lark, Grasshopper Sparrow, and Savannah Sparrow, Passerculus sandwichensis).

  6. Restoration handbook for sagebrush steppe ecosystems with emphasis on greater sage-grouse habitat - Part 3: Site level restoration decisions

    Science.gov (United States)

    David A. Pyke; Jeanne C. Chambers; Mike Pellant; Richard F. Miller; Jeffrey L. Beck; Paul S. Doescher; Bruce A. Roundy; Eugene W. Schupp; Steven T. Knick; Mark Brunson; James D. McIver

    2017-01-01

    Sagebrush steppe ecosystems in the United States currently (2016) occur on only about one-half of their historical land area because of changes in land use, urban growth, and degradation of land, including invasions of non-native plants. The existence of many animal species depends on the existence of sagebrush steppe habitat. The greater sage-grouse (Centrocercus...

  7. Evapotranspiration over spatially extensive plant communities in the Big Cypress National Preserve, southern Florida, 2007-2010

    Science.gov (United States)

    Shoemaker, W. Barclay; Lopez, Christian D.; Duever, Michael J.

    2011-01-01

    Evapotranspiration (ET) was quantified over plant communities within the Big Cypress National Preserve (BCNP) using the eddy covariance method for a period of 3 years from October 2007 to September 2010. Plant communities selected for study included Pine Upland, Wet Prairie, Marsh, Cypress Swamp, and Dwarf Cypress. These plant communities are spatially extensive in southern Florida, and thus, the ET measurements described herein can be applied to other humid subtropical locations such as the Everglades.

  8. Big Data and Big Science

    OpenAIRE

    Di Meglio, Alberto

    2014-01-01

    Brief introduction to the challenges of big data in scientific research based on the work done by the HEP community at CERN and how the CERN openlab promotes collaboration among research institutes and industrial IT companies. Presented at the FutureGov 2014 conference in Singapore.

  9. Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: learning from past climate patterns and Landsat imagery to predict the future

    Science.gov (United States)

    Homer, Collin G.; Xian, George Z.; Aldridge, Cameron L.; Meyer, Debra K.; Loveland, Thomas R.; O'Donnell, Michael S.

    2015-01-01

    Sagebrush (Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances have altered and reduced this ecosystem historically, but climate change may ultimately represent the greatest future risk. Improved ways to quantify, monitor, and predict climate-driven gradual change in this ecosystem is vital to its future management. We examined the annual change of Daymet precipitation (daily gridded climate data) and five remote sensing ecosystem sagebrush vegetation and soil components (bare ground, herbaceous, litter, sagebrush, and shrub) from 1984 to 2011 in southwestern Wyoming. Bare ground displayed an increasing trend in abundance over time, and herbaceous, litter, shrub, and sagebrush showed a decreasing trend. Total precipitation amounts show a downward trend during the same period. We established statistically significant correlations between each sagebrush component and historical precipitation records using a simple least squares linear regression. Using the historical relationship between sagebrush component abundance and precipitation in a linear model, we forecasted the abundance of the sagebrush components in 2050 using Intergovernmental Panel on Climate Change (IPCC) precipitation scenarios A1B and A2. Bare ground was the only component that increased under both future scenarios, with a net increase of 48.98 km2 (1.1%) across the study area under the A1B scenario and 41.15 km2 (0.9%) under the A2 scenario. The remaining components decreased under both future scenarios: litter had the highest net reductions with 49.82 km2 (4.1%) under A1B and 50.8 km2 (4.2%) under A2, and herbaceous had the smallest net reductions with 39.95 km2 (3.8%) under A1B and 40.59 km2 (3.3%) under A2. We applied the 2050 forecast sagebrush component values to contemporary (circa 2006) greater sage-grouse (Centrocercus

  10. Mechanical mastication of Utah juniper encroaching sagebrush steppe increases inorganic soil N

    Science.gov (United States)

    Juniper (Juniperus spp.) has encroached millions of hectares of sagebrush (Artemisia spp.) steppe. Juniper mechanical mastication increases cover of understory species, but could increase resource availability and subsequently invasive plant species. We quantified the effects of juniper mastication ...

  11. Big Surveys, Big Data Centres

    Science.gov (United States)

    Schade, D.

    2016-06-01

    Well-designed astronomical surveys are powerful and have consistently been keystones of scientific progress. The Byurakan Surveys using a Schmidt telescope with an objective prism produced a list of about 3000 UV-excess Markarian galaxies but these objects have stimulated an enormous amount of further study and appear in over 16,000 publications. The CFHT Legacy Surveys used a wide-field imager to cover thousands of square degrees and those surveys are mentioned in over 1100 publications since 2002. Both ground and space-based astronomy have been increasing their investments in survey work. Survey instrumentation strives toward fair samples and large sky coverage and therefore strives to produce massive datasets. Thus we are faced with the "big data" problem in astronomy. Survey datasets require specialized approaches to data management. Big data places additional challenging requirements for data management. If the term "big data" is defined as data collections that are too large to move then there are profound implications for the infrastructure that supports big data science. The current model of data centres is obsolete. In the era of big data the central problem is how to create architectures that effectively manage the relationship between data collections, networks, processing capabilities, and software, given the science requirements of the projects that need to be executed. A stand alone data silo cannot support big data science. I'll describe the current efforts of the Canadian community to deal with this situation and our successes and failures. I'll talk about how we are planning in the next decade to try to create a workable and adaptable solution to support big data science.

  12. Big data analytics to aid developing livable communities.

    Science.gov (United States)

    2015-12-31

    In transportation, ubiquitous deployment of low-cost sensors combined with powerful : computer hardware and high-speed network makes big data available. USDOT defines big : data research in transportation as a number of advanced techniques applied to...

  13. Birds of a Great Basin Sagebrush Habitat in East-Central Nevada

    OpenAIRE

    United States Department of Agriculture, Forest Service

    1992-01-01

    Breeding bird populations ranged from 3.35 to 3.48 individuals/ha over a 3-year study conducted from 1981 to 1983. Brewer's sparrows, sage sparrows, sage thrashers, and black-throated sparrows were numerically dominant. Horned larks and western meadowlarks were less common. Results are compared with bird populations in Great Basin sagebrush habitats elsewhere in the United States.

  14. Big data integration: scalability and sustainability

    KAUST Repository

    Zhang, Zhang

    2016-01-26

    Integration of various types of omics data is critically indispensable for addressing most important and complex biological questions. In the era of big data, however, data integration becomes increasingly tedious, time-consuming and expensive, posing a significant obstacle to fully exploit the wealth of big biological data. Here we propose a scalable and sustainable architecture that integrates big omics data through community-contributed modules. Community modules are contributed and maintained by different committed groups and each module corresponds to a specific data type, deals with data collection, processing and visualization, and delivers data on-demand via web services. Based on this community-based architecture, we build Information Commons for Rice (IC4R; http://ic4r.org), a rice knowledgebase that integrates a variety of rice omics data from multiple community modules, including genome-wide expression profiles derived entirely from RNA-Seq data, resequencing-based genomic variations obtained from re-sequencing data of thousands of rice varieties, plant homologous genes covering multiple diverse plant species, post-translational modifications, rice-related literatures, and community annotations. Taken together, such architecture achieves integration of different types of data from multiple community-contributed modules and accordingly features scalable, sustainable and collaborative integration of big data as well as low costs for database update and maintenance, thus helpful for building IC4R into a comprehensive knowledgebase covering all aspects of rice data and beneficial for both basic and translational researches.

  15. Ecosystem functional response across precipitation extremes in a sagebrush steppe.

    Science.gov (United States)

    Tredennick, Andrew T; Kleinhesselink, Andrew R; Taylor, J Bret; Adler, Peter B

    2018-01-01

    Precipitation is predicted to become more variable in the western United States, meaning years of above and below average precipitation will become more common. Periods of extreme precipitation are major drivers of interannual variability in ecosystem functioning in water limited communities, but how ecosystems respond to these extremes over the long-term may shift with precipitation means and variances. Long-term changes in ecosystem functional response could reflect compensatory changes in species composition or species reaching physiological thresholds at extreme precipitation levels. We conducted a five year precipitation manipulation experiment in a sagebrush steppe ecosystem in Idaho, United States. We used drought and irrigation treatments (approximately 50% decrease/increase) to investigate whether ecosystem functional response remains consistent under sustained high or low precipitation. We recorded data on aboveground net primary productivity (ANPP), species abundance, and soil moisture. We fit a generalized linear mixed effects model to determine if the relationship between ANPP and soil moisture differed among treatments. We used nonmetric multidimensional scaling to quantify community composition over the five years. Ecosystem functional response, defined as the relationship between soil moisture and ANPP, was similar among irrigation and control treatments, but the drought treatment had a greater slope than the control treatment. However, all estimates for the effect of soil moisture on ANPP overlapped zero, indicating the relationship is weak and uncertain regardless of treatment. There was also large spatial variation in ANPP within-years, which contributes to the uncertainty of the soil moisture effect. Plant community composition was remarkably stable over the course of the experiment and did not differ among treatments. Despite some evidence that ecosystem functional response became more sensitive under sustained drought conditions, the response

  16. The big data-big model (BDBM) challenges in ecological research

    Science.gov (United States)

    Luo, Y.

    2015-12-01

    The field of ecology has become a big-data science in the past decades due to development of new sensors used in numerous studies in the ecological community. Many sensor networks have been established to collect data. For example, satellites, such as Terra and OCO-2 among others, have collected data relevant on global carbon cycle. Thousands of field manipulative experiments have been conducted to examine feedback of terrestrial carbon cycle to global changes. Networks of observations, such as FLUXNET, have measured land processes. In particular, the implementation of the National Ecological Observatory Network (NEON), which is designed to network different kinds of sensors at many locations over the nation, will generate large volumes of ecological data every day. The raw data from sensors from those networks offer an unprecedented opportunity for accelerating advances in our knowledge of ecological processes, educating teachers and students, supporting decision-making, testing ecological theory, and forecasting changes in ecosystem services. Currently, ecologists do not have the infrastructure in place to synthesize massive yet heterogeneous data into resources for decision support. It is urgent to develop an ecological forecasting system that can make the best use of multiple sources of data to assess long-term biosphere change and anticipate future states of ecosystem services at regional and continental scales. Forecasting relies on big models that describe major processes that underlie complex system dynamics. Ecological system models, despite great simplification of the real systems, are still complex in order to address real-world problems. For example, Community Land Model (CLM) incorporates thousands of processes related to energy balance, hydrology, and biogeochemistry. Integration of massive data from multiple big data sources with complex models has to tackle Big Data-Big Model (BDBM) challenges. Those challenges include interoperability of multiple

  17. Main Issues in Big Data Security

    Directory of Open Access Journals (Sweden)

    Julio Moreno

    2016-09-01

    Full Text Available Data is currently one of the most important assets for companies in every field. The continuous growth in the importance and volume of data has created a new problem: it cannot be handled by traditional analysis techniques. This problem was, therefore, solved through the creation of a new paradigm: Big Data. However, Big Data originated new issues related not only to the volume or the variety of the data, but also to data security and privacy. In order to obtain a full perspective of the problem, we decided to carry out an investigation with the objective of highlighting the main issues regarding Big Data security, and also the solutions proposed by the scientific community to solve them. In this paper, we explain the results obtained after applying a systematic mapping study to security in the Big Data ecosystem. It is almost impossible to carry out detailed research into the entire topic of security, and the outcome of this research is, therefore, a big picture of the main problems related to security in a Big Data system, along with the principal solutions to them proposed by the research community.

  18. Are Big Food's corporate social responsibility strategies valuable to communities? A qualitative study with parents and children.

    Science.gov (United States)

    Richards, Zoe; Phillipson, Lyn

    2017-12-01

    Recent studies have identified parents and children as two target groups whom Big Food hopes to positively influence through its corporate social responsibility (CSR) strategies. The current preliminary study aimed to gain an in-depth understanding of parents and children's awareness and interpretation of Big Food's CSR strategies to understand how CSR shapes their beliefs about companies. Community-based qualitative semi-structured interviews. New South Wales, Australia. Parents (n 15) and children aged 8-12 years (n 15). Parents and children showed unprompted recognition of CSR activities when shown McDonald's and Coca-Cola brand logos, indicating a strong level of association between the brands and activities that target the settings of children. When discussing CSR strategies some parents and most children saw value in the activities, viewing them as acts of merit or worth. For some parents and children, the companies' CSR activities were seen as a reflection of the company's moral attributes, which resonated with their own values of charity and health. For others, CSR strategies were in conflict with companies' core business. Finally, some also viewed the activities as harmful, representing a deceit of the public and a smokescreen for the companies' ultimately unethical behaviour. A large proportion of participants valued the CSR activities, signalling that denormalising CSR to sever the strong ties between the community and Big Food will be a difficult process for the public health community. Efforts to gain public acceptance for action on CSR may need greater levels of persuasion to gain public support of a comprehensive and restrictive approach.

  19. Semantic Web Technologies and Big Data Infrastructures: SPARQL Federated Querying of Heterogeneous Big Data Stores

    OpenAIRE

    Konstantopoulos, Stasinos; Charalambidis, Angelos; Mouchakis, Giannis; Troumpoukis, Antonis; Jakobitsch, Jürgen; Karkaletsis, Vangelis

    2016-01-01

    The ability to cross-link large scale data with each other and with structured Semantic Web data, and the ability to uniformly process Semantic Web and other data adds value to both the Semantic Web and to the Big Data community. This paper presents work in progress towards integrating Big Data infrastructures with Semantic Web technologies, allowing for the cross-linking and uniform retrieval of data stored in both Big Data infrastructures and Semantic Web data. The technical challenges invo...

  20. Mercury distribution in two Sierran forest and one desert sagebrush steppe ecosystems and the effects of fire

    International Nuclear Information System (INIS)

    Engle, Mark A.; Sexauer Gustin, Mae; Johnson, Dale W.; Murphy, James F.; Miller, Wally W.; Walker, Roger F.; Wright, Joan; Markee, Melissa

    2006-01-01

    Mercury (Hg) concentration, reservoir mass, and Hg reservoir size were determined for vegetation components, litter, and mineral soil for two Sierran forest sites and one desert sagebrush steppe site. Mercury was found to be held primarily in the mineral soil (maximum depth of 60 to 100 cm), which contained more than 90% of the total ecosystem reservoir. However, Hg in foliage, bark, and litter plays a more dominant role in Hg cycling than the mineral soil. Mercury partitioning into ecosystem components at the Sierran forest sites was similar to that observed for other US forest sites. Vegetation and litter Hg reservoirs were significantly smaller in the sagebrush steppe system because of lower biomass. Data collected from these ecosystems after wildfire and prescribed burns showed a significant decrease in the Hg pool from certain reservoirs. No loss from mineral soil was observed for the study areas but data from fire severity points suggested that Hg in the upper few millimeters of surface soil may be volatilized due to exposure to elevated temperatures. Comparison of data from burned and unburned plots suggested that the only significant source of atmospheric Hg from the prescribed burn was combustion of litter. Differences in unburned versus burned Hg reservoirs at the forest wildfire site demonstrated that drastic reduction in the litter and above ground live biomass Hg reservoirs after burning had occurred. Sagebrush and litter were absent in the burned plots after a wildfire suggesting that both reservoirs were released during the fire. Mercury emissions due to fire from the forest prescribed burn, forest wildfire, and sagebrush steppe wildfire sites were roughly estimated at 2.0 to 5.1, 2.2 to 4.9, and 0.36 ± 0.13 g ha -1 , respectively, with litter and vegetation being the most important sources

  1. Tapping soil survey information for rapid assessment of sagebrush ecosystem resilience and resistance

    Science.gov (United States)

    Jeremy D. Maestas; Steven B. Campbell; Jeanne C. Chambers; Mike Pellant; Richard F. Miller

    2016-01-01

    A new ecologically-based approach to risk abatement has emerged that can aid land managers in grappling with escalating impacts of large-scale wildfire and invasive annual grasses in sagebrush ecosystems, particularly in the Great Basin. Specifically, ecosystem resilience and resistance (R&R) concepts have been more fully operationalized from regional...

  2. Using resistance and resilience concepts to reduce impacts of annual grasses and altered fire regimes on the sagebrush ecosystem and sage-grouse- A strategic multi-scale approach

    Science.gov (United States)

    Chambers, Jeanne C.; Pyke, David A.; Maestas, Jeremy D.; Boyd, Chad S.; Campbell, Steve; Espinosa, Shawn; Havlina, Doug; Mayer, Kenneth F.; Wuenschel, Amarina

    2014-01-01

    This Report provides a strategic approach for conservation of sagebrush ecosystems and Greater Sage- Grouse (sage-grouse) that focuses specifically on habitat threats caused by invasive annual grasses and altered fire regimes. It uses information on factors that influence (1) sagebrush ecosystem resilience to disturbance and resistance to invasive annual grasses and (2) distribution, relative abundance, and persistence of sage-grouse populations to develop management strategies at both landscape and site scales. A sage-grouse habitat matrix links relative resilience and resistance of sagebrush ecosystems with sage-grouse habitat requirements for landscape cover of sagebrush to help decision makers assess risks and determine appropriate management strategies at landscape scales. Focal areas for management are assessed by overlaying matrix components with sage-grouse Priority Areas for Conservation (PACs), breeding bird densities, and specific habitat threats. Decision tools are discussed for determining the suitability of focal areas for treatment and the most appropriate management treatments.

  3. A cross-scale approach to understand drought-induced variability of sagebrush ecosystem productivity

    Science.gov (United States)

    Assal, T.; Anderson, P. J.

    2016-12-01

    Sagebrush (Artemisia spp.) mortality has recently been reported in the Upper Green River Basin (Wyoming, USA) of the sagebrush steppe of western North America. Numerous causes have been suggested, but recent drought (2012-13) is the likely mechanism of mortality in this water-limited ecosystem which provides critical habitat for many species of wildlife. An understanding of the variability in patterns of productivity with respect to climate is essential to exploit landscape scale remote sensing for detection of subtle changes associated with mortality in this sparse, uniformly vegetated ecosystem. We used the standardized precipitation index to characterize drought conditions and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery (250-m resolution) to characterize broad characteristics of growing season productivity. We calculated per-pixel growing season anomalies over a 16-year period (2000-2015) to identify the spatial and temporal variability in productivity. Metrics derived from Landsat satellite imagery (30-m resolution) were used to further investigate trends within anomalous areas at local scales. We found evidence to support an initial hypothesis that antecedent winter drought was most important in explaining reduced productivity. The results indicate drought effects were inconsistent over space and time. MODIS derived productivity deviated by more than four standard deviations in heavily impacted areas, but was well within the interannual variability in other areas. Growing season anomalies highlighted dramatic declines in productivity during the 2012 and 2013 growing seasons. However, large negative anomalies persisted in other areas during the 2014 growing season, indicating lag effects of drought. We are further investigating if the reduction in productivity is mediated by local biophysical properties. Our analysis identified spatially explicit patterns of ecosystem properties altered by severe drought which are consistent with

  4. Investigation of Artemisia tridentata as a biogeochemical uranium indicator

    Energy Technology Data Exchange (ETDEWEB)

    Diebold, F E; McGrath, S [Montana Coll. of Mineral Science and Technology, Butte (USA)

    1985-01-01

    Hydroponic experiments were conducted with seedlings of Artemisia tridentata subsp. tridentata (big sagebrush) to test the effect of the phosphate speciation of uranium in solution on its uptake by big sagebrush. No single complex could be identified as being preferentially taken up by the plant, but the varying aqueous phosphate concentrations did affect uranium uptake by the plants at the higher uranium concentrations in solution. The data also substantiate the tendency for uranium to behave as an essential element in this plant species. The implications for the use of Artemisia tridentata as a biogeochemical uranium indicator are discussed.

  5. Using resilience and resistance concepts to manage threats to sagebrush ecosystems, Gunnison sage-grouse, and Greater sage-grouse in their eastern range: A strategic multi-scale approach

    Science.gov (United States)

    Jeanne C. Chambers; Jeffrey L. Beck; Steve Campbell; John Carlson; Thomas J. Christiansen; Karen J. Clause; Jonathan B. Dinkins; Kevin E. Doherty; Kathleen A. Griffin; Douglas W. Havlina; Kenneth F. Henke; Jacob D. Hennig; Laurie L. Kurth; Jeremy D. Maestas; Mary Manning; Kenneth E. Mayer; Brian A. Mealor; Clinton McCarthy; Marco A. Perea; David A. Pyke

    2016-01-01

    This report provides a strategic approach developed by a Western Association of Fish and Wildlife Agencies interagency working group for conservation of sagebrush ecosystems, Greater sage-grouse, and Gunnison sage-grouse. It uses information on (1) factors that influence sagebrush ecosystem resilience to disturbance and resistance to nonnative invasive annual grasses...

  6. Science framework for conservation and restoration of the sagebrush biome: Linking the Department of the Interior’s Integrated Rangeland Fire Management Strategy to long-term strategic conservation actions, Part 1. Science basis and applications

    Science.gov (United States)

    Chambers, Jeanne C.; Beck, Jeffrey L.; Bradford, John B.; Bybee, Jared; Campbell, Steve; Carlson, John; Christiansen, Thomas J; Clause, Karen J.; Collins, Gail; Crist, Michele R.; Dinkins, Jonathan B.; Doherty, Kevin E.; Edwards, Fred; Espinosa, Shawn; Griffin, Kathleen A.; Griffin, Paul; Haas, Jessica R.; Hanser, Steven E.; Havlina, Douglas W.; Henke, Kenneth F.; Hennig, Jacob D.; Joyce, Linda A; Kilkenny, Francis F.; Kulpa, Sarah M; Kurth, Laurie L; Maestas, Jeremy D; Manning, Mary E.; Mayer, Kenneth E.; Mealor, Brian A.; McCarthy, Clinton; Pellant, Mike; Perea, Marco A.; Prentice, Karen L.; Pyke, David A.; Wiechman , Lief A.; Wuenschel, Amarina

    2017-01-01

    The Science Framework is intended to link the Department of the Interior’s Integrated Rangeland Fire Management Strategy with long-term strategic conservation actions in the sagebrush biome. The Science Framework provides a multiscale approach for prioritizing areas for management and determining effective management strategies within the sagebrush biome. The emphasis is on sagebrush (Artemisia spp.) ecosystems and Greater sage-grouse (Centrocercus urophasianus). The approach provided in the Science Framework links sagebrush ecosystem resilience to disturbance and resistance to nonnative, invasive plant species to species habitat information based on the distribution and abundance of focal species. A geospatial process is presented that overlays information on ecosystem resilience and resistance, species habitats, and predominant threats and that can be used at the mid-scale to prioritize areas for management. A resilience and resistance habitat matrix is provided that can help decisionmakers evaluate risks and determine appropriate management strategies. Prioritized areas and management strategies can be refined by managers and stakeholders at the local scale based on higher resolution data and local knowledge. Decision tools are discussed for determining appropriate management actions for areas that are prioritized for management. Geospatial data, maps, and models are provided through the U.S. Geological Survey (USGS) ScienceBase and Bureau of Land Management (BLM) Landscape Approach Data Portal. The Science Framework is intended to be adaptive and will be updated as additional data become available on other values and species at risk. It is anticipated that the Science Framework will be widely used to: (1) inform emerging strategies to conserve sagebrush ecosystems, sagebrush dependent species, and human uses of the sagebrush system, and (2) assist managers in prioritizing and planning on-the-ground restoration and mitigation actions across the sagebrush biome.

  7. Putting Big Data to Work: Community Colleges Use Detailed Reports to Design Smarter Workforce Training and Education Programs

    Science.gov (United States)

    Woods, Bob

    2013-01-01

    In this article, Bob Woods reports that "Big data" is all the rage on college campuses, and it makes sense that administrators would use what they know to boost student outcomes. Woods points out that community colleges around the country are using the data: (1) to guide the systematic expansion of its curriculum, providing targeted…

  8. Science framework for conservation and restoration of the sagebrush biome: Linking the Department of the Interior’s Integrated Rangeland Fire Management Strategy to long-term strategic conservation actions

    Science.gov (United States)

    J.C. Chambers; J.L. Beck; J.B. Bradford; J. Bybee; S. Campbell; J. Carlson; T.J. Christiansen; K.J. Clause; G. Collins; M.R. Crist; J.B. Dinkins; K.E. Doherty; F. Edwards; S. Espinosa; K.A. Griffin; P. Griffin; J.R. Haas; S.E. Hanser; D.W. Havlina; K.F. Henke; J.D. Hennig; L.A. Joyce; F.M. Kilkenny; S.M. Kulpa; L.L. Kurth; J.D. Maestas; M. Manning; K.E. Mayer; B.A. Mealor; C. McCarthy; M. Pellant; M.A. Perea; K.L. Prentice; D.A. Pyke; L.A. Wiechman; A. Wuenschel

    2017-01-01

    The Science Framework is intended to link the Department of the Interior’s Integrated Rangeland Fire Management Strategy with long-term strategic conservation actions in the sagebrush biome. The Science Framework provides a multiscale approach for prioritizing areas for management and determining effective management strategies within the sagebrush biome. The emphasis...

  9. Big Data - What is it and why it matters.

    Science.gov (United States)

    Tattersall, Andy; Grant, Maria J

    2016-06-01

    Big data, like MOOCs, altmetrics and open access, is a term that has been commonplace in the library community for some time yet, despite its prevalence, many in the library and information sector remain unsure of the relationship between big data and their roles. This editorial explores what big data could mean for the day-to-day practice of health library and information workers, presenting examples of big data in action, considering the ethics of accessing big data sets and the potential for new roles for library and information workers. © 2016 Health Libraries Group.

  10. 75 FR 77801 - Endangered and Threatened Wildlife and Plants; Endangered Status for Dunes Sagebrush Lizard

    Science.gov (United States)

    2010-12-14

    ... public lands in Texas. It is evident that the dunes sagebrush lizard is still present at the park, but... expected to contribute to habitat loss, modification, or fragmentation in the future include wind and solar... and Solar Energy Development Eastern New Mexico and western Texas are highly suitable areas for wind...

  11. Using resistance and resilience concepts to reduce impacts of invasive annual grasses and altered fire regimes on the sagebrush ecosystem and greater sage-grouse: A strategic multi-scale approach

    Science.gov (United States)

    Jeanne C. Chambers; David A. Pyke; Jeremy D. Maestas; Mike Pellant; Chad S. Boyd; Steven B. Campbell; Shawn Espinosa; Douglas W. Havlina; Kenneth E. Mayer; Amarina Wuenschel

    2014-01-01

    This Report provides a strategic approach for conservation of sagebrush ecosystems and Greater Sage- Grouse (sage-grouse) that focuses specifically on habitat threats caused by invasive annual grasses and altered fire regimes. It uses information on factors that influence (1) sagebrush ecosystem resilience to disturbance and resistance to invasive annual grasses and (2...

  12. Big data has big potential for applications to climate change adaptation

    NARCIS (Netherlands)

    Ford, James D.; Tilleard, Simon E.; Berrang-Ford, Lea; Araos, Malcolm; Biesbroek, Robbert; Lesnikowski, Alexandra C.; MacDonald, Graham K.; Hsu, Angel; Chen, Chen; Bizikova, Livia

    2016-01-01

    The capacity to collect and analyze massive amounts
    of data is transforming research in the natural and social
    sciences (1). And yet, the climate change adaptation
    community has largely overlooked these developments.
    Here, we examine how “big data” can inform adaptation
    research

  13. The challenges of big data.

    Science.gov (United States)

    Mardis, Elaine R

    2016-05-01

    The largely untapped potential of big data analytics is a feeding frenzy that has been fueled by the production of many next-generation-sequencing-based data sets that are seeking to answer long-held questions about the biology of human diseases. Although these approaches are likely to be a powerful means of revealing new biological insights, there are a number of substantial challenges that currently hamper efforts to harness the power of big data. This Editorial outlines several such challenges as a means of illustrating that the path to big data revelations is paved with perils that the scientific community must overcome to pursue this important quest. © 2016. Published by The Company of Biologists Ltd.

  14. Susceptibility and antibody response of Vesper Sparrows (Pooecetes gramineus) to West Nile virus: A potential amplification host in sagebrush-grassland habitat

    Science.gov (United States)

    Hofmeister, Erik K.; Dusek, Robert J.; Fassbinder-Orth, Carol; Owen, Benjamin; Franson, J. Christian

    2016-01-01

    West Nile virus (WNV) spread to the US western plains states in 2003, when a significant mortality event attributed to WNV occurred in Greater Sage-grouse ( Centrocercus urophasianus ). The role of avian species inhabiting sagebrush in the amplification of WNV in arid and semiarid regions of the North America is unknown. We conducted an experimental WNV challenge study in Vesper Sparrows ( Pooecetes gramineus ), a species common to sagebrush and grassland habitats found throughout much of North America. We found Vesper Sparrows to be moderately susceptible to WNV, developing viremia considered sufficient to transmit WNV to feeding mosquitoes, but the majority of birds were capable of surviving infection and developing a humoral immune response to the WNV nonstructural 1 and envelope proteins. Despite clearance of viremia, after 6 mo, WNV was detected molecularly in three birds and cultured from one bird. Surviving Vesper Sparrows were resistant to reinfection 6 mo after the initial challenge. Vesper sparrows could play a role in the amplification of WNV in sagebrush habitat and other areas of their range, but rapid clearance of WNV may limit their importance as competent amplification hosts of WNV.

  15. An assessment of the hypervariable domains of the 16S rRNA genes for their value in determining microbial community diversity: the paradox of traditional ecological indices.

    Science.gov (United States)

    Mills, DeEtta K; Entry, James A; Voss, Joshua D; Gillevet, Patrick M; Mathee, Kalai

    2006-09-01

    Amplicon length heterogeneity PCR (LH-PCR) was investigated for its ability to distinguish between microbial community patterns from the same soil type under different land management practices. Natural sagebrush and irrigated mouldboard-ploughed soils from Idaho were queried as to which hypervariable domains, or combinations of 16S rRNA gene domains, were the best molecular markers. Using standard ecological indices to measure richness, diversity and evenness, the combination of three domains, V1, V3 and V1+V2, or the combined V1 and V3 domains were the markers that could best distinguish the undisturbed natural sagebrush communities from the mouldboard-ploughed microbial communities. Bray-Curtis similarity and multidimensional scaling were found to be better metrics to ordinate and cluster the LH-PCR community profiling data. The use/misuse of traditional ecological indices such as diversity and evenness to study microbial community profiles will remain a major point to consider when performing metagenomic studies.

  16. Range-wide assessment of livestock grazing across the sagebrush biome

    Science.gov (United States)

    Veblen, Kari E.; Pyke, David A.; Jones, Christopher A.; Casazza, Michael L.; Assal, Timothy J.; Farinha, Melissa A.

    2011-01-01

    Domestic livestock grazing occurs in virtually all sagebrush habitats and is a prominent disturbance factor. By affecting habitat condition and trend, grazing influences the resources required by, and thus, the distribution and abundance of sagebrush-obligate wildlife species (for example, sage-grouse Centrocercus spp.). Yet, the risks that livestock grazing may pose to these species and their habitats are not always clear. Although livestock grazing intensity and associated habitat condition may be known in many places at the local level, we have not yet been able to answer questions about use, condition, and trend at the landscape scale or at the range-wide scale for wildlife species. A great deal of information about grazing use, management regimes, and ecological condition exists at the local level (for individual livestock management units) under the oversight of organizations such as the Bureau of Land Management (BLM). However, the extent, quality, and types of existing data are unknown, which hinders the compilation, mapping, or analysis of these data. Once compiled, these data may be helpful for drawing conclusions about rangeland status, and we may be able to identify relationships between those data and wildlife habitat at the landscape scale. The overall objective of our study was to perform a range-wide assessment of livestock grazing effects (and the relevant supporting data) in sagebrush ecosystems managed by the BLM. Our assessments and analyses focused primarily on local-level management and data collected at the scale of BLM grazing allotments (that is, individual livestock management units). Specific objectives included the following: 1. Identify and refine existing range-wide datasets to be used for analyses of livestock grazing effects on sagebrush ecosystems. 2. Assess the extent, quality, and types of livestock grazing-related natural resource data collected by BLM range-wide (i.e., across allotments, districts and regions). 3. Compile and

  17. Plant litter effects on soil nutrient availability and vegetation dynamics: changes that occur when annual grasses invade shrub-steppe communities

    Science.gov (United States)

    Sheel Bansal; Roger L. Sheley; Bob Blank; Edward A. Vasquez

    2014-01-01

    Changes in the quantity and quality of plant litter occur in many ecosystems as they are invaded by exotic species, which impact soil nutrient cycling and plant community composition. Such changes in sagebrush-steppe communities are occurring with invasion of annual grasses (AG) into a perennial grass (PG) dominated system. We conducted a 5-year litter manipulation...

  18. Establishing Artemisia tridentata ssp wyomingensis on mined lands: Science and economics

    Energy Technology Data Exchange (ETDEWEB)

    Schuman, G.E.; Vicklund, L.E.; Belden, S.E. [ARS, Cheyenne, WY (United States). High Plains Grasslands Research Station

    2005-12-01

    In 1996, the Wyoming Department of Environmental Quality enacted regulations governing the reestablishment of woody shrubs on mined lands. The regulation required that an average density of one shrub m{sup -2} be reestablished on at least 20% of the disturbed land area and that the shrub composition must include dominant premine species. In Wyoming, and much of the Northern Great Plains, that meant that Artemisia tridentata Nutt. ssp. wyomingensis (Beetle and Young) (Wyoming big sagebrush) had to be reestablished on mined lands. Artemisia tridentata Nutt. ssp. wyomingensis had proven difficult to reestablish on mined lands because of poor quality seed, seed dormancy and a poor understanding of the seedbed ecology of this species. Research in the last two decades has produced significant knowledge in the area of direct-seed establishment of Artemisia tridentata Nutt. ssp. wyomingensis on mined lands. Our research has shown that reducing grass seeding rates will reduce competition and result in larger sagebrush plants that are more likely to survive and provide greater structural diversity to the plant community. Economic analyses demonstrated that big sagebrush can be established at a cost of $0.01-0.05 per seedling using direct seeding methods compared to transplanting nursery grown seedlings, estimated to cost $0.72-$1.65 per seedling (depending on size) to grow and from $1.30-$2.40 to plant (flat land to 2:1 slopes). An adequate level of precipitation will be necessary to ensure successful establishment of this species no matter what method of propagation is selected and direct seeding gives greater opportunity for success because of the demonstrated longevity of the seed to germinate 3-5 years after the initial seeding.

  19. Big Earth Data Initiative: Metadata Improvement: Case Studies

    Science.gov (United States)

    Kozimor, John; Habermann, Ted; Farley, John

    2016-01-01

    Big Earth Data Initiative (BEDI) The Big Earth Data Initiative (BEDI) invests in standardizing and optimizing the collection, management and delivery of U.S. Government's civil Earth observation data to improve discovery, access use, and understanding of Earth observations by the broader user community. Complete and consistent standard metadata helps address all three goals.

  20. Big data analytics turning big data into big money

    CERN Document Server

    Ohlhorst, Frank J

    2012-01-01

    Unique insights to implement big data analytics and reap big returns to your bottom line Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportuni

  1. Plant age, communication, and resistance to herbivores: young sagebrush plants are better emitters and receivers.

    Science.gov (United States)

    Shiojiri, Kaori; Karban, Richard

    2006-08-01

    Plants progress through a series of distinct stages during development, although the role of plant ontogeny in their defenses against herbivores is poorly understood. Recent work indicates that many plants activate systemic induced resistance after herbivore attack, although the relationship between resistance and ontogeny has not been a focus of this work. In addition, for sagebrush and a few other species, individuals near neighbors that experience simulated herbivory become more resistant to subsequent attack. Volatile, airborne cues are required for both systemic induced resistance among branches and for communication among individuals. We conducted experiments in stands of sagebrush of mixed ages to determine effects of plant age on volatile signaling between branches and individuals. Young and old control plants did not differ in levels of chewing damage that they experienced. Systemic induced resistance among branches was only observed for young plants. Young plants showed strong evidence of systemic resistance only if airflow was permitted among branches; plants with only vascular connections showed no systemic resistance. We also found evidence for volatile communication between individuals. For airborne communication, young plants were more effective emitters of cues as well as more responsive receivers of volatile cues.

  2. Conservation and restoration of sagebrush ecosystems and sage-grouse: An assessment of USDA Forest Service Science

    Science.gov (United States)

    Deborah M. Finch; Douglas A. Boyce; Jeanne C. Chambers; Chris J. Colt; Kas Dumroese; Stanley G. Kitchen; Clinton McCarthy; Susan E. Meyer; Bryce A. Richardson; Mary M. Rowland; Mark A. Rumble; Michael K. Schwartz; Monica S. Tomosy; Michael J. Wisdom

    2016-01-01

    Sagebrush ecosystems are among the largest and most threatened ecosystems in North America. Greater sage-grouse has served as the bellwether for species conservation in these ecosystems and has been considered for listing under the Endangered Species Act eight times. In September 2015, the decision was made not to list greater sage-grouse, but to reevaluate its status...

  3. Evolutionary and ecological implications of genome size in the North American endemic sagebrushes and allies (Artemisia, Asteraceae)

    Science.gov (United States)

    Sonia Garcia; Miguel A. Canela; Teresa Garnatje; E. Durant McArthur; Jaume Pellicer; Stewart C. Sanderson; Joan Valles

    2008-01-01

    The genome size of 51 populations of 20 species of the North American endemic sagebrushes (subgenus Tridentatae), related species, and some hybrid taxa were assessed by flow cytometry, and were analysed in a phylogenetic framework. Results were similar for most Tridentatae species, with the exception of three taxonomically conflictive species: Artemisia bigelovii Gray...

  4. Influence of climate and environment on post-fire recovery of mountain big sagebrush

    Science.gov (United States)

    Zachary J. Nelson; Peter J. Weisberg; Stanley G. Kitchen

    2014-01-01

    In arid and semi-arid landscapes around the world, wildfire plays a key role in maintaining species diversity. Dominant plant associations may depend upon particular fire regime characteristics for their persistence. Mountain shrub communities in high-elevation landscapes of the Intermountain West, USA, are strongly influenced by the post-fire recovery dynamics of the...

