Georeferenced Point Clouds: A Survey of Features and Point Cloud Management

Directory of Open Access Journals (Sweden)

Johannes Otepka

2013-10-01

Full Text Available This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point clouds are reviewed. This includes the data structures, but also spatial processing concepts. We suggest a categorization of features into levels that reflect the amount of processing. Point clouds are found across many disciplines, which is reflected in the versatility of the literature suggesting specific features.

New public dataset for spotting patterns in medieval document images

Science.gov (United States)

En, Sovann; Nicolas, Stéphane; Petitjean, Caroline; Jurie, Frédéric; Heutte, Laurent

2017-01-01

With advances in technology, a large part of our cultural heritage is becoming digitally available. In particular, in the field of historical document image analysis, there is now a growing need for indexing and data mining tools, thus allowing us to spot and retrieve the occurrences of an object of interest, called a pattern, in a large database of document images. Patterns may present some variability in terms of color, shape, or context, making the spotting of patterns a challenging task. Pattern spotting is a relatively new field of research, still hampered by the lack of available annotated resources. We present a new publicly available dataset named DocExplore dedicated to spotting patterns in historical document images. The dataset contains 1500 images and 1464 queries, and allows the evaluation of two tasks: image retrieval and pattern localization. A standardized benchmark protocol along with ad hoc metrics is provided for a fair comparison of the submitted approaches. We also provide some first results obtained with our baseline system on this new dataset, which show that there is room for improvement and that should encourage researchers of the document image analysis community to design new systems and submit improved results.

Object Georeferencing in UAV-Based SAR Terrain Images

Directory of Open Access Journals (Sweden)

Łabowski Michał

2016-12-01

Full Text Available Synthetic aperture radars (SAR allow to obtain high resolution terrain images comparable with the resolution of optical methods. Radar imaging is independent on the weather conditions and the daylight. The process of analysis of the SAR images consists primarily of identifying of interesting objects. The ability to determine their geographical coordinates can increase usability of the solution from a user point of view. The paper presents a georeferencing method of the radar terrain images. The presented images were obtained from the SAR system installed on board an Unmanned Aerial Vehicle (UAV. The system was developed within a project under acronym WATSAR realized by the Military University of Technology and WB Electronics S.A. The source of the navigation data was an INS/GNSS system integrated by the Kalman filter with a feed-backward correction loop. The paper presents the terrain images obtained during flight tests and results of selected objects georeferencing with an assessment of the accuracy of the method.

EPA Nanorelease Dataset

Data.gov (United States)

U.S. Environmental Protection Agency — EPA Nanorelease Dataset. This dataset is associated with the following publication: Wohlleben, W., C. Kingston, J. Carter, E. Sahle-Demessie, S. Vazquez-Campos, B....

A library of georeferenced photos from the field

Science.gov (United States)

Xiao, Xiangming; Dorovskoy, Pavel; Biradar, Chandrashekhar; Bridge, Eli

2011-12-01

A picture is worth a thousand of words, and every day hundreds of scientists, students, and environmentally aware citizens are taking field photos to document their observations of rocks, glaciers, soils, forests, wetlands, croplands, rangelands, livestock, and birds and mammals, as well as important events such as droughts, floods, wildfires, insect emergences, and infectious disease outbreaks. Where are those field photos stored? Can they be shared in a timely fashion to support education, research, and the leisure activities of citizens across the world? What are the financial and intellectual costs if those field photos are lost or not shared? Recently, researchers at the University of Oklahoma developed and released the Global Geo-Referenced Field Photo Library (hereinafter referred to as the Field Photo Library; http://www.eomf.ou.edu/photos/), a Web-based data portal designed for researchers and educators who wish to archive and share field photos from across the world, each tagged with exact positioning data (Figure 1). The data portal has a simple user interface that allows people to upload, query, and download georeferenced field photos in the library.

Datasets will not be made accessible to the public due to the fact that they include household level data with PII.

Data.gov (United States)

U.S. Environmental Protection Agency — Datasets will not be made accessible to the public due to the fact that they include household level data with PII. This dataset is not publicly accessible because:...

The StreamCat Dataset: Accumulated Attributes for NHDPlusV2 (Version 2.1) Catchments for the Conterminous United States: Facility Registry Services (FRS) : Toxic Release Inventory (TRI) , National Pollutant Discharge Elimination System (NPDES) , and Superfund Sites

Data.gov (United States)

U.S. Environmental Protection Agency — This dataset represents the estimated density of georeferenced sites within individual, local NHDPlusV2 catchments and upstream, contributing watersheds based on the...

The StreamCat Dataset: Accumulated Attributes for NHDPlusV2 (Version 2.1) Catchments Riparian Buffer for the Conterminous United States: Facility Registry Services (FRS) : Toxic Release Inventory (TRI) , National Pollutant Discharge Elimination System (NPDES) , and Superfund Sites

Data.gov (United States)

U.S. Environmental Protection Agency — This dataset represents the estimated density of georeferenced sites within individual, local NHDPlusV2 catchments and upstream, contributing watersheds riparian...

Real-Time and Post-Processed Georeferencing for Hyperpspectral Drone Remote Sensing

Science.gov (United States)

Oliveira, R. A.; Khoramshahi, E.; Suomalainen, J.; Hakala, T.; Viljanen, N.; Honkavaara, E.

2018-05-01

The use of drones and photogrammetric technologies are increasing rapidly in different applications. Currently, drone processing workflow is in most cases based on sequential image acquisition and post-processing, but there are great interests towards real-time solutions. Fast and reliable real-time drone data processing can benefit, for instance, environmental monitoring tasks in precision agriculture and in forest. Recent developments in miniaturized and low-cost inertial measurement systems and GNSS sensors, and Real-time kinematic (RTK) position data are offering new perspectives for the comprehensive remote sensing applications. The combination of these sensors and light-weight and low-cost multi- or hyperspectral frame sensors in drones provides the opportunity of creating near real-time or real-time remote sensing data of target object. We have developed a system with direct georeferencing onboard drone to be used combined with hyperspectral frame cameras in real-time remote sensing applications. The objective of this study is to evaluate the real-time georeferencing comparing with post-processing solutions. Experimental data sets were captured in agricultural and forested test sites using the system. The accuracy of onboard georeferencing data were better than 0.5 m. The results showed that the real-time remote sensing is promising and feasible in both test sites.

A dataset for examining trends in publication of new Australian insects

Directory of Open Access Journals (Sweden)

Robert Mesibov

2014-07-01

Full Text Available Australian Faunal Directory data were used to create a new, publicly available dataset, nai50, which lists 18318 species and subspecies names for Australian insects described in the period 1961–2010, together with associated publishing data. The number of taxonomic publications introducing the new names varied little around a long-term average of 70 per year, with ca 420 new names published per year during the 30-year period 1981–2010. Within this stable pattern there were steady increases in multi-authored and 'Smith in Jones and Smith' names, and a decline in publication of names in entomology journals and books. For taxonomic works published in Australia, a publications peak around 1990 reflected increases in museum, scientific society and government agency publishing, but a subsequent decline is largely explained by a steep drop in the number of papers on insect taxonomy published by Australia's national science agency, CSIRO.

Dataset of Building and Environment Publication in 2016, A reference method for measuring emissions of SVOCs in small chambers

Data.gov (United States)

U.S. Environmental Protection Agency — The data presented in this data file is a product of a journal publication. The dataset contains DEHP air concentrations in the emission test chamber. This dataset...

Integrated GNSS attitude determination and positioning for direct geo-referencing

NARCIS (Netherlands)

Nadarajah, N.; Paffenholz, J.A.; Teunissen, P.J.G.

2014-01-01

Direct geo-referencing is an efficient methodology for the fast acquisition of 3D spatial data. It requires the fusion of spatial data acquisition sensors with navigation sensors, such as Global Navigation Satellite System (GNSS) receivers. In this contribution, we consider an integrated GNSS

Real-time geo-referenced video mosaicking with the MATISSE system

DEFF Research Database (Denmark)

Vincent, Anne-Gaelle; Pessel, Nathalie; Borgetto, Manon

This paper presents the MATISSE system: Mosaicking Advanced Technologies Integrated in a Single Software Environment. This system aims at producing in-line and off-line geo-referenced video mosaics of seabed given a video input and navigation data. It is based upon several techniques of image...

Public Availability to ECS Collected Datasets

Science.gov (United States)

Henderson, J. F.; Warnken, R.; McLean, S. J.; Lim, E.; Varner, J. D.

2013-12-01

Coastal nations have spent considerable resources exploring the limits of their extended continental shelf (ECS) beyond 200 nm. Although these studies are funded to fulfill requirements of the UN Convention on the Law of the Sea, the investments are producing new data sets in frontier areas of Earth's oceans that will be used to understand, explore, and manage the seafloor and sub-seafloor for decades to come. Although many of these datasets are considered proprietary until a nation's potential ECS has become 'final and binding' an increasing amount of data are being released and utilized by the public. Data sets include multibeam, seismic reflection/refraction, bottom sampling, and geophysical data. The U.S. ECS Project, a multi-agency collaboration whose mission is to establish the full extent of the continental shelf of the United States consistent with international law, relies heavily on data and accurate, standard metadata. The United States has made it a priority to make available to the public all data collected with ECS-funding as quickly as possible. The National Oceanic and Atmospheric Administration's (NOAA) National Geophysical Data Center (NGDC) supports this objective by partnering with academia and other federal government mapping agencies to archive, inventory, and deliver marine mapping data in a coordinated, consistent manner. This includes ensuring quality, standard metadata and developing and maintaining data delivery capabilities built on modern digital data archives. Other countries, such as Ireland, have submitted their ECS data for public availability and many others have made pledges to participate in the future. The data services provided by NGDC support the U.S. ECS effort as well as many developing nation's ECS effort through the U.N. Environmental Program. Modern discovery, visualization, and delivery of scientific data and derived products that span national and international sources of data ensure the greatest re-use of data and

Standardization of a geo-referenced fishing data set for the Indian Ocean bigeye tuna, Thunnus obesus (1952-2014)

Science.gov (United States)

Wibawa, Teja A.; Lehodey, Patrick; Senina, Inna

2017-02-01

Geo-referenced catch and fishing effort data of the bigeye tuna fisheries in the Indian Ocean over 1952-2014 were analyzed and standardized to facilitate population dynamics modeling studies. During this 62-year historical period of exploitation, many changes occurred both in the fishing techniques and the monitoring of activity. This study includes a series of processing steps used for standardization of spatial resolution, conversion and standardization of catch and effort units, raising of geo-referenced catch into nominal catch level, screening and correction of outliers, and detection of major catchability changes over long time series of fishing data, i.e., the Japanese longline fleet operating in the tropical Indian Ocean. A total of 30 fisheries were finally determined from longline, purse seine and other-gears data sets, from which 10 longline and 4 purse seine fisheries represented 96 % of the whole historical geo-referenced catch. Nevertheless, one-third of total nominal catch is still not included due to a total lack of geo-referenced information and would need to be processed separately, accordingly to the requirements of the study. The geo-referenced records of catch, fishing effort and associated length frequency samples of all fisheries are available at PANGAEA.864154" target="_blank">doi:10.1594/PANGAEA.864154.

Development of a georeferenced data bank of radionuclides in typical food of Latin America - SIGLARA

International Nuclear Information System (INIS)

Nascimento, Lucia Maria Evangelista do

2014-01-01

The related management information related to the environmental assessment activity aims to provide the world community with better access to meaningful environmental information and help use this information in making decisions in case of contamination due to accident or deliberate actions. In recent years, the geotechnologies acquired are fundamental to research and environmental monitoring, once it possible, efficiently obtaining large amount of data natural resources. The result of this work was the development of a database system to store georeferenced data values of radionuclides in typical foods in Latin America (SIGLARA), defined in three languages (Spanish, Portuguese and English), using free software. The developed system meets the primary need of the RLA 09/72 ARCAL Project, funded by the International Atomic Energy Agency (IAEA), as having eleven participants countries in Latin America. The database of georeferenced created for SIGLARA system was tested in its applicability through the entry and manipulation of real data analyzed, which showed that the system is able to store, retrieve, view reports and maps of the samples of registered food. Interfaces that connect the user with the database show up efficient, making the system easy operability. Their application to environmental management is already showing results, it is hoped that these results will encourage its widespread adoption by other countries, institutions, the scientific community and the general public. (author)

Near Real-Time Dissemination of Geo-Referenced Imagery by an Enterprise Server

National Research Council Canada - National Science Library

Brown, Alison; Gilbert, Chris; Holland, Heather; Lu, Yan

2006-01-01

.... The payload is connected through a data link to a ground-based server that can process the georegistered data in near-real-time using our GeoReferenced Information Manager (GRIM) Enterprise Server...

Generation of High-Resolution Geo-referenced Photo-Mosaics From Navigation Data

Science.gov (United States)

Delaunoy, O.; Elibol, A.; Garcia, R.; Escartin, J.; Fornari, D.; Humphris, S.

2006-12-01

mosaic, with the intention of locally improving image alignment. Tests have been conducted using the data acquired during the cruise LUSTRE'96 (LUcky STRike Exploration, 37°17'N 32°17'W) by WHOI. During this cruise, the ARGO-II tethered vehicle acquired ~21,000 images in a ~1Km2 area of the seafloor to map at high resolution the geology of this hydrothermal field. The obtained geo-referenced photo-mosaic has a resolution of 1.5cm per pixel, with a coverage of ~25% of the Lucky Strike area. Data and software will be made publicly available.

An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement.

Science.gov (United States)

Souza, Roberto; Lucena, Oeslle; Garrafa, Julia; Gobbi, David; Saluzzi, Marina; Appenzeller, Simone; Rittner, Letícia; Frayne, Richard; Lotufo, Roberto

2018-04-15

This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. CC-359 is comprised of 359 datasets, approximately 60 subjects per vendor and magnetic field strength. The dataset is approximately age and gender balanced, subject to the constraints of the available images. It provides consensus brain extraction masks for all volumes generated using supervised classification. Manual segmentation results for twelve randomly selected subjects performed by an expert are also provided. The CC-359 dataset allows investigation of 1) the influences of both vendor and magnetic field strength on quantitative analysis of brain MR; 2) parameter optimization for automatic segmentation methods; and potentially 3) machine learning classifiers with big data, specifically those based on deep learning methods, as these approaches require a large amount of data. To illustrate the utility of this dataset, we compared to the results of a supervised classifier, the results of eight publicly available skull stripping methods and one publicly available consensus algorithm. A linear mixed effects model analysis indicated that vendor (p-valuefield strength (p-value<0.001) have statistically significant impacts on skull stripping results. Copyright © 2017 Elsevier Inc. All rights reserved.

Analysis of Public Datasets for Wearable Fall Detection Systems.

Science.gov (United States)

Casilari, Eduardo; Santoyo-Ramón, José-Antonio; Cano-García, José-Manuel

2017-06-27

Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.

3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds

Directory of Open Access Journals (Sweden)

Miguel Garrido

2015-12-01

Full Text Available 3D crop reconstruction with a high temporal resolution and by the use of non-destructive measuring technologies can support the automation of plant phenotyping processes. Thereby, the availability of such 3D data can give valuable information about the plant development and the interaction of the plant genotype with the environment. This article presents a new methodology for georeferenced 3D reconstruction of maize plant structure. For this purpose a total station, an IMU, and several 2D LiDARs with different orientations were mounted on an autonomous vehicle. By the multistep methodology presented, based on the application of the ICP algorithm for point cloud fusion, it was possible to perform the georeferenced point clouds overlapping. The overlapping point cloud algorithm showed that the aerial points (corresponding mainly to plant parts were reduced to 1.5%–9% of the total registered data. The remaining were redundant or ground points. Through the inclusion of different LiDAR point of views of the scene, a more realistic representation of the surrounding is obtained by the incorporation of new useful information but also of noise. The use of georeferenced 3D maize plant reconstruction at different growth stages, combined with the total station accuracy could be highly useful when performing precision agriculture at the crop plant level.

Socio-economic data for global environmental change research

DEFF Research Database (Denmark)

Otto, Ilona; Biewald, Anne; Coumou, Dim

2015-01-01

Subnational socio-economic datasets are required if we are to assess the impacts of global environmental changes and to improve adaptation responses. Institutional and community efforts should concentrate on standardization of data collection methodologies, free public access, and geo-referencing....

DIRECT GEOREFERENCING : A NEW STANDARD IN PHOTOGRAMMETRY FOR HIGH ACCURACY MAPPING

Directory of Open Access Journals (Sweden)

A. Rizaldy

2012-07-01

Full Text Available Direct georeferencing is a new method in photogrammetry, especially in the digital camera era. Theoretically, this method does not require ground control points (GCP and the Aerial Triangulation (AT, to process aerial photography into ground coordinates. Compared with the old method, this method has three main advantages: faster data processing, simple workflow and less expensive project, at the same accuracy. Direct georeferencing using two devices, GPS and IMU. GPS recording the camera coordinates (X, Y, Z, and IMU recording the camera orientation (omega, phi, kappa. Both parameters merged into Exterior Orientation (EO parameter. This parameters required for next steps in the photogrammetric projects, such as stereocompilation, DSM generation, orthorectification and mosaic. Accuracy of this method was tested on topographic map project in Medan, Indonesia. Large-format digital camera Ultracam X from Vexcel is used, while the GPS / IMU is IGI AeroControl. 19 Independent Check Point (ICP were used to determine the accuracy. Horizontal accuracy is 0.356 meters and vertical accuracy is 0.483 meters. Data with this accuracy can be used for 1:2.500 map scale project.

Analysis of Public Datasets for Wearable Fall Detection Systems

Directory of Open Access Journals (Sweden)

Eduardo Casilari

2017-06-01

Full Text Available Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs. In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.. Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.

Editorial: Datasets for Learning Analytics

NARCIS (Netherlands)

Dietze, Stefan; George, Siemens; Davide, Taibi; Drachsler, Hendrik

2018-01-01

The European LinkedUp and LACE (Learning Analytics Community Exchange) project have been responsible for setting up a series of data challenges at the LAK conferences 2013 and 2014 around the LAK dataset. The LAK datasets consists of a rich collection of full text publications in the domain of

Publishing descriptions of non-public clinical datasets: proposed guidance for researchers, repositories, editors and funding organisations.

Science.gov (United States)

Hrynaszkiewicz, Iain; Khodiyar, Varsha; Hufton, Andrew L; Sansone, Susanna-Assunta

2016-01-01

Sharing of experimental clinical research data usually happens between individuals or research groups rather than via public repositories, in part due to the need to protect research participant privacy. This approach to data sharing makes it difficult to connect journal articles with their underlying datasets and is often insufficient for ensuring access to data in the long term. Voluntary data sharing services such as the Yale Open Data Access (YODA) and Clinical Study Data Request (CSDR) projects have increased accessibility to clinical datasets for secondary uses while protecting patient privacy and the legitimacy of secondary analyses but these resources are generally disconnected from journal articles-where researchers typically search for reliable information to inform future research. New scholarly journal and article types dedicated to increasing accessibility of research data have emerged in recent years and, in general, journals are developing stronger links with data repositories. There is a need for increased collaboration between journals, data repositories, researchers, funders, and voluntary data sharing services to increase the visibility and reliability of clinical research. Using the journal Scientific Data as a case study, we propose and show examples of changes to the format and peer-review process for journal articles to more robustly link them to data that are only available on request. We also propose additional features for data repositories to better accommodate non-public clinical datasets, including Data Use Agreements (DUAs).

Georeferenced historical forest maps of Bukovina (Northern Romania) - important tool for paleoenvironmental analyses

Science.gov (United States)

Popa, Ionel; Crǎciunescu, Vasile; Candrea, Bogdan; Timár, Gábor

2010-05-01

The historical region of Bukovina is one of the most forested areas of Romania. The name itself, beech land, suggest the high wood resources located here. The systematic wood exploitation started in Bukovina during the Austrian rule (1775 - 1918). To fully asses the region's wood potential and to make the exploitation and replantation processes more efficient, the Austrian engineers developed a dedicated mapping system. The result was a series of maps, surveyed for each forest district. In the first editions, we can find maps crafted at different scales (e.g. 1:50 000, 1: 20 000, 1: 25 000). Later on (after 1900), the map sheets scale was standardized to 1: 25 000. Each sheet was accompanied by a register with information regarding the forest parcels. The system was kept after 1918, when Bukovina become a part of Romania. For another 20 years, the forest districts were periodically surveyed and the maps updated. The basemap content also changed during time. For most of the maps, the background was compiled from the Austrian Third Military Survey maps. After the Second World War, the Romanian military maps ("planurile directoare de tragere") were also used. The forest surveys were positioned using the Austrian triangulation network with the closest baseline at Rădăuţi. Considered lost after WWII, an important part of this maps were recently recovered by a fortunate and accidental finding. Such informations are highly valuable for the today forest planners. By careful studying this kind of documents, a modern forest manager can better understand the way forests were managed in the past and the implications of that management in today's forest reality. In order to do that, the maps should be first georeferenced into a known coordinate system of the Third Survey and integrated with recent geospatial datasets using a GIS environment. The paper presents the challenges of finding and applying the right informations regarding the datum and projection used by the

Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

Directory of Open Access Journals (Sweden)

Xiangyu Zhuo

2017-04-01

Full Text Available Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles. As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.

Dataset of Atmospheric Environment Publication in 2016, Characterization of organophosphorus flame retardants’ sorption on building materials and consumer products

Data.gov (United States)

U.S. Environmental Protection Agency — The data presented in this data file is a product of a journal publication. The dataset contains OPFR sorption concentrations on building materials and consumer...

Isfahan MISP Dataset.

Science.gov (United States)

Kashefpur, Masoud; Kafieh, Rahele; Jorjandi, Sahar; Golmohammadi, Hadis; Khodabande, Zahra; Abbasi, Mohammadreza; Teifuri, Nilufar; Fakharzadeh, Ali Akbar; Kashefpoor, Maryam; Rabbani, Hossein

2017-01-01

An online depository was introduced to share clinical ground truth with the public and provide open access for researchers to evaluate their computer-aided algorithms. PHP was used for web programming and MySQL for database managing. The website was entitled "biosigdata.com." It was a fast, secure, and easy-to-use online database for medical signals and images. Freely registered users could download the datasets and could also share their own supplementary materials while maintaining their privacies (citation and fee). Commenting was also available for all datasets, and automatic sitemap and semi-automatic SEO indexing have been set for the site. A comprehensive list of available websites for medical datasets is also presented as a Supplementary (http://journalonweb.com/tempaccess/4800.584.JMSS_55_16I3253.pdf).

Tables and figure datasets

Data.gov (United States)

U.S. Environmental Protection Agency — Soil and air concentrations of asbestos in Sumas study. This dataset is associated with the following publication: Wroble, J., T. Frederick, A. Frame, and D....

DactyLoc : A minimally geo-referenced WiFi+GSM-fingerprint-based localization method for positioning in urban spaces

DEFF Research Database (Denmark)

Cujia, Kristian; Wirz, Martin; Kjærgaard, Mikkel Baun

2012-01-01

Fingerprinting-based localization methods relying on WiFi and GSM information provide sufficient localization accuracy for many mobile phone applications. Most of the existing approaches require a training set consisting of geo-referenced fingerprints to build a reference database. We propose...... a collaborative, semi-supervised WiFi+GSM fingerprinting method where only a small fraction of all fingerprints needs to be geo-referenced. Our approach enables indexing of areas in the absence of GPS reception as often found in urban spaces and indoors without manual labeling of fingerprints. The method takes...

Something From Nothing (There): Collecting Global IPv6 Datasets from DNS

NARCIS (Netherlands)

Fiebig, T.; Borgolte, Kevin; Hao, Shuang; Kruegel, Christopher; Vigna, Giovanny; Spring, Neil; Riley, George F.

2017-01-01

Current large-scale IPv6 studies mostly rely on non-public datasets, asmost public datasets are domain specific. For instance, traceroute-based datasetsare biased toward network equipment. In this paper, we present a new methodologyto collect IPv6 address datasets that does not require access to

The GTZAN dataset

DEFF Research Database (Denmark)

Sturm, Bob L.

2013-01-01

The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR). Our recent work, however, shows GTZAN has several faults (repetitions, mislabelings, and distortions), which challenge...... of GTZAN, and provide a catalog of its faults. We review how GTZAN has been used in MGR research, and find few indications that its faults have been known and considered. Finally, we rigorously study the effects of its faults on evaluating five different MGR systems. The lesson is not to banish GTZAN...

Experiences and lessons learned from creating a generalized workflow for data publication of field campaign datasets

Science.gov (United States)

Santhana Vannan, S. K.; Ramachandran, R.; Deb, D.; Beaty, T.; Wright, D.

2017-12-01

This paper summarizes the workflow challenges of curating and publishing data produced from disparate data sources and provides a generalized workflow solution to efficiently archive data generated by researchers. The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for biogeochemical dynamics and the Global Hydrology Resource Center (GHRC) DAAC have been collaborating on the development of a generalized workflow solution to efficiently manage the data publication process. The generalized workflow presented here are built on lessons learned from implementations of the workflow system. Data publication consists of the following steps: Accepting the data package from the data providers, ensuring the full integrity of the data files. Identifying and addressing data quality issues Assembling standardized, detailed metadata and documentation, including file level details, processing methodology, and characteristics of data files Setting up data access mechanisms Setup of the data in data tools and services for improved data dissemination and user experience Registering the dataset in online search and discovery catalogues Preserving the data location through Digital Object Identifiers (DOI) We will describe the steps taken to automate, and realize efficiencies to the above process. The goals of the workflow system are to reduce the time taken to publish a dataset, to increase the quality of documentation and metadata, and to track individual datasets through the data curation process. Utilities developed to achieve these goal will be described. We will also share metrics driven value of the workflow system and discuss the future steps towards creation of a common software framework.

Aaron Journal article datasets

Data.gov (United States)

U.S. Environmental Protection Agency — All figures used in the journal article are in netCDF format. This dataset is associated with the following publication: Sims, A., K. Alapaty , and S. Raman....

Road Bridges and Culverts, Bridge dataset only includes bridges maintained by Johnson County Public Works in the unincorporated areas, Published in Not Provided, Johnson County Government.

Data.gov (United States)

NSGIC Local Govt | GIS Inventory — Road Bridges and Culverts dataset current as of unknown. Bridge dataset only includes bridges maintained by Johnson County Public Works in the unincorporated areas.

Develop Direct Geo-referencing System Based on Open Source Software and Hardware Platform

Directory of Open Access Journals (Sweden)

H. S. Liu

2015-08-01

Full Text Available Direct geo-referencing system uses the technology of remote sensing to quickly grasp images, GPS tracks, and camera position. These data allows the construction of large volumes of images with geographic coordinates. So that users can be measured directly on the images. In order to properly calculate positioning, all the sensor signals must be synchronized. Traditional aerial photography use Position and Orientation System (POS to integrate image, coordinates and camera position. However, it is very expensive. And users could not use the result immediately because the position information does not embed into image. To considerations of economy and efficiency, this study aims to develop a direct geo-referencing system based on open source software and hardware platform. After using Arduino microcontroller board to integrate the signals, we then can calculate positioning with open source software OpenCV. In the end, we use open source panorama browser, panini, and integrate all these to open source GIS software, Quantum GIS. A wholesome collection of data – a data processing system could be constructed.

Develop Direct Geo-referencing System Based on Open Source Software and Hardware Platform

Science.gov (United States)

Liu, H. S.; Liao, H. M.

2015-08-01

Direct geo-referencing system uses the technology of remote sensing to quickly grasp images, GPS tracks, and camera position. These data allows the construction of large volumes of images with geographic coordinates. So that users can be measured directly on the images. In order to properly calculate positioning, all the sensor signals must be synchronized. Traditional aerial photography use Position and Orientation System (POS) to integrate image, coordinates and camera position. However, it is very expensive. And users could not use the result immediately because the position information does not embed into image. To considerations of economy and efficiency, this study aims to develop a direct geo-referencing system based on open source software and hardware platform. After using Arduino microcontroller board to integrate the signals, we then can calculate positioning with open source software OpenCV. In the end, we use open source panorama browser, panini, and integrate all these to open source GIS software, Quantum GIS. A wholesome collection of data - a data processing system could be constructed.

Integration of georeferencing, habitat, sampling, and genetic data for documentation of wild plant genetic resources

Science.gov (United States)

Plant genetic resource collections provide novel materials to the breeding and research communities. Availability of detailed documentation of passport, phenotypic, and genetic data increases the value of the genebank accessions. Inclusion of georeferenced sources, habitats, and sampling data in co...

Chemical product and function dataset

Data.gov (United States)

U.S. Environmental Protection Agency — Merged product weight fraction and chemical function data. This dataset is associated with the following publication: Isaacs , K., M. Goldsmith, P. Egeghy , K....

Obesity and Fast Food in Urban Markets: A New Approach Using Geo-referenced Micro Data

NARCIS (Netherlands)

Chen, S.E.; Florax, R.J.G.M.; Snyder, S.D.

2013-01-01

This paper presents a new method of assessing the relationship between features of the built environment and obesity, particularly in urban areas. Our empirical application combines georeferenced data on the location of fast-food restaurants with data about personal health, behavioral, and

Georeferenced cartography dataset of the La Fossa crater fumarolic field at Vulcano Island (Aeolian Archipelago, Italy: conversion and comparison of data from local to global positioning methods

Directory of Open Access Journals (Sweden)

Carmelo Sammarco

2011-07-01

Full Text Available The present study illustrates the procedures applied for the coordinate system conversion of the historical fumarole positions at La Fossa crater, to allow their comparison with newly acquired global positioning system (GPS data. Due to the absence of ground control points in the field and on both the old Gauss Boaga and the new UTM WGS 1984 maps, we had to model the transformation errors between the two systems using differential GPS techniques. Once corrected, the maps show a residual Easting shifting, due to erroneous georeferencing of the original base maps; this is corrected by morphological comparative methods. The good correspondence between the corrected positions of the historical data and the results of the new GPS survey that was carried out in 2009 highlights the good quality of the old surveys, although they were carried out without the use of accurate topographical instruments.

Turkey Run Landfill Emissions Dataset

Data.gov (United States)

U.S. Environmental Protection Agency — landfill emissions measurements for the Turkey run landfill in Georgia. This dataset is associated with the following publication: De la Cruz, F., R. Green, G....

Dataset of NRDA emission data

Data.gov (United States)

U.S. Environmental Protection Agency — Emissions data from open air oil burns. This dataset is associated with the following publication: Gullett, B., J. Aurell, A. Holder, B. Mitchell, D. Greenwell, M....

Accuracy assessment of minimum control points for UAV photography and georeferencing

Science.gov (United States)

Skarlatos, D.; Procopiou, E.; Stavrou, G.; Gregoriou, M.

2013-08-01

In recent years, Autonomous Unmanned Aerial Vehicles (AUAV) became popular among researchers across disciplines because they combine many advantages. One major application is monitoring and mapping. Their ability to fly beyond eye sight autonomously, collecting data over large areas whenever, wherever, makes them excellent platform for monitoring hazardous areas or disasters. In both cases rapid mapping is needed while human access isn't always a given. Indeed, current automatic processing of aerial photos using photogrammetry and computer vision algorithms allows for rapid orthophomap production and Digital Surface Model (DSM) generation, as tools for monitoring and damage assessment. In such cases, control point measurement using GPS is either impossible, or time consuming or costly. This work investigates accuracies that can be attained using few or none control points over areas of one square kilometer, in two test sites; a typical block and a corridor survey. On board GPS data logged during AUAV's flight are being used for direct georeferencing, while ground check points are being used for evaluation. In addition various control point layouts are being tested using bundle adjustment for accuracy evaluation. Results indicate that it is possible to use on board single frequency GPS for direct georeferencing in cases of disaster management or areas without easy access, or even over featureless areas. Due to large numbers of tie points in the bundle adjustment, horizontal accuracy can be fulfilled with a rather small number of control points, but vertical accuracy may not.

Public Health Applications of Remotely-sensed Environmental Datasets for the Conterminous United States

Science.gov (United States)

Al-Hamdan, Mohammad; Crosson, William; Economou, Sigrid; Estes, Marice Jr; Estes, Sue; Hemmings, Sarah; Kent, Shia; Puckett, Mark; Quattrochi, Dale; Wade, Gina

2013-01-01

NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) National Center for Public Health Informatics to address issues of environmental health and enhance public health decision-making using NASA remotely-sensed data and products. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the linked data sets and associated analyses to local, state and federal end-user groups. Three daily environmental data sets were developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid using the US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Incoming Solar Radiation (Insolation) and heat-related products using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets were linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline, stroke and other health outcomes. These environmental datasets and the results of the public health linkage analyses will be disseminated to end-users for decision-making through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system and through peer-reviewed publications respectively. The linkage of these data with the CDC WONDER system substantially expands public access to NASA data, making their use by a wide range of decision makers feasible. By successful

Geoseq: a tool for dissecting deep-sequencing datasets

Directory of Open Access Journals (Sweden)

Homann Robert

2010-10-01

Full Text Available Abstract Background Datasets generated on deep-sequencing platforms have been deposited in various public repositories such as the Gene Expression Omnibus (GEO, Sequence Read Archive (SRA hosted by the NCBI, or the DNA Data Bank of Japan (ddbj. Despite being rich data sources, they have not been used much due to the difficulty in locating and analyzing datasets of interest. Results Geoseq http://geoseq.mssm.edu provides a new method of analyzing short reads from deep sequencing experiments. Instead of mapping the reads to reference genomes or sequences, Geoseq maps a reference sequence against the sequencing data. It is web-based, and holds pre-computed data from public libraries. The analysis reduces the input sequence to tiles and measures the coverage of each tile in a sequence library through the use of suffix arrays. The user can upload custom target sequences or use gene/miRNA names for the search and get back results as plots and spreadsheet files. Geoseq organizes the public sequencing data using a controlled vocabulary, allowing identification of relevant libraries by organism, tissue and type of experiment. Conclusions Analysis of small sets of sequences against deep-sequencing datasets, as well as identification of public datasets of interest, is simplified by Geoseq. We applied Geoseq to, a identify differential isoform expression in mRNA-seq datasets, b identify miRNAs (microRNAs in libraries, and identify mature and star sequences in miRNAS and c to identify potentially mis-annotated miRNAs. The ease of using Geoseq for these analyses suggests its utility and uniqueness as an analysis tool.

A Tenebrionid beetle's dataset (Coleoptera, Tenebrionidae) from Peninsula Valdés (Chubut, Argentina).

Science.gov (United States)

Cheli, Germán H; Flores, Gustavo E; Román, Nicolás Martínez; Podestá, Darío; Mazzanti, Renato; Miyashiro, Lidia

2013-12-18

The Natural Protected Area Peninsula Valdés, located in Northeastern Patagonia, is one of the largest conservation units of arid lands in Argentina. Although this area has been in the UNESCO World Heritage List since 1999, it has been continually exposed to sheep grazing and cattle farming for more than a century which have had a negative impact on the local environment. Our aim is to describe the first dataset of tenebrionid beetle species living in Peninsula Valdés and their relationship to sheep grazing. The dataset contains 118 records on 11 species and 198 adult individuals collected. Beetles were collected using pitfall traps in the two major environmental units of Peninsula Valdés, taking into account grazing intensities over a three year time frame from 2005-2007. The Data quality was enhanced following the best practices suggested in the literature during the digitalization and geo-referencing processes. Moreover, identification of specimens and current accurate spelling of scientific names were reviewed. Finally, post-validation processes using DarwinTest software were applied. Specimens have been deposited at Entomological Collection of the Centro Nacional Patagónico (CENPAT-CONICET). The dataset is part of the database of this collection and has been published on the internet through GBIF Integrated Publishing Toolkit (IPT) (http://data.gbif.org/datasets/resource/14669/). Furthermore, it is the first dataset for tenebrionid beetles of arid Patagonia available in GBIF database, and it is the first one based on a previously designed and standardized sampling to assess the interaction between these beetles and grazing in the area. The main purposes of this dataset are to ensure accessibility to data associated with Tenebrionidae specimens from Peninsula Valdés (Chubut, Argentina), also to contribute to GBIF with primary data about Patagonian tenebrionids and finally, to promote the Entomological Collection of Centro Nacional Patagónico (CENPAT

Direct Georeferencing of Uav Data Based on Simple Building Structures

Science.gov (United States)

Tampubolon, W.; Reinhardt, W.

