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

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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 (

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....

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

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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...

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

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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,...

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

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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...

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

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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.

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

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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

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

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

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...

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 (...

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

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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

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

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.

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...

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...

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...

Multiscale sagebrush rangeland habitat modeling in the Gunnison Basin of Colorado

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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

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

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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.

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

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

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.

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.

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.

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....

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...

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).

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

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...

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.

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.

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....

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

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...

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

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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.

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

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

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).

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.

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...

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.

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...

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

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

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...

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...

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

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

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.

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

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,...

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...

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

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.

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.

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.

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.

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...

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

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...

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...

Intelligent Control of Micro Grid: A Big Data-Based Control Center

Science.gov (United States)

Liu, Lu; Wang, Yanping; Liu, Li; Wang, Zhiseng

2018-01-01

In this paper, a structure of micro grid system with big data-based control center is introduced. Energy data from distributed generation, storage and load are analized through the control center, and from the results new trends will be predicted and applied as a feedback to optimize the control. Therefore, each step proceeded in micro grid can be adjusted and orgnized in a form of comprehensive management. A framework of real-time data collection, data processing and data analysis will be proposed by employing big data technology. Consequently, a integrated distributed generation and a optimized energy storage and transmission process can be implemented in the micro grid system.

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

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.

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. ...

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...

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

Interactions between fire and nonnative, annual plant species (that is, “the grass/fire cycle”) represent one of the greatest threats to sagebrush (Artemisia spp.) ecosystems and associated wildlife, including the greater sage-grouse (Centrocercus urophasianus). In 2015, U.S. Department of the Interior called for a “science-based strategy to reduce the threat of large-scale rangeland fire to habitat for the greater sage-grouse and the sagebrush-steppe ecosystem.” An associated guidance document, the “Integrated Rangeland Fire Management Strategy Actionable Science Plan,” identified fuel breaks as high priority areas for scientific research. Fuel breaks are intended to reduce fire size and frequency, and potentially they can compartmentalize wildfire spatial distribution in a landscape. Fuel breaks are designed to reduce flame length, fireline intensity, and rates of fire spread in order to enhance firefighter access, improve response times, and provide safe and strategic anchor points for wildland fire-fighting activities. To accomplish these objectives, fuel breaks disrupt fuel continuity, reduce fuel accumulation, and (or) increase plants with high moisture content through the removal or modification of vegetation in strategically placed strips or blocks of land.Fuel breaks are being newly constructed, enhanced, or proposed across large areas of the Great Basin to reduce wildfire risk and to protect remaining sagebrush ecosystems (including greater sage-grouse habitat). These projects are likely to result in thousands of linear miles of fuel breaks that will have direct ecological effects across hundreds of thousands of acres through habitat loss and conversion. These projects may also affect millions of acres indirectly because of edge effects and habitat fragmentation created by networks of fuel breaks. Hence, land managers are often faced with a potentially paradoxical situation: the need to substantially alter sagebrush habitats with fuel breaks

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

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

Big Data for weed control and crop protection

NARCIS (Netherlands)

Evert, van F.K.; Fountas, S.; Jakovetic, D.; Crnojevic, V.; Travlos, I.; Kempenaar, C.

2017-01-01

Farmers have access to many data-intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in

Big Data, data integrity, and the fracturing of the control zone

Directory of Open Access Journals (Sweden)

Carl Lagoze

2014-11-01

Full Text Available Despite all the attention to Big Data and the claims that it represents a “paradigm shift” in science, we lack understanding about what are the qualities of Big Data that may contribute to this revolutionary impact. In this paper, we look beyond the quantitative aspects of Big Data (i.e. lots of data and examine it from a sociotechnical perspective. We argue that a key factor that distinguishes “Big Data” from “lots of data” lies in changes to the traditional, well-established “control zones” that facilitated clear provenance of scientific data, thereby ensuring data integrity and providing the foundation for credible science. The breakdown of these control zones is a consequence of the manner in which our network technology and culture enable and encourage open, anonymous sharing of information, participation regardless of expertise, and collaboration across geographic, disciplinary, and institutional barriers. We are left with the conundrum—how to reap the benefits of Big Data while re-creating a trust fabric and an accountable chain of responsibility that make credible science possible.

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.

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

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...

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.

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

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 ...

Tuberculosis control in big cities and urban risk groups in the European Union: a consensus statement.

Science.gov (United States)

van Hest, N A; Aldridge, R W; de Vries, G; Sandgren, A; Hauer, B; Hayward, A; Arrazola de Oñate, W; Haas, W; Codecasa, L R; Caylà, J A; Story, A; Antoine, D; Gori, A; Quabeck, L; Jonsson, J; Wanlin, M; Orcau, Å; Rodes, A; Dedicoat, M; Antoun, F; van Deutekom, H; Keizer, St; Abubakar, I

2014-03-06

In low-incidence countries in the European Union (EU), tuberculosis (TB) is concentrated in big cities, especially among certain urban high-risk groups including immigrants from TB high-incidence countries, homeless people, and those with a history of drug and alcohol misuse. Elimination of TB in European big cities requires control measures focused on multiple layers of the urban population. The particular complexities of major EU metropolises, for example high population density and social structure, create specific opportunities for transmission, but also enable targeted TB control interventions, not efficient in the general population, to be effective or cost effective. Lessons can be learnt from across the EU and this consensus statement on TB control in big cities and urban risk groups was prepared by a working group representing various EU big cities, brought together on the initiative of the European Centre for Disease Prevention and Control. The consensus statement describes general and specific social, educational, operational, organisational, legal and monitoring TB control interventions in EU big cities, as well as providing recommendations for big city TB control, based upon a conceptual TB transmission and control model.

The Design of Intelligent Repair Welding Mechanism and Relative Control System of Big Gear

Directory of Open Access Journals (Sweden)

Hong-Yu LIU

2014-10-01

Full Text Available Effective repair of worn big gear has large influence on ensuring safety production and enhancing economic benefits. A kind of intelligent repair welding method was put forward mainly aimed at the big gear restriction conditions of high production cost, long production cycle and high- intensity artificial repair welding work. Big gear repair welding mechanism was designed in this paper. The work principle and part selection of big gear repair welding mechanism was introduced. The three dimensional mode of big gear repair welding mechanism was constructed by Pro/E three dimensional design software. Three dimensional motions can be realized by motor controlling ball screw. According to involute gear feature, the complicated curve motion on curved gear surface can be transformed to linear motion by orientation. By this way, the repair welding on worn gear area can be realized. In the design of big gear repair welding mechanism control system, Siemens S7-200 series hardware was chosen. Siemens STEP7 programming software was chosen as system design tool. The entire repair welding process was simulated by experiment simulation. It provides a kind of practical and feasible method for the intelligent repair welding of big worn gear.

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.

Two applications of airtightness control techniques on big assemblies

CERN Document Server

Devallan, C; Marcellin, J

1973-01-01

Deals with two airtightness control techniques respectively applied on intersecting storage rings (ISR) at CERN in Geneva and on a liquid methane storage tank. These two big assemblies called for two different control techniques which use helium and ammonia respectively as tracer gas. Existing practical leakage detection techniques to meet industrial needs are discussed at the end of the article. (2 refs).

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

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

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

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...

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.

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

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.

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...

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.

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...

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...

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...

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.

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.

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

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...

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

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...

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...

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…

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.

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.

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...

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.

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

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…

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...

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

What do Big Data do in Global Governance?

DEFF Research Database (Denmark)

Krause Hansen, Hans; Porter, Tony

2017-01-01

Two paradoxes associated with big data are relevant to global governance. First, while promising to increase the capacities of humans in governance, big data also involve an increasingly independent role for algorithms, technical artifacts, the Internet of things, and other objects, which can...... reduce the control of human actors. Second, big data involve new boundary transgressions as data are brought together from multiple sources while also creating new boundary conflicts as powerful actors seek to gain advantage by controlling big data and excluding competitors. These changes are not just...... about new data sources for global decision-makers, but instead signal more profound changes in the character of global governance....

Knowledge Discovery for Smart Grid Operation, Control, and Situation Awareness -- A Big Data Visualization Platform

Energy Technology Data Exchange (ETDEWEB)

Gu, Yi; Jiang, Huaiguang; Zhang, Yingchen; Zhang, Jun Jason; Gao, Tianlu; Muljadi, Eduard

2016-11-21

In this paper, a big data visualization platform is designed to discover the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. The spawn of smart sensors at both grid side and customer side can provide large volume of heterogeneous data that collect information in all time spectrums. Extracting useful knowledge from this big-data poll is still challenging. In this paper, the Apache Spark, an open source cluster computing framework, is used to process the big-data to effectively discover the hidden knowledge. A high-speed communication architecture utilizing the Open System Interconnection (OSI) model is designed to transmit the data to a visualization platform. This visualization platform uses Google Earth, a global geographic information system (GIS) to link the geological information with the SG knowledge and visualize the information in user defined fashion. The University of Denver's campus grid is used as a SG test bench and several demonstrations are presented for the proposed platform.

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).

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…

[The technological innovation strategy for quality control of Chinese medicine based on Big Data].