  5. Big Opportunities and Big Concerns of Big Data in Education

    Science.gov (United States)

    Wang, Yinying

    2016-01-01

    Against the backdrop of the ever-increasing influx of big data, this article examines the opportunities and concerns over big data in education. Specifically, this article first introduces big data, followed by delineating the potential opportunities of using big data in education in two areas: learning analytics and educational policy. Then, the…

  6. The Big Sky inside

    Science.gov (United States)

    Adams, Earle; Ward, Tony J.; Vanek, Diana; Marra, Nancy; Hester, Carolyn; Knuth, Randy; Spangler, Todd; Jones, David; Henthorn, Melissa; Hammill, Brock; Smith, Paul; Salisbury, Rob; Reckin, Gene; Boulafentis, Johna

    2009-01-01

    The University of Montana (UM)-Missoula has implemented a problem-based program in which students perform scientific research focused on indoor air pollution. The Air Toxics Under the Big Sky program (Jones et al. 2007; Adams et al. 2008; Ward et al. 2008) provides a community-based framework for understanding the complex relationship between poor…

  7. Machine learning for Big Data analytics in plants.

    Science.gov (United States)

    Ma, Chuang; Zhang, Hao Helen; Wang, Xiangfeng

    2014-12-01

    Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Utilizing Big Data and Twitter to Discover Emergent Online Communities of Cannabis Users

    Directory of Open Access Journals (Sweden)

    Peter Baumgartner

    2017-05-01

    Full Text Available Large shifts in medical, recreational, and illicit cannabis consumption in the United States have implications for personalizing treatment and prevention programs to a wide variety of populations. As such, considerable research has investigated clinical presentations of cannabis users in clinical and population-based samples. Studies leveraging big data, social media, and social network analysis have emerged as a promising mechanism to generate timely insights that can inform treatment and prevention research. This study extends a novel method called stochastic block modeling to derive communities of cannabis consumers as part of a complex social network on Twitter. A set of examples illustrate how this method can ascertain candidate samples of medical, recreational, and illicit cannabis users. Implications for research planning, intervention design, and public health surveillance are discussed.

  9. Using Unmanned Aerial Vehicles to Assess Vegetative Cover in Sagebrush Steppe Ecosytstems

    Energy Technology Data Exchange (ETDEWEB)

    Robert P. Breckenridge

    2005-09-01

    The Idaho National Laboratory (INL), in conjunction with the University of Idaho, is evaluating novel approaches for using unmanned aerial vehicles (UAVs) as a quicker and safer method for monitoring biotic resources. Evaluating vegetative cover is an important factor in understanding the sustainability of many ecosystems. In assessing vegetative cover, methods that improve accuracy and cost efficiency could revolutionize how biotic resources are monitored on western federal lands. Sagebrush steppe ecosystems provide important habitat for a variety of species, some of which are important indicator species (e.g., sage grouse). Improved methods are needed to support monitoring these habitats because there are not enough resource specialists or funds available for comprehensive ground evaluation of these ecosystems. In this project, two types of UAV platforms (fixed wing and helicopter) were used to collect still-frame imagery to assess cover in sagebrush steppe ecosystems. This paper discusses the process for collecting and analyzing imagery from the UAVs to (1) estimate total percent cover, (2) estimate percent cover for six different types of vegetation, and (3) locate sage grouse based on representative decoys. The field plots were located on the INL site west of Idaho Falls, Idaho, in areas with varying amounts and types of vegetative cover. A software program called SamplePoint developed by the U.S. Department of Agriculture, Agricultural Research Service (USDA-ARS) was used to evaluate the imagery for percent cover for the six vegetation types (bare ground, litter, shrubs, dead shrubs, grasses, and forbs). Results were compared against standard field measurements to assess accuracy.

  10. Big Data in the Earth Observing System Data and Information System

    Science.gov (United States)

    Lynnes, Chris; Baynes, Katie; McInerney, Mark

    2016-01-01

    Approaches that are being pursued for the Earth Observing System Data and Information System (EOSDIS) data system to address the challenges of Big Data were presented to the NASA Big Data Task Force. Cloud prototypes are underway to tackle the volume challenge of Big Data. However, advances in computer hardware or cloud won't help (much) with variety. Rather, interoperability standards, conventions, and community engagement are the key to addressing variety.

  11. Integrated disposal Facility Sagebrush Habitat Mitigation Project: FY2007 Compensation Area Monitoring Report

    Energy Technology Data Exchange (ETDEWEB)

    Durham, Robin E.; Sackschewsky, Michael R.

    2007-09-01

    This report summarizes the first year survival of sagebrush seedlings planted as compensatory mitigation for the Integrated Disposal Facility Project. Approximately 42,600 bare root seedlings and 26,000 pluglings were planted at a mitigation site along Army Loop Road in February 2007. Initial baseline monitoring occurred in March 2007, and first summer survival was assessed in September 2007. Overall survival was 19%, with bare root survival being marginally better than pluglings (21% versus 14%). Likely major factors contributing to low survival were late season planting and insufficient soil moisture during seedling establishment.

  12. Ecology of greater sage-grouse in the Dakotas

    Science.gov (United States)

    Christopher C. Swanson

    2009-01-01

    Greater sage-grouse (Centrocercus urophasianus) populations and the sagebrush (Artemisia spp.) communities that they rely on have dramatically declined from historic levels. Moreover, information regarding sage-grouse annual life-history requirements at the eastern-most extension of sagebrush steppe communities is lacking....

  13. Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications.

    Science.gov (United States)

    Karyotis, Vasileios; Tsitseklis, Konstantinos; Sotiropoulos, Konstantinos; Papavassiliou, Symeon

    2018-04-15

    In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

  14. Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

    Directory of Open Access Journals (Sweden)

    Vasileios Karyotis

    2018-04-01

    Full Text Available In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

  15. BigData as a Driver for Capacity Building in Astrophysics

    Science.gov (United States)

    Shastri, Prajval

    2015-08-01

    Exciting public interest in astrophysics acquires new significance in the era of Big Data. Since Big Data involves advanced technologies of both software and hardware, astrophysics with Big Data has the potential to inspire young minds with diverse inclinations - i.e., not just those attracted to physics but also those pursuing engineering careers. Digital technologies have become steadily cheaper, which can enable expansion of the Big Data user pool considerably, especially to communities that may not yet be in the astrophysics mainstream, but have high potential because of access to thesetechnologies. For success, however, capacity building at the early stages becomes key. The development of on-line pedagogical resources in astrophysics, astrostatistics, data-mining and data visualisation that are designed around the big facilities of the future can be an important effort that drives such capacity building, especially if facilitated by the IAU.

  16. How Big Are "Martin's Big Words"? Thinking Big about the Future.

    Science.gov (United States)

    Gardner, Traci

    "Martin's Big Words: The Life of Dr. Martin Luther King, Jr." tells of King's childhood determination to use "big words" through biographical information and quotations. In this lesson, students in grades 3 to 5 explore information on Dr. King to think about his "big" words, then they write about their own…

  17. Big Data Analytics in Chemical Engineering.

    Science.gov (United States)

    Chiang, Leo; Lu, Bo; Castillo, Ivan

    2017-06-07

    Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.

  18. Big data and biomedical informatics: a challenging opportunity.

    Science.gov (United States)

    Bellazzi, R

    2014-05-22

    Big data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations.

  19. Big Data, Big Problems: A Healthcare Perspective.

    Science.gov (United States)

    Househ, Mowafa S; Aldosari, Bakheet; Alanazi, Abdullah; Kushniruk, Andre W; Borycki, Elizabeth M

    2017-01-01

    Much has been written on the benefits of big data for healthcare such as improving patient outcomes, public health surveillance, and healthcare policy decisions. Over the past five years, Big Data, and the data sciences field in general, has been hyped as the "Holy Grail" for the healthcare industry promising a more efficient healthcare system with the promise of improved healthcare outcomes. However, more recently, healthcare researchers are exposing the potential and harmful effects Big Data can have on patient care associating it with increased medical costs, patient mortality, and misguided decision making by clinicians and healthcare policy makers. In this paper, we review the current Big Data trends with a specific focus on the inadvertent negative impacts that Big Data could have on healthcare, in general, and specifically, as it relates to patient and clinical care. Our study results show that although Big Data is built up to be as a the "Holy Grail" for healthcare, small data techniques using traditional statistical methods are, in many cases, more accurate and can lead to more improved healthcare outcomes than Big Data methods. In sum, Big Data for healthcare may cause more problems for the healthcare industry than solutions, and in short, when it comes to the use of data in healthcare, "size isn't everything."

  20. Big Data and Biomedical Informatics: A Challenging Opportunity

    Science.gov (United States)

    2014-01-01

    Summary Big data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations. PMID:24853034

  1. Estudiarán el Big Bang por Internet

    CERN Multimedia

    2007-01-01

    The most powerful Internet, star of the present, goes for another challenge that mixes past and future: to join the scientific world community to clarify the orígines of the universe, the Big Bang. (1/2 page)

  2. The BIG Data Center: from deposition to integration to translation.

    Science.gov (United States)

    2017-01-04

    Biological data are generated at unprecedentedly exponential rates, posing considerable challenges in big data deposition, integration and translation. The BIG Data Center, established at Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, provides a suite of database resources, including (i) Genome Sequence Archive, a data repository specialized for archiving raw sequence reads, (ii) Gene Expression Nebulas, a data portal of gene expression profiles based entirely on RNA-Seq data, (iii) Genome Variation Map, a comprehensive collection of genome variations for featured species, (iv) Genome Warehouse, a centralized resource housing genome-scale data with particular focus on economically important animals and plants, (v) Methylation Bank, an integrated database of whole-genome single-base resolution methylomes and (vi) Science Wikis, a central access point for biological wikis developed for community annotations. The BIG Data Center is dedicated to constructing and maintaining biological databases through big data integration and value-added curation, conducting basic research to translate big data into big knowledge and providing freely open access to a variety of data resources in support of worldwide research activities in both academia and industry. All of these resources are publicly available and can be found at http://bigd.big.ac.cn. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  3. Recht voor big data, big data voor recht

    NARCIS (Netherlands)

    Lafarre, Anne

    Big data is een niet meer weg te denken fenomeen in onze maatschappij. Het is de hype cycle voorbij en de eerste implementaties van big data-technieken worden uitgevoerd. Maar wat is nu precies big data? Wat houden de vijf V's in die vaak genoemd worden in relatie tot big data? Ter inleiding van

  4. Nursing Knowledge: Big Data Science-Implications for Nurse Leaders.

    Science.gov (United States)

    Westra, Bonnie L; Clancy, Thomas R; Sensmeier, Joyce; Warren, Judith J; Weaver, Charlotte; Delaney, Connie W

    2015-01-01

    The integration of Big Data from electronic health records and other information systems within and across health care enterprises provides an opportunity to develop actionable predictive models that can increase the confidence of nursing leaders' decisions to improve patient outcomes and safety and control costs. As health care shifts to the community, mobile health applications add to the Big Data available. There is an evolving national action plan that includes nursing data in Big Data science, spearheaded by the University of Minnesota School of Nursing. For the past 3 years, diverse stakeholders from practice, industry, education, research, and professional organizations have collaborated through the "Nursing Knowledge: Big Data Science" conferences to create and act on recommendations for inclusion of nursing data, integrated with patient-generated, interprofessional, and contextual data. It is critical for nursing leaders to understand the value of Big Data science and the ways to standardize data and workflow processes to take advantage of newer cutting edge analytics to support analytic methods to control costs and improve patient quality and safety.

  5. A practical guide to big data research in psychology.

    Science.gov (United States)

    Chen, Eric Evan; Wojcik, Sean P

    2016-12-01

    The massive volume of data that now covers a wide variety of human behaviors offers researchers in psychology an unprecedented opportunity to conduct innovative theory- and data-driven field research. This article is a practical guide to conducting big data research, covering data management, acquisition, processing, and analytics (including key supervised and unsupervised learning data mining methods). It is accompanied by walkthrough tutorials on data acquisition, text analysis with latent Dirichlet allocation topic modeling, and classification with support vector machines. Big data practitioners in academia, industry, and the community have built a comprehensive base of tools and knowledge that makes big data research accessible to researchers in a broad range of fields. However, big data research does require knowledge of software programming and a different analytical mindset. For those willing to acquire the requisite skills, innovative analyses of unexpected or previously untapped data sources can offer fresh ways to develop, test, and extend theories. When conducted with care and respect, big data research can become an essential complement to traditional research. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  6. Average biomass of four Northwest shrubs by fuel size class and crown cover.

    Science.gov (United States)

    Robert E. Martin; David W. Frewing; James L. McClanahan

    1981-01-01

    The average biomass of big sagebrush (Artemisia tridentata Nutt.), antelope bitterbrush (Purshia tridentata (Pursh) DC.), snowbrush ceanothus (Ceanothus velutinus Dougl. ex Hook.), and greenleaf manzanita (Arctostaphylos patula Greene) was 6.1, 5.1, 10.7, and 16.2 tons per acre (13.9,...

  7. A modelling framework for improving plant establishment during ecological restoration

    Science.gov (United States)

    Plants seeded during ecological restoration projects often perish en masse, and researchers are currently searching for traits promoting increased survival. In this study of a big sagebrush (Artemisia tridentata Nutt.) ecosystem, we found survivorship rankings of seeded grass species varied across 3...

  8. Indicators of ecosystem function identify alternate states in the sagebrush steppe.

    Science.gov (United States)

    Kachergis, Emily; Rocca, Monique E; Fernandez-Gimenez, Maria E

    2011-10-01

    Models of ecosystem change that incorporate nonlinear dynamics and thresholds, such as state-and-transition models (STMs), are increasingly popular tools for land management decision-making. However, few models are based on systematic collection and documentation of ecological data, and of these, most rely solely on structural indicators (species composition) to identify states and transitions. As STMs are adopted as an assessment framework throughout the United States, finding effective and efficient ways to create data-driven models that integrate ecosystem function and structure is vital. This study aims to (1) evaluate the utility of functional indicators (indicators of rangeland health, IRH) as proxies for more difficult ecosystem function measurements and (2) create a data-driven STM for the sagebrush steppe of Colorado, USA, that incorporates both ecosystem structure and function. We sampled soils, plant communities, and IRH at 41 plots with similar clayey soils but different site histories to identify potential states and infer the effects of management practices and disturbances on transitions. We found that many IRH were correlated with quantitative measures of functional indicators, suggesting that the IRH can be used to approximate ecosystem function. In addition to a reference state that functions as expected for this soil type, we identified four biotically and functionally distinct potential states, consistent with the theoretical concept of alternate states. Three potential states were related to management practices (chemical and mechanical shrub treatments and seeding history) while one was related only to ecosystem processes (erosion). IRH and potential states were also related to environmental variation (slope, soil texture), suggesting that there are environmental factors within areas with similar soils that affect ecosystem dynamics and should be noted within STMs. Our approach generated an objective, data-driven model of ecosystem dynamics

  9. [Structural Change, Contextuality, and Transfer in Health Promotion--Sustainable Implementation of the BIG Project].

    Science.gov (United States)

    Rütten, A; Frahsa, A; Rosenhäger, N; Wolff, A

    2015-09-01

    The BIG approach aims at promoting physical activity and health among socially disadvantaged women. BIG has been developed and sustainably implemented in Erlangen/Bavaria. Subsequently, it has been transferred to other communities and states in Germany. Crucial factors for sustainability and transfer in BIG are (1) lifestyle and policy analysis, (2) assets approach, (3) empowerment of target group, (4) enabling of policy-makers and professionals. © Georg Thieme Verlag KG Stuttgart · New York.

  10. Addressing big data challenges for scientific data infrastructure

    NARCIS (Netherlands)

    Demchenko, Y.; Zhao, Z.; Grosso, P.; Wibisono, A.; de Laat, C.

    2012-01-01

    This paper discusses the challenges that are imposed by Big Data Science on the modern and future Scientific Data Infrastructure (SDI). The paper refers to different scientific communities to define requirements on data management, access control and security. The paper introduces the Scientific

  11. Studies of a new hybrid taxon in the Artemisia tridentata (Asteraceae: Anthemideae) complex

    Science.gov (United States)

    Heather D. Garrison; Leila M. Shultz; E. Durant McArthur

    2013-01-01

    Members of the Artemisia tridentata complex (ASTERACEAE: Anthemideae: Artemisia subgen. Tridentatae) have adapted to changing environmental conditions through geographic migration, introgression, and hybridization. These processes have resulted in morphologic and genetic variation. A presumed hybrid ("Bonneville" big sagebrush) of the complex occurs in the...

  12. Biological zonation of the last unbound big river in the West Carpathians: reference scheme based on caddisfly communities

    Directory of Open Access Journals (Sweden)

    Čiliak M.

    2014-01-01

    Full Text Available A thorough understanding of biotic communities distribution in predisturbance state is essential for predictions of their future changes related to human activities. In this regard, pre-damming data on spatial distribution of benthic communities are highly valuable. Caddisflies were sampled at 14 sites of the Hron River and analysed in order to establish longitudinal zonation of the river and to determine environmental factors affecting assemblages’ distribution in the longitudinal profile. A total of 2600 individuals of caddisflies belonging to 40 taxa of 12 families were recorded. Diversity of caddisflies was found to be higher in the upper (rhithral part of the river. Major change, with shift to much more uniform caddisfly assemblages, occurred in the middle part of the river. Four zones (subzones were distinguished using caddisfly communities: epirhithral, metarhithral, hyporhithral and epipotamal. Canonical correspondence analysis demonstrated the determining influence of altitude and conductivity on the caddisflies. Pre-damming zonation patterns presented here could serve as basic information for management of the Hron River as well as a reference scheme for other, previously dammed big rivers in the West Carpathian region.

  13. EKALAVYA MODEL OF HIGHER EDUCATION – AN INNOVATION OF IBM’S BIG DATA UNIVERSITY

    OpenAIRE

    Dr. P. S. Aithal; Shubhrajyotsna Aithal

    2016-01-01

    Big Data Science is a new multi-disciplinary subject in the society, comprising of business intelligence, data analytics, and the related fields have become increasingly important in both the academic and the business communities during the 21st century. Many organizations and business intelligence experts have foreseen the significant development in the big data field as next big wave in future research arena in many industry sectors and the society. To become an expert and skilled in this n...

  14. BigOP: Generating Comprehensive Big Data Workloads as a Benchmarking Framework

    OpenAIRE

    Zhu, Yuqing; Zhan, Jianfeng; Weng, Chuliang; Nambiar, Raghunath; Zhang, Jinchao; Chen, Xingzhen; Wang, Lei

    2014-01-01

    Big Data is considered proprietary asset of companies, organizations, and even nations. Turning big data into real treasure requires the support of big data systems. A variety of commercial and open source products have been unleashed for big data storage and processing. While big data users are facing the choice of which system best suits their needs, big data system developers are facing the question of how to evaluate their systems with regard to general big data processing needs. System b...

  15. Database Resources of the BIG Data Center in 2018.

    Science.gov (United States)

    2018-01-04

    The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides freely open access to a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of omics data generated at ever-greater scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big-data integration and value-added curation, including BioCode (a repository archiving bioinformatics tool codes), BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Gene Expression Nebulas (GEN, a database of gene expression profiles based on RNA-Seq data), Methylation Bank (MethBank, an integrated databank of DNA methylomes), and Science Wikis (a series of biological knowledge wikis for community annotations). In addition, three featured web services are provided, viz., BIG Search (search as a service; a scalable inter-domain text search engine), BIG SSO (single sign-on as a service; a user access control system to gain access to multiple independent systems with a single ID and password) and Gsub (submission as a service; a unified submission service for all relevant resources). All of these resources are publicly accessible through the home page of the BIG Data Center at http://bigd.big.ac.cn. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  16. Using resilience and resistance concepts to manage threats to sagebrush ecosystems, Gunnison sage-grouse, and Greater sage-grouse in their eastern range: A strategic multi-scale approach

    Science.gov (United States)

    Chambers, Jeanne C.; Beck, Jeffrey L.; Campbell, Steve; Carlson, John; Christiansen, Thomas J.; Clause, Karen J.; Dinkins, Jonathan B.; Doherty, Kevin E.; Griffin, Kathleen A.; Havlina, Douglas W.; Mayer, Kenneth F.; Hennig, Jacob D.; Kurth, Laurie L.; Maestas, Jeremy D.; Manning, Mary E.; Mealor, Brian A.; McCarthy, Clinton; Perea, Marco A.; Pyke, David A.

    2016-01-01

    This report provides a strategic approach developed by a Western Association of Fish and Wildlife Agencies interagency working group for conservation of sagebrush ecosystems, Greater sage-grouse, and Gunnison sage-grouse. It uses information on (1) factors that influence sagebrush ecosystem resilience to disturbance and resistance to nonnative invasive annual grasses and (2) distribution and relative abundance of sage-grouse populations to address persistent ecosystem threats, such as invasive annual grasses and wildfire, and land use and development threats, such as oil and gas development and cropland conversion, to develop effective management strategies. A sage-grouse habitat matrix links relative resilience and resistance of sagebrush ecosystems with modeled sage-grouse breeding habitat probabilities to help decisionmakers assess risks and determine appropriate management strategies at both landscape and site scales. Areas for targeted management are assessed by overlaying matrix components with Greater sage-grouse Priority Areas for Conservation and Gunnison sage-grouse critical habitat and linkages, breeding bird concentration areas, and specific habitat threats. Decision tools are discussed for determining the suitability of target areas for management and the most appropriate management actions. A similar approach was developed for the Great Basin that was incorporated into the Federal land use plan amendments and served as the basis of a Bureau of Land Management Fire and Invasives Assessment Tool, which was used to prioritize sage-grouse habitat for targeted management activities.

  17. How Big Is Too Big?

    Science.gov (United States)

    Cibes, Margaret; Greenwood, James

    2016-01-01

    Media Clips appears in every issue of Mathematics Teacher, offering readers contemporary, authentic applications of quantitative reasoning based on print or electronic media. This issue features "How Big is Too Big?" (Margaret Cibes and James Greenwood) in which students are asked to analyze the data and tables provided and answer a…

  18. Quantifying restoration effectiveness using multi-scale habitat models: implications for sage-grouse in the Great Basin

    Science.gov (United States)

    Arkle, Robert S.; Pilliod, David S.; Hanser, Steven E.; Brooks, Matthew L.; Chambers, Jeanne C.; Grace, James B.; Knutson, Kevin C.; Pyke, David A.; Welty, Justin L.

    2014-01-01

    A recurrent challenge in the conservation of wide-ranging, imperiled species is understanding which habitats to protect and whether we are capable of restoring degraded landscapes. For Greater Sage-grouse (Centrocercus urophasianus), a species of conservation concern in the western United States, we approached this problem by developing multi-scale empirical models of occupancy in 211 randomly located plots within a 40 million ha portion of the species' range. We then used these models to predict sage-grouse habitat quality at 826 plots associated with 101 post-wildfire seeding projects implemented from 1990 to 2003. We also compared conditions at restoration sites to published habitat guidelines. Sage-grouse occupancy was positively related to plot- and landscape-level dwarf sagebrush (Artemisia arbuscula, A. nova, A. tripartita) and big sagebrush steppe prevalence, and negatively associated with non-native plants and human development. The predicted probability of sage-grouse occupancy at treated plots was low on average (0.09) and not substantially different from burned areas that had not been treated. Restoration sites with quality habitat tended to occur at higher elevation locations with low annual temperatures, high spring precipitation, and high plant diversity. Of 313 plots seeded after fire, none met all sagebrush guidelines for breeding habitats, but approximately 50% met understory guidelines, particularly for perennial grasses. This pattern was similar for summer habitat. Less than 2% of treated plots met winter habitat guidelines. Restoration actions did not increase the probability of burned areas meeting most guideline criteria. The probability of meeting guidelines was influenced by a latitudinal gradient, climate, and topography. Our results suggest that sage-grouse are relatively unlikely to use many burned areas within 20 years of fire, regardless of treatment. Understory habitat conditions are more likely to be adequate than overstory

  19. Nursing Needs Big Data and Big Data Needs Nursing.

    Science.gov (United States)

    Brennan, Patricia Flatley; Bakken, Suzanne

    2015-09-01

    Contemporary big data initiatives in health care will benefit from greater integration with nursing science and nursing practice; in turn, nursing science and nursing practice has much to gain from the data science initiatives. Big data arises secondary to scholarly inquiry (e.g., -omics) and everyday observations like cardiac flow sensors or Twitter feeds. Data science methods that are emerging ensure that these data be leveraged to improve patient care. Big data encompasses data that exceed human comprehension, that exist at a volume unmanageable by standard computer systems, that arrive at a velocity not under the control of the investigator and possess a level of imprecision not found in traditional inquiry. Data science methods are emerging to manage and gain insights from big data. The primary methods included investigation of emerging federal big data initiatives, and exploration of exemplars from nursing informatics research to benchmark where nursing is already poised to participate in the big data revolution. We provide observations and reflections on experiences in the emerging big data initiatives. Existing approaches to large data set analysis provide a necessary but not sufficient foundation for nursing to participate in the big data revolution. Nursing's Social Policy Statement guides a principled, ethical perspective on big data and data science. There are implications for basic and advanced practice clinical nurses in practice, for the nurse scientist who collaborates with data scientists, and for the nurse data scientist. Big data and data science has the potential to provide greater richness in understanding patient phenomena and in tailoring interventional strategies that are personalized to the patient. © 2015 Sigma Theta Tau International.

  20. BIG Data - BIG Gains? Understanding the Link Between Big Data Analytics and Innovation

    OpenAIRE

    Niebel, Thomas; Rasel, Fabienne; Viete, Steffen

    2017-01-01

    This paper analyzes the relationship between firms’ use of big data analytics and their innovative performance for product innovations. Since big data technologies provide new data information practices, they create new decision-making possibilities, which firms can use to realize innovations. Applying German firm-level data we find suggestive evidence that big data analytics matters for the likelihood of becoming a product innovator as well as the market success of the firms’ product innovat...

  1. Networking for big data

    CERN Document Server

    Yu, Shui; Misic, Jelena; Shen, Xuemin (Sherman)

    2015-01-01

    Networking for Big Data supplies an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Starting with a comprehensive introduction to Big Data and its networking issues, it offers deep technical coverage of both theory and applications.The book is divided into four sections: introduction to Big Data, networking theory and design for Big Data, networking security for Big Data, and platforms and systems for Big Data applications. Focusing on key networking issues in Big Data, the book explains network design and implementation for Big Data. It exa

  2. Some experiences and opportunities for big data in translational research.

    Science.gov (United States)

    Chute, Christopher G; Ullman-Cullere, Mollie; Wood, Grant M; Lin, Simon M; He, Min; Pathak, Jyotishman

    2013-10-01

    Health care has become increasingly information intensive. The advent of genomic data, integrated into patient care, significantly accelerates the complexity and amount of clinical data. Translational research in the present day increasingly embraces new biomedical discovery in this data-intensive world, thus entering the domain of "big data." The Electronic Medical Records and Genomics consortium has taught us many lessons, while simultaneously advances in commodity computing methods enable the academic community to affordably manage and process big data. Although great promise can emerge from the adoption of big data methods and philosophy, the heterogeneity and complexity of clinical data, in particular, pose additional challenges for big data inferencing and clinical application. However, the ultimate comparability and consistency of heterogeneous clinical information sources can be enhanced by existing and emerging data standards, which promise to bring order to clinical data chaos. Meaningful Use data standards in particular have already simplified the task of identifying clinical phenotyping patterns in electronic health records.

  3. Global fluctuation spectra in big-crunch-big-bang string vacua

    International Nuclear Information System (INIS)

    Craps, Ben; Ovrut, Burt A.

    2004-01-01

    We study big-crunch-big-bang cosmologies that correspond to exact world-sheet superconformal field theories of type II strings. The string theory spacetime contains a big crunch and a big bang cosmology, as well as additional 'whisker' asymptotic and intermediate regions. Within the context of free string theory, we compute, unambiguously, the scalar fluctuation spectrum in all regions of spacetime. Generically, the big crunch fluctuation spectrum is altered while passing through the bounce singularity. The change in the spectrum is characterized by a function Δ, which is momentum and time dependent. We compute Δ explicitly and demonstrate that it arises from the whisker regions. The whiskers are also shown to lead to 'entanglement' entropy in the big bang region. Finally, in the Milne orbifold limit of our superconformal vacua, we show that Δ→1 and, hence, the fluctuation spectrum is unaltered by the big-crunch-big-bang singularity. We comment on, but do not attempt to resolve, subtleties related to gravitational back reaction and light winding modes when interactions are taken into account

  4. Big Argumentation?

    Directory of Open Access Journals (Sweden)

    Daniel Faltesek

    2013-08-01

    Full Text Available Big Data is nothing new. Public concern regarding the mass diffusion of data has appeared repeatedly with computing innovations, in the formation before Big Data it was most recently referred to as the information explosion. In this essay, I argue that the appeal of Big Data is not a function of computational power, but of a synergistic relationship between aesthetic order and a politics evacuated of a meaningful public deliberation. Understanding, and challenging, Big Data requires an attention to the aesthetics of data visualization and the ways in which those aesthetics would seem to depoliticize information. The conclusion proposes an alternative argumentative aesthetic as the appropriate response to the depoliticization posed by the popular imaginary of Big Data.

  5. Understanding Big Data for Industrial Innovation and Design: The Missing Information Systems Perspective

    Directory of Open Access Journals (Sweden)

    Miguel Baptista Nunes

    2017-12-01

    Full Text Available This paper identifies a need to complement the current rich technical and mathematical research agenda on big data with a more information systems and information science strand, which focuses on the business value of big data. An agenda of research for information systems would explore motives for using big data in real organizational contexts, and consider proposed benefits, such as increased effectiveness and efficiency, production of high-quality products/services, creation of added business value, and stimulation of innovation and design. Impacts of such research on the academic community, the industrial and business world, and policy-makers are discussed.

  6. Big Data in Drug Discovery.

    Science.gov (United States)

    Brown, Nathan; Cambruzzi, Jean; Cox, Peter J; Davies, Mark; Dunbar, James; Plumbley, Dean; Sellwood, Matthew A; Sim, Aaron; Williams-Jones, Bryn I; Zwierzyna, Magdalena; Sheppard, David W

    2018-01-01

    Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation. © 2018 Elsevier B.V. All rights reserved.

  7. Greater sage-grouse winter habitat use on the eastern edge of their range

    Science.gov (United States)

    Swanson, Christopher C.; Rumble, Mark A.; Grovenburg, Troy W.; Kaczor, Nicholas W.; Klaver, Robert W.; Herman-Brunson, Katie M.; Jenks, Jonathan A.; Jensen, Kent C.

    2013-01-01

    Greater sage-grouse (Centrocercus urophasianus) at the western edge of the Dakotas occur in the transition zone between sagebrush and grassland communities. These mixed sagebrush (Artemisia sp.) and grasslands differ from those habitats that comprise the central portions of the sage-grouse range; yet, no information is available on winter habitat selection within this region of their distribution. We evaluated factors influencing greater sage-grouse winter habitat use in North Dakota during 2005–2006 and 2006–2007 and in South Dakota during 2006–2007 and 2007–2008. We captured and radio-marked 97 breeding-age females and 54 breeding-age males from 2005 to 2007 and quantified habitat selection for 98 of these birds that were alive during winter. We collected habitat measurements at 340 (177 ND, 163 SD) sage-grouse use sites and 680 random (340 each at 250 m and 500 m from locations) dependent sites. Use sites differed from random sites with greater percent sagebrush cover (14.75% use vs. 7.29% random; P 2 use vs. 0.94 plants/m2 random; P ≤ 0.001), but lesser percent grass cover (11.76% use vs. 16.01% random; P ≤ 0.001) and litter cover (4.34% use vs. 5.55% random; P = 0.001) and lower sagebrush height (20.02 cm use vs. 21.35 cm random; P = 0.13) and grass height (21.47 cm use vs. 23.21 cm random; P = 0.15). We used conditional logistic regression to estimate winter habitat selection by sage-grouse on continuous scales. The model sagebrush cover + sagebrush height + sagebrush cover × sagebrush height (wi = 0.60) was the most supported of the 13 models we considered, indicating that percent sagebrush cover strongly influenced selection. Logistic odds ratios indicated that the probability of selection by sage-grouse increased by 1.867 for every 1% increase in sagebrush cover (95% CI = 1.627–2.141) and by 1.041 for every 1 cm increase in sagebrush height (95% CI = 1.002–1.082). The

  8. A Proposed Collaboration Against Big Tobacco: Common Ground Between the Vaping and Public Health Community in the United States.

    Science.gov (United States)

    Wagener, Theodore L; Meier, Ellen; Tackett, Alayna P; Matheny, James D; Pechacek, Terry F

    2016-05-01

    An unfortunate conflict is underway between the public health community and the vaping community over e-cigarettes' harmfulness or lack thereof. This conflict is made worse by an information vacuum that is being filled by vocal members on both sides of the debate; a perceived lack of credibility of public health officials by those in the vaping community; the tobacco industry's recent involvement in e-cigarettes; and the constant evolution of different styles and types of e-cigarettes. This conflict is avoidable; common ground exists. If both groups rally around what is in their own and the public's best interest-the end of combustible tobacco--all will benefit significantly. If not, the result may be missed opportunities, misguided alliances, and--ultimately-poorer public health. This study brings light to the contentious debate between the vaping and public health communities. It addresses how both sides are responsible for bringing misleading information to the public and vocal leaders on both sides are unknowingly intensifying and polarizing the debate-likely at the expense of public health. It also describes how this conflict is avoidable, and provides a starting point for potential positions of common ground against Big Tobacco. © The Author 2015. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  9. Big data

    DEFF Research Database (Denmark)

    Madsen, Anders Koed; Flyverbom, Mikkel; Hilbert, Martin

    2016-01-01

    is to outline a research agenda that can be used to raise a broader set of sociological and practice-oriented questions about the increasing datafication of international relations and politics. First, it proposes a way of conceptualizing big data that is broad enough to open fruitful investigations......The claim that big data can revolutionize strategy and governance in the context of international relations is increasingly hard to ignore. Scholars of international political sociology have mainly discussed this development through the themes of security and surveillance. The aim of this paper...... into the emerging use of big data in these contexts. This conceptualization includes the identification of three moments contained in any big data practice. Second, it suggests a research agenda built around a set of subthemes that each deserve dedicated scrutiny when studying the interplay between big data...

  10. Big data computing

    CERN Document Server

    Akerkar, Rajendra

    2013-01-01

    Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book i

  11. Development and Validation of Big Four Personality Scales for the Schedule for Nonadaptive and Adaptive Personality-2nd Edition (SNAP-2)

    Science.gov (United States)

    Calabrese, William R.; Rudick, Monica M.; Simms, Leonard J.; Clark, Lee Anna

    2012-01-01

    Recently, integrative, hierarchical models of personality and personality disorder (PD)—such as the Big Three, Big Four and Big Five trait models—have gained support as a unifying dimensional framework for describing PD. However, no measures to date can simultaneously represent each of these potentially interesting levels of the personality hierarchy. To unify these measurement models psychometrically, we sought to develop Big Five trait scales within the Schedule for Adaptive and Nonadaptive Personality–2nd Edition (SNAP-2). Through structural and content analyses, we examined relations between the SNAP-2, Big Five Inventory (BFI), and NEO-Five Factor Inventory (NEO-FFI) ratings in a large data set (N = 8,690), including clinical, military, college, and community participants. Results yielded scales consistent with the Big Four model of personality (i.e., Neuroticism, Conscientiousness, Introversion, and Antagonism) and not the Big Five as there were insufficient items related to Openness. Resulting scale scores demonstrated strong internal consistency and temporal stability. Structural and external validity was supported by strong convergent and discriminant validity patterns between Big Four scale scores and other personality trait scores and expectable patterns of self-peer agreement. Descriptive statistics and community-based norms are provided. The SNAP-2 Big Four Scales enable researchers and clinicians to assess personality at multiple levels of the trait hierarchy and facilitate comparisons among competing “Big Trait” models. PMID:22250598

  12. Quantifying phenology metrics from Great Basin plant communities and their relationship to seasonal water availability

    Science.gov (United States)

    Background/Question/Methods Sagebrush steppe is critical habitat in the Great Basin for wildlife and provides important ecosystem goods and services. Expansion of pinyon (Pinus spp.) and juniper (Juniperus spp.) in the Great Basin has reduced the extent of sagebrush steppe causing habitat, fire, and...

  13. From big bang to big crunch and beyond

    International Nuclear Information System (INIS)

    Elitzur, Shmuel; Rabinovici, Eliezer; Giveon, Amit; Kutasov, David

    2002-01-01

    We study a quotient Conformal Field Theory, which describes a 3+1 dimensional cosmological spacetime. Part of this spacetime is the Nappi-Witten (NW) universe, which starts at a 'big bang' singularity, expands and then contracts to a 'big crunch' singularity at a finite time. The gauged WZW model contains a number of copies of the NW spacetime, with each copy connected to the preceding one and to the next one at the respective big bang/big crunch singularities. The sequence of NW spacetimes is further connected at the singularities to a series of non-compact static regions with closed timelike curves. These regions contain boundaries, on which the observables of the theory live. This suggests a holographic interpretation of the physics. (author)

  14. Development and validation of Big Four personality scales for the Schedule for Nonadaptive and Adaptive Personality--Second Edition (SNAP-2).

    Science.gov (United States)

    Calabrese, William R; Rudick, Monica M; Simms, Leonard J; Clark, Lee Anna

    2012-09-01

    Recently, integrative, hierarchical models of personality and personality disorder (PD)--such as the Big Three, Big Four, and Big Five trait models--have gained support as a unifying dimensional framework for describing PD. However, no measures to date can simultaneously represent each of these potentially interesting levels of the personality hierarchy. To unify these measurement models psychometrically, we sought to develop Big Five trait scales within the Schedule for Nonadaptive and Adaptive Personality--Second Edition (SNAP-2). Through structural and content analyses, we examined relations between the SNAP-2, the Big Five Inventory (BFI), and the NEO Five-Factor Inventory (NEO-FFI) ratings in a large data set (N = 8,690), including clinical, military, college, and community participants. Results yielded scales consistent with the Big Four model of personality (i.e., Neuroticism, Conscientiousness, Introversion, and Antagonism) and not the Big Five, as there were insufficient items related to Openness. Resulting scale scores demonstrated strong internal consistency and temporal stability. Structural validity and external validity were supported by strong convergent and discriminant validity patterns between Big Four scale scores and other personality trait scores and expectable patterns of self-peer agreement. Descriptive statistics and community-based norms are provided. The SNAP-2 Big Four Scales enable researchers and clinicians to assess personality at multiple levels of the trait hierarchy and facilitate comparisons among competing big-trait models. PsycINFO Database Record (c) 2012 APA, all rights reserved.

  15. BIG data - BIG gains? Empirical evidence on the link between big data analytics and innovation

    OpenAIRE

    Niebel, Thomas; Rasel, Fabienne; Viete, Steffen

    2017-01-01

    This paper analyzes the relationship between firms’ use of big data analytics and their innovative performance in terms of product innovations. Since big data technologies provide new data information practices, they create novel decision-making possibilities, which are widely believed to support firms’ innovation process. Applying German firm-level data within a knowledge production function framework we find suggestive evidence that big data analytics is a relevant determinant for the likel...

  16. NASA EOSDIS Evolution in the BigData Era

    Science.gov (United States)

    Lynnes, Christopher

    2015-01-01

    NASA's EOSDIS system faces several challenges in the Big Data Era. Although volumes are large (but not unmanageably so), the variety of different data collections is daunting. That variety also brings with it a large and diverse user community. One key evolution EOSDIS is working toward is to enable more science analysis to be performed close to the data.

  17. Indoglish as adaptation of english to Indonesian: change of society in big cities of Indonesia

    Science.gov (United States)

    Saddhono, K.; Sulaksono, D.