2016-06-01

Unmanned Aerial Vehicle (UAV) data acquisition is more flexible compared with the more complex traditional airborne data acquisition. This advantage puts UAV platforms in a position as an alternative acquisition method in many applications including Large Scale Topographical Mapping (LSTM). LSTM, i.e. larger or equal than 1:10.000 map scale, is one of a number of prominent priority tasks to be solved in an accelerated way especially in third world developing countries such as Indonesia. As one component of fundamental geospatial data sets, large scale topographical maps are mandatory in order to enable detailed spatial planning. However, the accuracy of the products derived from the UAV data are normally not sufficient for LSTM as it needs robust georeferencing, which requires additional costly efforts such as the incorporation of sophisticated GPS Inertial Navigation System (INS) or Inertial Measurement Unit (IMU) on the platform and/or Ground Control Point (GCP) data on the ground. To reduce the costs and the weight on the UAV alternative solutions have to be found. This paper outlines a direct georeferencing method of UAV data by providing image orientation parameters derived from simple building structures and presents results of an investigation on the achievable results in a LSTM application. In this case, the image orientation determination has been performed through sequential images without any input from INS/IMU equipment. The simple building structures play a significant role in such a way that geometrical characteristics have been considered. Some instances are the orthogonality of the building's wall/rooftop and the local knowledge of the building orientation in the field. In addition, we want to include the Structure from Motion (SfM) approach in order to reduce the number of required GCPs especially for the absolute orientation purpose. The SfM technique applied to the UAV data and simple building structures additionally presents an effective tool

DIRECT GEOREFERENCING OF UAV DATA BASED ON SIMPLE BUILDING STRUCTURES

Directory of Open Access Journals (Sweden)

W. Tampubolon

2016-06-01

Full Text Available Unmanned Aerial Vehicle (UAV data acquisition is more flexible compared with the more complex traditional airborne data acquisition. This advantage puts UAV platforms in a position as an alternative acquisition method in many applications including Large Scale Topographical Mapping (LSTM. LSTM, i.e. larger or equal than 1:10.000 map scale, is one of a number of prominent priority tasks to be solved in an accelerated way especially in third world developing countries such as Indonesia. As one component of fundamental geospatial data sets, large scale topographical maps are mandatory in order to enable detailed spatial planning. However, the accuracy of the products derived from the UAV data are normally not sufficient for LSTM as it needs robust georeferencing, which requires additional costly efforts such as the incorporation of sophisticated GPS Inertial Navigation System (INS or Inertial Measurement Unit (IMU on the platform and/or Ground Control Point (GCP data on the ground. To reduce the costs and the weight on the UAV alternative solutions have to be found. This paper outlines a direct georeferencing method of UAV data by providing image orientation parameters derived from simple building structures and presents results of an investigation on the achievable results in a LSTM application. In this case, the image orientation determination has been performed through sequential images without any input from INS/IMU equipment. The simple building structures play a significant role in such a way that geometrical characteristics have been considered. Some instances are the orthogonality of the building’s wall/rooftop and the local knowledge of the building orientation in the field. In addition, we want to include the Structure from Motion (SfM approach in order to reduce the number of required GCPs especially for the absolute orientation purpose. The SfM technique applied to the UAV data and simple building structures additionally presents an

A Research Graph dataset for connecting research data repositories using RD-Switchboard.

Science.gov (United States)

Aryani, Amir; Poblet, Marta; Unsworth, Kathryn; Wang, Jingbo; Evans, Ben; Devaraju, Anusuriya; Hausstein, Brigitte; Klas, Claus-Peter; Zapilko, Benjamin; Kaplun, Samuele

2018-05-29

This paper describes the open access graph dataset that shows the connections between Dryad, CERN, ANDS and other international data repositories to publications and grants across multiple research data infrastructures. The graph dataset was created using the Research Graph data model and the Research Data Switchboard (RD-Switchboard), a collaborative project by the Research Data Alliance DDRI Working Group (DDRI WG) with the aim to discover and connect the related research datasets based on publication co-authorship or jointly funded grants. The graph dataset allows researchers to trace and follow the paths to understanding a body of work. By mapping the links between research datasets and related resources, the graph dataset improves both their discovery and visibility, while avoiding duplicate efforts in data creation. Ultimately, the linked datasets may spur novel ideas, facilitate reproducibility and re-use in new applications, stimulate combinatorial creativity, and foster collaborations across institutions.

Geopan AT@S: a Brokering Based Gateway to Georeferenced Historical Maps for Risk Analysis

Science.gov (United States)

Previtali, M.

2017-08-01

Importance of ancient and historical maps is nowadays recognized in many applications (e.g., urban planning, landscape valorisation and preservation, land changes identification, etc.). In the last years a great effort has been done by different institutions, such as Geographical Institutes, Public Administrations, and collaborative communities, for digitizing and publishing online collections of historical maps. In spite of this variety and availability of data, information overload makes difficult their discovery and management: without knowing the specific repository where the data are stored, it is difficult to find the information required. In addition, problems of interconnection between different data sources and their restricted interoperability may arise. This paper describe a new brokering based gateway developed to assure interoperability between data, in particular georeferenced historical maps and geographic data, gathered from different data providers, with various features and referring to different historical periods. The developed approach is exemplified by a new application named GeoPAN Atl@s that is aimed at linking in Northern Italy area land changes with risk analysis (local seismicity amplification and flooding risk) by using multi-temporal data sources and historic maps.

Publishing datasets with eSciDoc and panMetaDocs

Science.gov (United States)

Ulbricht, D.; Klump, J.; Bertelmann, R.

2012-04-01

Currently serveral research institutions worldwide undertake considerable efforts to have their scientific datasets published and to syndicate them to data portals as extensively described objects identified by a persistent identifier. This is done to foster the reuse of data, to make scientific work more transparent, and to create a citable entity that can be referenced unambigously in written publications. GFZ Potsdam established a publishing workflow for file based research datasets. Key software components are an eSciDoc infrastructure [1] and multiple instances of the data curation tool panMetaDocs [2]. The eSciDoc repository holds data objects and their associated metadata in container objects, called eSciDoc items. A key metadata element in this context is the publication status of the referenced data set. PanMetaDocs, which is based on PanMetaWorks [3], is a PHP based web application that allows to describe data with any XML-based metadata schema. The metadata fields can be filled with static or dynamic content to reduce the number of fields that require manual entries to a minimum and make use of contextual information in a project setting. Access rights can be applied to set visibility of datasets to other project members and allow collaboration on and notifying about datasets (RSS) and interaction with the internal messaging system, that was inherited from panMetaWorks. When a dataset is to be published, panMetaDocs allows to change the publication status of the eSciDoc item from status "private" to "submitted" and prepare the dataset for verification by an external reviewer. After quality checks, the item publication status can be changed to "published". This makes the data and metadata available through the internet worldwide. PanMetaDocs is developed as an eSciDoc application. It is an easy to use graphical user interface to eSciDoc items, their data and metadata. It is also an application supporting a DOI publication agent during the process of

A Tenebrionid beetle’s dataset (Coleoptera, Tenebrionidae from Peninsula Valdés (Chubut, Argentina

Directory of Open Access Journals (Sweden)

German Cheli

2013-12-01

Full Text Available The Natural Protected Area Peninsula Valdés, located in Northeastern Patagonia, is one of the largest conservation units of arid lands in Argentina. Although this area has been in the UNESCO World Heritage List since 1999, it has been continually exposed to sheep grazing and cattle farming for more than a century which have had a negative impact on the local environment. Our aim is to describe the first dataset of tenebrionid beetle species living in Peninsula Valdés and their relationship to sheep grazing. The dataset contains 118 records on 11 species and 198 adult individuals collected. Beetles were collected using pitfall traps in the two major environmental units of Peninsula Valdés, taking into account grazing intensities over a three year time frame from 2005–2007. The Data quality was enhanced following the best practices suggested in the literature during the digitalization and geo-referencing processes. Moreover, identification of specimens and current accurate spelling of scientific names were reviewed. Finally, post-validation processes using DarwinTest software were applied. Specimens have been deposited at Entomological Collection of the Centro Nacional Patagónico (CENPAT-CONICET. The dataset is part of the database of this collection and has been published on the internet through GBIF Integrated Publishing Toolkit (IPT (http://data.gbif.org/datasets/resource/14669/. Furthermore, it is the first dataset for tenebrionid beetles of arid Patagonia available in GBIF database, and it is the first one based on a previously designed and standardized sampling to assess the interaction between these beetles and grazing in the area. The main purposes of this dataset are to ensure accessibility to data associated with Tenebrionidae specimens from Peninsula Valdés (Chubut, Argentina, also to contribute to GBIF with primary data about Patagonian tenebrionids and finally, to promote the Entomological Collection of Centro Nacional Patag

A Tenebrionid beetle’s dataset (Coleoptera, Tenebrionidae) from Peninsula Valdés (Chubut, Argentina)

Science.gov (United States)

Cheli, Germán H.; Flores, Gustavo E.; Román, Nicolás Martínez; Podestá, Darío; Mazzanti, Renato; Miyashiro, Lidia

2013-01-01

Abstract The Natural Protected Area Peninsula Valdés, located in Northeastern Patagonia, is one of the largest conservation units of arid lands in Argentina. Although this area has been in the UNESCO World Heritage List since 1999, it has been continually exposed to sheep grazing and cattle farming for more than a century which have had a negative impact on the local environment. Our aim is to describe the first dataset of tenebrionid beetle species living in Peninsula Valdés and their relationship to sheep grazing. The dataset contains 118 records on 11 species and 198 adult individuals collected. Beetles were collected using pitfall traps in the two major environmental units of Peninsula Valdés, taking into account grazing intensities over a three year time frame from 2005–2007. The Data quality was enhanced following the best practices suggested in the literature during the digitalization and geo-referencing processes. Moreover, identification of specimens and current accurate spelling of scientific names were reviewed. Finally, post-validation processes using DarwinTest software were applied. Specimens have been deposited at Entomological Collection of the Centro Nacional Patagónico (CENPAT-CONICET). The dataset is part of the database of this collection and has been published on the internet through GBIF Integrated Publishing Toolkit (IPT) (http://data.gbif.org/datasets/resource/14669/). Furthermore, it is the first dataset for tenebrionid beetles of arid Patagonia available in GBIF database, and it is the first one based on a previously designed and standardized sampling to assess the interaction between these beetles and grazing in the area. The main purposes of this dataset are to ensure accessibility to data associated with Tenebrionidae specimens from Peninsula Valdés (Chubut, Argentina), also to contribute to GBIF with primary data about Patagonian tenebrionids and finally, to promote the Entomological Collection of Centro Nacional Patag

Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset

Directory of Open Access Journals (Sweden)

Sokratis Vidros

2017-03-01

Full Text Available The critical process of hiring has relatively recently been ported to the cloud. Specifically, the automated systems responsible for completing the recruitment of new employees in an online fashion, aim to make the hiring process more immediate, accurate and cost-efficient. However, the online exposure of such traditional business procedures has introduced new points of failure that may lead to privacy loss for applicants and harm the reputation of organizations. So far, the most common case of Online Recruitment Frauds (ORF, is employment scam. Unlike relevant online fraud problems, the tackling of ORF has not yet received the proper attention, remaining largely unexplored until now. Responding to this need, the work at hand defines and describes the characteristics of this severe and timely novel cyber security research topic. At the same time, it contributes and evaluates the first to our knowledge publicly available dataset of 17,880 annotated job ads, retrieved from the use of a real-life system.

Methods for Georeferencing and Spectral Scaling of Remote Imagery using ArcView, ArcGIS, and ENVI

Science.gov (United States)

Remote sensing images can be used to support variable-rate (VR) application of material from aircraft. Geographic coordinates must be assigned to an image (georeferenced) so that the variable-rate system can determine where in the field to apply these inputs and adjust the system when a zone has bee...

Development of a SPARK Training Dataset

Energy Technology Data Exchange (ETDEWEB)

Sayre, Amanda M. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Olson, Jarrod R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2015-03-01

In its first five years, the National Nuclear Security Administration’s (NNSA) Next Generation Safeguards Initiative (NGSI) sponsored more than 400 undergraduate, graduate, and post-doctoral students in internships and research positions (Wyse 2012). In the past seven years, the NGSI program has, and continues to produce a large body of scientific, technical, and policy work in targeted core safeguards capabilities and human capital development activities. Not only does the NGSI program carry out activities across multiple disciplines, but also across all U.S. Department of Energy (DOE)/NNSA locations in the United States. However, products are not readily shared among disciplines and across locations, nor are they archived in a comprehensive library. Rather, knowledge of NGSI-produced literature is localized to the researchers, clients, and internal laboratory/facility publication systems such as the Electronic Records and Information Capture Architecture (ERICA) at the Pacific Northwest National Laboratory (PNNL). There is also no incorporated way of analyzing existing NGSI literature to determine whether the larger NGSI program is achieving its core safeguards capabilities and activities. A complete library of NGSI literature could prove beneficial to a cohesive, sustainable, and more economical NGSI program. The Safeguards Platform for Automated Retrieval of Knowledge (SPARK) has been developed to be a knowledge storage, retrieval, and analysis capability to capture safeguards knowledge to exist beyond the lifespan of NGSI. During the development process, it was necessary to build a SPARK training dataset (a corpus of documents) for initial entry into the system and for demonstration purposes. We manipulated these data to gain new information about the breadth of NGSI publications, and they evaluated the science-policy interface at PNNL as a practical demonstration of SPARK’s intended analysis capability. The analysis demonstration sought to answer the

Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV Imagery

Directory of Open Access Journals (Sweden)

Arko Lucieer

2012-05-01

Full Text Available Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud ( < 1–3 cm point spacing is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25–40 mm can be obtained from imagery acquired from 50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion can be monitored.

panMetaDocs, eSciDoc, and DOIDB - an infrastructure for the curation and publication of file-based datasets for 'GFZ Data Services'

Science.gov (United States)

Ulbricht, Damian; Elger, Kirsten; Bertelmann, Roland; Klump, Jens

2016-04-01

With the foundation of DataCite in 2009 and the technical infrastructure installed in the last six years it has become very easy to create citable dataset DOIs. Nowadays, dataset DOIs are increasingly accepted and required by journals in reference lists of manuscripts. In addition, DataCite provides usage statistics [1] of assigned DOIs and offers a public search API to make research data count. By linking related information to the data, they become more useful for future generations of scientists. For this purpose, several identifier systems, as ISBN for books, ISSN for journals, DOI for articles or related data, Orcid for authors, and IGSN for physical samples can be attached to DOIs using the DataCite metadata schema [2]. While these are good preconditions to publish data, free and open solutions that help with the curation of data, the publication of research data, and the assignment of DOIs in one software seem to be rare. At GFZ Potsdam we built a modular software stack that is made of several free and open software solutions and we established 'GFZ Data Services'. 'GFZ Data Services' provides storage, a metadata editor for publication and a facility to moderate minted DOIs. All software solutions are connected through web APIs, which makes it possible to reuse and integrate established software. Core component of 'GFZ Data Services' is an eSciDoc [3] middleware that is used as central storage, and has been designed along the OAIS reference model for digital preservation. Thus, data are stored in self-contained packages that are made of binary file-based data and XML-based metadata. The eSciDoc infrastructure provides access control to data and it is able to handle half-open datasets, which is useful in embargo situations when a subset of the research data are released after an adequate period. The data exchange platform panMetaDocs [4] makes use of eSciDoc's REST API to upload file-based data into eSciDoc and uses a metadata editor [5] to annotate the files

Towards an Automatic Framework for Urban Settlement Mapping from Satellite Images: Applications of Geo-referenced Social Media and One Class Classification

Science.gov (United States)

Miao, Zelang

2017-04-01

Currently, urban dwellers comprise more than half of the world's population and this percentage is still dramatically increasing. The explosive urban growth over the next two decades poses long-term profound impact on people as well as the environment. Accurate and up-to-date delineation of urban settlements plays a fundamental role in defining planning strategies and in supporting sustainable development of urban settlements. In order to provide adequate data about urban extents and land covers, classifying satellite data has become a common practice, usually with accurate enough results. Indeed, a number of supervised learning methods have proven effective in urban area classification, but they usually depend on a large amount of training samples, whose collection is a time and labor expensive task. This issue becomes particularly serious when classifying large areas at the regional/global level. As an alternative to manual ground truth collection, in this work we use geo-referenced social media data. Cities and densely populated areas are an extremely fertile land for the production of individual geo-referenced data (such as GPS and social network data). Training samples derived from geo-referenced social media have several advantages: they are easy to collect, usually they are freely exploitable; and, finally, data from social media are spatially available in many locations, and with no doubt in most urban areas around the world. Despite these advantages, the selection of training samples from social media meets two challenges: 1) there are many duplicated points; 2) method is required to automatically label them as "urban/non-urban". The objective of this research is to validate automatic sample selection from geo-referenced social media and its applicability in one class classification for urban extent mapping from satellite images. The findings in this study shed new light on social media applications in the field of remote sensing.

Development of a SPARK Training Dataset

International Nuclear Information System (INIS)

Sayre, Amanda M.; Olson, Jarrod R.

2015-01-01

In its first five years, the National Nuclear Security Administration's (NNSA) Next Generation Safeguards Initiative (NGSI) sponsored more than 400 undergraduate, graduate, and post-doctoral students in internships and research positions (Wyse 2012). In the past seven years, the NGSI program has, and continues to produce a large body of scientific, technical, and policy work in targeted core safeguards capabilities and human capital development activities. Not only does the NGSI program carry out activities across multiple disciplines, but also across all U.S. Department of Energy (DOE)/NNSA locations in the United States. However, products are not readily shared among disciplines and across locations, nor are they archived in a comprehensive library. Rather, knowledge of NGSI-produced literature is localized to the researchers, clients, and internal laboratory/facility publication systems such as the Electronic Records and Information Capture Architecture (ERICA) at the Pacific Northwest National Laboratory (PNNL). There is also no incorporated way of analyzing existing NGSI literature to determine whether the larger NGSI program is achieving its core safeguards capabilities and activities. A complete library of NGSI literature could prove beneficial to a cohesive, sustainable, and more economical NGSI program. The Safeguards Platform for Automated Retrieval of Knowledge (SPARK) has been developed to be a knowledge storage, retrieval, and analysis capability to capture safeguards knowledge to exist beyond the lifespan of NGSI. During the development process, it was necessary to build a SPARK training dataset (a corpus of documents) for initial entry into the system and for demonstration purposes. We manipulated these data to gain new information about the breadth of NGSI publications, and they evaluated the science-policy interface at PNNL as a practical demonstration of SPARK's intended analysis capability. The analysis demonstration sought to answer

Process mining in oncology using the MIMIC-III dataset

Science.gov (United States)

Prima Kurniati, Angelina; Hall, Geoff; Hogg, David; Johnson, Owen

2018-03-01

Process mining is a data analytics approach to discover and analyse process models based on the real activities captured in information systems. There is a growing body of literature on process mining in healthcare, including oncology, the study of cancer. In earlier work we found 37 peer-reviewed papers describing process mining research in oncology with a regular complaint being the limited availability and accessibility of datasets with suitable information for process mining. Publicly available datasets are one option and this paper describes the potential to use MIMIC-III, for process mining in oncology. MIMIC-III is a large open access dataset of de-identified patient records. There are 134 publications listed as using the MIMIC dataset, but none of them have used process mining. The MIMIC-III dataset has 16 event tables which are potentially useful for process mining and this paper demonstrates the opportunities to use MIMIC-III for process mining in oncology. Our research applied the L* lifecycle method to provide a worked example showing how process mining can be used to analyse cancer pathways. The results and data quality limitations are discussed along with opportunities for further work and reflection on the value of MIMIC-III for reproducible process mining research.

Vision-Based Georeferencing of GPR in Urban Areas

Directory of Open Access Journals (Sweden)

Riccardo Barzaghi

2016-01-01

Full Text Available Ground Penetrating Radar (GPR surveying is widely used to gather accurate knowledge about the geometry and position of underground utilities. The sensor arrays need to be coupled to an accurate positioning system, like a geodetic-grade Global Navigation Satellite System (GNSS device. However, in urban areas this approach is not always feasible because GNSS accuracy can be substantially degraded due to the presence of buildings, trees, tunnels, etc. In this work, a photogrammetric (vision-based method for GPR georeferencing is presented. The method can be summarized in three main steps: tie point extraction from the images acquired during the survey, computation of approximate camera extrinsic parameters and finally a refinement of the parameter estimation using a rigorous implementation of the collinearity equations. A test under operational conditions is described, where accuracy of a few centimeters has been achieved. The results demonstrate that the solution was robust enough for recovering vehicle trajectories even in critical situations, such as poorly textured framed surfaces, short baselines, and low intersection angles.

Mobile TDR for geo-referenced measurement of soil water content and electrical conductivity

DEFF Research Database (Denmark)

Thomsen, Anton; Schelde, Kirsten; Drøscher, Per

2007-01-01

The development of site-specific crop management is constrained by the availability of sensors for monitoring important soil and crop related conditions. A mobile time-domain reflectometry (TDR) unit for geo-referenced soil measurements has been developed and used for detailed mapping of soil wat...... analysis of the soil water measurements, recommendations are made with respect to sampling strategies. Depending on the variability of a given area, between 15 and 30 ha can be mapped with respect to soil moisture and electrical conductivity with sufficient detail within 8 h...

Dataset of Atmospheric Environment Publication in 2016, Source emission and model evaluation of formaldehyde from composite and solid wood furniture in a full-scale chamber

Data.gov (United States)

U.S. Environmental Protection Agency — The data presented in this data file is a product of a journal publication. The dataset contains formaldehyde air concentrations in the emission test chamber and...

Toward computational cumulative biology by combining models of biological datasets.

Science.gov (United States)

Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations-for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.

Using Multiple Big Datasets and Machine Learning to Produce a New Global Particulate Dataset: A Technology Challenge Case Study

Science.gov (United States)

Lary, D. J.

2013-12-01

A BigData case study is described where multiple datasets from several satellites, high-resolution global meteorological data, social media and in-situ observations are combined using machine learning on a distributed cluster using an automated workflow. The global particulate dataset is relevant to global public health studies and would not be possible to produce without the use of the multiple big datasets, in-situ data and machine learning.To greatly reduce the development time and enhance the functionality a high level language capable of parallel processing has been used (Matlab). A key consideration for the system is high speed access due to the large data volume, persistence of the large data volumes and a precise process time scheduling capability.

Public Access Points, Location of public beach access along the Oregon Coast. Boat ramp locations were added to the dataset to allow users to view the location of boat ramps along the Columbia River and the Willamete River north of the Oregon City Dam., Published in 2005, 1:100000 (1in=8333ft) scale, Oregon Geospatial Enterprise Office (GEO).

Data.gov (United States)

NSGIC State | GIS Inventory — Public Access Points dataset current as of 2005. Location of public beach access along the Oregon Coast. Boat ramp locations were added to the dataset to allow users...

Design and development of a geo-referenced database to radionuclides in food

Science.gov (United States)

Nascimento, L. M. E.; Ferreira, A. C. M.; Gonzalez, S. A.

2018-03-01

The primary purpose of the range of activities concerning the info management of the environmental assessment is to provide to scientific community an improved access to environmental data, as well as to support the decision making loop, in case of contamination events due either to accidental or intentional causes. In recent years, geotechnologies became a key reference in environmental research and monitoring, since they deliver an efficient data retrieval and subsequent processing about natural resources. This study aimed at the development of a georeferenced database (SIGLARA – SIstema Georeferenciado Latino Americano de Radionuclídeos em Alimentos), designed to radioactivity in food data storage, available in three languages (Spanish, Portuguese and English), employing free software[l].

Design and development of a geo-referenced database to radionuclides in food

Energy Technology Data Exchange (ETDEWEB)

Nascimento, Lucia Maria Evangelista do; Ferreira, Ana Cristina de Melo; Gonzalez, Sergio de Albuquerque, E-mail: anacris@ird.gov.br [Instituto de Radioproteção e Dosimetria (RD/CNEN-RJ), Rio de Janeiro, RJ (Brazil)

2017-07-01

The primary purpose of the range of activities concerning the info management of the environmental assessment is to provide to scientific community an improved access to environmental data, as well as to support the decision making loop, in case of contamination events due either to accidental or intentional causes. In recent years, geotechnologies became a key reference in environmental research and monitoring, since they deliver an efficient data retrieval and subsequent processing about natural resources. This study aimed at the development of a georeferenced database (SIGLARA - Sistema Georeferenciado Latino Americano de Radionuclídeos em Alimentos), designed to radioactivity in food data storage, available in three languages (Spanish, Portuguese and English), employing free software. (author)

Design and development of a geo-referenced database to radionuclides in food

International Nuclear Information System (INIS)

Nascimento, Lucia Maria Evangelista do; Ferreira, Ana Cristina de Melo; Gonzalez, Sergio de Albuquerque

2017-01-01

The primary purpose of the range of activities concerning the info management of the environmental assessment is to provide to scientific community an improved access to environmental data, as well as to support the decision making loop, in case of contamination events due either to accidental or intentional causes. In recent years, geotechnologies became a key reference in environmental research and monitoring, since they deliver an efficient data retrieval and subsequent processing about natural resources. This study aimed at the development of a georeferenced database (SIGLARA - Sistema Georeferenciado Latino Americano de Radionuclídeos em Alimentos), designed to radioactivity in food data storage, available in three languages (Spanish, Portuguese and English), employing free software. (author)

Harvard Aging Brain Study: Dataset and accessibility.

Science.gov (United States)

Dagley, Alexander; LaPoint, Molly; Huijbers, Willem; Hedden, Trey; McLaren, Donald G; Chatwal, Jasmeer P; Papp, Kathryn V; Amariglio, Rebecca E; Blacker, Deborah; Rentz, Dorene M; Johnson, Keith A; Sperling, Reisa A; Schultz, Aaron P

2017-01-01

The Harvard Aging Brain Study is sharing its data with the global research community. The longitudinal dataset consists of a 284-subject cohort with the following modalities acquired: demographics, clinical assessment, comprehensive neuropsychological testing, clinical biomarkers, and neuroimaging. To promote more extensive analyses, imaging data was designed to be compatible with other publicly available datasets. A cloud-based system enables access to interested researchers with blinded data available contingent upon completion of a data usage agreement and administrative approval. Data collection is ongoing and currently in its fifth year. Copyright © 2015 Elsevier Inc. All rights reserved.

VideoWeb Dataset for Multi-camera Activities and Non-verbal Communication

Science.gov (United States)

Denina, Giovanni; Bhanu, Bir; Nguyen, Hoang Thanh; Ding, Chong; Kamal, Ahmed; Ravishankar, Chinya; Roy-Chowdhury, Amit; Ivers, Allen; Varda, Brenda

Human-activity recognition is one of the most challenging problems in computer vision. Researchers from around the world have tried to solve this problem and have come a long way in recognizing simple motions and atomic activities. As the computer vision community heads toward fully recognizing human activities, a challenging and labeled dataset is needed. To respond to that need, we collected a dataset of realistic scenarios in a multi-camera network environment (VideoWeb) involving multiple persons performing dozens of different repetitive and non-repetitive activities. This chapter describes the details of the dataset. We believe that this VideoWeb Activities dataset is unique and it is one of the most challenging datasets available today. The dataset is publicly available online at http://vwdata.ee.ucr.edu/ along with the data annotation.

A multi-environment dataset for activity of daily living recognition in video streams.

Science.gov (United States)

Borreo, Alessandro; Onofri, Leonardo; Soda, Paolo

2015-08-01

Public datasets played a key role in the increasing level of interest that vision-based human action recognition has attracted in last years. While the production of such datasets has been influenced by the variability introduced by various actors performing the actions, the different modalities of interactions with the environment introduced by the variation of the scenes around the actors has been scarcely took into account. As a consequence, public datasets do not provide a proper test-bed for recognition algorithms that aim at achieving high accuracy, irrespective of the environment where actions are performed. This is all the more so, when systems are designed to recognize activities of daily living (ADL), which are characterized by a high level of human-environment interaction. For that reason, we present in this manuscript the MEA dataset, a new multi-environment ADL dataset, which permitted us to show how the change of scenario can affect the performances of state-of-the-art approaches for action recognition.

CERC Dataset (Full Hadza Data)

DEFF Research Database (Denmark)

2016-01-01

The dataset includes demographic, behavioral, and religiosity data from eight different populations from around the world. The samples were drawn from: (1) Coastal and (2) Inland Tanna, Vanuatu; (3) Hadzaland, Tanzania; (4) Lovu, Fiji; (5) Pointe aux Piment, Mauritius; (6) Pesqueiro, Brazil; (7......) Kyzyl, Tyva Republic; and (8) Yasawa, Fiji. Related publication: Purzycki, et al. (2016). Moralistic Gods, Supernatural Punishment and the Expansion of Human Sociality. Nature, 530(7590): 327-330....

RetroTransformDB: A Dataset of Generic Transforms for Retrosynthetic Analysis

Directory of Open Access Journals (Sweden)

Svetlana Avramova

2018-04-01

Full Text Available Presently, software tools for retrosynthetic analysis are widely used by organic, medicinal, and computational chemists. Rule-based systems extensively use collections of retro-reactions (transforms. While there are many public datasets with reactions in synthetic direction (usually non-generic reactions, there are no publicly-available databases with generic reactions in computer-readable format which can be used for the purposes of retrosynthetic analysis. Here we present RetroTransformDB—a dataset of transforms, compiled and coded in SMIRKS line notation by us. The collection is comprised of more than 100 records, with each one including the reaction name, SMIRKS linear notation, the functional group to be obtained, and the transform type classification. All SMIRKS transforms were tested syntactically, semantically, and from a chemical point of view in different software platforms. The overall dataset design and the retrosynthetic fitness were analyzed and curated by organic chemistry experts. The RetroTransformDB dataset may be used by open-source and commercial software packages, as well as chemoinformatics tools.

Passive Containment DataSet

Science.gov (United States)

This data is for Figures 6 and 7 in the journal article. The data also includes the two EPANET input files used for the analysis described in the paper, one for the looped system and one for the block system.This dataset is associated with the following publication:Grayman, W., R. Murray , and D. Savic. Redesign of Water Distribution Systems for Passive Containment of Contamination. JOURNAL OF THE AMERICAN WATER WORKS ASSOCIATION. American Water Works Association, Denver, CO, USA, 108(7): 381-391, (2016).

Feature extraction and descriptor calculation methods for automatic georeferencing of Philippines' first microsatellite imagery

Science.gov (United States)

Tupas, M. E. A.; Dasallas, J. A.; Jiao, B. J. D.; Magallon, B. J. P.; Sempio, J. N. H.; Ramos, M. K. F.; Aranas, R. K. D.; Tamondong, A. M.

2017-10-01

The FAST-SIFT corner detector and descriptor extractor combination was used to automatically georeference DIWATA-1 Spaceborne Multispectral Imager images. Features from the Fast Accelerated Segment Test (FAST) algorithm detects corners or keypoints in an image, and these robustly detected keypoints have well-defined positions. Descriptors were computed using Scale-Invariant Feature Transform (SIFT) extractor. FAST-SIFT method effectively SMI same-subscene images detected by the NIR sensor. The method was also tested in stitching NIR images with varying subscene swept by the camera. The slave images were matched to the master image. The keypoints served as the ground control points. Random sample consensus was used to eliminate fall-out matches and ensure accuracy of the feature points from which the transformation parameters were derived. Keypoints are matched based on their descriptor vector. Nearest-neighbor matching is employed based on a metric distance between the descriptors. The metrics include Euclidean and city block, among others. Rough matching outputs not only the correct matches but also the faulty matches. A previous work in automatic georeferencing incorporates a geometric restriction. In this work, we applied a simplified version of the learning method. RANSAC was used to eliminate fall-out matches and ensure accuracy of the feature points. This method identifies if a point fits the transformation function and returns inlier matches. The transformation matrix was solved by Affine, Projective, and Polynomial models. The accuracy of the automatic georeferencing method were determined by calculating the RMSE of interest points, selected randomly, between the master image and transformed slave image.

The Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap.

Science.gov (United States)

Mann, Richard P; Mushtaq, Faisal; White, Alan D; Mata-Cervantes, Gabriel; Pike, Tom; Coker, Dalton; Murdoch, Stuart; Hiles, Tim; Smith, Clare; Berridge, David; Hinchliffe, Suzanne; Hall, Geoff; Smye, Stephen; Wilkie, Richard M; Lodge, J Peter A; Mon-Williams, Mark

2016-01-01

Big datasets have the potential to revolutionize public health. However, there is a mismatch between the political and scientific optimism surrounding big data and the public's perception of its benefit. We suggest a systematic and concerted emphasis on developing models derived from smaller datasets to illustrate to the public how big data can produce tangible benefits in the long term. In order to highlight the immediate value of a small data approach, we produced a proof-of-concept model predicting hospital length of stay. The results demonstrate that existing small datasets can be used to create models that generate a reasonable prediction, facilitating health-care delivery. We propose that greater attention (and funding) needs to be directed toward the utilization of existing information resources in parallel with current efforts to create and exploit "big data."

Accuracy analysis of indirect georeferencing about TH-1 satellite in Weinan test area

International Nuclear Information System (INIS)

Yunlan, Yang; Haiyan, Hu

2014-01-01

Optical linear scanning sensors can be divided into single-lens sensors and multi-lens sensors according to the number of lenses. In order to build stereo imaging, for single-lens optical systems such as aerial mapping camera ADS40 and ADS80, there are more than two parallel linear arrays placed on the focal plane. And for a multi-lens optical system there is only one linear CCD arrays placed on the center of every focal plan for each lens which is often carried on spacecraft. The difference of design between these two kinds of optical systems leads to the systematic errors, calibration in orbit and approach of data adjustment are different completely. Recent years the domestic space optical sensor systems are focused on multi-lens linear CCD sensor in China, such as TH-1 and ZY-3 both belong to multi-lens optical systems. The parameters influencing the position accuracy of the satellite system which are unknown or unknown precisely even changed after sensor posted launch can be estimated by self-calibration in orbit. So after self-calibration in orbit the accuracy of mapping satellite will often be improved strongly. Comparing to direct georeferencing, the indirect georeferencing as a research approach is introduced to TH-1 satellite in this paper considering the systematic errors completely. Parameters about geometry position systematic error are introduced to the basic co-linearity equations for multi-lenses linear array CCD sensor, and based on the extended model the method of space multi-lens linear array CCD sensor self-calibration bundle adjustment is presented. The test field is in some area of Weinan, Shaanxi province, and the observation data of GCPs and orbit are collected. The extended rigors model is used in bundle adjustment and the accuracy analysis shown that TH-1 has a satisfied metric performance

External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.

Science.gov (United States)

Benndorf, Matthias; Burnside, Elizabeth S; Herda, Christoph; Langer, Mathias; Kotter, Elmar

2015-08-01

Lesions detected at mammography are described with a highly standardized terminology: the breast imaging-reporting and data system (BI-RADS) lexicon. Up to now, no validated semantic computer assisted classification algorithm exists to interactively link combinations of morphological descriptors from the lexicon to a probabilistic risk estimate of malignancy. The authors therefore aim at the external validation of the mammographic mass diagnosis (MMassDx) algorithm. A classification algorithm like MMassDx must perform well in a variety of clinical circumstances and in datasets that were not used to generate the algorithm in order to ultimately become accepted in clinical routine. The MMassDx algorithm uses a naïve Bayes network and calculates post-test probabilities of malignancy based on two distinct sets of variables, (a) BI-RADS descriptors and age ("descriptor model") and (b) BI-RADS descriptors, age, and BI-RADS assessment categories ("inclusive model"). The authors evaluate both the MMassDx (descriptor) and MMassDx (inclusive) models using two large publicly available datasets of mammographic mass lesions: the digital database for screening mammography (DDSM) dataset, which contains two subsets from the same examinations-a medio-lateral oblique (MLO) view and cranio-caudal (CC) view dataset-and the mammographic mass (MM) dataset. The DDSM contains 1220 mass lesions and the MM dataset contains 961 mass lesions. The authors evaluate discriminative performance using area under the receiver-operating-characteristic curve (AUC) and compare this to the BI-RADS assessment categories alone (i.e., the clinical performance) using the DeLong method. The authors also evaluate whether assigned probabilistic risk estimates reflect the lesions' true risk of malignancy using calibration curves. The authors demonstrate that the MMassDx algorithms show good discriminatory performance. AUC for the MMassDx (descriptor) model in the DDSM data is 0.876/0.895 (MLO/CC view) and AUC

BanglaLekha-Isolated: A multi-purpose comprehensive dataset of Handwritten Bangla Isolated characters

Directory of Open Access Journals (Sweden)

Mithun Biswas

2017-06-01

Full Text Available BanglaLekha-Isolated, a Bangla handwritten isolated character dataset is presented in this article. This dataset contains 84 different characters comprising of 50 Bangla basic characters, 10 Bangla numerals and 24 selected compound characters. 2000 handwriting samples for each of the 84 characters were collected, digitized and pre-processed. After discarding mistakes and scribbles, 1,66,105 handwritten character images were included in the final dataset. The dataset also includes labels indicating the age and the gender of the subjects from whom the samples were collected. This dataset could be used not only for optical handwriting recognition research but also to explore the influence of gender and age on handwriting. The dataset is publicly available at https://data.mendeley.com/datasets/hf6sf8zrkc/2.

A dataset of forest biomass structure for Eurasia.