Science.gov (United States)

Li, Zhen-hao; Qian, Zhong-zhi; Cheng, Yi-yu

2015-09-01

The evolution of the quality control concepts of medical products within the global context and the development of the quality control technology of Chinese medicine are briefly described. Aimed at the bottlenecks in the regulation and quality control of Chinese medicine, using Big Data technology to address the significant challenges in Chinese medicine industry is proposed. For quality standard refinements and internationalization of Chinese medicine, a technological innovation strategy encompassing its methodology, and the R&D direction of the subsequent core technology are also presented.

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.

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.

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.

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."

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.

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

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,...

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...

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.

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...

Pupils' Self-Perceptions: The Role of Teachers' Judgment Controlling for Big-Fish-Little-Pond Effect

Science.gov (United States)

Bressoux, Pascal; Pansu, Pascal

2016-01-01

This article aims to study the relationship between teachers' judgment and pupils' self-perceptions controlling for the big-fish-little-pond effect (BFLPE). Three studies were conducted among third-grade pupils. Study 1 (n = 585) focused on pupils' perceptions of their scholastic competence. Teachers' judgment and BFLPE were found to have an…

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.

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.

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...

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.

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…

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

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...

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

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

Big Data Analytics for Industrial Process Control

DEFF Research Database (Denmark)

Khan, Abdul Rauf; Schioler, Henrik; Kulahci, Murat

2017-01-01

Today, in modern factories, each step in manufacturing produces a bulk of valuable as well as highly precise information. This provides a great opportunity for understanding the hidden statistical dependencies in the process. Systematic analysis and utilization of advanced analytical methods can ...... lead towards more informed decisions. In this article we discuss some of the challenges related to big data analysis in manufacturing and relevant solutions to some of these challenges....

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.

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.

A Hierarchical Visualization Analysis Model of Power Big Data

Science.gov (United States)

Li, Yongjie; Wang, Zheng; Hao, Yang

2018-01-01

Based on the conception of integrating VR scene and power big data analysis, a hierarchical visualization analysis model of power big data is proposed, in which levels are designed, targeting at different abstract modules like transaction, engine, computation, control and store. The regularly departed modules of power data storing, data mining and analysis, data visualization are integrated into one platform by this model. It provides a visual analysis solution for the power big data.

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

Interventions for treating osteoarthritis of the big toe joint.

Science.gov (United States)

Zammit, Gerard V; Menz, Hylton B; Munteanu, Shannon E; Landorf, Karl B; Gilheany, Mark F

2010-09-08

Osteoarthritis affecting of the big toe joint of the foot (hallux limitus or rigidus) is a common and painful condition. Although several treatments have been proposed, few have been adequately evaluated. To identify controlled trials evaluating interventions for osteoarthritis of the big toe joint and to determine the optimum intervention(s). Literature searches were conducted across the following electronic databases: CENTRAL; MEDLINE; EMBASE; CINAHL; and PEDro (to 14th January 2010). No language restrictions were applied. Randomised controlled trials, quasi-randomised trials, or controlled clinical trials that assessed treatment outcomes for osteoarthritis of the big toe joint. Participants of any age or gender with osteoarthritis of the big toe joint (defined either radiographically or clinically) were included. Two authors examined the list of titles and abstracts identified by the literature searches. One content area expert and one methodologist independently applied the pre-determined inclusion and exclusion criteria to the full text of identified trials. To minimise error and reduce potential bias, data were extracted independently by two content experts. Only one trial satisfactorily fulfilled the inclusion criteria and was included in this review. This trial evaluated the effectiveness of two physical therapy programs in 20 individuals with osteoarthritis of the big toe joint. Assessment outcomes included pain levels, big toe joint range of motion and plantar flexion strength of the hallux. Mean differences at four weeks follow up were 3.80 points (95% CI 2.74 to 4.86) for self reported pain, 28.30 degrees (95% CI 21.37 to 35.23) for big toe joint range of motion, and 2.80 kg (95% CI 2.13 to 3.47) for muscle strength. Although differences in outcomes between treatment and control groups were reported, the risk of bias was high. The trial failed to employ appropriate randomisation or adequate allocation concealment, used a relatively small sample and

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...

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

[Contemplation on the application of big data in clinical medicine].

Science.gov (United States)

Lian, Lei

2015-01-01

Medicine is another area where big data is being used. The link between clinical treatment and outcome is the key step when applying big data in medicine. In the era of big data, it is critical to collect complete outcome data. Patient follow-up, comprehensive integration of data resources, quality control and standardized data management are the predominant approaches to avoid missing data and data island. Therefore, establishment of systemic patients follow-up protocol and prospective data management strategy are the important aspects of big data in medicine.

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)

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...

Release plan for Big Pete

International Nuclear Information System (INIS)

Edwards, T.A.

1996-11-01

This release plan is to provide instructions for the Radiological Control Technician (RCT) to conduct surveys for the unconditional release of ''Big Pete,'' which was used in the removal of ''Spacers'' from the N-Reactor. Prior to performing surveys on the rear end portion of ''Big Pete,'' it shall be cleaned (i.e., free of oil, grease, caked soil, heavy dust). If no contamination is found, the vehicle may be released with the permission of the area RCT Supervisor. If contamination is found by any of the surveys, contact the cognizant Radiological Engineer for decontamination instructions

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

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.

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.

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...

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.

Codimension-Two Big-Bang Bifurcation in a ZAD-Controlled Boost DC-DC Converter

Science.gov (United States)

Amador, A.; Casanova, S.; Granada, H. A.; Olivar, G.; Hurtado, J.

In this paper, we study some nonlinear behaviors in a two-dimensional system defined by a Boost Converter controlled by CPWM (Centered Pulse-Width Modulation) and a ZAD (Zero Average Dynamics) strategy. The dynamics was analyzed using a discrete-time map, which consists of a sampled system at each switching cycle. The structure of the two-parametric space is characterized analytically. This allows proving the existence and stability of an infinite number of codimension-one curves that intersect at the same point in the two-parametric space. This phenomenon has been called a big-bang bifurcation.

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.

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.

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.

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).

The Big Data Tools Impact on Development of Simulation-Concerned Academic Disciplines

Directory of Open Access Journals (Sweden)

A. A. Sukhobokov

2015-01-01

Full Text Available The article gives a definition of Big Data on the basis of 5V (Volume, Variety, Velocity, Veracity, Value as well as shows examples of tasks that require using Big Data tools in a diversity of areas, namely: health, education, financial services, industry, agriculture, logistics, retail, information technology, telecommunications and others. An overview of Big Data tools is delivered, including open source products, IBM Bluemix and SAP HANA platforms. Examples of architecture of corporate data processing and management systems using Big Data tools are shown for big Internet companies and for enterprises in traditional industries. Within the overview, a classification of Big Data tools is proposed that fills gaps of previously developed similar classifications. The new classification contains 19 classes and allows embracing several hundreds of existing and emerging products.The uprise and use of Big Data tools, in addition to solving practical problems, affects the development of scientific disciplines concerning the simulation of technical, natural or socioeconomic systems and the solution of practical problems based on developed models. New schools arise in these disciplines. These new schools decide peculiar to each discipline tasks, but for systems with a much bigger number of internal elements and connections between them. Characteristics of the problems to be solved under new schools, not always meet the criteria for Big Data. It is suggested to identify the Big Data as a part of the theory of sorting and searching algorithms. In other disciplines the new schools are called by analogy with Big Data: Big Calculation in numerical methods, Big Simulation in imitational modeling, Big Management in the management of socio-economic systems, Big Optimal Control in the optimal control theory. The paper shows examples of tasks and methods to be developed within new schools. The educed tendency is not limited to the considered disciplines: there are

Surveillance, Snowden, and Big Data: Capacities, consequences, critique

Directory of Open Access Journals (Sweden)

David Lyon

2014-07-01

Full Text Available The Snowden revelations about National Security Agency surveillance, starting in 2013, along with the ambiguous complicity of internet companies and the international controversies that followed provide a perfect segue into contemporary conundrums of surveillance and Big Data. Attention has shifted from late C20th information technologies and networks to a C21st focus on data, currently crystallized in “Big Data.” Big Data intensifies certain surveillance trends associated with information technology and networks, and is thus implicated in fresh but fluid configurations. This is considered in three main ways: One, the capacities of Big Data (including metadata intensify surveillance by expanding interconnected datasets and analytical tools. Existing dynamics of influence, risk-management, and control increase their speed and scope through new techniques, especially predictive analytics. Two, while Big Data appears to be about size, qualitative change in surveillance practices is also perceptible, accenting consequences. Important trends persist – the control motif, faith in technology, public-private synergies, and user-involvement – but the future-orientation increasingly severs surveillance from history and memory and the quest for pattern-discovery is used to justify unprecedented access to data. Three, the ethical turn becomes more urgent as a mode of critique. Modernity's predilection for certain definitions of privacy betrays the subjects of surveillance who, so far from conforming to the abstract, disembodied image of both computing and legal practices, are engaged and embodied users-in-relation whose activities both fuel and foreclose surveillance.

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...

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...

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.

Biomedical Big Data: New Models of Control Over Access, Use and Governance.

Science.gov (United States)

Vayena, Effy; Blasimme, Alessandro

2017-12-01

Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be openly stated in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it.