    2018-03-01

    Indoglish is a term that is often used for the use of Indonesian culture language nuances. Indoglish studies focus on the community, especially on the big cities in Indonesia. The use of language in society is chosen because the emerging form is the natural language, which in the context of linguistic research should actually be used in preference to describe large cities in Indonesia in actual language situations. The data of this study are various kinds of discourse obtained in the society, especially in five big cities in Indonesia where there is a form of linguistic language mixture of Indonesian and English. The main research data source is the community in big cities in Indonesia. The basic assumption for determining locational data sources is the consideration that people in large cities have diverse social, economic, and cultural backgrounds that are expected to reflect the condition of society. The major cities used as research sites are: (1) Jakarta, (2) Surakarta, (3) Surabaya, (4) Denpasar, and (5) Bandung. The data set used refers to the usual method of linguistic research. Data analysis is done by applying the usual method of distribution to linguistics. The method of analysis is performed after data is collected and classified and interpreted correctly. The results showed that in general the mastery of Indonesian language by the community was not good enough. Motivation to learn Indonesian in general is also not high enough in the community in big cities in Indonesia. With this background, then Indoglish emerged as a form of public utterance that occurs in the social. Indoglish also emerged as a form of community identity that has a prestigious sense if it smells of foreign cultural elements, including in it is the use of language.

  18. Deserts in the Deluge: TerraPopulus and Big Human-Environment Data.

    Science.gov (United States)

    Manson, S M; Kugler, T A; Haynes, D

    2016-01-01

    Terra Populus, or TerraPop, is a cyberinfrastructure project that integrates, preserves, and disseminates massive data collections describing characteristics of the human population and environment over the last six decades. TerraPop has made a number of GIScience advances in the handling of big spatial data to make information interoperable between formats and across scientific communities. In this paper, we describe challenges of these data, or 'deserts in the deluge' of data, that are common to spatial big data more broadly, and explore computational solutions specific to microdata, raster, and vector data models.

  19. Personality correlates of pathological gambling derived from Big Three and Big Five personality models.

    Science.gov (United States)

    Miller, Joshua D; Mackillop, James; Fortune, Erica E; Maples, Jessica; Lance, Charles E; Keith Campbell, W; Goodie, Adam S

    2013-03-30

    Personality traits have proved to be consistent and important factors in a variety of externalizing behaviors including addiction, aggression, and antisocial behavior. Given the comorbidity of these behaviors with pathological gambling (PG), it is important to test the degree to which PG shares these trait correlates. In a large community sample of regular gamblers (N=354; 111 with diagnoses of pathological gambling), the relations between measures of two major models of personality - Big Three and Big Five - were examined in relation to PG symptoms derived from a semi-structured diagnostic interview. Across measures, traits related to the experience of strong negative emotions were the most consistent correlates of PG, regardless of whether they were analyzed using bivariate or multivariate analyses. In several instances, however, the relations between personality and PG were moderated by demographic variable such as gender, race, and age. It will be important for future empirical work of this nature to pay closer attention to potentially important moderators of these relations. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  20. Corporate Social Responsibility programs of Big Food in Australia: a content analysis of industry documents.

    Science.gov (United States)

    Richards, Zoe; Thomas, Samantha L; Randle, Melanie; Pettigrew, Simone

    2015-12-01

    To examine Corporate Social Responsibility (CSR) tactics by identifying the key characteristics of CSR strategies as described in the corporate documents of selected 'Big Food' companies. A mixed methods content analysis was used to analyse the information contained on Australian Big Food company websites. Data sources included company CSR reports and web-based content that related to CSR initiatives employed in Australia. A total of 256 CSR activities were identified across six organisations. Of these, the majority related to the categories of environment (30.5%), responsibility to consumers (25.0%) or community (19.5%). Big Food companies appear to be using CSR activities to: 1) build brand image through initiatives associated with the environment and responsibility to consumers; 2) target parents and children through community activities; and 3) align themselves with respected organisations and events in an effort to transfer their positive image attributes to their own brands. Results highlight the type of CSR strategies Big Food companies are employing. These findings serve as a guide to mapping and monitoring CSR as a specific form of marketing. © 2015 Public Health Association of Australia.

  1. The Meaningful Use of Big Data: Four Perspectives - Four Challenges

    NARCIS (Netherlands)

    C. Bizer; P.A. Boncz (Peter); M Brodie; O. Erling (Orri)

    2011-01-01

    textabstractTwenty-five Semantic Web and Database researchers met at the 2011 STI Semantic Summit in Riga, Latvia July 6-8, 2011 to discuss the opportunities and challenges posed by Big Data for the Semantic Web, Semantic Technologies, and Database communities. The unanimous conclusion was that the

  2. BIG DATA AND E-LEARNING: THE IMPACT ON THE FUTURE OF LEARNING INDUSTRY

    Directory of Open Access Journals (Sweden)

    Valentin PAU

    2015-11-01

    Full Text Available In nowadays, one of the most interesting aspects of e-Learning is that he is continuously evolving, where, the big data architecture represents an important component over which the e-Learning communities has stopped more and more. In our work paper we will analyze the technological benefits of the big data concept and the impact on the future of e-Learning but also we will mention the critical aspects regarding the integrity of the data.

  3. Big data, big knowledge: big data for personalized healthcare.

    Science.gov (United States)

    Viceconti, Marco; Hunter, Peter; Hose, Rod

    2015-07-01

    The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the "physiological envelope" during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority.

  4. Conociendo Big Data

    Directory of Open Access Journals (Sweden)

    Juan José Camargo-Vega

    2014-12-01

    Full Text Available Teniendo en cuenta la importancia que ha adquirido el término Big Data, la presente investigación buscó estudiar y analizar de manera exhaustiva el estado del arte del Big Data; además, y como segundo objetivo, analizó las características, las herramientas, las tecnologías, los modelos y los estándares relacionados con Big Data, y por último buscó identificar las características más relevantes en la gestión de Big Data, para que con ello se pueda conocer todo lo concerniente al tema central de la investigación.La metodología utilizada incluyó revisar el estado del arte de Big Data y enseñar su situación actual; conocer las tecnologías de Big Data; presentar algunas de las bases de datos NoSQL, que son las que permiten procesar datos con formatos no estructurados, y mostrar los modelos de datos y las tecnologías de análisis de ellos, para terminar con algunos beneficios de Big Data.El diseño metodológico usado para la investigación fue no experimental, pues no se manipulan variables, y de tipo exploratorio, debido a que con esta investigación se empieza a conocer el ambiente del Big Data.

  5. BigDansing

    KAUST Repository

    Khayyat, Zuhair

    2015-06-02

    Data cleansing approaches have usually focused on detecting and fixing errors with little attention to scaling to big datasets. This presents a serious impediment since data cleansing often involves costly computations such as enumerating pairs of tuples, handling inequality joins, and dealing with user-defined functions. In this paper, we present BigDansing, a Big Data Cleansing system to tackle efficiency, scalability, and ease-of-use issues in data cleansing. The system can run on top of most common general purpose data processing platforms, ranging from DBMSs to MapReduce-like frameworks. A user-friendly programming interface allows users to express data quality rules both declaratively and procedurally, with no requirement of being aware of the underlying distributed platform. BigDansing takes these rules into a series of transformations that enable distributed computations and several optimizations, such as shared scans and specialized joins operators. Experimental results on both synthetic and real datasets show that BigDansing outperforms existing baseline systems up to more than two orders of magnitude without sacrificing the quality provided by the repair algorithms.

  6. A peek into the future of radiology using big data applications

    Directory of Open Access Journals (Sweden)

    Amit T Kharat

    2017-01-01

    Full Text Available Big data is extremely large amount of data which is available in the radiology department. Big data is identified by four Vs – Volume, Velocity, Variety, and Veracity. By applying different algorithmic tools and converting raw data to transformed data in such large datasets, there is a possibility of understanding and using radiology data for gaining new knowledge and insights. Big data analytics consists of 6Cs – Connection, Cloud, Cyber, Content, Community, and Customization. The global technological prowess and per-capita capacity to save digital information has roughly doubled every 40 months since the 1980's. By using big data, the planning and implementation of radiological procedures in radiology departments can be given a great boost. Potential applications of big data in the future are scheduling of scans, creating patient-specific personalized scanning protocols, radiologist decision support, emergency reporting, virtual quality assurance for the radiologist, etc. Targeted use of big data applications can be done for images by supporting the analytic process. Screening software tools designed on big data can be used to highlight a region of interest, such as subtle changes in parenchymal density, solitary pulmonary nodule, or focal hepatic lesions, by plotting its multidimensional anatomy. Following this, we can run more complex applications such as three-dimensional multi planar reconstructions (MPR, volumetric rendering (VR, and curved planar reconstruction, which consume higher system resources on targeted data subsets rather than querying the complete cross-sectional imaging dataset. This pre-emptive selection of dataset can substantially reduce the system requirements such as system memory, server load and provide prompt results. However, a word of caution, “big data should not become “dump data” due to inadequate and poor analysis and non-structured improperly stored data. In the near future, big data can ring in the

  7. A peek into the future of radiology using big data applications.

    Science.gov (United States)

    Kharat, Amit T; Singhal, Shubham

    2017-01-01

    Big data is extremely large amount of data which is available in the radiology department. Big data is identified by four Vs - Volume, Velocity, Variety, and Veracity. By applying different algorithmic tools and converting raw data to transformed data in such large datasets, there is a possibility of understanding and using radiology data for gaining new knowledge and insights. Big data analytics consists of 6Cs - Connection, Cloud, Cyber, Content, Community, and Customization. The global technological prowess and per-capita capacity to save digital information has roughly doubled every 40 months since the 1980's. By using big data, the planning and implementation of radiological procedures in radiology departments can be given a great boost. Potential applications of big data in the future are scheduling of scans, creating patient-specific personalized scanning protocols, radiologist decision support, emergency reporting, virtual quality assurance for the radiologist, etc. Targeted use of big data applications can be done for images by supporting the analytic process. Screening software tools designed on big data can be used to highlight a region of interest, such as subtle changes in parenchymal density, solitary pulmonary nodule, or focal hepatic lesions, by plotting its multidimensional anatomy. Following this, we can run more complex applications such as three-dimensional multi planar reconstructions (MPR), volumetric rendering (VR), and curved planar reconstruction, which consume higher system resources on targeted data subsets rather than querying the complete cross-sectional imaging dataset. This pre-emptive selection of dataset can substantially reduce the system requirements such as system memory, server load and provide prompt results. However, a word of caution, "big data should not become "dump data" due to inadequate and poor analysis and non-structured improperly stored data. In the near future, big data can ring in the era of personalized and

  8. A peek into the future of radiology using big data applications

    Science.gov (United States)

    Kharat, Amit T.; Singhal, Shubham

    2017-01-01

    Big data is extremely large amount of data which is available in the radiology department. Big data is identified by four Vs – Volume, Velocity, Variety, and Veracity. By applying different algorithmic tools and converting raw data to transformed data in such large datasets, there is a possibility of understanding and using radiology data for gaining new knowledge and insights. Big data analytics consists of 6Cs – Connection, Cloud, Cyber, Content, Community, and Customization. The global technological prowess and per-capita capacity to save digital information has roughly doubled every 40 months since the 1980's. By using big data, the planning and implementation of radiological procedures in radiology departments can be given a great boost. Potential applications of big data in the future are scheduling of scans, creating patient-specific personalized scanning protocols, radiologist decision support, emergency reporting, virtual quality assurance for the radiologist, etc. Targeted use of big data applications can be done for images by supporting the analytic process. Screening software tools designed on big data can be used to highlight a region of interest, such as subtle changes in parenchymal density, solitary pulmonary nodule, or focal hepatic lesions, by plotting its multidimensional anatomy. Following this, we can run more complex applications such as three-dimensional multi planar reconstructions (MPR), volumetric rendering (VR), and curved planar reconstruction, which consume higher system resources on targeted data subsets rather than querying the complete cross-sectional imaging dataset. This pre-emptive selection of dataset can substantially reduce the system requirements such as system memory, server load and provide prompt results. However, a word of caution, “big data should not become “dump data” due to inadequate and poor analysis and non-structured improperly stored data. In the near future, big data can ring in the era of personalized

  9. Nest mortality of sagebrush songbirds due to a severe hailstorm

    Science.gov (United States)

    Hightower, Jessica N.; Carlisle, Jason D.; Chalfoun, Anna D.

    2018-01-01

    Demographic assessments of nesting birds typically focus on failures due to nest predation or brood parasitism. Extreme weather events such as hailstorms, however, can also destroy eggs and injure or kill juvenile and adult birds at the nest. We documented the effects of a severe hailstorm on 3 species of sagebrush-associated songbirds: Sage Thrasher (Oreoscoptes montanus), Brewer's Sparrow (Spizella breweri), and Vesper Sparrow (Pooecetes gramineus), nesting at eight 24 ha study plots in central Wyoming, USA. Across all plots, 17% of 128 nests failed due to the hailstorm; however, all failed nests were located at a subset of study plots (n = 3) where the hailstorm was most intense, and 45% of all nests failures on those plots were due to hail. Mortality rates varied by species, nest architecture, and nest placement. Nests with more robust architecture and those sited more deeply under the shrub canopy were more likely to survive the hailstorm, suggesting that natural history traits may modulate mortality risk due to hailstorms. While sporadic in nature, hailstorms may represent a significant source of nest failure to songbirds in certain locations, especially with increasing storm frequency and severity forecasted in some regions with ongoing climate change.

  10. Characterizing Big Data Management

    Directory of Open Access Journals (Sweden)

    Rogério Rossi

    2015-06-01

    Full Text Available Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dimension that is related to storage, analytics and visualization of big data; the human aspects of big data; and, in addition, the process management dimension that involves in a technological and business approach the aspects of big data management.

  11. Big science

    CERN Multimedia

    Nadis, S

    2003-01-01

    " "Big science" is moving into astronomy, bringing large experimental teams, multi-year research projects, and big budgets. If this is the wave of the future, why are some astronomers bucking the trend?" (2 pages).

  12. Summary of science, activities, programs, and policies that influence the rangewide conservation of Greater Sage-Grouse (Centrocercus urophasianus)

    Science.gov (United States)

    Manier, D.J.; Wood, David J.A.; Bowen, Z.H.; Donovan, R.M.; Holloran, M.J.; Juliusson, L.M.; Mayne, K.S.; Oyler-McCance, S.J.; Quamen, F.R.; Saher, D.J.; Titolo, A.J.

    2013-01-01

    The Greater Sage-Grouse, has been observed, hunted, and counted for decades. The sagebrush biome, home to the Greater Sage-Grouse, includes sagebrush-steppe and Great Basin sagebrush communities, interspersed with grasslands, salt flats, badlands, mountain ranges, springs, intermittent creeks and washes, and major river systems, and is one of the most widespread and enigmatic components of Western U.S. landscapes. Over time, habitat conversion, degradation, and fragmentation have accumulated across the entire range such that local conditions as well as habitat distributions at local and regional scales are negatively affecting the long-term persistence of this species. Historic patterns of human use and settlement of the sagebrush ecosystem have contributed to the current condition and status of sage-grouse populations. The accumulation of habitat loss, persistent habitat degradation, and fragmentation by industry and urban infrastructure, as indicated by U.S. Fish and Wildlife Service (USFWS) findings, presents a significant challenge for conservation of this species and sustainable management of the sagebrush ecosystem. Because of the wide variations in natural and human history across these landscapes, no single prescription for management of sagebrush ecosystems (including sage-grouse habitats) will suffice to guide the collective efforts of public and private entities to conserve the species and its habitat. This report documents and summarizes several decades of work on sage-grouse populations, sagebrush as habitat, and sagebrush community and ecosystem functions based on the recent assessment and findings of the USFWS under consideration of the Endangered Species Act. As reflected here, some of these topics receive a greater depth of discussion because of the perceived importance of the issue for sagebrush ecosystems and sage-grouse populations. Drawing connections between the direct effects on sagebrush ecosystems and the effect of ecosystem condition on

  13. A strategy for maximizing native plant material diversity for ecological restoration, germplasm conservation and genecology research

    Science.gov (United States)

    Berta Youtie; Nancy Shaw; Matt Fisk; Scott Jensen

    2012-01-01

    One of the most important steps in planning a restoration project is careful selection of ecologically adapted native plant material. As species-specific seed zone maps are not available for most species in the Artemisia tridentata ssp. wyomingensis (Wyoming big sagebrush) ecoregion in the Great Basin, USA, we are employing a provisional seed zone map based on annual...

  14. Flowering branches cause injuries to second-year main stems of Artemisia tridentata nutt. subspecies tridentata

    Science.gov (United States)

    Lance S. Evans; Angela Citta; Stewart C. Sanderson

    2012-01-01

    Eccentricity of stems of Artemisia tridentata Nutt. (big sagebrush) has been reported previously. Analysis of samples observed over 2 years documented that each stem terminal produces about 8-10 branches each year, and during second-year growth, 3-8 of these develop into short, flowering, determinate branches. Each flowering branch produces hundreds of seeds and then...

  15. Using Unmanned Aerial Vehicles to Assess Vegetative Cover and Identify Biotic Resources in Sagebrush Steppe Ecosystems: Preliminary Evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Robert P. Breckenridge

    2006-04-01

    The Idaho National Laboratory (INL), in conjunction with the University of Idaho, is evaluating novel approaches for using unmanned aerial vehicles (UAVs) as a quicker and safer method for monitoring biotic resources. Evaluating vegetative cover is an important factor in understanding the sustainability of many ecosystems. In assessing vegetative cover, methods that improve accuracy and cost efficiency could revolutionize how biotic resources are monitored on western federal lands. Sagebrush steppe ecosystems provide important habitat for a variety of species, some of which are important indicator species (e.g., sage grouse). Improved methods are needed to support monitoring these habitats because there are not enough resource specialists or funds available for comprehensive ground evaluation of these ecosystems. In this project, two types of UAV platforms (fixed wing and helicopter) were used to collect still-frame imagery to assess cover in sagebrush steppe ecosystems. This paper discusses the process for collecting and analyzing imagery from the UAVs to (1) estimate total percent cover, (2) estimate percent cover for six different types of vegetation, and (3) locate sage grouse based on representative decoys. The field plots were located on the INL site west of Idaho Falls, Idaho, in areas with varying amounts and types of vegetative cover. A software program called SamplePoint developed by the U.S. Department of Agriculture, Agricultural Research Service was used to evaluate the imagery for percent cover for the six vegetation types (bare ground, litter, shrubs, dead shrubs, grasses, and forbs). Results were compared against standard field measurements to assess accuracy.

  16. Big bang and big crunch in matrix string theory

    OpenAIRE

    Bedford, J; Papageorgakis, C; Rodríguez-Gómez, D; Ward, J

    2007-01-01

    Following the holographic description of linear dilaton null Cosmologies with a Big Bang in terms of Matrix String Theory put forward by Craps, Sethi and Verlinde, we propose an extended background describing a Universe including both Big Bang and Big Crunch singularities. This belongs to a class of exact string backgrounds and is perturbative in the string coupling far away from the singularities, both of which can be resolved using Matrix String Theory. We provide a simple theory capable of...

  17. Evolution of the Air Toxics under the Big Sky Program

    Science.gov (United States)

    Marra, Nancy; Vanek, Diana; Hester, Carolyn; Holian, Andrij; Ward, Tony; Adams, Earle; Knuth, Randy

    2011-01-01

    As a yearlong exploration of air quality and its relation to respiratory health, the "Air Toxics Under the Big Sky" program offers opportunities for students to learn and apply science process skills through self-designed inquiry-based research projects conducted within their communities. The program follows a systematic scope and sequence…

  18. Adoption of geodemographic and ethno-cultural taxonomies for analysing Big Data

    Directory of Open Access Journals (Sweden)

    Richard James Webber

    2015-05-01

    Full Text Available This paper is intended to contribute to the discussion of the differential level of adoption of Big Data among research communities. Recognising the impracticality of conducting an audit across all forms and uses of Big Data, we have restricted our enquiry to one very specific form of Big Data, namely general purpose taxonomies, of which Mosaic, Acorn and Origins are examples, that rely on data from a variety of Big Data feeds. The intention of these taxonomies is to enable the records of consumers and citizens held on Big Data datasets to be coded according to type of residential neighbourhood or ethno-cultural heritage without any use of questionnaires. Based on our respective experience in the academic social sciences, in government and in the design and marketing of these taxonomies, we identify the features of these classifications which appear to render them attractive or problematic to different categories of potential user or researcher depending on how the relationship is conceived. We conclude by identifying seven classifications of user or potential user who, on account of their background, current position and future career expectations, tend to respond in different ways to the opportunity to adopt these generic systems as aids for understanding social processes.

  19. Greater sage-grouse winter habitat use on the eastern edge of their range

    Science.gov (United States)

    Christopher C. Swanson; Mark A. Rumble; Nicholas W. Kaczor; Robert W. Klaver; Katie M. Herman-Brunson; Jonathan A. Jenks; Kent C. Jensen

    2013-01-01

    Greater sage-grouse (Centrocercus urophasianus) at the western edge of the Dakotas occur in the transition zone between sagebrush and grassland communities. These mixed sagebrush (Artemisia sp.) and grasslands differ from those habitats that comprise the central portions of the sage-grouse range; yet, no information is available on winter habitat selection within this...

  20. Bliver big data til big business?

    DEFF Research Database (Denmark)

    Ritter, Thomas

    2015-01-01

    Danmark har en digital infrastruktur, en registreringskultur og it-kompetente medarbejdere og kunder, som muliggør en førerposition, men kun hvis virksomhederne gør sig klar til næste big data-bølge.......Danmark har en digital infrastruktur, en registreringskultur og it-kompetente medarbejdere og kunder, som muliggør en førerposition, men kun hvis virksomhederne gør sig klar til næste big data-bølge....

  1. Big data uncertainties.

    Science.gov (United States)

    Maugis, Pierre-André G

    2018-07-01

    Big data-the idea that an always-larger volume of information is being constantly recorded-suggests that new problems can now be subjected to scientific scrutiny. However, can classical statistical methods be used directly on big data? We analyze the problem by looking at two known pitfalls of big datasets. First, that they are biased, in the sense that they do not offer a complete view of the populations under consideration. Second, that they present a weak but pervasive level of dependence between all their components. In both cases we observe that the uncertainty of the conclusion obtained by statistical methods is increased when used on big data, either because of a systematic error (bias), or because of a larger degree of randomness (increased variance). We argue that the key challenge raised by big data is not only how to use big data to tackle new problems, but to develop tools and methods able to rigorously articulate the new risks therein. Copyright © 2016. Published by Elsevier Ltd.

  2. over time

    Directory of Open Access Journals (Sweden)

    Sara K. Hanna

    2015-01-01

    Full Text Available Sagebrush steppe ecosystems of the Intermountain West have experienced a decline over the past 150 years due to changing fire regimes, invasive species and conifer encroachment. Prescribed fire is a common and cost-effective tool used in sagebrush restoration and fuels management. We examined the post-fire succession of a sagebrush steppe community over a nearly 30-year period at two study sites in northeastern California. The long-term nature of this study was particularly significant, as invasive annual grasses dominated the plant community in the years immediately following fire, but native perennial grasses and shrubs successfully out-competed them in the long term. Shrubs were slow to recover but had returned to pre-fire levels by the end of the study period. There was also notable increase in western juniper throughout the study sites, particularly in areas that had not been burned. Our results indicate that mean fire return intervals of 50 years or less would help reduce western juniper encroachment and preserve sagebrush habitat, especially for potentially threatened species such as the sage grouse.

  3. Biomedical Big Data Training Collaborative (BBDTC): An effort to bridge the talent gap in biomedical science and research.

    Science.gov (United States)

    Purawat, Shweta; Cowart, Charles; Amaro, Rommie E; Altintas, Ilkay

    2016-06-01

    The BBDTC (https://biobigdata.ucsd.edu) is a community-oriented platform to encourage high-quality knowledge dissemination with the aim of growing a well-informed biomedical big data community through collaborative efforts on training and education. The BBDTC collaborative is an e-learning platform that supports the biomedical community to access, develop and deploy open training materials. The BBDTC supports Big Data skill training for biomedical scientists at all levels, and from varied backgrounds. The natural hierarchy of courses allows them to be broken into and handled as modules . Modules can be reused in the context of multiple courses and reshuffled, producing a new and different, dynamic course called a playlist . Users may create playlists to suit their learning requirements and share it with individual users or the wider public. BBDTC leverages the maturity and design of the HUBzero content-management platform for delivering educational content. To facilitate the migration of existing content, the BBDTC supports importing and exporting course material from the edX platform. Migration tools will be extended in the future to support other platforms. Hands-on training software packages, i.e., toolboxes , are supported through Amazon EC2 and Virtualbox virtualization technologies, and they are available as: ( i ) downloadable lightweight Virtualbox Images providing a standardized software tool environment with software packages and test data on their personal machines, and ( ii ) remotely accessible Amazon EC2 Virtual Machines for accessing biomedical big data tools and scalable big data experiments. At the moment, the BBDTC site contains three open Biomedical big data training courses with lecture contents, videos and hands-on training utilizing VM toolboxes, covering diverse topics. The courses have enhanced the hands-on learning environment by providing structured content that users can use at their own pace. A four course biomedical big data series is

  4. HARNESSING BIG DATA VOLUMES

    Directory of Open Access Journals (Sweden)

    Bogdan DINU

    2014-04-01

    Full Text Available Big Data can revolutionize humanity. Hidden within the huge amounts and variety of the data we are creating we may find information, facts, social insights and benchmarks that were once virtually impossible to find or were simply inexistent. Large volumes of data allow organizations to tap in real time the full potential of all the internal or external information they possess. Big data calls for quick decisions and innovative ways to assist customers and the society as a whole. Big data platforms and product portfolio will help customers harness to the full the value of big data volumes. This paper deals with technical and technological issues related to handling big data volumes in the Big Data environment.

  5. The Big Bang: UK Young Scientists' and Engineers' Fair 2010

    Science.gov (United States)

    Allison, Simon

    2010-01-01

    The Big Bang: UK Young Scientists' and Engineers' Fair is an annual three-day event designed to promote science, technology, engineering and maths (STEM) careers to young people aged 7-19 through experiential learning. It is supported by stakeholders from business and industry, government and the community, and brings together people from various…

  6. BigNeuron: Large-scale 3D Neuron Reconstruction from Optical Microscopy Images

    OpenAIRE

    Peng, Hanchuan; Hawrylycz, Michael; Roskams, Jane; Hill, Sean; Spruston, Nelson; Meijering, Erik; Ascoli, Giorgio A.

    2015-01-01

    textabstractUnderstanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons. Understanding the structure of single neurons is critical for unde...

  7. Big bang and big crunch in matrix string theory

    International Nuclear Information System (INIS)

    Bedford, J.; Ward, J.; Papageorgakis, C.; Rodriguez-Gomez, D.

    2007-01-01

    Following the holographic description of linear dilaton null cosmologies with a big bang in terms of matrix string theory put forward by Craps, Sethi, and Verlinde, we propose an extended background describing a universe including both big bang and big crunch singularities. This belongs to a class of exact string backgrounds and is perturbative in the string coupling far away from the singularities, both of which can be resolved using matrix string theory. We provide a simple theory capable of describing the complete evolution of this closed universe

  8. Big data a primer

    CERN Document Server

    Bhuyan, Prachet; Chenthati, Deepak

    2015-01-01

    This book is a collection of chapters written by experts on various aspects of big data. The book aims to explain what big data is and how it is stored and used. The book starts from  the fundamentals and builds up from there. It is intended to serve as a review of the state-of-the-practice in the field of big data handling. The traditional framework of relational databases can no longer provide appropriate solutions for handling big data and making it available and useful to users scattered around the globe. The study of big data covers a wide range of issues including management of heterogeneous data, big data frameworks, change management, finding patterns in data usage and evolution, data as a service, service-generated data, service management, privacy and security. All of these aspects are touched upon in this book. It also discusses big data applications in different domains. The book will prove useful to students, researchers, and practicing database and networking engineers.

  9. Big data and virtual communities: methodological issues

    OpenAIRE

    Martínez Torres, María del Rocío; Toral, S. L.; Fornara, Nicoletta

    2014-01-01

    Virtual communities represent today en emergent phenomenon through which users get together to create ideas, to obtain help from one another, or just to casually engage in discussions. Their increasing popularity as well as their utility as a source of business value and marketing strategies justify the necessity of defi ning some specifi c methodologies for analyzing them. The aim of this paper is providing new insights into virtual communities from a methodological viewpoint, hi...

  10. Microsoft big data solutions

    CERN Document Server

    Jorgensen, Adam; Welch, John; Clark, Dan; Price, Christopher; Mitchell, Brian

    2014-01-01

    Tap the power of Big Data with Microsoft technologies Big Data is here, and Microsoft's new Big Data platform is a valuable tool to help your company get the very most out of it. This timely book shows you how to use HDInsight along with HortonWorks Data Platform for Windows to store, manage, analyze, and share Big Data throughout the enterprise. Focusing primarily on Microsoft and HortonWorks technologies but also covering open source tools, Microsoft Big Data Solutions explains best practices, covers on-premises and cloud-based solutions, and features valuable case studies. Best of all,

  11. Optimal management strategies in variable environments: Stochastic optimal control methods

    Science.gov (United States)

    Williams, B.K.

    1985-01-01

    Dynamic optimization was used to investigate the optimal defoliation of salt desert shrubs in north-western Utah. Management was formulated in the context of optimal stochastic control theory, with objective functions composed of discounted or time-averaged biomass yields. Climatic variability and community patterns of salt desert shrublands make the application of stochastic optimal control both feasible and necessary. A primary production model was used to simulate shrub responses and harvest yields under a variety of climatic regimes and defoliation patterns. The simulation results then were used in an optimization model to determine optimal defoliation strategies. The latter model encodes an algorithm for finite state, finite action, infinite discrete time horizon Markov decision processes. Three questions were addressed: (i) What effect do changes in weather patterns have on optimal management strategies? (ii) What effect does the discounting of future returns have? (iii) How do the optimal strategies perform relative to certain fixed defoliation strategies? An analysis was performed for the three shrub species, winterfat (Ceratoides lanata), shadscale (Atriplex confertifolia) and big sagebrush (Artemisia tridentata). In general, the results indicate substantial differences among species in optimal control strategies, which are associated with differences in physiological and morphological characteristics. Optimal policies for big sagebrush varied less with variation in climate, reserve levels and discount rates than did either shadscale or winterfat. This was attributed primarily to the overwintering of photosynthetically active tissue and to metabolic activity early in the growing season. Optimal defoliation of shadscale and winterfat generally was more responsive to differences in plant vigor and climate, reflecting the sensitivity of these species to utilization and replenishment of carbohydrate reserves. Similarities could be seen in the influence of both

  12. Summary big data

    CERN Document Server

    2014-01-01

    This work offers a summary of Cukier the book: "Big Data: A Revolution That Will Transform How we Live, Work, and Think" by Viktor Mayer-Schonberg and Kenneth. Summary of the ideas in Viktor Mayer-Schonberg's and Kenneth Cukier's book: " Big Data " explains that big data is where we use huge quantities of data to make better predictions based on the fact we identify patters in the data rather than trying to understand the underlying causes in more detail. This summary highlights that big data will be a source of new economic value and innovation in the future. Moreover, it shows that it will

  13. BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images

    NARCIS (Netherlands)

    H. Peng (Hanchuan); M. Hawrylycz (Michael); J. Roskams (Jane); S. Hill (Sean); N. Spruston (Nelson); E. Meijering (Erik); G.A. Ascoli (Giorgio A.)

    2015-01-01

    textabstractUnderstanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and

  14. Big data in pharmacy practice: current use, challenges, and the future.

    Science.gov (United States)

    Ma, Carolyn; Smith, Helen Wong; Chu, Cherie; Juarez, Deborah T

    2015-01-01

    Pharmacy informatics is defined as the use and integration of data, information, knowledge, technology, and automation in the medication-use process for the purpose of improving health outcomes. The term "big data" has been coined and is often defined in three V's: volume, velocity, and variety. This paper describes three major areas in which pharmacy utilizes big data, including: 1) informed decision making (clinical pathways and clinical practice guidelines); 2) improved care delivery in health care settings such as hospitals and community pharmacy practice settings; and 3) quality performance measurement for the Centers for Medicare and Medicaid and medication management activities such as tracking medication adherence and medication reconciliation.

  15. Long-Term Developmental Changes in Children's Lower-Order Big Five Personality Facets

    NARCIS (Netherlands)

    A.D. de Haan (Amaranta); S.S.W. de Pauw (Sarah); A.L. van den Akker (Alithe); M. Deković (Maja); P.J. Prinzie (Peter)

    2016-01-01

    markdownabstract__Objective:__ This study examined long-term developmental changes in mother-rated lower-order facets of children's Big Five dimensions. __Method:__ Two independent community samples covering early childhood (2-4.5 years; N=365, 39% girls) and middle childhood to the end of middle

  16. Big Data en surveillance, deel 1 : Definities en discussies omtrent Big Data

    NARCIS (Netherlands)

    Timan, Tjerk

    2016-01-01

    Naar aanleiding van een (vrij kort) college over surveillance en Big Data, werd me gevraagd iets dieper in te gaan op het thema, definities en verschillende vraagstukken die te maken hebben met big data. In dit eerste deel zal ik proberen e.e.a. uiteen te zetten betreft Big Data theorie en

  17. Characterizing Big Data Management

    OpenAIRE

    Rogério Rossi; Kechi Hirama

    2015-01-01

    Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: t...

  18. Science framework for the conservation and restoration strategy of DOI secretarial order 3336: Utilizing resilience and resistance concepts to assess threats to sagebrush ecosystems and greater sage-grouse, prioritize conservation and restoration actions, and inform management strategies

    Science.gov (United States)

    Chambers, Jeanne C.; Campbell, Steve; Carlson, John; Beck, Jeffrey L.; Clause, Karen J.; Dinkins, Jonathan B.; Doherty, Kevin E.; Espinosa, Shawn; Griffin, Kathleen A.; Christiansen, Thomas J.; Crist, Michele R.; Hanser, Steven E.; Havlina, Douglas W.; Henke, Kenneth F.; Hennig, Jacob D.; Kurth, Laurie L.; Maestas, Jeremy D.; Mayer, Kenneth E.; Manning, Mary E.; Mealor, Brian A.; McCarthy, Clinton; Pellant, Mike; Prentice, Karen L.; Perea, Marco A.; Pyke, David A.; Wiechman , Lief A.; Wuenschel, Amarina

    2016-01-01

    The Science Framework for the Conservation and Restoration Strategy of the Department of the Interior, Secretarial Order 3336 (SO 3336), Rangeland Fire Prevention, Management and Restoration, provides a strategic, multiscale approach for prioritizing areas for management and determining effective management strategies across the sagebrush biome. The emphasis of this version is on sagebrush ecosystems and greater sage-grouse. The Science Framework uses a six step process in which sagebrush ecosystem resilience to disturbance and resistance to nonnative, invasive annual grasses is linked to species habitat information based on the distribution and abundance of focal species. The predominant ecosystem and anthropogenic threats are assessed, and a habitat matrix is developed that helps decision makers evaluate risks and determine appropriate management strategies at regional and local scales. Areas are prioritized for management action using a geospatial approach that overlays resilience and resistance, species habitat information, and predominant threats. Decision tools are discussed for determining the suitability of priority areas for management and the most appropriate management actions at regional to local scales. The Science Framework and geospatial crosscut are intended to complement the mitigation strategies associated with the Greater Sage-Grouse Land Use Plan amendments for the Department of the Interior Bureaus, such as the Bureau of Land Management, and the U.S. Forest Service.

  19. The Big Challenge in Big Earth Science Data: Maturing to Transdisciplinary Data Platforms that are Relevant to Government, Research and Industry

    Science.gov (United States)

    Wyborn, Lesley; Evans, Ben

    2016-04-01

    scientific domain silos, including into the humanities and social sciences. Furthermore there is increasing desire for these 'Big Data' data infrastructures to prove their value not only as platforms for scientific discovery, but to also support the development of evidence-based government policies, economic growth, and private-sector opportunities. The capacity of these transdisciplinary data repositories leads to many new exciting opportunities for the next generation of large-scale data integration, but there is an emerging suite of data challenges that now need to be tackled. Many large volume data sets have historically been developed within traditional domain silos and issues such as difference of standards (informal and formal), the data conventions, the lack of controlled or even uniform vocabularies, the non-existent/not machine-accessible semantic information, and bespoke or unclear copyrights and licensing are becoming apparent. The different perspectives and approaches of the various communities have also started to come to the fore; particularly the dominant file based approach of the big data generating science communities versus the database approach of the point observational communities; and the multidimensional approach of the climate and oceans community versus the traditional 2D approach of the GIS/spatial community. Addressing such challenges is essential to fully unlock online access to all relevant data to enable the maturing of research to the transdisciplinary paradigm.

  20. Big Data in der Cloud

    DEFF Research Database (Denmark)

    Leimbach, Timo; Bachlechner, Daniel

    2014-01-01

    Technology assessment of big data, in particular cloud based big data services, for the Office for Technology Assessment at the German federal parliament (Bundestag)......Technology assessment of big data, in particular cloud based big data services, for the Office for Technology Assessment at the German federal parliament (Bundestag)...

  1. An analysis of cross-sectional differences in big and non-big public accounting firms' audit programs

    NARCIS (Netherlands)

    Blokdijk, J.H. (Hans); Drieenhuizen, F.; Stein, M.T.; Simunic, D.A.

    2006-01-01

    A significant body of prior research has shown that audits by the Big 5 (now Big 4) public accounting firms are quality differentiated relative to non-Big 5 audits. This result can be derived analytically by assuming that Big 5 and non-Big 5 firms face different loss functions for "audit failures"

  2. Big Data is invading big places as CERN

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Big Data technologies are becoming more popular with the constant grow of data generation in different fields such as social networks, internet of things and laboratories like CERN. How is CERN making use of such technologies? How machine learning is applied at CERN with Big Data technologies? How much data we move and how it is analyzed? All these questions will be answered during the talk.

  3. The big bang

    International Nuclear Information System (INIS)

    Chown, Marcus.

    1987-01-01

    The paper concerns the 'Big Bang' theory of the creation of the Universe 15 thousand million years ago, and traces events which physicists predict occurred soon after the creation. Unified theory of the moment of creation, evidence of an expanding Universe, the X-boson -the particle produced very soon after the big bang and which vanished from the Universe one-hundredth of a second after the big bang, and the fate of the Universe, are all discussed. (U.K.)

  4. GraphStore: A Distributed Graph Storage System for Big Data Networks

    Science.gov (United States)

    Martha, VenkataSwamy

    2013-01-01

    Networks, such as social networks, are a universal solution for modeling complex problems in real time, especially in the Big Data community. While previous studies have attempted to enhance network processing algorithms, none have paved a path for the development of a persistent storage system. The proposed solution, GraphStore, provides an…

  5. Small Big Data Congress 2017

    NARCIS (Netherlands)

    Doorn, J.