Science.gov (United States)

Schepaschenko, Dmitry; Shvidenko, Anatoly; Usoltsev, Vladimir; Lakyda, Petro; Luo, Yunjian; Vasylyshyn, Roman; Lakyda, Ivan; Myklush, Yuriy; See, Linda; McCallum, Ian; Fritz, Steffen; Kraxner, Florian; Obersteiner, Michael

2017-05-16

The most comprehensive dataset of in situ destructive sampling measurements of forest biomass in Eurasia have been compiled from a combination of experiments undertaken by the authors and from scientific publications. Biomass is reported as four components: live trees (stem, bark, branches, foliage, roots); understory (above- and below ground); green forest floor (above- and below ground); and coarse woody debris (snags, logs, dead branches of living trees and dead roots), consisting of 10,351 unique records of sample plots and 9,613 sample trees from ca 1,200 experiments for the period 1930-2014 where there is overlap between these two datasets. The dataset also contains other forest stand parameters such as tree species composition, average age, tree height, growing stock volume, etc., when available. Such a dataset can be used for the development of models of biomass structure, biomass extension factors, change detection in biomass structure, investigations into biodiversity and species distribution and the biodiversity-productivity relationship, as well as the assessment of the carbon pool and its dynamics, among many others.

The MetabolomeExpress Project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets

Directory of Open Access Journals (Sweden)

Carroll Adam J

2010-07-01

Full Text Available Abstract Background Standardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis. Description Here, we present MetabolomeExpress, a new File Transfer Protocol (FTP server and web-tool for the online storage, processing, visualisation and statistical re-analysis of publicly submitted GC/MS metabolomics datasets. Users may search a quality-controlled database of metabolite response statistics from publicly submitted datasets by a number of parameters (eg. metabolite, species, organ/biofluid etc.. Users may also perform meta-analysis comparisons of multiple independent experiments or re-analyse public primary datasets via user-friendly tools for t-test, principal components analysis, hierarchical cluster analysis and correlation analysis. They may interact with chromatograms, mass spectra and peak detection results via an integrated raw data viewer. Researchers who register for a free account may upload (via FTP their own data to the server for online processing via a novel raw data processing pipeline. Conclusions MetabolomeExpress https://www.metabolome-express.org provides a new opportunity for the general metabolomics community to transparently present online the raw and processed GC/MS data underlying their metabolomics publications. Transparent sharing of these data will allow researchers to

Ecohydrological Index, Native Fish, and Climate Trends and Relationships in the Kansas River Basin_dataset

Data.gov (United States)

U.S. Environmental Protection Agency — The dataset is an excel file that contain data for the figures in the manuscript. This dataset is associated with the following publication: Sinnathamby, S., K....

Importing, Working With, and Sharing Microstructural Data in the StraboSpot Digital Data System, Including an Example Dataset from the Pilbara Craton, Western Australia.

Science.gov (United States)

Roberts, N.; Cunningham, H.; Snell, A.; Newman, J.; Tikoff, B.; Chatzaras, V.; Walker, J. D.; Williams, R. T.

2017-12-01

There is currently no repository where a geologist can survey microstructural datasets that have been collected from a specific field area or deformation experiment. New development of the StraboSpot digital data system provides a such a repository as well as visualization and analysis tools. StraboSpot is a graph database that allows field geologists to share primary data and develop new types of scientific questions. The database can be accessed through: 1) a field-based mobile application that runs on iOS and Android mobile devices; and 2) a desktop system. We are expanding StraboSpot to include the handling of a variety of microstructural data types. Presented here is the detailed vocabulary and logic used for the input of microstructural data, and how this system operates with the anticipated workflow of users. Microstructural data include observations and interpretations from photomicrographs, scanning electron microscope images, electron backscatter diffraction, and transmission electron microscopy data. The workflow for importing microstructural data into StraboSpot is organized into the following tabs: Images, Mineralogy & Composition; Sedimentary; Igneous; Metamorphic; Fault Rocks; Grain size & configuration; Crystallographic Preferred Orientation; Reactions; Geochronology; Relationships; and Interpretations. Both the sample and the thin sections are also spots. For the sample spot, the user can specify whether a sample is experimental or natural; natural samples are inherently linked to their field context. For the thin section (sub-sample) spot, the user can select between different options for sample preparation, geometry, and methods. A universal framework for thin section orientation is given, which allows users to overlay different microscope images of the same area and keeps georeferenced orientation. We provide an example dataset of field and microstructural data from the Mt Edgar dome, a granitic complex in the Paleoarchean East Pilbara craton

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.

Directory of Open Access Journals (Sweden)

Nilotpal Chowdhury

Full Text Available Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis.The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets.Four microarray series (having 742 patients were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA.Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed.To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.

Science.gov (United States)

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting

Development of a georeferenced data bank of radionuclides in typical food of Latin America - SIGLARA; Desenvolvimento de um banco de dados georeferenciado de radionuclideos em alimentos tipicos na America Latina - SIGLARA

Energy Technology Data Exchange (ETDEWEB)

Nascimento, Lucia Maria Evangelista do

2014-07-01

The related management information related to the environmental assessment activity aims to provide the world community with better access to meaningful environmental information and help use this information in making decisions in case of contamination due to accident or deliberate actions. In recent years, the geotechnologies acquired are fundamental to research and environmental monitoring, once it possible, efficiently obtaining large amount of data natural resources. The result of this work was the development of a database system to store georeferenced data values of radionuclides in typical foods in Latin America (SIGLARA), defined in three languages (Spanish, Portuguese and English), using free software. The developed system meets the primary need of the RLA 09/72 ARCAL Project, funded by the International Atomic Energy Agency (IAEA), as having eleven participants countries in Latin America. The database of georeferenced created for SIGLARA system was tested in its applicability through the entry and manipulation of real data analyzed, which showed that the system is able to store, retrieve, view reports and maps of the samples of registered food. Interfaces that connect the user with the database show up efficient, making the system easy operability. Their application to environmental management is already showing results, it is hoped that these results will encourage its widespread adoption by other countries, institutions, the scientific community and the general public. (author)

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery: A new, publicly-available dataset

Science.gov (United States)

Richardson, A. D.

2015-12-01

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is highly sensitive to climate change and variability, and is thus a key aspect of global change ecology. The goal of the PhenoCam network is to serve as a long-term, continental-scale, phenological observatory. The network uses repeat digital photography—images captured using conventional, visible-wavelength, automated digital cameras—to characterize vegetation phenology in diverse ecosystems across North America and around the world. At present, imagery from over 200 research sites, spanning a wide range of ecoregions, climate zones, and plant functional types, is currently being archived and processed in near-real-time through the PhenoCam project web page (http://phenocam.sr.unh.edu/). Data derived from PhenoCam imagery have been previously used to evaluate satellite phenology products, to constrain and test new phenology models, to understand relationships between canopy phenology and ecosystem processes, and to study the seasonal changes in leaf-level physiology that are associated with changes in leaf color. I will describe a new, publicly-available phenological dataset, derived from over 600 site-years of PhenoCam imagery. For each archived image (ca. 5 million), we extracted RGB (red, green, blue) color channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 minute) imagery, we derived time series characterizing vegetation color, including "canopy greenness", processed to 1- and 3-day intervals. For ecosystems with a single annual cycle of vegetation activity, we derived estimates, with uncertainties, for the start, middle, and end of spring and autumn phenological transitions. Given the lack of multi-year, standardized, and geographically distributed phenological data for North America, we

Wind and wave dataset for Matara, Sri Lanka

Science.gov (United States)

Luo, Yao; Wang, Dongxiao; Priyadarshana Gamage, Tilak; Zhou, Fenghua; Madusanka Widanage, Charith; Liu, Taiwei

2018-01-01

We present a continuous in situ hydro-meteorology observational dataset from a set of instruments first deployed in December 2012 in the south of Sri Lanka, facing toward the north Indian Ocean. In these waters, simultaneous records of wind and wave data are sparse due to difficulties in deploying measurement instruments, although the area hosts one of the busiest shipping lanes in the world. This study describes the survey, deployment, and measurements of wind and waves, with the aim of offering future users of the dataset the most comprehensive and as much information as possible. This dataset advances our understanding of the nearshore hydrodynamic processes and wave climate, including sea waves and swells, in the north Indian Ocean. Moreover, it is a valuable resource for ocean model parameterization and validation. The archived dataset (Table 1) is examined in detail, including wave data at two locations with water depths of 20 and 10 m comprising synchronous time series of wind, ocean astronomical tide, air pressure, etc. In addition, we use these wave observations to evaluate the ERA-Interim reanalysis product. Based on Buoy 2 data, the swells are the main component of waves year-round, although monsoons can markedly alter the proportion between swell and wind sea. The dataset (Luo et al., 2017) is publicly available from Science Data Bank (https://doi.org/10.11922/sciencedb.447).

Wind and wave dataset for Matara, Sri Lanka

Directory of Open Access Journals (Sweden)

Y. Luo

2018-01-01

Full Text Available We present a continuous in situ hydro-meteorology observational dataset from a set of instruments first deployed in December 2012 in the south of Sri Lanka, facing toward the north Indian Ocean. In these waters, simultaneous records of wind and wave data are sparse due to difficulties in deploying measurement instruments, although the area hosts one of the busiest shipping lanes in the world. This study describes the survey, deployment, and measurements of wind and waves, with the aim of offering future users of the dataset the most comprehensive and as much information as possible. This dataset advances our understanding of the nearshore hydrodynamic processes and wave climate, including sea waves and swells, in the north Indian Ocean. Moreover, it is a valuable resource for ocean model parameterization and validation. The archived dataset (Table 1 is examined in detail, including wave data at two locations with water depths of 20 and 10 m comprising synchronous time series of wind, ocean astronomical tide, air pressure, etc. In addition, we use these wave observations to evaluate the ERA-Interim reanalysis product. Based on Buoy 2 data, the swells are the main component of waves year-round, although monsoons can markedly alter the proportion between swell and wind sea. The dataset (Luo et al., 2017 is publicly available from Science Data Bank (https://doi.org/10.11922/sciencedb.447.

Discovery and Reuse of Open Datasets: An Exploratory Study

Directory of Open Access Journals (Sweden)

Sara

2016-07-01

Full Text Available Objective: This article analyzes twenty cited or downloaded datasets and the repositories that house them, in order to produce insights that can be used by academic libraries to encourage discovery and reuse of research data in institutional repositories. Methods: Using Thomson Reuters’ Data Citation Index and repository download statistics, we identified twenty cited/downloaded datasets. We documented the characteristics of the cited/downloaded datasets and their corresponding repositories in a self-designed rubric. The rubric includes six major categories: basic information; funding agency and journal information; linking and sharing; factors to encourage reuse; repository characteristics; and data description. Results: Our small-scale study suggests that cited/downloaded datasets generally comply with basic recommendations for facilitating reuse: data are documented well; formatted for use with a variety of software; and shared in established, open access repositories. Three significant factors also appear to contribute to dataset discovery: publishing in discipline-specific repositories; indexing in more than one location on the web; and using persistent identifiers. The cited/downloaded datasets in our analysis came from a few specific disciplines, and tended to be funded by agencies with data publication mandates. Conclusions: The results of this exploratory research provide insights that can inform academic librarians as they work to encourage discovery and reuse of institutional datasets. Our analysis also suggests areas in which academic librarians can target open data advocacy in their communities in order to begin to build open data success stories that will fuel future advocacy efforts.

GEOREFERENCING IN GNSS-CHALLENGED ENVIRONMENT: INTEGRATING UWB AND IMU TECHNOLOGIES

Directory of Open Access Journals (Sweden)

C. K. Toth

2017-05-01

Full Text Available Acquiring geospatial data in GNSS compromised environments remains a problem in mapping and positioning in general. Urban canyons, heavily vegetated areas, indoor environments represent different levels of GNSS signal availability from weak to no signal reception. Even outdoors, with multiple GNSS systems, with an ever-increasing number of satellites, there are many situations with limited or no access to GNSS signals. Independent navigation sensors, such as IMU can provide high-data rate information but their initial accuracy degrades quickly, as the measurement data drift over time unless positioning fixes are provided from another source. At The Ohio State University’s Satellite Positioning and Inertial Navigation (SPIN Laboratory, as one feasible solution, Ultra- Wideband (UWB radio units are used to aid positioning and navigating in GNSS compromised environments, including indoor and outdoor scenarios. Here we report about experiences obtained with georeferencing a pushcart based sensor system under canopied areas. The positioning system is based on UWB and IMU sensor integration, and provides sensor platform orientation for an electromagnetic inference (EMI sensor. Performance evaluation results are provided for various test scenarios, confirming acceptable results for applications where high accuracy is not required.

The Role of Datasets on Scientific Influence within Conflict Research.

Science.gov (United States)

Van Holt, Tracy; Johnson, Jeffery C; Moates, Shiloh; Carley, Kathleen M

2016-01-01

We inductively tested if a coherent field of inquiry in human conflict research emerged in an analysis of published research involving "conflict" in the Web of Science (WoS) over a 66-year period (1945-2011). We created a citation network that linked the 62,504 WoS records and their cited literature. We performed a critical path analysis (CPA), a specialized social network analysis on this citation network (~1.5 million works), to highlight the main contributions in conflict research and to test if research on conflict has in fact evolved to represent a coherent field of inquiry. Out of this vast dataset, 49 academic works were highlighted by the CPA suggesting a coherent field of inquiry; which means that researchers in the field acknowledge seminal contributions and share a common knowledge base. Other conflict concepts that were also analyzed-such as interpersonal conflict or conflict among pharmaceuticals, for example, did not form their own CP. A single path formed, meaning that there was a cohesive set of ideas that built upon previous research. This is in contrast to a main path analysis of conflict from 1957-1971 where ideas didn't persist in that multiple paths existed and died or emerged reflecting lack of scientific coherence (Carley, Hummon, and Harty, 1993). The critical path consisted of a number of key features: 1) Concepts that built throughout include the notion that resource availability drives conflict, which emerged in the 1960s-1990s and continued on until 2011. More recent intrastate studies that focused on inequalities emerged from interstate studies on the democracy of peace earlier on the path. 2) Recent research on the path focused on forecasting conflict, which depends on well-developed metrics and theories to model. 3) We used keyword analysis to independently show how the CP was topically linked (i.e., through democracy, modeling, resources, and geography). Publically available conflict datasets developed early on helped shape the

The OXL format for the exchange of integrated datasets

Directory of Open Access Journals (Sweden)

Taubert Jan

2007-12-01

Full Text Available A prerequisite for systems biology is the integration and analysis of heterogeneous experimental data stored in hundreds of life-science databases and millions of scientific publications. Several standardised formats for the exchange of specific kinds of biological information exist. Such exchange languages facilitate the integration process; however they are not designed to transport integrated datasets. A format for exchanging integrated datasets needs to i cover data from a broad range of application domains, ii be flexible and extensible to combine many different complex data structures, iii include metadata and semantic definitions, iv include inferred information, v identify the original data source for integrated entities and vi transport large integrated datasets. Unfortunately, none of the exchange formats from the biological domain (e.g. BioPAX, MAGE-ML, PSI-MI, SBML or the generic approaches (RDF, OWL fulfil these requirements in a systematic way.

Review of ATLAS Open Data 8 TeV datasets, tools and activities

CERN Document Server

The ATLAS collaboration

2018-01-01

The ATLAS Collaboration has released two 8 TeV datasets and relevant simulated samples to the public for educational use. A number of groups within ATLAS have used these ATLAS Open Data 8 TeV datasets, developing tools and educational material to promote particle physics. The general aim of these activities is to provide simple and user-friendly interactive interfaces to simulate the procedures used by high-energy physics researchers. International Masterclasses introduce particle physics to high school students and have been studying 8 TeV ATLAS Open Data since 2015. Inspired by this success, a new ATLAS Open Data initiative was launched in 2016 for university students. A comprehensive educational platform was thus developed featuring a second 8 TeV dataset and a new set of educational tools. The 8 TeV datasets and associated tools are presented and discussed here, as well as a selection of activities studying the ATLAS Open Data 8 TeV datasets.

Public Schools

Data.gov (United States)

Department of Homeland Security — This Public Schools feature dataset is composed of all Public elementary and secondary education in the United States as defined by the Common Core of Data, National...

Chemical elements in the environment: multi-element geochemical datasets from continental to national scale surveys on four continents

Science.gov (United States)

Caritat, Patrice de; Reimann, Clemens; Smith, David; Wang, Xueqiu

2017-01-01

During the last 10-20 years, Geological Surveys around the world have undertaken a major effort towards delivering fully harmonized and tightly quality-controlled low-density multi-element soil geochemical maps and datasets of vast regions including up to whole continents. Concentrations of between 45 and 60 elements commonly have been determined in a variety of different regolith types (e.g., sediment, soil). The multi-element datasets are published as complete geochemical atlases and made available to the general public. Several other geochemical datasets covering smaller areas but generally at a higher spatial density are also available. These datasets may, however, not be found by superficial internet-based searches because the elements are not mentioned individually either in the title or in the keyword lists of the original references. This publication attempts to increase the visibility and discoverability of these fundamental background datasets covering large areas up to whole continents.

Se-SAD serial femtosecond crystallography datasets from selenobiotinyl-streptavidin

Science.gov (United States)

Yoon, Chun Hong; Demirci, Hasan; Sierra, Raymond G.; Dao, E. Han; Ahmadi, Radman; Aksit, Fulya; Aquila, Andrew L.; Batyuk, Alexander; Ciftci, Halilibrahim; Guillet, Serge; Hayes, Matt J.; Hayes, Brandon; Lane, Thomas J.; Liang, Meng; Lundström, Ulf; Koglin, Jason E.; Mgbam, Paul; Rao, Yashas; Rendahl, Theodore; Rodriguez, Evan; Zhang, Lindsey; Wakatsuki, Soichi; Boutet, Sébastien; Holton, James M.; Hunter, Mark S.

2017-04-01

We provide a detailed description of selenobiotinyl-streptavidin (Se-B SA) co-crystal datasets recorded using the Coherent X-ray Imaging (CXI) instrument at the Linac Coherent Light Source (LCLS) for selenium single-wavelength anomalous diffraction (Se-SAD) structure determination. Se-B SA was chosen as the model system for its high affinity between biotin and streptavidin where the sulfur atom in the biotin molecule (C10H16N2O3S) is substituted with selenium. The dataset was collected at three different transmissions (100, 50, and 10%) using a serial sample chamber setup which allows for two sample chambers, a front chamber and a back chamber, to operate simultaneously. Diffraction patterns from Se-B SA were recorded to a resolution of 1.9 Å. The dataset is publicly available through the Coherent X-ray Imaging Data Bank (CXIDB) and also on LCLS compute nodes as a resource for research and algorithm development.

The influence of the in situ camera calibration for direct georeferencing of aerial imagery

Science.gov (United States)

Mitishita, E.; Barrios, R.; Centeno, J.

2014-11-01

The direct determination of exterior orientation parameters (EOPs) of aerial images via GNSS/INS technologies is an essential prerequisite in photogrammetric mapping nowadays. Although direct sensor orientation technologies provide a high degree of automation in the process due to the GNSS/INS technologies, the accuracies of the obtained results depend on the quality of a group of parameters that models accurately the conditions of the system at the moment the job is performed. One sub-group of parameters (lever arm offsets and boresight misalignments) models the position and orientation of the sensors with respect to the IMU body frame due to the impossibility of having all sensors on the same position and orientation in the airborne platform. Another sub-group of parameters models the internal characteristics of the sensor (IOP). A system calibration procedure has been recommended by worldwide studies to obtain accurate parameters (mounting and sensor characteristics) for applications of the direct sensor orientation. Commonly, mounting and sensor characteristics are not stable; they can vary in different flight conditions. The system calibration requires a geometric arrangement of the flight and/or control points to decouple correlated parameters, which are not available in the conventional photogrammetric flight. Considering this difficulty, this study investigates the feasibility of the in situ camera calibration to improve the accuracy of the direct georeferencing of aerial images. The camera calibration uses a minimum image block, extracted from the conventional photogrammetric flight, and control point arrangement. A digital Vexcel UltraCam XP camera connected to POS AV TM system was used to get two photogrammetric image blocks. The blocks have different flight directions and opposite flight line. In situ calibration procedures to compute different sets of IOPs are performed and their results are analyzed and used in photogrammetric experiments. The IOPs

Benchmarking Deep Learning Models on Large Healthcare Datasets.

Science.gov (United States)

Purushotham, Sanjay; Meng, Chuizheng; Che, Zhengping; Liu, Yan

2018-06-04

Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the 'raw' clinical time series data is used as input features to the models. Copyright © 2018 Elsevier Inc. All rights reserved.

Sensor Fusion of a Mobile Device to Control and Acquire Videos or Images of Coffee Branches and for Georeferencing Trees

Directory of Open Access Journals (Sweden)

Paula Jimena Ramos Giraldo

2017-04-01

Full Text Available Smartphones show potential for controlling and monitoring variables in agriculture. Their processing capacity, instrumentation, connectivity, low cost, and accessibility allow farmers (among other users in rural areas to operate them easily with applications adjusted to their specific needs. In this investigation, the integration of inertial sensors, a GPS, and a camera are presented for the monitoring of a coffee crop. An Android-based application was developed with two operating modes: (i Navigation: for georeferencing trees, which can be as close as 0.5 m from each other; and (ii Acquisition: control of video acquisition, based on the movement of the mobile device over a branch, and measurement of image quality, using clarity indexes to select the most appropriate frames for application in future processes. The integration of inertial sensors in navigation mode, shows a mean relative error of ±0.15 m, and total error ±5.15 m. In acquisition mode, the system correctly identifies the beginning and end of mobile phone movement in 99% of cases, and image quality is determined by means of a sharpness factor which measures blurriness. With the developed system, it will be possible to obtain georeferenced information about coffee trees, such as their production, nutritional state, and presence of plagues or diseases.

Sensor Fusion of a Mobile Device to Control and Acquire Videos or Images of Coffee Branches and for Georeferencing Trees.

Science.gov (United States)

Giraldo, Paula Jimena Ramos; Aguirre, Álvaro Guerrero; Muñoz, Carlos Mario; Prieto, Flavio Augusto; Oliveros, Carlos Eugenio

2017-04-06

Smartphones show potential for controlling and monitoring variables in agriculture. Their processing capacity, instrumentation, connectivity, low cost, and accessibility allow farmers (among other users in rural areas) to operate them easily with applications adjusted to their specific needs. In this investigation, the integration of inertial sensors, a GPS, and a camera are presented for the monitoring of a coffee crop. An Android-based application was developed with two operating modes: ( i ) Navigation: for georeferencing trees, which can be as close as 0.5 m from each other; and ( ii ) Acquisition: control of video acquisition, based on the movement of the mobile device over a branch, and measurement of image quality, using clarity indexes to select the most appropriate frames for application in future processes. The integration of inertial sensors in navigation mode, shows a mean relative error of ±0.15 m, and total error ±5.15 m. In acquisition mode, the system correctly identifies the beginning and end of mobile phone movement in 99% of cases, and image quality is determined by means of a sharpness factor which measures blurriness. With the developed system, it will be possible to obtain georeferenced information about coffee trees, such as their production, nutritional state, and presence of plagues or diseases.

OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets.

Science.gov (United States)

Perez-Riverol, Yasset; Ternent, Tobias; Koch, Maximilian; Barsnes, Harald; Vrousgou, Olga; Jupp, Simon; Vizcaíno, Juan Antonio

2017-10-01

The availability of user-friendly software to annotate biological datasets and experimental details is becoming essential in data management practices, both in local storage systems and in public databases. The Ontology Lookup Service (OLS, http://www.ebi.ac.uk/ols) is a popular centralized service to query, browse and navigate biomedical ontologies and controlled vocabularies. Recently, the OLS framework has been completely redeveloped (version 3.0), including enhancements in the data model, like the added support for Web Ontology Language based ontologies, among many other improvements. However, the new OLS is not backwards compatible and new software tools are needed to enable access to this widely used framework now that the previous version is no longer available. We here present the OLS Client as a free, open-source Java library to retrieve information from the new version of the OLS. It enables rapid tool creation by providing a robust, pluggable programming interface and common data model to programmatically access the OLS. The library has already been integrated and is routinely used by several bioinformatics resources and related data annotation tools. Secondly, we also introduce an updated version of the OLS Dialog (version 2.0), a Java graphical user interface that can be easily plugged into Java desktop applications to access the OLS. The software and related documentation are freely available at https://github.com/PRIDE-Utilities/ols-client and https://github.com/PRIDE-Toolsuite/ols-dialog. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A framework for automatic creation of gold-standard rigid 3D-2D registration datasets.

Science.gov (United States)

Madan, Hennadii; Pernuš, Franjo; Likar, Boštjan; Špiclin, Žiga

2017-02-01

Advanced image-guided medical procedures incorporate 2D intra-interventional information into pre-interventional 3D image and plan of the procedure through 3D/2D image registration (32R). To enter clinical use, and even for publication purposes, novel and existing 32R methods have to be rigorously validated. The performance of a 32R method can be estimated by comparing it to an accurate reference or gold standard method (usually based on fiducial markers) on the same set of images (gold standard dataset). Objective validation and comparison of methods are possible only if evaluation methodology is standardized, and the gold standard  dataset is made publicly available. Currently, very few such datasets exist and only one contains images of multiple patients acquired during a procedure. To encourage the creation of gold standard 32R datasets, we propose an automatic framework. The framework is based on rigid registration of fiducial markers. The main novelty is spatial grouping of fiducial markers on the carrier device, which enables automatic marker localization and identification across the 3D and 2D images. The proposed framework was demonstrated on clinical angiograms of 20 patients. Rigid 32R computed by the framework was more accurate than that obtained manually, with the respective target registration error below 0.027 mm compared to 0.040 mm. The framework is applicable for gold standard setup on any rigid anatomy, provided that the acquired images contain spatially grouped fiducial markers. The gold standard datasets and software will be made publicly available.

A large-scale dataset of solar event reports from automated feature recognition modules

Science.gov (United States)

Schuh, Michael A.; Angryk, Rafal A.; Martens, Petrus C.

2016-05-01

The massive repository of images of the Sun captured by the Solar Dynamics Observatory (SDO) mission has ushered in the era of Big Data for Solar Physics. In this work, we investigate the entire public collection of events reported to the Heliophysics Event Knowledgebase (HEK) from automated solar feature recognition modules operated by the SDO Feature Finding Team (FFT). With the SDO mission recently surpassing five years of operations, and over 280,000 event reports for seven types of solar phenomena, we present the broadest and most comprehensive large-scale dataset of the SDO FFT modules to date. We also present numerous statistics on these modules, providing valuable contextual information for better understanding and validating of the individual event reports and the entire dataset as a whole. After extensive data cleaning through exploratory data analysis, we highlight several opportunities for knowledge discovery from data (KDD). Through these important prerequisite analyses presented here, the results of KDD from Solar Big Data will be overall more reliable and better understood. As the SDO mission remains operational over the coming years, these datasets will continue to grow in size and value. Future versions of this dataset will be analyzed in the general framework established in this work and maintained publicly online for easy access by the community.

GPS receivers for georeferencing of spatial variability of soil attributes Receptores GPS para georreferenciamento da variabilidade espacial de atributos do solo

Directory of Open Access Journals (Sweden)

David L Rosalen

2011-12-01

Full Text Available The characterization of the spatial variability of soil attributes is essential to support agricultural practices in a sustainable manner. The use of geostatistics to characterize spatial variability of these attributes, such as soil resistance to penetration (RP and gravimetric soil moisture (GM is now usual practice in precision agriculture. The result of geostatistical analysis is dependent on the sample density and other factors according to the georeferencing methodology used. Thus, this study aimed to compare two methods of georeferencing to characterize the spatial variability of RP and GM as well as the spatial correlation of these variables. Sampling grid of 60 points spaced 20 m was used. For RP measurements, an electronic penetrometer was used and to determine the GM, a Dutch auger (0.0-0.1 m depth was used. The samples were georeferenced using a GPS navigation receiver, Simple Point Positioning (SPP with navigation GPS receiver, and Semi-Kinematic Relative Positioning (SKRP with an L1 geodetic GPS receiver. The results indicated that the georeferencing conducted by PPS did not affect the characterization of spatial variability of RP or GM, neither the spatial structure relationship of these attributes.A caracterização da variabilidade espacial dos atributos do solo é indispensável para subsidiar práticas agrícolas de maneira sustentável. A utilização da geoestatística para caracterizar a variabilidade espacial desses atributos, como a resistência mecânica do solo à penetração (RP e a umidade gravimétrica do solo (UG, é, hoje, prática usual na agricultura de precisão. O resultado da análise geoestatística é dependente da densidade amostral e de outros fatores, como o método de georreferencimento utilizado. Desta forma, o presente trabalho teve como objetivo comparar dois métodos de georreferenciamento para a caracterização da variabilidade espacial da RP e da UG, bem como a correlação espacial dessas vari

The Role of Datasets on Scientific Influence within Conflict Research.

Directory of Open Access Journals (Sweden)

Tracy Van Holt

Full Text Available We inductively tested if a coherent field of inquiry in human conflict research emerged in an analysis of published research involving "conflict" in the Web of Science (WoS over a 66-year period (1945-2011. We created a citation network that linked the 62,504 WoS records and their cited literature. We performed a critical path analysis (CPA, a specialized social network analysis on this citation network (~1.5 million works, to highlight the main contributions in conflict research and to test if research on conflict has in fact evolved to represent a coherent field of inquiry. Out of this vast dataset, 49 academic works were highlighted by the CPA suggesting a coherent field of inquiry; which means that researchers in the field acknowledge seminal contributions and share a common knowledge base. Other conflict concepts that were also analyzed-such as interpersonal conflict or conflict among pharmaceuticals, for example, did not form their own CP. A single path formed, meaning that there was a cohesive set of ideas that built upon previous research. This is in contrast to a main path analysis of conflict from 1957-1971 where ideas didn't persist in that multiple paths existed and died or emerged reflecting lack of scientific coherence (Carley, Hummon, and Harty, 1993. The critical path consisted of a number of key features: 1 Concepts that built throughout include the notion that resource availability drives conflict, which emerged in the 1960s-1990s and continued on until 2011. More recent intrastate studies that focused on inequalities emerged from interstate studies on the democracy of peace earlier on the path. 2 Recent research on the path focused on forecasting conflict, which depends on well-developed metrics and theories to model. 3 We used keyword analysis to independently show how the CP was topically linked (i.e., through democracy, modeling, resources, and geography. Publically available conflict datasets developed early on helped

The Role of Datasets on Scientific Influence within Conflict Research

Science.gov (United States)

Van Holt, Tracy; Johnson, Jeffery C.; Moates, Shiloh; Carley, Kathleen M.

2016-01-01

We inductively tested if a coherent field of inquiry in human conflict research emerged in an analysis of published research involving “conflict” in the Web of Science (WoS) over a 66-year period (1945–2011). We created a citation network that linked the 62,504 WoS records and their cited literature. We performed a critical path analysis (CPA), a specialized social network analysis on this citation network (~1.5 million works), to highlight the main contributions in conflict research and to test if research on conflict has in fact evolved to represent a coherent field of inquiry. Out of this vast dataset, 49 academic works were highlighted by the CPA suggesting a coherent field of inquiry; which means that researchers in the field acknowledge seminal contributions and share a common knowledge base. Other conflict concepts that were also analyzed—such as interpersonal conflict or conflict among pharmaceuticals, for example, did not form their own CP. A single path formed, meaning that there was a cohesive set of ideas that built upon previous research. This is in contrast to a main path analysis of conflict from 1957–1971 where ideas didn’t persist in that multiple paths existed and died or emerged reflecting lack of scientific coherence (Carley, Hummon, and Harty, 1993). The critical path consisted of a number of key features: 1) Concepts that built throughout include the notion that resource availability drives conflict, which emerged in the 1960s-1990s and continued on until 2011. More recent intrastate studies that focused on inequalities emerged from interstate studies on the democracy of peace earlier on the path. 2) Recent research on the path focused on forecasting conflict, which depends on well-developed metrics and theories to model. 3) We used keyword analysis to independently show how the CP was topically linked (i.e., through democracy, modeling, resources, and geography). Publically available conflict datasets developed early on helped

Toward Automatic Georeferencing of Archival Aerial Photogrammetric Surveys

Science.gov (United States)

Giordano, S.; Le Bris, A.; Mallet, C.

2018-05-01

Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes over the past 100 years. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Such time series image analysis is a mandatory baseline for a large variety of long-term environmental monitoring studies. The current bottleneck for accurate comparison between epochs is their fine georeferencing step. No fully automatic method has been proposed yet and existing studies are rather limited in terms of area and number of dates. State-of-the art shows that the major challenge is the identification of ground references: cartographic coordinates and their position in the archival images. This task is manually performed, and extremely time-consuming. This paper proposes to use a photogrammetric approach, and states that the 3D information that can be computed is the key to full automation. Its original idea lies in a 2-step approach: (i) the computation of a coarse absolute image orientation; (ii) the use of the coarse Digital Surface Model (DSM) information for automatic absolute image orientation. It only relies on a recent orthoimage+DSM, used as master reference for all epochs. The coarse orthoimage, compared with such a reference, allows the identification of dense ground references and the coarse DSM provides their position in the archival images. Results on two areas and 5 dates show that this method is compatible with long and dense archival aerial image series. Satisfactory planimetric and altimetric accuracies are reported, with variations depending on the ground sampling distance of the images and the location of the Ground Control Points.

TOWARD AUTOMATIC GEOREFERENCING OF ARCHIVAL AERIAL PHOTOGRAMMETRIC SURVEYS

Directory of Open Access Journals (Sweden)

S. Giordano

2018-05-01

Full Text Available Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes over the past 100 years. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Such time series image analysis is a mandatory baseline for a large variety of long-term environmental monitoring studies. The current bottleneck for accurate comparison between epochs is their fine georeferencing step. No fully automatic method has been proposed yet and existing studies are rather limited in terms of area and number of dates. State-of-the art shows that the major challenge is the identification of ground references: cartographic coordinates and their position in the archival images. This task is manually performed, and extremely time-consuming. This paper proposes to use a photogrammetric approach, and states that the 3D information that can be computed is the key to full automation. Its original idea lies in a 2-step approach: (i the computation of a coarse absolute image orientation; (ii the use of the coarse Digital Surface Model (DSM information for automatic absolute image orientation. It only relies on a recent orthoimage+DSM, used as master reference for all epochs. The coarse orthoimage, compared with such a reference, allows the identification of dense ground references and the coarse DSM provides their position in the archival images. Results on two areas and 5 dates show that this method is compatible with long and dense archival aerial image series. Satisfactory planimetric and altimetric accuracies are reported, with variations depending on the ground sampling distance of the images and the location of the Ground Control Points.

Dataset of herbarium specimens of threatened vascular plants in Catalonia.

Science.gov (United States)

Nualart, Neus; Ibáñez, Neus; Luque, Pere; Pedrol, Joan; Vilar, Lluís; Guàrdia, Roser

2017-01-01

This data paper describes a specimens' dataset of the Catalonian threatened vascular plants conserved in five public Catalonian herbaria (BC, BCN, HGI, HBIL and MTTE). Catalonia is an administrative region of Spain that includes large autochthon plants diversity and 199 taxa with IUCN threatened categories (EX, EW, RE, CR, EN and VU). This dataset includes 1,618 records collected from 17 th century to nowadays. For each specimen, the species name, locality indication, collection date, collector, ecology and revision label are recorded. More than 94% of the taxa are represented in the herbaria, which evidence the paper of the botanical collections as an essential source of occurrence data.

Datasets used in ORD-018902: Bisphenol A alternatives can effectively substitute for estradiol

Data.gov (United States)

U.S. Environmental Protection Agency — Gene Expression Omnibus numbers only. This dataset is associated with the following publication: Mesnage, R., A. Phedonos, M. Arno, S. Balu, C. Corton, and M....

USAID Public-Private Partnerships Database

Data.gov (United States)

US Agency for International Development — This dataset brings together information collected since 2001 on PPPs that have been supported by USAID. For the purposes of this dataset a Public-Private...

Proteomics dataset

DEFF Research Database (Denmark)

Bennike, Tue Bjerg; Carlsen, Thomas Gelsing; Ellingsen, Torkell

2017-01-01

The datasets presented in this article are related to the research articles entitled “Neutrophil Extracellular Traps in Ulcerative Colitis: A Proteome Analysis of Intestinal Biopsies” (Bennike et al., 2015 [1]), and “Proteome Analysis of Rheumatoid Arthritis Gut Mucosa” (Bennike et al., 2017 [2])...... been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD001608 for ulcerative colitis and control samples, and PXD003082 for rheumatoid arthritis samples....

A curated compendium of monocyte transcriptome datasets of relevance to human monocyte immunobiology research [version 2; referees: 2 approved

Directory of Open Access Journals (Sweden)

Darawan Rinchai

2016-04-01

Full Text Available Systems-scale profiling approaches have become widely used in translational research settings. The resulting accumulation of large-scale datasets in public repositories represents a critical opportunity to promote insight and foster knowledge discovery. However, resources that can serve as an interface between biomedical researchers and such vast and heterogeneous dataset collections are needed in order to fulfill this potential. Recently, we have developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB. This tool can be used to overlay deep molecular phenotyping data with rich contextual information about analytes, samples and studies along with ancillary clinical or immunological profiling data. In this note, we describe a curated compendium of 93 public datasets generated in the context of human monocyte immunological studies, representing a total of 4,516 transcriptome profiles. Datasets were uploaded to an instance of GXB along with study description and sample annotations. Study samples were arranged in different groups. Ranked gene lists were generated based on relevant group comparisons. This resource is publicly available online at http://monocyte.gxbsidra.org/dm3/landing.gsp.