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...

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....

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.

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.

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.

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

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.

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,

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.

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

Big endothelin changes the cellular miRNA environment in TMOb osteoblasts and increases mineralization.

Science.gov (United States)

Johnson, Michael G; Kristianto, Jasmin; Yuan, Baozhi; Konicke, Kathryn; Blank, Robert

2014-08-01

Endothelin (ET1) promotes the growth of osteoblastic breast and prostate cancer metastases. Conversion of big ET1 to mature ET1, catalyzed primarily by endothelin converting enzyme 1 (ECE1), is necessary for ET1's biological activity. We previously identified the Ece1, locus as a positional candidate gene for a pleiotropic quantitative trait locus affecting femoral size, shape, mineralization, and biomechanical performance. We exposed TMOb osteoblasts continuously to 25 ng/ml big ET1. Cells were grown for 6 days in growth medium and then switched to mineralization medium for an additional 15 days with or without big ET1, by which time the TMOb cells form mineralized nodules. We quantified mineralization by alizarin red staining and analyzed levels of miRNAs known to affect osteogenesis. Micro RNA 126-3p was identified by search as a potential regulator of sclerostin (SOST) translation. TMOb cells exposed to big ET1 showed greater mineralization than control cells. Big ET1 repressed miRNAs targeting transcripts of osteogenic proteins. Big ET1 increased expression of miRNAs that target transcripts of proteins that inhibit osteogenesis. Big ET1 increased expression of 126-3p 121-fold versus control. To begin to assess the effect of big ET1 on SOST production we analyzed both SOST transcription and protein production with and without the presence of big ET1 demonstrating that transcription and translation were uncoupled. Our data show that big ET1 signaling promotes mineralization. Moreover, the results suggest that big ET1's osteogenic effects are potentially mediated through changes in miRNA expression, a previously unrecognized big ET1 osteogenic mechanism.

Editorial: Big data through the power lens: Marker for regulating innovation

OpenAIRE

Ulbricht, Lena; von Grafenstein, Maximilian

2016-01-01

Facing general conceptions of the power effects of big data, this thematic edition is interested in studies that scrutinise big data and power in concrete fields of application. It brings together scholars from different disciplines who analyse the fields agriculture, education, border control and consumer policy. As will be made explicit in the following, each of the articles tells us something about firstly, what big data is and how it relates to power. They secondly also shed light on how ...

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

From big data to deep insight in developmental science.

Science.gov (United States)

Gilmore, Rick O

2016-01-01

The use of the term 'big data' has grown substantially over the past several decades and is now widespread. In this review, I ask what makes data 'big' and what implications the size, density, or complexity of datasets have for the science of human development. A survey of existing datasets illustrates how existing large, complex, multilevel, and multimeasure data can reveal the complexities of developmental processes. At the same time, significant technical, policy, ethics, transparency, cultural, and conceptual issues associated with the use of big data must be addressed. Most big developmental science data are currently hard to find and cumbersome to access, the field lacks a culture of data sharing, and there is no consensus about who owns or should control research data. But, these barriers are dissolving. Developmental researchers are finding new ways to collect, manage, store, share, and enable others to reuse data. This promises a future in which big data can lead to deeper insights about some of the most profound questions in behavioral science. © 2016 The Authors. WIREs Cognitive Science published by Wiley Periodicals, Inc.

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...

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.

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.

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)...

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"

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.

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.)

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

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.

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.

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...

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.

[Applications of eco-environmental big data: Progress and prospect].

Science.gov (United States)

Zhao, Miao Miao; Zhao, Shi Cheng; Zhang, Li Yun; Zhao, Fen; Shao, Rui; Liu, Li Xiang; Zhao, Hai Feng; Xu, Ming

2017-05-18

With the advance of internet and wireless communication technology, the fields of ecology and environment have entered a new digital era with the amount of data growing explosively and big data technologies attracting more and more attention. The eco-environmental big data is based airborne and space-/land-based observations of ecological and environmental factors and its ultimate goal is to integrate multi-source and multi-scale data for information mining by taking advantages of cloud computation, artificial intelligence, and modeling technologies. In comparison with other fields, the eco-environmental big data has its own characteristics, such as diverse data formats and sources, data collected with various protocols and standards, and serving different clients and organizations with special requirements. Big data technology has been applied worldwide in ecological and environmental fields including global climate prediction, ecological network observation and modeling, and regional air pollution control. The development of eco-environmental big data in China is facing many problems, such as data sharing issues, outdated monitoring facilities and techno-logies, and insufficient data mining capacity. Despite all this, big data technology is critical to solving eco-environmental problems, improving prediction and warning accuracy on eco-environmental catastrophes, and boosting scientific research in the field in China. We expected that the eco-environmental big data would contribute significantly to policy making and environmental services and management, and thus the sustainable development and eco-civilization construction in China in the coming decades.

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

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.

Big Data for Precision Medicine

Directory of Open Access Journals (Sweden)

Daniel Richard Leff

2015-09-01

Full Text Available This article focuses on the potential impact of big data analysis to improve health, prevent and detect disease at an earlier stage, and personalize interventions. The role that big data analytics may have in interrogating the patient electronic health record toward improved clinical decision support is discussed. We examine developments in pharmacogenetics that have increased our appreciation of the reasons why patients respond differently to chemotherapy. We also assess the expansion of online health communications and the way in which this data may be capitalized on in order to detect public health threats and control or contain epidemics. Finally, we describe how a new generation of wearable and implantable body sensors may improve wellbeing, streamline management of chronic diseases, and improve the quality of surgical implants.

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....

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.

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.

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....

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

[Active surveillance of adverse drug reaction in the era of big data: challenge and opportunity for control selection].

Science.gov (United States)

Wang, S F; Zhan, S Y

2016-07-01

Electronic healthcare databases have become an important source for active surveillance of drug safety in the era of big data. The traditional epidemiology research designs are needed to confirm the association between drug use and adverse events based on these datasets, and the selection of the comparative control is essential to each design. This article aims to explain the principle and application of each type of control selection, introduce the methods and parameters for method comparison, and describe the latest achievements in the batch processing of control selection, which would provide important methodological reference for the use of electronic healthcare databases to conduct post-marketing drug safety surveillance in China.

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...

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

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...

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...

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

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

Big Data Analytics for Prostate Radiotherapy.

Science.gov (United States)

Coates, James; Souhami, Luis; El Naqa, Issam

2016-01-01

Radiation therapy is a first-line treatment option for localized prostate cancer and radiation-induced normal tissue damage are often the main limiting factor for modern radiotherapy regimens. Conversely, under-dosing of target volumes in an attempt to spare adjacent healthy tissues limits the likelihood of achieving local, long-term control. Thus, the ability to generate personalized data-driven risk profiles for radiotherapy outcomes would provide valuable prognostic information to help guide both clinicians and patients alike. Big data applied to radiation oncology promises to deliver better understanding of outcomes by harvesting and integrating heterogeneous data types, including patient-specific clinical parameters, treatment-related dose-volume metrics, and biological risk factors. When taken together, such variables make up the basis for a multi-dimensional space (the "RadoncSpace") in which the presented modeling techniques search in order to identify significant predictors. Herein, we review outcome modeling and big data-mining techniques for both tumor control and radiotherapy-induced normal tissue effects. We apply many of the presented modeling approaches onto a cohort of hypofractionated prostate cancer patients taking into account different data types and a large heterogeneous mix of physical and biological parameters. Cross-validation techniques are also reviewed for the refinement of the proposed framework architecture and checking individual model performance. We conclude by considering advanced modeling techniques that borrow concepts from big data analytics, such as machine learning and artificial intelligence, before discussing the potential future impact of systems radiobiology approaches.

Modeling and Analysis in Marine Big Data: Advances and Challenges

Directory of Open Access Journals (Sweden)

Dongmei Huang

2015-01-01

Full Text Available It is aware that big data has gathered tremendous attentions from academic research institutes, governments, and enterprises in all aspects of information sciences. With the development of diversity of marine data acquisition techniques, marine data grow exponentially in last decade, which forms marine big data. As an innovation, marine big data is a double-edged sword. On the one hand, there are many potential and highly useful values hidden in the huge volume of marine data, which is widely used in marine-related fields, such as tsunami and red-tide warning, prevention, and forecasting, disaster inversion, and visualization modeling after disasters. There is no doubt that the future competitions in marine sciences and technologies will surely converge into the marine data explorations. On the other hand, marine big data also brings about many new challenges in data management, such as the difficulties in data capture, storage, analysis, and applications, as well as data quality control and data security. To highlight theoretical methodologies and practical applications of marine big data, this paper illustrates a broad view about marine big data and its management, makes a survey on key methods and models, introduces an engineering instance that demonstrates the management architecture, and discusses the existing challenges.

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.

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....

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.

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.

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.'

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

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…

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.

Performance of automatic exposure control of digital imaging systems in three big hospitals

International Nuclear Information System (INIS)

Avramova-Cholakova, S.; Dyakov, I.