    2017-01-01

    TNO, in collaboration with the Big Data Value Center, presents the fourth Small Big Data Congress! Our congress aims at providing an overview of practical and innovative applications based on big data. Do you want to know what is happening in applied research with big data? And what can already be

  6. Big data opportunities and challenges

    CERN Document Server

    2014-01-01

    This ebook aims to give practical guidance for all those who want to understand big data better and learn how to make the most of it. Topics range from big data analysis, mobile big data and managing unstructured data to technologies, governance and intellectual property and security issues surrounding big data.

  7. Big Data and Neuroimaging.

    Science.gov (United States)

    Webb-Vargas, Yenny; Chen, Shaojie; Fisher, Aaron; Mejia, Amanda; Xu, Yuting; Crainiceanu, Ciprian; Caffo, Brian; Lindquist, Martin A

    2017-12-01

    Big Data are of increasing importance in a variety of areas, especially in the biosciences. There is an emerging critical need for Big Data tools and methods, because of the potential impact of advancements in these areas. Importantly, statisticians and statistical thinking have a major role to play in creating meaningful progress in this arena. We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big Data problems. We use the field of statistical neuroimaging to demonstrate these points. As such, this paper covers several applications and novel methodological developments of Big Data tools applied to neuroimaging data.

  8. Big Data; A Management Revolution : The emerging role of big data in businesses

    OpenAIRE

    Blasiak, Kevin

    2014-01-01

    Big data is a term that was coined in 2012 and has since then emerged to one of the top trends in business and technology. Big data is an agglomeration of different technologies resulting in data processing capabilities that have been unreached before. Big data is generally characterized by 4 factors. Volume, velocity and variety. These three factors distinct it from the traditional data use. The possibilities to utilize this technology are vast. Big data technology has touch points in differ...

  9. Social big data mining

    CERN Document Server

    Ishikawa, Hiroshi

    2015-01-01

    Social Media. Big Data and Social Data. Hypotheses in the Era of Big Data. Social Big Data Applications. Basic Concepts in Data Mining. Association Rule Mining. Clustering. Classification. Prediction. Web Structure Mining. Web Content Mining. Web Access Log Mining, Information Extraction and Deep Web Mining. Media Mining. Scalability and Outlier Detection.

  10. Observatories, think tanks, and community models in the hydrologic and environmental sciences: How does it affect me?

    Science.gov (United States)

    Torgersen, Thomas

    2006-06-01

    Multiple issues in hydrologic and environmental sciences are now squarely in the public focus and require both government and scientific study. Two facts also emerge: (1) The new approach being touted publicly for advancing the hydrologic and environmental sciences is the establishment of community-operated "big science" (observatories, think tanks, community models, and data repositories). (2) There have been important changes in the business of science over the last 20 years that make it important for the hydrologic and environmental sciences to demonstrate the "value" of public investment in hydrological and environmental science. Given that community-operated big science (observatories, think tanks, community models, and data repositories) could become operational, I argue that such big science should not mean a reduction in the importance of single-investigator science. Rather, specific linkages between the large-scale, team-built, community-operated big science and the single investigator should provide context data, observatory data, and systems models for a continuing stream of hypotheses by discipline-based, specialized research and a strong rationale for continued, single-PI ("discovery-based") research. I also argue that big science can be managed to provide a better means of demonstrating the value of public investment in the hydrologic and environmental sciences. Decisions regarding policy will still be political, but big science could provide an integration of the best scientific understanding as a guide for the best policy.

  11. Cryptography for Big Data Security

    Science.gov (United States)

    2015-07-13

    Cryptography for Big Data Security Book Chapter for Big Data: Storage, Sharing, and Security (3S) Distribution A: Public Release Ariel Hamlin1 Nabil...Email: arkady@ll.mit.edu ii Contents 1 Cryptography for Big Data Security 1 1.1 Introduction...48 Chapter 1 Cryptography for Big Data Security 1.1 Introduction With the amount

  12. Data: Big and Small.

    Science.gov (United States)

    Jones-Schenk, Jan

    2017-02-01

    Big data is a big topic in all leadership circles. Leaders in professional development must develop an understanding of what data are available across the organization that can inform effective planning for forecasting. Collaborating with others to integrate data sets can increase the power of prediction. Big data alone is insufficient to make big decisions. Leaders must find ways to access small data and triangulate multiple types of data to ensure the best decision making. J Contin Educ Nurs. 2017;48(2):60-61. Copyright 2017, SLACK Incorporated.

  13. Big Data Revisited

    DEFF Research Database (Denmark)

    Kallinikos, Jannis; Constantiou, Ioanna

    2015-01-01

    We elaborate on key issues of our paper New games, new rules: big data and the changing context of strategy as a means of addressing some of the concerns raised by the paper’s commentators. We initially deal with the issue of social data and the role it plays in the current data revolution...... and the technological recording of facts. We further discuss the significance of the very mechanisms by which big data is produced as distinct from the very attributes of big data, often discussed in the literature. In the final section of the paper, we qualify the alleged importance of algorithms and claim...... that the structures of data capture and the architectures in which data generation is embedded are fundamental to the phenomenon of big data....

  14. Big Data in industry

    Science.gov (United States)

    Latinović, T. S.; Preradović, D. M.; Barz, C. R.; Latinović, M. T.; Petrica, P. P.; Pop-Vadean, A.

    2016-08-01

    The amount of data at the global level has grown exponentially. Along with this phenomena, we have a need for a new unit of measure like exabyte, zettabyte, and yottabyte as the last unit measures the amount of data. The growth of data gives a situation where the classic systems for the collection, storage, processing, and visualization of data losing the battle with a large amount, speed, and variety of data that is generated continuously. Many of data that is created by the Internet of Things, IoT (cameras, satellites, cars, GPS navigation, etc.). It is our challenge to come up with new technologies and tools for the management and exploitation of these large amounts of data. Big Data is a hot topic in recent years in IT circles. However, Big Data is recognized in the business world, and increasingly in the public administration. This paper proposes an ontology of big data analytics and examines how to enhance business intelligence through big data analytics as a service by presenting a big data analytics services-oriented architecture. This paper also discusses the interrelationship between business intelligence and big data analytics. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, and business intelligence as well as intelligent agents.

  15. Long-term developmental changes in children's lower-order Big Five personality facets

    NARCIS (Netherlands)

    Haan, A.D. de; Pauw, S. de; Akker, A.J. van den; Dekovic, M.; Prinzie, P.

    2017-01-01

    Objective: This study examined long-term developmental changes in mother-rated lower-order facets of children's Big Five dimensions. Method: Two independent community samples covering early childhood (2-4.5 years; N = 365, 39% girls) and middle childhood to the end of middle adolescence (6-17 years;

  16. Big Data Analytics An Overview

    Directory of Open Access Journals (Sweden)

    Jayshree Dwivedi

    2015-08-01

    Full Text Available Big data is a data beyond the storage capacity and beyond the processing power is called big data. Big data term is used for data sets its so large or complex that traditional data it involves data sets with sizes. Big data size is a constantly moving target year by year ranging from a few dozen terabytes to many petabytes of data means like social networking sites the amount of data produced by people is growing rapidly every year. Big data is not only a data rather it become a complete subject which includes various tools techniques and framework. It defines the epidemic possibility and evolvement of data both structured and unstructured. Big data is a set of techniques and technologies that require new forms of assimilate to uncover large hidden values from large datasets that are diverse complex and of a massive scale. It is difficult to work with using most relational database management systems and desktop statistics and visualization packages exacting preferably massively parallel software running on tens hundreds or even thousands of servers. Big data environment is used to grab organize and resolve the various types of data. In this paper we describe applications problems and tools of big data and gives overview of big data.

  17. Research Data Alliance: Understanding Big Data Analytics Applications in Earth Science

    Science.gov (United States)

    Riedel, Morris; Ramachandran, Rahul; Baumann, Peter

    2014-01-01

    The Research Data Alliance (RDA) enables data to be shared across barriers through focused working groups and interest groups, formed of experts from around the world - from academia, industry and government. Its Big Data Analytics (BDA) interest groups seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. BDA seeks to analyze different scientific domain applications (e.g. earth science use cases) and their potential use of various big data analytics techniques. These techniques reach from hardware deployment models up to various different algorithms (e.g. machine learning algorithms such as support vector machines for classification). A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. This contribution will outline initial parts of such a classification and recommendations in the specific context of the field of Earth Sciences. Given lessons learned and experiences are based on a survey of use cases and also providing insights in a few use cases in detail.

  18. Urbanising Big

    DEFF Research Database (Denmark)

    Ljungwall, Christer

    2013-01-01

    Development in China raises the question of how big a city can become, and at the same time be sustainable, writes Christer Ljungwall of the Swedish Agency for Growth Policy Analysis.......Development in China raises the question of how big a city can become, and at the same time be sustainable, writes Christer Ljungwall of the Swedish Agency for Growth Policy Analysis....

  19. Big bang nucleosynthesis

    International Nuclear Information System (INIS)

    Boyd, Richard N.

    2001-01-01

    The precision of measurements in modern cosmology has made huge strides in recent years, with measurements of the cosmic microwave background and the determination of the Hubble constant now rivaling the level of precision of the predictions of big bang nucleosynthesis. However, these results are not necessarily consistent with the predictions of the Standard Model of big bang nucleosynthesis. Reconciling these discrepancies may require extensions of the basic tenets of the model, and possibly of the reaction rates that determine the big bang abundances

  20. Health level seven interoperability strategy: big data, incrementally structured.

    Science.gov (United States)

    Dolin, R H; Rogers, B; Jaffe, C

    2015-01-01

    Describe how the HL7 Clinical Document Architecture (CDA), a foundational standard in US Meaningful Use, contributes to a "big data, incrementally structured" interoperability strategy, whereby data structured incrementally gets large amounts of data flowing faster. We present cases showing how this approach is leveraged for big data analysis. To support the assertion that semi-structured narrative in CDA format can be a useful adjunct in an overall big data analytic approach, we present two case studies. The first assesses an organization's ability to generate clinical quality reports using coded data alone vs. coded data supplemented by CDA narrative. The second leverages CDA to construct a network model for referral management, from which additional observations can be gleaned. The first case shows that coded data supplemented by CDA narrative resulted in significant variances in calculated performance scores. In the second case, we found that the constructed network model enables the identification of differences in patient characteristics among different referral work flows. The CDA approach goes after data indirectly, by focusing first on the flow of narrative, which is then incrementally structured. A quantitative assessment of whether this approach will lead to a greater flow of data and ultimately a greater flow of structured data vs. other approaches is planned as a future exercise. Along with growing adoption of CDA, we are now seeing the big data community explore the standard, particularly given its potential to supply analytic en- gines with volumes of data previously not possible.

  1. Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data.

    Science.gov (United States)

    Dinov, Ivo D

    2016-01-01

    Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be 'team science'.

  2. The ethics of big data in big agriculture

    OpenAIRE

    Carbonell (Isabelle M.)

    2016-01-01

    This paper examines the ethics of big data in agriculture, focusing on the power asymmetry between farmers and large agribusinesses like Monsanto. Following the recent purchase of Climate Corp., Monsanto is currently the most prominent biotech agribusiness to buy into big data. With wireless sensors on tractors monitoring or dictating every decision a farmer makes, Monsanto can now aggregate large quantities of previously proprietary farming data, enabling a privileged position with unique in...

  3. The role of administrative data in the big data revolution in social science research.

    Science.gov (United States)

    Connelly, Roxanne; Playford, Christopher J; Gayle, Vernon; Dibben, Chris

    2016-09-01

    The term big data is currently a buzzword in social science, however its precise meaning is ambiguous. In this paper we focus on administrative data which is a distinctive form of big data. Exciting new opportunities for social science research will be afforded by new administrative data resources, but these are currently under appreciated by the research community. The central aim of this paper is to discuss the challenges associated with administrative data. We emphasise that it is critical for researchers to carefully consider how administrative data has been produced. We conclude that administrative datasets have the potential to contribute to the development of high-quality and impactful social science research, and should not be overlooked in the emerging field of big data. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  4. A Big Video Manifesto

    DEFF Research Database (Denmark)

    Mcilvenny, Paul Bruce; Davidsen, Jacob

    2017-01-01

    and beautiful visualisations. However, we also need to ask what the tools of big data can do both for the Humanities and for more interpretative approaches and methods. Thus, we prefer to explore how the power of computation, new sensor technologies and massive storage can also help with video-based qualitative......For the last few years, we have witnessed a hype about the potential results and insights that quantitative big data can bring to the social sciences. The wonder of big data has moved into education, traffic planning, and disease control with a promise of making things better with big numbers...

  5. Identifying Dwarfs Workloads in Big Data Analytics

    OpenAIRE

    Gao, Wanling; Luo, Chunjie; Zhan, Jianfeng; Ye, Hainan; He, Xiwen; Wang, Lei; Zhu, Yuqing; Tian, Xinhui

    2015-01-01

    Big data benchmarking is particularly important and provides applicable yardsticks for evaluating booming big data systems. However, wide coverage and great complexity of big data computing impose big challenges on big data benchmarking. How can we construct a benchmark suite using a minimum set of units of computation to represent diversity of big data analytics workloads? Big data dwarfs are abstractions of extracting frequently appearing operations in big data computing. One dwarf represen...

  6. Applications of Big Data in Education

    OpenAIRE

    Faisal Kalota

    2015-01-01

    Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners' needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in educa...

  7. Big Data Semantics

    NARCIS (Netherlands)

    Ceravolo, Paolo; Azzini, Antonia; Angelini, Marco; Catarci, Tiziana; Cudré-Mauroux, Philippe; Damiani, Ernesto; Mazak, Alexandra; van Keulen, Maurice; Jarrar, Mustafa; Santucci, Giuseppe; Sattler, Kai-Uwe; Scannapieco, Monica; Wimmer, Manuel; Wrembel, Robert; Zaraket, Fadi

    2018-01-01

    Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be

  8. Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.

    Science.gov (United States)

    Popescu, George V; Noutsos, Christos; Popescu, Sorina C

    2016-01-01

    In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.

  9. Comparative validity of brief to medium-length Big Five and Big Six personality questionnaires

    NARCIS (Netherlands)

    Thalmayer, A.G.; Saucier, G.; Eigenhuis, A.

    2011-01-01

    A general consensus on the Big Five model of personality attributes has been highly generative for the field of personality psychology. Many important psychological and life outcome correlates with Big Five trait dimensions have been established. But researchers must choose between multiple Big Five

  10. Ocean Networks Canada's "Big Data" Initiative

    Science.gov (United States)

    Dewey, R. K.; Hoeberechts, M.; Moran, K.; Pirenne, B.; Owens, D.

    2013-12-01

    Ocean Networks Canada operates two large undersea observatories that collect, archive, and deliver data in real time over the Internet. These data contribute to our understanding of the complex changes taking place on our ocean planet. Ocean Networks Canada's VENUS was the world's first cabled seafloor observatory to enable researchers anywhere to connect in real time to undersea experiments and observations. Its NEPTUNE observatory is the largest cabled ocean observatory, spanning a wide range of ocean environments. Most recently, we installed a new small observatory in the Arctic. Together, these observatories deliver "Big Data" across many disciplines in a cohesive manner using the Oceans 2.0 data management and archiving system that provides national and international users with open access to real-time and archived data while also supporting a collaborative work environment. Ocean Networks Canada operates these observatories to support science, innovation, and learning in four priority areas: study of the impact of climate change on the ocean; the exploration and understanding the unique life forms in the extreme environments of the deep ocean and below the seafloor; the exchange of heat, fluids, and gases that move throughout the ocean and atmosphere; and the dynamics of earthquakes, tsunamis, and undersea landslides. To date, the Ocean Networks Canada archive contains over 130 TB (collected over 7 years) and the current rate of data acquisition is ~50 TB per year. This data set is complex and diverse. Making these "Big Data" accessible and attractive to users is our priority. In this presentation, we share our experience as a "Big Data" institution where we deliver simple and multi-dimensional calibrated data cubes to a diverse pool of users. Ocean Networks Canada also conducts extensive user testing. Test results guide future tool design and development of "Big Data" products. We strive to bridge the gap between the raw, archived data and the needs and

  11. Big data need big theory too.

    Science.gov (United States)

    Coveney, Peter V; Dougherty, Edward R; Highfield, Roger R

    2016-11-13

    The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their 'depth' and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote 'blind' big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'. © 2015 The Authors.

  12. Preliminary development and validation of an Australian community participation questionnaire: types of participation and associations with distress in a coastal community.

    Science.gov (United States)

    Berry, Helen Louise; Rodgers, Bryan; Dear, Keith B G

    2007-04-01

    Participating in the social and civic life of communities is protectively associated with the onset and course of physical and mental disorders, and is considered important in achieving health promotion goals. Despite its importance in health research, there is no systematically developed measure of community participation. Our aim was to undertake the preliminary development of a community participation questionnaire, including validating it against an external reference, general psychological distress. Participants were 963 randomly selected community members, aged 19-97, from coastal New South Wales, Australia, who completed an anonymous postal survey. There were 14 types of community participation, most of which were characterised by personal involvement, initiative and effort. Frequency of participation varied across types and between women and men. Based on multiple linear regression analyses, controlling for socio-demographic factors, nine types of participation were independently and significantly associated with general psychological distress. Unexpectedly, for two of these, "expressing opinions publicly" and "political protest", higher levels of participation were associated with higher levels of distress. The other seven were: contact with immediate household, extended family, friends, and neighbours; participating in organised community activities; taking an active interest in current affairs; and religious observance. We called these the "Big 7". Higher levels of participation in the Big 7 were associated with lower levels of distress. Participating in an increasing number of the Big 7 types of participation was strongly associated in linear fashion with decreasing distress.

  13. Big Data in HEP: A comprehensive use case study

    OpenAIRE

    Gutsche, Oliver; Cremonesi, Matteo; Elmer, Peter; Jayatilaka, Bo; Kowalkowski, Jim; Pivarski, Jim; Sehrish, Saba; Surez, Cristina Mantilla; Svyatkovskiy, Alexey; Tran, Nhan

    2017-01-01

    Experimental Particle Physics has been at the forefront of analyzing the worlds largest datasets for decades. The HEP community was the first to develop suitable software and computing tools for this task. In recent times, new toolkits and systems collectively called Big Data technologies have emerged to support the analysis of Petabyte and Exabyte datasets in industry. While the principles of data analysis in HEP have not changed (filtering and transforming experiment-specific data formats),...

  14. Big Data and medicine: a big deal?

    Science.gov (United States)

    Mayer-Schönberger, V; Ingelsson, E

    2018-05-01

    Big Data promises huge benefits for medical research. Looking beyond superficial increases in the amount of data collected, we identify three key areas where Big Data differs from conventional analyses of data samples: (i) data are captured more comprehensively relative to the phenomenon under study; this reduces some bias but surfaces important trade-offs, such as between data quantity and data quality; (ii) data are often analysed using machine learning tools, such as neural networks rather than conventional statistical methods resulting in systems that over time capture insights implicit in data, but remain black boxes, rarely revealing causal connections; and (iii) the purpose of the analyses of data is no longer simply answering existing questions, but hinting at novel ones and generating promising new hypotheses. As a consequence, when performed right, Big Data analyses can accelerate research. Because Big Data approaches differ so fundamentally from small data ones, research structures, processes and mindsets need to adjust. The latent value of data is being reaped through repeated reuse of data, which runs counter to existing practices not only regarding data privacy, but data management more generally. Consequently, we suggest a number of adjustments such as boards reviewing responsible data use, and incentives to facilitate comprehensive data sharing. As data's role changes to a resource of insight, we also need to acknowledge the importance of collecting and making data available as a crucial part of our research endeavours, and reassess our formal processes from career advancement to treatment approval. © 2017 The Association for the Publication of the Journal of Internal Medicine.

  15. Assessing Big Data

    DEFF Research Database (Denmark)

    Leimbach, Timo; Bachlechner, Daniel

    2015-01-01

    In recent years, big data has been one of the most controversially discussed technologies in terms of its possible positive and negative impact. Therefore, the need for technology assessments is obvious. This paper first provides, based on the results of a technology assessment study, an overview...... of the potential and challenges associated with big data and then describes the problems experienced during the study as well as methods found helpful to address them. The paper concludes with reflections on how the insights from the technology assessment study may have an impact on the future governance of big...... data....

  16. Big data, big responsibilities

    Directory of Open Access Journals (Sweden)

    Primavera De Filippi

    2014-01-01

    Full Text Available Big data refers to the collection and aggregation of large quantities of data produced by and about people, things or the interactions between them. With the advent of cloud computing, specialised data centres with powerful computational hardware and software resources can be used for processing and analysing a humongous amount of aggregated data coming from a variety of different sources. The analysis of such data is all the more valuable to the extent that it allows for specific patterns to be found and new correlations to be made between different datasets, so as to eventually deduce or infer new information, as well as to potentially predict behaviours or assess the likelihood for a certain event to occur. This article will focus specifically on the legal and moral obligations of online operators collecting and processing large amounts of data, to investigate the potential implications of big data analysis on the privacy of individual users and on society as a whole.

  17. Comparative validity of brief to medium-length Big Five and Big Six Personality Questionnaires.

    Science.gov (United States)

    Thalmayer, Amber Gayle; Saucier, Gerard; Eigenhuis, Annemarie

    2011-12-01

    A general consensus on the Big Five model of personality attributes has been highly generative for the field of personality psychology. Many important psychological and life outcome correlates with Big Five trait dimensions have been established. But researchers must choose between multiple Big Five inventories when conducting a study and are faced with a variety of options as to inventory length. Furthermore, a 6-factor model has been proposed to extend and update the Big Five model, in part by adding a dimension of Honesty/Humility or Honesty/Propriety. In this study, 3 popular brief to medium-length Big Five measures (NEO Five Factor Inventory, Big Five Inventory [BFI], and International Personality Item Pool), and 3 six-factor measures (HEXACO Personality Inventory, Questionnaire Big Six Scales, and a 6-factor version of the BFI) were placed in competition to best predict important student life outcomes. The effect of test length was investigated by comparing brief versions of most measures (subsets of items) with original versions. Personality questionnaires were administered to undergraduate students (N = 227). Participants' college transcripts and student conduct records were obtained 6-9 months after data was collected. Six-factor inventories demonstrated better predictive ability for life outcomes than did some Big Five inventories. Additional behavioral observations made on participants, including their Facebook profiles and cell-phone text usage, were predicted similarly by Big Five and 6-factor measures. A brief version of the BFI performed surprisingly well; across inventory platforms, increasing test length had little effect on predictive validity. Comparative validity of the models and measures in terms of outcome prediction and parsimony is discussed.

  18. Big Machines and Big Science: 80 Years of Accelerators at Stanford

    Energy Technology Data Exchange (ETDEWEB)

    Loew, Gregory

    2008-12-16

    Longtime SLAC physicist Greg Loew will present a trip through SLAC's origins, highlighting its scientific achievements, and provide a glimpse of the lab's future in 'Big Machines and Big Science: 80 Years of Accelerators at Stanford.'

  19. Dual of big bang and big crunch

    International Nuclear Information System (INIS)

    Bak, Dongsu

    2007-01-01

    Starting from the Janus solution and its gauge theory dual, we obtain the dual gauge theory description of the cosmological solution by the procedure of double analytic continuation. The coupling is driven either to zero or to infinity at the big-bang and big-crunch singularities, which are shown to be related by the S-duality symmetry. In the dual Yang-Mills theory description, these are nonsingular as the coupling goes to zero in the N=4 super Yang-Mills theory. The cosmological singularities simply signal the failure of the supergravity description of the full type IIB superstring theory

  20. Comparative Validity of Brief to Medium-Length Big Five and Big Six Personality Questionnaires

    Science.gov (United States)

    Thalmayer, Amber Gayle; Saucier, Gerard; Eigenhuis, Annemarie

    2011-01-01

    A general consensus on the Big Five model of personality attributes has been highly generative for the field of personality psychology. Many important psychological and life outcome correlates with Big Five trait dimensions have been established. But researchers must choose between multiple Big Five inventories when conducting a study and are…

  1. Big data for health.

    Science.gov (United States)

    Andreu-Perez, Javier; Poon, Carmen C Y; Merrifield, Robert D; Wong, Stephen T C; Yang, Guang-Zhong

    2015-07-01

    This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

  2. Big Data and Intelligence: Applications, Human Capital, and Education

    Directory of Open Access Journals (Sweden)

    Michael Landon-Murray

    2016-06-01

    Full Text Available The potential for big data to contribute to the US intelligence mission goes beyond bulk collection, social media and counterterrorism. Applications will speak to a range of issues of major concern to intelligence agencies, from military operations to climate change to cyber security. There are challenges too: procurement lags, data stovepiping, separating signal from noise, sources and methods, a range of normative issues, and central to managing these challenges, human capital. These potential applications and challenges are discussed and a closer look at what data scientists do in the Intelligence Community (IC is offered. Effectively filling the ranks of the IC’s data science workforce will depend on the provision of well-trained data scientists from the higher education system. Program offerings at America’s top fifty universities will thus be surveyed (just a few years ago there were reportedly no degrees in data science. One Master’s program that has melded data science with intelligence is examined as well as a university big data research center focused on security and intelligence. This discussion goes a long way to clarify the prospective uses of data science in intelligence while probing perhaps the key challenge to optimal application of big data in the IC.

  3. Big Data: Implications for Health System Pharmacy.

    Science.gov (United States)

    Stokes, Laura B; Rogers, Joseph W; Hertig, John B; Weber, Robert J

    2016-07-01

    Big Data refers to datasets that are so large and complex that traditional methods and hardware for collecting, sharing, and analyzing them are not possible. Big Data that is accurate leads to more confident decision making, improved operational efficiency, and reduced costs. The rapid growth of health care information results in Big Data around health services, treatments, and outcomes, and Big Data can be used to analyze the benefit of health system pharmacy services. The goal of this article is to provide a perspective on how Big Data can be applied to health system pharmacy. It will define Big Data, describe the impact of Big Data on population health, review specific implications of Big Data in health system pharmacy, and describe an approach for pharmacy leaders to effectively use Big Data. A few strategies involved in managing Big Data in health system pharmacy include identifying potential opportunities for Big Data, prioritizing those opportunities, protecting privacy concerns, promoting data transparency, and communicating outcomes. As health care information expands in its content and becomes more integrated, Big Data can enhance the development of patient-centered pharmacy services.

  4. Generalized formal model of Big Data

    OpenAIRE

    Shakhovska, N.; Veres, O.; Hirnyak, M.

    2016-01-01

    This article dwells on the basic characteristic features of the Big Data technologies. It is analyzed the existing definition of the “big data” term. The article proposes and describes the elements of the generalized formal model of big data. It is analyzed the peculiarities of the application of the proposed model components. It is described the fundamental differences between Big Data technology and business analytics. Big Data is supported by the distributed file system Google File System ...

  5. BigWig and BigBed: enabling browsing of large distributed datasets.

    Science.gov (United States)

    Kent, W J; Zweig, A S; Barber, G; Hinrichs, A S; Karolchik, D

    2010-09-01

    BigWig and BigBed files are compressed binary indexed files containing data at several resolutions that allow the high-performance display of next-generation sequencing experiment results in the UCSC Genome Browser. The visualization is implemented using a multi-layered software approach that takes advantage of specific capabilities of web-based protocols and Linux and UNIX operating systems files, R trees and various indexing and compression tricks. As a result, only the data needed to support the current browser view is transmitted rather than the entire file, enabling fast remote access to large distributed data sets. Binaries for the BigWig and BigBed creation and parsing utilities may be downloaded at http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/. Source code for the creation and visualization software is freely available for non-commercial use at http://hgdownload.cse.ucsc.edu/admin/jksrc.zip, implemented in C and supported on Linux. The UCSC Genome Browser is available at http://genome.ucsc.edu.

  6. Data privacy foundations, new developments and the big data challenge

    CERN Document Server

    Torra, Vicenç

    2017-01-01

    This book offers a broad, cohesive overview of the field of data privacy. It discusses, from a technological perspective, the problems and solutions of the three main communities working on data privacy: statistical disclosure control (those with a statistical background), privacy-preserving data mining (those working with data bases and data mining), and privacy-enhancing technologies (those involved in communications and security) communities. Presenting different approaches, the book describes alternative privacy models and disclosure risk measures as well as data protection procedures for respondent, holder and user privacy. It also discusses specific data privacy problems and solutions for readers who need to deal with big data.

  7. Long-term developmental changes in children’s lower-order big five personality facets

    NARCIS (Netherlands)

    de Haan, A.; de Pauw, S.; van den Akker, A.; Deković, M.; Prinzie, P.

    2017-01-01

    Objective This study examined long-term developmental changes in mother-rated lower-order facets of children's Big Five dimensions. Method Two independent community samples covering early childhood (2–4.5 years; N = 365, 39% girls) and middle childhood to the end of middle adolescence (6–17 years;

  8. Long-term developmental changes in children’s lower-order Big Five personality facets

    NARCIS (Netherlands)

    de Haan, A.D.; de Pauw, S.; van den Akker, A.L.; Dekovic, M.; Prinzie, Peter

    2017-01-01

    Objective This study examined long-term developmental changes in mother-rated lower-order facets of children's Big Five dimensions. Method Two independent community samples covering early childhood (2–4.5 years; N = 365, 39% girls) and middle childhood to the end of middle adolescence (6–17 years;

  9. Big data-driven business how to use big data to win customers, beat competitors, and boost profits

    CERN Document Server

    Glass, Russell

    2014-01-01

    Get the expert perspective and practical advice on big data The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits makes the case that big data is for real, and more than just big hype. The book uses real-life examples-from Nate Silver to Copernicus, and Apple to Blackberry-to demonstrate how the winners of the future will use big data to seek the truth. Written by a marketing journalist and the CEO of a multi-million-dollar B2B marketing platform that reaches more than 90% of the U.S. business population, this book is a comprehens

  10. Big Game Reporting Stations

    Data.gov (United States)

    Vermont Center for Geographic Information — Point locations of big game reporting stations. Big game reporting stations are places where hunters can legally report harvested deer, bear, or turkey. These are...

  11. Stalin's Big Fleet Program

    National Research Council Canada - National Science Library

    Mauner, Milan

    2002-01-01

    Although Dr. Milan Hauner's study 'Stalin's Big Fleet program' has focused primarily on the formation of Big Fleets during the Tsarist and Soviet periods of Russia's naval history, there are important lessons...

  12. Patient Privacy in the Era of Big Data.

    Science.gov (United States)

    Kayaalp, Mehmet

    2018-01-20

    Privacy was defined as a fundamental human right in the Universal Declaration of Human Rights at the 1948 United Nations General Assembly. However, there is still no consensus on what constitutes privacy. In this review, we look at the evolution of privacy as a concept from the era of Hippocrates to the era of social media and big data. To appreciate the modern measures of patient privacy protection and correctly interpret the current regulatory framework in the United States, we need to analyze and understand the concepts of individually identifiable information, individually identifiable health information, protected health information, and de-identification. The Privacy Rule of the Health Insurance Portability and Accountability Act defines the regulatory framework and casts a balance between protective measures and access to health information for secondary (scientific) use. The rule defines the conditions when health information is protected by law and how protected health information can be de-identified for secondary use. With the advents of artificial intelligence and computational linguistics, computational text de-identification algorithms produce de-identified results nearly as well as those produced by human experts, but much faster, more consistently and basically for free. Modern clinical text de-identification systems now pave the road to big data and enable scientists to access de-identified clinical information while firmly protecting patient privacy. However, clinical text de-identification is not a perfect process. In order to maximize the protection of patient privacy and to free clinical and scientific information from the confines of electronic healthcare systems, all stakeholders, including patients, health institutions and institutional review boards, scientists and the scientific communities, as well as regulatory and law enforcement agencies must collaborate closely. On the one hand, public health laws and privacy regulations define rules

  13. Five Big, Big Five Issues : Rationale, Content, Structure, Status, and Crosscultural Assessment

    NARCIS (Netherlands)

    De Raad, Boele

    1998-01-01

    This article discusses the rationale, content, structure, status, and crosscultural assessment of the Big Five trait factors, focusing on topics of dispute and misunderstanding. Taxonomic restrictions of the original Big Five forerunner, the "Norman Five," are discussed, and criticisms regarding the

  14. Big data challenges

    DEFF Research Database (Denmark)

    Bachlechner, Daniel; Leimbach, Timo

    2016-01-01

    Although reports on big data success stories have been accumulating in the media, most organizations dealing with high-volume, high-velocity and high-variety information assets still face challenges. Only a thorough understanding of these challenges puts organizations into a position in which...... they can make an informed decision for or against big data, and, if the decision is positive, overcome the challenges smoothly. The combination of a series of interviews with leading experts from enterprises, associations and research institutions, and focused literature reviews allowed not only...... framework are also relevant. For large enterprises and startups specialized in big data, it is typically easier to overcome the challenges than it is for other enterprises and public administration bodies....

  15. Big Data and HPC collocation: Using HPC idle resources for Big Data Analytics

    OpenAIRE

    MERCIER , Michael; Glesser , David; Georgiou , Yiannis; Richard , Olivier

    2017-01-01

    International audience; Executing Big Data workloads upon High Performance Computing (HPC) infrastractures has become an attractive way to improve their performances. However, the collocation of HPC and Big Data workloads is not an easy task, mainly because of their core concepts' differences. This paper focuses on the challenges related to the scheduling of both Big Data and HPC workloads on the same computing platform. In classic HPC workloads, the rigidity of jobs tends to create holes in ...

  16. Forget the hype or reality. Big data presents new opportunities in Earth Science.

    Science.gov (United States)

    Lee, T. J.

    2015-12-01

    Earth science is arguably one of the most mature science discipline which constantly acquires, curates, and utilizes a large volume of data with diverse variety. We deal with big data before there is big data. For example, while developing the EOS program in the 1980s, the EOS data and information system (EOSDIS) was developed to manage the vast amount of data acquired by the EOS fleet of satellites. EOSDIS continues to be a shining example of modern science data systems in the past two decades. With the explosion of internet, the usage of social media, and the provision of sensors everywhere, the big data era has bring new challenges. First, Goggle developed the search algorithm and a distributed data management system. The open source communities quickly followed up and developed Hadoop file system to facility the map reduce workloads. The internet continues to generate tens of petabytes of data every day. There is a significant shortage of algorithms and knowledgeable manpower to mine the data. In response, the federal government developed the big data programs that fund research and development projects and training programs to tackle these new challenges. Meanwhile, comparatively to the internet data explosion, Earth science big data problem has become quite small. Nevertheless, the big data era presents an opportunity for Earth science to evolve. We learned about the MapReduce algorithms, in memory data mining, machine learning, graph analysis, and semantic web technologies. How do we apply these new technologies to our discipline and bring the hype to Earth? In this talk, I will discuss how we might want to apply some of the big data technologies to our discipline and solve many of our challenging problems. More importantly, I will propose new Earth science data system architecture to enable new type of scientific inquires.

  17. Big Data as Governmentality

    DEFF Research Database (Denmark)

    Flyverbom, Mikkel; Madsen, Anders Koed; Rasche, Andreas

    This paper conceptualizes how large-scale data and algorithms condition and reshape knowledge production when addressing international development challenges. The concept of governmentality and four dimensions of an analytics of government are proposed as a theoretical framework to examine how big...... data is constituted as an aspiration to improve the data and knowledge underpinning development efforts. Based on this framework, we argue that big data’s impact on how relevant problems are governed is enabled by (1) new techniques of visualizing development issues, (2) linking aspects...... shows that big data problematizes selected aspects of traditional ways to collect and analyze data for development (e.g. via household surveys). We also demonstrate that using big data analyses to address development challenges raises a number of questions that can deteriorate its impact....

  18. Boarding to Big data

    Directory of Open Access Journals (Sweden)

    Oana Claudia BRATOSIN

    2016-05-01

    Full Text Available Today Big data is an emerging topic, as the quantity of the information grows exponentially, laying the foundation for its main challenge, the value of the information. The information value is not only defined by the value extraction from huge data sets, as fast and optimal as possible, but also by the value extraction from uncertain and inaccurate data, in an innovative manner using Big data analytics. At this point, the main challenge of the businesses that use Big data tools is to clearly define the scope and the necessary output of the business so that the real value can be gained. This article aims to explain the Big data concept, its various classifications criteria, architecture, as well as the impact in the world wide processes.

  19. Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm.

    Science.gov (United States)

    Huang, Xiuzhen; Jennings, Steven F; Bruce, Barry; Buchan, Alison; Cai, Liming; Chen, Pengyin; Cramer, Carole L; Guan, Weihua; Hilgert, Uwe Kk; Jiang, Hongmei; Li, Zenglu; McClure, Gail; McMullen, Donald F; Nanduri, Bindu; Perkins, Andy; Rekepalli, Bhanu; Salem, Saeed; Specker, Jennifer; Walker, Karl; Wunsch, Donald; Xiong, Donghai; Zhang, Shuzhong; Zhang, Yu; Zhao, Zhongming; Moore, Jason H

    2015-01-01

    Whether your interests lie in scientific arenas, the corporate world, or in government, you have certainly heard the praises of big data: Big data will give you new insights, allow you to become more efficient, and/or will solve your problems. While big data has had some outstanding successes, many are now beginning to see that it is not the Silver Bullet that it has been touted to be. Here our main concern is the overall impact of big data; the current manifestation of big data is constructing a Maginot Line in science in the 21st century. Big data is not "lots of data" as a phenomena anymore; The big data paradigm is putting the spirit of the Maginot Line into lots of data. Big data overall is disconnecting researchers and science challenges. We propose No-Boundary Thinking (NBT), applying no-boundary thinking in problem defining to address science challenges.

  20. Big Egos in Big Science

    DEFF Research Database (Denmark)

    Andersen, Kristina Vaarst; Jeppesen, Jacob

    In this paper we investigate the micro-mechanisms governing structural evolution and performance of scientific collaboration. Scientific discovery tends not to be lead by so called lone ?stars?, or big egos, but instead by collaboration among groups of researchers, from a multitude of institutions...

  1. Direct and indirect effects of petroleum production activities on the western fence lizard (Sceloporus occidentalis) as a surrogate for the dunes sagebrush lizard (Sceloporus arenicolus).

    Science.gov (United States)

    Weir, Scott M; Knox, Ami; Talent, Larry G; Anderson, Todd A; Salice, Christopher J

    2016-05-01

    The dunes sagebrush lizard (Sceloporus arenicolus) is a habitat specialist of conservation concern limited to shin oak sand dune systems of New Mexico and Texas (USA). Because much of the dunes sagebrush lizard's habitat occurs in areas of high oil and gas production, there may be direct and indirect effects of these activities. The congeneric Western fence lizard (Sceloporus occidentalis) was used as a surrogate species to determine direct effects of 2 contaminants associated with oil and gas drilling activities in the Permian Basin (NM and TX, USA): herbicide formulations (Krovar and Quest) and hydrogen sulfide gas (H2S). Lizards were exposed to 2 concentrations of H2 S (30 ppm or 90 ppm) and herbicide formulations (1× or 2× label application rate) representing high-end exposure scenarios. Sublethal behavioral endpoints were evaluated, including sprint speed and time to prey detection and capture. Neither H2S nor herbicide formulations caused significant behavioral effects compared to controls. To understand potential indirect effects of oil and gas drilling on the prey base, terrestrial invertebrate biomass and order diversity were quantified at impacted sites to compare with nonimpacted sites. A significant decrease in biomass was found at impacted sites, but no significant effects on diversity. The results suggest little risk from direct toxic effects, but the potential for indirect effects should be further explored. © 2015 SETAC.