A collection of annotated and harmonized human breast cancer transcriptome datasets, including immunologic classification [version 2; referees: 2 approved

Directory of Open Access Journals (Sweden)

Jessica Roelands

2018-02-01

Full Text Available The increased application of high-throughput approaches in translational research has expanded the number of publicly available data repositories. Gathering additional valuable information contained in the datasets represents a crucial opportunity in the biomedical field. To facilitate and stimulate utilization of these datasets, we have recently developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB. In this note, we describe a curated compendium of 13 public datasets on human breast cancer, representing a total of 2142 transcriptome profiles. We classified the samples according to different immune based classification systems and integrated this information into the datasets. Annotated and harmonized datasets were uploaded to GXB. Study samples were categorized in different groups based on their immunologic tumor response profiles, intrinsic molecular subtypes and multiple clinical parameters. Ranked gene lists were generated based on relevant group comparisons. In this data note, we demonstrate the utility of GXB to evaluate the expression of a gene of interest, find differential gene expression between groups and investigate potential associations between variables with a specific focus on immunologic classification in breast cancer. This interactive resource is publicly available online at: http://breastcancer.gxbsidra.org/dm3/geneBrowser/list.

Palmprint and Palmvein Recognition Based on DCNN and A New Large-Scale Contactless Palmvein Dataset

Directory of Open Access Journals (Sweden)

Lin Zhang

2018-03-01

Full Text Available Among the members of biometric identifiers, the palmprint and the palmvein have received significant attention due to their stability, uniqueness, and non-intrusiveness. In this paper, we investigate the problem of palmprint/palmvein recognition and propose a Deep Convolutional Neural Network (DCNN based scheme, namely P a l m R CNN (short for palmprint/palmvein recognition using CNNs. The effectiveness and efficiency of P a l m R CNN have been verified through extensive experiments conducted on benchmark datasets. In addition, though substantial effort has been devoted to palmvein recognition, it is still quite difficult for the researchers to know the potential discriminating capability of the contactless palmvein. One of the root reasons is that a large-scale and publicly available dataset comprising high-quality, contactless palmvein images is still lacking. To this end, a user-friendly acquisition device for collecting high quality contactless palmvein images is at first designed and developed in this work. Then, a large-scale palmvein image dataset is established, comprising 12,000 images acquired from 600 different palms in two separate collection sessions. The collected dataset now is publicly available.

GLEAM version 3: Global Land Evaporation Datasets and Model

Science.gov (United States)

Martens, B.; Miralles, D. G.; Lievens, H.; van der Schalie, R.; de Jeu, R.; Fernandez-Prieto, D.; Verhoest, N.

2015-12-01

Terrestrial evaporation links energy, water and carbon cycles over land and is therefore a key variable of the climate system. However, the global-scale magnitude and variability of the flux, and the sensitivity of the underlying physical process to changes in environmental factors, are still poorly understood due to limitations in in situ measurements. As a result, several methods have risen to estimate global patterns of land evaporation from satellite observations. However, these algorithms generally differ in their approach to model evaporation, resulting in large differences in their estimates. One of these methods is GLEAM, the Global Land Evaporation: the Amsterdam Methodology. GLEAM estimates terrestrial evaporation based on daily satellite observations of meteorological variables, vegetation characteristics and soil moisture. Since the publication of the first version of the algorithm (2011), the model has been widely applied to analyse trends in the water cycle and land-atmospheric feedbacks during extreme hydrometeorological events. A third version of the GLEAM global datasets is foreseen by the end of 2015. Given the relevance of having a continuous and reliable record of global-scale evaporation estimates for climate and hydrological research, the establishment of an online data portal to host these data to the public is also foreseen. In this new release of the GLEAM datasets, different components of the model have been updated, with the most significant change being the revision of the data assimilation algorithm. In this presentation, we will highlight the most important changes of the methodology and present three new GLEAM datasets and their validation against in situ observations and an alternative dataset of terrestrial evaporation (ERA-Land). Results of the validation exercise indicate that the magnitude and the spatiotemporal variability of the modelled evaporation agree reasonably well with the estimates of ERA-Land and the in situ

Dataset of anomalies and malicious acts in a cyber-physical subsystem.

Science.gov (United States)

Laso, Pedro Merino; Brosset, David; Puentes, John

2017-10-01

This article presents a dataset produced to investigate how data and information quality estimations enable to detect aNomalies and malicious acts in cyber-physical systems. Data were acquired making use of a cyber-physical subsystem consisting of liquid containers for fuel or water, along with its automated control and data acquisition infrastructure. Described data consist of temporal series representing five operational scenarios - Normal, aNomalies, breakdown, sabotages, and cyber-attacks - corresponding to 15 different real situations. The dataset is publicly available in the .zip file published with the article, to investigate and compare faulty operation detection and characterization methods for cyber-physical systems.

A New Heuristic Anonymization Technique for Privacy Preserved Datasets Publication on Cloud Computing

Science.gov (United States)

Aldeen Yousra, S.; Mazleena, Salleh

2018-05-01

Recent advancement in Information and Communication Technologies (ICT) demanded much of cloud services to sharing users’ private data. Data from various organizations are the vital information source for analysis and research. Generally, this sensitive or private data information involves medical, census, voter registration, social network, and customer services. Primary concern of cloud service providers in data publishing is to hide the sensitive information of individuals. One of the cloud services that fulfill the confidentiality concerns is Privacy Preserving Data Mining (PPDM). The PPDM service in Cloud Computing (CC) enables data publishing with minimized distortion and absolute privacy. In this method, datasets are anonymized via generalization to accomplish the privacy requirements. However, the well-known privacy preserving data mining technique called K-anonymity suffers from several limitations. To surmount those shortcomings, I propose a new heuristic anonymization framework for preserving the privacy of sensitive datasets when publishing on cloud. The advantages of K-anonymity, L-diversity and (α, k)-anonymity methods for efficient information utilization and privacy protection are emphasized. Experimental results revealed the superiority and outperformance of the developed technique than K-anonymity, L-diversity, and (α, k)-anonymity measure.

Plant traits, productivity, biomass and soil properties from forest sites in the Pacific Northwest, 1999-2014

Science.gov (United States)

Berner, Logan T.; Law, Beverly E.

2016-01-01

Plant trait measurements are needed for evaluating ecological responses to environmental conditions and for ecosystem process model development, parameterization, and testing. We present a standardized dataset integrating measurements from projects conducted by the Terrestrial Ecosystem Research and Regional Analysis- Pacific Northwest (TERRA-PNW) research group between 1999 and 2014 across Oregon and Northern California, where measurements were collected for scaling and modeling regional terrestrial carbon processes with models such as Biome-BGC and the Community Land Model. The dataset contains measurements of specific leaf area, leaf longevity, leaf carbon and nitrogen for 35 tree and shrub species derived from more than 1,200 branch samples collected from over 200 forest plots, including several AmeriFlux sites. The dataset also contains plot-level measurements of forest composition, structure (e.g., tree biomass), and productivity, as well as measurements of soil structure (e.g., bulk density) and chemistry (e.g., carbon). Publically-archiving regional datasets of standardized, co-located, and geo-referenced plant trait measurements will advance the ability of earth system models to capture species-level climate sensitivity at regional to global scales.

Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets.

Science.gov (United States)

Boareto, Marcelo; Cesar, Jonatas; Leite, Vitor B P; Caticha, Nestor

2015-01-01

We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.

Genome-wide gene expression dataset used to identify potential therapeutic targets in androgenetic alopecia

Directory of Open Access Journals (Sweden)

R. Dey-Rao

2017-08-01

Full Text Available The microarray dataset attached to this report is related to the research article with the title: “A genomic approach to susceptibility and pathogenesis leads to identifying potential novel therapeutic targets in androgenetic alopecia” (Dey-Rao and Sinha, 2017 [1]. Male-pattern hair loss that is induced by androgens (testosterone in genetically predisposed individuals is known as androgenetic alopecia (AGA. The raw dataset is being made publicly available to enable critical and/or extended analyses. Our related research paper utilizes the attached raw dataset, for genome-wide gene-expression associated investigations. Combined with several in silico bioinformatics-based analyses we were able to delineate five strategic molecular elements as potential novel targets towards future AGA-therapy.

FigS7.txt

Data.gov (United States)

U.S. Environmental Protection Agency — This is an ascii file with georeferencing information containing the data plotted in Figure S7 of the supplemental information manuscript section. This dataset is...

RARD: The Related-Article Recommendation Dataset

OpenAIRE

Beel, Joeran; Carevic, Zeljko; Schaible, Johann; Neusch, Gabor

2017-01-01

Recommender-system datasets are used for recommender-system evaluations, training machine-learning algorithms, and exploring user behavior. While there are many datasets for recommender systems in the domains of movies, books, and music, there are rather few datasets from research-paper recommender systems. In this paper, we introduce RARD, the Related-Article Recommendation Dataset, from the digital library Sowiport and the recommendation-as-a-service provider Mr. DLib. The dataset contains ...

Satellite-Based Precipitation Datasets

Science.gov (United States)

Munchak, S. J.; Huffman, G. J.

2017-12-01

Of the possible sources of precipitation data, those based on satellites provide the greatest spatial coverage. There is a wide selection of datasets, algorithms, and versions from which to choose, which can be confusing to non-specialists wishing to use the data. The International Precipitation Working Group (IPWG) maintains tables of the major publicly available, long-term, quasi-global precipitation data sets (http://www.isac.cnr.it/ ipwg/data/datasets.html), and this talk briefly reviews the various categories. As examples, NASA provides two sets of quasi-global precipitation data sets: the older Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and current Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG). Both provide near-real-time and post-real-time products that are uniformly gridded in space and time. The TMPA products are 3-hourly 0.25°x0.25° on the latitude band 50°N-S for about 16 years, while the IMERG products are half-hourly 0.1°x0.1° on 60°N-S for over 3 years (with plans to go to 16+ years in Spring 2018). In addition to the precipitation estimates, each data set provides fields of other variables, such as the satellite sensor providing estimates and estimated random error. The discussion concludes with advice about determining suitability for use, the necessity of being clear about product names and versions, and the need for continued support for satellite- and surface-based observation.

Sullivan County 30 centimeter Resolution Natural Color Orthoimagery Sullivan County 60 centimeter Resolution Panchromatic Orthoimagery

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

New Jersey 2007 - 2008 High Resolution Orthophotography

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, CAMDEN COUNTY, NEW JERSEY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, DARKE COUNTY, OHIO

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

OrthoImagery Submission for Christian County, Illinois, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has beeen removed for...

ORTHOIMAGERY, CONCORD WATERSHED, MIDDLESEX COUNTY, MASSACHUSETTS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, Santa Clara, CA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

OrthoImagery Submission for Moultrie County, Illinois, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has beeen removed for...

ORTHOIMAGERY, CITY OF SHISHMAREF, ALASKA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, Otsego County, NY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, CITY OF HOONAH, ALASKA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, Pittsylvania County, VA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Washburn County, WI, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

OrthoImagery Submission for Monmouth County, New Jersey

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, TALBOT COUNTY, MD USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, Cecil County, MD

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, Anne Arundel County, MD

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Walworth County, WI, USA - MIP Walworth Portion Rock River RiskMap DFIRM Update

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Dodge County, WI, USA - MIP Dodge Countywide DFIRM

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Columbia County, WI, USA - MIP Columbia Portion Baraboo River Watershed RiskMap DFIRM Update

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Outagamie County, WI, USA - MIP Outagamie Countywide DFIRM

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Crawford County, WI, USA - MIP Crawford Countywide DFIRM

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Columbia County, WI, USA - MIP Columbia Portion Baraboo River RiskMap DFIRM Update

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Waukesha County, WI, USA - MIP Waukesha Portion Rock River RiskMap DFIRM Update

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Sauk County, WI, USA - MIP Sauk Countywide DFIRM

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Jefferson County, WI, USA - MIP Jefferson Portion Rock River RiskMap DFIRM Update

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Rock County, WI, USA - MIP Rock County Portion Rock River RiskMap DFIRM Update

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Oconto County, WI, USA - MIP Oconto Countywide DFIRM

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Waupaca County, WI, USA - MIP Waupaca Countywide DFIRM

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Barron County, WI, USA - MIP Barron Countywide DFIRM

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Vernon County, WI, USA - MIP Vernon Countywide DFIRM

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Washington County, WI, USA - MIP Washington Portion Rock River RiskMap DFIRM Update

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Sauk County, WI, USA - MIP Sauk Portion Baraboo River RiskMap DFIRM Update

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

OrthoImagery Submission for Douglas County, Illinois, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has beeen removed for...

ORTHOIMAGERY, PIRECE COUNTY, WISCONSIN

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

HOUSTON COUNTY, ALABAMA ORTHOIMAGERY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Dodge County, WI, USA - Fox Lake Physical Map Revision

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY Submission for McLean COUNTY, ND

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, Botetourt County, VA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, Alleghany County, VA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Dane County, WI, USA - MIP Dane Portion Rock River RiskMap DFIRM Update

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, MARICOPA COUNTY, AZ

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

USDA-FSA-APFO Digital Ortho Mosaic

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Spring 2007 Orthophotography of Lake County, IL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthophotography of Indiana

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

FigS8.txt

Data.gov (United States)

U.S. Environmental Protection Agency — This is an ASCII file with georeferencing information containing data plotted in Figure S8 of the supplemental information section of the manuscript. This dataset is...

OrthoImagery submittal for Scott County, Indiana

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

OrthoImagery submittal for Switzerland County, Indiana

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

OrthoImagery Submission for Albany County, New York

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Orthoimagery Submission for Schenectady County, New York

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

OrthoImagery Submission for Putnam County, New York

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Tioga County - 24-inch Resolution Natural Color Orthoimagery

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, CARBON COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Brown County, Wisconsin Digital Orthophotography

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth's surface, collected by a sensor in which object displacement has been removed for...

Proliferative diabetic retinopathy characterization based on fractal features: Evaluation on a publicly available dataset.

Science.gov (United States)

Orlando, José Ignacio; van Keer, Karel; Barbosa Breda, João; Manterola, Hugo Luis; Blaschko, Matthew B; Clausse, Alejandro

2017-12-01

Diabetic retinopathy (DR) is one of the most widespread causes of preventable blindness in the world. The most dangerous stage of this condition is proliferative DR (PDR), in which the risk of vision loss is high and treatments are less effective. Fractal features of the retinal vasculature have been previously explored as potential biomarkers of DR, yet the current literature is inconclusive with respect to their correlation with PDR. In this study, we experimentally assess their discrimination ability to recognize PDR cases. A statistical analysis of the viability of using three reference fractal characterization schemes - namely box, information, and correlation dimensions - to identify patients with PDR is presented. These descriptors are also evaluated as input features for training ℓ1 and ℓ2 regularized logistic regression classifiers, to estimate their performance. Our results on MESSIDOR, a public dataset of 1200 fundus photographs, indicate that patients with PDR are more likely to exhibit a higher fractal dimension than healthy subjects or patients with mild levels of DR (P≤1.3×10-2). Moreover, a supervised classifier trained with both fractal measurements and red lesion-based features reports an area under the ROC curve of 0.93 for PDR screening and 0.96 for detecting patients with optic disc neovascularizations. The fractal dimension of the vasculature increases with the level of DR. Furthermore, PDR screening using multiscale fractal measurements is more feasible than using their derived fractal dimensions. Code and further resources are provided at https://github.com/ignaciorlando/fundus-fractal-analysis. © 2017 American Association of Physicists in Medicine.

Integrated web system of geospatial data services for climate research

Science.gov (United States)

Okladnikov, Igor; Gordov, Evgeny; Titov, Alexander

2016-04-01

Georeferenced datasets are currently actively used for modeling, interpretation and forecasting of climatic and ecosystem changes on different spatial and temporal scales. Due to inherent heterogeneity of environmental datasets as well as their huge size (up to tens terabytes for a single dataset) a special software supporting studies in the climate and environmental change areas is required. An approach for integrated analysis of georefernced climatological data sets based on combination of web and GIS technologies in the framework of spatial data infrastructure paradigm is presented. According to this approach a dedicated data-processing web system for integrated analysis of heterogeneous georeferenced climatological and meteorological data is being developed. It is based on Open Geospatial Consortium (OGC) standards and involves many modern solutions such as object-oriented programming model, modular composition, and JavaScript libraries based on GeoExt library, ExtJS Framework and OpenLayers software. This work is supported by the Ministry of Education and Science of the Russian Federation, Agreement #14.613.21.0037.

PLÉIADES PROJECT: ASSESSMENT OF GEOREFERENCING ACCURACY, IMAGE QUALITY, PANSHARPENING PERFORMENCE AND DSM/DTM QUALITY

Directory of Open Access Journals (Sweden)

H. Topan

2016-06-01

Full Text Available Pléiades 1A and 1B are twin optical satellites of Optical and Radar Federated Earth Observation (ORFEO program jointly running by France and Italy. They are the first satellites of Europe with sub-meter resolution. Airbus DS (formerly Astrium Geo runs a MyGIC (formerly Pléiades Users Group program to validate Pléiades images worldwide for various application purposes. The authors conduct three projects, one is within this program, the second is supported by BEU Scientific Research Project Program, and the third is supported by TÜBİTAK. Assessment of georeferencing accuracy, image quality, pansharpening performance and Digital Surface Model/Digital Terrain Model (DSM/DTM quality subjects are investigated in these projects. For these purposes, triplet panchromatic (50 cm Ground Sampling Distance (GSD and VNIR (2 m GSD Pléiades 1A images were investigated over Zonguldak test site (Turkey which is urbanised, mountainous and covered by dense forest. The georeferencing accuracy was estimated with a standard deviation in X and Y (SX, SY in the range of 0.45m by bias corrected Rational Polynomial Coefficient (RPC orientation, using ~170 Ground Control Points (GCPs. 3D standard deviation of ±0.44m in X, ±0.51m in Y, and ±1.82m in Z directions have been reached in spite of the very narrow angle of convergence by bias corrected RPC orientation. The image quality was also investigated with respect to effective resolution, Signal to Noise Ratio (SNR and blur coefficient. The effective resolution was estimated with factor slightly below 1.0, meaning that the image quality corresponds to the nominal resolution of 50cm. The blur coefficients were achieved between 0.39-0.46 for triplet panchromatic images, indicating a satisfying image quality. SNR is in the range of other comparable space borne images which may be caused by de-noising of Pléiades images. The pansharpened images were generated by various methods, and are validated by most common

ORTHOIMAGERY, LAMAR COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

OrthoImagery Submission for Colfax County NE

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the surface of the Earth, collected by a sensor in which object displacement has been removed...

OrthoImagery submittal for Clinton County, Indiana

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, Crisp COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

OrthoImagery submittal for Gibson County, Indiana

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

OrthoImagery submittal for Allen County, Indiana

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, SPALDING COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, Dougherty COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, LEE COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, UPSON COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, SUMTER COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, TERRELL COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, MACON COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, CALHOUN COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, DOOLY COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, THOMAS COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, TAYLOR COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, BUTTS COUNTY, GA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

ORTHOIMAGERY, WEBSTER COUNTY, GA, USA

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Federal Emergency Management Agency, Department of Homeland Security — Digital orthographic imagery datasets contain georeferenced images of the Earth?s surface, collected by a sensor in which object displacement has been removed for...

Lake Bathymetric DEM Shaded Relief Image

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Minnesota Department of Natural Resources — Geo-referenced, shaded relief image of lake bathymetry classified at 5-foot depth intervals. This dataset has a cell resolution of 5 meters (occasionally 10m) as...

Psychogeography in the Age of the Quantified Self — Mental Map modelling with Georeferenced Personal Activity Data

Science.gov (United States)

Meier, Sebastian; Glinka, Katrin

2018-05-01

Personal and subjective perceptions of urban space have been a focus of various research projects in the area of cartography, geography, and related fields such as urban planning. This paper illustrates how personal georeferenced activity data can be used in algorithmic modelling of certain aspects of mental maps and customised spatial visualisations. The technical implementation of the algorithm is accompanied by a preliminary study which evaluates the performance of the algorithm. As a linking element between personal perception, interpretation, and depiction of space and the field of cartography and geography, we include perspectives from artistic practice and cultural theory. By developing novel visualisation concepts based on personal data, the paper in part mitigates the challenges presented by user modelling that is, amongst others, used in LBS applications.

Open University Learning Analytics dataset.

Science.gov (United States)

Kuzilek, Jakub; Hlosta, Martin; Zdrahal, Zdenek

2017-11-28

Learning Analytics focuses on the collection and analysis of learners' data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students' interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries). The dataset is freely available at https://analyse.kmi.open.ac.uk/open_dataset under a CC-BY 4.0 license.

Public Water Supply Systems (PWS)

Data.gov (United States)

Kansas Data Access and Support Center — This dataset includes boundaries for most public water supply systems (PWS) in Kansas (525 municipalities, 289 rural water districts and 13 public wholesale water...

Dataset for Calibration and performance of synchronous SIM/scan mode for simultaneous targeted and discovery (non-targeted) analysis of exhaled breath samples from firefighters

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U.S. Environmental Protection Agency — This dataset includes the tables and supplementary information from the journal article. This dataset is associated with the following publication: Wallace, A., J....

Dataset on information strategies for energy conservation: A field experiment in India.

Science.gov (United States)

Chen, Victor L; Delmas, Magali A; Locke, Stephen L; Singh, Amarjeet

2018-02-01

The data presented in this article are related to the research article entitled: "Information strategies for energy conservation: a field experiment in India" (Chen et al., 2017) [1]. The availability of high-resolution electricity data offers benefits to both utilities and consumers to understand the dynamics of energy consumption for example, between billing periods or times of peak demand. However, few public datasets with high-temporal resolution have been available to researchers on electricity use, especially at the appliance-level. This article describes data collected in a residential field experiment for 19 apartments at an Indian faculty housing complex during the period from August 1, 2013 to May 12, 2014. The dataset includes detailed information about electricity consumption. It also includes information on apartment characteristics and hourly weather variation to enable further studies of energy performance. These data can be used by researchers as training datasets to evaluate electricity usage consumption.

Use of country of birth as an indicator of refugee background in health datasets

Science.gov (United States)

2014-01-01

Background Routine public health databases contain a wealth of data useful for research among vulnerable or isolated groups, who may be under-represented in traditional medical research. Identifying specific vulnerable populations, such as resettled refugees, can be particularly challenging; often country of birth is the sole indicator of whether an individual has a refugee background. The objective of this article was to review strengths and weaknesses of different methodological approaches to identifying resettled refugees and comparison groups from routine health datasets and to propose the application of additional methodological rigour in future research. Discussion Methodological approaches to selecting refugee and comparison groups from existing routine health datasets vary widely and are often explained in insufficient detail. Linked data systems or datasets from specialized refugee health services can accurately select resettled refugee and asylum seeker groups but have limited availability and can be selective. In contrast, country of birth is commonly collected in routine health datasets but a robust method for selecting humanitarian source countries based solely on this information is required. The authors recommend use of national immigration data to objectively identify countries of birth with high proportions of humanitarian entrants, matched by time period to the study dataset. When available, additional migration indicators may help to better understand migration as a health determinant. Methodologically, if multiple countries of birth are combined, the proportion of the sample represented by each country of birth should be included, with sub-analysis of individual countries of birth potentially providing further insights, if population size allows. United Nations-defined world regions provide an objective framework for combining countries of birth when necessary. A comparison group of economic migrants from the same world region may be appropriate

BLOND, a building-level office environment dataset of typical electrical appliances

Science.gov (United States)

Kriechbaumer, Thomas; Jacobsen, Hans-Arno

2018-03-01

Energy metering has gained popularity as conventional meters are replaced by electronic smart meters that promise energy savings and higher comfort levels for occupants. Achieving these goals requires a deeper understanding of consumption patterns to reduce the energy footprint: load profile forecasting, power disaggregation, appliance identification, startup event detection, etc. Publicly available datasets are used to test, verify, and benchmark possible solutions to these problems. For this purpose, we present the BLOND dataset: continuous energy measurements of a typical office environment at high sampling rates with common appliances and load profiles. We provide voltage and current readings for aggregated circuits and matching fully-labeled ground truth data (individual appliance measurements). The dataset contains 53 appliances (16 classes) in a 3-phase power grid. BLOND-50 contains 213 days of measurements sampled at 50kSps (aggregate) and 6.4kSps (individual appliances). BLOND-250 consists of the same setup: 50 days, 250kSps (aggregate), 50kSps (individual appliances). These are the longest continuous measurements at such high sampling rates and fully-labeled ground truth we are aware of.

BLOND, a building-level office environment dataset of typical electrical appliances.

Science.gov (United States)

Kriechbaumer, Thomas; Jacobsen, Hans-Arno

2018-03-27

Energy metering has gained popularity as conventional meters are replaced by electronic smart meters that promise energy savings and higher comfort levels for occupants. Achieving these goals requires a deeper understanding of consumption patterns to reduce the energy footprint: load profile forecasting, power disaggregation, appliance identification, startup event detection, etc. Publicly available datasets are used to test, verify, and benchmark possible solutions to these problems. For this purpose, we present the BLOND dataset: continuous energy measurements of a typical office environment at high sampling rates with common appliances and load profiles. We provide voltage and current readings for aggregated circuits and matching fully-labeled ground truth data (individual appliance measurements). The dataset contains 53 appliances (16 classes) in a 3-phase power grid. BLOND-50 contains 213 days of measurements sampled at 50kSps (aggregate) and 6.4kSps (individual appliances). BLOND-250 consists of the same setup: 50 days, 250kSps (aggregate), 50kSps (individual appliances). These are the longest continuous measurements at such high sampling rates and fully-labeled ground truth we are aware of.

Bibliometric Analysis to Scan and Scrape New Datasets: It’s all about that BASS

Directory of Open Access Journals (Sweden)

Karen Tingay

2017-04-01

Results focus on: • Using bibliometric methods in the context of DPUK cohort publications • Identifying emerging trends in the field of dementia research.  • Identifying and prioritising datasets which might be useful for the SAIL Databank to acquire

Georeferenced Population Datasets of Mexico (GEO-MEX): Urban Place GIS Coverage of Mexico

Data.gov (United States)

National Aeronautics and Space Administration — The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is...

Check your biosignals here: a new dataset for off-the-person ECG biometrics.

Science.gov (United States)

da Silva, Hugo Plácido; Lourenço, André; Fred, Ana; Raposo, Nuno; Aires-de-Sousa, Marta

2014-02-01

The Check Your Biosignals Here initiative (CYBHi) was developed as a way of creating a dataset and consistently repeatable acquisition framework, to further extend research in electrocardiographic (ECG) biometrics. In particular, our work targets the novel trend towards off-the-person data acquisition, which opens a broad new set of challenges and opportunities both for research and industry. While datasets with ECG signals collected using medical grade equipment at the chest can be easily found, for off-the-person ECG data the solution is generally for each team to collect their own corpus at considerable expense of resources. In this paper we describe the context, experimental considerations, methods, and preliminary findings of two public datasets created by our team, one for short-term and another for long-term assessment, with ECG data collected at the hand palms and fingers. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

DOIs for Data: Progress in Data Citation and Publication in the Geosciences

Science.gov (United States)

Callaghan, S.; Murphy, F.; Tedds, J.; Allan, R.

2012-12-01

Identifiers for data are the bedrock on which data citation and publication rests. These, in their turn, are widely proposed as methods for encouraging researchers to share their datasets, and at the same time receive academic credit for their efforts in producing them. However, neither data citation nor publication can be properly achieved without a method of identifying clearly what is, and what isn't, part of the dataset. Once a dataset becomes part of the scientific record (either through formal data publication or through being cited) then issues such as dataset stability and permanence become vital to address. In the geosciences, several projects in the UK are concentrating on issues of dataset identification, citation and publication. The UK's Natural Environment Research Council's (NERC) Science Information Strategy data citation and publication project is addressing the issue of identifiers for data, stability, transparency, and credit for data producers through data citation. At a data publication level, 2012 has seen the launch of the new Wiley title Geoscience Data Journal and the PREPARDE (Peer Review for Publication & Accreditation of Research Data in the Earth sciences) project, both aiming to encourage data publication by addressing issues such as data paper submission workflows and the scientific peer-review of data. All of these initiatives work with a range of partners including academic institutions, learned societies, data centers and commercial publishers, both nationally and internationally, with a cross-project aim of developing the mechanisms so data can be identified, cited and published with confidence. This involves investigating barriers and drivers to data publishing and sharing, peer review, and re-use of geoscientific datasets, and specifically such topics as dataset requirements for citation, workflows for dataset ingestion into data centers and publishers, procedures and policies for editors, reviewers and authors of data

Creation of the Naturalistic Engagement in Secondary Tasks (NEST) distracted driving dataset.

Science.gov (United States)

Owens, Justin M; Angell, Linda; Hankey, Jonathan M; Foley, James; Ebe, Kazutoshi

2015-09-01

Distracted driving has become a topic of critical importance to driving safety research over the past several decades. Naturalistic driving data offer a unique opportunity to study how drivers engage with secondary tasks in real-world driving; however, the complexities involved with identifying and coding relevant epochs of naturalistic data have limited its accessibility to the general research community. This project was developed to help address this problem by creating an accessible dataset of driver behavior and situational factors observed during distraction-related safety-critical events and baseline driving epochs, using the Strategic Highway Research Program 2 (SHRP2) naturalistic dataset. The new NEST (Naturalistic Engagement in Secondary Tasks) dataset was created using crashes and near-crashes from the SHRP2 dataset that were identified as including secondary task engagement as a potential contributing factor. Data coding included frame-by-frame video analysis of secondary task and hands-on-wheel activity, as well as summary event information. In addition, information about each secondary task engagement within the trip prior to the crash/near-crash was coded at a higher level. Data were also coded for four baseline epochs and trips per safety-critical event. 1,180 events and baseline epochs were coded, and a dataset was constructed. The project team is currently working to determine the most useful way to allow broad public access to the dataset. We anticipate that the NEST dataset will be extraordinarily useful in allowing qualified researchers access to timely, real-world data concerning how drivers interact with secondary tasks during safety-critical events and baseline driving. The coded dataset developed for this project will allow future researchers to have access to detailed data on driver secondary task engagement in the real world. It will be useful for standalone research, as well as for integration with additional SHRP2 data to enable the

Mridangam stroke dataset

OpenAIRE

CompMusic

2014-01-01

The audio examples were recorded from a professional Carnatic percussionist in a semi-anechoic studio conditions by Akshay Anantapadmanabhan using SM-58 microphones and an H4n ZOOM recorder. The audio was sampled at 44.1 kHz and stored as 16 bit wav files. The dataset can be used for training models for each Mridangam stroke. /n/nA detailed description of the Mridangam and its strokes can be found in the paper below. A part of the dataset was used in the following paper. /nAkshay Anantapadman...

2008 TIGER/Line Nationwide Dataset

Data.gov (United States)

California Natural Resource Agency — This dataset contains a nationwide build of the 2008 TIGER/Line datasets from the US Census Bureau downloaded in April 2009. The TIGER/Line Shapefiles are an extract...

Design of an audio advertisement dataset

Science.gov (United States)

Fu, Yutao; Liu, Jihong; Zhang, Qi; Geng, Yuting

2015-12-01

Since more and more advertisements swarm into radios, it is necessary to establish an audio advertising dataset which could be used to analyze and classify the advertisement. A method of how to establish a complete audio advertising dataset is presented in this paper. The dataset is divided into four different kinds of advertisements. Each advertisement's sample is given in *.wav file format, and annotated with a txt file which contains its file name, sampling frequency, channel number, broadcasting time and its class. The classifying rationality of the advertisements in this dataset is proved by clustering the different advertisements based on Principal Component Analysis (PCA). The experimental results show that this audio advertisement dataset offers a reliable set of samples for correlative audio advertisement experimental studies.

Background qualitative analysis of the European reference life cycle database (ELCD) energy datasets - part II: electricity datasets.

Science.gov (United States)

Garraín, Daniel; Fazio, Simone; de la Rúa, Cristina; Recchioni, Marco; Lechón, Yolanda; Mathieux, Fabrice

2015-01-01

The aim of this paper is to identify areas of potential improvement of the European Reference Life Cycle Database (ELCD) electricity datasets. The revision is based on the data quality indicators described by the International Life Cycle Data system (ILCD) Handbook, applied on sectorial basis. These indicators evaluate the technological, geographical and time-related representativeness of the dataset and the appropriateness in terms of completeness, precision and methodology. Results show that ELCD electricity datasets have a very good quality in general terms, nevertheless some findings and recommendations in order to improve the quality of Life-Cycle Inventories have been derived. Moreover, these results ensure the quality of the electricity-related datasets to any LCA practitioner, and provide insights related to the limitations and assumptions underlying in the datasets modelling. Giving this information, the LCA practitioner will be able to decide whether the use of the ELCD electricity datasets is appropriate based on the goal and scope of the analysis to be conducted. The methodological approach would be also useful for dataset developers and reviewers, in order to improve the overall Data Quality Requirements of databases.

RIGOROUS GEOREFERENCING OF ALSAT-2A PANCHROMATIC AND MULTISPECTRAL IMAGERY

Directory of Open Access Journals (Sweden)

I. Boukerch

2013-04-01

Full Text Available The exploitation of the full geometric capabilities of the High-Resolution Satellite Imagery (HRSI, require the development of an appropriate sensor orientation model. Several authors studied this problem; generally we have two categories of geometric models: physical and empirical models. Based on the analysis of the metadata provided with ALSAT-2A, a rigorous pushbroom camera model can be developed. This model has been successfully applied to many very high resolution imagery systems. The relation between the image and ground coordinates by the time dependant collinearity involving many coordinates systems has been tested. The interior orientation parameters must be integrated in the model, the interior parameters can be estimated from the viewing angles corresponding to the pointing directions of any detector, these values are derived from cubic polynomials provided in the metadata. The developed model integrates all the necessary elements with 33 unknown. All the approximate values of the 33 unknowns parameters may be derived from the informations contained in the metadata files provided with the imagery technical specifications or they are simply fixed to zero, so the condition equation is linearized and solved using SVD in a least square sense in order to correct the initial values using a suitable number of well-distributed GCPs. Using Alsat-2A images over the town of Toulouse in the south west of France, three experiments are done. The first is about 2D accuracy analysis using several sets of parameters. The second is about GCPs number and distribution. The third experiment is about georeferencing multispectral image by applying the model calculated from panchromatic image.

Plant traits, productivity, biomass and soil properties from forest sites in the Pacific Northwest, 1999–2014

Science.gov (United States)

Berner, Logan T.; Law, Beverly E.

2016-01-01

Plant trait measurements are needed for evaluating ecological responses to environmental conditions and for ecosystem process model development, parameterization, and testing. We present a standardized dataset integrating measurements from projects conducted by the Terrestrial Ecosystem Research and Regional Analysis- Pacific Northwest (TERRA-PNW) research group between 1999 and 2014 across Oregon and Northern California, where measurements were collected for scaling and modeling regional terrestrial carbon processes with models such as Biome-BGC and the Community Land Model. The dataset contains measurements of specific leaf area, leaf longevity, leaf carbon and nitrogen for 35 tree and shrub species derived from more than 1,200 branch samples collected from over 200 forest plots, including several AmeriFlux sites. The dataset also contains plot-level measurements of forest composition, structure (e.g., tree biomass), and productivity, as well as measurements of soil structure (e.g., bulk density) and chemistry (e.g., carbon). Publically-archiving regional datasets of standardized, co-located, and geo-referenced plant trait measurements will advance the ability of earth system models to capture species-level climate sensitivity at regional to global scales. PMID:26784559

The African Crane Database (1978-2014): Records of three threatened crane species (Family: Gruidae) from southern and eastern Africa

Science.gov (United States)

Smith, Tanya; Page-Nicholson, Samantha; Gibbons, Bradley; Jones, M. Genevieve W.; van Niekerk, Mark; Botha, Bronwyn; Oliver, Kirsten; McCann, Kevin

2016-01-01

Abstract Background The International Crane Foundation (ICF) / Endangered Wildlife Trust’s (EWT) African Crane Conservation Programme has recorded 26 403 crane sightings in its database from 1978 to 2014. This sightings collection is currently ongoing and records are continuously added to the database by the EWT field staff, ICF/EWT Partnership staff, various partner organizations and private individuals. The dataset has two peak collection periods: 1994-1996 and 2008-2012. The dataset collection spans five African countries: Kenya, Rwanda, South Africa, Uganda and Zambia; 98% of the data were collected in South Africa. Georeferencing of the dataset was verified before publication of the data. The dataset contains data on three African crane species: Blue Crane Anthropoides paradiseus, Grey Crowned Crane Balearica regulorum and Wattled Crane Bugeranus carunculatus. The Blue and Wattled Cranes are classified by the IUCN Red List of Threatened Species as Vulnerable and the Grey Crowned Crane as Endangered. New information This is the single most comprehensive dataset published on African Crane species that adds new information about the distribution of these three threatened species. We hope this will further aid conservation authorities to monitor and protect these species. The dataset continues to grow and especially to expand in geographic coverage into new countries in Africa and new sites within countries. The dataset can be freely accessed through the Global Biodiversity Information Facility data portal. PMID:27956850

ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers.