2012-01-01

The digital imaging systems are quite new in Bulgaria and there is still no clear evidence between all the servicing companies how to set the automatic exposure control (AEC) of the systems. The aim of this work is to study the AEC settings of the digital imaging systems in three big hospitals, serviced by different engineering companies. This study included seven systems, one with digital radiography (DR) and the others with computed radiography (CR) detectors. The AEC settings were tested in terms of mean pixel value (MPV), air kerma at phantom exit and detector dose indicator (DDI) dependence from AEC chamber selection, tube voltage and phantom thickness. Relatively small variations in MPV up to 8 % were observed for 4 of the CR systems, usually with small variations in DDI as well (except for one system, for which up to 40 % difference in DDI consistency between chambers was found). Air kerma at phantom exit for these systems had bigger variations up to 29 %. The other CR systems had big variations in MPV up to 57 % with DDI variations up to 30 % while air kerma changes were not small - from 8 to 38 %. The DR system showed smaller variation in air kerma at phantom exit up to 8 % and bigger variations in MPV and DDI up to 20 % and 15 % respectively. There is no systematic approach in the AEC settings used in the 3 hospitals. Further investigation and collaboration with the servicing companies is needed aiming to establish the optimized selection of AEC calibration parameters in each case. (authors)

A Control Approach for Performance of Big Data Systems

OpenAIRE

Berekmeri , Mihaly; Serrano , Damián; Bouchenak , Sara; Marchand , Nicolas; Robu , Bogdan

2014-01-01

International audience; We are at the dawn of a huge data explosion therefore companies have fast growing amounts of data to process. For this purpose Google developed MapReduce, a parallel programming paradigm which is slowly becoming the de facto tool for Big Data analytics. Although to some extent its use is already wide-spread in the industry, ensuring performance constraints for such a complex system poses great challenges and its management requires a high level of expertise. This paper...

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.

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.

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 ...

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.

Stability analysis for the Big Dee upgrade of the Doublet III tokamak

International Nuclear Information System (INIS)

Helton, F.J.; Luxon, J.L.

1987-01-01

Ideal magnetohydrodynamic stability analysis has been carried out for configurations expected in the Big Dee tokamak, an upgrade of the Doublet III tokamak into a non-circular cross-section device which began operation early in 1986. The results of this analysis support theoretical predictions as follows: Since the maximum value of beta stable to ballooning and Mercier modes, which we denote β c , increases with inverse aspect ratio, elongation and triangularity, the Big Dee is particularly suited to obtain high values of β c and there exist high β c Big Dee equilibria for large variations in all relevant plasma parameters. The beta limits for the Big Dee are consistent with established theory as summarized in present scaling laws. High beta Big Dee equilibria are continuously accessible when approached through changes in all relevant input parameters and are structurally stable with respect to variations of input plasma parameters. Big Dee beta limits have a smooth dependence on plasma parameters such as β p and elongation. These calculations indicate that in the actual running of the device the Big Dee high beta equilibria should be smoothly accessible. Theory predicts that the limiting plasma parameters, such as beta, total plasma current and plasma pressure, which can be obtained within the operating limits of the Big Dee are reactor relevant. Thus the Big Dee should be able to use its favourable ideal MHD scaling and controlled plasma shaping to attain reactor relevant parameters in a moderate sized device. (author)

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

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...

Survey of Cyber Crime in Big Data

Science.gov (United States)

Rajeswari, C.; Soni, Krishna; Tandon, Rajat

2017-11-01

Big data is like performing computation operations and database operations for large amounts of data, automatically from the data possessor’s business. Since a critical strategic offer of big data access to information from numerous and various areas, security and protection will assume an imperative part in big data research and innovation. The limits of standard IT security practices are notable, with the goal that they can utilize programming sending to utilize programming designers to incorporate pernicious programming in a genuine and developing risk in applications and working frameworks, which are troublesome. The impact gets speedier than big data. In this way, one central issue is that security and protection innovation are sufficient to share controlled affirmation for countless direct get to. For powerful utilization of extensive information, it should be approved to get to the information of that space or whatever other area from a space. For a long time, dependable framework improvement has arranged a rich arrangement of demonstrated ideas of demonstrated security to bargain to a great extent with the decided adversaries, however this procedure has been to a great extent underestimated as “needless excess” and sellers In this discourse, essential talks will be examined for substantial information to exploit this develop security and protection innovation, while the rest of the exploration difficulties will be investigated.

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...

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

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....

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 ...

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....

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.

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.

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...

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.

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.

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....

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 ...

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…

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

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?”. ...

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.

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.

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.

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.

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).

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'.

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...

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

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

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

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.

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.)

The measurement equivalence of Big Five factor markers for persons with different levels of education.

Science.gov (United States)

Rammstedt, Beatrice; Goldberg, Lewis R; Borg, Ingwer

2010-02-01

Previous findings suggest that the Big-Five factor structure is not guaranteed in samples with lower educational levels. The present study investigates the Big-Five factor structure in two large samples representative of the German adult population. In both samples, the Big-Five factor structure emerged only in a blurry way at lower educational levels, whereas for highly educated persons it emerged with textbook-like clarity. Because well-educated persons are most comparable to the usual subjects of psychological research, it might be asked if the Big Five are limited to such persons. Our data contradict this conclusion. There are strong individual differences in acquiescence response tendencies among less highly educated persons. After controlling for this bias the Big-Five model holds at all educational levels.

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...

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.

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

Anger and hostility from the perspective of the Big Five personality model.

Science.gov (United States)

Sanz, Jesús; García-Vera, María Paz; Magán, Inés

2010-06-01

This study was aimed at examining the relationships of the personality dimensions of the five-factor model or Big Five with trait anger and with two specific traits of hostility (mistrust and confrontational attitude), and identifying the similarities and differences between trait anger and hostility in the framework of the Big Five. In a sample of 353 male and female adults, the Big Five explained a significant percentage of individual differences in trait anger and hostility after controlling the effects due to the relationship between both constructs and content overlapping across scales. In addition, trait anger was primarily associated with neuroticism, whereas mistrust and confrontational attitude were principally related to low agreeableness. These findings are discussed in the context of the anger-hostility-aggression syndrome and the capability of the Big Five for organizing and clarifying related personality constructs.

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.

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...

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.

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.

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…

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...

Risks due to fires at Big Rock Point

International Nuclear Information System (INIS)

Brinsfield, W.A.; Blanchard, D.P.

1983-01-01

The unique and older designs of the Big Rock Point nuclear plant is such that fires contribute significantly to the probability of core damage predicted in the probabilistic risk assessment performed for this plant. The methodology employed to determine this contribution reflects the unique, as constructed, plant design, while systematically and logically addressing the true effect of fires on the operation of the plant and the safety of the public. As a result of the methodology utilized in the PRA, recommendations are made which minimize the risk of core damage due to fires. Included in these recommendations is a proposal for equipment and controls to be included on the Big Rock Point alternate shutdown panel

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.

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...

Big Rock Point severe accident management strategies

International Nuclear Information System (INIS)

Brogan, B.A.; Gabor, J.R.

1996-01-01

December 1994, the Nuclear Energy Institute (NEI) issued guidance relative to the formal industry position on Severe Accident Management (SAM) approved by the NEI Strategic Issues Advisory Committee on November 4, 1994. This paper summarizes how Big Rock Point (BRP) has and continues to address SAM strategies. The historical accounting portion of this presentation includes a description of how the following projects identified and defined the current Big Rock Point SAM strategies: the 1981 Level 3 Probabilistic Risk Assessment performance; the development of the Plant Specific Technical Guidelines from which the symptom oriented Emergency Operating Procedures (EOPs) were developed; the Control Room Design Review; and, the recent completion of the Individual Plant Evaluation (IPE). In addition to the historical presentation deliberation, this paper the present activities that continue to stress SAM strategies

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...

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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.

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)

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.

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...

A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

Science.gov (United States)

Faizrahnemoon, Mahsa; Schlote, Arieh; Maggi, Lorenzo; Crisostomi, Emanuele; Shorten, Robert

2015-11-01

This paper describes a Markov-chain-based approach to modelling multi-modal transportation networks. An advantage of the model is the ability to accommodate complex dynamics and handle huge amounts of data. The transition matrix of the Markov chain is built and the model is validated using the data extracted from a traffic simulator. A realistic test-case using multi-modal data from the city of London is given to further support the ability of the proposed methodology to handle big quantities of data. Then, we use the Markov chain as a control tool to improve the overall efficiency of a transportation network, and some practical examples are described to illustrate the potentials of the approach.

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.

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...

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

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.

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

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.

Drosophila Big bang regulates the apical cytocortex and wing growth through junctional tension.

Science.gov (United States)

Tsoumpekos, Giorgos; Nemetschke, Linda; Knust, Elisabeth

2018-03-05

Growth of epithelial tissues is regulated by a plethora of components, including signaling and scaffolding proteins, but also by junctional tension, mediated by the actomyosin cytoskeleton. However, how these players are spatially organized and functionally coordinated is not well understood. Here, we identify the Drosophila melanogaster scaffolding protein Big bang as a novel regulator of growth in epithelial cells of the wing disc by ensuring proper junctional tension. Loss of big bang results in the reduction of the regulatory light chain of nonmuscle myosin, Spaghetti squash. This is associated with an increased apical cell surface, decreased junctional tension, and smaller wings. Strikingly, these phenotypic traits of big bang mutant discs can be rescued by expressing constitutively active Spaghetti squash. Big bang colocalizes with Spaghetti squash in the apical cytocortex and is found in the same protein complex. These results suggest that in epithelial cells of developing wings, the scaffolding protein Big bang controls apical cytocortex organization, which is important for regulating cell shape and tissue growth. © 2018 Tsoumpekos et al.