  2. Challenges of Big Data Analysis.

    Science.gov (United States)

    Fan, Jianqing; Han, Fang; Liu, Han

    2014-06-01

    Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

  3. Big data is not a monolith

    CERN Document Server

    Ekbia, Hamid R; Mattioli, Michael

    2016-01-01

    Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies. The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control throu...

  4. Big universe, big data

    DEFF Research Database (Denmark)

    Kremer, Jan; Stensbo-Smidt, Kristoffer; Gieseke, Fabian Cristian

    2017-01-01

    , modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing......, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications....

  5. Poker Player Behavior After Big Wins and Big Losses

    OpenAIRE

    Gary Smith; Michael Levere; Robert Kurtzman

    2009-01-01

    We find that experienced poker players typically change their style of play after winning or losing a big pot--most notably, playing less cautiously after a big loss, evidently hoping for lucky cards that will erase their loss. This finding is consistent with Kahneman and Tversky's (Kahneman, D., A. Tversky. 1979. Prospect theory: An analysis of decision under risk. Econometrica 47(2) 263-292) break-even hypothesis and suggests that when investors incur a large loss, it might be time to take ...

  6. Big Data and Chemical Education

    Science.gov (United States)

    Pence, Harry E.; Williams, Antony J.

    2016-01-01

    The amount of computerized information that organizations collect and process is growing so large that the term Big Data is commonly being used to describe the situation. Accordingly, Big Data is defined by a combination of the Volume, Variety, Velocity, and Veracity of the data being processed. Big Data tools are already having an impact in…

  7. Supporting Imagers' VOICE: A National Training Program in Comparative Effectiveness Research and Big Data Analytics.

    Science.gov (United States)

    Kang, Stella K; Rawson, James V; Recht, Michael P

    2017-12-05

    Provided methodologic training, more imagers can contribute to the evidence basis on improved health outcomes and value in diagnostic imaging. The Value of Imaging Through Comparative Effectiveness Research Program was developed to provide hands-on, practical training in five core areas for comparative effectiveness and big biomedical data research: decision analysis, cost-effectiveness analysis, evidence synthesis, big data principles, and applications of big data analytics. The program's mixed format consists of web-based modules for asynchronous learning as well as in-person sessions for practical skills and group discussion. Seven diagnostic radiology subspecialties and cardiology are represented in the first group of program participants, showing the collective potential for greater depth of comparative effectiveness research in the imaging community. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  8. Big data in Finnish financial services

    OpenAIRE

    Laurila, M. (Mikko)

    2017-01-01

    Abstract This thesis aims to explore the concept of big data, and create understanding of big data maturity in the Finnish financial services industry. The research questions of this thesis are “What kind of big data solutions are being implemented in the Finnish financial services sector?” and “Which factors impede faster implementation of big data solutions in the Finnish financial services sector?”. ...

  9. Big data in fashion industry

    Science.gov (United States)

    Jain, S.; Bruniaux, J.; Zeng, X.; Bruniaux, P.

    2017-10-01

    Significant work has been done in the field of big data in last decade. The concept of big data includes analysing voluminous data to extract valuable information. In the fashion world, big data is increasingly playing a part in trend forecasting, analysing consumer behaviour, preference and emotions. The purpose of this paper is to introduce the term fashion data and why it can be considered as big data. It also gives a broad classification of the types of fashion data and briefly defines them. Also, the methodology and working of a system that will use this data is briefly described.

  10. Grasshopper populations inhabiting the B-C Cribs and REDOX Pond Sites, 200 Area Plateau, United States Energy Research and Development Administration's Hanford Reservation

    International Nuclear Information System (INIS)

    Sheldon, J.K.; Rogers, L.E.

    1976-02-01

    The purpose of this study was to determine the taxonomic composition, abundance, and food habits of grasshopper populations inhabiting the 200 Area plateau. Two sites were selected for detailed study, one near the B-C Cribs control zone and the other near the former REDOX Pond. A total of 14 grasshopper species were collected from the B-C Cribs study area and 16 species from the REDOX Pond area. Thirteen of these species occurred at both locations. Population density was low throughout most of the spring, increased in late May, and reached a peak of about 4 grasshoppers per square meter in early July. A dietary analysis showed that 7 of the 28 species of vascular plants recorded from the area were major components in grasshopper diets. These included needle-and-thread grass (Stipa comata), turpentine cymopterus (Cymopterus terebinthinus), Carey's balsamroot (Balsamorhiza careyana), western tansymustard (Descurainia pinnata), Jim Hill mustard (Sisymbrium altissimum), big sagebrush (Artemisia tridentata) and green rabbitbrush (Chrysothamnus viscidiflorus). The plant most heavily utilized was big sagebrush, followed by turpentine cymopterus, green rabbitbrush, and Carey's balsamroot. Other species were less frequently eaten. Several plants were present in the diet at a much higher frequency than they occurred in the environment, indicating that they were preferred food items.

  11. Symbiotic regulation of plant growth, development and reproduction

    Science.gov (United States)

    Rodriguez, R.J.; Freeman, D. Carl; McArthur, E.D.; Kim, Y.-O.; Redman, R.S.

    2009-01-01

    The growth and development of rice (Oryzae sativa) seedlings was shown to be regulated epigenetically by a fungal endophyte. In contrast to un-inoculated (nonsymbiotic) plants, endophyte colonized (symbiotic) plants preferentially allocated resources into root growth until root hairs were well established. During that time symbiotic roots expanded at five times the rate observed in nonsymbiotic plants. Endophytes also influenced sexual reproduction of mature big sagebrush (Artemisia tridentata) plants. Two spatially distinct big sagebrush subspecies and their hybrids were symbiotic with unique fungal endophytes, despite being separated by only 380 m distance and 60 m elevation. A double reciprocal transplant experiment of parental and hybrid plants, and soils across the hybrid zone showed that fungal endophytes interact with the soils and different plant genotypes to confer enhanced plant reproduction in soil native to the endophyte and reduced reproduction in soil alien to the endophyte. Moreover, the most prevalent endophyte of the hybrid zone reduced the fitness of both parental subspecies. Because these endophytes are passed to the next generation of plants on seed coats, this interaction provides a selective advantage, habitat specificity, and the means of restricting gene flow, thereby making the hybrid zone stable, narrow and potentially leading to speciation. ?? 2009 Landes Bioscience.

  12. Multi-model comparison highlights consistency in predicted effect of warming on a semi-arid shrub

    Science.gov (United States)

    Renwick, Katherine M.; Curtis, Caroline; Kleinhesselink, Andrew R.; Schlaepfer, Daniel R.; Bradley, Bethany A.; Aldridge, Cameron L.; Poulter, Benjamin; Adler, Peter B.

    2018-01-01

    A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi-model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi-model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.

  13. Act-Frequency Signatures of the Big Five.

    Science.gov (United States)

    Chapman, Benjamin P; Goldberg, Lewis R

    2017-10-01

    The traditional focus of work on personality and behavior has tended toward "major outcomes" such as health or antisocial behavior, or small sets of behaviors observable over short periods in laboratories or in convenience samples. In a community sample, we examined a wide set (400) of mundane, incidental or "every day" behavioral acts, the frequencies of which were reported over the past year. Using an exploratory methodology similar to genomic approaches (relying on the False Discovery Rate) revealed 26 prototypical acts for Intellect, 24 acts for Extraversion, 13 for Emotional Stability, nine for Conscientiousness, and six for Agreeableness. Many links were consistent with general intuition-for instance, low Conscientiousness with work and procrastination. Some of the most robust associations, however, were for acts too specific for a priori hypothesis. For instance, Extraversion was strongly associated with telling dirty jokes, Intellect with "loung[ing] around [the] house without clothes on", and Agreeableness with singing in the shower. Frequency categories for these acts changed with markedly non-linearity across Big Five Z-scores. Findings may help ground trait scores in emblematic acts, and enrich understanding of mundane or common behavioral signatures of the Big Five.

  14. Big data bioinformatics.

    Science.gov (United States)

    Greene, Casey S; Tan, Jie; Ung, Matthew; Moore, Jason H; Cheng, Chao

    2014-12-01

    Recent technological advances allow for high throughput profiling of biological systems in a cost-efficient manner. The low cost of data generation is leading us to the "big data" era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this review, we introduce key concepts in the analysis of big data, including both "machine learning" algorithms as well as "unsupervised" and "supervised" examples of each. We note packages for the R programming language that are available to perform machine learning analyses. In addition to programming based solutions, we review webservers that allow users with limited or no programming background to perform these analyses on large data compendia. © 2014 Wiley Periodicals, Inc.

  15. Changing the personality of a face: Perceived Big Two and Big Five personality factors modeled in real photographs.

    Science.gov (United States)

    Walker, Mirella; Vetter, Thomas

    2016-04-01

    General, spontaneous evaluations of strangers based on their faces have been shown to reflect judgments of these persons' intention and ability to harm. These evaluations can be mapped onto a 2D space defined by the dimensions trustworthiness (intention) and dominance (ability). Here we go beyond general evaluations and focus on more specific personality judgments derived from the Big Two and Big Five personality concepts. In particular, we investigate whether Big Two/Big Five personality judgments can be mapped onto the 2D space defined by the dimensions trustworthiness and dominance. Results indicate that judgments of the Big Two personality dimensions almost perfectly map onto the 2D space. In contrast, at least 3 of the Big Five dimensions (i.e., neuroticism, extraversion, and conscientiousness) go beyond the 2D space, indicating that additional dimensions are necessary to describe more specific face-based personality judgments accurately. Building on this evidence, we model the Big Two/Big Five personality dimensions in real facial photographs. Results from 2 validation studies show that the Big Two/Big Five are perceived reliably across different samples of faces and participants. Moreover, results reveal that participants differentiate reliably between the different Big Two/Big Five dimensions. Importantly, this high level of agreement and differentiation in personality judgments from faces likely creates a subjective reality which may have serious consequences for those being perceived-notably, these consequences ensue because the subjective reality is socially shared, irrespective of the judgments' validity. The methodological approach introduced here might prove useful in various psychological disciplines. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  16. The BigBOSS Experiment

    Energy Technology Data Exchange (ETDEWEB)

    Schelgel, D.; Abdalla, F.; Abraham, T.; Ahn, C.; Allende Prieto, C.; Annis, J.; Aubourg, E.; Azzaro, M.; Bailey, S.; Baltay, C.; Baugh, C.; /APC, Paris /Brookhaven /IRFU, Saclay /Marseille, CPPM /Marseille, CPT /Durham U. / /IEU, Seoul /Fermilab /IAA, Granada /IAC, La Laguna

    2011-01-01

    BigBOSS will obtain observational constraints that will bear on three of the four 'science frontier' questions identified by the Astro2010 Cosmology and Fundamental Phyics Panel of the Decadal Survey: Why is the universe accelerating; what is dark matter and what are the properties of neutrinos? Indeed, the BigBOSS project was recommended for substantial immediate R and D support the PASAG report. The second highest ground-based priority from the Astro2010 Decadal Survey was the creation of a funding line within the NSF to support a 'Mid-Scale Innovations' program, and it used BigBOSS as a 'compelling' example for support. This choice was the result of the Decadal Survey's Program Priorization panels reviewing 29 mid-scale projects and recommending BigBOSS 'very highly'.

  17. Big game hunting practices, meanings, motivations and constraints: a survey of Oregon big game hunters

    Science.gov (United States)

    Suresh K. Shrestha; Robert C. Burns

    2012-01-01

    We conducted a self-administered mail survey in September 2009 with randomly selected Oregon hunters who had purchased big game hunting licenses/tags for the 2008 hunting season. Survey questions explored hunting practices, the meanings of and motivations for big game hunting, the constraints to big game hunting participation, and the effects of age, years of hunting...

  18. The big data potential of epidemiological studies for criminology and forensics.

    Science.gov (United States)

    DeLisi, Matt

    2018-07-01

    Big data, the analysis of original datasets with large samples ranging from ∼30,000 to one million participants to mine unexplored data, has been under-utilized in criminology. However, there have been recent calls for greater synthesis between epidemiology and criminology and a small number of scholars have utilized epidemiological studies that were designed to measure alcohol and substance use to harvest behavioral and psychiatric measures that relate to the study of crime. These studies have been helpful in producing knowledge about the most serious, violent, and chronic offenders, but applications to more pathological forensic populations is lagging. Unfortunately, big data relating to crime and justice are restricted and limited to criminal justice purposes and not easily available to the research community. Thus, the study of criminal and forensic populations is limited in terms of data volume, velocity, and variety. Additional forays into epidemiology, increased use of available online judicial and correctional data, and unknown new frontiers are needed to bring criminology up to speed in the big data arena. Copyright © 2016 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  19. Google BigQuery analytics

    CERN Document Server

    Tigani, Jordan

    2014-01-01

    How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasets Google BigQuery Analytics is the perfect guide for business and data analysts who want the latest tips on running complex queries and writing code to communicate with the BigQuery API. The book uses real-world examples to demonstrate current best practices and techniques, and also explains and demonstrates streaming ingestion, transformation via Hadoop in Google Compute engine, AppEngine datastore integration, and using GViz with Tableau to generate charts of query results. In addit

  20. Big data for dummies

    CERN Document Server

    Hurwitz, Judith; Halper, Fern; Kaufman, Marcia

    2013-01-01

    Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it m

  1. Exploring complex and big data

    Directory of Open Access Journals (Sweden)

    Stefanowski Jerzy

    2017-12-01

    Full Text Available This paper shows how big data analysis opens a range of research and technological problems and calls for new approaches. We start with defining the essential properties of big data and discussing the main types of data involved. We then survey the dedicated solutions for storing and processing big data, including a data lake, virtual integration, and a polystore architecture. Difficulties in managing data quality and provenance are also highlighted. The characteristics of big data imply also specific requirements and challenges for data mining algorithms, which we address as well. The links with related areas, including data streams and deep learning, are discussed. The common theme that naturally emerges from this characterization is complexity. All in all, we consider it to be the truly defining feature of big data (posing particular research and technological challenges, which ultimately seems to be of greater importance than the sheer data volume.

  2. Was there a big bang

    International Nuclear Information System (INIS)

    Narlikar, J.

    1981-01-01

    In discussing the viability of the big-bang model of the Universe relative evidence is examined including the discrepancies in the age of the big-bang Universe, the red shifts of quasars, the microwave background radiation, general theory of relativity aspects such as the change of the gravitational constant with time, and quantum theory considerations. It is felt that the arguments considered show that the big-bang picture is not as soundly established, either theoretically or observationally, as it is usually claimed to be, that the cosmological problem is still wide open and alternatives to the standard big-bang picture should be seriously investigated. (U.K.)

  3. BIG DATA-DRIVEN MARKETING: AN ABSTRACT

    OpenAIRE

    Suoniemi, Samppa; Meyer-Waarden, Lars; Munzel, Andreas

    2017-01-01

    Customer information plays a key role in managing successful relationships with valuable customers. Big data customer analytics use (BD use), i.e., the extent to which customer information derived from big data analytics guides marketing decisions, helps firms better meet customer needs for competitive advantage. This study addresses three research questions: What are the key antecedents of big data customer analytics use? How, and to what extent, does big data customer an...

  4. Big Five personality and health in adults with and without cancer.

    Science.gov (United States)

    Rochefort, Catherine; Hoerger, Michael; Turiano, Nicholas A; Duberstein, Paul

    2018-01-01

    Personality is associated with health, but examinations in patients with illnesses are lacking. We aimed to determine whether personality-physical health associations differed between community and cancer samples. This cross-sectional study involved 168 participants without cancer, 212 men with prostate cancer, and 55 women with breast cancer. We examined whether the Big Five personality dimensions were associated with health behaviors and multiple health indicators. Higher conscientiousness and lower neuroticism were associated with better health behaviors and health ( r max  = .31), with few differences between community and cancer samples. Findings call for research on the implications of personality in patients with serious illnesses.

  5. Big Data Analytics in Medicine and Healthcare.

    Science.gov (United States)

    Ristevski, Blagoj; Chen, Ming

    2018-05-10

    This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various - omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given.

  6. The trashing of Big Green

    International Nuclear Information System (INIS)

    Felten, E.

    1990-01-01

    The Big Green initiative on California's ballot lost by a margin of 2-to-1. Green measures lost in five other states, shocking ecology-minded groups. According to the postmortem by environmentalists, Big Green was a victim of poor timing and big spending by the opposition. Now its supporters plan to break up the bill and try to pass some provisions in the Legislature

  7. The Big Bang Singularity

    Science.gov (United States)

    Ling, Eric

    The big bang theory is a model of the universe which makes the striking prediction that the universe began a finite amount of time in the past at the so called "Big Bang singularity." We explore the physical and mathematical justification of this surprising result. After laying down the framework of the universe as a spacetime manifold, we combine physical observations with global symmetrical assumptions to deduce the FRW cosmological models which predict a big bang singularity. Next we prove a couple theorems due to Stephen Hawking which show that the big bang singularity exists even if one removes the global symmetrical assumptions. Lastly, we investigate the conditions one needs to impose on a spacetime if one wishes to avoid a singularity. The ideas and concepts used here to study spacetimes are similar to those used to study Riemannian manifolds, therefore we compare and contrast the two geometries throughout.

  8. Reframing Open Big Data

    DEFF Research Database (Denmark)

    Marton, Attila; Avital, Michel; Jensen, Tina Blegind

    2013-01-01

    Recent developments in the techniques and technologies of collecting, sharing and analysing data are challenging the field of information systems (IS) research let alone the boundaries of organizations and the established practices of decision-making. Coined ‘open data’ and ‘big data......’, these developments introduce an unprecedented level of societal and organizational engagement with the potential of computational data to generate new insights and information. Based on the commonalities shared by open data and big data, we develop a research framework that we refer to as open big data (OBD......) by employing the dimensions of ‘order’ and ‘relationality’. We argue that these dimensions offer a viable approach for IS research on open and big data because they address one of the core value propositions of IS; i.e. how to support organizing with computational data. We contrast these dimensions with two...

  9. Rethinking climate change adaptation and place through a situated pathways framework: A case study from the Big Hole Valley, USA

    Science.gov (United States)

    Daniel J. Murphy; Laurie Yung; Carina Wyborn; Daniel R. Williams

    2017-01-01

    This paper critically examines the temporal and spatial dynamics of adaptation in climate change science and explores how dynamic notions of 'place' elucidate novel ways of understanding community vulnerability and adaptation. Using data gathered from a narrative scenario-building process carried out among communities of the Big Hole Valley in Montana, the...

  10. Medical big data: promise and challenges.

    Science.gov (United States)

    Lee, Choong Ho; Yoon, Hyung-Jin

    2017-03-01

    The concept of big data, commonly characterized by volume, variety, velocity, and veracity, goes far beyond the data type and includes the aspects of data analysis, such as hypothesis-generating, rather than hypothesis-testing. Big data focuses on temporal stability of the association, rather than on causal relationship and underlying probability distribution assumptions are frequently not required. Medical big data as material to be analyzed has various features that are not only distinct from big data of other disciplines, but also distinct from traditional clinical epidemiology. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics frequently exploits analytic methods developed in data mining, including classification, clustering, and regression. Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation. Recently, propensity score analysis and instrumental variable analysis have been introduced to overcome these limitations, and they have accomplished a great deal. Many challenges, such as the absence of evidence of practical benefits of big data, methodological issues including legal and ethical issues, and clinical integration and utility issues, must be overcome to realize the promise of medical big data as the fuel of a continuous learning healthcare system that will improve patient outcome and reduce waste in areas including nephrology.

  11. Medical big data: promise and challenges

    Directory of Open Access Journals (Sweden)

    Choong Ho Lee

    2017-03-01

    Full Text Available The concept of big data, commonly characterized by volume, variety, velocity, and veracity, goes far beyond the data type and includes the aspects of data analysis, such as hypothesis-generating, rather than hypothesis-testing. Big data focuses on temporal stability of the association, rather than on causal relationship and underlying probability distribution assumptions are frequently not required. Medical big data as material to be analyzed has various features that are not only distinct from big data of other disciplines, but also distinct from traditional clinical epidemiology. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics frequently exploits analytic methods developed in data mining, including classification, clustering, and regression. Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation. Recently, propensity score analysis and instrumental variable analysis have been introduced to overcome these limitations, and they have accomplished a great deal. Many challenges, such as the absence of evidence of practical benefits of big data, methodological issues including legal and ethical issues, and clinical integration and utility issues, must be overcome to realize the promise of medical big data as the fuel of a continuous learning healthcare system that will improve patient outcome and reduce waste in areas including nephrology.

  12. What is beyond the big five?

    Science.gov (United States)

    Saucier, G; Goldberg, L R

    1998-08-01

    Previous investigators have proposed that various kinds of person-descriptive content--such as differences in attitudes or values, in sheer evaluation, in attractiveness, or in height and girth--are not adequately captured by the Big Five Model. We report on a rather exhaustive search for reliable sources of Big Five-independent variation in data from person-descriptive adjectives. Fifty-three candidate clusters were developed in a college sample using diverse approaches and sources. In a nonstudent adult sample, clusters were evaluated with respect to a minimax criterion: minimum multiple correlation with factors from Big Five markers and maximum reliability. The most clearly Big Five-independent clusters referred to Height, Girth, Religiousness, Employment Status, Youthfulness and Negative Valence (or low-base-rate attributes). Clusters referring to Fashionableness, Sensuality/Seductiveness, Beauty, Masculinity, Frugality, Humor, Wealth, Prejudice, Folksiness, Cunning, and Luck appeared to be potentially beyond the Big Five, although each of these clusters demonstrated Big Five multiple correlations of .30 to .45, and at least one correlation of .20 and over with a Big Five factor. Of all these content areas, Religiousness, Negative Valence, and the various aspects of Attractiveness were found to be represented by a substantial number of distinct, common adjectives. Results suggest directions for supplementing the Big Five when one wishes to extend variable selection outside the domain of personality traits as conventionally defined.

  13. Big Data Analytics and Its Applications

    Directory of Open Access Journals (Sweden)

    Mashooque A. Memon

    2017-10-01

    Full Text Available The term, Big Data, has been authored to refer to the extensive heave of data that can't be managed by traditional data handling methods or techniques. The field of Big Data plays an indispensable role in various fields, such as agriculture, banking, data mining, education, chemistry, finance, cloud computing, marketing, health care stocks. Big data analytics is the method for looking at big data to reveal hidden patterns, incomprehensible relationship and other important data that can be utilize to resolve on enhanced decisions. There has been a perpetually expanding interest for big data because of its fast development and since it covers different areas of applications. Apache Hadoop open source technology created in Java and keeps running on Linux working framework was used. The primary commitment of this exploration is to display an effective and free solution for big data application in a distributed environment, with its advantages and indicating its easy use. Later on, there emerge to be a required for an analytical review of new developments in the big data technology. Healthcare is one of the best concerns of the world. Big data in healthcare imply to electronic health data sets that are identified with patient healthcare and prosperity. Data in the healthcare area is developing past managing limit of the healthcare associations and is relied upon to increment fundamentally in the coming years.

  14. Measuring the Promise of Big Data Syllabi

    Science.gov (United States)

    Friedman, Alon

    2018-01-01

    Growing interest in Big Data is leading industries, academics and governments to accelerate Big Data research. However, how teachers should teach Big Data has not been fully examined. This article suggests criteria for redesigning Big Data syllabi in public and private degree-awarding higher education establishments. The author conducted a survey…

  15. 77 FR 27245 - Big Stone National Wildlife Refuge, Big Stone and Lac Qui Parle Counties, MN

    Science.gov (United States)

    2012-05-09

    ... DEPARTMENT OF THE INTERIOR Fish and Wildlife Service [FWS-R3-R-2012-N069; FXRS1265030000S3-123-FF03R06000] Big Stone National Wildlife Refuge, Big Stone and Lac Qui Parle Counties, MN AGENCY: Fish and... plan (CCP) and environmental assessment (EA) for Big Stone National Wildlife Refuge (Refuge, NWR) for...

  16. The BigBoss Experiment

    Energy Technology Data Exchange (ETDEWEB)

    Schelgel, D.; Abdalla, F.; Abraham, T.; Ahn, C.; Allende Prieto, C.; Annis, J.; Aubourg, E.; Azzaro, M.; Bailey, S.; Baltay, C.; Baugh, C.; Bebek, C.; Becerril, S.; Blanton, M.; Bolton, A.; Bromley, B.; Cahn, R.; Carton, P.-H.; Cervanted-Cota, J.L.; Chu, Y.; Cortes, M.; /APC, Paris /Brookhaven /IRFU, Saclay /Marseille, CPPM /Marseille, CPT /Durham U. / /IEU, Seoul /Fermilab /IAA, Granada /IAC, La Laguna / /IAC, Mexico / / /Madrid, IFT /Marseille, Lab. Astrophys. / / /New York U. /Valencia U.

    2012-06-07

    BigBOSS is a Stage IV ground-based dark energy experiment to study baryon acoustic oscillations (BAO) and the growth of structure with a wide-area galaxy and quasar redshift survey over 14,000 square degrees. It has been conditionally accepted by NOAO in response to a call for major new instrumentation and a high-impact science program for the 4-m Mayall telescope at Kitt Peak. The BigBOSS instrument is a robotically-actuated, fiber-fed spectrograph capable of taking 5000 simultaneous spectra over a wavelength range from 340 nm to 1060 nm, with a resolution R = {lambda}/{Delta}{lambda} = 3000-4800. Using data from imaging surveys that are already underway, spectroscopic targets are selected that trace the underlying dark matter distribution. In particular, targets include luminous red galaxies (LRGs) up to z = 1.0, extending the BOSS LRG survey in both redshift and survey area. To probe the universe out to even higher redshift, BigBOSS will target bright [OII] emission line galaxies (ELGs) up to z = 1.7. In total, 20 million galaxy redshifts are obtained to measure the BAO feature, trace the matter power spectrum at smaller scales, and detect redshift space distortions. BigBOSS will provide additional constraints on early dark energy and on the curvature of the universe by measuring the Ly-alpha forest in the spectra of over 600,000 2.2 < z < 3.5 quasars. BigBOSS galaxy BAO measurements combined with an analysis of the broadband power, including the Ly-alpha forest in BigBOSS quasar spectra, achieves a FOM of 395 with Planck plus Stage III priors. This FOM is based on conservative assumptions for the analysis of broad band power (k{sub max} = 0.15), and could grow to over 600 if current work allows us to push the analysis to higher wave numbers (k{sub max} = 0.3). BigBOSS will also place constraints on theories of modified gravity and inflation, and will measure the sum of neutrino masses to 0.024 eV accuracy.

  17. Big data and educational research

    OpenAIRE

    Beneito-Montagut, Roser

    2017-01-01

    Big data and data analytics offer the promise to enhance teaching and learning, improve educational research and progress education governance. This chapter aims to contribute to the conceptual and methodological understanding of big data and analytics within educational research. It describes the opportunities and challenges that big data and analytics bring to education as well as critically explore the perils of applying a data driven approach to education. Despite the claimed value of the...

  18. Thick-Big Descriptions

    DEFF Research Database (Denmark)

    Lai, Signe Sophus

    The paper discusses the rewards and challenges of employing commercial audience measurements data – gathered by media industries for profitmaking purposes – in ethnographic research on the Internet in everyday life. It questions claims to the objectivity of big data (Anderson 2008), the assumption...... communication systems, language and behavior appear as texts, outputs, and discourses (data to be ‘found’) – big data then documents things that in earlier research required interviews and observations (data to be ‘made’) (Jensen 2014). However, web-measurement enterprises build audiences according...... to a commercial logic (boyd & Crawford 2011) and is as such directed by motives that call for specific types of sellable user data and specific segmentation strategies. In combining big data and ‘thick descriptions’ (Geertz 1973) scholars need to question how ethnographic fieldwork might map the ‘data not seen...

  19. U.S. Geological Survey shrub/grass products provide new approach to shrubland monitoring

    Science.gov (United States)

    Young, Steven M.

    2017-12-11

    In the Western United States, shrubland ecosystems provide vital ecological, hydrological, biological, agricultural, and recreational services. However, disturbances such as livestock grazing, exotic species invasion, conversion to agriculture, climate change, urban expansion, and energy development are altering these ecosystems.Improving our understanding of how shrublands are distributed, where they are changing, the extent of the historical change, and likely future change directions is critical for successful management of these ecosystems. Remote-sensing technologies provide the most likely data source for large-area monitoring of ecosystem disturbance—both near-real time and historically. A monitoring framework supported by remote-sensing data can offer efficient and accurate analysis of change across a range of spatial and temporal scales.The U.S. Geological Survey has been working to develop new remote-sensing data, tools, and products to characterize and monitor these changing shrubland landscapes. Nine individual map products (components) have been developed that quantify the percent of shrub, sagebrush, big sagebrush, herbaceous, annual herbaceous, litter, bare ground, shrub height, and sagebrush height at 1-percent intervals in each 30-meter grid cell. These component products are designed to be combined and customized to widely support different applications in rangeland monitoring, analysis of wildlife habitat, resource inventory, adaptive management, and environmental review.

  20. Characterization of shrubland ecosystem components as continuous fields in the northwest United States

    Science.gov (United States)

    Xian, George Z.; Homer, Collin G.; Rigge, Matthew B.; Shi, Hua; Meyer, Debbie

    2015-01-01

    Accurate and consistent estimates of shrubland ecosystem components are crucial to a better understanding of ecosystem conditions in arid and semiarid lands. An innovative approach was developed by integrating multiple sources of information to quantify shrubland components as continuous field products within the National Land Cover Database (NLCD). The approach consists of several procedures including field sample collections, high-resolution mapping of shrubland components using WorldView-2 imagery and regression tree models, Landsat 8 radiometric balancing and phenological mosaicking, medium resolution estimates of shrubland components following different climate zones using Landsat 8 phenological mosaics and regression tree models, and product validation. Fractional covers of nine shrubland components were estimated: annual herbaceous, bare ground, big sagebrush, herbaceous, litter, sagebrush, shrub, sagebrush height, and shrub height. Our study area included the footprint of six Landsat 8 scenes in the northwestern United States. Results show that most components have relatively significant correlations with validation data, have small normalized root mean square errors, and correspond well with expected ecological gradients. While some uncertainties remain with height estimates, the model formulated in this study provides a cross-validated, unbiased, and cost effective approach to quantify shrubland components at a regional scale and advances knowledge of horizontal and vertical variability of these components.

  1. Big Data Analytics Tools as Applied to ATLAS Event Data

    CERN Document Server

    Vukotic, Ilija; The ATLAS collaboration

    2016-01-01

    Big Data technologies have proven to be very useful for storage, processing and visualization of derived metrics associated with ATLAS distributed computing (ADC) services. Log file data and database records, and metadata from a diversity of systems have been aggregated and indexed to create an analytics platform for ATLAS ADC operations analysis. Dashboards, wide area data access cost metrics, user analysis patterns, and resource utilization efficiency charts are produced flexibly through queries against a powerful analytics cluster. Here we explore whether these techniques and analytics ecosystem can be applied to add new modes of open, quick, and pervasive access to ATLAS event data so as to simplify access and broaden the reach of ATLAS public data to new communities of users. An ability to efficiently store, filter, search and deliver ATLAS data at the event and/or sub-event level in a widely supported format would enable or significantly simplify usage of big data, statistical and machine learning tools...

  2. Big Data's Role in Precision Public Health.

    Science.gov (United States)

    Dolley, Shawn

    2018-01-01

    Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

  3. Increasing the value of geospatial informatics with open approaches for Big Data

    Science.gov (United States)

    Percivall, G.; Bermudez, L. E.

    2017-12-01

    Open approaches to big data provide geoscientists with new capabilities to address problems of unmatched size and complexity. Consensus approaches for Big Geo Data have been addressed in multiple international workshops and testbeds organized by the Open Geospatial Consortium (OGC) in the past year. Participants came from government (NASA, ESA, USGS, NOAA, DOE); research (ORNL, NCSA, IU, JPL, CRIM, RENCI); industry (ESRI, Digital Globe, IBM, rasdaman); standards (JTC 1/NIST); and open source software communities. Results from the workshops and testbeds are documented in Testbed reports and a White Paper published by the OGC. The White Paper identifies the following set of use cases: Collection and Ingest: Remote sensed data processing; Data stream processing Prepare and Structure: SQL and NoSQL databases; Data linking; Feature identification Analytics and Visualization: Spatial-temporal analytics; Machine Learning; Data Exploration Modeling and Prediction: Integrated environmental models; Urban 4D models. Open implementations were developed in the Arctic Spatial Data Pilot using Discrete Global Grid Systems (DGGS) and in Testbeds using WPS and ESGF to publish climate predictions. Further development activities to advance open implementations of Big Geo Data include the following: Open Cloud Computing: Avoid vendor lock-in through API interoperability and Application portability. Open Source Extensions: Implement geospatial data representations in projects from Apache, Location Tech, and OSGeo. Investigate parallelization strategies for N-Dimensional spatial data. Geospatial Data Representations: Schemas to improve processing and analysis using geospatial concepts: Features, Coverages, DGGS. Use geospatial encodings like NetCDF and GeoPackge. Big Linked Geodata: Use linked data methods scaled to big geodata. Analysis Ready Data: Support "Download as last resort" and "Analytics as a service". Promote elements common to "datacubes."

  4. Big-Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities.

    Science.gov (United States)

    Del Sol, Antonio; Thiesen, Hans J; Imitola, Jaime; Carazo Salas, Rafael E

    2017-02-02

    Achieving the promises of stem cell science to generate precise disease models and designer cell samples for personalized therapeutics will require harnessing pheno-genotypic cell-level data quantitatively and predictively in the lab and clinic. Those requirements could be met by developing a Big-Data-driven stem cell science strategy and community. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Big Data, indispensable today

    Directory of Open Access Journals (Sweden)

    Radu-Ioan ENACHE

    2015-10-01

    Full Text Available Big data is and will be used more in the future as a tool for everything that happens both online and offline. Of course , online is a real hobbit, Big Data is found in this medium , offering many advantages , being a real help for all consumers. In this paper we talked about Big Data as being a plus in developing new applications, by gathering useful information about the users and their behaviour.We've also presented the key aspects of real-time monitoring and the architecture principles of this technology. The most important benefit brought to this paper is presented in the cloud section.

  6. Antigravity and the big crunch/big bang transition

    Science.gov (United States)

    Bars, Itzhak; Chen, Shih-Hung; Steinhardt, Paul J.; Turok, Neil

    2012-08-01

    We point out a new phenomenon which seems to be generic in 4d effective theories of scalar fields coupled to Einstein gravity, when applied to cosmology. A lift of such theories to a Weyl-invariant extension allows one to define classical evolution through cosmological singularities unambiguously, and hence construct geodesically complete background spacetimes. An attractor mechanism ensures that, at the level of the effective theory, generic solutions undergo a big crunch/big bang transition by contracting to zero size, passing through a brief antigravity phase, shrinking to zero size again, and re-emerging into an expanding normal gravity phase. The result may be useful for the construction of complete bouncing cosmologies like the cyclic model.

  7. Antigravity and the big crunch/big bang transition

    Energy Technology Data Exchange (ETDEWEB)

    Bars, Itzhak [Department of Physics and Astronomy, University of Southern California, Los Angeles, CA 90089-2535 (United States); Chen, Shih-Hung [Perimeter Institute for Theoretical Physics, Waterloo, ON N2L 2Y5 (Canada); Department of Physics and School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404 (United States); Steinhardt, Paul J., E-mail: steinh@princeton.edu [Department of Physics and Princeton Center for Theoretical Physics, Princeton University, Princeton, NJ 08544 (United States); Turok, Neil [Perimeter Institute for Theoretical Physics, Waterloo, ON N2L 2Y5 (Canada)

    2012-08-29

    We point out a new phenomenon which seems to be generic in 4d effective theories of scalar fields coupled to Einstein gravity, when applied to cosmology. A lift of such theories to a Weyl-invariant extension allows one to define classical evolution through cosmological singularities unambiguously, and hence construct geodesically complete background spacetimes. An attractor mechanism ensures that, at the level of the effective theory, generic solutions undergo a big crunch/big bang transition by contracting to zero size, passing through a brief antigravity phase, shrinking to zero size again, and re-emerging into an expanding normal gravity phase. The result may be useful for the construction of complete bouncing cosmologies like the cyclic model.

  8. Antigravity and the big crunch/big bang transition

    International Nuclear Information System (INIS)

    Bars, Itzhak; Chen, Shih-Hung; Steinhardt, Paul J.; Turok, Neil

    2012-01-01

    We point out a new phenomenon which seems to be generic in 4d effective theories of scalar fields coupled to Einstein gravity, when applied to cosmology. A lift of such theories to a Weyl-invariant extension allows one to define classical evolution through cosmological singularities unambiguously, and hence construct geodesically complete background spacetimes. An attractor mechanism ensures that, at the level of the effective theory, generic solutions undergo a big crunch/big bang transition by contracting to zero size, passing through a brief antigravity phase, shrinking to zero size again, and re-emerging into an expanding normal gravity phase. The result may be useful for the construction of complete bouncing cosmologies like the cyclic model.

  9. Back to the Future: The Resurgence of Community in American Society, and Community Journalism in the Newspaper Industry and Higher Education.

    Science.gov (United States)

    Lauterer, Jock

    America is in the midst of the age of the emergent and enlightened community. Citizens increasingly demand from their newspapers high-quality, explanatory coverage of local issues. Newspapers large and small are responding. Community newspapers are growing, and many big city media outlets are rethinking their news coverage philosophy in terms of…

  10. Big data: een zoektocht naar instituties

    NARCIS (Netherlands)

    van der Voort, H.G.; Crompvoets, J

    2016-01-01

    Big data is a well-known phenomenon, even a buzzword nowadays. It refers to an abundance of data and new possibilities to process and use them. Big data is subject of many publications. Some pay attention to the many possibilities of big data, others warn us for their consequences. This special

  11. Data, Data, Data : Big, Linked & Open

    NARCIS (Netherlands)

    Folmer, E.J.A.; Krukkert, D.; Eckartz, S.M.