Directory of Open Access Journals (Sweden)

Douglas Teodoro

Full Text Available The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms.

ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers

Science.gov (United States)

Sundvall, Erik; João Junior, Mario; Ruch, Patrick; Miranda Freire, Sergio

2018-01-01

The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms. PMID:29293556

ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers.

Science.gov (United States)

Teodoro, Douglas; Sundvall, Erik; João Junior, Mario; Ruch, Patrick; Miranda Freire, Sergio

2018-01-01

The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms.

The Stream-Catchment (StreamCat) Dataset: A database of watershed metrics for the conterminous USA

Science.gov (United States)

We developed an extensive database of landscape metrics for ~2.65 million streams, and their associated catchments, within the conterminous USA: The Stream-Catchment (StreamCat) Dataset. These data are publically available and greatly reduce the specialized geospatial expertise n...

Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation

Directory of Open Access Journals (Sweden)

Jing Wu

2016-01-01

Full Text Available Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT, used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not publicly available, come from only one device, or use different evaluation methodologies making them difficult to compare. Thus we present and evaluate a multiple expert annotated reference dataset for the problem of intraretinal cystoid fluid (IRF segmentation, a key indicator in exudative macular disease. In addition, a standardized framework for segmentation accuracy evaluation, applicable to other pathological structures, is presented. Integral to this work is the dataset used which must be fit for purpose for IRF segmentation algorithm training and testing. We describe here a multivendor dataset comprised of 30 scans. Each OCT scan for system training has been annotated by multiple graders using a proprietary system. Evaluation of the intergrader annotations shows a good correlation, thus making the reproducibly annotated scans suitable for the training and validation of image processing and machine learning based segmentation methods. The dataset will be made publicly available in the form of a segmentation Grand Challenge.

Online Education in Public Affairs

Science.gov (United States)

Ginn, Martha H.; Hammond, Augustine

2012-01-01

This exploratory study provides an overview of the current landscape of online education in the fields of Master of Public Administration and Master of Public Policy (MPA/MPP) utilizing a dataset compiled from content analysis of MPA/MPP programs' websites and survey of 96 National Association of Schools of Public Affairs and Administration…

Two datasets of defect reports labeled by a crowd of annotators of unknown reliability

Directory of Open Access Journals (Sweden)

Jerónimo Hernández-González

2018-06-01

Full Text Available Classifying software defects according to any defined taxonomy is not straightforward. In order to be used for automatizing the classification of software defects, two sets of defect reports were collected from public issue tracking systems from two different real domains. Due to the lack of a domain expert, the collected defects were categorized by a set of annotators of unknown reliability according to their impact from IBM's orthogonal defect classification taxonomy. Both datasets are prepared to solve the defect classification problem by means of techniques of the learning from crowds paradigm (Hernández-González et al. [1].Two versions of both datasets are publicly shared. In the first version, the raw data is given: the text description of defects together with the category assigned by each annotator. In the second version, the text of each defect has been transformed to a descriptive vector using text-mining techniques.

Libraries, The locations and contact information for academic, private and public libraries in Rhode Island. The intention of this dataset was to provide an overview of data. Additional information pertinent to the state is also available from the RI Department of, Published in 2007, 1:4800 (1in=400ft) scale, Rhode Island and Providence Plantations.

Data.gov (United States)

NSGIC State | GIS Inventory — Libraries dataset current as of 2007. The locations and contact information for academic, private and public libraries in Rhode Island. The intention of this dataset...

AN ACCURACY ASSESSMENT OF GEOREFERENCED POINT CLOUDS PRODUCED VIA MULTI-VIEW STEREO TECHNIQUES APPLIED TO IMAGERY ACQUIRED VIA UNMANNED AERIAL VEHICLE

Directory of Open Access Journals (Sweden)

S. Harwin

2012-08-01

Full Text Available Low-cost Unmanned Aerial Vehicles (UAVs are becoming viable environmental remote sensing tools. Sensor and battery technology is expanding the data capture opportunities. The UAV, as a close range remote sensing platform, can capture high resolution photography on-demand. This imagery can be used to produce dense point clouds using multi-view stereopsis techniques (MVS combining computer vision and photogrammetry. This study examines point clouds produced using MVS techniques applied to UAV and terrestrial photography. A multi-rotor micro UAV acquired aerial imagery from a altitude of approximately 30–40 m. The point clouds produced are extremely dense (<1–3 cm point spacing and provide a detailed record of the surface in the study area, a 70 m section of sheltered coastline in southeast Tasmania. Areas with little surface texture were not well captured, similarly, areas with complex geometry such as grass tussocks and woody scrub were not well mapped. The process fails to penetrate vegetation, but extracts very detailed terrain in unvegetated areas. Initially the point clouds are in an arbitrary coordinate system and need to be georeferenced. A Helmert transformation is applied based on matching ground control points (GCPs identified in the point clouds to GCPs surveying with differential GPS. These point clouds can be used, alongside laser scanning and more traditional techniques, to provide very detailed and precise representations of a range of landscapes at key moments. There are many potential applications for the UAV-MVS technique, including coastal erosion and accretion monitoring, mine surveying and other environmental monitoring applications. For the generated point clouds to be used in spatial applications they need to be converted to surface models that reduce dataset size without loosing too much detail. Triangulated meshes are one option, another is Poisson Surface Reconstruction. This latter option makes use of point normal

Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests

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Mathias Neumann

2016-06-01

Full Text Available Net primary production (NPP is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country and gradients (elevation, location, tree age, dominant species, etc.. The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO.

Accurate Reconstruction of the Roman Circus in Milan by Georeferencing Heterogeneous Data Sources with GIS

Directory of Open Access Journals (Sweden)

Gabriele Guidi

2017-09-01

Full Text Available This paper presents the methodological approach and the actual workflow for creating the 3D digital reconstruction in time of the ancient Roman Circus of Milan, which is presently covered completely by the urban fabric of the modern city. The diachronic reconstruction is based on a proper mix of quantitative data originated by current 3D surveys and historical sources, such as ancient maps, drawings, archaeological reports, restrictions decrees, and old photographs. When possible, such heterogeneous sources have been georeferenced and stored in a GIS system. In this way the sources have been analyzed in depth, allowing the deduction of geometrical information not explicitly revealed by the material available. A reliable reconstruction of the area in different historical periods has been therefore hypothesized. This research has been carried on in the framework of the project Cultural Heritage Through Time—CHT2, funded by the Joint Programming Initiative on Cultural Heritage (JPI-CH, supported by the Italian Ministry for Cultural Heritage (MiBACT, the Italian Ministry for University and Research (MIUR, and the European Commission.

Geo-referenced modelling of metal concentrations in river basins at the catchment scale

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Hüffmeyer, N.; Berlekamp, J.; Klasmeier, J.

2009-04-01

1. Introduction The European Water Framework Directive demands the good ecological and chemical state of surface waters [1]. This implies the reduction of unwanted metal concentrations in surface waters. To define reasonable environmental target values and to develop promising mitigation strategies a detailed exposure assessment is required. This includes the identification of emission sources and the evaluation of their effect on local and regional surface water concentrations. Point source emissions via municipal or industrial wastewater that collect metal loads from a wide variety of applications and products are important anthropogenic pathways into receiving waters. Natural background and historical influences from ore-mining activities may be another important factor. Non-point emissions occur via surface runoff and erosion from drained land area. Besides deposition metals can be deposited by fertilizer application or the use of metal products such as wires or metal fences. Surface water concentrations vary according to the emission strength of sources located nearby and upstream of the considered location. A direct link between specific emission sources and pathways on the one hand and observed concentrations can hardly be established by monitoring alone. Geo-referenced models such as GREAT-ER (Geo-referenced Regional Exposure Assessment Tool for European Rivers) deliver spatially resolved concentrations in a whole river basin and allow for evaluating the causal relationship between specific emissions and resulting concentrations. This study summarizes the results of investigations for the metals zinc and copper in three German catchments. 2. The model GREAT-ER The geo-referenced model GREAT-ER has originally been developed to simulate and assess chemical burden of European river systems from multiple emission sources [2]. Emission loads from private households and rainwater runoff are individually estimated based on average consumption figures, runoff rates

Multilayered complex network datasets for three supply chain network archetypes on an urban road grid.

Science.gov (United States)

Viljoen, Nadia M; Joubert, Johan W

2018-02-01

This article presents the multilayered complex network formulation for three different supply chain network archetypes on an urban road grid and describes how 500 instances were randomly generated for each archetype. Both the supply chain network layer and the urban road network layer are directed unweighted networks. The shortest path set is calculated for each of the 1 500 experimental instances. The datasets are used to empirically explore the impact that the supply chain's dependence on the transport network has on its vulnerability in Viljoen and Joubert (2017) [1]. The datasets are publicly available on Mendeley (Joubert and Viljoen, 2017) [2].

Quantifying uncertainty in observational rainfall datasets

Science.gov (United States)

Lennard, Chris; Dosio, Alessandro; Nikulin, Grigory; Pinto, Izidine; Seid, Hussen

2015-04-01

The CO-ordinated Regional Downscaling Experiment (CORDEX) has to date seen the publication of at least ten journal papers that examine the African domain during 2012 and 2013. Five of these papers consider Africa generally (Nikulin et al. 2012, Kim et al. 2013, Hernandes-Dias et al. 2013, Laprise et al. 2013, Panitz et al. 2013) and five have regional foci: Tramblay et al. (2013) on Northern Africa, Mariotti et al. (2014) and Gbobaniyi el al. (2013) on West Africa, Endris et al. (2013) on East Africa and Kalagnoumou et al. (2013) on southern Africa. There also are a further three papers that the authors know about under review. These papers all use an observed rainfall and/or temperature data to evaluate/validate the regional model output and often proceed to assess projected changes in these variables due to climate change in the context of these observations. The most popular reference rainfall data used are the CRU, GPCP, GPCC, TRMM and UDEL datasets. However, as Kalagnoumou et al. (2013) point out there are many other rainfall datasets available for consideration, for example, CMORPH, FEWS, TAMSAT & RIANNAA, TAMORA and the WATCH & WATCH-DEI data. They, with others (Nikulin et al. 2012, Sylla et al. 2012) show that the observed datasets can have a very wide spread at a particular space-time coordinate. As more ground, space and reanalysis-based rainfall products become available, all which use different methods to produce precipitation data, the selection of reference data is becoming an important factor in model evaluation. A number of factors can contribute to a uncertainty in terms of the reliability and validity of the datasets such as radiance conversion algorithims, the quantity and quality of available station data, interpolation techniques and blending methods used to combine satellite and guage based products. However, to date no comprehensive study has been performed to evaluate the uncertainty in these observational datasets. We assess 18 gridded

Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets.

Science.gov (United States)

Paxton, Alexandra; Griffiths, Thomas L

2017-10-01

Today, people generate and store more data than ever before as they interact with both real and virtual environments. These digital traces of behavior and cognition offer cognitive scientists and psychologists an unprecedented opportunity to test theories outside the laboratory. Despite general excitement about big data and naturally occurring datasets among researchers, three "gaps" stand in the way of their wider adoption in theory-driven research: the imagination gap, the skills gap, and the culture gap. We outline an approach to bridging these three gaps while respecting our responsibilities to the public as participants in and consumers of the resulting research. To that end, we introduce Data on the Mind ( http://www.dataonthemind.org ), a community-focused initiative aimed at meeting the unprecedented challenges and opportunities of theory-driven research with big data and naturally occurring datasets. We argue that big data and naturally occurring datasets are most powerfully used to supplement-not supplant-traditional experimental paradigms in order to understand human behavior and cognition, and we highlight emerging ethical issues related to the collection, sharing, and use of these powerful datasets.

Public participation in GIS via mobile applications

Science.gov (United States)

Brovelli, Maria Antonia; Minghini, Marco; Zamboni, Giorgio

2016-04-01

Driven by the recent trends in the GIS domain including Volunteered Geographic Information, geo-crowdsourcing and citizen science, and fostered by the constant technological advances, collection and dissemination of geospatial information by ordinary people has become commonplace. However, applications involving user-generated geospatial content show dramatically diversified patterns in terms of incentive, type and level of participation, purpose of the activity, data/metadata provided and data quality. This study contributes to this heterogeneous context by investigating public participation in GIS within the field of mobile-based applications. Results not only show examples of how to technically build GIS applications enabling user collection and interaction with geospatial data, but they also draw conclusions about the methods and needs of public participation. We describe three projects with different scales and purposes in the context of urban monitoring and planning, and tourism valorisation. In each case, an open source architecture is used, allowing users to exploit their mobile devices to collect georeferenced information. This data is then made publicly available on specific Web viewers. Analysis of user involvement in these projects provides insights related to participation patterns which suggests some generalized conclusions.

The Geometry of Finite Equilibrium Datasets

DEFF Research Database (Denmark)

Balasko, Yves; Tvede, Mich

We investigate the geometry of finite datasets defined by equilibrium prices, income distributions, and total resources. We show that the equilibrium condition imposes no restrictions if total resources are collinear, a property that is robust to small perturbations. We also show that the set...... of equilibrium datasets is pathconnected when the equilibrium condition does impose restrictions on datasets, as for example when total resources are widely non collinear....

Automatic 3D relief acquisition and georeferencing of road sides by low-cost on-motion SfM

Science.gov (United States)

Voumard, Jérémie; Bornemann, Perrick; Malet, Jean-Philippe; Derron, Marc-Henri; Jaboyedoff, Michel

2017-04-01

3D terrain relief acquisition is important for a large part of geosciences. Several methods have been developed to digitize terrains, such as total station, LiDAR, GNSS or photogrammetry. To digitize road (or rail tracks) sides on long sections, mobile spatial imaging system or UAV are commonly used. In this project, we compare a still fairly new method -the SfM on-motion technics- with some traditional technics of terrain digitizing (terrestrial laser scanning, traditional SfM, UAS imaging solutions, GNSS surveying systems and total stations). The SfM on-motion technics generates 3D spatial data by photogrammetric processing of images taken from a moving vehicle. Our mobile system consists of six action cameras placed on a vehicle. Four fisheye cameras mounted on a mast on the vehicle roof are placed at 3.2 meters above the ground. Three of them have a GNNS chip providing geotagged images. Two pictures were acquired every second by each camera. 4K resolution fisheye videos were also used to extract 8.3M not geotagged pictures. All these pictures are then processed with the Agisoft PhotoScan Professional software. Results from the SfM on-motion technics are compared with results from classical SfM photogrammetry on a 500 meters long alpine track. They were also compared with mobile laser scanning data on the same road section. First results seem to indicate that slope structures are well observable up to decimetric accuracy. For the georeferencing, the planimetric (XY) accuracy of few meters is much better than the altimetric (Z) accuracy. There is indeed a Z coordinate shift of few tens of meters between GoPro cameras and Garmin camera. This makes necessary to give a greater freedom to altimetric coordinates in the processing software. Benefits of this low-cost SfM on-motion method are: 1) a simple setup to use in the field (easy to switch between vehicle types as car, train, bike, etc.), 2) a low cost and 3) an automatic georeferencing of 3D points clouds. Main

Using kittens to unlock photo-sharing website datasets for environmental applications

Science.gov (United States)

Gascoin, Simon

2016-04-01

Mining photo-sharing websites is a promising approach to complement in situ and satellite observations of the environment, however a challenge is to deal with the large degree of noise inherent to online social datasets. Here I explored the value of the Flickr image hosting website database to monitor the snow cover in the Pyrenees. Using the Flickr application programming interface (API) I queried all the public images metadata tagged at least with one of the following words: "snow", "neige", "nieve", "neu" (snow in French, Spanish and Catalan languages). The search was limited to the geo-tagged pictures taken in the Pyrenees area. However, the number of public pictures available in the Flickr database for a given time interval depends on several factors, including the Flickr website popularity and the development of digital photography. Thus, I also searched for all Flickr images tagged with "chat", "gat" or "gato" (cat in French, Spanish and Catalan languages). The tag "cat" was not considered in order to exclude the results from North America where Flickr got popular earlier than in Europe. The number of "cat" images per month was used to fit a model of the number of images uploaded in Flickr with time. This model was used to remove this trend in the numbers of snow-tagged photographs. The resulting time series was compared to a time series of the snow cover area derived from the MODIS satellite over the same region. Both datasets are well correlated; in particular they exhibit the same seasonal evolution, although the inter-annual variabilities are less similar. I will also discuss which other factors may explain the main discrepancies in order to further decrease the noise in the Flickr dataset.

Automated Generation of Geo-Referenced Mosaics From Video Data Collected by Deep-Submergence Vehicles: Preliminary Results

Science.gov (United States)

Rhzanov, Y.; Beaulieu, S.; Soule, S. A.; Shank, T.; Fornari, D.; Mayer, L. A.

2005-12-01

Many advances in understanding geologic, tectonic, biologic, and sedimentologic processes in the deep ocean are facilitated by direct observation of the seafloor. However, making such observations is both difficult and expensive. Optical systems (e.g., video, still camera, or direct observation) will always be constrained by the severe attenuation of light in the deep ocean, limiting the field of view to distances that are typically less than 10 meters. Acoustic systems can 'see' much larger areas, but at the cost of spatial resolution. Ultimately, scientists want to study and observe deep-sea processes in the same way we do land-based phenomena so that the spatial distribution and juxtaposition of processes and features can be resolved. We have begun development of algorithms that will, in near real-time, generate mosaics from video collected by deep-submergence vehicles. Mosaics consist of >>10 video frames and can cover 100's of square-meters. This work builds on a publicly available still and video mosaicking software package developed by Rzhanov and Mayer. Here we present the results of initial tests of data collection methodologies (e.g., transects across the seafloor and panoramas across features of interest), algorithm application, and GIS integration conducted during a recent cruise to the Eastern Galapagos Spreading Center (0 deg N, 86 deg W). We have developed a GIS database for the region that will act as a means to access and display mosaics within a geospatially-referenced framework. We have constructed numerous mosaics using both video and still imagery and assessed the quality of the mosaics (including registration errors) under different lighting conditions and with different navigation procedures. We have begun to develop algorithms for efficient and timely mosaicking of collected video as well as integration with navigation data for georeferencing the mosaics. Initial results indicate that operators must be properly versed in the control of the

Direct Georeferencing of a Pushbroom, Lightweight Hyperspectral System for Mini-UAV Applications

Directory of Open Access Journals (Sweden)

Marion Jaud

2018-01-01

Full Text Available Hyperspectral imagery has proven its potential in many research applications, especially in the field of environmental sciences. Currently, hyperspectral imaging is generally performed by satellite or aircraft platforms, but mini-UAV (Unmanned Aerial Vehicle platforms (<20 kg are now emerging. On such platforms, payload restrictions are critical, so sensors must be selected according to stringent specifications. This article presents the integration of a light pushbroom hyperspectral sensor onboard a multirotor UAV, which we have called Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations. This article depicts the system design: the UAV platform, the imaging module, the navigation module, and the interfacing between the different elements. Pushbroom sensors offer a better combination of spatial and spectral resolution than full-frame cameras. Nevertheless, data georectification has to be performed line by line, the quality of direct georeferencing being limited by mechanical stability, good timing accuracy, and the resolution and accuracy of the proprioceptive sensors. A georegistration procedure is proposed for geometrical pre-processing of hyperspectral data. The specifications of Hyper-DRELIO surveys are described through two examples of surveys above coastal or inland waters, with different flight altitudes. This system can collect hyperspectral data in VNIR (Visible and Near InfraRed domain above small study sites (up to about 4 ha with both high spatial resolution (<10 cm and high spectral resolution (1.85 nm and with georectification accuracy on the order of 1 to 2 m.

Georeferenced measurement of soil EC as a tool to detect susceptible areas to water erosion.

Science.gov (United States)

Fabian Sallesses, Leonardo; Aparicio, Virginia Carolina; Costa, Jose Luis

2017-04-01

The Southeast region of Buenos Aires Province, Argentina, is one of the main region for the cultivation of potato (Solanum tuberosum L.) in that country. The implementation of complementary irrigation for potato cultivation meant an increase in yield of up to 60%. Therefore, all potato production in the region is under irrigation. In this way, the area under central pivot irrigation has increased to 150% in the last two decades. The water used for irrigation in that region is underground with a high concentration of sodium bicarbonate. The combination of irrigation and rain increases the sodium absorption ratio of soil (SARs), consequently raising the clay dispersion and reducing infiltration. A reduction in infiltration means greater partitioning of precipitation into runoff. The degree of slope of the terrain, added to its length, increases the erosive potential of runoff water. The content of dissolved salts, in combination with the water content, affect the apparent Electrical Conductivity of the soil (EC), which is directly related to the concentration of Na + 2 in the soil solution. In August 2016, severe rill erosion was detected in a productive plot of 300 ha. The predecessor crop was a potato under irrigation campaign. However the history of the lot consists of various winter and summer crops, always made in dry land and no till. Cumulative rainfall from harvest to erosion detection (four months) was 250 mm. A georeferenced EC measurement was performed using the Verys 3100® contact sensor. With the data obtained, a geostatistical analysis was performed using Kriging spatial interpolation. The maps obtained were processed, dividing them into 4 EC ranges. The values and amplitude of the CEa ranges for each lot were determined according to the distribution observed in the generated histograms. It was observed a distribution of elevated EC ranges and consequently of a higher concentration of Na+ 2 coincident with the irrigation areas of the pivots. These

RAINBIO: a mega-database of tropical African vascular plants distributions

Directory of Open Access Journals (Sweden)

Dauby Gilles

2016-11-01

Full Text Available The tropical vegetation of Africa is characterized by high levels of species diversity but is undergoing important shifts in response to ongoing climate change and increasing anthropogenic pressures. Although our knowledge of plant species distribution patterns in the African tropics has been improving over the years, it remains limited. Here we present RAINBIO, a unique comprehensive mega-database of georeferenced records for vascular plants in continental tropical Africa. The geographic focus of the database is the region south of the Sahel and north of Southern Africa, and the majority of data originate from tropical forest regions. RAINBIO is a compilation of 13 datasets either publicly available or personal ones. Numerous in depth data quality checks, automatic and manual via several African flora experts, were undertaken for georeferencing, standardization of taxonomic names and identification and merging of duplicated records. The resulting RAINBIO data allows exploration and extraction of distribution data for 25,356 native tropical African vascular plant species, which represents ca. 89% of all known plant species in the area of interest. Habit information is also provided for 91% of these species.

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

Science.gov (United States)

Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

2016-12-01

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

CHARMe Commentary metadata for Climate Science: collecting, linking and sharing user feedback on climate datasets

Science.gov (United States)

Blower, Jon; Lawrence, Bryan; Kershaw, Philip; Nagni, Maurizio

2014-05-01

The research process can be thought of as an iterative activity, initiated based on prior domain knowledge, as well on a number of external inputs, and producing a range of outputs including datasets, studies and peer reviewed publications. These outputs may describe the problem under study, the methodology used, the results obtained, etc. In any new publication, the author may cite or comment other papers or datasets in order to support their research hypothesis. However, as their work progresses, the researcher may draw from many other latent channels of information. These could include for example, a private conversation following a lecture or during a social dinner; an opinion expressed concerning some significant event such as an earthquake or for example a satellite failure. In addition, other sources of information of grey literature are important public such as informal papers such as the arxiv deposit, reports and studies. The climate science community is not an exception to this pattern; the CHARMe project, funded under the European FP7 framework, is developing an online system for collecting and sharing user feedback on climate datasets. This is to help users judge how suitable such climate data are for an intended application. The user feedback could be comments about assessments, citations, or provenance of the dataset, or other information such as descriptions of uncertainty or data quality. We define this as a distinct category of metadata called Commentary or C-metadata. We link C-metadata with target climate datasets using a Linked Data approach via the Open Annotation data model. In the context of Linked Data, C-metadata plays the role of a resource which, depending on its nature, may be accessed as simple text or as more structured content. The project is implementing a range of software tools to create, search or visualize C-metadata including a JavaScript plugin enabling this functionality to be integrated in situ with data provider portals

An Annotated Dataset of 14 Meat Images

DEFF Research Database (Denmark)

Stegmann, Mikkel Bille

2002-01-01

This note describes a dataset consisting of 14 annotated images of meat. Points of correspondence are placed on each image. As such, the dataset can be readily used for building statistical models of shape. Further, format specifications and terms of use are given.......This note describes a dataset consisting of 14 annotated images of meat. Points of correspondence are placed on each image. As such, the dataset can be readily used for building statistical models of shape. Further, format specifications and terms of use are given....

Comparison of recent SnIa datasets

International Nuclear Information System (INIS)

Sanchez, J.C. Bueno; Perivolaropoulos, L.; Nesseris, S.

2009-01-01

We rank the six latest Type Ia supernova (SnIa) datasets (Constitution (C), Union (U), ESSENCE (Davis) (E), Gold06 (G), SNLS 1yr (S) and SDSS-II (D)) in the context of the Chevalier-Polarski-Linder (CPL) parametrization w(a) = w 0 +w 1 (1−a), according to their Figure of Merit (FoM), their consistency with the cosmological constant (ΛCDM), their consistency with standard rulers (Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillations (BAO)) and their mutual consistency. We find a significant improvement of the FoM (defined as the inverse area of the 95.4% parameter contour) with the number of SnIa of these datasets ((C) highest FoM, (U), (G), (D), (E), (S) lowest FoM). Standard rulers (CMB+BAO) have a better FoM by about a factor of 3, compared to the highest FoM SnIa dataset (C). We also find that the ranking sequence based on consistency with ΛCDM is identical with the corresponding ranking based on consistency with standard rulers ((S) most consistent, (D), (C), (E), (U), (G) least consistent). The ranking sequence of the datasets however changes when we consider the consistency with an expansion history corresponding to evolving dark energy (w 0 ,w 1 ) = (−1.4,2) crossing the phantom divide line w = −1 (it is practically reversed to (G), (U), (E), (S), (D), (C)). The SALT2 and MLCS2k2 fitters are also compared and some peculiar features of the SDSS-II dataset when standardized with the MLCS2k2 fitter are pointed out. Finally, we construct a statistic to estimate the internal consistency of a collection of SnIa datasets. We find that even though there is good consistency among most samples taken from the above datasets, this consistency decreases significantly when the Gold06 (G) dataset is included in the sample

SIMADL: Simulated Activities of Daily Living Dataset

Directory of Open Access Journals (Sweden)

Talal Alshammari

2018-04-01

Full Text Available With the realisation of the Internet of Things (IoT paradigm, the analysis of the Activities of Daily Living (ADLs, in a smart home environment, is becoming an active research domain. The existence of representative datasets is a key requirement to advance the research in smart home design. Such datasets are an integral part of the visualisation of new smart home concepts as well as the validation and evaluation of emerging machine learning models. Machine learning techniques that can learn ADLs from sensor readings are used to classify, predict and detect anomalous patterns. Such techniques require data that represent relevant smart home scenarios, for training, testing and validation. However, the development of such machine learning techniques is limited by the lack of real smart home datasets, due to the excessive cost of building real smart homes. This paper provides two datasets for classification and anomaly detection. The datasets are generated using OpenSHS, (Open Smart Home Simulator, which is a simulation software for dataset generation. OpenSHS records the daily activities of a participant within a virtual environment. Seven participants simulated their ADLs for different contexts, e.g., weekdays, weekends, mornings and evenings. Eighty-four files in total were generated, representing approximately 63 days worth of activities. Forty-two files of classification of ADLs were simulated in the classification dataset and the other forty-two files are for anomaly detection problems in which anomalous patterns were simulated and injected into the anomaly detection dataset.

The NOAA Dataset Identifier Project

Science.gov (United States)

de la Beaujardiere, J.; Mccullough, H.; Casey, K. S.

2013-12-01

The US National Oceanic and Atmospheric Administration (NOAA) initiated a project in 2013 to assign persistent identifiers to datasets archived at NOAA and to create informational landing pages about those datasets. The goals of this project are to enable the citation of datasets used in products and results in order to help provide credit to data producers, to support traceability and reproducibility, and to enable tracking of data usage and impact. A secondary goal is to encourage the submission of datasets for long-term preservation, because only archived datasets will be eligible for a NOAA-issued identifier. A team was formed with representatives from the National Geophysical, Oceanographic, and Climatic Data Centers (NGDC, NODC, NCDC) to resolve questions including which identifier scheme to use (answer: Digital Object Identifier - DOI), whether or not to embed semantics in identifiers (no), the level of granularity at which to assign identifiers (as coarsely as reasonable), how to handle ongoing time-series data (do not break into chunks), creation mechanism for the landing page (stylesheet from formal metadata record preferred), and others. Decisions made and implementation experience gained will inform the writing of a Data Citation Procedural Directive to be issued by the Environmental Data Management Committee in 2014. Several identifiers have been issued as of July 2013, with more on the way. NOAA is now reporting the number as a metric to federal Open Government initiatives. This paper will provide further details and status of the project.

Control Measure Dataset

Data.gov (United States)

U.S. Environmental Protection Agency — The EPA Control Measure Dataset is a collection of documents describing air pollution control available to regulated facilities for the control and abatement of air...

Georeferenced Population Datasets of Mexico (GEO-MEX): Raster Based GIS Coverage of Mexican Population

Data.gov (United States)

National Aeronautics and Space Administration — The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The...

Dataset on the energy performance of atrium type hotel buildings.

Science.gov (United States)

Vujosevic, Milica; Krstic-Furundzic, Aleksandra

2018-04-01

The data presented in this article are related to the research article entitled "The Influence of Atrium on Energy Performance of Hotel Building" (Vujosevic and Krstic-Furundzic, 2017) [1], which describes the annual energy performance of atrium type hotel building in Belgrade climate conditions, with the objective to present the impact of the atrium on the hotel building's energy demands for space heating and cooling. This dataset is made publicly available to show energy performance of selected hotel design alternatives, in order to enable extended analyzes of these data for other researchers.

GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

Directory of Open Access Journals (Sweden)

Zheng Huiru

2009-01-01

Full Text Available Abstract Background Information about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases and non-interacting proteins (negative cases are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task. Results GRIP (Gold Reference dataset constructor from Information on Protein complexes is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in Saccharomyces cerevisiae. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in Saccharomyces cerevisiae. Conclusion GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at http://rosalind.infj.ulst.ac.uk/GRIP/.

The Kinetics Human Action Video Dataset

OpenAIRE

Kay, Will; Carreira, Joao; Simonyan, Karen; Zhang, Brian; Hillier, Chloe; Vijayanarasimhan, Sudheendra; Viola, Fabio; Green, Tim; Back, Trevor; Natsev, Paul; Suleyman, Mustafa; Zisserman, Andrew

2017-01-01

We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focussed and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. We describe the statistics of the dataset, how it was collected, and give some ...

DIRECT GEOREFERENCING ON SMALL UNMANNED AERIAL PLATFORMS FOR IMPROVED RELIABILITY AND ACCURACY OF MAPPING WITHOUT THE NEED FOR GROUND CONTROL POINTS

Directory of Open Access Journals (Sweden)

O. Mian

2015-08-01

Full Text Available This paper presents results from a Direct Mapping Solution (DMS comprised of an Applanix APX-15 UAV GNSS-Inertial system integrated with a Sony a7R camera to produce highly accurate ortho-rectified imagery without Ground Control Points on a Microdrones md4-1000 platform. A 55 millimeter Nikkor f/1.8 lens was mounted on the Sony a7R and the camera was then focused and calibrated terrestrially using the Applanix camera calibration facility, and then integrated with the APX-15 UAV GNSS-Inertial system using a custom mount specifically designed for UAV applications. In July 2015, Applanix and Avyon carried out a test flight of this system. The goal of the test flight was to assess the performance of DMS APX-15 UAV direct georeferencing system on the md4-1000. The area mapped during the test was a 250 x 300 meter block in a rural setting in Ontario, Canada. Several ground control points are distributed within the test area. The test included 8 North-South lines and 1 cross strip flown at 80 meters AGL, resulting in a ~1 centimeter Ground Sample Distance (GSD. Map products were generated from the test flight using Direct Georeferencing, and then compared for accuracy against the known positions of ground control points in the test area. The GNSS-Inertial data collected by the APX-15 UAV was post-processed in Single Base mode, using a base station located in the project area via POSPac UAV. The base-station’s position was precisely determined by processing a 12-hour session using the CSRS-PPP Post Processing service. The ground control points were surveyed in using differential GNSS post-processing techniques with respect to the base-station.

Comparison of CORA and EN4 in-situ datasets validation methods, toward a better quality merged dataset.

Science.gov (United States)

Szekely, Tanguy; Killick, Rachel; Gourrion, Jerome; Reverdin, Gilles

2017-04-01

CORA and EN4 are both global delayed time mode validated in-situ ocean temperature and salinity datasets distributed by the Met Office (http://www.metoffice.gov.uk/) and Copernicus (www.marine.copernicus.eu). A large part of the profiles distributed by CORA and EN4 in recent years are Argo profiles from the ARGO DAC, but profiles are also extracted from the World Ocean Database and TESAC profiles from GTSPP. In the case of CORA, data coming from the EUROGOOS Regional operationnal oserving system( ROOS) operated by European institutes no managed by National Data Centres and other datasets of profiles povided by scientific sources can also be found (Sea mammals profiles from MEOP, XBT datasets from cruises ...). (EN4 also takes data from the ASBO dataset to supplement observations in the Arctic). First advantage of this new merge product is to enhance the space and time coverage at global and european scales for the period covering 1950 till a year before the current year. This product is updated once a year and T&S gridded fields are alos generated for the period 1990-year n-1. The enhancement compared to the revious CORA product will be presented Despite the fact that the profiles distributed by both datasets are mostly the same, the quality control procedures developed by the Met Office and Copernicus teams differ, sometimes leading to different quality control flags for the same profile. Started in 2016 a new study started that aims to compare both validation procedures to move towards a Copernicus Marine Service dataset with the best features of CORA and EN4 validation.A reference data set composed of the full set of in-situ temperature and salinity measurements collected by Coriolis during 2015 is used. These measurements have been made thanks to wide range of instruments (XBTs, CTDs, Argo floats, Instrumented sea mammals,...), covering the global ocean. The reference dataset has been validated simultaneously by both teams.An exhaustive comparison of the

Enhancing Conservation with High Resolution Productivity Datasets for the Conterminous United States

Science.gov (United States)

Robinson, Nathaniel Paul

across the CONUS domain. The main results of this work are three publicly available datasets: 1) 30 m Landsat NDVI; 2) 250 m MODIS based GPP and NPP; and 3) 30 m Landsat based GPP and NPP. My goal is that these products prove useful for the wider scientific, conservation, and land management communities as we continue to strive for better conservation and management practices.

Mining Open Datasets for Transparency in Taxi Transport in Metropolitan Environments

OpenAIRE

Noulas, Anastasios; Salnikov, Vsevolod; Lambiotte, Renaud; Mascolo, Cecilia

2015-01-01

Uber has recently been introducing novel practices in urban taxi transport. Journey prices can change dynamically in almost real time and also vary geographically from one area to another in a city, a strategy known as surge pricing. In this paper, we explore the power of the new generation of open datasets towards understanding the impact of the new disruption technologies that emerge in the area of public transport. With our primary goal being a more transparent economic landscape for urban...

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database.

Science.gov (United States)

Li, Yan; Hu, Qingwu; Wu, Meng; Gao, Yang

2016-01-28

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m.

Introducing a Web API for Dataset Submission into a NASA Earth Science Data Center

Science.gov (United States)

Moroni, D. F.; Quach, N.; Francis-Curley, W.

2016-12-01

facilitate rapid and efficient updating of dataset metadata records by external data producers. Here we present this new service and demonstrate the variety of ways in which a multitude of Earth Science datasets may be submitted in a manner that significantly reduces the time in ensuring that new, vital data reaches the public domain.

Comparison and evaluation of datasets for off-angle iris recognition

Science.gov (United States)

Kurtuncu, Osman M.; Cerme, Gamze N.; Karakaya, Mahmut

2016-05-01

In this paper, we investigated the publicly available iris recognition datasets and their data capture procedures in order to determine if they are suitable for the stand-off iris recognition research. Majority of the iris recognition datasets include only frontal iris images. Even if a few datasets include off-angle iris images, the frontal and off-angle iris images are not captured at the same time. The comparison of the frontal and off-angle iris images shows not only differences in the gaze angle but also change in pupil dilation and accommodation as well. In order to isolate the effect of the gaze angle from other challenging issues including dilation and accommodation, the frontal and off-angle iris images are supposed to be captured at the same time by using two different cameras. Therefore, we developed an iris image acquisition platform by using two cameras in this work where one camera captures frontal iris image and the other one captures iris images from off-angle. Based on the comparison of Hamming distance between frontal and off-angle iris images captured with the two-camera- setup and one-camera-setup, we observed that Hamming distance in two-camera-setup is less than one-camera-setup ranging from 0.05 to 0.001. These results show that in order to have accurate results in the off-angle iris recognition research, two-camera-setup is necessary in order to distinguish the challenging issues from each other.