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.

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.

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.

[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.

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...

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.

[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.

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...

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.

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, ...

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

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.

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.

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…

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

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

Intra- and interspecific responses to Rafinesque’s big-eared bat (Corynorhinus rafinesquii) social calls.

Energy Technology Data Exchange (ETDEWEB)

Loeb, Susan, C.; Britzke, Eric, R.

2010-07-01

Bats respond to the calls of conspecifics as well as to calls of other species; however, few studies have attempted to quantify these responses or understand the functions of these calls. We tested the response of Rafinesque’s big-eared bats (Corynorhinus rafinesquii) to social calls as a possible method to increase capture success and to understand the function of social calls. We also tested if calls of bats within the range of the previously designated subspecies differed, if the responses of Rafinesque’s big-eared bats varied with geographic origin of the calls, and if other species responded to the calls of C. rafinesquii. We recorded calls of Rafinesque’s big-eared bats at two colony roost sites in South Carolina, USA. Calls were recorded while bats were in the roosts and as they exited. Playback sequences for each site were created by copying typical pulses into the playback file. Two mist nets were placed approximately 50–500 m from known roost sites; the net with the playback equipment served as the Experimental net and the one without the equipment served as the Control net. Call structures differed significantly between the Mountain and Coastal Plains populations with calls from the Mountains being of higher frequency and longer duration. Ten of 11 Rafinesque’s big-eared bats were caught in the Control nets and, 13 of 19 bats of other species were captured at Experimental nets even though overall bat activity did not differ significantly between Control and Experimental nets. Our results suggest that Rafinesque’s big-eared bats are not attracted to conspecifics’ calls and that these calls may act as an intraspecific spacing mechanism during foraging.

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...

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.

About the Big Graphs Arising when Forming the Diagnostic Models in a Reconfigurable Computing Field of Functional Monitoring and Diagnostics System of the Spacecraft Onboard Control Complex

Directory of Open Access Journals (Sweden)

L. V. Savkin

2015-01-01

Full Text Available One of the problems in implementation of the multipurpose complete systems based on the reconfigurable computing fields (RCF is the problem of optimum redistribution of logicalarithmetic resources in growing scope of functional tasks. Irrespective of complexity, all of them are transformed into an orgraph, which functional and topological structure is appropriately imposed on the RCF based, as a rule, on the field programmable gate array (FPGA.Due to limitation of the hardware configurations and functions realized by means of the switched logical blocks (SLB, the abovementioned problem becomes even more critical when there is a need, within the strictly allocated RCF fragment, to realize even more complex challenge in comparison with the problem which was solved during the previous computing step. In such cases it is possible to speak about graphs of big dimensions with respect to allocated RCF fragment.The article considers this problem through development of diagnostic algorithms to implement diagnostics and control of an onboard control complex of the spacecraft using RCF. It gives examples of big graphs arising with respect to allocated RCF fragment when forming the hardware levels of a diagnostic model, which, in this case, is any hardware-based algorithm of diagnostics in RCF.The article reviews examples of arising big graphs when forming the complicated diagnostic models due to drastic difference in formation of hardware levels on closely located RCF fragments. It also pays attention to big graphs emerging when the multichannel diagnostic models are formed.Three main ways to solve the problem of big graphs with respect to allocated RCF fragment are given. These are: splitting the graph into fragments, use of pop-up windows with relocating and memorizing intermediate values of functions of high hardware levels of diagnostic models, and deep adaptive update of diagnostic model.It is shown that the last of three ways is the most efficient

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

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

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.

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...

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.

Weak interspecific interactions in a sagebrush steppe? Conflicting evidence from observations and experiments.

Science.gov (United States)

Adler, Peter B; Kleinhesselink, Andrew; Giles, Hooker; Taylor, J Bret; Teller, Brittany; Ellner, Stephen P

2018-04-28

Stable coexistence requires intraspecific limitations to be stronger than interspecific limitations. The greater the difference between intra- and interspecific limitations, the more stable the coexistence, and the weaker the competitive release any species should experience following removal of competitors. We conducted a removal experiment to test whether a previously estimated model, showing surprisingly weak interspecific competition for four dominant species in a sagebrush steppe, accurately predicts competitive release. Our treatments were 1) removal of all perennial grasses and 2) removal of the dominant shrub, Artemisia tripartita. We regressed survival, growth and recruitment on the locations, sizes, and species identities of neighboring plants, along with an indicator variable for removal treatment. If our "baseline" regression model, which accounts for local plant-plant interactions, accurately explains the observed responses to removals, then the removal coefficient should be non-significant. For survival, the removal coefficients were never significantly different from zero, and only A. tripartita showed a (negative) response to removals at the recruitment stage. For growth, the removal treatment effect was significant and positive for two species, Poa secunda and Pseudoroegneria spicata, indicating that the baseline model underestimated interspecific competition. For all three grass species, population models based on the vital rate regressions that included removal effects projected 1.4 to 3-fold increases in equilibrium population size relative to the baseline model (no removal effects). However, we found no evidence of higher response to removal in quadrats with higher pretreatment cover of A. tripartita, or by plants experiencing higher pre-treatment crowding by A. tripartita, raising questions about the mechanisms driving the positive response to removal. While our results show the value of combining observations with a simple removal experiment

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

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.

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.

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.

"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.

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.

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.

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.

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 ...

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

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.

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.

The New Possibilities from "Big Data" to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis.

Science.gov (United States)

Kruse, Christian

2018-06-01

To review current practices and technologies within the scope of "Big Data" that can further our understanding of diabetes mellitus and osteoporosis from large volumes of data. "Big Data" techniques involving supervised machine learning, unsupervised machine learning, and deep learning image analysis are presented with examples of current literature. Supervised machine learning can allow us to better predict diabetes-induced osteoporosis and understand relative predictor importance of diabetes-affected bone tissue. Unsupervised machine learning can allow us to understand patterns in data between diabetic pathophysiology and altered bone metabolism. Image analysis using deep learning can allow us to be less dependent on surrogate predictors and use large volumes of images to classify diabetes-induced osteoporosis and predict future outcomes directly from images. "Big Data" techniques herald new possibilities to understand diabetes-induced osteoporosis and ascertain our current ability to classify, understand, and predict this condition.

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.

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.

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

Privacy and Big Data

CERN Document Server

Craig, Terence

2011-01-01

Much of what constitutes Big Data is information about us. Through our online activities, we leave an easy-to-follow trail of digital footprints that reveal who we are, what we buy, where we go, and much more. This eye-opening book explores the raging privacy debate over the use of personal data, with one undeniable conclusion: once data's been collected, we have absolutely no control over who uses it or how it is used. Personal data is the hottest commodity on the market today-truly more valuable than gold. We are the asset that every company, industry, non-profit, and government wants. Pri

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.

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.

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

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...

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.

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.

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.

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...

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.

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...

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.

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.

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.

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.

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 ...

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)

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...

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

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.

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...

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.

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.)

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.

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...

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 ...

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.

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.

The faces of Big Science.

Science.gov (United States)

Schatz, Gottfried

2014-06-01

Fifty years ago, academic science was a calling with few regulations or financial rewards. Today, it is a huge enterprise confronted by a plethora of bureaucratic and political controls. This change was not triggered by specific events or decisions but reflects the explosive 'knee' in the exponential growth that science has sustained during the past three-and-a-half centuries. Coming to terms with the demands and benefits of 'Big Science' is a major challenge for today's scientific generation. Since its foundation 50 years ago, the European Molecular Biology Organization (EMBO) has been of invaluable help in meeting this challenge.

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...

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.)

Modeling canopy-level productivity: is the "big-leaf" simplification acceptable?

Science.gov (United States)

Sprintsin, M.; Chen, J. M.

2009-05-01

The "big-leaf" approach to calculating the carbon balance of plant canopies assumes that canopy carbon fluxes have the same relative responses to the environment as any single unshaded leaf in the upper canopy. Widely used light use efficiency models are essentially simplified versions of the big-leaf model. Despite its wide acceptance, subsequent developments in the modeling of leaf photosynthesis and measurements of canopy physiology have brought into question the assumptions behind this approach showing that big leaf approximation is inadequate for simulating canopy photosynthesis because of the additional leaf internal control on carbon assimilation and because of the non-linear response of photosynthesis on leaf nitrogen and absorbed light, and changes in leaf microenvironment with canopy depth. To avoid this problem a sunlit/shaded leaf separation approach, within which the vegetation is treated as two big leaves under different illumination conditions, is gradually replacing the "big-leaf" strategy, for applications at local and regional scales. Such separation is now widely accepted as a more accurate and physiologically based approach for modeling canopy photosynthesis. Here we compare both strategies for Gross Primary Production (GPP) modeling using the Boreal Ecosystem Productivity Simulator (BEPS) at local (tower footprint) scale for different land cover types spread over North America: two broadleaf forests (Harvard, Massachusetts and Missouri Ozark, Missouri); two coniferous forests (Howland, Maine and Old Black Spruce, Saskatchewan); Lost Creek shrubland site (Wisconsin) and Mer Bleue petland (Ontario). BEPS calculates carbon fixation by scaling Farquhar's leaf biochemical model up to canopy level with stomatal conductance estimated by a modified version of the Ball-Woodrow-Berry model. The "big-leaf" approach was parameterized using derived leaf level parameters scaled up to canopy level by means of Leaf Area Index. The influence of sunlit

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.