    2013-01-01

    De gehele business en IT-wereld praat op dit moment over Big Data, een trend die medio 2013 Cloud Computing is gepasseerd (op basis van Google Trends). Ook beleidsmakers houden zich actief bezig met Big Data. Neelie Kroes, vice-president van de Europese Commissie, spreekt over de ‘Big Data

  12. Visualizing the knowledge structure and evolution of big data research in healthcare informatics.

    Science.gov (United States)

    Gu, Dongxiao; Li, Jingjing; Li, Xingguo; Liang, Changyong

    2017-02-01

    In recent years, the literature associated with healthcare big data has grown rapidly, but few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of the healthcare big data field. To explore the foundational knowledge and research hotspots of big data research in the field of healthcare informatics, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the trend of each paper's co-author number, the distribution of core institutions and countries, the core literature distribution, the related information of prolific authors and innovation paths in the field, a keyword co-occurrence analysis, and research hotspots and trends for the future. By conducting a literature content analysis and structure analysis, we found the following: (a) In the early stage, researchers from the United States, the People's Republic of China, the United Kingdom, and Germany made the most contributions to the literature associated with healthcare big data research and the innovation path in this field. (b) The innovation path in healthcare big data consists of three stages: the disease early detection, diagnosis, treatment, and prognosis phase, the life and health promotion phase, and the nursing phase. (c) Research hotspots are mainly concentrated in three dimensions: the disease dimension (e.g., epidemiology, breast cancer, obesity, and diabetes), the technical dimension (e.g., data mining and machine learning), and the health service dimension (e.g., customized service and elderly nursing). This study will provide scholars in the healthcare informatics community with panoramic knowledge of healthcare big data research, as well as research hotspots and future research directions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  13. Methods and tools for big data visualization

    OpenAIRE

    Zubova, Jelena; Kurasova, Olga

    2015-01-01

    In this paper, methods and tools for big data visualization have been investigated. Challenges faced by the big data analysis and visualization have been identified. Technologies for big data analysis have been discussed. A review of methods and tools for big data visualization has been done. Functionalities of the tools have been demonstrated by examples in order to highlight their advantages and disadvantages.

  14. Big data analytics methods and applications

    CERN Document Server

    Rao, BLS; Rao, SB

    2016-01-01

    This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.

  15. The Big bang and the Quantum

    Science.gov (United States)

    Ashtekar, Abhay

    2010-06-01

    General relativity predicts that space-time comes to an end and physics comes to a halt at the big-bang. Recent developments in loop quantum cosmology have shown that these predictions cannot be trusted. Quantum geometry effects can resolve singularities, thereby opening new vistas. Examples are: The big bang is replaced by a quantum bounce; the `horizon problem' disappears; immediately after the big bounce, there is a super-inflationary phase with its own phenomenological ramifications; and, in presence of a standard inflation potential, initial conditions are naturally set for a long, slow roll inflation independently of what happens in the pre-big bang branch. As in my talk at the conference, I will first discuss the foundational issues and then the implications of the new Planck scale physics near the Big Bang.

  16. Big Bang baryosynthesis

    International Nuclear Information System (INIS)

    Turner, M.S.; Chicago Univ., IL

    1983-01-01

    In these lectures I briefly review Big Bang baryosynthesis. In the first lecture I discuss the evidence which exists for the BAU, the failure of non-GUT symmetrical cosmologies, the qualitative picture of baryosynthesis, and numerical results of detailed baryosynthesis calculations. In the second lecture I discuss the requisite CP violation in some detail, further the statistical mechanics of baryosynthesis, possible complications to the simplest scenario, and one cosmological implication of Big Bang baryosynthesis. (orig./HSI)

  17. Exploiting big data for critical care research.

    Science.gov (United States)

    Docherty, Annemarie B; Lone, Nazir I

    2015-10-01

    Over recent years the digitalization, collection and storage of vast quantities of data, in combination with advances in data science, has opened up a new era of big data. In this review, we define big data, identify examples of critical care research using big data, discuss the limitations and ethical concerns of using these large datasets and finally consider scope for future research. Big data refers to datasets whose size, complexity and dynamic nature are beyond the scope of traditional data collection and analysis methods. The potential benefits to critical care are significant, with faster progress in improving health and better value for money. Although not replacing clinical trials, big data can improve their design and advance the field of precision medicine. However, there are limitations to analysing big data using observational methods. In addition, there are ethical concerns regarding maintaining confidentiality of patients who contribute to these datasets. Big data have the potential to improve medical care and reduce costs, both by individualizing medicine, and bringing together multiple sources of data about individual patients. As big data become increasingly mainstream, it will be important to maintain public confidence by safeguarding data security, governance and confidentiality.

  18. Empathy and the Big Five

    OpenAIRE

    Paulus, Christoph

    2016-01-01

    Del Barrio et al. (2004) haben vor mehr als 10 Jahren versucht, eine direkte Beziehung zwischen Empathie und den Big Five herzustellen. Im Mittel hatten in ihrer Stichprobe Frauen höhere Werte in der Empathie und auf den Big Five-Faktoren mit Ausnahme des Faktors Neurotizismus. Zusammenhänge zu Empathie fanden sie in den Bereichen Offenheit, Verträglichkeit, Gewissenhaftigkeit und Extraversion. In unseren Daten besitzen Frauen sowohl in der Empathie als auch den Big Five signifikant höhere We...

  19. Big domains are novel Ca²+-binding modules: evidences from big domains of Leptospira immunoglobulin-like (Lig) proteins.

    Science.gov (United States)

    Raman, Rajeev; Rajanikanth, V; Palaniappan, Raghavan U M; Lin, Yi-Pin; He, Hongxuan; McDonough, Sean P; Sharma, Yogendra; Chang, Yung-Fu

    2010-12-29

    Many bacterial surface exposed proteins mediate the host-pathogen interaction more effectively in the presence of Ca²+. Leptospiral immunoglobulin-like (Lig) proteins, LigA and LigB, are surface exposed proteins containing Bacterial immunoglobulin like (Big) domains. The function of proteins which contain Big fold is not known. Based on the possible similarities of immunoglobulin and βγ-crystallin folds, we here explore the important question whether Ca²+ binds to a Big domains, which would provide a novel functional role of the proteins containing Big fold. We selected six individual Big domains for this study (three from the conserved part of LigA and LigB, denoted as Lig A3, Lig A4, and LigBCon5; two from the variable region of LigA, i.e., 9(th) (Lig A9) and 10(th) repeats (Lig A10); and one from the variable region of LigB, i.e., LigBCen2. We have also studied the conserved region covering the three and six repeats (LigBCon1-3 and LigCon). All these proteins bind the calcium-mimic dye Stains-all. All the selected four domains bind Ca²+ with dissociation constants of 2-4 µM. Lig A9 and Lig A10 domains fold well with moderate thermal stability, have β-sheet conformation and form homodimers. Fluorescence spectra of Big domains show a specific doublet (at 317 and 330 nm), probably due to Trp interaction with a Phe residue. Equilibrium unfolding of selected Big domains is similar and follows a two-state model, suggesting the similarity in their fold. We demonstrate that the Lig are Ca²+-binding proteins, with Big domains harbouring the binding motif. We conclude that despite differences in sequence, a Big motif binds Ca²+. This work thus sets up a strong possibility for classifying the proteins containing Big domains as a novel family of Ca²+-binding proteins. Since Big domain is a part of many proteins in bacterial kingdom, we suggest a possible function these proteins via Ca²+ binding.

  20. Quantum fields in a big-crunch-big-bang spacetime

    International Nuclear Information System (INIS)

    Tolley, Andrew J.; Turok, Neil

    2002-01-01

    We consider quantum field theory on a spacetime representing the big-crunch-big-bang transition postulated in ekpyrotic or cyclic cosmologies. We show via several independent methods that an essentially unique matching rule holds connecting the incoming state, in which a single extra dimension shrinks to zero, to the outgoing state in which it reexpands at the same rate. For free fields in our construction there is no particle production from the incoming adiabatic vacuum. When interactions are included the particle production for fixed external momentum is finite at the tree level. We discuss a formal correspondence between our construction and quantum field theory on de Sitter spacetime

  1. Turning big bang into big bounce: II. Quantum dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Malkiewicz, Przemyslaw; Piechocki, Wlodzimierz, E-mail: pmalk@fuw.edu.p, E-mail: piech@fuw.edu.p [Theoretical Physics Department, Institute for Nuclear Studies, Hoza 69, 00-681 Warsaw (Poland)

    2010-11-21

    We analyze the big bounce transition of the quantum Friedmann-Robertson-Walker model in the setting of the nonstandard loop quantum cosmology (LQC). Elementary observables are used to quantize composite observables. The spectrum of the energy density operator is bounded and continuous. The spectrum of the volume operator is bounded from below and discrete. It has equally distant levels defining a quantum of the volume. The discreteness may imply a foamy structure of spacetime at a semiclassical level which may be detected in astro-cosmo observations. The nonstandard LQC method has a free parameter that should be fixed in some way to specify the big bounce transition.

  2. Scaling Big Data Cleansing

    KAUST Repository

    Khayyat, Zuhair

    2017-07-31

    Data cleansing approaches have usually focused on detecting and fixing errors with little attention to big data scaling. This presents a serious impediment since identify- ing and repairing dirty data often involves processing huge input datasets, handling sophisticated error discovery approaches and managing huge arbitrary errors. With large datasets, error detection becomes overly expensive and complicated especially when considering user-defined functions. Furthermore, a distinctive algorithm is de- sired to optimize inequality joins in sophisticated error discovery rather than na ̈ıvely parallelizing them. Also, when repairing large errors, their skewed distribution may obstruct effective error repairs. In this dissertation, I present solutions to overcome the above three problems in scaling data cleansing. First, I present BigDansing as a general system to tackle efficiency, scalability, and ease-of-use issues in data cleansing for Big Data. It automatically parallelizes the user’s code on top of general-purpose distributed platforms. Its programming inter- face allows users to express data quality rules independently from the requirements of parallel and distributed environments. Without sacrificing their quality, BigDans- ing also enables parallel execution of serial repair algorithms by exploiting the graph representation of discovered errors. The experimental results show that BigDansing outperforms existing baselines up to more than two orders of magnitude. Although BigDansing scales cleansing jobs, it still lacks the ability to handle sophisticated error discovery requiring inequality joins. Therefore, I developed IEJoin as an algorithm for fast inequality joins. It is based on sorted arrays and space efficient bit-arrays to reduce the problem’s search space. By comparing IEJoin against well- known optimizations, I show that it is more scalable, and several orders of magnitude faster. BigDansing depends on vertex-centric graph systems, i.e., Pregel

  3. The ethics of big data in big agriculture

    Directory of Open Access Journals (Sweden)

    Isabelle M. Carbonell

    2016-03-01

    Full Text Available This paper examines the ethics of big data in agriculture, focusing on the power asymmetry between farmers and large agribusinesses like Monsanto. Following the recent purchase of Climate Corp., Monsanto is currently the most prominent biotech agribusiness to buy into big data. With wireless sensors on tractors monitoring or dictating every decision a farmer makes, Monsanto can now aggregate large quantities of previously proprietary farming data, enabling a privileged position with unique insights on a field-by-field basis into a third or more of the US farmland. This power asymmetry may be rebalanced through open-sourced data, and publicly-funded data analytic tools which rival Climate Corp. in complexity and innovation for use in the public domain.

  4. Homogeneous and isotropic big rips?

    CERN Document Server

    Giovannini, Massimo

    2005-01-01

    We investigate the way big rips are approached in a fully inhomogeneous description of the space-time geometry. If the pressure and energy densities are connected by a (supernegative) barotropic index, the spatial gradients and the anisotropic expansion decay as the big rip is approached. This behaviour is contrasted with the usual big-bang singularities. A similar analysis is performed in the case of sudden (quiescent) singularities and it is argued that the spatial gradients may well be non-negligible in the vicinity of pressure singularities.

  5. Rate Change Big Bang Theory

    Science.gov (United States)

    Strickland, Ken

    2013-04-01

    The Rate Change Big Bang Theory redefines the birth of the universe with a dramatic shift in energy direction and a new vision of the first moments. With rate change graph technology (RCGT) we can look back 13.7 billion years and experience every step of the big bang through geometrical intersection technology. The analysis of the Big Bang includes a visualization of the first objects, their properties, the astounding event that created space and time as well as a solution to the mystery of anti-matter.

  6. Intelligent Test Mechanism Design of Worn Big Gear

    Directory of Open Access Journals (Sweden)

    Hong-Yu LIU

    2014-10-01

    Full Text Available With the continuous development of national economy, big gear was widely applied in metallurgy and mine domains. So, big gear plays an important role in above domains. In practical production, big gear abrasion and breach take place often. It affects normal production and causes unnecessary economic loss. A kind of intelligent test method was put forward on worn big gear mainly aimed at the big gear restriction conditions of high production cost, long production cycle and high- intensity artificial repair welding work. The measure equations transformations were made on involute straight gear. Original polar coordinate equations were transformed into rectangular coordinate equations. Big gear abrasion measure principle was introduced. Detection principle diagram was given. Detection route realization method was introduced. OADM12 laser sensor was selected. Detection on big gear abrasion area was realized by detection mechanism. Tested data of unworn gear and worn gear were led in designed calculation program written by Visual Basic language. Big gear abrasion quantity can be obtained. It provides a feasible method for intelligent test and intelligent repair welding on worn big gear.

  7. [Big data in medicine and healthcare].

    Science.gov (United States)

    Rüping, Stefan

    2015-08-01

    Healthcare is one of the business fields with the highest Big Data potential. According to the prevailing definition, Big Data refers to the fact that data today is often too large and heterogeneous and changes too quickly to be stored, processed, and transformed into value by previous technologies. The technological trends drive Big Data: business processes are more and more executed electronically, consumers produce more and more data themselves - e.g. in social networks - and finally ever increasing digitalization. Currently, several new trends towards new data sources and innovative data analysis appear in medicine and healthcare. From the research perspective, omics-research is one clear Big Data topic. In practice, the electronic health records, free open data and the "quantified self" offer new perspectives for data analytics. Regarding analytics, significant advances have been made in the information extraction from text data, which unlocks a lot of data from clinical documentation for analytics purposes. At the same time, medicine and healthcare is lagging behind in the adoption of Big Data approaches. This can be traced to particular problems regarding data complexity and organizational, legal, and ethical challenges. The growing uptake of Big Data in general and first best-practice examples in medicine and healthcare in particular, indicate that innovative solutions will be coming. This paper gives an overview of the potentials of Big Data in medicine and healthcare.

  8. From Big Data to Big Business

    DEFF Research Database (Denmark)

    Lund Pedersen, Carsten

    2017-01-01

    Idea in Brief: Problem: There is an enormous profit potential for manufacturing firms in big data, but one of the key barriers to obtaining data-driven growth is the lack of knowledge about which capabilities are needed to extract value and profit from data. Solution: We (BDBB research group at C...

  9. Making big sense from big data in toxicology by read-across.

    Science.gov (United States)

    Hartung, Thomas

    2016-01-01

    Modern information technologies have made big data available in safety sciences, i.e., extremely large data sets that may be analyzed only computationally to reveal patterns, trends and associations. This happens by (1) compilation of large sets of existing data, e.g., as a result of the European REACH regulation, (2) the use of omics technologies and (3) systematic robotized testing in a high-throughput manner. All three approaches and some other high-content technologies leave us with big data--the challenge is now to make big sense of these data. Read-across, i.e., the local similarity-based intrapolation of properties, is gaining momentum with increasing data availability and consensus on how to process and report it. It is predominantly applied to in vivo test data as a gap-filling approach, but can similarly complement other incomplete datasets. Big data are first of all repositories for finding similar substances and ensure that the available data is fully exploited. High-content and high-throughput approaches similarly require focusing on clusters, in this case formed by underlying mechanisms such as pathways of toxicity. The closely connected properties, i.e., structural and biological similarity, create the confidence needed for predictions of toxic properties. Here, a new web-based tool under development called REACH-across, which aims to support and automate structure-based read-across, is presented among others.

  10. [Big data in official statistics].

    Science.gov (United States)

    Zwick, Markus

    2015-08-01

    The concept of "big data" stands to change the face of official statistics over the coming years, having an impact on almost all aspects of data production. The tasks of future statisticians will not necessarily be to produce new data, but rather to identify and make use of existing data to adequately describe social and economic phenomena. Until big data can be used correctly in official statistics, a lot of questions need to be answered and problems solved: the quality of data, data protection, privacy, and the sustainable availability are some of the more pressing issues to be addressed. The essential skills of official statisticians will undoubtedly change, and this implies a number of challenges to be faced by statistical education systems, in universities, and inside the statistical offices. The national statistical offices of the European Union have concluded a concrete strategy for exploring the possibilities of big data for official statistics, by means of the Big Data Roadmap and Action Plan 1.0. This is an important first step and will have a significant influence on implementing the concept of big data inside the statistical offices of Germany.

  11. Big-Leaf Mahogany on CITES Appendix II: Big Challenge, Big Opportunity

    Science.gov (United States)

    JAMES GROGAN; PAULO BARRETO

    2005-01-01

    On 15 November 2003, big-leaf mahogany (Swietenia macrophylla King, Meliaceae), the most valuable widely traded Neotropical timber tree, gained strengthened regulatory protection from its listing on Appendix II of the Convention on International Trade in Endangered Species ofWild Fauna and Flora (CITES). CITES is a United Nations-chartered agreement signed by 164...

  12. Big Data as Information Barrier

    Directory of Open Access Journals (Sweden)

    Victor Ya. Tsvetkov

    2014-07-01

    Full Text Available The article covers analysis of ‘Big Data’ which has been discussed over last 10 years. The reasons and factors for the issue are revealed. It has proved that the factors creating ‘Big Data’ issue has existed for quite a long time, and from time to time, would cause the informational barriers. Such barriers were successfully overcome through the science and technologies. The conducted analysis refers the “Big Data” issue to a form of informative barrier. This issue may be solved correctly and encourages development of scientific and calculating methods.

  13. Big Data in Space Science

    OpenAIRE

    Barmby, Pauline

    2018-01-01

    It seems like “big data” is everywhere these days. In planetary science and astronomy, we’ve been dealing with large datasets for a long time. So how “big” is our data? How does it compare to the big data that a bank or an airline might have? What new tools do we need to analyze big datasets, and how can we make better use of existing tools? What kinds of science problems can we address with these? I’ll address these questions with examples including ESA’s Gaia mission, ...

  14. Big Data in Medicine is Driving Big Changes

    Science.gov (United States)

    Verspoor, K.

    2014-01-01

    Summary Objectives To summarise current research that takes advantage of “Big Data” in health and biomedical informatics applications. Methods Survey of trends in this work, and exploration of literature describing how large-scale structured and unstructured data sources are being used to support applications from clinical decision making and health policy, to drug design and pharmacovigilance, and further to systems biology and genetics. Results The survey highlights ongoing development of powerful new methods for turning that large-scale, and often complex, data into information that provides new insights into human health, in a range of different areas. Consideration of this body of work identifies several important paradigm shifts that are facilitated by Big Data resources and methods: in clinical and translational research, from hypothesis-driven research to data-driven research, and in medicine, from evidence-based practice to practice-based evidence. Conclusions The increasing scale and availability of large quantities of health data require strategies for data management, data linkage, and data integration beyond the limits of many existing information systems, and substantial effort is underway to meet those needs. As our ability to make sense of that data improves, the value of the data will continue to increase. Health systems, genetics and genomics, population and public health; all areas of biomedicine stand to benefit from Big Data and the associated technologies. PMID:25123716

  15. Harnessing the Power of Big Data to Improve Graduate Medical Education: Big Idea or Bust?

    Science.gov (United States)

    Arora, Vineet M

    2018-06-01

    With the advent of electronic medical records (EMRs) fueling the rise of big data, the use of predictive analytics, machine learning, and artificial intelligence are touted as transformational tools to improve clinical care. While major investments are being made in using big data to transform health care delivery, little effort has been directed toward exploiting big data to improve graduate medical education (GME). Because our current system relies on faculty observations of competence, it is not unreasonable to ask whether big data in the form of clinical EMRs and other novel data sources can answer questions of importance in GME such as when is a resident ready for independent practice.The timing is ripe for such a transformation. A recent National Academy of Medicine report called for reforms to how GME is delivered and financed. While many agree on the need to ensure that GME meets our nation's health needs, there is little consensus on how to measure the performance of GME in meeting this goal. During a recent workshop at the National Academy of Medicine on GME outcomes and metrics in October 2017, a key theme emerged: Big data holds great promise to inform GME performance at individual, institutional, and national levels. In this Invited Commentary, several examples are presented, such as using big data to inform clinical experience and provide clinically meaningful data to trainees, and using novel data sources, including ambient data, to better measure the quality of GME training.

  16. A SWOT Analysis of Big Data

    Science.gov (United States)

    Ahmadi, Mohammad; Dileepan, Parthasarati; Wheatley, Kathleen K.

    2016-01-01

    This is the decade of data analytics and big data, but not everyone agrees with the definition of big data. Some researchers see it as the future of data analysis, while others consider it as hype and foresee its demise in the near future. No matter how it is defined, big data for the time being is having its glory moment. The most important…

  17. A survey of big data research

    Science.gov (United States)

    Fang, Hua; Zhang, Zhaoyang; Wang, Chanpaul Jin; Daneshmand, Mahmoud; Wang, Chonggang; Wang, Honggang

    2015-01-01

    Big data create values for business and research, but pose significant challenges in terms of networking, storage, management, analytics and ethics. Multidisciplinary collaborations from engineers, computer scientists, statisticians and social scientists are needed to tackle, discover and understand big data. This survey presents an overview of big data initiatives, technologies and research in industries and academia, and discusses challenges and potential solutions. PMID:26504265

  18. Big Data in Action for Government : Big Data Innovation in Public Services, Policy, and Engagement

    OpenAIRE

    World Bank

    2017-01-01

    Governments have an opportunity to harness big data solutions to improve productivity, performance and innovation in service delivery and policymaking processes. In developing countries, governments have an opportunity to adopt big data solutions and leapfrog traditional administrative approaches

  19. 78 FR 3911 - Big Stone National Wildlife Refuge, Big Stone and Lac Qui Parle Counties, MN; Final Comprehensive...

    Science.gov (United States)

    2013-01-17

    ... DEPARTMENT OF THE INTERIOR Fish and Wildlife Service [FWS-R3-R-2012-N259; FXRS1265030000-134-FF03R06000] Big Stone National Wildlife Refuge, Big Stone and Lac Qui Parle Counties, MN; Final Comprehensive... significant impact (FONSI) for the environmental assessment (EA) for Big Stone National Wildlife Refuge...

  20. Grass-Shrub Associations over a Precipitation Gradient and Their Implications for Restoration in the Great Basin, USA.

    Directory of Open Access Journals (Sweden)

    Maike F Holthuijzen

    Full Text Available As environmental stress increases positive (facilitative plant interactions often predominate. Plant-plant associations (or lack thereof can indicate whether certain plant species favor particular types of microsites (e.g., shrub canopies or plant-free interspaces and can provide valuable insights into whether "nurse plants" will contribute to seeding or planting success during ecological restoration. It can be difficult, however, to anticipate how relationships between nurse plants and plants used for restoration may change over large-ranging, regional stress gradients. We investigated associations between the shrub, Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis, and three common native grasses (Poa secunda, Elymus elymoides, and Pseudoroegneria spicata, representing short-, medium-, and deep-rooted growth forms, respectively, across an annual rainfall gradient (220-350 mm in the Great Basin, USA. We hypothesized that positive shrub-grass relationships would become more frequent at lower rainfall levels, as indicated by greater cover of grasses in shrub canopies than vegetation-free interspaces. We sampled aerial cover, density, height, basal width, grazing status, and reproductive status of perennial grasses in canopies and interspaces of 25-33 sagebrush individuals at 32 sites along a rainfall gradient. We found that aerial cover of the shallow rooted grass, P. secunda, was higher in sagebrush canopy than interspace microsites at lower levels of rainfall. Cover and density of the medium-rooted grass, E. elymoides were higher in sagebrush canopies than interspaces at all but the highest rainfall levels. Neither annual rainfall nor sagebrush canopy microsite significantly affected P. spicata cover. E. elymoides and P. spicata plants were taller, narrower, and less likely to be grazed in shrub canopy microsites than interspaces. Our results suggest that exploring sagebrush canopy microsites for restoration of native perennial

  1. Big domains are novel Ca²+-binding modules: evidences from big domains of Leptospira immunoglobulin-like (Lig proteins.

    Directory of Open Access Journals (Sweden)

    Rajeev Raman

    Full Text Available BACKGROUND: Many bacterial surface exposed proteins mediate the host-pathogen interaction more effectively in the presence of Ca²+. Leptospiral immunoglobulin-like (Lig proteins, LigA and LigB, are surface exposed proteins containing Bacterial immunoglobulin like (Big domains. The function of proteins which contain Big fold is not known. Based on the possible similarities of immunoglobulin and βγ-crystallin folds, we here explore the important question whether Ca²+ binds to a Big domains, which would provide a novel functional role of the proteins containing Big fold. PRINCIPAL FINDINGS: We selected six individual Big domains for this study (three from the conserved part of LigA and LigB, denoted as Lig A3, Lig A4, and LigBCon5; two from the variable region of LigA, i.e., 9(th (Lig A9 and 10(th repeats (Lig A10; and one from the variable region of LigB, i.e., LigBCen2. We have also studied the conserved region covering the three and six repeats (LigBCon1-3 and LigCon. All these proteins bind the calcium-mimic dye Stains-all. All the selected four domains bind Ca²+ with dissociation constants of 2-4 µM. Lig A9 and Lig A10 domains fold well with moderate thermal stability, have β-sheet conformation and form homodimers. Fluorescence spectra of Big domains show a specific doublet (at 317 and 330 nm, probably due to Trp interaction with a Phe residue. Equilibrium unfolding of selected Big domains is similar and follows a two-state model, suggesting the similarity in their fold. CONCLUSIONS: We demonstrate that the Lig are Ca²+-binding proteins, with Big domains harbouring the binding motif. We conclude that despite differences in sequence, a Big motif binds Ca²+. This work thus sets up a strong possibility for classifying the proteins containing Big domains as a novel family of Ca²+-binding proteins. Since Big domain is a part of many proteins in bacterial kingdom, we suggest a possible function these proteins via Ca²+ binding.

  2. Big data in pharmacy practice: current use, challenges, and the future

    Directory of Open Access Journals (Sweden)

    Ma C

    2015-08-01

    Full Text Available Carolyn Ma, Helen Wong Smith, Cherie Chu, Deborah T JuarezDepartment of Pharmacy Practice, The Daniel K Inouye College of Pharmacy, University of Hawai'i at Hilo, Hilo, HI, USAAbstract: Pharmacy informatics is defined as the use and integration of data, information, knowledge, technology, and automation in the medication-use process for the purpose of improving health outcomes. The term “big data” has been coined and is often defined in three V's: volume, velocity, and variety. This paper describes three major areas in which pharmacy utilizes big data, including: 1 informed decision making (clinical pathways and clinical practice guidelines; 2 improved care delivery in health care settings such as hospitals and community pharmacy practice settings; and 3 quality performance measurement for the Centers for Medicare and Medicaid and medication management activities such as tracking medication adherence and medication reconciliation.Keywords: clinical pharmacy data base, pharmacy informatics, patient outcomes

  3. New 'bigs' in cosmology

    International Nuclear Information System (INIS)

    Yurov, Artyom V.; Martin-Moruno, Prado; Gonzalez-Diaz, Pedro F.

    2006-01-01

    This paper contains a detailed discussion on new cosmic solutions describing the early and late evolution of a universe that is filled with a kind of dark energy that may or may not satisfy the energy conditions. The main distinctive property of the resulting space-times is that they make to appear twice the single singular events predicted by the corresponding quintessential (phantom) models in a manner which can be made symmetric with respect to the origin of cosmic time. Thus, big bang and big rip singularity are shown to take place twice, one on the positive branch of time and the other on the negative one. We have also considered dark energy and phantom energy accretion onto black holes and wormholes in the context of these new cosmic solutions. It is seen that the space-times of these holes would then undergo swelling processes leading to big trip and big hole events taking place on distinct epochs along the evolution of the universe. In this way, the possibility is considered that the past and future be connected in a non-paradoxical manner in the universes described by means of the new symmetric solutions

  4. Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems

    Science.gov (United States)

    Casazza, Michael L.; Coates, Peter S.

    2013-01-01

    The development of anthropogenic structures, especially those related to energy resources, in sagebrush ecosystems is an important concern among developers, conservationists, and land managers in relation to greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) populations. Sage-grouse are dependent on sagebrush ecosystems to meet their seasonal life-phase requirements, and research indicates that anthropogenic structures can adversely affect sage-grouse populations. Land management agencies have attempted to reduce the negative effects of anthropogenic development by assigning surface use (SU) designations, such as no surface occupancy, to areas around leks (breeding grounds). However, rationale for the size of these areas is often challenged. To help inform this issue, we used a spatial analysis of sage-grouse utilization distributions (UDs) to quantify seasonal (spring, summer and fall, winter) sage-grouse space use in relation to leks. We sampled UDs from 193 sage-grouse (11,878 telemetry locations) across 4 subpopulations within the Bi-State Distinct Population Segment (DPS, bordering California and Nevada) during 2003–2009. We quantified the volume of each UD (vUD) within a range of areas that varied in size and were centered on leks, up to a distance of 30 km from leks. We also quantified the percentage of nests within those areas. We then estimated the diminishing gains of vUD as area increased and produced continuous response curves that allow for flexibility in land management decisions. We found nearly 90% of the total vUD (all seasons combined) was contained within 5 km of leks, and we identified variation in vUD for a given distance related to season and migratory status. Five kilometers also represented the 95th percentile of the distribution of nesting distances. Because diminishing gains of vUD was not substantial until distances exceeded 8 km, managers should consider the theoretical optimal distances for SU designation

  5. 2nd INNS Conference on Big Data

    CERN Document Server

    Manolopoulos, Yannis; Iliadis, Lazaros; Roy, Asim; Vellasco, Marley

    2017-01-01

    The book offers a timely snapshot of neural network technologies as a significant component of big data analytics platforms. It promotes new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on different computing platforms (e.g. neuromorphic, graphics processing units (GPUs), clouds, clusters); and big data analytics applications to solve real-world problems (e.g. weather prediction, transportation, energy management). The book, which reports on the second edition of the INNS Conference on Big Data, held on October 23–25, 2016, in Thessaloniki, Greece, depicts an interesting collaborative adventure of neural networks with big data and other learning technologies.

  6. The ethics of biomedical big data

    CERN Document Server

    Mittelstadt, Brent Daniel

    2016-01-01

    This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. ‘Biomedical Big Data’ refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understan...

  7. Scalable privacy-preserving big data aggregation mechanism

    Directory of Open Access Journals (Sweden)

    Dapeng Wu

    2016-08-01

    Full Text Available As the massive sensor data generated by large-scale Wireless Sensor Networks (WSNs recently become an indispensable part of ‘Big Data’, the collection, storage, transmission and analysis of the big sensor data attract considerable attention from researchers. Targeting the privacy requirements of large-scale WSNs and focusing on the energy-efficient collection of big sensor data, a Scalable Privacy-preserving Big Data Aggregation (Sca-PBDA method is proposed in this paper. Firstly, according to the pre-established gradient topology structure, sensor nodes in the network are divided into clusters. Secondly, sensor data is modified by each node according to the privacy-preserving configuration message received from the sink. Subsequently, intra- and inter-cluster data aggregation is employed during the big sensor data reporting phase to reduce energy consumption. Lastly, aggregated results are recovered by the sink to complete the privacy-preserving big data aggregation. Simulation results validate the efficacy and scalability of Sca-PBDA and show that the big sensor data generated by large-scale WSNs is efficiently aggregated to reduce network resource consumption and the sensor data privacy is effectively protected to meet the ever-growing application requirements.

  8. Community Engagement for Big Epidemiology: Deliberative Democracy as a Tool

    Directory of Open Access Journals (Sweden)

    Rebekah E. McWhirter

    2014-11-01

    Full Text Available Public trust is critical in any project requiring significant public support, both in monetary terms and to encourage participation. The research community has widely recognized the centrality of public trust, garnered through community consultation, to the success of large-scale epidemiology. This paper examines the potential utility of the deliberative democracy methodology within the public health research setting. A deliberative democracy event was undertaken in Tasmania, Australia, as part of a wider program of community consultation regarding the potential development of a Tasmanian Biobank. Twenty-five Tasmanians of diverse backgrounds participated in two weekends of deliberation; involving elements of information gathering; discussion; identification of issues and formation of group resolutions. Participants demonstrated strong support for a Tasmanian Biobank and their deliberations resulted in specific proposals in relation to consent; privacy; return of results; governance; funding; and, commercialization and benefit sharing. They exhibited a high degree of satisfaction with the event, and confidence in the outcomes. Deliberative democracy methodology is a useful tool for community engagement that addresses some of the limitations of traditional consultation methods.

  9. Community Engagement for Big Epidemiology: Deliberative Democracy as a Tool

    Science.gov (United States)

    McWhirter, Rebekah E.; Critchley, Christine R.; Nicol, Dianne; Chalmers, Don; Whitton, Tess; Otlowski, Margaret; Burgess, Michael M.; Dickinson, Joanne L.

    2014-01-01

    Public trust is critical in any project requiring significant public support, both in monetary terms and to encourage participation. The research community has widely recognized the centrality of public trust, garnered through community consultation, to the success of large-scale epidemiology. This paper examines the potential utility of the deliberative democracy methodology within the public health research setting. A deliberative democracy event was undertaken in Tasmania, Australia, as part of a wider program of community consultation regarding the potential development of a Tasmanian Biobank. Twenty-five Tasmanians of diverse backgrounds participated in two weekends of deliberation; involving elements of information gathering; discussion; identification of issues and formation of group resolutions. Participants demonstrated strong support for a Tasmanian Biobank and their deliberations resulted in specific proposals in relation to consent; privacy; return of results; governance; funding; and, commercialization and benefit sharing. They exhibited a high degree of satisfaction with the event, and confidence in the outcomes. Deliberative democracy methodology is a useful tool for community engagement that addresses some of the limitations of traditional consultation methods. PMID:25563457

  10. Ethische aspecten van big data

    NARCIS (Netherlands)

    N. (Niek) van Antwerpen; Klaas Jan Mollema

    2017-01-01

    Big data heeft niet alleen geleid tot uitdagende technische vraagstukken, ook gaat het gepaard met allerlei nieuwe ethische en morele kwesties. Om verantwoord met big data om te gaan, moet ook over deze kwesties worden nagedacht. Want slecht datagebruik kan nadelige gevolgen hebben voor

  11. Epidemiology in wonderland: Big Data and precision medicine.

    Science.gov (United States)

    Saracci, Rodolfo

    2018-03-01

    Big Data and precision medicine, two major contemporary challenges for epidemiology, are critically examined from two different angles. In Part 1 Big Data collected for research purposes (Big research Data) and Big Data used for research although collected for other primary purposes (Big secondary Data) are discussed in the light of the fundamental common requirement of data validity, prevailing over "bigness". Precision medicine is treated developing the key point that high relative risks are as a rule required to make a variable or combination of variables suitable for prediction of disease occurrence, outcome or response to treatment; the commercial proliferation of allegedly predictive tests of unknown or poor validity is commented. Part 2 proposes a "wise epidemiology" approach to: (a) choosing in a context imprinted by Big Data and precision medicine-epidemiological research projects actually relevant to population health, (b) training epidemiologists, (c) investigating the impact on clinical practices and doctor-patient relation of the influx of Big Data and computerized medicine and (d) clarifying whether today "health" may be redefined-as some maintain in purely technological terms.

  12. Big Data viewed through the lens of management fashion theory

    OpenAIRE

    Madsen, Dag Øivind; Stenheim, Tonny

    2016-01-01

    Big Data (BD) is currently one of the most talked about management ideas in the business community. Many call it the “buzzword of the day.” In books and media articles, BD has been referred to as a “revolution” and “new era.” There is lots of optimistic and upbeat rhetoric surrounding BD. This has led some to question whether BD is a hyped-up management fashion. In this paper, the BD phenomenon is viewed through the lens of management fashion theory. Management fashion provides an analytical ...

  13. Big Data and Analytics in Healthcare.

    Science.gov (United States)

    Tan, S S-L; Gao, G; Koch, S

    2015-01-01

    This editorial is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". The amount of data being generated in the healthcare industry is growing at a rapid rate. This has generated immense interest in leveraging the availability of healthcare data (and "big data") to improve health outcomes and reduce costs. However, the nature of healthcare data, and especially big data, presents unique challenges in processing and analyzing big data in healthcare. This Focus Theme aims to disseminate some novel approaches to address these challenges. More specifically, approaches ranging from efficient methods of processing large clinical data to predictive models that could generate better predictions from healthcare data are presented.

  14. Big Data for Business Ecosystem Players

    Directory of Open Access Journals (Sweden)

    Perko Igor

    2016-06-01

    Full Text Available In the provided research, some of the Big Data most prospective usage domains connect with distinguished player groups found in the business ecosystem. Literature analysis is used to identify the state of the art of Big Data related research in the major domains of its use-namely, individual marketing, health treatment, work opportunities, financial services, and security enforcement. System theory was used to identify business ecosystem major player types disrupted by Big Data: individuals, small and mid-sized enterprises, large organizations, information providers, and regulators. Relationships between the domains and players were explained through new Big Data opportunities and threats and by players’ responsive strategies. System dynamics was used to visualize relationships in the provided model.

  15. "Big data" in economic history.

    Science.gov (United States)

    Gutmann, Myron P; Merchant, Emily Klancher; Roberts, Evan

    2018-03-01

    Big data is an exciting prospect for the field of economic history, which has long depended on the acquisition, keying, and cleaning of scarce numerical information about the past. This article examines two areas in which economic historians are already using big data - population and environment - discussing ways in which increased frequency of observation, denser samples, and smaller geographic units allow us to analyze the past with greater precision and often to track individuals, places, and phenomena across time. We also explore promising new sources of big data: organically created economic data, high resolution images, and textual corpora.

  16. Big Data Knowledge in Global Health Education.

    Science.gov (United States)

    Olayinka, Olaniyi; Kekeh, Michele; Sheth-Chandra, Manasi; Akpinar-Elci, Muge

    The ability to synthesize and analyze massive amounts of data is critical to the success of organizations, including those that involve global health. As countries become highly interconnected, increasing the risk for pandemics and outbreaks, the demand for big data is likely to increase. This requires a global health workforce that is trained in the effective use of big data. To assess implementation of big data training in global health, we conducted a pilot survey of members of the Consortium of Universities of Global Health. More than half the respondents did not have a big data training program at their institution. Additionally, the majority agreed that big data training programs will improve global health deliverables, among other favorable outcomes. Given the observed gap and benefits, global health educators may consider investing in big data training for students seeking a career in global health. Copyright © 2017 Icahn School of Medicine at Mount Sinai. Published by Elsevier Inc. All rights reserved.

  17. GEOSS: Addressing Big Data Challenges

    Science.gov (United States)

    Nativi, S.; Craglia, M.; Ochiai, O.