PHYSICS PERFORMANCE AND DATASET (PPD)

CERN Multimedia

L. Silvestris

2013-01-01

The PPD activities, in the first part of 2013, have been focused mostly on the final physics validation and preparation for the data reprocessing of the full 8 TeV datasets with the latest calibrations. These samples will be the basis for the preliminary results for summer 2013 but most importantly for the final publications on the 8 TeV Run 1 data. The reprocessing involves also the reconstruction of a significant fraction of “parked data” that will allow CMS to perform a whole new set of precision analyses and searches. In this way the CMSSW release 53X is becoming the legacy release for the 8 TeV Run 1 data. The regular operation activities have included taking care of the prolonged proton-proton data taking and the run with proton-lead collisions that ended in February. The DQM and Data Certification team has deployed a continuous effort to promptly certify the quality of the data. The luminosity-weighted certification efficiency (requiring all sub-detectors to be certified as usab...

Public Use Airport Runways, Geographic WGS84, BTS (2006) [public_use_airport_runway_BTS_2006

Data.gov (United States)

Louisiana Geographic Information Center — The Public Use Airport Runways database is a geographic dataset of runways in the United States and US territories containing information on the physical...

Fluxnet Synthesis Dataset Collaboration Infrastructure

Energy Technology Data Exchange (ETDEWEB)

Agarwal, Deborah A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Humphrey, Marty [Univ. of Virginia, Charlottesville, VA (United States); van Ingen, Catharine [Microsoft. San Francisco, CA (United States); Beekwilder, Norm [Univ. of Virginia, Charlottesville, VA (United States); Goode, Monte [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Jackson, Keith [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Rodriguez, Matt [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Weber, Robin [Univ. of California, Berkeley, CA (United States)

2008-02-06

The Fluxnet synthesis dataset originally compiled for the La Thuile workshop contained approximately 600 site years. Since the workshop, several additional site years have been added and the dataset now contains over 920 site years from over 240 sites. A data refresh update is expected to increase those numbers in the next few months. The ancillary data describing the sites continues to evolve as well. There are on the order of 120 site contacts and 60proposals have been approved to use thedata. These proposals involve around 120 researchers. The size and complexity of the dataset and collaboration has led to a new approach to providing access to the data and collaboration support and the support team attended the workshop and worked closely with the attendees and the Fluxnet project office to define the requirements for the support infrastructure. As a result of this effort, a new website (http://www.fluxdata.org) has been created to provide access to the Fluxnet synthesis dataset. This new web site is based on a scientific data server which enables browsing of the data on-line, data download, and version tracking. We leverage database and data analysis tools such as OLAP data cubes and web reports to enable browser and Excel pivot table access to the data.

Multilayered complex network datasets for three supply chain network archetypes on an urban road grid

Directory of Open Access Journals (Sweden)

Nadia M. Viljoen

2018-02-01

Full Text Available This article presents the multilayered complex network formulation for three different supply chain network archetypes on an urban road grid and describes how 500 instances were randomly generated for each archetype. Both the supply chain network layer and the urban road network layer are directed unweighted networks. The shortest path set is calculated for each of the 1 500 experimental instances. The datasets are used to empirically explore the impact that the supply chain's dependence on the transport network has on its vulnerability in Viljoen and Joubert (2017 [1]. The datasets are publicly available on Mendeley (Joubert and Viljoen, 2017 [2]. Keywords: Multilayered complex networks, Supply chain vulnerability, Urban road networks

Scanning and georeferencing historical USGS quadrangles

Science.gov (United States)

Fishburn, Kristin A.; Davis, Larry R.; Allord, Gregory J.

2017-06-23

The U.S. Geological Survey (USGS) National Geospatial Program is scanning published USGS 1:250,000-scale and larger topographic maps printed between 1884, the inception of the topographic mapping program, and 2006. The goal of this project, which began publishing the Historical Topographic Map Collection in 2011, is to provide access to a digital repository of USGS topographic maps that is available to the public at no cost. For more than 125 years, USGS topographic maps have accurately portrayed the complex geography of the Nation. The USGS is the Nation’s largest producer of traditional topographic maps, and, prior to 2006, USGS topographic maps were created using traditional cartographic methods and printed using a lithographic process. The next generation of topographic maps, US Topo, is being released by the USGS in digital form, and newer technologies make it possible to also deliver historical maps in the same electronic format that is more publicly accessible.

Simulation of Smart Home Activity Datasets

Directory of Open Access Journals (Sweden)

Jonathan Synnott

2015-06-01

Full Text Available A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.

Simulation of Smart Home Activity Datasets.

Science.gov (United States)

Synnott, Jonathan; Nugent, Chris; Jeffers, Paul

2015-06-16

A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.

Data Publications Correlate with Citation Impact.

Science.gov (United States)

Leitner, Florian; Bielza, Concha; Hill, Sean L; Larrañaga, Pedro

2016-01-01

Neuroscience and molecular biology have been generating large datasets over the past years that are reshaping how research is being conducted. In their wake, open data sharing has been singled out as a major challenge for the future of research. We conducted a comparative study of citations of data publications in both fields, showing that the average publication tagged with a data-related term by the NCBI MeSH (Medical Subject Headings) curators achieves a significantly larger citation impact than the average in either field. We introduce a new metric, the data article citation index (DAC-index), to identify the most prolific authors among those data-related publications. The study is fully reproducible from an executable Rmd (R Markdown) script together with all the citation datasets. We hope these results can encourage authors to more openly publish their data.

Solar Integration National Dataset Toolkit | Grid Modernization | NREL

Science.gov (United States)

Solar Integration National Dataset Toolkit Solar Integration National Dataset Toolkit NREL is working on a Solar Integration National Dataset (SIND) Toolkit to enable researchers to perform U.S . regional solar generation integration studies. It will provide modeled, coherent subhourly solar power data

PROVIDING GEOGRAPHIC DATASETS AS LINKED DATA IN SDI

Directory of Open Access Journals (Sweden)

E. Hietanen

2016-06-01

Full Text Available In this study, a prototype service to provide data from Web Feature Service (WFS as linked data is implemented. At first, persistent and unique Uniform Resource Identifiers (URI are created to all spatial objects in the dataset. The objects are available from those URIs in Resource Description Framework (RDF data format. Next, a Web Ontology Language (OWL ontology is created to describe the dataset information content using the Open Geospatial Consortium’s (OGC GeoSPARQL vocabulary. The existing data model is modified in order to take into account the linked data principles. The implemented service produces an HTTP response dynamically. The data for the response is first fetched from existing WFS. Then the Geographic Markup Language (GML format output of the WFS is transformed on-the-fly to the RDF format. Content Negotiation is used to serve the data in different RDF serialization formats. This solution facilitates the use of a dataset in different applications without replicating the whole dataset. In addition, individual spatial objects in the dataset can be referred with URIs. Furthermore, the needed information content of the objects can be easily extracted from the RDF serializations available from those URIs. A solution for linking data objects to the dataset URI is also introduced by using the Vocabulary of Interlinked Datasets (VoID. The dataset is divided to the subsets and each subset is given its persistent and unique URI. This enables the whole dataset to be explored with a web browser and all individual objects to be indexed by search engines.

Wind Integration National Dataset Toolkit | Grid Modernization | NREL

Science.gov (United States)

Integration National Dataset Toolkit Wind Integration National Dataset Toolkit The Wind Integration National Dataset (WIND) Toolkit is an update and expansion of the Eastern Wind Integration Data Set and Western Wind Integration Data Set. It supports the next generation of wind integration studies. WIND

What are we 'tweeting' about obesity? Mapping tweets with Topic Modeling and Geographic Information System.

Science.gov (United States)

Ghosh, Debarchana Debs; Guha, Rajarshi

2013-01-01

Public health related tweets are difficult to identify in large conversational datasets like Twitter.com. Even more challenging is the visualization and analyses of the spatial patterns encoded in tweets. This study has the following objectives: How can topic modeling be used to identify relevant public health topics such as obesity on Twitter.com? What are the common obesity related themes? What is the spatial pattern of the themes? What are the research challenges of using large conversational datasets from social networking sites? Obesity is chosen as a test theme to demonstrate the effectiveness of topic modeling using Latent Dirichlet Allocation (LDA) and spatial analysis using Geographic Information System (GIS). The dataset is constructed from tweets (originating from the United States) extracted from Twitter.com on obesity-related queries. Examples of such queries are 'food deserts', 'fast food', and 'childhood obesity'. The tweets are also georeferenced and time stamped. Three cohesive and meaningful themes such as 'childhood obesity and schools', 'obesity prevention', and 'obesity and food habits' are extracted from the LDA model. The GIS analysis of the extracted themes show distinct spatial pattern between rural and urban areas, northern and southern states, and between coasts and inland states. Further, relating the themes with ancillary datasets such as US census and locations of fast food restaurants based upon the location of the tweets in a GIS environment opened new avenues for spatial analyses and mapping. Therefore the techniques used in this study provide a possible toolset for computational social scientists in general and health researchers in specific to better understand health problems from large conversational datasets.

Dataset - Evaluation of Standardized Sample Collection, Packaging, and Decontamination Procedures to Assess Cross-Contamination Potential during Bacillus anthracis Incident Response Operations

Data.gov (United States)

U.S. Environmental Protection Agency — Spore recovery data during sample packaging decontamination tests. This dataset is associated with the following publication: Calfee, W., J. Tufts, K. Meyer, K....

A New Outlier Detection Method for Multidimensional Datasets

KAUST Repository

Abdel Messih, Mario A.

2012-07-01

This study develops a novel hybrid method for outlier detection (HMOD) that combines the idea of distance based and density based methods. The proposed method has two main advantages over most of the other outlier detection methods. The first advantage is that it works well on both dense and sparse datasets. The second advantage is that, unlike most other outlier detection methods that require careful parameter setting and prior knowledge of the data, HMOD is not very sensitive to small changes in parameter values within certain parameter ranges. The only required parameter to set is the number of nearest neighbors. In addition, we made a fully parallelized implementation of HMOD that made it very efficient in applications. Moreover, we proposed a new way of using the outlier detection for redundancy reduction in datasets where the confidence level that evaluates how accurate the less redundant dataset can be used to represent the original dataset can be specified by users. HMOD is evaluated on synthetic datasets (dense and mixed “dense and sparse”) and a bioinformatics problem of redundancy reduction of dataset of position weight matrices (PWMs) of transcription factor binding sites. In addition, in the process of assessing the performance of our redundancy reduction method, we developed a simple tool that can be used to evaluate the confidence level of reduced dataset representing the original dataset. The evaluation of the results shows that our method can be used in a wide range of problems.

NP-PAH Interaction Dataset

Data.gov (United States)

U.S. Environmental Protection Agency — Dataset presents concentrations of organic pollutants, such as polyaromatic hydrocarbon compounds, in water samples. Water samples of known volume and concentration...

A dataset on tail risk of commodities markets.

Science.gov (United States)

Powell, Robert J; Vo, Duc H; Pham, Thach N; Singh, Abhay K

2017-12-01

This article contains the datasets related to the research article "The long and short of commodity tails and their relationship to Asian equity markets"(Powell et al., 2017) [1]. The datasets contain the daily prices (and price movements) of 24 different commodities decomposed from the S&P GSCI index and the daily prices (and price movements) of three share market indices including World, Asia, and South East Asia for the period 2004-2015. Then, the dataset is divided into annual periods, showing the worst 5% of price movements for each year. The datasets are convenient to examine the tail risk of different commodities as measured by Conditional Value at Risk (CVaR) as well as their changes over periods. The datasets can also be used to investigate the association between commodity markets and share markets.

A Geo-referenced 3D model of the Juan de Fuca Slab and associated seismicity

Science.gov (United States)

Blair, J.L.; McCrory, P.A.; Oppenheimer, D.H.; Waldhauser, F.

2011-01-01

We present a Geographic Information System (GIS) of a new 3-dimensional (3D) model of the subducted Juan de Fuca Plate beneath western North America and associated seismicity of the Cascadia subduction system. The geo-referenced 3D model was constructed from weighted control points that integrate depth information from hypocenter locations and regional seismic velocity studies. We used the 3D model to differentiate earthquakes that occur above the Juan de Fuca Plate surface from earthquakes that occur below the plate surface. This GIS project of the Cascadia subduction system supersedes the one previously published by McCrory and others (2006). Our new slab model updates the model with new constraints. The most significant updates to the model include: (1) weighted control points to incorporate spatial uncertainty, (2) an additional gridded slab surface based on the Generic Mapping Tools (GMT) Surface program which constructs surfaces based on splines in tension (see expanded description below), (3) double-differenced hypocenter locations in northern California to better constrain slab location there, and (4) revised slab shape based on new hypocenter profiles that incorporate routine depth uncertainties as well as data from new seismic-reflection and seismic-refraction studies. We also provide a 3D fly-through animation of the model for use as a visualization tool.

Obesity and fast food in urban markets: a new approach using geo-referenced micro data.

Science.gov (United States)

Chen, Susan Elizabeth; Florax, Raymond J; Snyder, Samantha D

2013-07-01

This paper presents a new method of assessing the relationship between features of the built environment and obesity, particularly in urban areas. Our empirical application combines georeferenced data on the location of fast-food restaurants with data about personal health, behavioral, and neighborhood characteristics. We define a 'local food environment' for every individual utilizing buffers around a person's home address. Individual food landscapes are potentially endogenous because of spatial sorting of the population and food outlets, and the body mass index (BMI) values for individuals living close to each other are likely to be spatially correlated because of observed and unobserved individual and neighborhood effects. The potential biases associated with endogeneity and spatial correlation are handled using spatial econometric estimation techniques. Our application provides quantitative estimates of the effect of proximity to fast-food restaurants on obesity in an urban food market. We also present estimates of a policy simulation that focuses on reducing the density of fast-food restaurants in urban areas. In the simulations, we account for spatial heterogeneity in both the policy instruments and individual neighborhoods and find a small effect for the hypothesized relationships between individual BMI values and the density of fast-food restaurants. Copyright © 2012 John Wiley & Sons, Ltd.

Proteomics dataset

DEFF Research Database (Denmark)

Bennike, Tue Bjerg; Carlsen, Thomas Gelsing; Ellingsen, Torkell

2017-01-01

patients (Morgan et al., 2012; Abraham and Medzhitov, 2011; Bennike, 2014) [8–10. Therefore, we characterized the proteome of colon mucosa biopsies from 10 inflammatory bowel disease ulcerative colitis (UC) patients, 11 gastrointestinal healthy rheumatoid arthritis (RA) patients, and 10 controls. We...... been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD001608 for ulcerative colitis and control samples, and PXD003082 for rheumatoid arthritis samples....

Standardization of GIS datasets for emergency preparedness of NPPs

International Nuclear Information System (INIS)

Saindane, Shashank S.; Suri, M.M.K.; Otari, Anil; Pradeepkumar, K.S.

2012-01-01

Probability of a major nuclear accident which can lead to large scale release of radioactivity into environment is extremely small by the incorporation of safety systems and defence-in-depth philosophy. Nevertheless emergency preparedness for implementation of counter measures to reduce the consequences are required for all major nuclear facilities. Iodine prophylaxis, Sheltering, evacuation etc. are protective measures to be implemented for members of public in the unlikely event of any significant releases from nuclear facilities. Bhabha Atomic Research Centre has developed a GIS supported Nuclear Emergency Preparedness Program. Preparedness for Response to Nuclear emergencies needs geographical details of the affected locations specially Nuclear Power Plant Sites and nearby public domain. Geographical information system data sets which the planners are looking for will have appropriate details in order to take decision and mobilize the resources in time and follow the Standard Operating Procedures. Maps are 2-dimensional representations of our real world and GIS makes it possible to manipulate large amounts of geo-spatially referenced data and convert it into information. This has become an integral part of the nuclear emergency preparedness and response planning. This GIS datasets consisting of layers such as village settlements, roads, hospitals, police stations, shelters etc. is standardized and effectively used during the emergency. The paper focuses on the need of standardization of GIS datasets which in turn can be used as a tool to display and evaluate the impact of standoff distances and selected zones in community planning. It will also highlight the database specifications which will help in fast processing of data and analysis to derive useful and helpful information. GIS has the capability to store, manipulate, analyze and display the large amount of required spatial and tabular data. This study intends to carry out a proper response and preparedness

Free internet datasets for streamflow modelling using SWAT in the Johor river basin, Malaysia

International Nuclear Information System (INIS)

Tan, M L

2014-01-01

Streamflow modelling is a mathematical computational approach that represents terrestrial hydrology cycle digitally and is used for water resources assessment. However, such modelling endeavours require a large amount of data. Generally, governmental departments produce and maintain these data sets which make it difficult to obtain this data due to bureaucratic constraints. In some countries, the availability and quality of geospatial and climate datasets remain a critical issue due to many factors such as lacking of ground station, expertise, technology, financial support and war time. To overcome this problem, this research used public domain datasets from the Internet as ''input'' to a streamflow model. The intention is simulate daily and monthly streamflow of the Johor River Basin in Malaysia. The model used is the Soil and Water Assessment Tool (SWAT). As input free data including a digital elevation model (DEM), land use information, soil and climate data were used. The model was validated by in-situ streamflow information obtained from Rantau Panjang station for the year 2006. The coefficient of determination and Nash-Sutcliffe efficiency were 0.35/0.02 for daily simulated streamflow and 0.92/0.21 for monthly simulated streamflow, respectively. The results show that free data can provide a better simulation at a monthly scale compared to a daily basis in a tropical region. A sensitivity analysis and calibration procedure should be conducted in order to maximize the ''goodness-of-fit'' between simulated and observed streamflow. The application of Internet datasets promises an acceptable performance of streamflow modelling. This research demonstrates that public domain data is suitable for streamflow modelling in a tropical river basin within acceptable accuracy

Free internet datasets for streamflow modelling using SWAT in the Johor river basin, Malaysia

Science.gov (United States)

Tan, M. L.

2014-02-01

Streamflow modelling is a mathematical computational approach that represents terrestrial hydrology cycle digitally and is used for water resources assessment. However, such modelling endeavours require a large amount of data. Generally, governmental departments produce and maintain these data sets which make it difficult to obtain this data due to bureaucratic constraints. In some countries, the availability and quality of geospatial and climate datasets remain a critical issue due to many factors such as lacking of ground station, expertise, technology, financial support and war time. To overcome this problem, this research used public domain datasets from the Internet as "input" to a streamflow model. The intention is simulate daily and monthly streamflow of the Johor River Basin in Malaysia. The model used is the Soil and Water Assessment Tool (SWAT). As input free data including a digital elevation model (DEM), land use information, soil and climate data were used. The model was validated by in-situ streamflow information obtained from Rantau Panjang station for the year 2006. The coefficient of determination and Nash-Sutcliffe efficiency were 0.35/0.02 for daily simulated streamflow and 0.92/0.21 for monthly simulated streamflow, respectively. The results show that free data can provide a better simulation at a monthly scale compared to a daily basis in a tropical region. A sensitivity analysis and calibration procedure should be conducted in order to maximize the "goodness-of-fit" between simulated and observed streamflow. The application of Internet datasets promises an acceptable performance of streamflow modelling. This research demonstrates that public domain data is suitable for streamflow modelling in a tropical river basin within acceptable accuracy.

Demonstrating the value of publishing open data by linking DOI-based citations of source datasets to uses in research and policy

Science.gov (United States)

Copas, K.; Legind, J. K.; Hahn, A.; Braak, K.; Høftt, M.; Noesgaard, D.; Robertson, T.; Méndez Hernández, F.; Schigel, D.; Ko, C.

2017-12-01

GBIF—the Global Biodiversity Information Facility—has recently demonstrated a system that tracks publications back to individual datasets, giving data providers demonstrable evidence of the benefit and utility of sharing data to support an array of scholarly topics and practical applications. GBIF is an open-data network and research infrastructure funded by the world's governments. Its community consists of more than 90 formal participants and almost 1,000 data-publishing institutions, which currently make tens of thousands of datasets containing nearly 800 million species occurrence records freely and publicly available for discovery, use and reuse across a wide range of biodiversity-related research and policy investigations. Starting in 2015 with the help of DataONE, GBIF introduced DOIs as persistent identifiers for the datasets shared through its network. This enhancement soon extended to the assignment of DOIs to user downloads from GBIF.org, which typically filter the available records with a variety of taxonomic, geographic, temporal and other search terms. Despite the lack of widely accepted standards for citing data among researchers and publications, this technical infrastructure is beginning to take hold and support open, transparent, persistent and repeatable use and reuse of species occurrence data. These `download DOIs' provide canonical references for the search results researchers process and use in peer-reviewed articles—a practice GBIF encourages by confirming new DOIs with each download and offering guidelines on citation. GBIF has recently started linking these citation results back to dataset and publisher pages, offering more consistent, traceable evidence of the value of sharing data to support others' research. GBIF's experience may be a useful model for other repositories to follow.

Comparison of Shallow Survey 2012 Multibeam Datasets

Science.gov (United States)

Ramirez, T. M.

2012-12-01

The purpose of the Shallow Survey common dataset is a comparison of the different technologies utilized for data acquisition in the shallow survey marine environment. The common dataset consists of a series of surveys conducted over a common area of seabed using a variety of systems. It provides equipment manufacturers the opportunity to showcase their latest systems while giving hydrographic researchers and scientists a chance to test their latest algorithms on the dataset so that rigorous comparisons can be made. Five companies collected data for the Common Dataset in the Wellington Harbor area in New Zealand between May 2010 and May 2011; including Kongsberg, Reson, R2Sonic, GeoAcoustics, and Applied Acoustics. The Wellington harbor and surrounding coastal area was selected since it has a number of well-defined features, including the HMNZS South Seas and HMNZS Wellington wrecks, an armored seawall constructed of Tetrapods and Akmons, aquifers, wharves and marinas. The seabed inside the harbor basin is largely fine-grained sediment, with gravel and reefs around the coast. The area outside the harbor on the southern coast is an active environment, with moving sand and exposed reefs. A marine reserve is also in this area. For consistency between datasets, the coastal research vessel R/V Ikatere and crew were used for all surveys conducted for the common dataset. Using Triton's Perspective processing software multibeam datasets collected for the Shallow Survey were processed for detail analysis. Datasets from each sonar manufacturer were processed using the CUBE algorithm developed by the Center for Coastal and Ocean Mapping/Joint Hydrographic Center (CCOM/JHC). Each dataset was gridded at 0.5 and 1.0 meter resolutions for cross comparison and compliance with International Hydrographic Organization (IHO) requirements. Detailed comparisons were made of equipment specifications (transmit frequency, number of beams, beam width), data density, total uncertainty, and

SAR image dataset of military ground targets with multiple poses for ATR

Science.gov (United States)

Belloni, Carole; Balleri, Alessio; Aouf, Nabil; Merlet, Thomas; Le Caillec, Jean-Marc

2017-10-01

Automatic Target Recognition (ATR) is the task of automatically detecting and classifying targets. Recognition using Synthetic Aperture Radar (SAR) images is interesting because SAR images can be acquired at night and under any weather conditions, whereas optical sensors operating in the visible band do not have this capability. Existing SAR ATR algorithms have mostly been evaluated using the MSTAR dataset.1 The problem with the MSTAR is that some of the proposed ATR methods have shown good classification performance even when targets were hidden,2 suggesting the presence of a bias in the dataset. Evaluations of SAR ATR techniques are currently challenging due to the lack of publicly available data in the SAR domain. In this paper, we present a high resolution SAR dataset consisting of images of a set of ground military target models taken at various aspect angles, The dataset can be used for a fair evaluation and comparison of SAR ATR algorithms. We applied the Inverse Synthetic Aperture Radar (ISAR) technique to echoes from targets rotating on a turntable and illuminated with a stepped frequency waveform. The targets in the database consist of four variants of two 1.7m-long models of T-64 and T-72 tanks. The gun, the turret position and the depression angle are varied to form 26 different sequences of images. The emitted signal spanned the frequency range from 13 GHz to 18 GHz to achieve a bandwidth of 5 GHz sampled with 4001 frequency points. The resolution obtained with respect to the size of the model targets is comparable to typical values obtained using SAR airborne systems. Single polarized images (Horizontal-Horizontal) are generated using the backprojection algorithm.3 A total of 1480 images are produced using a 20° integration angle. The images in the dataset are organized in a suggested training and testing set to facilitate a standard evaluation of SAR ATR algorithms.

National Hydrography Dataset (NHD)

Data.gov (United States)

Kansas Data Access and Support Center — The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that comprise the...

The Harvard organic photovoltaic dataset.

Science.gov (United States)

Lopez, Steven A; Pyzer-Knapp, Edward O; Simm, Gregor N; Lutzow, Trevor; Li, Kewei; Seress, Laszlo R; Hachmann, Johannes; Aspuru-Guzik, Alán

2016-09-27

The Harvard Organic Photovoltaic Dataset (HOPV15) presented in this work is a collation of experimental photovoltaic data from the literature, and corresponding quantum-chemical calculations performed over a range of conformers, each with quantum chemical results using a variety of density functionals and basis sets. It is anticipated that this dataset will be of use in both relating electronic structure calculations to experimental observations through the generation of calibration schemes, as well as for the creation of new semi-empirical methods and the benchmarking of current and future model chemistries for organic electronic applications.

Human Health Screening and Public Health Significance of Contaminants of Emerging Concern Detected in Public Water Supplies

Data.gov (United States)

U.S. Environmental Protection Agency — Background information for human health margin of exposure paper. This dataset is associated with the following publication: Benson , B., O. Conerly , W. Sander, A....

A method and software framework for enriching private biomedical sources with data from public online repositories.

Science.gov (United States)

Anguita, Alberto; García-Remesal, Miguel; Graf, Norbert; Maojo, Victor

2016-04-01

Modern biomedical research relies on the semantic integration of heterogeneous data sources to find data correlations. Researchers access multiple datasets of disparate origin, and identify elements-e.g. genes, compounds, pathways-that lead to interesting correlations. Normally, they must refer to additional public databases in order to enrich the information about the identified entities-e.g. scientific literature, published clinical trial results, etc. While semantic integration techniques have traditionally focused on providing homogeneous access to private datasets-thus helping automate the first part of the research, and there exist different solutions for browsing public data, there is still a need for tools that facilitate merging public repositories with private datasets. This paper presents a framework that automatically locates public data of interest to the researcher and semantically integrates it with existing private datasets. The framework has been designed as an extension of traditional data integration systems, and has been validated with an existing data integration platform from a European research project by integrating a private biological dataset with data from the National Center for Biotechnology Information (NCBI). Copyright © 2016 Elsevier Inc. All rights reserved.

The importance of accurate road data for spatial applications in public health: customizing a road network

Directory of Open Access Journals (Sweden)

Laraia Barbara A

2009-05-01

Full Text Available Abstract Background Health researchers have increasingly adopted the use of geographic information systems (GIS for analyzing environments in which people live and how those environments affect health. One aspect of this research that is often overlooked is the quality and detail of the road data and whether or not it is appropriate for the scale of analysis. Many readily available road datasets, both public domain and commercial, contain positional errors or generalizations that may not be compatible with highly accurate geospatial locations. This study examined the accuracy, completeness, and currency of four readily available public and commercial sources for road data (North Carolina Department of Transportation, StreetMap Pro, TIGER/Line 2000, TIGER/Line 2007 relative to a custom road dataset which we developed and used for comparison. Methods and Results A custom road network dataset was developed to examine associations between health behaviors and the environment among pregnant and postpartum women living in central North Carolina in the United States. Three analytical measures were developed to assess the comparative accuracy and utility of four publicly and commercially available road datasets and the custom dataset in relation to participants' residential locations over three time periods. The exclusion of road segments and positional errors in the four comparison road datasets resulted in between 5.9% and 64.4% of respondents lying farther than 15.24 meters from their nearest road, the distance of the threshold set by the project to facilitate spatial analysis. Agreement, using a Pearson's correlation coefficient, between the customized road dataset and the four comparison road datasets ranged from 0.01 to 0.82. Conclusion This study demonstrates the importance of examining available road datasets and assessing their completeness, accuracy, and currency for their particular study area. This paper serves as an example for assessing

Preparedness for the Rio 2016 Olympic Games: hospital treatment capacity in georeferenced areas

Directory of Open Access Journals (Sweden)

Carolina Figueiredo Freitas

2016-01-01

Full Text Available Abstract: Recently, Brazil has hosted mass events with recognized international relevance. The 2014 FIFA World Cup was held in 12 Brazilian state capitals and health sector preparedness drew on the history of other World Cups and Brazil's own experience with the 2013 FIFA Confederations Cup. The current article aims to analyze the treatment capacity of hospital facilities in georeferenced areas for sports events in the 2016 Olympic Games in the city of Rio de Janeiro, based on a model built drawing on references from the literature. Source of data were Brazilian health databases and the Rio 2016 website. Sports venues for the Olympic Games and surrounding hospitals in a 10km radius were located by geoprocessing and designated a "health area" referring to the probable inflow of persons to be treated in case of hospital referral. Six different factors were used to calculate needs for surge and one was used to calculate needs in case of disasters (20/1,000. Hospital treatment capacity is defined by the coincidence of beds and life support equipment, namely the number of cardiac monitors (electrocardiographs and ventilators in each hospital unit. Maracanã followed by the Olympic Stadium (Engenhão and the Sambódromo would have the highest single demand for hospitalizations (1,572, 1,200 and 600, respectively. Hospital treatment capacity proved capable of accommodating surges, but insufficient in cases of mass casualties. In mass events most treatments involve easy clinical management, it is expected that the current capacity will not have negative consequences for participants.

Open source platform for collaborative construction of wearable sensor datasets for human motion analysis and an application for gait analysis.

Science.gov (United States)

Llamas, César; González, Manuel A; Hernández, Carmen; Vegas, Jesús

2016-10-01

Nearly every practical improvement in modeling human motion is well founded in a properly designed collection of data or datasets. These datasets must be made publicly available for the community could validate and accept them. It is reasonable to concede that a collective, guided enterprise could serve to devise solid and substantial datasets, as a result of a collaborative effort, in the same sense as the open software community does. In this way datasets could be complemented, extended and expanded in size with, for example, more individuals, samples and human actions. For this to be possible some commitments must be made by the collaborators, being one of them sharing the same data acquisition platform. In this paper, we offer an affordable open source hardware and software platform based on inertial wearable sensors in a way that several groups could cooperate in the construction of datasets through common software suitable for collaboration. Some experimental results about the throughput of the overall system are reported showing the feasibility of acquiring data from up to 6 sensors with a sampling frequency no less than 118Hz. Also, a proof-of-concept dataset is provided comprising sampled data from 12 subjects suitable for gait analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

PHYSICS PERFORMANCE AND DATASET (PPD)

CERN Multimedia

L. Silvestris

2013-01-01

The first part of the Long Shutdown period has been dedicated to the preparation of the samples for the analysis targeting the summer conferences. In particular, the 8 TeV data acquired in 2012, including most of the “parked datasets”, have been reconstructed profiting from improved alignment and calibration conditions for all the sub-detectors. A careful planning of the resources was essential in order to deliver the datasets well in time to the analysts, and to schedule the update of all the conditions and calibrations needed at the analysis level. The newly reprocessed data have undergone detailed scrutiny by the Dataset Certification team allowing to recover some of the data for analysis usage and further improving the certification efficiency, which is now at 91% of the recorded luminosity. With the aim of delivering a consistent dataset for 2011 and 2012, both in terms of conditions and release (53X), the PPD team is now working to set up a data re-reconstruction and a new MC pro...

Data publication - policies and procedures from the PREPARDE project

Science.gov (United States)

Callaghan, Sarah; Murphy, Fiona; Tedds, Jonathan; Kunze, John; Lawrence, Rebecca; Mayernik, , Matthew S.; Whyte, Angus; Roberts, Timothy

2013-04-01

Data are widely acknowledged as a first class scientific output. Increases in researchers' abilities to create data need to be matched by corresponding infrastructures for them to manage and share their data. At the same time, the quality and persistence of the datasets need to be ensured, providing the dataset creators with the recognition they deserve for their efforts. Formal publication of data takes advantage of the processes and procedures already in place to publish academic articles about scientific results, enabling data to be reviewed and more broadly disseminated. Data are vastly more varied in format than papers, and so the policies required to manage and publish data must take into account the complexities associated with different data types, scientific fields, licensing rules etc. The Peer REview for Publication & Accreditation of Research Data in the Earth sciences (PREPARDE) project is JISC- and NERC-funded, and aims to investigate the policies and procedures required for the formal publication of research data. The project is investigating the whole workflow of data publication, from ingestion into a data repository, through to formal publication in a data journal. To limit the scope of the project, the focus is primarily on the policies required for the Royal Meteorological Society and Wiley's Geoscience Data Journal, though members of the project team include representatives from the life sciences (F1000Research), and will generalise the policies to other disciplines. PREPARDE addresses key issues arising in the data publication paradigm, such as: what criteria are needed for a repository to be considered objectively trustworthy; how does one peer-review a dataset; and how can datasets and journal publications be effectively cross-linked for the benefit of the wider research community and the completeness of the scientific record? To answer these questions, the project is hosting workshops addressing these issues, with interactions from key

Geolokit: An interactive tool for visualising and exploring geoscientific data in Google Earth

Science.gov (United States)

Triantafyllou, Antoine; Watlet, Arnaud; Bastin, Christophe

2017-10-01

Virtual globes have been developed to showcase different types of data combining a digital elevation model and basemaps of high resolution satellite imagery. Hence, they became a standard to share spatial data and information, although they suffer from a lack of toolboxes dedicated to the formatting of large geoscientific dataset. From this perspective, we developed Geolokit: a free and lightweight software that allows geoscientists - and every scientist working with spatial data - to import their data (e.g., sample collections, structural geology, cross-sections, field pictures, georeferenced maps), to handle and to transcribe them to Keyhole Markup Language (KML) files. KML files are then automatically opened in the Google Earth virtual globe and the spatial data accessed and shared. Geolokit comes with a large number of dedicated tools that can process and display: (i) multi-points data, (ii) scattered data interpolations, (iii) structural geology features in 2D and 3D, (iv) rose diagrams, stereonets and dip-plunge polar histograms, (v) cross-sections and oriented rasters, (vi) georeferenced field pictures, (vii) georeferenced maps and projected gridding. Therefore, together with Geolokit, Google Earth becomes not only a powerful georeferenced data viewer but also a stand-alone work platform. The toolbox (available online at http://www.geolokit.org) is written in Python, a high-level, cross-platform programming language and is accessible through a graphical user interface, designed to run in parallel with Google Earth, through a workflow that requires no additional third party software. Geolokit features are demonstrated in this paper using typical datasets gathered from two case studies illustrating its applicability at multiple scales of investigation: a petro-structural investigation of the Ile d'Yeu orthogneissic unit (Western France) and data collection of the Mariana oceanic subduction zone (Western Pacific).

Integrated Surface Dataset (Global)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Integrated Surface (ISD) Dataset (ISD) is composed of worldwide surface weather observations from over 35,000 stations, though the best spatial coverage is...

Market Squid Ecology Dataset

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This dataset contains ecological information collected on the major adult spawning and juvenile habitats of market squid off California and the US Pacific Northwest....

Determination of Exterior Orientation Parameters Through Direct Geo-Referencing in a Real-Time Aerial Monitoring System

Science.gov (United States)

Kim, H.; Lee, J.; Choi, K.; Lee, I.