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.

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.

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...

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.

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.

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

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,

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.

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...

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.

Functional magnetic resonance imaging of divergent and convergent thinking in Big-C creativity.

Science.gov (United States)

Japardi, Kevin; Bookheimer, Susan; Knudsen, Kendra; Ghahremani, Dara G; Bilder, Robert M

2018-02-15

The cognitive and physiological processes underlying creativity remain unclear, and very few studies to date have attempted to identify the behavioral and brain characteristics that distinguish exceptional ("Big-C") from everyday ("little-c") creativity. The Big-C Project examined functional brain responses during tasks demanding divergent and convergent thinking in 35 Big-C Visual Artists (VIS), 41 Big-C Scientists (SCI), and 31 individuals in a "smart comparison group" (SCG) matched to the Big-C groups on parental educational attainment and estimated IQ. Functional MRI (fMRI) scans included two activation paradigms widely used in prior creativity research, the Alternate Uses Task (AUT) and Remote Associates Task (RAT), to assess brain function during divergent and convergent thinking, respectively. Task performance did not differ between groups. Functional MRI activation in Big-C and SCG groups differed during the divergent thinking task. No differences in activation were seen during the convergent thinking task. Big-C groups had less activation than SCG in frontal pole, right frontal operculum, left middle frontal gyrus, and bilaterally in occipital cortex. SCI displayed lower frontal and parietal activation relative to the SCG when generating alternate uses in the AUT, while VIS displayed lower frontal activation than SCI and SCG when generating typical qualities (the control condition in the AUT). VIS showed more activation in right inferior frontal gyrus and left supramarginal gyrus relative to SCI. All groups displayed considerable overlapping activation during the RAT. The results confirm substantial overlap in functional activation across groups, but suggest that exceptionally creative individuals may depend less on task-positive networks during tasks that demand divergent thinking. Published by Elsevier Ltd.

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

Capillary electrophoresis of Big-Dye terminator sequencing reactions for human mtDNA Control Region haplotyping in the identification of human remains.

Science.gov (United States)

Montesino, Marta; Prieto, Lourdes

2012-01-01

Cycle sequencing reaction with Big-Dye terminators provides the methodology to analyze mtDNA Control Region amplicons by means of capillary electrophoresis. DNA sequencing with ddNTPs or terminators was developed by (1). The progressive automation of the method by combining the use of fluorescent-dye terminators with cycle sequencing has made it possible to increase the sensibility and efficiency of the method and hence has allowed its introduction into the forensic field. PCR-generated mitochondrial DNA products are the templates for sequencing reactions. Different set of primers can be used to generate amplicons with different sizes according to the quality and quantity of the DNA extract providing sequence data for different ranges inside the Control Region.

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.

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.

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

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

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...

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.

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.

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

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...

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.

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.

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.

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.

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.

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

Processes meet big data : connecting data science with process science

NARCIS (Netherlands)

van der Aalst, W.; Damiani, E.

2015-01-01

As more and more companies are embracing Big data, it has become apparent that the ultimate challenge is to relate massive amounts of event data to processes that are highly dynamic. To unleash the value of event data, events need to be tightly connected to the control and management of operational

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,

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.

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

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.

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.

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 ...

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...

Field Testing of Activated Carbon Injection Options for Mercury Control at TXU's Big Brown Station

Energy Technology Data Exchange (ETDEWEB)

John Pavlish; Jeffrey Thompson; Christopher Martin; Mark Musich; Lucinda Hamre

2009-01-07

The primary objective of the project was to evaluate the long-term feasibility of using activated carbon injection (ACI) options to effectively reduce mercury emissions from Texas electric generation plants in which a blend of lignite and subbituminous coal is fired. Field testing of ACI options was performed on one-quarter of Unit 2 at TXU's Big Brown Steam Electric Station. Unit 2 has a design output of 600 MW and burns a blend of 70% Texas Gulf Coast lignite and 30% subbituminous Powder River Basin coal. Big Brown employs a COHPAC configuration, i.e., high air-to-cloth baghouses following cold-side electrostatic precipitators (ESPs), for particulate control. When sorbent injection is added between the ESP and the baghouse, the combined technology is referred to as TOXECON{trademark} and is patented by the Electric Power Research Institute in the United States. Key benefits of the TOXECON configuration include better mass transfer characteristics of a fabric filter compared to an ESP for mercury capture and contamination of only a small percentage of the fly ash with AC. The field testing consisted of a baseline sampling period, a parametric screening of three sorbent injection options, and a month long test with a single mercury control technology. During the baseline sampling, native mercury removal was observed to be less than 10%. Parametric testing was conducted for three sorbent injection options: injection of standard AC alone; injection of an EERC sorbent enhancement additive, SEA4, with ACI; and injection of an EERC enhanced AC. Injection rates were determined for all of the options to achieve the minimum target of 55% mercury removal as well as for higher removals approaching 90%. Some of the higher injection rates were not sustainable because of increased differential pressure across the test baghouse module. After completion of the parametric testing, a month long test was conducted using the enhanced AC at a nominal rate of 1.5 lb/Macf. During

[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.

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).

'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.

Passive and Active Observation: Experimental Design Issues in Big Data

OpenAIRE

Pesce, Elena; Riccomagno, Eva; Wynn, Henry P.

2017-01-01

Data can be collected in scientific studies via a controlled experiment or passive observation. Big data is often collected in a passive way, e.g. from social media. Understanding the difference between active and passive observation is critical to the analysis. For example in studies of causation great efforts are made to guard against hidden confounders or feedback which can destroy the identification of causation by corrupting or omitting counterfactuals (controls). Various solutions of th...

The BIG protein distinguishes the process of CO2 -induced stomatal closure from the inhibition of stomatal opening by CO2.

Science.gov (United States)

He, Jingjing; Zhang, Ruo-Xi; Peng, Kai; Tagliavia, Cecilia; Li, Siwen; Xue, Shaowu; Liu, Amy; Hu, Honghong; Zhang, Jingbo; Hubbard, Katharine E; Held, Katrin; McAinsh, Martin R; Gray, Julie E; Kudla, Jörg; Schroeder, Julian I; Liang, Yun-Kuan; Hetherington, Alistair M

2018-04-01

We conducted an infrared thermal imaging-based genetic screen to identify Arabidopsis mutants displaying aberrant stomatal behavior in response to elevated concentrations of CO 2 . This approach resulted in the isolation of a novel allele of the Arabidopsis BIG locus (At3g02260) that we have called CO 2 insensitive 1 (cis1). BIG mutants are compromised in elevated CO 2 -induced stomatal closure and bicarbonate activation of S-type anion channel currents. In contrast with the wild-type, they fail to exhibit reductions in stomatal density and index when grown in elevated CO 2 . However, like the wild-type, BIG mutants display inhibition of stomatal opening when exposed to elevated CO 2 . BIG mutants also display wild-type stomatal aperture responses to the closure-inducing stimulus abscisic acid (ABA). Our results indicate that BIG is a signaling component involved in the elevated CO 2 -mediated control of stomatal development. In the control of stomatal aperture by CO 2 , BIG is only required in elevated CO 2 -induced closure and not in the inhibition of stomatal opening by this environmental signal. These data show that, at the molecular level, the CO 2 -mediated inhibition of opening and promotion of stomatal closure signaling pathways are separable and BIG represents a distinguishing element in these two CO 2 -mediated responses. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.

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...

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

[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.

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.

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.

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.

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…

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.

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

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.

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.

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...

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.

Big data analytics in healthcare: promise and potential.

Science.gov (United States)

Raghupathi, Wullianallur; Raghupathi, Viju

2014-01-01

To describe the promise and potential of big data analytics in healthcare. The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.

Data warehousing in the age of big data

CERN Document Server

Krishnan, Krish

2013-01-01

Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture

The Death of the Big Men

DEFF Research Database (Denmark)

Martin, Keir

2010-01-01

Recently Tolai people og Papua New Guinea have adopted the term 'Big Shot' to decribe an emerging post-colonial political elite. The mergence of the term is a negative moral evaluation of new social possibilities that have arisen as a consequence of the Big Shots' privileged position within a glo...

Big data and software defined networks

CERN Document Server

Taheri, Javid

2018-01-01

Big Data Analytics and Software Defined Networking (SDN) are helping to drive the management of data usage of the extraordinary increase of computer processing power provided by Cloud Data Centres (CDCs). This new book investigates areas where Big-Data and SDN can help each other in delivering more efficient services.

Big Data-Survey

Directory of Open Access Journals (Sweden)

P.S.G. Aruna Sri

2016-03-01

Full Text Available Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.