    2014-12-01

    In the sector of Earth Observation, the explosion of data is due to many factors including: new satellite constellations, the increased capabilities of sensor technologies, social media, crowdsourcing, and the need for multidisciplinary and collaborative research to face Global Changes. In this area, there are many expectations and concerns about Big Data. Vendors have attempted to use this term for their commercial purposes. It is necessary to understand whether Big Data is a radical shift or an incremental change for the existing digital infrastructures. This presentation tries to explore and discuss the impact of Big Data challenges and new capabilities on the Global Earth Observation System of Systems (GEOSS) and particularly on its common digital infrastructure called GCI. GEOSS is a global and flexible network of content providers allowing decision makers to access an extraordinary range of data and information at their desk. The impact of the Big Data dimensionalities (commonly known as 'V' axes: volume, variety, velocity, veracity, visualization) on GEOSS is discussed. The main solutions and experimentation developed by GEOSS along these axes are introduced and analyzed. GEOSS is a pioneering framework for global and multidisciplinary data sharing in the Earth Observation realm; its experience on Big Data is valuable for the many lessons learned.

  18. Big data for bipolar disorder.

    Science.gov (United States)

    Monteith, Scott; Glenn, Tasha; Geddes, John; Whybrow, Peter C; Bauer, Michael

    2016-12-01

    The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.

  19. BIG DATA IN TAMIL: OPPORTUNITIES, BENEFITS AND CHALLENGES

    OpenAIRE

    R.S. Vignesh Raj; Babak Khazaei; Ashik Ali

    2015-01-01

    This paper gives an overall introduction on big data and has tried to introduce Big Data in Tamil. It discusses the potential opportunities, benefits and likely challenges from a very Tamil and Tamil Nadu perspective. The paper has also made original contribution by proposing the ‘big data’s’ terminology in Tamil. The paper further suggests a few areas to explore using big data Tamil on the lines of the Tamil Nadu Government ‘vision 2023’. Whilst, big data has something to offer everyone, it ...

  20. Big data in biomedicine.

    Science.gov (United States)

    Costa, Fabricio F

    2014-04-01

    The increasing availability and growth rate of biomedical information, also known as 'big data', provides an opportunity for future personalized medicine programs that will significantly improve patient care. Recent advances in information technology (IT) applied to biomedicine are changing the landscape of privacy and personal information, with patients getting more control of their health information. Conceivably, big data analytics is already impacting health decisions and patient care; however, specific challenges need to be addressed to integrate current discoveries into medical practice. In this article, I will discuss the major breakthroughs achieved in combining omics and clinical health data in terms of their application to personalized medicine. I will also review the challenges associated with using big data in biomedicine and translational science. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Big Data’s Role in Precision Public Health

    Science.gov (United States)

    Dolley, Shawn

    2018-01-01

    Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts. PMID:29594091

  2. Big inquiry

    Energy Technology Data Exchange (ETDEWEB)

    Wynne, B [Lancaster Univ. (UK)

    1979-06-28

    The recently published report entitled 'The Big Public Inquiry' from the Council for Science and Society and the Outer Circle Policy Unit is considered, with especial reference to any future enquiry which may take place into the first commercial fast breeder reactor. Proposals embodied in the report include stronger rights for objectors and an attempt is made to tackle the problem that participation in a public inquiry is far too late to be objective. It is felt by the author that the CSS/OCPU report is a constructive contribution to the debate about big technology inquiries but that it fails to understand the deeper currents in the economic and political structure of technology which so influence the consequences of whatever formal procedures are evolved.

  3. Big data analytics with R and Hadoop

    CERN Document Server

    Prajapati, Vignesh

    2013-01-01

    Big Data Analytics with R and Hadoop is a tutorial style book that focuses on all the powerful big data tasks that can be achieved by integrating R and Hadoop.This book is ideal for R developers who are looking for a way to perform big data analytics with Hadoop. This book is also aimed at those who know Hadoop and want to build some intelligent applications over Big data with R packages. It would be helpful if readers have basic knowledge of R.

  4. Big data in forensic science and medicine.

    Science.gov (United States)

    Lefèvre, Thomas

    2018-07-01

    In less than a decade, big data in medicine has become quite a phenomenon and many biomedical disciplines got their own tribune on the topic. Perspectives and debates are flourishing while there is a lack for a consensual definition for big data. The 3Vs paradigm is frequently evoked to define the big data principles and stands for Volume, Variety and Velocity. Even according to this paradigm, genuine big data studies are still scarce in medicine and may not meet all expectations. On one hand, techniques usually presented as specific to the big data such as machine learning techniques are supposed to support the ambition of personalized, predictive and preventive medicines. These techniques are mostly far from been new and are more than 50 years old for the most ancient. On the other hand, several issues closely related to the properties of big data and inherited from other scientific fields such as artificial intelligence are often underestimated if not ignored. Besides, a few papers temper the almost unanimous big data enthusiasm and are worth attention since they delineate what is at stakes. In this context, forensic science is still awaiting for its position papers as well as for a comprehensive outline of what kind of contribution big data could bring to the field. The present situation calls for definitions and actions to rationally guide research and practice in big data. It is an opportunity for grounding a true interdisciplinary approach in forensic science and medicine that is mainly based on evidence. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  5. Surface fluxes and water balance of spatially varying vegetation within a small mountainous headwater catchment

    Directory of Open Access Journals (Sweden)

    G. N. Flerchinger

    2010-06-01

    Full Text Available Precipitation variability and complex topography often create a mosaic of vegetation communities in mountainous headwater catchments, creating a challenge for measuring and interpreting energy and mass fluxes. Understanding the role of these communities in modulating energy, water and carbon fluxes is critical to quantifying the variability in energy, carbon, and water balances across landscapes. The focus of this paper was: (1 to demonstrate the utility of eddy covariance (EC systems in estimating the evapotranspiration component of the water balance of complex headwater mountain catchments; and (2 to compare and contrast the seasonal surface energy and carbon fluxes across a headwater catchment characterized by large variability in precipitation and vegetation cover. Eddy covariance systems were used to measure surface fluxes over sagebrush (Artemesia arbuscula and Artemesia tridentada vaseyana, aspen (Populus tremuloides and the understory of grasses and forbs beneath the aspen canopy. Peak leaf area index of the sagebrush, aspen, and aspen understory was 0.77, 1.35, and 1.20, respectively. The sagebrush and aspen canopies were subject to similar meteorological forces, while the understory of the aspen was sheltered from the wind. Missing periods of measured data were common and made it necessary to extrapolate measured fluxes to the missing periods using a combination of measured and simulated data. Estimated cumulative evapotranspiratation from the sagebrush, aspen trees, and aspen understory were 384 mm, 314 mm and 185 mm. A water balance of the catchment indicated that of the 699 mm of areal average precipitation, 421 mm was lost to evapotranspiration, and 254 mm of streamflow was measured from the catchment; water balance closure for the catchment was within 22 mm. Fluxes of latent heat and carbon for all sites were minimal through the winter. Growing season fluxes of latent heat and carbon were consistently higher

  6. Big Science, co-publication and collaboration: getting to the core

    Energy Technology Data Exchange (ETDEWEB)

    Kahn, M.

    2016-07-01

    International collaboration in science has risen considerably in the last two decades (UNESCO, 2010). In the same period Big Science collaborations have proliferated in physics, astronomy, astrophysics, and medicine. Publications that use Big Science data draw on the expertise of those who design and build the equipment and software, as well as the scientific community. Over time a set of ‘rules of use’ has emerged that protects their intellectual property but that may have the unintended consequence of enhancing co-publication counts. This in turn distorts the use of co-publication data as a proxy for collaboration. The distorting effects are illustrated by means of a case study of the BRICS countries that recently issued a declaration on scientific and technological cooperation with specific fields allocated to each country. It is found that with a single exception the dominant research areas of collaboration are different to individual country specializations. The disjuncture between such ‘collaboration’ and the intent of the declaration raises questions of import to science policy, for the BRICS in particular and the measurement of scientific collaboration more generally. (Author)

  7. NASA's Big Data Task Force

    Science.gov (United States)

    Holmes, C. P.; Kinter, J. L.; Beebe, R. F.; Feigelson, E.; Hurlburt, N. E.; Mentzel, C.; Smith, G.; Tino, C.; Walker, R. J.

    2017-12-01

    Two years ago NASA established the Ad Hoc Big Data Task Force (BDTF - https://science.nasa.gov/science-committee/subcommittees/big-data-task-force), an advisory working group with the NASA Advisory Council system. The scope of the Task Force included all NASA Big Data programs, projects, missions, and activities. The Task Force focused on such topics as exploring the existing and planned evolution of NASA's science data cyber-infrastructure that supports broad access to data repositories for NASA Science Mission Directorate missions; best practices within NASA, other Federal agencies, private industry and research institutions; and Federal initiatives related to big data and data access. The BDTF has completed its two-year term and produced several recommendations plus four white papers for NASA's Science Mission Directorate. This presentation will discuss the activities and results of the TF including summaries of key points from its focused study topics. The paper serves as an introduction to the papers following in this ESSI session.

  8. Big Data Technologies

    Science.gov (United States)

    Bellazzi, Riccardo; Dagliati, Arianna; Sacchi, Lucia; Segagni, Daniele

    2015-01-01

    The so-called big data revolution provides substantial opportunities to diabetes management. At least 3 important directions are currently of great interest. First, the integration of different sources of information, from primary and secondary care to administrative information, may allow depicting a novel view of patient’s care processes and of single patient’s behaviors, taking into account the multifaceted nature of chronic care. Second, the availability of novel diabetes technologies, able to gather large amounts of real-time data, requires the implementation of distributed platforms for data analysis and decision support. Finally, the inclusion of geographical and environmental information into such complex IT systems may further increase the capability of interpreting the data gathered and extract new knowledge from them. This article reviews the main concepts and definitions related to big data, it presents some efforts in health care, and discusses the potential role of big data in diabetes care. Finally, as an example, it describes the research efforts carried on in the MOSAIC project, funded by the European Commission. PMID:25910540

  9. The Berlin Inventory of Gambling behavior - Screening (BIG-S): Validation using a clinical sample.

    Science.gov (United States)

    Wejbera, Martin; Müller, Kai W; Becker, Jan; Beutel, Manfred E

    2017-05-18

    Published diagnostic questionnaires for gambling disorder in German are either based on DSM-III criteria or focus on aspects other than life time prevalence. This study was designed to assess the usability of the DSM-IV criteria based Berlin Inventory of Gambling Behavior Screening tool in a clinical sample and adapt it to DSM-5 criteria. In a sample of 432 patients presenting for behavioral addiction assessment at the University Medical Center Mainz, we checked the screening tool's results against clinical diagnosis and compared a subsample of n=300 clinically diagnosed gambling disorder patients with a comparison group of n=132. The BIG-S produced a sensitivity of 99.7% and a specificity of 96.2%. The instrument's unidimensionality and the diagnostic improvements of DSM-5 criteria were verified by exploratory and confirmatory factor analysis as well as receiver operating characteristic analysis. The BIG-S is a reliable and valid screening tool for gambling disorder and demonstrated its concise and comprehensible operationalization of current DSM-5 criteria in a clinical setting.

  10. Biomedical Big Data Training Collaborative (BBDTC): An effort to bridge the talent gap in biomedical science and research.

    Science.gov (United States)

    Purawat, Shweta; Cowart, Charles; Amaro, Rommie E; Altintas, Ilkay

    2017-05-01

    The BBDTC (https://biobigdata.ucsd.edu) is a community-oriented platform to encourage high-quality knowledge dissemination with the aim of growing a well-informed biomedical big data community through collaborative efforts on training and education. The BBDTC is an e-learning platform that empowers the biomedical community to develop, launch and share open training materials. It deploys hands-on software training toolboxes through virtualization technologies such as Amazon EC2 and Virtualbox. The BBDTC facilitates migration of courses across other course management platforms. The framework encourages knowledge sharing and content personalization through the playlist functionality that enables unique learning experiences and accelerates information dissemination to a wider community.

  11. Traffic information computing platform for big data

    Energy Technology Data Exchange (ETDEWEB)

    Duan, Zongtao, E-mail: ztduan@chd.edu.cn; Li, Ying, E-mail: ztduan@chd.edu.cn; Zheng, Xibin, E-mail: ztduan@chd.edu.cn; Liu, Yan, E-mail: ztduan@chd.edu.cn; Dai, Jiting, E-mail: ztduan@chd.edu.cn; Kang, Jun, E-mail: ztduan@chd.edu.cn [Chang' an University School of Information Engineering, Xi' an, China and Shaanxi Engineering and Technical Research Center for Road and Traffic Detection, Xi' an (China)

    2014-10-06

    Big data environment create data conditions for improving the quality of traffic information service. The target of this article is to construct a traffic information computing platform for big data environment. Through in-depth analysis the connotation and technology characteristics of big data and traffic information service, a distributed traffic atomic information computing platform architecture is proposed. Under the big data environment, this type of traffic atomic information computing architecture helps to guarantee the traffic safety and efficient operation, more intelligent and personalized traffic information service can be used for the traffic information users.

  12. Traffic information computing platform for big data

    International Nuclear Information System (INIS)

    Duan, Zongtao; Li, Ying; Zheng, Xibin; Liu, Yan; Dai, Jiting; Kang, Jun

    2014-01-01

    Big data environment create data conditions for improving the quality of traffic information service. The target of this article is to construct a traffic information computing platform for big data environment. Through in-depth analysis the connotation and technology characteristics of big data and traffic information service, a distributed traffic atomic information computing platform architecture is proposed. Under the big data environment, this type of traffic atomic information computing architecture helps to guarantee the traffic safety and efficient operation, more intelligent and personalized traffic information service can be used for the traffic information users

  13. Fremtidens landbrug bliver big business

    DEFF Research Database (Denmark)

    Hansen, Henning Otte

    2016-01-01

    Landbrugets omverdensforhold og konkurrencevilkår ændres, og det vil nødvendiggøre en udvikling i retning af “big business“, hvor landbrugene bliver endnu større, mere industrialiserede og koncentrerede. Big business bliver en dominerende udvikling i dansk landbrug - men ikke den eneste...

  14. Soil biogeochemistry in the age of big data

    Science.gov (United States)

    Cécillon, Lauric; Barré, Pierre; Coissac, Eric; Plante, Alain; Rasse, Daniel

    2015-04-01

    already been made thanks to meta-analysis, chemometrics, machine-learning systems and bioinformatics. Some techniques like structural equation modeling eventually propose to explore causalities opening a way towards the mechanistic understanding of soil big data rather than simple correlations. We claim that data science should be fully integrated into soil biogeochemists basic education schemes. We expect the blooming of a new generation of soil biogeochemists highly skilled in manipulating big data. Will big data represent a net gain for soil biogeochemistry? Increasing the amount of data will increase associated biases that may further be exacerbated by the increasing distance between data manipulators, soil sampling and data acquisition. Integrating data science into soil biogeochemistry should thus not be done at the expenses of pedology and metrology. We further expect that the more data, the more spurious correlations will appear leading to possible misinterpretation of data. Finally, big data on soils characteristics and processes will always need to be confronted to biogeochemical theories and socio-economic knowledge to be useful. Big data could revolutionize soil biogeochemistry, fostering new scientific and business models around the conservation of the soil natural capital, but our community should go into this new era with clear-sightedness and discernment.

  15. Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets.

    Science.gov (United States)

    Paxton, Alexandra; Griffiths, Thomas L

    2017-10-01

    Today, people generate and store more data than ever before as they interact with both real and virtual environments. These digital traces of behavior and cognition offer cognitive scientists and psychologists an unprecedented opportunity to test theories outside the laboratory. Despite general excitement about big data and naturally occurring datasets among researchers, three "gaps" stand in the way of their wider adoption in theory-driven research: the imagination gap, the skills gap, and the culture gap. We outline an approach to bridging these three gaps while respecting our responsibilities to the public as participants in and consumers of the resulting research. To that end, we introduce Data on the Mind ( http://www.dataonthemind.org ), a community-focused initiative aimed at meeting the unprecedented challenges and opportunities of theory-driven research with big data and naturally occurring datasets. We argue that big data and naturally occurring datasets are most powerfully used to supplement-not supplant-traditional experimental paradigms in order to understand human behavior and cognition, and we highlight emerging ethical issues related to the collection, sharing, and use of these powerful datasets.

  16. Quantum nature of the big bang.

    Science.gov (United States)

    Ashtekar, Abhay; Pawlowski, Tomasz; Singh, Parampreet

    2006-04-14

    Some long-standing issues concerning the quantum nature of the big bang are resolved in the context of homogeneous isotropic models with a scalar field. Specifically, the known results on the resolution of the big-bang singularity in loop quantum cosmology are significantly extended as follows: (i) the scalar field is shown to serve as an internal clock, thereby providing a detailed realization of the "emergent time" idea; (ii) the physical Hilbert space, Dirac observables, and semiclassical states are constructed rigorously; (iii) the Hamiltonian constraint is solved numerically to show that the big bang is replaced by a big bounce. Thanks to the nonperturbative, background independent methods, unlike in other approaches the quantum evolution is deterministic across the deep Planck regime.

  17. Mentoring in Schools: An Impact Study of Big Brothers Big Sisters School-Based Mentoring

    Science.gov (United States)

    Herrera, Carla; Grossman, Jean Baldwin; Kauh, Tina J.; McMaken, Jennifer

    2011-01-01

    This random assignment impact study of Big Brothers Big Sisters School-Based Mentoring involved 1,139 9- to 16-year-old students in 10 cities nationwide. Youth were randomly assigned to either a treatment group (receiving mentoring) or a control group (receiving no mentoring) and were followed for 1.5 school years. At the end of the first school…

  18. Using Ethical Reasoning to Amplify the Reach and Resonance of Professional Codes of Conduct in Training Big Data Scientists.

    Science.gov (United States)

    Tractenberg, Rochelle E; Russell, Andrew J; Morgan, Gregory J; FitzGerald, Kevin T; Collmann, Jeff; Vinsel, Lee; Steinmann, Michael; Dolling, Lisa M

    2015-12-01

    The use of Big Data--however the term is defined--involves a wide array of issues and stakeholders, thereby increasing numbers of complex decisions around issues including data acquisition, use, and sharing. Big Data is becoming a significant component of practice in an ever-increasing range of disciplines; however, since it is not a coherent "discipline" itself, specific codes of conduct for Big Data users and researchers do not exist. While many institutions have created, or will create, training opportunities (e.g., degree programs, workshops) to prepare people to work in and around Big Data, insufficient time, space, and thought have been dedicated to training these people to engage with the ethical, legal, and social issues in this new domain. Since Big Data practitioners come from, and work in, diverse contexts, neither a relevant professional code of conduct nor specific formal ethics training are likely to be readily available. This normative paper describes an approach to conceptualizing ethical reasoning and integrating it into training for Big Data use and research. Our approach is based on a published framework that emphasizes ethical reasoning rather than topical knowledge. We describe the formation of professional community norms from two key disciplines that contribute to the emergent field of Big Data: computer science and statistics. Historical analogies from these professions suggest strategies for introducing trainees and orienting practitioners both to ethical reasoning and to a code of professional conduct itself. We include two semester course syllabi to strengthen our thesis that codes of conduct (including and beyond those we describe) can be harnessed to support the development of ethical reasoning in, and a sense of professional identity among, Big Data practitioners.

  19. Big Data in HEP: A comprehensive use case study

    Science.gov (United States)

    Gutsche, Oliver; Cremonesi, Matteo; Elmer, Peter; Jayatilaka, Bo; Kowalkowski, Jim; Pivarski, Jim; Sehrish, Saba; Mantilla Surez, Cristina; Svyatkovskiy, Alexey; Tran, Nhan

    2017-10-01

    Experimental Particle Physics has been at the forefront of analyzing the worlds largest datasets for decades. The HEP community was the first to develop suitable software and computing tools for this task. In recent times, new toolkits and systems collectively called Big Data technologies have emerged to support the analysis of Petabyte and Exabyte datasets in industry. While the principles of data analysis in HEP have not changed (filtering and transforming experiment-specific data formats), these new technologies use different approaches and promise a fresh look at analysis of very large datasets and could potentially reduce the time-to-physics with increased interactivity. In this talk, we present an active LHC Run 2 analysis, searching for dark matter with the CMS detector, as a testbed for Big Data technologies. We directly compare the traditional NTuple-based analysis with an equivalent analysis using Apache Spark on the Hadoop ecosystem and beyond. In both cases, we start the analysis with the official experiment data formats and produce publication physics plots. We will discuss advantages and disadvantages of each approach and give an outlook on further studies needed.

  20. Big data processing in the cloud - Challenges and platforms

    Science.gov (United States)

    Zhelev, Svetoslav; Rozeva, Anna

    2017-12-01

    Choosing the appropriate architecture and technologies for a big data project is a difficult task, which requires extensive knowledge in both the problem domain and in the big data landscape. The paper analyzes the main big data architectures and the most widely implemented technologies used for processing and persisting big data. Clouds provide for dynamic resource scaling, which makes them a natural fit for big data applications. Basic cloud computing service models are presented. Two architectures for processing big data are discussed, Lambda and Kappa architectures. Technologies for big data persistence are presented and analyzed. Stream processing as the most important and difficult to manage is outlined. The paper highlights main advantages of cloud and potential problems.

  1. Ethics and Epistemology in Big Data Research.

    Science.gov (United States)

    Lipworth, Wendy; Mason, Paul H; Kerridge, Ian; Ioannidis, John P A

    2017-12-01

    Biomedical innovation and translation are increasingly emphasizing research using "big data." The hope is that big data methods will both speed up research and make its results more applicable to "real-world" patients and health services. While big data research has been embraced by scientists, politicians, industry, and the public, numerous ethical, organizational, and technical/methodological concerns have also been raised. With respect to technical and methodological concerns, there is a view that these will be resolved through sophisticated information technologies, predictive algorithms, and data analysis techniques. While such advances will likely go some way towards resolving technical and methodological issues, we believe that the epistemological issues raised by big data research have important ethical implications and raise questions about the very possibility of big data research achieving its goals.

  2. Victoria Stodden: Scholarly Communication in the Era of Big Data and Big Computation

    OpenAIRE

    Stodden, Victoria

    2015-01-01

    Victoria Stodden gave the keynote address for Open Access Week 2015. "Scholarly communication in the era of big data and big computation" was sponsored by the University Libraries, Computational Modeling and Data Analytics, the Department of Computer Science, the Department of Statistics, the Laboratory for Interdisciplinary Statistical Analysis (LISA), and the Virginia Bioinformatics Institute. Victoria Stodden is an associate professor in the Graduate School of Library and Information Scien...

  3. Big Data: Concept, Potentialities and Vulnerabilities

    Directory of Open Access Journals (Sweden)

    Fernando Almeida

    2018-03-01

    Full Text Available The evolution of information systems and the growth in the use of the Internet and social networks has caused an explosion in the amount of available data relevant to the activities of the companies. Therefore, the treatment of these available data is vital to support operational, tactical and strategic decisions. This paper aims to present the concept of big data and the main technologies that support the analysis of large data volumes. The potential of big data is explored considering nine sectors of activity, such as financial, retail, healthcare, transports, agriculture, energy, manufacturing, public, and media and entertainment. In addition, the main current opportunities, vulnerabilities and privacy challenges of big data are discussed. It was possible to conclude that despite the potential for using the big data to grow in the previously identified areas, there are still some challenges that need to be considered and mitigated, namely the privacy of information, the existence of qualified human resources to work with Big Data and the promotion of a data-driven organizational culture.

  4. Big data analytics a management perspective

    CERN Document Server

    Corea, Francesco

    2016-01-01

    This book is about innovation, big data, and data science seen from a business perspective. Big data is a buzzword nowadays, and there is a growing necessity within practitioners to understand better the phenomenon, starting from a clear stated definition. This book aims to be a starting reading for executives who want (and need) to keep the pace with the technological breakthrough introduced by new analytical techniques and piles of data. Common myths about big data will be explained, and a series of different strategic approaches will be provided. By browsing the book, it will be possible to learn how to implement a big data strategy and how to use a maturity framework to monitor the progress of the data science team, as well as how to move forward from one stage to the next. Crucial challenges related to big data will be discussed, where some of them are more general - such as ethics, privacy, and ownership – while others concern more specific business situations (e.g., initial public offering, growth st...

  5. Human factors in Big Data

    NARCIS (Netherlands)

    Boer, J. de

    2016-01-01

    Since 2014 I am involved in various (research) projects that try to make the hype around Big Data more concrete and tangible for the industry and government. Big Data is about multiple sources of (real-time) data that can be analysed, transformed to information and be used to make 'smart' decisions.

  6. Slaves to Big Data. Or Are We?

    Directory of Open Access Journals (Sweden)

    Mireille Hildebrandt

    2013-10-01

    Full Text Available

    In this contribution, the notion of Big Data is discussed in relation to the monetisation of personal data. The claim of some proponents, as well as adversaries, that Big Data implies that ‘n = all’, meaning that we no longer need to rely on samples because we have all the data, is scrutinised and found to be both overly optimistic and unnecessarily pessimistic. A set of epistemological and ethical issues is presented, focusing on the implications of Big Data for our perception, cognition, fairness, privacy and due process. The article then looks into the idea of user-centric personal data management to investigate to what extent it provides solutions for some of the problems triggered by the Big Data conundrum. Special attention is paid to the core principle of data protection legislation, namely purpose binding. Finally, this contribution seeks to inquire into the influence of Big Data politics on self, mind and society, and asks how we can prevent ourselves from becoming slaves to Big Data.

  7. Will Organization Design Be Affected By Big Data?

    Directory of Open Access Journals (Sweden)

    Giles Slinger

    2014-12-01

    Full Text Available Computing power and analytical methods allow us to create, collate, and analyze more data than ever before. When datasets are unusually large in volume, velocity, and variety, they are referred to as “big data.” Some observers have suggested that in order to cope with big data (a organizational structures will need to change and (b the processes used to design organizations will be different. In this article, we differentiate big data from relatively slow-moving, linked people data. We argue that big data will change organizational structures as organizations pursue the opportunities presented by big data. The processes by which organizations are designed, however, will be relatively unaffected by big data. Instead, organization design processes will be more affected by the complex links found in people data.

  8. Official statistics and Big Data

    Directory of Open Access Journals (Sweden)

    Peter Struijs

    2014-07-01

    Full Text Available The rise of Big Data changes the context in which organisations producing official statistics operate. Big Data provides opportunities, but in order to make optimal use of Big Data, a number of challenges have to be addressed. This stimulates increased collaboration between National Statistical Institutes, Big Data holders, businesses and universities. In time, this may lead to a shift in the role of statistical institutes in the provision of high-quality and impartial statistical information to society. In this paper, the changes in context, the opportunities, the challenges and the way to collaborate are addressed. The collaboration between the various stakeholders will involve each partner building on and contributing different strengths. For national statistical offices, traditional strengths include, on the one hand, the ability to collect data and combine data sources with statistical products and, on the other hand, their focus on quality, transparency and sound methodology. In the Big Data era of competing and multiplying data sources, they continue to have a unique knowledge of official statistical production methods. And their impartiality and respect for privacy as enshrined in law uniquely position them as a trusted third party. Based on this, they may advise on the quality and validity of information of various sources. By thus positioning themselves, they will be able to play their role as key information providers in a changing society.

  9. Big Data

    OpenAIRE

    Bútora, Matúš

    2017-01-01

    Cieľom bakalárskej práca je popísať problematiku Big Data a agregačné operácie OLAP pre podporu rozhodovania, ktoré sú na ne aplikované pomocou technológie Apache Hadoop. Prevažná časť práce je venovaná popisu práve tejto technológie. Posledná kapitola sa zaoberá spôsobom aplikovania agregačných operácií a problematikou ich realizácie. Nasleduje celkové zhodnotenie práce a možnosti využitia výsledného systému do budúcna. The aim of the bachelor thesis is to describe the Big Data issue and ...

  10. A seasonal comparison of deposition velocities and retention half-times for Cs-134 and Ce-141 on cool desert vegetation

    International Nuclear Information System (INIS)

    Millard, Gloria C.; Fraley, Leslie Jr.; Markham, O.D.

    1978-01-01

    Due to a scarcity of reliable deposition velocity estimates for radionuclides (particularly those in the submicron range) pooled estimates have been used to predict population doses resulting from atmospheric releases of radioactive particulates. The use of these estimates has led to large uncertainties in whole body dose estimates. Deposition velocities and retention half-times were therefore determined for submicron aerosols of 141 Ce (biologically inactive) and 134 Cs (biologically active) on sagebrush dominated desert vegetation in SE Idaho. Approximately 250 mCi (9.3 GBq) of each radionuclide were released over stands of Artemisia tridentata (big sagebrush) and bottlebrush grass (Sitanion hystrix) during three stages of plant development - spring vegetative growth, seed development, and plant dormancy. Air filters and vegetation samples were collected immediately following each release for use in deposition velocity calculations. Vegetation sampling was continued for a period of three months to obtain retention data. Deposition velocity values were 0.20 cm/s for sagebrush and 0.025 cm/s for grass. The loss of activity on the vegetation seemed to best fit a two component exponential loss function. Short component half-times were 1 to 2 days for both species. Long component half-times were two to three weeks for the shrub species and one to two weeks for the grass species. No significant difference was observed between nuclides. (author)

  11. Science Framework for the Conservation and Restoration Strategy of the Department of the Interior, Secretarial Order 3336: Using resilience and resistance concepts to assess threats to sagebrush ecosystems and sage-grouse, prioritize conservation and restoration actions, and inform management strategies

    Science.gov (United States)

    Jeanne C. Chambers; Jeffrey L. Beck; Steve Campbell; John Carlson; Thomas J. Christiansen; Karen J. Clause; Michele R. Crist; Jonathan B. Dinkins; Kevin E. Doherty; Shawn Espinosa; Kathleen A. Griffin; Steven E. Hanser; Douglas W. Havlina; Kenneth F. Henke; Jacob D. Hennig; Laurie L. Kurth; Jeremy D. Maestas; Mary Manning; Kenneth E. Mayer; Brian A. Mealor; Clinton McCarthy; Mike Pellant; Marco A. Perea; Karen L. Prentice; David A. Pyke; Lief A. Wiechman; Amarina Wuenschel

    2016-01-01

    The Science Framework for the Conservation and Restoration Strategy of the Department of the Interior, Secretarial Order 3336 (SO 3336), Rangeland Fire Prevention, Management and Restoration, provides a strategic, multiscale approach for prioritizing areas for management and determining effective management strategies across the sagebrush biome. The emphasis of this...

  12. BigDansing

    KAUST Repository

    Khayyat, Zuhair; Ilyas, Ihab F.; Jindal, Alekh; Madden, Samuel; Ouzzani, Mourad; Papotti, Paolo; Quiané -Ruiz, Jorge-Arnulfo; Tang, Nan; Yin, Si

    2015-01-01

    of the underlying distributed platform. BigDansing takes these rules into a series of transformations that enable distributed computations and several optimizations, such as shared scans and specialized joins operators. Experimental results on both synthetic

  13. Leveraging Mobile Network Big Data for Developmental Policy ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Some argue that big data and big data users offer advantages to generate evidence. ... Supported by IDRC, this research focused on transportation planning in urban ... Using mobile network big data for land use classification CPRsouth 2015.

  14. Practice Variation in Big-4 Transparency Reports

    DEFF Research Database (Denmark)

    Girdhar, Sakshi; Klarskov Jeppesen, Kim

    2018-01-01

    Purpose: The purpose of this paper is to examine the transparency reports published by the Big-4 public accounting firms in the UK, Germany and Denmark to understand the determinants of their content within the networks of big accounting firms. Design/methodology/approach: The study draws...... on a qualitative research approach, in which the content of transparency reports is analyzed and semi-structured interviews are conducted with key people from the Big-4 firms who are responsible for developing the transparency reports. Findings: The findings show that the content of transparency reports...... is inconsistent and the transparency reporting practice is not uniform within the Big-4 networks. Differences were found in the way in which the transparency reporting practices are coordinated globally by the respective central governing bodies of the Big-4. The content of the transparency reports...

  15. [Contribution and challenges of Big Data in oncology].

    Science.gov (United States)

    Saintigny, Pierre; Foy, Jean-Philippe; Ferrari, Anthony; Cassier, Philippe; Viari, Alain; Puisieux, Alain

    2017-03-01

    Since the first draft of the human genome sequence published in 2001, the cost of sequencing has dramatically decreased. The development of new technologies such as next generation sequencing led to a comprehensive characterization of a large number of tumors of various types as well as to significant advances in precision medicine. Despite the valuable information this technological revolution has allowed to produce, the vast amount of data generated resulted in the emergence of new challenges for the biomedical community, such as data storage, processing and mining. Here, we describe the contribution and challenges of Big Data in oncology. Copyright © 2016 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  16. Community support essential to better malaria testing | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Home · Funding · In their own words: IDRC awardees share their experiences. Community support essential to better malaria testing ... “As a research consultant, this big picture thinking now guides my approach to planning and delivery for ...

  17. Getting Open Source Right for Big Data Analytics: Software Sharing, Governance, Collaboration and Most of All, Fun!

    Science.gov (United States)

    Mattmann, C. A.

    2013-12-01

    A wave of open source big data analytic infrastructure is currently shaping government, private sector, and academia. Projects are consuming, adapting, and contributing back to various ecosystems of software e.g., the Apache Hadoop project and its ecosystem of related efforts including Hive, HBase, Pig, Oozie, Ambari, Knox, Tez and Yarn, to name a few; the Berkeley AMPLab stack which includes Spark, Shark, Mesos, Tachyon, BlinkDB, MLBase, and other emerging efforts; MapR and its related stack of technologies, offerings from commercial companies building products around these tools e.g., Hortonworks Data Platform (HDP), Cloudera's CDH project, etc. Though the technologies all offer different capabilities including low latency support/in-memory, versus record oriented file I/O, high availability, support for the Map Reduce programming paradigm or other dataflow/workflow constructs, there is a common thread that binds these products - they are all released under an open source license e.g., Apache2, MIT, BSD, GPL/LGPL, etc.; all thrive in various ecosystems, such as Apache, or Berkeley AMPLab; all are developed collaboratively, and all technologies provide plug in architecture models and methodologies for allowing others to contribute, and participate via various community models. This talk will cover the open source aspects and governance aspects of the aforementioned Big Data ecosystems and point out the differences, subtleties, and implications of those differences. The discussion will be by example, using several national deployments and Big Data initiatives stemming from the Administration including DARPA's XDATA program; NASA's CMAC program; NSF's EarthCube and geosciences BigData projects. Lessons learned from these efforts in terms of the open source aspects of these technologies will help guide the AGU community in their use, deployment and understanding.

  18. Was the big bang hot

    International Nuclear Information System (INIS)

    Wright, E.L.

    1983-01-01

    The author considers experiments to confirm the substantial deviations from a Planck curve in the Woody and Richards spectrum of the microwave background, and search for conducting needles in our galaxy. Spectral deviations and needle-shaped grains are expected for a cold Big Bang, but are not required by a hot Big Bang. (Auth.)

  19. Passport to the Big Bang

    CERN Multimedia

    De Melis, Cinzia

    2013-01-01

    Le 2 juin 2013, le CERN inaugure le projet Passeport Big Bang lors d'un grand événement public. Affiche et programme. On 2 June 2013 CERN launches a scientific tourist trail through the Pays de Gex and the Canton of Geneva known as the Passport to the Big Bang. Poster and Programme.

  20. Keynote: Big Data, Big Opportunities

    OpenAIRE

    Borgman, Christine L.

    2014-01-01

    The enthusiasm for big data is obscuring the complexity and diversity of data in scholarship and the challenges for stewardship. Inside the black box of data are a plethora of research, technology, and policy issues. Data are not shiny objects that are easily exchanged. Rather, data are representations of observations, objects, or other entities used as evidence of phenomena for the purposes of research or scholarship. Data practices are local, varying from field to field, individual to indiv...

  1. Integrating R and Hadoop for Big Data Analysis

    OpenAIRE

    Bogdan Oancea; Raluca Mariana Dragoescu

    2014-01-01

    Analyzing and working with big data could be very diffi cult using classical means like relational database management systems or desktop software packages for statistics and visualization. Instead, big data requires large clusters with hundreds or even thousands of computing nodes. Offi cial statistics is increasingly considering big data for deriving new statistics because big data sources could produce more relevant and timely statistics than traditional sources. One of the software tools ...

  2. Big³. Editorial.

    Science.gov (United States)

    Lehmann, C U; Séroussi, B; Jaulent, M-C

    2014-05-22

    To provide an editorial introduction into the 2014 IMIA Yearbook of Medical Informatics with an overview of the content, the new publishing scheme, and upcoming 25th anniversary. A brief overview of the 2014 special topic, Big Data - Smart Health Strategies, and an outline of the novel publishing model is provided in conjunction with a call for proposals to celebrate the 25th anniversary of the Yearbook. 'Big Data' has become the latest buzzword in informatics and promise new approaches and interventions that can improve health, well-being, and quality of life. This edition of the Yearbook acknowledges the fact that we just started to explore the opportunities that 'Big Data' will bring. However, it will become apparent to the reader that its pervasive nature has invaded all aspects of biomedical informatics - some to a higher degree than others. It was our goal to provide a comprehensive view at the state of 'Big Data' today, explore its strengths and weaknesses, as well as its risks, discuss emerging trends, tools, and applications, and stimulate the development of the field through the aggregation of excellent survey papers and working group contributions to the topic. For the first time in history will the IMIA Yearbook be published in an open access online format allowing a broader readership especially in resource poor countries. For the first time, thanks to the online format, will the IMIA Yearbook be published twice in the year, with two different tracks of papers. We anticipate that the important role of the IMIA yearbook will further increase with these changes just in time for its 25th anniversary in 2016.

  3. Big questions, big science: meeting the challenges of global ecology.

    Science.gov (United States)

    Schimel, David; Keller, Michael

    2015-04-01

    Ecologists are increasingly tackling questions that require significant infrastucture, large experiments, networks of observations, and complex data and computation. Key hypotheses in ecology increasingly require more investment, and larger data sets to be tested than can be collected by a single investigator's or s group of investigator's labs, sustained for longer than a typical grant. Large-scale projects are expensive, so their scientific return on the investment has to justify the opportunity cost-the science foregone because resources were expended on a large project rather than supporting a number of individual projects. In addition, their management must be accountable and efficient in the use of significant resources, requiring the use of formal systems engineering and project management to mitigate risk of failure. Mapping the scientific method into formal project management requires both scientists able to work in the context, and a project implementation team sensitive to the unique requirements of ecology. Sponsoring agencies, under pressure from external and internal forces, experience many pressures that push them towards counterproductive project management but a scientific community aware and experienced in large project science can mitigate these tendencies. For big ecology to result in great science, ecologists must become informed, aware and engaged in the advocacy and governance of large ecological projects.

  4. Cloud Based Big Data Infrastructure: Architectural Components and Automated Provisioning

    OpenAIRE

    Demchenko, Yuri; Turkmen, Fatih; Blanchet, Christophe; Loomis, Charles; Laat, Caees de

    2016-01-01

    This paper describes the general architecture and functional components of the cloud based Big Data Infrastructure (BDI). The proposed BDI architecture is based on the analysis of the emerging Big Data and data intensive technologies and supported by the definition of the Big Data Architecture Framework (BDAF) that defines the following components of the Big Data technologies: Big Data definition, Data Management including data lifecycle and data structures, Big Data Infrastructure (generical...