2012-07-01

Rapid responses for emergency situations such as natural disasters or accidents often require geo-spatial information describing the on-going status of the affected area. Such geo-spatial information can be promptly acquired by a manned or unmanned aerial vehicle based multi-sensor system that can monitor the emergent situations in near real-time from the air using several kinds of sensors. Thus, we are in progress of developing such a real-time aerial monitoring system (RAMS) consisting of both aerial and ground segments. The aerial segment acquires the sensory data about the target areas by a low-altitude helicopter system equipped with sensors such as a digital camera and a GPS/IMU system and transmits them to the ground segment through a RF link in real-time. The ground segment, which is a deployable ground station installed on a truck, receives the sensory data and rapidly processes them to generate ortho-images, DEMs, etc. In order to generate geo-spatial information, in this system, exterior orientation parameters (EOP) of the acquired images are obtained through direct geo-referencing because it is difficult to acquire coordinates of ground points in disaster area. The main process, since the data acquisition stage until the measurement of EOP, is discussed as follows. First, at the time of data acquisition, image acquisition time synchronized by GPS time is recorded as part of image file name. Second, the acquired data are then transmitted to the ground segment in real-time. Third, by processing software for ground segment, positions/attitudes of acquired images are calculated through a linear interpolation using the GPS time of the received position/attitude data and images. Finally, the EOPs of images are obtained from position/attitude data by deriving the relationships between a camera coordinate system and a GPS/IMU coordinate system. In this study, we evaluated the accuracy of the EOP decided by direct geo-referencing in our system. To perform this

DETERMINATION OF EXTERIOR ORIENTATION PARAMETERS THROUGH DIRECT GEO-REFERENCING IN A REAL-TIME AERIAL MONITORING SYSTEM

Directory of Open Access Journals (Sweden)

H. Kim

2012-07-01

Full Text Available Rapid responses for emergency situations such as natural disasters or accidents often require geo-spatial information describing the on-going status of the affected area. Such geo-spatial information can be promptly acquired by a manned or unmanned aerial vehicle based multi-sensor system that can monitor the emergent situations in near real-time from the air using several kinds of sensors. Thus, we are in progress of developing such a real-time aerial monitoring system (RAMS consisting of both aerial and ground segments. The aerial segment acquires the sensory data about the target areas by a low-altitude helicopter system equipped with sensors such as a digital camera and a GPS/IMU system and transmits them to the ground segment through a RF link in real-time. The ground segment, which is a deployable ground station installed on a truck, receives the sensory data and rapidly processes them to generate ortho-images, DEMs, etc. In order to generate geo-spatial information, in this system, exterior orientation parameters (EOP of the acquired images are obtained through direct geo-referencing because it is difficult to acquire coordinates of ground points in disaster area. The main process, since the data acquisition stage until the measurement of EOP, is discussed as follows. First, at the time of data acquisition, image acquisition time synchronized by GPS time is recorded as part of image file name. Second, the acquired data are then transmitted to the ground segment in real-time. Third, by processing software for ground segment, positions/attitudes of acquired images are calculated through a linear interpolation using the GPS time of the received position/attitude data and images. Finally, the EOPs of images are obtained from position/attitude data by deriving the relationships between a camera coordinate system and a GPS/IMU coordinate system. In this study, we evaluated the accuracy of the EOP decided by direct geo-referencing in our system

Advancements in Wind Integration Study Data Modeling: The Wind Integration National Dataset (WIND) Toolkit; Preprint

Energy Technology Data Exchange (ETDEWEB)

Draxl, C.; Hodge, B. M.; Orwig, K.; Jones, W.; Searight, K.; Getman, D.; Harrold, S.; McCaa, J.; Cline, J.; Clark, C.

2013-10-01

Regional wind integration studies in the United States require detailed wind power output data at many locations to perform simulations of how the power system will operate under high-penetration scenarios. The wind data sets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as be time synchronized with available load profiles. The Wind Integration National Dataset (WIND) Toolkit described in this paper fulfills these requirements. A wind resource dataset, wind power production time series, and simulated forecasts from a numerical weather prediction model run on a nationwide 2-km grid at 5-min resolution will be made publicly available for more than 110,000 onshore and offshore wind power production sites.

ATLAS File and Dataset Metadata Collection and Use

CERN Document Server

Albrand, S; The ATLAS collaboration; Lambert, F; Gallas, E J

2012-01-01

The ATLAS Metadata Interface (“AMI”) was designed as a generic cataloguing system, and as such it has found many uses in the experiment including software release management, tracking of reconstructed event sizes and control of dataset nomenclature. The primary use of AMI is to provide a catalogue of datasets (file collections) which is searchable using physics criteria. In this paper we discuss the various mechanisms used for filling the AMI dataset and file catalogues. By correlating information from different sources we can derive aggregate information which is important for physics analysis; for example the total number of events contained in dataset, and possible reasons for missing events such as a lost file. Finally we will describe some specialized interfaces which were developed for the Data Preparation and reprocessing coordinators. These interfaces manipulate information from both the dataset domain held in AMI, and the run-indexed information held in the ATLAS COMA application (Conditions and ...

Norwegian Hydrological Reference Dataset for Climate Change Studies

Energy Technology Data Exchange (ETDEWEB)

Magnussen, Inger Helene; Killingland, Magnus; Spilde, Dag

2012-07-01

Based on the Norwegian hydrological measurement network, NVE has selected a Hydrological Reference Dataset for studies of hydrological change. The dataset meets international standards with high data quality. It is suitable for monitoring and studying the effects of climate change on the hydrosphere and cryosphere in Norway. The dataset includes streamflow, groundwater, snow, glacier mass balance and length change, lake ice and water temperature in rivers and lakes.(Author)

The Harvard organic photovoltaic dataset

Science.gov (United States)

Lopez, Steven A.; Pyzer-Knapp, Edward O.; Simm, Gregor N.; Lutzow, Trevor; Li, Kewei; Seress, Laszlo R.; Hachmann, Johannes; Aspuru-Guzik, Alán

2016-01-01

The Harvard Organic Photovoltaic Dataset (HOPV15) presented in this work is a collation of experimental photovoltaic data from the literature, and corresponding quantum-chemical calculations performed over a range of conformers, each with quantum chemical results using a variety of density functionals and basis sets. It is anticipated that this dataset will be of use in both relating electronic structure calculations to experimental observations through the generation of calibration schemes, as well as for the creation of new semi-empirical methods and the benchmarking of current and future model chemistries for organic electronic applications. PMID:27676312

Synthetic and Empirical Capsicum Annuum Image Dataset

NARCIS (Netherlands)

Barth, R.

2016-01-01

This dataset consists of per-pixel annotated synthetic (10500) and empirical images (50) of Capsicum annuum, also known as sweet or bell pepper, situated in a commercial greenhouse. Furthermore, the source models to generate the synthetic images are included. The aim of the datasets are to

Solutions for research data from a publisher's perspective

Science.gov (United States)

Cotroneo, P.

2015-12-01

Sharing research data has the potential to make research more efficient and reproducible. Elsevier has developed several initiatives to address the different needs of research data users. These include PANGEA Linked data, which provides geo-referenced, citable datasets from earth and life sciences, archived as supplementary data from publications by the PANGEA data repository; Mendeley Data, which allows users to freely upload and share their data; a database linking program that creates links between articles on ScienceDirect and datasets held in external data repositories such as EarthRef and EarthChem; a pilot for searching for research data through a map interface; an open data pilot that allows authors publishing in Elsevier journals to store and share research data and make this publicly available as a supplementary file alongside their article; and data journals, including Data in Brief, which allow researchers to share their data open access. Through these initiatives, researchers are not only encouraged to share their research data, but also supported in optimizing their research data management. By making data more readily citable and visible, and hence generating citations for authors, these initiatives also aim to ensure that researchers get the recognition they deserve for publishing their data.

The Direct Georeferencing Application and Performance Analysis of Uav Helicopter in Gcp-Free Area

Science.gov (United States)

Lo, C. F.; Tsai, M. L.; Chiang, K. W.; Chu, C. H.; Tsai, G. J.; Cheng, C. K.; El-Sheimy, N.; Ayman, H.

2015-08-01

There are many disasters happened because the weather changes extremely in these years. To facilitate applications such as environment detection or monitoring becomes very important. Therefore, the development of rapid low cost systems for collecting near real-time spatial information is very critical. Rapid spatial information collection has become an emerging trend for remote sensing and mapping applications. This study develops a Direct Georeferencing (DG) based Unmanned Aerial Vehicle (UAV) helicopter photogrammetric platform where an Inertial Navigation System (INS)/Global Navigation Satellite System (GNSS) integrated Positioning and Orientation System (POS) system is implemented to provide the DG capability of the platform. The performance verification indicates that the proposed platform can capture aerial images successfully. A flight test is performed to verify the positioning accuracy in DG mode without using Ground Control Points (GCP). The preliminary results illustrate that horizontal DG positioning accuracies in the x and y axes are around 5 meter with 100 meter flight height. The positioning accuracy in the z axis is less than 10 meter. Such accuracy is good for near real-time disaster relief. The DG ready function of proposed platform guarantees mapping and positioning capability even in GCP free environments, which is very important for rapid urgent response for disaster relief. Generally speaking, the data processing time for the DG module, including POS solution generalization, interpolation, Exterior Orientation Parameters (EOP) generation, and feature point measurements, is less than 1 hour.

Datasets in Gene Expression Omnibus used in the study ORD-020382: Evaluation of estrogen receptor alpha activation by glyphosate-based herbicide constituents

Data.gov (United States)

U.S. Environmental Protection Agency — GEO accession number of the microarray study. This dataset is associated with the following publication: Mesnage, R., A. Phedonos, M. Biserni, M. Arno, S. Balu, C....

EEG datasets for motor imagery brain-computer interface.

Science.gov (United States)

Cho, Hohyun; Ahn, Minkyu; Ahn, Sangtae; Kwon, Moonyoung; Jun, Sung Chan

2017-07-01

Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states. © The Authors 2017. Published by Oxford University Press.

Job Satisfaction and Public Service Motivation

OpenAIRE

Kaiser, Lutz C.

2014-01-01

Based on a unique case study-dataset, the paper analyses job satisfaction and public service motivation in Germany. A special issue of the investigation is related to the evaluation of performance pay scales that were introduced some years ago to German public employees within the frame of fostering New Public Management. The findings display a general dominance of intrinsic motivators. Additionally, this kind of motivators plays an important role with regard to building up and keeping job sa...

A high-resolution European dataset for hydrologic modeling

Science.gov (United States)

Ntegeka, Victor; Salamon, Peter; Gomes, Goncalo; Sint, Hadewij; Lorini, Valerio; Thielen, Jutta

2013-04-01

There is an increasing demand for large scale hydrological models not only in the field of modeling the impact of climate change on water resources but also for disaster risk assessments and flood or drought early warning systems. These large scale models need to be calibrated and verified against large amounts of observations in order to judge their capabilities to predict the future. However, the creation of large scale datasets is challenging for it requires collection, harmonization, and quality checking of large amounts of observations. For this reason, only a limited number of such datasets exist. In this work, we present a pan European, high-resolution gridded dataset of meteorological observations (EFAS-Meteo) which was designed with the aim to drive a large scale hydrological model. Similar European and global gridded datasets already exist, such as the HadGHCND (Caesar et al., 2006), the JRC MARS-STAT database (van der Goot and Orlandi, 2003) and the E-OBS gridded dataset (Haylock et al., 2008). However, none of those provide similarly high spatial resolution and/or a complete set of variables to force a hydrologic model. EFAS-Meteo contains daily maps of precipitation, surface temperature (mean, minimum and maximum), wind speed and vapour pressure at a spatial grid resolution of 5 x 5 km for the time period 1 January 1990 - 31 December 2011. It furthermore contains calculated radiation, which is calculated by using a staggered approach depending on the availability of sunshine duration, cloud cover and minimum and maximum temperature, and evapotranspiration (potential evapotranspiration, bare soil and open water evapotranspiration). The potential evapotranspiration was calculated using the Penman-Monteith equation with the above-mentioned meteorological variables. The dataset was created as part of the development of the European Flood Awareness System (EFAS) and has been continuously updated throughout the last years. The dataset variables are used as

ASSISTments Dataset from Multiple Randomized Controlled Experiments

Science.gov (United States)

Selent, Douglas; Patikorn, Thanaporn; Heffernan, Neil

2016-01-01

In this paper, we present a dataset consisting of data generated from 22 previously and currently running randomized controlled experiments inside the ASSISTments online learning platform. This dataset provides data mining opportunities for researchers to analyze ASSISTments data in a convenient format across multiple experiments at the same time.…

MiSTIC, an integrated platform for the analysis of heterogeneity in large tumour transcriptome datasets.

Science.gov (United States)

Lemieux, Sebastien; Sargeant, Tobias; Laperrière, David; Ismail, Houssam; Boucher, Geneviève; Rozendaal, Marieke; Lavallée, Vincent-Philippe; Ashton-Beaucage, Dariel; Wilhelm, Brian; Hébert, Josée; Hilton, Douglas J; Mader, Sylvie; Sauvageau, Guy

2017-07-27

Genome-wide transcriptome profiling has enabled non-supervised classification of tumours, revealing different sub-groups characterized by specific gene expression features. However, the biological significance of these subtypes remains for the most part unclear. We describe herein an interactive platform, Minimum Spanning Trees Inferred Clustering (MiSTIC), that integrates the direct visualization and comparison of the gene correlation structure between datasets, the analysis of the molecular causes underlying co-variations in gene expression in cancer samples, and the clinical annotation of tumour sets defined by the combined expression of selected biomarkers. We have used MiSTIC to highlight the roles of specific transcription factors in breast cancer subtype specification, to compare the aspects of tumour heterogeneity targeted by different prognostic signatures, and to highlight biomarker interactions in AML. A version of MiSTIC preloaded with datasets described herein can be accessed through a public web server (http://mistic.iric.ca); in addition, the MiSTIC software package can be obtained (github.com/iric-soft/MiSTIC) for local use with personalized datasets. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Would the ‘real’ observed dataset stand up? A critical examination of eight observed gridded climate datasets for China

International Nuclear Information System (INIS)

Sun, Qiaohong; Miao, Chiyuan; Duan, Qingyun; Kong, Dongxian; Ye, Aizhong; Di, Zhenhua; Gong, Wei

2014-01-01

This research compared and evaluated the spatio-temporal similarities and differences of eight widely used gridded datasets. The datasets include daily precipitation over East Asia (EA), the Climate Research Unit (CRU) product, the Global Precipitation Climatology Centre (GPCC) product, the University of Delaware (UDEL) product, Precipitation Reconstruction over Land (PREC/L), the Asian Precipitation Highly Resolved Observational (APHRO) product, the Institute of Atmospheric Physics (IAP) dataset from the Chinese Academy of Sciences, and the National Meteorological Information Center dataset from the China Meteorological Administration (CN05). The meteorological variables focus on surface air temperature (SAT) or precipitation (PR) in China. All datasets presented general agreement on the whole spatio-temporal scale, but some differences appeared for specific periods and regions. On a temporal scale, EA shows the highest amount of PR, while APHRO shows the lowest. CRU and UDEL show higher SAT than IAP or CN05. On a spatial scale, the most significant differences occur in western China for PR and SAT. For PR, the difference between EA and CRU is the largest. When compared with CN05, CRU shows higher SAT in the central and southern Northwest river drainage basin, UDEL exhibits higher SAT over the Southwest river drainage system, and IAP has lower SAT in the Tibetan Plateau. The differences in annual mean PR and SAT primarily come from summer and winter, respectively. Finally, potential factors impacting agreement among gridded climate datasets are discussed, including raw data sources, quality control (QC) schemes, orographic correction, and interpolation techniques. The implications and challenges of these results for climate research are also briefly addressed. (paper)

Mr-Moose: An advanced SED-fitting tool for heterogeneous multi-wavelength datasets

Science.gov (United States)

Drouart, G.; Falkendal, T.

2018-04-01

We present the public release of Mr-Moose, a fitting procedure that is able to perform multi-wavelength and multi-object spectral energy distribution (SED) fitting in a Bayesian framework. This procedure is able to handle a large variety of cases, from an isolated source to blended multi-component sources from an heterogeneous dataset (i.e. a range of observation sensitivities and spectral/spatial resolutions). Furthermore, Mr-Moose handles upper-limits during the fitting process in a continuous way allowing models to be gradually less probable as upper limits are approached. The aim is to propose a simple-to-use, yet highly-versatile fitting tool fro handling increasing source complexity when combining multi-wavelength datasets with fully customisable filter/model databases. The complete control of the user is one advantage, which avoids the traditional problems related to the "black box" effect, where parameter or model tunings are impossible and can lead to overfitting and/or over-interpretation of the results. Also, while a basic knowledge of Python and statistics is required, the code aims to be sufficiently user-friendly for non-experts. We demonstrate the procedure on three cases: two artificially-generated datasets and a previous result from the literature. In particular, the most complex case (inspired by a real source, combining Herschel, ALMA and VLA data) in the context of extragalactic SED fitting, makes Mr-Moose a particularly-attractive SED fitting tool when dealing with partially blended sources, without the need for data deconvolution.

Estimating parameters for probabilistic linkage of privacy-preserved datasets.

Science.gov (United States)

Brown, Adrian P; Randall, Sean M; Ferrante, Anna M; Semmens, James B; Boyd, James H

2017-07-10

Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters. Our method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data. Linkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher

Viking Seismometer PDS Archive Dataset

Science.gov (United States)

Lorenz, R. D.

2016-12-01

The Viking Lander 2 seismometer operated successfully for over 500 Sols on the Martian surface, recording at least one likely candidate Marsquake. The Viking mission, in an era when data handling hardware (both on board and on the ground) was limited in capability, predated modern planetary data archiving, and ad-hoc repositories of the data, and the very low-level record at NSSDC, were neither convenient to process nor well-known. In an effort supported by the NASA Mars Data Analysis Program, we have converted the bulk of the Viking dataset (namely the 49,000 and 270,000 records made in High- and Event- modes at 20 and 1 Hz respectively) into a simple ASCII table format. Additionally, since wind-generated lander motion is a major component of the signal, contemporaneous meteorological data are included in summary records to facilitate correlation. These datasets are being archived at the PDS Geosciences Node. In addition to brief instrument and dataset descriptions, the archive includes code snippets in the freely-available language 'R' to demonstrate plotting and analysis. Further, we present examples of lander-generated noise, associated with the sampler arm, instrument dumps and other mechanical operations.

Enrichment of Data Publications in Earth Sciences - Data Reports as a Missing Link

Science.gov (United States)

Elger, Kirsten; Bertelmann, Roland; Haberland, Christian; Evans, Peter L.

2015-04-01

During the past decade, the relevance of research data stewardship has been rising significantly. Preservation and publication of scientific data for long-term use, including the storage in adequate repositories has been identified as a key issue by the scientific community as well as by bodies like research agencies. Essential for any kind of re-use is a proper description of the datasets. As a result of the increasing interest, data repositories have been developed and the included research data is accompanied with at least a minimum set of metadata. This metadata is useful for data discovery and a first insight to the content of a dataset. But often data re-use needs more and extended information. Many datasets are accompanied by a small 'readme file' with basic information on the data structure, or other accompanying documents. A source of additional information could be an article published in one of the newly emerging data journals (e.g. Copernicus's ESSD Earth System Science Data or Nature's Scientific Data). Obviously there is an information gap between a 'readme file', that is only accessible after data download (which often leads to less usage of published datasets than if the information was available beforehand) and the much larger effort to prepare an article for a peer-reviewed data journal. For many years, GFZ German Research Centre for Geosciences publishes 'Scientific Technical Reports (STR)' as a report series which is electronically persistently available and citable with assigned DOIs. This series was opened for the description of parallel published datasets as 'STR Data'. These are internally reviewed and offer a flexible publication format describing published data in depth, suitable for different datasets ranging from long-term monitoring time series of observatories to field data, to (meta-)databases, and software publications. STR Data offer a full and consistent overview and description to all relevant parameters of a linked published

Terrestrial Lidar Datasets of New Orleans, Louisiana, Levee Failures from Hurricane Katrina, August 29, 2005

Science.gov (United States)

Collins, Brian D.; Kayen, Robert; Minasian, Diane L.; Reiss, Thomas

2009-01-01

Hurricane Katrina made landfall with the northern Gulf Coast on August 29, 2005, as one of the strongest hurricanes on record. The storm damage incurred in Louisiana included a number of levee failures that led to the inundation of approximately 85 percent of the metropolitan New Orleans area. Whereas extreme levels of storm damage were expected from such an event, the catastrophic failure of the New Orleans levees prompted a quick mobilization of engineering experts to assess why and how particular levees failed. As part of this mobilization, civil engineering members of the United States Geological Survey (USGS) performed terrestrial lidar topographic surveys at major levee failures in the New Orleans area. The focus of the terrestrial lidar effort was to obtain precise measurements of the ground surface to map soil displacements at each levee site, the nonuniformity of levee height freeboard, depth of erosion where scour occurred, and distress in structures at incipient failure. In total, we investigated eight sites in the New Orleans region, including both earth and concrete floodwall levee breaks. The datasets extend from the 17th Street Canal in the Orleans East Bank area to the intersection of the Gulf Intracoastal Waterway (GIWW) with the Mississippi River Gulf Outlet (MRGO) in the New Orleans East area. The lidar scan data consists of electronic files containing millions of surveyed points. These points characterize the topography of each levee's postfailure or incipient condition and are available for download through online hyperlinks. The data serve as a permanent archive of the catastrophic damage of Hurricane Katrina on the levee systems of New Orleans. Complete details of the data collection, processing, and georeferencing methodologies are provided in this report to assist in the visualization and analysis of the data by future users.

Homogenised Australian climate datasets used for climate change monitoring

International Nuclear Information System (INIS)

Trewin, Blair; Jones, David; Collins; Dean; Jovanovic, Branislava; Braganza, Karl

2007-01-01

Full text: The Australian Bureau of Meteorology has developed a number of datasets for use in climate change monitoring. These datasets typically cover 50-200 stations distributed as evenly as possible over the Australian continent, and have been subject to detailed quality control and homogenisation.The time period over which data are available for each element is largely determined by the availability of data in digital form. Whilst nearly all Australian monthly and daily precipitation data have been digitised, a significant quantity of pre-1957 data (for temperature and evaporation) or pre-1987 data (for some other elements) remains to be digitised, and is not currently available for use in the climate change monitoring datasets. In the case of temperature and evaporation, the start date of the datasets is also determined by major changes in instruments or observing practices for which no adjustment is feasible at the present time. The datasets currently available cover: Monthly and daily precipitation (most stations commence 1915 or earlier, with many extending back to the late 19th century, and a few to the mid-19th century); Annual temperature (commences 1910); Daily temperature (commences 1910, with limited station coverage pre-1957); Twice-daily dewpoint/relative humidity (commences 1957); Monthly pan evaporation (commences 1970); Cloud amount (commences 1957) (Jovanovic etal. 2007). As well as the station-based datasets listed above, an additional dataset being developed for use in climate change monitoring (and other applications) covers tropical cyclones in the Australian region. This is described in more detail in Trewin (2007). The datasets already developed are used in analyses of observed climate change, which are available through the Australian Bureau of Meteorology website (http://www.bom.gov.au/silo/products/cli_chg/). They are also used as a basis for routine climate monitoring, and in the datasets used for the development of seasonal

Mammals in the MZNA Vertebrate Collection of University of Navarra, Spain.

Science.gov (United States)

Escribano, Nora; Galicia, David; Ariño, Arturo H; Escala, Carmen

2016-01-01

In this paper five datasets are described that provide information about records of mammals in the Vertebrate Collection of the Museum of Zoology of the University of Navarra (MZNA-VERT). The datasets contain 3,466 records belonging to 20 species of mammals sampled across the transition zone between the Atlantic and Mediterranean biogeographical regions (north Iberian Peninsula). The datasets include both distributional data (georeferenced records) and basic biometric data of most of the vouchered specimens stored in the museum facilities. The samples originated mainly within research projects and PhD theses carried out in the former department of Zoology and Ecology of the University of Navarra between 1982 and 2011. The Darwin Core Archive Format datasets are accessible through GBIF.

Introduction of a simple-model-based land surface dataset for Europe

Science.gov (United States)

Orth, Rene; Seneviratne, Sonia I.

2015-04-01

Land surface hydrology can play a crucial role during extreme events such as droughts, floods and even heat waves. We introduce in this study a new hydrological dataset for Europe that consists of soil moisture, runoff and evapotranspiration (ET). It is derived with a simple water balance model (SWBM) forced with precipitation, temperature and net radiation. The SWBM dataset extends over the period 1984-2013 with a daily time step and 0.5° × 0.5° resolution. We employ a novel calibration approach, in which we consider 300 random parameter sets chosen from an observation-based range. Using several independent validation datasets representing soil moisture (or terrestrial water content), ET and streamflow, we identify the best performing parameter set and hence the new dataset. To illustrate its usefulness, the SWBM dataset is compared against several state-of-the-art datasets (ERA-Interim/Land, MERRA-Land, GLDAS-2-Noah, simulations of the Community Land Model Version 4), using all validation datasets as reference. For soil moisture dynamics it outperforms the benchmarks. Therefore the SWBM soil moisture dataset constitutes a reasonable alternative to sparse measurements, little validated model results, or proxy data such as precipitation indices. Also in terms of runoff the SWBM dataset performs well, whereas the evaluation of the SWBM ET dataset is overall satisfactory, but the dynamics are less well captured for this variable. This highlights the limitations of the dataset, as it is based on a simple model that uses uniform parameter values. Hence some processes impacting ET dynamics may not be captured, and quality issues may occur in regions with complex terrain. Even though the SWBM is well calibrated, it cannot replace more sophisticated models; but as their calibration is a complex task the present dataset may serve as a benchmark in future. In addition we investigate the sources of skill of the SWBM dataset and find that the parameter set has a similar

Data Mining for Imbalanced Datasets: An Overview

Science.gov (United States)

Chawla, Nitesh V.

A dataset is imbalanced if the classification categories are not approximately equally represented. Recent years brought increased interest in applying machine learning techniques to difficult "real-world" problems, many of which are characterized by imbalanced data. Additionally the distribution of the testing data may differ from that of the training data, and the true misclassification costs may be unknown at learning time. Predictive accuracy, a popular choice for evaluating performance of a classifier, might not be appropriate when the data is imbalanced and/or the costs of different errors vary markedly. In this Chapter, we discuss some of the sampling techniques used for balancing the datasets, and the performance measures more appropriate for mining imbalanced datasets.

Multi-facetted Metadata - Describing datasets with different metadata schemas at the same time

Science.gov (United States)

Ulbricht, Damian; Klump, Jens; Bertelmann, Roland

2013-04-01

Inspired by the wish to re-use research data a lot of work is done to bring data systems of the earth sciences together. Discovery metadata is disseminated to data portals to allow building of customized indexes of catalogued dataset items. Data that were once acquired in the context of a scientific project are open for reappraisal and can now be used by scientists that were not part of the original research team. To make data re-use easier, measurement methods and measurement parameters must be documented in an application metadata schema and described in a written publication. Linking datasets to publications - as DataCite [1] does - requires again a specific metadata schema and every new use context of the measured data may require yet another metadata schema sharing only a subset of information with the meta information already present. To cope with the problem of metadata schema diversity in our common data repository at GFZ Potsdam we established a solution to store file-based research data and describe these with an arbitrary number of metadata schemas. Core component of the data repository is an eSciDoc infrastructure that provides versioned container objects, called eSciDoc [2] "items". The eSciDoc content model allows assigning files to "items" and adding any number of metadata records to these "items". The eSciDoc items can be submitted, revised, and finally published, which makes the data and metadata available through the internet worldwide. GFZ Potsdam uses eSciDoc to support its scientific publishing workflow, including mechanisms for data review in peer review processes by providing temporary web links for external reviewers that do not have credentials to access the data. Based on the eSciDoc API, panMetaDocs [3] provides a web portal for data management in research projects. PanMetaDocs, which is based on panMetaWorks [4], is a PHP based web application that allows to describe data with any XML-based schema. It uses the eSciDoc infrastructures

The personnel economics approach to public workforce research.

Science.gov (United States)

Gibbs, Michael

2009-11-01

This article argues that the relatively new field of personnel economics (PE) holds strong potential as a tool for studying public sector workforces. This subfield of labor economics is based on a strong foundation of microeconomics, which provides a robust theoretical foundation for studying workforce and organizational design issues. PE has evolved on this foundation to a strong practical emphasis, with theoretical insights designed for practical use and with strong focus on empirical research. The field is also characterized by creative data entrepreneurship. The types of datasets that personnel economists use are described. If similar datasets can be obtained for public sector workforces, PE should be a very useful approach for studying them.

A hybrid organic-inorganic perovskite dataset

Science.gov (United States)

Kim, Chiho; Huan, Tran Doan; Krishnan, Sridevi; Ramprasad, Rampi

2017-05-01

Hybrid organic-inorganic perovskites (HOIPs) have been attracting a great deal of attention due to their versatility of electronic properties and fabrication methods. We prepare a dataset of 1,346 HOIPs, which features 16 organic cations, 3 group-IV cations and 4 halide anions. Using a combination of an atomic structure search method and density functional theory calculations, the optimized structures, the bandgap, the dielectric constant, and the relative energies of the HOIPs are uniformly prepared and validated by comparing with relevant experimental and/or theoretical data. We make the dataset available at Dryad Digital Repository, NoMaD Repository, and Khazana Repository (http://khazana.uconn.edu/), hoping that it could be useful for future data-mining efforts that can explore possible structure-property relationships and phenomenological models. Progressive extension of the dataset is expected as new organic cations become appropriate within the HOIP framework, and as additional properties are calculated for the new compounds found.

Genomics dataset of unidentified disclosed isolates

Directory of Open Access Journals (Sweden)

Bhagwan N. Rekadwad

2016-09-01

Full Text Available Analysis of DNA sequences is necessary for higher hierarchical classification of the organisms. It gives clues about the characteristics of organisms and their taxonomic position. This dataset is chosen to find complexities in the unidentified DNA in the disclosed patents. A total of 17 unidentified DNA sequences were thoroughly analyzed. The quick response codes were generated. AT/GC content of the DNA sequences analysis was carried out. The QR is helpful for quick identification of isolates. AT/GC content is helpful for studying their stability at different temperatures. Additionally, a dataset on cleavage code and enzyme code studied under the restriction digestion study, which helpful for performing studies using short DNA sequences was reported. The dataset disclosed here is the new revelatory data for exploration of unique DNA sequences for evaluation, identification, comparison and analysis. Keywords: BioLABs, Blunt ends, Genomics, NEB cutter, Restriction digestion, Short DNA sequences, Sticky ends

IPCC Socio-Economic Baseline Dataset

Data.gov (United States)

National Aeronautics and Space Administration — The Intergovernmental Panel on Climate Change (IPCC) Socio-Economic Baseline Dataset consists of population, human development, economic, water resources, land...

The LANDFIRE Refresh strategy: updating the national dataset

Science.gov (United States)

Nelson, Kurtis J.; Connot, Joel A.; Peterson, Birgit E.; Martin, Charley

2013-01-01

The LANDFIRE Program provides comprehensive vegetation and fuel datasets for the entire United States. As with many large-scale ecological datasets, vegetation and landscape conditions must be updated periodically to account for disturbances, growth, and natural succession. The LANDFIRE Refresh effort was the first attempt to consistently update these products nationwide. It incorporated a combination of specific systematic improvements to the original LANDFIRE National data, remote sensing based disturbance detection methods, field collected disturbance information, vegetation growth and succession modeling, and vegetation transition processes. This resulted in the creation of two complete datasets for all 50 states: LANDFIRE Refresh 2001, which includes the systematic improvements, and LANDFIRE Refresh 2008, which includes the disturbance and succession updates to the vegetation and fuel data. The new datasets are comparable for studying landscape changes in vegetation type and structure over a decadal period, and provide the most recent characterization of fuel conditions across the country. The applicability of the new layers is discussed and the effects of using the new fuel datasets are demonstrated through a fire behavior modeling exercise using the 2011 Wallow Fire in eastern Arizona as an example.

ISED: Constructing a high-resolution elevation road dataset from massive, low-quality in-situ observations derived from geosocial fitness tracking data.

Directory of Open Access Journals (Sweden)

Grant McKenzie

Full Text Available Gaining access to inexpensive, high-resolution, up-to-date, three-dimensional road network data is a top priority beyond research, as such data would fuel applications in industry, governments, and the broader public alike. Road network data are openly available via user-generated content such as OpenStreetMap (OSM but lack the resolution required for many tasks, e.g., emergency management. More importantly, however, few publicly available data offer information on elevation and slope. For most parts of the world, up-to-date digital elevation products with a resolution of less than 10 meters are a distant dream and, if available, those datasets have to be matched to the road network through an error-prone process. In this paper we present a radically different approach by deriving road network elevation data from massive amounts of in-situ observations extracted from user-contributed data from an online social fitness tracking application. While each individual observation may be of low-quality in terms of resolution and accuracy, taken together they form an accurate, high-resolution, up-to-date, three-dimensional road network that excels where other technologies such as LiDAR fail, e.g., in case of overpasses, overhangs, and so forth. In fact, the 1m spatial resolution dataset created in this research based on 350 million individual 3D location fixes has an RMSE of approximately 3.11m compared to a LiDAR-based ground-truth and can be used to enhance existing road network datasets where individual elevation fixes differ by up to 60m. In contrast, using interpolated data from the National Elevation Dataset (NED results in 4.75m RMSE compared to the base line. We utilize Linked Data technologies to integrate the proposed high-resolution dataset with OpenStreetMap road geometries without requiring any changes to the OSM data model.

ISED: Constructing a high-resolution elevation road dataset from massive, low-quality in-situ observations derived from geosocial fitness tracking data.

Science.gov (United States)

McKenzie, Grant; Janowicz, Krzysztof

2017-01-01

Gaining access to inexpensive, high-resolution, up-to-date, three-dimensional road network data is a top priority beyond research, as such data would fuel applications in industry, governments, and the broader public alike. Road network data are openly available via user-generated content such as OpenStreetMap (OSM) but lack the resolution required for many tasks, e.g., emergency management. More importantly, however, few publicly available data offer information on elevation and slope. For most parts of the world, up-to-date digital elevation products with a resolution of less than 10 meters are a distant dream and, if available, those datasets have to be matched to the road network through an error-prone process. In this paper we present a radically different approach by deriving road network elevation data from massive amounts of in-situ observations extracted from user-contributed data from an online social fitness tracking application. While each individual observation may be of low-quality in terms of resolution and accuracy, taken together they form an accurate, high-resolution, up-to-date, three-dimensional road network that excels where other technologies such as LiDAR fail, e.g., in case of overpasses, overhangs, and so forth. In fact, the 1m spatial resolution dataset created in this research based on 350 million individual 3D location fixes has an RMSE of approximately 3.11m compared to a LiDAR-based ground-truth and can be used to enhance existing road network datasets where individual elevation fixes differ by up to 60m. In contrast, using interpolated data from the National Elevation Dataset (NED) results in 4.75m RMSE compared to the base line. We utilize Linked Data technologies to integrate the proposed high-resolution dataset with OpenStreetMap road geometries without requiring any changes to the OSM data model.

Formerly Used Defense Sites (FUDS) Public Properties

Data.gov (United States)

Department of Homeland Security — The FUDS Public GIS dataset contains point location information for the 2,709 Formerly Used Defense Sites (FUDS) properties where the U.S. Army Corps of Engineers is...

Omicseq: a web-based search engine for exploring omics datasets

Science.gov (United States)

Sun, Xiaobo; Pittard, William S.; Xu, Tianlei; Chen, Li; Zwick, Michael E.; Jiang, Xiaoqian; Wang, Fusheng

2017-01-01

Abstract The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve ‘findability’ of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. PMID:28402462

Nanoparticle-organic pollutant interaction dataset

Data.gov (United States)

U.S. Environmental Protection Agency — Dataset presents concentrations of organic pollutants, such as polyaromatic hydrocarbon compounds, in water samples. Water samples of known volume and concentration...

Framework for Interactive Parallel Dataset Analysis on the Grid

Energy Technology Data Exchange (ETDEWEB)

Alexander, David A.; Ananthan, Balamurali; /Tech-X Corp.; Johnson, Tony; Serbo, Victor; /SLAC

2007-01-10

We present a framework for use at a typical Grid site to facilitate custom interactive parallel dataset analysis targeting terabyte-scale datasets of the type typically produced by large multi-institutional science experiments. We summarize the needs for interactive analysis and show a prototype solution that satisfies those needs. The solution consists of desktop client tool and a set of Web Services that allow scientists to sign onto a Grid site, compose analysis script code to carry out physics analysis on datasets, distribute the code and datasets to worker nodes, collect the results back to the client, and to construct professional-quality visualizations of the results.

The effects of spatial population dataset choice on estimates of population at risk of disease

Directory of Open Access Journals (Sweden)

Gething Peter W

2011-02-01

consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets. Conclusions Detailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

Large-scale Labeled Datasets to Fuel Earth Science Deep Learning Applications

Science.gov (United States)

Maskey, M.; Ramachandran, R.; Miller, J.

2017-12-01

Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. However, generic large-scale labeled datasets such as the ImageNet are the fuel that drives the impressive accuracy of deep learning results. Large-scale labeled datasets already exist in domains such as medical science, but creating them in the Earth science domain is a challenge. While there are ways to apply deep learning using limited labeled datasets, there is a need in the Earth sciences for creating large-scale labeled datasets for benchmarking and scaling deep learning applications. At the NASA Marshall Space Flight Center, we are using deep learning for a variety of Earth science applications where we have encountered the need for large-scale labeled datasets. We will discuss our approaches for creating such datasets and why these datasets are just as valuable as deep learning algorithms. We will also describe successful usage of these large-scale labeled datasets with our deep learning based applications.

An Affinity Propagation Clustering Algorithm for Mixed Numeric and Categorical Datasets

Directory of Open Access Journals (Sweden)

Kang Zhang

2014-01-01

Full Text Available Clustering has been widely used in different fields of science, technology, social science, and so forth. In real world, numeric as well as categorical features are usually used to describe the data objects. Accordingly, many clustering methods can process datasets that are either numeric or categorical. Recently, algorithms that can handle the mixed data clustering problems have been developed. Affinity propagation (AP algorithm is an exemplar-based clustering method which has demonstrated good performance on a wide variety of datasets. However, it has limitations on processing mixed datasets. In this paper, we propose a novel similarity measure for mixed type datasets and an adaptive AP clustering algorithm is proposed to cluster the mixed datasets. Several real world datasets are studied to evaluate the performance of the proposed algorithm. Comparisons with other clustering algorithms demonstrate that the proposed method works well not only on mixed datasets but also on pure numeric and categorical datasets.