Burnable absorber-integrated Guide Thimble (BigT) - 1. Design concepts and neutronic characterization on the fuel assembly benchmarks

International Nuclear Information System (INIS)

Yahya, Mohd-Syukri; Yu, Hwanyeal; Kim, Yonghee

2016-01-01

This paper presents the conceptual designs of a new burnable absorber (BA) for the pressurized water reactor (PWR), which is named 'Burnable absorber-integrated Guide Thimble' (BigT). The BigT integrates BA materials into standard guide thimble in a PWR fuel assembly. Neutronic sensitivities and practical design considerations of the BigT concept are points of highlight in the first half of the paper. Specifically, the BigT concepts are characterized in view of its BA material and spatial self-shielding variations. In addition, the BigT replaceability requirement, bottom-end design specifications and thermal-hydraulic considerations are also deliberated. Meanwhile, much of the second half of the paper is devoted to demonstrate practical viability of the BigT absorbers via comparative evaluations against the conventional BA technologies in representative 17x17 and 16x16 fuel assembly lattices. For the 17x17 lattice evaluations, all three BigT variants are benchmarked against Westinghouse's existing BA technologies, while in the 16x16 assembly analyses, the BigT designs are compared against traditional integral gadolinia-urania rod design. All analyses clearly show that the BigT absorbers perform as well as the commercial BA technologies in terms of reactivity and power peaking management. In addition, it has been shown that sufficiently high control rod worth can be obtained with the BigT absorbers in place. All neutronic simulations were completed using the Monte Carlo Serpent code with ENDF/B-VII.0 library. (author)

Big Data Analytics, Infectious Diseases and Associated Ethical Impacts

OpenAIRE

Garattini, C.; Raffle, J.; Aisyah, D. N.; Sartain, F.; Kozlakidis, Z.

2017-01-01

The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes ...

Evaluation of Data Management Systems for Geospatial Big Data

OpenAIRE

Amirian, Pouria; Basiri, Anahid; Winstanley, Adam C.

2014-01-01

Big Data encompasses collection, management, processing and analysis of the huge amount of data that varies in types and changes with high frequency. Often data component of Big Data has a positional component as an important part of it in various forms, such as postal address, Internet Protocol (IP) address and geographical location. If the positional components in Big Data extensively used in storage, retrieval, analysis, processing, visualization and knowledge discovery (geospatial Big Dat...

A New Look at Big History

Science.gov (United States)

Hawkey, Kate

2014-01-01

The article sets out a "big history" which resonates with the priorities of our own time. A globalizing world calls for new spacial scales to underpin what the history curriculum addresses, "big history" calls for new temporal scales, while concern over climate change calls for a new look at subject boundaries. The article…

West Virginia's big trees: setting the record straight

Science.gov (United States)

Melissa Thomas-Van Gundy; Robert. Whetsell

2016-01-01

People love big trees, people love to find big trees, and people love to find big trees in the place they call home. Having been suspicious for years, my coauthor and historian Rob Whetsell, approached me with a species identification challenge. There are several photographs of giant trees used by many people to illustrate the past forests of West Virginia,...

Sosiaalinen asiakassuhdejohtaminen ja big data

OpenAIRE

Toivonen, Topi-Antti

2015-01-01

Tässä tutkielmassa käsitellään sosiaalista asiakassuhdejohtamista sekä hyötyjä, joita siihen voidaan saada big datan avulla. Sosiaalinen asiakassuhdejohtaminen on terminä uusi ja monille tuntematon. Tutkimusta motivoi aiheen vähäinen tutkimus, suomenkielisen tutkimuksen puuttuminen kokonaan sekä sosiaalisen asiakassuhdejohtamisen mahdollinen olennainen rooli yritysten toiminnassa tulevaisuudessa. Big dataa käsittelevissä tutkimuksissa keskitytään monesti sen tekniseen puoleen, eikä sovellutuk...

D-branes in a big bang/big crunch universe: Misner space

International Nuclear Information System (INIS)

Hikida, Yasuaki; Nayak, Rashmi R.; Panigrahi, Kamal L.

2005-01-01

We study D-branes in a two-dimensional lorentzian orbifold R 1,1 /Γ with a discrete boost Γ. This space is known as Misner or Milne space, and includes big crunch/big bang singularity. In this space, there are D0-branes in spiral orbits and D1-branes with or without flux on them. In particular, we observe imaginary parts of partition functions, and interpret them as the rates of open string pair creation for D0-branes and emission of winding closed strings for D1-branes. These phenomena occur due to the time-dependence of the background. Open string 2→2 scattering amplitude on a D1-brane is also computed and found to be less singular than closed string case

D-branes in a big bang/big crunch universe: Misner space

Energy Technology Data Exchange (ETDEWEB)

Hikida, Yasuaki [Theory Group, High Energy Accelerator Research Organization (KEK), Tukuba, Ibaraki 305-0801 (Japan); Nayak, Rashmi R. [Dipartimento di Fisica and INFN, Sezione di Roma 2, ' Tor Vergata' , Rome 00133 (Italy); Panigrahi, Kamal L. [Dipartimento di Fisica and INFN, Sezione di Roma 2, ' Tor Vergata' , Rome 00133 (Italy)

2005-09-01

We study D-branes in a two-dimensional lorentzian orbifold R{sup 1,1}/{gamma} with a discrete boost {gamma}. This space is known as Misner or Milne space, and includes big crunch/big bang singularity. In this space, there are D0-branes in spiral orbits and D1-branes with or without flux on them. In particular, we observe imaginary parts of partition functions, and interpret them as the rates of open string pair creation for D0-branes and emission of winding closed strings for D1-branes. These phenomena occur due to the time-dependence of the background. Open string 2{yields}2 scattering amplitude on a D1-brane is also computed and found to be less singular than closed string case.

Astroinformatics: the big data of the universe

OpenAIRE

Barmby, Pauline

2016-01-01

In astrophysics we like to think that our field was the originator of big data, back when it had to be carried around in big sky charts and books full of tables. These days, it's easier to move astrophysics data around, but we still have a lot of it, and upcoming telescope  facilities will generate even more. I discuss how astrophysicists approach big data in general, and give examples from some Western Physics & Astronomy research projects.  I also give an overview of ho...

Recent big flare

International Nuclear Information System (INIS)

Moriyama, Fumio; Miyazawa, Masahide; Yamaguchi, Yoshisuke

1978-01-01

The features of three big solar flares observed at Tokyo Observatory are described in this paper. The active region, McMath 14943, caused a big flare on September 16, 1977. The flare appeared on both sides of a long dark line which runs along the boundary of the magnetic field. Two-ribbon structure was seen. The electron density of the flare observed at Norikura Corona Observatory was 3 x 10 12 /cc. Several arc lines which connect both bright regions of different magnetic polarity were seen in H-α monochrome image. The active region, McMath 15056, caused a big flare on December 10, 1977. At the beginning, several bright spots were observed in the region between two main solar spots. Then, the area and the brightness increased, and the bright spots became two ribbon-shaped bands. A solar flare was observed on April 8, 1978. At first, several bright spots were seen around the solar spot in the active region, McMath 15221. Then, these bright spots developed to a large bright region. On both sides of a dark line along the magnetic neutral line, bright regions were generated. These developed to a two-ribbon flare. The time required for growth was more than one hour. A bright arc which connects two ribbons was seen, and this arc may be a loop prominence system. (Kato, T.)

Big Bang Day : The Great Big Particle Adventure - 3. Origins

CERN Multimedia

2008-01-01

In this series, comedian and physicist Ben Miller asks the CERN scientists what they hope to find. If the LHC is successful, it will explain the nature of the Universe around us in terms of a few simple ingredients and a few simple rules. But the Universe now was forged in a Big Bang where conditions were very different, and the rules were very different, and those early moments were crucial to determining how things turned out later. At the LHC they can recreate conditions as they were billionths of a second after the Big Bang, before atoms and nuclei existed. They can find out why matter and antimatter didn't mutually annihilate each other to leave behind a Universe of pure, brilliant light. And they can look into the very structure of space and time - the fabric of the Universe

Inflated granularity: Spatial “Big Data” and geodemographics

Directory of Open Access Journals (Sweden)

Craig M Dalton

2015-08-01

Full Text Available Data analytics, particularly the current rhetoric around “Big Data”, tend to be presented as new and innovative, emerging ahistorically to revolutionize modern life. In this article, we situate one branch of Big Data analytics, spatial Big Data, through a historical predecessor, geodemographic analysis, to help develop a critical approach to current data analytics. Spatial Big Data promises an epistemic break in marketing, a leap from targeting geodemographic areas to targeting individuals. Yet it inherits characteristics and problems from geodemographics, including a justification through the market, and a process of commodification through the black-boxing of technology. As researchers develop sustained critiques of data analytics and its effects on everyday life, we must so with a grounding in the cultural and historical contexts from which data technologies emerged. This article and others (Barnes and Wilson, 2014 develop a historically situated, critical approach to spatial Big Data. This history illustrates connections to the critical issues of surveillance, redlining, and the production of consumer subjects and geographies. The shared histories and structural logics of spatial Big Data and geodemographics create the space for a continued critique of data analyses’ role in society.