  5. Physics with Big Karl Brainstorming. Abstracts

    International Nuclear Information System (INIS)

    Machner, H.; Lieb, J.

    2000-08-01

    Before summarizing details of the meeting, a short description of the spectrometer facility Big Karl is given. The facility is essentially a new instrument using refurbished dipole magnets from its predecessor. The large acceptance quadrupole magnets and the beam optics are new. Big Karl has a design very similar as the focussing spectrometers at MAMI (Mainz), AGOR (Groningen) and the high resolution spectrometer (HRS) in Hall A at Jefferson Laboratory with ΔE/E = 10 -4 but at some lower maximum momentum. The focal plane detectors consisting of multiwire drift chambers and scintillating hodoscopes are similar. Unlike HRS, Big Karl still needs Cerenkov counters and polarimeters in its focal plane; detectors which are necessary to perform some of the experiments proposed during the brainstorming. In addition, BIG KARL allows emission angle reconstruction via track measurements in its focal plane with high resolution. In the following the physics highlights, the proposed and potential experiments are summarized. During the meeting it became obvious that the physics to be explored at Big Karl can be grouped into five distinct categories, and this summary is organized accordingly. (orig.)

  6. Application and Prospect of Big Data in Water Resources

    Science.gov (United States)

    Xi, Danchi; Xu, Xinyi

    2017-04-01

    Because of developed information technology and affordable data storage, we h ave entered the era of data explosion. The term "Big Data" and technology relate s to it has been created and commonly applied in many fields. However, academic studies just got attention on Big Data application in water resources recently. As a result, water resource Big Data technology has not been fully developed. This paper introduces the concept of Big Data and its key technologies, including the Hadoop system and MapReduce. In addition, this paper focuses on the significance of applying the big data in water resources and summarizing prior researches by others. Most studies in this field only set up theoretical frame, but we define the "Water Big Data" and explain its tridimensional properties which are time dimension, spatial dimension and intelligent dimension. Based on HBase, the classification system of Water Big Data is introduced: hydrology data, ecology data and socio-economic data. Then after analyzing the challenges in water resources management, a series of solutions using Big Data technologies such as data mining and web crawler, are proposed. Finally, the prospect of applying big data in water resources is discussed, it can be predicted that as Big Data technology keeps developing, "3D" (Data Driven Decision) will be utilized more in water resources management in the future.

  7. Big Data in food and agriculture

    Directory of Open Access Journals (Sweden)

    Kelly Bronson

    2016-06-01

    Full Text Available Farming is undergoing a digital revolution. Our existing review of current Big Data applications in the agri-food sector has revealed several collection and analytics tools that may have implications for relationships of power between players in the food system (e.g. between farmers and large corporations. For example, Who retains ownership of the data generated by applications like Monsanto Corproation's Weed I.D . “app”? Are there privacy implications with the data gathered by John Deere's precision agricultural equipment? Systematically tracing the digital revolution in agriculture, and charting the affordances as well as the limitations of Big Data applied to food and agriculture, should be a broad research goal for Big Data scholarship. Such a goal brings data scholarship into conversation with food studies and it allows for a focus on the material consequences of big data in society.

  8. Big data optimization recent developments and challenges

    CERN Document Server

    2016-01-01

    The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.

  9. Una aproximación a Big Data = An approach to Big Data

    OpenAIRE

    Puyol Moreno, Javier

    2014-01-01

    Big Data puede ser considerada como una tendencia en el avance de la tecnología que ha abierto la puerta a un nuevo enfoque para la comprensión y la toma de decisiones, que se utiliza para describir las enormes cantidades de datos (estructurados, no estructurados y semi- estructurados) que sería demasiado largo y costoso para cargar una base de datos relacional para su análisis. Así, el concepto de Big Data se aplica a toda la información que no puede ser procesada o analizada utilizando herr...

  10. Toward a Literature-Driven Definition of Big Data in Healthcare.

    Science.gov (United States)

    Baro, Emilie; Degoul, Samuel; Beuscart, Régis; Chazard, Emmanuel

    2015-01-01

    The aim of this study was to provide a definition of big data in healthcare. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. A total of 196 papers were included. Big data can be defined as datasets with Log(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.

  11. Big Data Analytic, Big Step for Patient Management and Care in Puerto Rico.

    Science.gov (United States)

    Borrero, Ernesto E

    2018-01-01

    This letter provides an overview of the application of big data in health care system to improve quality of care, including predictive modelling for risk and resource use, precision medicine and clinical decision support, quality of care and performance measurement, public health and research applications, among others. The author delineates the tremendous potential for big data analytics and discuss how it can be successfully implemented in clinical practice, as an important component of a learning health-care system.

  12. Big data governance an emerging imperative

    CERN Document Server

    Soares, Sunil

    2012-01-01

    Written by a leading expert in the field, this guide focuses on the convergence of two major trends in information management-big data and information governance-by taking a strategic approach oriented around business cases and industry imperatives. With the advent of new technologies, enterprises are expanding and handling very large volumes of data; this book, nontechnical in nature and geared toward business audiences, encourages the practice of establishing appropriate governance over big data initiatives and addresses how to manage and govern big data, highlighting the relevant processes,

  13. Big Data and historical social science

    Directory of Open Access Journals (Sweden)

    Peter Bearman

    2015-11-01

    Full Text Available “Big Data” can revolutionize historical social science if it arises from substantively important contexts and is oriented towards answering substantively important questions. Such data may be especially important for answering previously largely intractable questions about the timing and sequencing of events, and of event boundaries. That said, “Big Data” makes no difference for social scientists and historians whose accounts rest on narrative sentences. Since such accounts are the norm, the effects of Big Data on the practice of historical social science may be more limited than one might wish.

  14. The Inverted Big-Bang

    OpenAIRE

    Vaas, Ruediger

    2004-01-01

    Our universe appears to have been created not out of nothing but from a strange space-time dust. Quantum geometry (loop quantum gravity) makes it possible to avoid the ominous beginning of our universe with its physically unrealistic (i.e. infinite) curvature, extreme temperature, and energy density. This could be the long sought after explanation of the big-bang and perhaps even opens a window into a time before the big-bang: Space itself may have come from an earlier collapsing universe tha...

  15. Minsky on "Big Government"

    Directory of Open Access Journals (Sweden)

    Daniel de Santana Vasconcelos

    2014-03-01

    Full Text Available This paper objective is to assess, in light of the main works of Minsky, his view and analysis of what he called the "Big Government" as that huge institution which, in parallels with the "Big Bank" was capable of ensuring stability in the capitalist system and regulate its inherently unstable financial system in mid-20th century. In this work, we analyze how Minsky proposes an active role for the government in a complex economic system flawed by financial instability.

  16. Classical propagation of strings across a big crunch/big bang singularity

    International Nuclear Information System (INIS)

    Niz, Gustavo; Turok, Neil

    2007-01-01

    One of the simplest time-dependent solutions of M theory consists of nine-dimensional Euclidean space times 1+1-dimensional compactified Milne space-time. With a further modding out by Z 2 , the space-time represents two orbifold planes which collide and re-emerge, a process proposed as an explanation of the hot big bang [J. Khoury, B. A. Ovrut, P. J. Steinhardt, and N. Turok, Phys. Rev. D 64, 123522 (2001).][P. J. Steinhardt and N. Turok, Science 296, 1436 (2002).][N. Turok, M. Perry, and P. J. Steinhardt, Phys. Rev. D 70, 106004 (2004).]. When the two planes are near, the light states of the theory consist of winding M2-branes, describing fundamental strings in a particular ten-dimensional background. They suffer no blue-shift as the M theory dimension collapses, and their equations of motion are regular across the transition from big crunch to big bang. In this paper, we study the classical evolution of fundamental strings across the singularity in some detail. We also develop a simple semiclassical approximation to the quantum evolution which allows one to compute the quantum production of excitations on the string and implement it in a simplified example

  17. The Information Panopticon in the Big Data Era

    Directory of Open Access Journals (Sweden)

    Martin Berner

    2014-04-01

    Full Text Available Taking advantage of big data opportunities is challenging for traditional organizations. In this article, we take a panoptic view of big data – obtaining information from more sources and making it visible to all organizational levels. We suggest that big data requires the transformation from command and control hierarchies to post-bureaucratic organizational structures wherein employees at all levels can be empowered while simultaneously being controlled. We derive propositions that show how to best exploit big data technologies in organizations.

  18. The Promise and Potential Perils of Big Data for Advancing Symptom Management Research in Populations at Risk for Health Disparities.

    Science.gov (United States)

    Bakken, Suzanne; Reame, Nancy

    2016-01-01

    Symptom management research is a core area of nursing science and one of the priorities for the National Institute of Nursing Research, which specifically focuses on understanding the biological and behavioral aspects of symptoms such as pain and fatigue, with the goal of developing new knowledge and new strategies for improving patient health and quality of life. The types and volume of data related to the symptom experience, symptom management strategies, and outcomes are increasingly accessible for research. Traditional data streams are now complemented by consumer-generated (i.e., quantified self) and "omic" data streams. Thus, the data available for symptom science can be considered big data. The purposes of this chapter are to (a) briefly summarize the current drivers for the use of big data in research; (b) describe the promise of big data and associated data science methods for advancing symptom management research; (c) explicate the potential perils of big data and data science from the perspective of the ethical principles of autonomy, beneficence, and justice; and (d) illustrate strategies for balancing the promise and the perils of big data through a case study of a community at high risk for health disparities. Big data and associated data science methods offer the promise of multidimensional data sources and new methods to address significant research gaps in symptom management. If nurse scientists wish to apply big data and data science methods to advance symptom management research and promote health equity, they must carefully consider both the promise and perils.

  19. WE-H-BRB-00: Big Data in Radiation Oncology

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2016-06-15

    Big Data in Radiation Oncology: (1) Overview of the NIH 2015 Big Data Workshop, (2) Where do we stand in the applications of big data in radiation oncology?, and (3) Learning Health Systems for Radiation Oncology: Needs and Challenges for Future Success The overriding goal of this trio panel of presentations is to improve awareness of the wide ranging opportunities for big data impact on patient quality care and enhancing potential for research and collaboration opportunities with NIH and a host of new big data initiatives. This presentation will also summarize the Big Data workshop that was held at the NIH Campus on August 13–14, 2015 and sponsored by AAPM, ASTRO, and NIH. The workshop included discussion of current Big Data cancer registry initiatives, safety and incident reporting systems, and other strategies that will have the greatest impact on radiation oncology research, quality assurance, safety, and outcomes analysis. Learning Objectives: To discuss current and future sources of big data for use in radiation oncology research To optimize our current data collection by adopting new strategies from outside radiation oncology To determine what new knowledge big data can provide for clinical decision support for personalized medicine L. Xing, NIH/NCI Google Inc.

  20. WE-H-BRB-00: Big Data in Radiation Oncology

    International Nuclear Information System (INIS)

    2016-01-01

    Big Data in Radiation Oncology: (1) Overview of the NIH 2015 Big Data Workshop, (2) Where do we stand in the applications of big data in radiation oncology?, and (3) Learning Health Systems for Radiation Oncology: Needs and Challenges for Future Success The overriding goal of this trio panel of presentations is to improve awareness of the wide ranging opportunities for big data impact on patient quality care and enhancing potential for research and collaboration opportunities with NIH and a host of new big data initiatives. This presentation will also summarize the Big Data workshop that was held at the NIH Campus on August 13–14, 2015 and sponsored by AAPM, ASTRO, and NIH. The workshop included discussion of current Big Data cancer registry initiatives, safety and incident reporting systems, and other strategies that will have the greatest impact on radiation oncology research, quality assurance, safety, and outcomes analysis. Learning Objectives: To discuss current and future sources of big data for use in radiation oncology research To optimize our current data collection by adopting new strategies from outside radiation oncology To determine what new knowledge big data can provide for clinical decision support for personalized medicine L. Xing, NIH/NCI Google Inc.

  1. De impact van Big Data op Internationale Betrekkingen

    NARCIS (Netherlands)

    Zwitter, Andrej

    Big Data changes our daily lives, but does it also change international politics? In this contribution, Andrej Zwitter (NGIZ chair at Groningen University) argues that Big Data impacts on international relations in ways that we only now start to understand. To comprehend how Big Data influences

  2. Epidemiology in the Era of Big Data

    Science.gov (United States)

    Mooney, Stephen J; Westreich, Daniel J; El-Sayed, Abdulrahman M

    2015-01-01

    Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called ‘3 Vs’: variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that, while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field’s future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future. PMID:25756221

  3. Big data and analytics strategic and organizational impacts

    CERN Document Server

    Morabito, Vincenzo

    2015-01-01

    This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book analyzes strategic issues relating to the growing relevance of Big Data and analytics for competitive advantage, which is also attributable to empowerment of activities such as consumer profiling, market segmentation, and development of new products or services. Detailed consideration is also given to the strategic impact of Big Data and analytics on innovation in domains such as government and education and to Big Data-driven business models. The second part of the book addresses the impact of Big Data and analytics on management and organizations, focusing on challenges for governance, evaluation, and change management, while the concluding part reviews real examples of Big Data and analytics innovation at the global level. The text is supported by informative illustrations and case studies, so that practitioners...

  4. Big Science and Long-tail Science

    CERN Document Server

    2008-01-01

    Jim Downing and I were privileged to be the guests of Salavtore Mele at CERN yesterday and to see the Atlas detector of the Large Hadron Collider . This is a wow experience - although I knew it was big, I hadnt realised how big.

  5. Toward a Literature-Driven Definition of Big Data in Healthcare

    Directory of Open Access Journals (Sweden)

    Emilie Baro

    2015-01-01

    Full Text Available Objective. The aim of this study was to provide a definition of big data in healthcare. Methods. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n and the number of variables (p for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. Results. A total of 196 papers were included. Big data can be defined as datasets with Log⁡(n*p≥7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Conclusion. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR data.

  6. Toward a Literature-Driven Definition of Big Data in Healthcare

    Science.gov (United States)

    Baro, Emilie; Degoul, Samuel; Beuscart, Régis; Chazard, Emmanuel

    2015-01-01

    Objective. The aim of this study was to provide a definition of big data in healthcare. Methods. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. Results. A total of 196 papers were included. Big data can be defined as datasets with Log⁡(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Conclusion. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data. PMID:26137488

  7. Big-Eyed Bugs Have Big Appetite for Pests

    Science.gov (United States)

    Many kinds of arthropod natural enemies (predators and parasitoids) inhabit crop fields in Arizona and can have a large negative impact on several pest insect species that also infest these crops. Geocoris spp., commonly known as big-eyed bugs, are among the most abundant insect predators in field c...

  8. Research on information security in big data era

    Science.gov (United States)

    Zhou, Linqi; Gu, Weihong; Huang, Cheng; Huang, Aijun; Bai, Yongbin

    2018-05-01

    Big data is becoming another hotspot in the field of information technology after the cloud computing and the Internet of Things. However, the existing information security methods can no longer meet the information security requirements in the era of big data. This paper analyzes the challenges and a cause of data security brought by big data, discusses the development trend of network attacks under the background of big data, and puts forward my own opinions on the development of security defense in technology, strategy and product.

  9. BIG DATA IN BUSINESS ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    Logica BANICA

    2015-06-01

    Full Text Available In recent years, dealing with a lot of data originating from social media sites and mobile communications among data from business environments and institutions, lead to the definition of a new concept, known as Big Data. The economic impact of the sheer amount of data produced in a last two years has increased rapidly. It is necessary to aggregate all types of data (structured and unstructured in order to improve current transactions, to develop new business models, to provide a real image of the supply and demand and thereby, generate market advantages. So, the companies that turn to Big Data have a competitive advantage over other firms. Looking from the perspective of IT organizations, they must accommodate the storage and processing Big Data, and provide analysis tools that are easily integrated into business processes. This paper aims to discuss aspects regarding the Big Data concept, the principles to build, organize and analyse huge datasets in the business environment, offering a three-layer architecture, based on actual software solutions. Also, the article refers to the graphical tools for exploring and representing unstructured data, Gephi and NodeXL.

  10. Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm

    Directory of Open Access Journals (Sweden)

    Baljit Singh Khehra

    2015-03-01

    Full Text Available The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA-based, Biogeography-based Optimization (BBO-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.

  11. A little big history of Tiananmen

    NARCIS (Netherlands)

    Quaedackers, E.; Grinin, L.E.; Korotayev, A.V.; Rodrigue, B.H.

    2011-01-01

    This contribution aims at demonstrating the usefulness of studying small-scale subjects such as Tiananmen, or the Gate of Heavenly Peace, in Beijing - from a Big History perspective. By studying such a ‘little big history’ of Tiananmen, previously overlooked yet fundamental explanations for why

  12. Addressing big data issues in Scientific Data Infrastructure

    NARCIS (Netherlands)

    Demchenko, Y.; Membrey, P.; Grosso, P.; de Laat, C.; Smari, W.W.; Fox, G.C.

    2013-01-01

    Big Data are becoming a new technology focus both in science and in industry. This paper discusses the challenges that are imposed by Big Data on the modern and future Scientific Data Infrastructure (SDI). The paper discusses a nature and definition of Big Data that include such features as Volume,

  13. Improving Healthcare Using Big Data Analytics

    Directory of Open Access Journals (Sweden)

    Revanth Sonnati

    2017-03-01

    Full Text Available In daily terms we call the current era as Modern Era which can also be named as the era of Big Data in the field of Information Technology. Our daily lives in todays world are rapidly advancing never quenching ones thirst. The fields of science engineering and technology are producing data at an exponential rate leading to Exabytes of data every day. Big data helps us to explore and re-invent many areas not limited to education health and law. The primary purpose of this paper is to provide an in-depth analysis in the area of Healthcare using the big data and analytics. The main purpose is to emphasize on the usage of the big data which is being stored all the time helping to look back in the history but this is the time to emphasize on the analyzation to improve the medication and services. Although many big data implementations happen to be in-house development this proposed implementation aims to propose a broader extent using Hadoop which just happen to be the tip of the iceberg. The focus of this paper is not limited to the improvement and analysis of the data it also focusses on the strengths and drawbacks compared to the conventional techniques available.

  14. Big Data - Smart Health Strategies

    Science.gov (United States)

    2014-01-01

    Summary Objectives To select best papers published in 2013 in the field of big data and smart health strategies, and summarize outstanding research efforts. Methods A systematic search was performed using two major bibliographic databases for relevant journal papers. The references obtained were reviewed in a two-stage process, starting with a blinded review performed by the two section editors, and followed by a peer review process operated by external reviewers recognized as experts in the field. Results The complete review process selected four best papers, illustrating various aspects of the special theme, among them: (a) using large volumes of unstructured data and, specifically, clinical notes from Electronic Health Records (EHRs) for pharmacovigilance; (b) knowledge discovery via querying large volumes of complex (both structured and unstructured) biological data using big data technologies and relevant tools; (c) methodologies for applying cloud computing and big data technologies in the field of genomics, and (d) system architectures enabling high-performance access to and processing of large datasets extracted from EHRs. Conclusions The potential of big data in biomedicine has been pinpointed in various viewpoint papers and editorials. The review of current scientific literature illustrated a variety of interesting methods and applications in the field, but still the promises exceed the current outcomes. As we are getting closer towards a solid foundation with respect to common understanding of relevant concepts and technical aspects, and the use of standardized technologies and tools, we can anticipate to reach the potential that big data offer for personalized medicine and smart health strategies in the near future. PMID:25123721

  15. About Big Data and its Challenges and Benefits in Manufacturing

    OpenAIRE

    Bogdan NEDELCU

    2013-01-01

    The aim of this article is to show the importance of Big Data and its growing influence on companies. It also shows what kind of big data is currently generated and how much big data is estimated to be generated. We can also see how much are the companies willing to invest in big data and how much are they currently gaining from their big data. There are also shown some major influences that big data has over one major segment in the industry (manufacturing) and the challenges that appear.

  16. Big Data Management in US Hospitals: Benefits and Barriers.

    Science.gov (United States)

    Schaeffer, Chad; Booton, Lawrence; Halleck, Jamey; Studeny, Jana; Coustasse, Alberto

    Big data has been considered as an effective tool for reducing health care costs by eliminating adverse events and reducing readmissions to hospitals. The purposes of this study were to examine the emergence of big data in the US health care industry, to evaluate a hospital's ability to effectively use complex information, and to predict the potential benefits that hospitals might realize if they are successful in using big data. The findings of the research suggest that there were a number of benefits expected by hospitals when using big data analytics, including cost savings and business intelligence. By using big data, many hospitals have recognized that there have been challenges, including lack of experience and cost of developing the analytics. Many hospitals will need to invest in the acquiring of adequate personnel with experience in big data analytics and data integration. The findings of this study suggest that the adoption, implementation, and utilization of big data technology will have a profound positive effect among health care providers.

  17. Big Data Strategy for Telco: Network Transformation

    OpenAIRE

    F. Amin; S. Feizi

    2014-01-01

    Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and ...

  18. Big Data in Shipping - Challenges and Opportunities

    OpenAIRE

    Rødseth, Ørnulf Jan; Perera, Lokukaluge Prasad; Mo, Brage

    2016-01-01

    Big Data is getting popular in shipping where large amounts of information is collected to better understand and improve logistics, emissions, energy consumption and maintenance. Constraints to the use of big data include cost and quality of on-board sensors and data acquisition systems, satellite communication, data ownership and technical obstacles to effective collection and use of big data. New protocol standards may simplify the process of collecting and organizing the data, including in...

  19. [Relevance of big data for molecular diagnostics].

    Science.gov (United States)

    Bonin-Andresen, M; Smiljanovic, B; Stuhlmüller, B; Sörensen, T; Grützkau, A; Häupl, T

    2018-04-01

    Big data analysis raises the expectation that computerized algorithms may extract new knowledge from otherwise unmanageable vast data sets. What are the algorithms behind the big data discussion? In principle, high throughput technologies in molecular research already introduced big data and the development and application of analysis tools into the field of rheumatology some 15 years ago. This includes especially omics technologies, such as genomics, transcriptomics and cytomics. Some basic methods of data analysis are provided along with the technology, however, functional analysis and interpretation requires adaptation of existing or development of new software tools. For these steps, structuring and evaluating according to the biological context is extremely important and not only a mathematical problem. This aspect has to be considered much more for molecular big data than for those analyzed in health economy or epidemiology. Molecular data are structured in a first order determined by the applied technology and present quantitative characteristics that follow the principles of their biological nature. These biological dependencies have to be integrated into software solutions, which may require networks of molecular big data of the same or even different technologies in order to achieve cross-technology confirmation. More and more extensive recording of molecular processes also in individual patients are generating personal big data and require new strategies for management in order to develop data-driven individualized interpretation concepts. With this perspective in mind, translation of information derived from molecular big data will also require new specifications for education and professional competence.

  20. Big data in psychology: A framework for research advancement.

    Science.gov (United States)

    Adjerid, Idris; Kelley, Ken

    2018-02-22

    The potential for big data to provide value for psychology is significant. However, the pursuit of big data remains an uncertain and risky undertaking for the average psychological researcher. In this article, we address some of this uncertainty by discussing the potential impact of big data on the type of data available for psychological research, addressing the benefits and most significant challenges that emerge from these data, and organizing a variety of research opportunities for psychology. Our article yields two central insights. First, we highlight that big data research efforts are more readily accessible than many researchers realize, particularly with the emergence of open-source research tools, digital platforms, and instrumentation. Second, we argue that opportunities for big data research are diverse and differ both in their fit for varying research goals, as well as in the challenges they bring about. Ultimately, our outlook for researchers in psychology using and benefiting from big data is cautiously optimistic. Although not all big data efforts are suited for all researchers or all areas within psychology, big data research prospects are diverse, expanding, and promising for psychology and related disciplines. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  1. 'Big data' in pharmaceutical science: challenges and opportunities.

    Science.gov (United States)

    Dossetter, Al G; Ecker, Gerhard; Laverty, Hugh; Overington, John

    2014-05-01

    Future Medicinal Chemistry invited a selection of experts to express their views on the current impact of big data in drug discovery and design, as well as speculate on future developments in the field. The topics discussed include the challenges of implementing big data technologies, maintaining the quality and privacy of data sets, and how the industry will need to adapt to welcome the big data era. Their enlightening responses provide a snapshot of the many and varied contributions being made by big data to the advancement of pharmaceutical science.

  2. Data set on the effects of conifer control and slash burning on soil carbon, total N, organic matter and extractable micro-nutrients

    Directory of Open Access Journals (Sweden)

    Jonathan D. Bates

    2017-10-01

    Full Text Available Conifer control in sagebrush steppe of the western United States causes various levels of site disturbance influencing vegetation recovery and resource availability. The data set presented in this article include growing season availability of soil micronutrients and levels of total soil carbon, organic matter, and N spanning a six year period following western juniper (Juniperus occidentalis spp. occidentalis reduction by mechanical cutting and prescribed fire of western juniper woodlands in southeast Oregon. These data can be useful to further evaluate the impacts of conifer woodland reduction to soil resources in sagebrush steppe plant communities.

  3. Soft computing in big data processing

    CERN Document Server

    Park, Seung-Jong; Lee, Jee-Hyong

    2014-01-01

    Big data is an essential key to build a smart world as a meaning of the streaming, continuous integration of large volume and high velocity data covering from all sources to final destinations. The big data range from data mining, data analysis and decision making, by drawing statistical rules and mathematical patterns through systematical or automatically reasoning. The big data helps serve our life better, clarify our future and deliver greater value. We can discover how to capture and analyze data. Readers will be guided to processing system integrity and implementing intelligent systems. With intelligent systems, we deal with the fundamental data management and visualization challenges in effective management of dynamic and large-scale data, and efficient processing of real-time and spatio-temporal data. Advanced intelligent systems have led to managing the data monitoring, data processing and decision-making in realistic and effective way. Considering a big size of data, variety of data and frequent chan...

  4. Non-target effects on songbirds from habitat manipulation for Greater Sage-Grouse: Implications for the umbrella species concept

    Science.gov (United States)

    Carlisle, Jason D.; Chalfoun, Anna D.; Smith, Kurt T.; Beck, Jeffery L.

    2018-01-01

    The “umbrella species” concept is a conservation strategy in which creating and managing reserve areas to meet the needs of one species is thought to benefit other species indirectly. Broad-scale habitat protections on behalf of an umbrella species are assumed to benefit co-occurring taxa, but targeted management actions to improve local habitat suitability for the umbrella species may produce unintended effects on other species. Our objective was to quantify the effects of a common habitat treatment (mowing of big sagebrush [Artemisia tridentata]) intended to benefit a high-profile umbrella species (Greater Sage-Grouse [Centrocercus urophasianus]) on 3 sympatric songbird species of concern. We used a before–after control-impact experimental design spanning 3 yr in Wyoming, USA, to quantify the effect of mowing on the abundance, nest-site selection, nestling condition, and nest survival of 2 sagebrush-obligate songbirds (Brewer's Sparrow [Spizella breweri] and Sage Thrasher [Oreoscoptes montanus]) and one open-habitat generalist songbird (Vesper Sparrow [Pooecetes gramineus]). Mowing was associated with lower abundance of Brewer's Sparrows and Sage Thrashers but higher abundance of Vesper Sparrows. We found no Brewer's Sparrows or Sage Thrashers nesting in the mowed footprint posttreatment, which suggests complete loss of nesting habitat for these species. Mowing was associated with higher nestling condition and nest survival for Vesper Sparrows but not for the sagebrush-obligate species. Management prescriptions that remove woody biomass within a mosaic of intact habitat may be tolerated by sagebrush-obligate songbirds but are likely more beneficial for open-habitat generalist species. By definition, umbrella species conservation entails habitat protections at broad spatial scales. We caution that habitat manipulations to benefit Greater Sage-Grouse could negatively affect nontarget species of conservation concern if implemented across large spatial extents.

  5. Heineken in the House: Improving Online Media Reputation through Featuring a Sponsored Brand Community

    NARCIS (Netherlands)

    Vermeer, S.; Remmelswaal, P.; Jacobs, S.

    2017-01-01

    Nowadays, more and more organizations use social media to promote their sponsorships of big events. Heineken has created a major brand community by facilitating the Holland Heineken House during the Olympic Games. This study investigates to what extent featuring a sponsored brand community on social

  6. Solution of a braneworld big crunch/big bang cosmology

    International Nuclear Information System (INIS)

    McFadden, Paul L.; Turok, Neil; Steinhardt, Paul J.

    2007-01-01

    We solve for the cosmological perturbations in a five-dimensional background consisting of two separating or colliding boundary branes, as an expansion in the collision speed V divided by the speed of light c. Our solution permits a detailed check of the validity of four-dimensional effective theory in the vicinity of the event corresponding to the big crunch/big bang singularity. We show that the four-dimensional description fails at the first nontrivial order in (V/c) 2 . At this order, there is nontrivial mixing of the two relevant four-dimensional perturbation modes (the growing and decaying modes) as the boundary branes move from the narrowly separated limit described by Kaluza-Klein theory to the well-separated limit where gravity is confined to the positive-tension brane. We comment on the cosmological significance of the result and compute other quantities of interest in five-dimensional cosmological scenarios

  7. [Big data and their perspectives in radiation therapy].

    Science.gov (United States)

    Guihard, Sébastien; Thariat, Juliette; Clavier, Jean-Baptiste

    2017-02-01

    The concept of big data indicates a change of scale in the use of data and data aggregation into large databases through improved computer technology. One of the current challenges in the creation of big data in the context of radiation therapy is the transformation of routine care items into dark data, i.e. data not yet collected, and the fusion of databases collecting different types of information (dose-volume histograms and toxicity data for example). Processes and infrastructures devoted to big data collection should not impact negatively on the doctor-patient relationship, the general process of care or the quality of the data collected. The use of big data requires a collective effort of physicians, physicists, software manufacturers and health authorities to create, organize and exploit big data in radiotherapy and, beyond, oncology. Big data involve a new culture to build an appropriate infrastructure legally and ethically. Processes and issues are discussed in this article. Copyright © 2016 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  8. Current applications of big data in obstetric anesthesiology.

    Science.gov (United States)

    Klumpner, Thomas T; Bauer, Melissa E; Kheterpal, Sachin

    2017-06-01

    The narrative review aims to highlight several recently published 'big data' studies pertinent to the field of obstetric anesthesiology. Big data has been used to study rare outcomes, to identify trends within the healthcare system, to identify variations in practice patterns, and to highlight potential inequalities in obstetric anesthesia care. Big data studies have helped define the risk of rare complications of obstetric anesthesia, such as the risk of neuraxial hematoma in thrombocytopenic parturients. Also, large national databases have been used to better understand trends in anesthesia-related adverse events during cesarean delivery as well as outline potential racial/ethnic disparities in obstetric anesthesia care. Finally, real-time analysis of patient data across a number of disparate health information systems through the use of sophisticated clinical decision support and surveillance systems is one promising application of big data technology on the labor and delivery unit. 'Big data' research has important implications for obstetric anesthesia care and warrants continued study. Real-time electronic surveillance is a potentially useful application of big data technology on the labor and delivery unit.

  9. Volume and Value of Big Healthcare Data.

    Science.gov (United States)

    Dinov, Ivo D

    Modern scientific inquiries require significant data-driven evidence and trans-disciplinary expertise to extract valuable information and gain actionable knowledge about natural processes. Effective evidence-based decisions require collection, processing and interpretation of vast amounts of complex data. The Moore's and Kryder's laws of exponential increase of computational power and information storage, respectively, dictate the need rapid trans-disciplinary advances, technological innovation and effective mechanisms for managing and interrogating Big Healthcare Data. In this article, we review important aspects of Big Data analytics and discuss important questions like: What are the challenges and opportunities associated with this biomedical, social, and healthcare data avalanche? Are there innovative statistical computing strategies to represent, model, analyze and interpret Big heterogeneous data? We present the foundation of a new compressive big data analytics (CBDA) framework for representation, modeling and inference of large, complex and heterogeneous datasets. Finally, we consider specific directions likely to impact the process of extracting information from Big healthcare data, translating that information to knowledge, and deriving appropriate actions.

  10. Using Big Book to Teach Things in My House

    OpenAIRE

    Effrien, Intan; Lailatus, Sa’diyah; Nuruliftitah Maja, Neneng

    2017-01-01

    The purpose of this study to determine students' interest in learning using the big book media. Big book is a big book from the general book. The big book contains simple words and images that match the content of sentences and spelling. From here researchers can know the interest and development of students' knowledge. As well as train researchers to remain crative in developing learning media for students.

  11. Body image and personality among British men: associations between the Big Five personality domains, drive for muscularity, and body appreciation.

    Science.gov (United States)

    Benford, Karis; Swami, Viren

    2014-09-01

    The present study examined associations between the Big Five personality domains and measures of men's body image. A total of 509 men from the community in London, UK, completed measures of drive for muscularity, body appreciation, the Big Five domains, and subjective social status, and provided their demographic details. The results of a hierarchical regression showed that, once the effects of participant body mass index (BMI) and subjective social status had been accounted for, men's drive for muscularity was significantly predicted by Neuroticism (β=.29). In addition, taking into account the effects of BMI and subjective social status, men's body appreciation was significantly predicted by Neuroticism (β=-.35) and Extraversion (β=.12). These findings highlight potential avenues for the development of intervention approaches based on the relationship between the Big Five personality traits and body image. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Big Data Analytics Methodology in the Financial Industry

    Science.gov (United States)

    Lawler, James; Joseph, Anthony

    2017-01-01

    Firms in industry continue to be attracted by the benefits of Big Data Analytics. The benefits of Big Data Analytics projects may not be as evident as frequently indicated in the literature. The authors of the study evaluate factors in a customized methodology that may increase the benefits of Big Data Analytics projects. Evaluating firms in the…

  13. Biotic components

    International Nuclear Information System (INIS)

    Uresk, D.W.; Fitzner, R.E.; Rogers, L.E.; Rickard, W.H.

    1977-01-01

    Representative plant communities are described. The major community is dominated by sagebrush/cheatgrass-sandberg blue grass. Mammal, bird and insect species inhabiting the 200 Area plateau are representative of surrounding regions. Prairie falcons are the only species present possibly threatened with extinction. They do not nest on the plateau but probably forage over the area

  14. Using DNA from hairs left at depredated greater sage-grouse nests to detect mammalian nest predators

    Science.gov (United States)

    Christopher P. Kirol; Kristine L. Pilgrim; Andrew L. Sutphin; Thomas L. Maechtle

    2018-01-01

    Despite a multitude of studies on sage-grouse (Centrocercus spp.), there is still sparse information on the predator communities that influence sage-grouse productivity and how these predator communities may change when sagebrush habitats are altered by human activities. As a proof-of-concept, we used mammalian hairs collected at depredated greater sage-grouse (C....

  15. Utility of Big Area Additive Manufacturing (BAAM) For The Rapid Manufacture of Customized Electric Vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Love, Lonnie J [ORNL

    2015-08-01

    This Oak Ridge National Laboratory (ORNL) Manufacturing Development Facility (MDF) technical collaboration project was conducted in two phases as a CRADA with Local Motors Inc. Phase 1 was previously reported as Advanced Manufacturing of Complex Cyber Mechanical Devices through Community Engagement and Micro-manufacturing and demonstrated the integration of components onto a prototype body part for a vehicle. Phase 2 was reported as Utility of Big Area Additive Manufacturing (BAAM) for the Rapid Manufacture of Customized Electric Vehicles and demonstrated the high profile live printing of an all-electric vehicle using ONRL s Big Area Additive Manufacturing (BAAM) technology. This demonstration generated considerable national attention and successfully demonstrated the capabilities of the BAAM system as developed by ORNL and Cincinnati, Inc. and the feasibility of additive manufacturing of a full scale electric vehicle as envisioned by the CRADA partner Local Motors, Inc.

  16. Big data: survey, technologies, opportunities, and challenges.

    Science.gov (United States)

    Khan, Nawsher; Yaqoob, Ibrar; Hashem, Ibrahim Abaker Targio; Inayat, Zakira; Ali, Waleed Kamaleldin Mahmoud; Alam, Muhammad; Shiraz, Muhammad; Gani, Abdullah

    2014-01-01

    Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

  17. Big Data: Survey, Technologies, Opportunities, and Challenges

    Science.gov (United States)

    Khan, Nawsher; Yaqoob, Ibrar; Hashem, Ibrahim Abaker Targio; Inayat, Zakira; Mahmoud Ali, Waleed Kamaleldin; Alam, Muhammad; Shiraz, Muhammad; Gani, Abdullah

    2014-01-01

    Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data. PMID:25136682

  18. Opportunity and Challenges for Migrating Big Data Analytics in Cloud

    Science.gov (United States)

    Amitkumar Manekar, S.; Pradeepini, G., Dr.

    2017-08-01

    Big Data Analytics is a big word now days. As per demanding and more scalable process data generation capabilities, data acquisition and storage become a crucial issue. Cloud storage is a majorly usable platform; the technology will become crucial to executives handling data powered by analytics. Now a day’s trend towards “big data-as-a-service” is talked everywhere. On one hand, cloud-based big data analytics exactly tackle in progress issues of scale, speed, and cost. But researchers working to solve security and other real-time problem of big data migration on cloud based platform. This article specially focused on finding possible ways to migrate big data to cloud. Technology which support coherent data migration and possibility of doing big data analytics on cloud platform is demanding in natute for new era of growth. This article also gives information about available technology and techniques for migration of big data in cloud.

  19. Hot big bang or slow freeze?

    Science.gov (United States)

    Wetterich, C.

    2014-09-01

    We confront the big bang for the beginning of the universe with an equivalent picture of a slow freeze - a very cold and slowly evolving universe. In the freeze picture the masses of elementary particles increase and the gravitational constant decreases with cosmic time, while the Newtonian attraction remains unchanged. The freeze and big bang pictures both describe the same observations or physical reality. We present a simple ;crossover model; without a big bang singularity. In the infinite past space-time is flat. Our model is compatible with present observations, describing the generation of primordial density fluctuations during inflation as well as the present transition to a dark energy-dominated universe.

  20. Big Data

    DEFF Research Database (Denmark)

    Aaen, Jon; Nielsen, Jeppe Agger

    2016-01-01

    Big Data byder sig til som en af tidens mest hypede teknologiske innovationer, udråbt til at rumme kimen til nye, værdifulde operationelle indsigter for private virksomheder og offentlige organisationer. Mens de optimistiske udmeldinger er mange, er forskningen i Big Data i den offentlige sektor...... indtil videre begrænset. Denne artikel belyser, hvordan den offentlige sundhedssektor kan genanvende og udnytte en stadig større mængde data under hensyntagen til offentlige værdier. Artiklen bygger på et casestudie af anvendelsen af store mængder sundhedsdata i Dansk AlmenMedicinsk Database (DAMD......). Analysen viser, at (gen)brug af data i nye sammenhænge er en flerspektret afvejning mellem ikke alene økonomiske rationaler og kvalitetshensyn, men også kontrol over personfølsomme data og etiske implikationer for borgeren. I DAMD-casen benyttes data på den ene side ”i den gode sags tjeneste” til...