Big data analysis for smart farming

NARCIS (Netherlands)

Kempenaar, C.; Lokhorst, C.; Bleumer, E.J.B.; Veerkamp, R.F.; Been, Th.; Evert, van F.K.; Boogaardt, M.J.; Ge, L.; Wolfert, J.; Verdouw, C.N.; Bekkum, van Michael; Feldbrugge, L.; Verhoosel, Jack P.C.; Waaij, B.D.; Persie, van M.; Noorbergen, H.

2016-01-01

In this report we describe results of a one-year TO2 institutes project on the development of big data technologies within the milk production chain. The goal of this project is to ‘create’ an integration platform for big data analysis for smart farming and to develop a show case. This includes both

A survey on Big Data Stream Mining

African Journals Online (AJOL)

pc

2018-03-05

Mar 5, 2018 ... Big Data can be static on one machine or distributed ... decision making, and process automation. Big data .... Concept Drifting: concept drifting mean the classifier .... transactions generated by a prefix tree structure. EstDec ...

Emerging technology and architecture for big-data analytics

CERN Document Server

Chang, Chip; Yu, Hao

2017-01-01

This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design and some interest in big-data analytics. Coverage includes emerging technology and devices for data-analytics, circuit design for data-analytics, and architecture and algorithms to support data-analytics. Readers will benefit from the realistic context used by the authors, which demonstrates what works, what doesn’t work, and what are the fundamental problems, solutions, upcoming challenges and opportunities. Provides a single-source reference to hardware architectures for big-data analytics; Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems; Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics.

Toward a manifesto for the 'public understanding of big data'.

Science.gov (United States)

Michael, Mike; Lupton, Deborah

2016-01-01

In this article, we sketch a 'manifesto' for the 'public understanding of big data'. On the one hand, this entails such public understanding of science and public engagement with science and technology-tinged questions as follows: How, when and where are people exposed to, or do they engage with, big data? Who are regarded as big data's trustworthy sources, or credible commentators and critics? What are the mechanisms by which big data systems are opened to public scrutiny? On the other hand, big data generate many challenges for public understanding of science and public engagement with science and technology: How do we address publics that are simultaneously the informant, the informed and the information of big data? What counts as understanding of, or engagement with, big data, when big data themselves are multiplying, fluid and recursive? As part of our manifesto, we propose a range of empirical, conceptual and methodological exhortations. We also provide Appendix 1 that outlines three novel methods for addressing some of the issues raised in the article. © The Author(s) 2015.

Big Data in Caenorhabditis elegans: quo vadis?

Science.gov (United States)

Hutter, Harald; Moerman, Donald

2015-11-05

A clear definition of what constitutes "Big Data" is difficult to identify, but we find it most useful to define Big Data as a data collection that is complete. By this criterion, researchers on Caenorhabditis elegans have a long history of collecting Big Data, since the organism was selected with the idea of obtaining a complete biological description and understanding of development. The complete wiring diagram of the nervous system, the complete cell lineage, and the complete genome sequence provide a framework to phrase and test hypotheses. Given this history, it might be surprising that the number of "complete" data sets for this organism is actually rather small--not because of lack of effort, but because most types of biological experiments are not currently amenable to complete large-scale data collection. Many are also not inherently limited, so that it becomes difficult to even define completeness. At present, we only have partial data on mutated genes and their phenotypes, gene expression, and protein-protein interaction--important data for many biological questions. Big Data can point toward unexpected correlations, and these unexpected correlations can lead to novel investigations; however, Big Data cannot establish causation. As a result, there is much excitement about Big Data, but there is also a discussion on just what Big Data contributes to solving a biological problem. Because of its relative simplicity, C. elegans is an ideal test bed to explore this issue and at the same time determine what is necessary to build a multicellular organism from a single cell. © 2015 Hutter and Moerman. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

76 FR 7810 - Big Horn County Resource Advisory Committee

Science.gov (United States)

2011-02-11

..., Wyoming 82801. Comments may also be sent via e-mail to [email protected] , with the words Big... DEPARTMENT OF AGRICULTURE Forest Service Big Horn County Resource Advisory Committee AGENCY: Forest Service, USDA. ACTION: Notice of meeting. SUMMARY: The Big Horn County Resource Advisory Committee...

Hot big bang or slow freeze?

Energy Technology Data Exchange (ETDEWEB)

Wetterich, C.

2014-09-07

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.

Hot big bang or slow freeze?

International Nuclear Information System (INIS)

Wetterich, C.

2014-01-01

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

Hot big bang or slow freeze?

Directory of Open Access Journals (Sweden)

C. Wetterich

2014-09-01

Full Text Available 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.

Big Data: Survey, Technologies, Opportunities, and Challenges

Directory of Open Access Journals (Sweden)

Nawsher Khan

2014-01-01

Full Text Available 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.

Pre-big bang cosmology and quantum fluctuations

International Nuclear Information System (INIS)

Ghosh, A.; Pollifrone, G.; Veneziano, G.

2000-01-01

The quantum fluctuations of a homogeneous, isotropic, open pre-big bang model are discussed. By solving exactly the equations for tensor and scalar perturbations we find that particle production is negligible during the perturbative Pre-Big Bang phase

Analysis of Big Data Maturity Stage in Hospitality Industry

OpenAIRE

Shabani, Neda; Munir, Arslan; Bose, Avishek

2017-01-01

Big data analytics has an extremely significant impact on many areas in all businesses and industries including hospitality. This study aims to guide information technology (IT) professionals in hospitality on their big data expedition. In particular, the purpose of this study is to identify the maturity stage of the big data in hospitality industry in an objective way so that hotels be able to understand their progress, and realize what it will take to get to the next stage of big data matur...

A Multidisciplinary Perspective of Big Data in Management Research

OpenAIRE

Sheng, Jie; Amankwah-Amoah, J.; Wang, X.

2017-01-01

In recent years, big data has emerged as one of the prominent buzzwords in business and management. In spite of the mounting body of research on big data across the social science disciplines, scholars have offered little synthesis on the current state of knowledge. To take stock of academic research that contributes to the big data revolution, this paper tracks scholarly work's perspectives on big data in the management domain over the past decade. We identify key themes emerging in manageme...

An embedding for the big bang

Science.gov (United States)

Wesson, Paul S.

1994-01-01

A cosmological model is given that has good physical properties for the early and late universe but is a hypersurface in a flat five-dimensional manifold. The big bang can therefore be regarded as an effect of a choice of coordinates in a truncated higher-dimensional geometry. Thus the big bang is in some sense a geometrical illusion.

Big Data as Governmentality in International Development

DEFF Research Database (Denmark)

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

2017-01-01

Statistics have long shaped the field of visibility for the governance of development projects. The introduction of big data has altered the field of visibility. Employing Dean's “analytics of government” framework, we analyze two cases—malaria tracking in Kenya and monitoring of food prices...... in Indonesia. Our analysis shows that big data introduces a bias toward particular types of visualizations. What problems are being made visible through big data depends to some degree on how the underlying data is visualized and who is captured in the visualizations. It is also influenced by technical factors...

A Brief Review on Leading Big Data Models

Directory of Open Access Journals (Sweden)

Sugam Sharma

2014-11-01

Full Text Available Today, science is passing through an era of transformation, where the inundation of data, dubbed data deluge is influencing the decision making process. The science is driven by the data and is being termed as data science. In this internet age, the volume of the data has grown up to petabytes, and this large, complex, structured or unstructured, and heterogeneous data in the form of “Big Data” has gained significant attention. The rapid pace of data growth through various disparate sources, especially social media such as Facebook, has seriously challenged the data analytic capabilities of traditional relational databases. The velocity of the expansion of the amount of data gives rise to a complete paradigm shift in how new age data is processed. Confidence in the data engineering of the existing data processing systems is gradually fading whereas the capabilities of the new techniques for capturing, storing, visualizing, and analyzing data are evolving. In this review paper, we discuss some of the modern Big Data models that are leading contributors in the NoSQL era and claim to address Big Data challenges in reliable and efficient ways. Also, we take the potential of Big Data into consideration and try to reshape the original operationaloriented definition of “Big Science” (Furner, 2003 into a new data-driven definition and rephrase it as “The science that deals with Big Data is Big Science.”

75 FR 71069 - Big Horn County Resource Advisory Committee

Science.gov (United States)

2010-11-22

....us , with the words Big Horn County RAC in the subject line. Facsimilies may be sent to 307-674-2668... DEPARTMENT OF AGRICULTURE Forest Service Big Horn County Resource Advisory Committee AGENCY: Forest Service, USDA. ACTION: Notice of meeting. SUMMARY: The Big Horn County Resource Advisory Committee...

76 FR 26240 - Big Horn County Resource Advisory Committee

Science.gov (United States)

2011-05-06

... words Big Horn County RAC in the subject line. Facsimilies may be sent to 307-674-2668. All comments... DEPARTMENT OF AGRICULTURE Forest Service Big Horn County Resource Advisory Committee AGENCY: Forest Service, USDA. ACTION: Notice of meeting. SUMMARY: The Big Horn County Resource Advisory Committee...