Quantifying selective reporting and the Proteus phenomenon for multiple datasets with similar bias.

Directory of Open Access Journals (Sweden)

Thomas Pfeiffer

2011-03-01

Full Text Available Meta-analyses play an important role in synthesizing evidence from diverse studies and datasets that address similar questions. A major obstacle for meta-analyses arises from biases in reporting. In particular, it is speculated that findings which do not achieve formal statistical significance are less likely reported than statistically significant findings. Moreover, the patterns of bias can be complex and may also depend on the timing of the research results and their relationship with previously published work. In this paper, we present an approach that is specifically designed to analyze large-scale datasets on published results. Such datasets are currently emerging in diverse research fields, particularly in molecular medicine. We use our approach to investigate a dataset on Alzheimer's disease (AD that covers 1167 results from case-control studies on 102 genetic markers. We observe that initial studies on a genetic marker tend to be substantially more biased than subsequent replications. The chances for initial, statistically non-significant results to be published are estimated to be about 44% (95% CI, 32% to 63% relative to statistically significant results, while statistically non-significant replications have almost the same chance to be published as statistically significant replications (84%; 95% CI, 66% to 107%. Early replications tend to be biased against initial findings, an observation previously termed Proteus phenomenon: The chances for non-significant studies going in the same direction as the initial result are estimated to be lower than the chances for non-significant studies opposing the initial result (73%; 95% CI, 55% to 96%. Such dynamic patterns in bias are difficult to capture by conventional methods, where typically simple publication bias is assumed to operate. Our approach captures and corrects for complex dynamic patterns of bias, and thereby helps generating conclusions from published results that are more robust

General Purpose Multimedia Dataset - GarageBand 2008

DEFF Research Database (Denmark)

Meng, Anders

This document describes a general purpose multimedia data-set to be used in cross-media machine learning problems. In more detail we describe the genre taxonomy applied at http://www.garageband.com, from where the data-set was collected, and how the taxonomy have been fused into a more human...... understandable taxonomy. Finally, a description of various features extracted from both the audio and text are presented....

Omicseq: a web-based search engine for exploring omics datasets.

Science.gov (United States)

Sun, Xiaobo; Pittard, William S; Xu, Tianlei; Chen, Li; Zwick, Michael E; Jiang, Xiaoqian; Wang, Fusheng; Qin, Zhaohui S

2017-07-03

The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve 'findability' of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Benchmarking undedicated cloud computing providers for analysis of genomic datasets.

Science.gov (United States)

Yazar, Seyhan; Gooden, George E C; Mackey, David A; Hewitt, Alex W

2014-01-01

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5-78.2) for E.coli and 53.5% (95% CI: 34.4-72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5-303.1) and 173.9% (95% CI: 134.6-213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.

Benchmarking undedicated cloud computing providers for analysis of genomic datasets.

Directory of Open Access Journals (Sweden)

Seyhan Yazar

Full Text Available A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR on Amazon EC2 instances and Google Compute Engine (GCE, using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5-78.2 for E.coli and 53.5% (95% CI: 34.4-72.6 for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5-303.1 and 173.9% (95% CI: 134.6-213.1 more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.

Topic modeling for cluster analysis of large biological and medical datasets.

Science.gov (United States)

Zhao, Weizhong; Zou, Wen; Chen, James J

2014-01-01

The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting

An Analysis of the GTZAN Music Genre Dataset

DEFF Research Database (Denmark)

Sturm, Bob L.

2012-01-01

Most research in automatic music genre recognition has used the dataset assembled by Tzanetakis et al. in 2001. The composition and integrity of this dataset, however, has never been formally analyzed. For the first time, we provide an analysis of its composition, and create a machine...

A curated transcriptome dataset collection to investigate the functional programming of human hematopoietic cells in early life.

Science.gov (United States)

Rahman, Mahbuba; Boughorbel, Sabri; Presnell, Scott; Quinn, Charlie; Cugno, Chiara; Chaussabel, Damien; Marr, Nico

2016-01-01

Compendia of large-scale datasets made available in public repositories provide an opportunity to identify and fill gaps in biomedical knowledge. But first, these data need to be made readily accessible to research investigators for interpretation. Here we make available a collection of transcriptome datasets to investigate the functional programming of human hematopoietic cells in early life. Thirty two datasets were retrieved from the NCBI Gene Expression Omnibus (GEO) and loaded in a custom web application called the Gene Expression Browser (GXB), which was designed for interactive query and visualization of integrated large-scale data. Quality control checks were performed. Multiple sample groupings and gene rank lists were created allowing users to reveal age-related differences in transcriptome profiles, changes in the gene expression of neonatal hematopoietic cells to a variety of immune stimulators and modulators, as well as during cell differentiation. Available demographic, clinical, and cell phenotypic information can be overlaid with the gene expression data and used to sort samples. Web links to customized graphical views can be generated and subsequently inserted in manuscripts to report novel findings. GXB also enables browsing of a single gene across projects, thereby providing new perspectives on age- and developmental stage-specific expression of a given gene across the human hematopoietic system. This dataset collection is available at: http://developmentalimmunology.gxbsidra.org/dm3/geneBrowser/list.

Dataset definition for CMS operations and physics analyses

Science.gov (United States)

Franzoni, Giovanni; Compact Muon Solenoid Collaboration

2016-04-01

Data recorded at the CMS experiment are funnelled into streams, integrated in the HLT menu, and further organised in a hierarchical structure of primary datasets and secondary datasets/dedicated skims. Datasets are defined according to the final-state particles reconstructed by the high level trigger, the data format and the use case (physics analysis, alignment and calibration, performance studies). During the first LHC run, new workflows have been added to this canonical scheme, to exploit at best the flexibility of the CMS trigger and data acquisition systems. The concepts of data parking and data scouting have been introduced to extend the physics reach of CMS, offering the opportunity of defining physics triggers with extremely loose selections (e.g. dijet resonance trigger collecting data at a 1 kHz). In this presentation, we review the evolution of the dataset definition during the LHC run I, and we discuss the plans for the run II.

Dataset definition for CMS operations and physics analyses

CERN Document Server

AUTHOR|(CDS)2051291

2016-01-01

Data recorded at the CMS experiment are funnelled into streams, integrated in the HLT menu, and further organised in a hierarchical structure of primary datasets, secondary datasets, and dedicated skims. Datasets are defined according to the final-state particles reconstructed by the high level trigger, the data format and the use case (physics analysis, alignment and calibration, performance studies). During the first LHC run, new workflows have been added to this canonical scheme, to exploit at best the flexibility of the CMS trigger and data acquisition systems. The concept of data parking and data scouting have been introduced to extend the physics reach of CMS, offering the opportunity of defining physics triggers with extremely loose selections (e.g. dijet resonance trigger collecting data at a 1 kHz). In this presentation, we review the evolution of the dataset definition during the first run, and we discuss the plans for the second LHC run.

Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

Science.gov (United States)

Kohli, Marc D; Summers, Ronald M; Geis, J Raymond

2017-08-01

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities.

Dataset on species incidence, species richness and forest characteristics in a Danish protected area

DEFF Research Database (Denmark)

Mazziotta, Adriano; Heilmann-Clausen, Jacob; Bruun, Hans Henrik

2016-01-01

The data presented in this article are related to the research article entitled "Restoring hydrology and old-growth structures in a former production forest: Modelling the long-term effects on biodiversity" (A. Mazziotta, J. Heilmann-Clausen, H. H.Bruun, Ö. Fritz, E. Aude, A.P. Tøttrup) [1......]. This article describes how the changes induced by restoration actions in forest hydrology and structure alter the biodiversity value of a Danish forest reserve. The field dataset is made publicly available to enable critical or extended analyses....

Visualization of conserved structures by fusing highly variable datasets.

Science.gov (United States)

Silverstein, Jonathan C; Chhadia, Ankur; Dech, Fred

2002-01-01

Skill, effort, and time are required to identify and visualize anatomic structures in three-dimensions from radiological data. Fundamentally, automating these processes requires a technique that uses symbolic information not in the dynamic range of the voxel data. We were developing such a technique based on mutual information for automatic multi-modality image fusion (MIAMI Fuse, University of Michigan). This system previously demonstrated facility at fusing one voxel dataset with integrated symbolic structure information to a CT dataset (different scale and resolution) from the same person. The next step of development of our technique was aimed at accommodating the variability of anatomy from patient to patient by using warping to fuse our standard dataset to arbitrary patient CT datasets. A standard symbolic information dataset was created from the full color Visible Human Female by segmenting the liver parenchyma, portal veins, and hepatic veins and overwriting each set of voxels with a fixed color. Two arbitrarily selected patient CT scans of the abdomen were used for reference datasets. We used the warping functions in MIAMI Fuse to align the standard structure data to each patient scan. The key to successful fusion was the focused use of multiple warping control points that place themselves around the structure of interest automatically. The user assigns only a few initial control points to align the scans. Fusion 1 and 2 transformed the atlas with 27 points around the liver to CT1 and CT2 respectively. Fusion 3 transformed the atlas with 45 control points around the liver to CT1 and Fusion 4 transformed the atlas with 5 control points around the portal vein. The CT dataset is augmented with the transformed standard structure dataset, such that the warped structure masks are visualized in combination with the original patient dataset. This combined volume visualization is then rendered interactively in stereo on the ImmersaDesk in an immersive Virtual

An Annotated Dataset of 14 Cardiac MR Images

DEFF Research Database (Denmark)

Stegmann, Mikkel Bille

2002-01-01

This note describes a dataset consisting of 14 annotated cardiac MR images. Points of correspondence are placed on each image at the left ventricle (LV). As such, the dataset can be readily used for building statistical models of shape. Further, format specifications and terms of use are given....

The Development of an UAV Borne Direct Georeferenced Photogrammetric Platform for Ground Control Point Free Applications

Directory of Open Access Journals (Sweden)

Chien-Hsun Chu

2012-07-01

Full Text Available To facilitate applications such as environment detection or disaster monitoring, the development of rapid low cost systems for collecting near real time spatial information is very critical. Rapid spatial information collection has become an emerging trend for remote sensing and mapping applications. In this study, a fixed-wing Unmanned Aerial Vehicle (UAV-based spatial information acquisition platform that can operate in Ground Control Point (GCP free environments is developed and evaluated. The proposed UAV based photogrammetric platform has a Direct Georeferencing (DG module that includes a low cost Micro Electro Mechanical Systems (MEMS Inertial Navigation System (INS/ Global Positioning System (GPS integrated system. The DG module is able to provide GPS single frequency carrier phase measurements for differential processing to obtain sufficient positioning accuracy. All necessary calibration procedures are implemented. Ultimately, a flight test is performed to verify the positioning accuracy in DG mode without using GCPs. The preliminary results of positioning accuracy in DG mode illustrate that horizontal positioning accuracies in the x and y axes are around 5 m at 300 m flight height above the ground. The positioning accuracy of the z axis is below 10 m. Therefore, the proposed platform is relatively safe and inexpensive for collecting critical spatial information for urgent response such as disaster relief and assessment applications where GCPs are not available.

Dataset - Adviesregel PPL 2010

NARCIS (Netherlands)

Evert, van F.K.; Schans, van der D.A.; Geel, van W.C.A.; Slabbekoorn, J.J.; Booij, R.; Jukema, J.N.; Meurs, E.J.J.; Uenk, D.

2011-01-01

This dataset contains experimental data from a number of field experiments with potato in The Netherlands (Van Evert et al., 2011). The data are presented as an SQL dump of a PostgreSQL database (version 8.4.4). An outline of the entity-relationship diagram of the database is given in an

Tension in the recent Type Ia supernovae datasets

International Nuclear Information System (INIS)

Wei, Hao

2010-01-01

In the present work, we investigate the tension in the recent Type Ia supernovae (SNIa) datasets Constitution and Union. We show that they are in tension not only with the observations of the cosmic microwave background (CMB) anisotropy and the baryon acoustic oscillations (BAO), but also with other SNIa datasets such as Davis and SNLS. Then, we find the main sources responsible for the tension. Further, we make this more robust by employing the method of random truncation. Based on the results of this work, we suggest two truncated versions of the Union and Constitution datasets, namely the UnionT and ConstitutionT SNIa samples, whose behaviors are more regular.

Viability of Controlling Prosthetic Hand Utilizing Electroencephalograph (EEG) Dataset Signal

Science.gov (United States)

Miskon, Azizi; A/L Thanakodi, Suresh; Raihan Mazlan, Mohd; Mohd Haziq Azhar, Satria; Nooraya Mohd Tawil, Siti

2016-11-01

This project presents the development of an artificial hand controlled by Electroencephalograph (EEG) signal datasets for the prosthetic application. The EEG signal datasets were used as to improvise the way to control the prosthetic hand compared to the Electromyograph (EMG). The EMG has disadvantages to a person, who has not used the muscle for a long time and also to person with degenerative issues due to age factor. Thus, the EEG datasets found to be an alternative for EMG. The datasets used in this work were taken from Brain Computer Interface (BCI) Project. The datasets were already classified for open, close and combined movement operations. It served the purpose as an input to control the prosthetic hand by using an Interface system between Microsoft Visual Studio and Arduino. The obtained results reveal the prosthetic hand to be more efficient and faster in response to the EEG datasets with an additional LiPo (Lithium Polymer) battery attached to the prosthetic. Some limitations were also identified in terms of the hand movements, weight of the prosthetic, and the suggestions to improve were concluded in this paper. Overall, the objective of this paper were achieved when the prosthetic hand found to be feasible in operation utilizing the EEG datasets.

Technical note: An inorganic water chemistry dataset (1972–2011 ...

African Journals Online (AJOL)

A national dataset of inorganic chemical data of surface waters (rivers, lakes, and dams) in South Africa is presented and made freely available. The dataset comprises more than 500 000 complete water analyses from 1972 up to 2011, collected from more than 2 000 sample monitoring stations in South Africa. The dataset ...

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

Directory of Open Access Journals (Sweden)

Daniela Micucci

2017-10-01

Full Text Available Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, there are only a few publicly available data sets, which often contain samples from subjects with too similar characteristics, and very often lack specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection. The dataset includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with four different classifiers and with two different feature vectors. We evaluated four different classification tasks: fall vs. no fall, 9 activities, 8 falls, 17 activities and falls. For each classification task, we performed a 5-fold cross-validation (i.e., including samples from all the subjects in both the training and the test dataset and a leave-one-subject-out cross-validation (i.e., the test data include the samples of a subject only, and the training data, the samples of all the other subjects. Regarding the

Heuristics for Relevancy Ranking of Earth Dataset Search Results

Science.gov (United States)

Lynnes, Christopher; Quinn, Patrick; Norton, James

2016-01-01

As the Variety of Earth science datasets increases, science researchers find it more challenging to discover and select the datasets that best fit their needs. The most common way of search providers to address this problem is to rank the datasets returned for a query by their likely relevance to the user. Large web page search engines typically use text matching supplemented with reverse link counts, semantic annotations and user intent modeling. However, this produces uneven results when applied to dataset metadata records simply externalized as a web page. Fortunately, data and search provides have decades of experience in serving data user communities, allowing them to form heuristics that leverage the structure in the metadata together with knowledge about the user community. Some of these heuristics include specific ways of matching the user input to the essential measurements in the dataset and determining overlaps of time range and spatial areas. Heuristics based on the novelty of the datasets can prioritize later, better versions of data over similar predecessors. And knowledge of how different user types and communities use data can be brought to bear in cases where characteristics of the user (discipline, expertise) or their intent (applications, research) can be divined. The Earth Observing System Data and Information System has begun implementing some of these heuristics in the relevancy algorithm of its Common Metadata Repository search engine.

QSAR ligand dataset for modelling mutagenicity, genotoxicity, and rodent carcinogenicity

Directory of Open Access Journals (Sweden)

Davy Guan

2018-04-01

Full Text Available Five datasets were constructed from ligand and bioassay result data from the literature. These datasets include bioassay results from the Ames mutagenicity assay, Greenscreen GADD-45a-GFP assay, Syrian Hamster Embryo (SHE assay, and 2 year rat carcinogenicity assay results. These datasets provide information about chemical mutagenicity, genotoxicity and carcinogenicity.

The Dataset of Countries at Risk of Electoral Violence

OpenAIRE

Birch, Sarah; Muchlinski, David

2017-01-01

Electoral violence is increasingly affecting elections around the world, yet researchers have been limited by a paucity of granular data on this phenomenon. This paper introduces and describes a new dataset of electoral violence – the Dataset of Countries at Risk of Electoral Violence (CREV) – that provides measures of 10 different types of electoral violence across 642 elections held around the globe between 1995 and 2013. The paper provides a detailed account of how and why the dataset was ...

Towards interoperable and reproducible QSAR analyses: Exchange of datasets.

Science.gov (United States)

Spjuth, Ola; Willighagen, Egon L; Guha, Rajarshi; Eklund, Martin; Wikberg, Jarl Es

2010-06-30

QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets and hence work collectively, but

Towards interoperable and reproducible QSAR analyses: Exchange of datasets

Directory of Open Access Journals (Sweden)

Spjuth Ola

2010-06-01

Full Text Available Abstract Background QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. Results We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Conclusions Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join

Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets

Directory of Open Access Journals (Sweden)

Karacali Bilge

2007-10-01

Full Text Available Abstract Background Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a all genes on the microarray platform and b a list of known disease-related genes (a priori selection. We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms. Results Highly discriminative expression profiles were produced on both simulated gene expression data and expression data from breast cancer and lymphoma datasets on the basis of ER and BCL-6 expression, respectively. Use of relapse-free status to identify profiles for prognosis prediction resulted in poorly discriminative decision rules. Supervised feature selection resulted in more accurate classifications than random or a priori selection, however, the difference in prediction error decreased as the number of features increased. These results held when decision rules were applied across-datasets to samples profiled on the same microarray platform. Conclusion Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine

Internationally coordinated glacier monitoring: strategy and datasets

Science.gov (United States)

Hoelzle, Martin; Armstrong, Richard; Fetterer, Florence; Gärtner-Roer, Isabelle; Haeberli, Wilfried; Kääb, Andreas; Kargel, Jeff; Nussbaumer, Samuel; Paul, Frank; Raup, Bruce; Zemp, Michael

2014-05-01

Internationally coordinated monitoring of long-term glacier changes provide key indicator data about global climate change and began in the year 1894 as an internationally coordinated effort to establish standardized observations. Today, world-wide monitoring of glaciers and ice caps is embedded within the Global Climate Observing System (GCOS) in support of the United Nations Framework Convention on Climate Change (UNFCCC) as an important Essential Climate Variable (ECV). The Global Terrestrial Network for Glaciers (GTN-G) was established in 1999 with the task of coordinating measurements and to ensure the continuous development and adaptation of the international strategies to the long-term needs of users in science and policy. The basic monitoring principles must be relevant, feasible, comprehensive and understandable to a wider scientific community as well as to policy makers and the general public. Data access has to be free and unrestricted, the quality of the standardized and calibrated data must be high and a combination of detailed process studies at selected field sites with global coverage by satellite remote sensing is envisaged. Recently a GTN-G Steering Committee was established to guide and advise the operational bodies responsible for the international glacier monitoring, which are the World Glacier Monitoring Service (WGMS), the US National Snow and Ice Data Center (NSIDC), and the Global Land Ice Measurements from Space (GLIMS) initiative. Several online databases containing a wealth of diverse data types having different levels of detail and global coverage provide fast access to continuously updated information on glacier fluctuation and inventory data. For world-wide inventories, data are now available through (a) the World Glacier Inventory containing tabular information of about 130,000 glaciers covering an area of around 240,000 km2, (b) the GLIMS-database containing digital outlines of around 118,000 glaciers with different time stamps and

3DSEM: A 3D microscopy dataset

Directory of Open Access Journals (Sweden)

Ahmad P. Tafti

2016-03-01

Full Text Available The Scanning Electron Microscope (SEM as a 2D imaging instrument has been widely used in many scientific disciplines including biological, mechanical, and materials sciences to determine the surface attributes of microscopic objects. However the SEM micrographs still remain 2D images. To effectively measure and visualize the surface properties, we need to truly restore the 3D shape model from 2D SEM images. Having 3D surfaces would provide anatomic shape of micro-samples which allows for quantitative measurements and informative visualization of the specimens being investigated. The 3DSEM is a dataset for 3D microscopy vision which is freely available at [1] for any academic, educational, and research purposes. The dataset includes both 2D images and 3D reconstructed surfaces of several real microscopic samples. Keywords: 3D microscopy dataset, 3D microscopy vision, 3D SEM surface reconstruction, Scanning Electron Microscope (SEM

Active Semisupervised Clustering Algorithm with Label Propagation for Imbalanced and Multidensity Datasets

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Mingwei Leng

2013-01-01

Full Text Available The accuracy of most of the existing semisupervised clustering algorithms based on small size of labeled dataset is low when dealing with multidensity and imbalanced datasets, and labeling data is quite expensive and time consuming in many real-world applications. This paper focuses on active data selection and semisupervised clustering algorithm in multidensity and imbalanced datasets and proposes an active semisupervised clustering algorithm. The proposed algorithm uses an active mechanism for data selection to minimize the amount of labeled data, and it utilizes multithreshold to expand labeled datasets on multidensity and imbalanced datasets. Three standard datasets and one synthetic dataset are used to demonstrate the proposed algorithm, and the experimental results show that the proposed semisupervised clustering algorithm has a higher accuracy and a more stable performance in comparison to other clustering and semisupervised clustering algorithms, especially when the datasets are multidensity and imbalanced.

A reanalysis dataset of the South China Sea

Science.gov (United States)

Zeng, Xuezhi; Peng, Shiqiu; Li, Zhijin; Qi, Yiquan; Chen, Rongyu

2014-01-01

Ocean reanalysis provides a temporally continuous and spatially gridded four-dimensional estimate of the ocean state for a better understanding of the ocean dynamics and its spatial/temporal variability. Here we present a 19-year (1992–2010) high-resolution ocean reanalysis dataset of the upper ocean in the South China Sea (SCS) produced from an ocean data assimilation system. A wide variety of observations, including in-situ temperature/salinity profiles, ship-measured and satellite-derived sea surface temperatures, and sea surface height anomalies from satellite altimetry, are assimilated into the outputs of an ocean general circulation model using a multi-scale incremental three-dimensional variational data assimilation scheme, yielding a daily high-resolution reanalysis dataset of the SCS. Comparisons between the reanalysis and independent observations support the reliability of the dataset. The presented dataset provides the research community of the SCS an important data source for studying the thermodynamic processes of the ocean circulation and meso-scale features in the SCS, including their spatial and temporal variability. PMID:25977803

A Dataset for Visual Navigation with Neuromorphic Methods

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Francisco eBarranco

2016-02-01

Full Text Available Standardized benchmarks in Computer Vision have greatly contributed to the advance of approaches to many problems in the field. If we want to enhance the visibility of event-driven vision and increase its impact, we will need benchmarks that allow comparison among different neuromorphic methods as well as comparison to Computer Vision conventional approaches. We present datasets to evaluate the accuracy of frame-free and frame-based approaches for tasks of visual navigation. Similar to conventional Computer Vision datasets, we provide synthetic and real scenes, with the synthetic data created with graphics packages, and the real data recorded using a mobile robotic platform carrying a dynamic and active pixel vision sensor (DAVIS and an RGB+Depth sensor. For both datasets the cameras move with a rigid motion in a static scene, and the data includes the images, events, optic flow, 3D camera motion, and the depth of the scene, along with calibration procedures. Finally, we also provide simulated event data generated synthetically from well-known frame-based optical flow datasets.

SPREAD: a high-resolution daily gridded precipitation dataset for Spain – an extreme events frequency and intensity overview

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R. Serrano-Notivoli

2017-09-01

Full Text Available A high-resolution daily gridded precipitation dataset was built from raw data of 12 858 observatories covering a period from 1950 to 2012 in peninsular Spain and 1971 to 2012 in Balearic and Canary islands. The original data were quality-controlled and gaps were filled on each day and location independently. Using the serially complete dataset, a grid with a 5 × 5 km spatial resolution was constructed by estimating daily precipitation amounts and their corresponding uncertainty at each grid node. Daily precipitation estimations were compared to original observations to assess the quality of the gridded dataset. Four daily precipitation indices were computed to characterise the spatial distribution of daily precipitation and nine extreme precipitation indices were used to describe the frequency and intensity of extreme precipitation events. The Mediterranean coast and the Central Range showed the highest frequency and intensity of extreme events, while the number of wet days and dry and wet spells followed a north-west to south-east gradient in peninsular Spain, from high to low values in the number of wet days and wet spells and reverse in dry spells. The use of the total available data in Spain, the independent estimation of precipitation for each day and the high spatial resolution of the grid allowed for a precise spatial and temporal assessment of daily precipitation that is difficult to achieve when using other methods, pre-selected long-term stations or global gridded datasets. SPREAD dataset is publicly available at https://doi.org/10.20350/digitalCSIC/7393.

Dataset on species incidence, species richness and forest characteristics in a Danish protected area

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Adriano Mazziotta

2016-12-01

Full Text Available The data presented in this article are related to the research article entitled “Restoring hydrology and old-growth structures in a former production forest: Modelling the long-term effects on biodiversity” (A. Mazziotta, J. Heilmann-Clausen, H. H.Bruun, Ö. Fritz, E. Aude, A.P. Tøttrup [1]. This article describes how the changes induced by restoration actions in forest hydrology and structure alter the biodiversity value of a Danish forest reserve. The field dataset is made publicly available to enable critical or extended analyses.

Sparse Group Penalized Integrative Analysis of Multiple Cancer Prognosis Datasets

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Liu, Jin; Huang, Jian; Xie, Yang; Ma, Shuangge

2014-01-01

SUMMARY In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Because of the “large d, small n” characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyzes multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the AFT (accelerated failure time) model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group MCP (minimax concave penalty) approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach. PMID:23938111

An Analysis on Better Testing than Training Performances on the Iris Dataset

NARCIS (Netherlands)

Schutten, Marten; Wiering, Marco

2016-01-01

The Iris dataset is a well known dataset containing information on three different types of Iris flowers. A typical and popular method for solving classification problems on datasets such as the Iris set is the support vector machine (SVM). In order to do so the dataset is separated in a set used

Interactive visualization and analysis of multimodal datasets for surgical applications.

Science.gov (United States)

Kirmizibayrak, Can; Yim, Yeny; Wakid, Mike; Hahn, James

2012-12-01

Surgeons use information from multiple sources when making surgical decisions. These include volumetric datasets (such as CT, PET, MRI, and their variants), 2D datasets (such as endoscopic videos), and vector-valued datasets (such as computer simulations). Presenting all the information to the user in an effective manner is a challenging problem. In this paper, we present a visualization approach that displays the information from various sources in a single coherent view. The system allows the user to explore and manipulate volumetric datasets, display analysis of dataset values in local regions, combine 2D and 3D imaging modalities and display results of vector-based computer simulations. Several interaction methods are discussed: in addition to traditional interfaces including mouse and trackers, gesture-based natural interaction methods are shown to control these visualizations with real-time performance. An example of a medical application (medialization laryngoplasty) is presented to demonstrate how the combination of different modalities can be used in a surgical setting with our approach.

Automatic processing of multimodal tomography datasets.

Science.gov (United States)

Parsons, Aaron D; Price, Stephen W T; Wadeson, Nicola; Basham, Mark; Beale, Andrew M; Ashton, Alun W; Mosselmans, J Frederick W; Quinn, Paul D

2017-01-01

With the development of fourth-generation high-brightness synchrotrons on the horizon, the already large volume of data that will be collected on imaging and mapping beamlines is set to increase by orders of magnitude. As such, an easy and accessible way of dealing with such large datasets as quickly as possible is required in order to be able to address the core scientific problems during the experimental data collection. Savu is an accessible and flexible big data processing framework that is able to deal with both the variety and the volume of data of multimodal and multidimensional scientific datasets output such as those from chemical tomography experiments on the I18 microfocus scanning beamline at Diamond Light Source.

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare.

Science.gov (United States)

Ali, Rahman; Siddiqi, Muhammad Hameed; Idris, Muhammad; Ali, Taqdir; Hussain, Shujaat; Huh, Eui-Nam; Kang, Byeong Ho; Lee, Sungyoung

2015-07-02

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a "data modeler" tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets.

An Emergency Georeferencing Framework for GF-4 Imagery Based on GCP Prediction and Dynamic RPC Refinement

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Pengfei Li

2017-10-01

Full Text Available GaoFen-4 (GF-4 imagery has very potential in terms of emergency response due to its gazing mode. However, only poor geometric accuracy can be obtained using the rational polynomial coefficient (RPC parameters provided, making ground control points (GCPs necessary for emergency response. However, selecting GCPs is traditionally time-consuming, labor-intensive, and not fully reliable. This is mainly due to the facts that (1 manual GCP selection is time-consuming and cumbersome because of too many human interventions, especially for the first few GCPs; (2 typically, GF-4 gives planar array imagery acquired at rather large tilt angles, and the distortion introduces problems in image matching; (3 reference data will not always be available, especially under emergency circumstances. This paper provides a novel emergency georeferencing framework for GF-4 Level 1 imagery. The key feature is GCP prediction based on dynamic RPC refinement, which is able to predict even the first GCP and the prediction will be dynamically refined as the selection goes on. This is done by two techniques: (1 GCP prediction using RPC parameters and (2 dynamic RPC refinement using as few as only one GCP. Besides, online map services are also adopted to automatically provide reference data. Experimental results show that (1 GCP predictions improve using dynamic RPC refinement; (2 GCP selection becomes more efficient with GCP prediction; (3 the integration of online map services constitutes a good example for emergency response.

Low aerial imagery - an assessment of georeferencing errors and the potential for use in environmental inventory

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Smaczyński, Maciej; Medyńska-Gulij, Beata

2017-06-01

Unmanned aerial vehicles are increasingly being used in close range photogrammetry. Real-time observation of the Earth's surface and the photogrammetric images obtained are used as material for surveying and environmental inventory. The following study was conducted on a small area (approximately 1 ha). In such cases, the classical method of topographic mapping is not accurate enough. The geodetic method of topographic surveying, on the other hand, is an overly precise measurement technique for the purpose of inventorying the natural environment components. The author of the following study has proposed using the unmanned aerial vehicle technology and tying in the obtained images to the control point network established with the aid of GNSS technology. Georeferencing the acquired images and using them to create a photogrammetric model of the studied area enabled the researcher to perform calculations, which yielded a total root mean square error below 9 cm. The performed comparison of the real lengths of the vectors connecting the control points and their lengths calculated on the basis of the photogrammetric model made it possible to fully confirm the RMSE calculated and prove the usefulness of the UAV technology in observing terrain components for the purpose of environmental inventory. Such environmental components include, among others, elements of road infrastructure, green areas, but also changes in the location of moving pedestrians and vehicles, as well as other changes in the natural environment that are not registered on classical base maps or topographic maps.

Does the Visibility of Greenery Increase Perceived Safety in Urban Areas? Evidence from the Place Pulse 1.0 Dataset

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Xiaojiang Li

2015-07-01

Full Text Available Urban green space provides a series of esthetic, environmental and psychological benefits to urban residents. However, the relationship between the visibility of green vegetation and perceived safety is still in debate. This research investigated whether green vegetation could help to increase the perceived safety based on a crowdsourced dataset: the Place Pulse 1.0 dataset. Place Pulse 1.0 dataset, which was generated from a large number of votes by online participants, includes geo-tagged Google Street View images and the corresponding perceived safety score for each image. In this study, we conducted statistical analyses to analyze the relationship between perceived safety and green vegetation characteristics, which were extracted from Google Street View images. Results show that the visibility of green vegetation plays an important role in increasing perceived safety in urban areas. For different land use types, the relationship between vegetation structures and perceived safety varies. In residential, urban public/institutional, commercial and open land areas, the visibility of vegetation higher than 2.5 m has significant positive correlations with perceived safety, but there exists no significant correlation between perceived safety and the percentage of green vegetation in transportation and industrial areas. The visibility of vegetation below 2.5 m has no significant relationship with the perceived safety in almost all land use types, except for multifamily residential land and urban public/institutional land. In general, this study provided insight for the relationship between green vegetation characteristics and the perception of environment, as well as valuable reference data for developing urban greening programs.

Veterans Affairs Suicide Prevention Synthetic Dataset

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Department of Veterans Affairs — The VA's Veteran Health Administration, in support of the Open Data Initiative, is providing the Veterans Affairs Suicide Prevention Synthetic Dataset (VASPSD). The...

Discovering New Global Climate Patterns: Curating a 21-Year High Temporal (Hourly) and Spatial (40km) Resolution Reanalysis Dataset

Science.gov (United States)

Hou, C. Y.; Dattore, R.; Peng, G. S.

2014-12-01

The National Center for Atmospheric Research's Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40km Reanalysis dataset is a dynamically downscaled dataset with high temporal and spatial resolution. The dataset contains three-dimensional hourly analyses in netCDF format for the global atmospheric state from 1985 to 2005 on a 40km horizontal grid (0.4°grid increment) with 28 vertical levels, providing good representation of local forcing and diurnal variation of processes in the planetary boundary layer. This project aimed to make the dataset publicly available, accessible, and usable in order to provide a unique resource to allow and promote studies of new climate characteristics. When the curation project started, it had been five years since the data files were generated. Also, although the Principal Investigator (PI) had generated a user document at the end of the project in 2009, the document had not been maintained. Furthermore, the PI had moved to a new institution, and the remaining team members were reassigned to other projects. These factors made data curation in the areas of verifying data quality, harvest metadata descriptions, documenting provenance information especially challenging. As a result, the project's curation process found that: Data curator's skill and knowledge helped make decisions, such as file format and structure and workflow documentation, that had significant, positive impact on the ease of the dataset's management and long term preservation. Use of data curation tools, such as the Data Curation Profiles Toolkit's guidelines, revealed important information for promoting the data's usability and enhancing preservation planning. Involving data curators during each stage of the data curation life cycle instead of at the end could improve the curation process' efficiency. Overall, the project showed that proper resources invested in the curation process would give datasets the best chance to fulfill their potential to

SAR image classification based on CNN in real and simulation datasets

Science.gov (United States)

Peng, Lijiang; Liu, Ming; Liu, Xiaohua; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-04-01

Convolution neural network (CNN) has made great success in image classification tasks. Even in the field of synthetic aperture radar automatic target recognition (SAR-ATR), state-of-art results has been obtained by learning deep representation of features on the MSTAR benchmark. However, the raw data of MSTAR have shortcomings in training a SAR-ATR model because of high similarity in background among the SAR images of each kind. This indicates that the CNN would learn the hierarchies of features of backgrounds as well as the targets. To validate the influence of the background, some other SAR images datasets have been made which contains the simulation SAR images of 10 manufactured targets such as tank and fighter aircraft, and the backgrounds of simulation SAR images are sampled from the whole original MSTAR data. The simulation datasets contain the dataset that the backgrounds of each kind images correspond to the one kind of backgrounds of MSTAR targets or clutters and the dataset that each image shares the random background of whole MSTAR targets or clutters. In addition, mixed datasets of MSTAR and simulation datasets had been made to use in the experiments. The CNN architecture proposed in this paper are trained on all datasets mentioned above. The experimental results shows that the architecture can get high performances on all datasets even the backgrounds of the images are miscellaneous, which indicates the architecture can learn a good representation of the targets even though the drastic changes on background.

On sample size and different interpretations of snow stability datasets

Science.gov (United States)

Schirmer, M.; Mitterer, C.; Schweizer, J.

2009-04-01

Interpretations of snow stability variations need an assessment of the stability itself, independent of the scale investigated in the study. Studies on stability variations at a regional scale have often chosen stability tests such as the Rutschblock test or combinations of various tests in order to detect differences in aspect and elevation. The question arose: ‘how capable are such stability interpretations in drawing conclusions'. There are at least three possible errors sources: (i) the variance of the stability test itself; (ii) the stability variance at an underlying slope scale, and (iii) that the stability interpretation might not be directly related to the probability of skier triggering. Various stability interpretations have been proposed in the past that provide partly different results. We compared a subjective one based on expert knowledge with a more objective one based on a measure derived from comparing skier-triggered slopes vs. slopes that have been skied but not triggered. In this study, the uncertainties are discussed and their effects on regional scale stability variations will be quantified in a pragmatic way. An existing dataset with very large sample sizes was revisited. This dataset contained the variance of stability at a regional scale for several situations. The stability in this dataset was determined using the subjective interpretation scheme based on expert knowledge. The question to be answered was how many measurements were needed to obtain similar results (mainly stability differences in aspect or elevation) as with the complete dataset. The optimal sample size was obtained in several ways: (i) assuming a nominal data scale the sample size was determined with a given test, significance level and power, and by calculating the mean and standard deviation of the complete dataset. With this method it can also be determined if the complete dataset consists of an appropriate sample size. (ii) Smaller subsets were created with similar

Really big data: Processing and analysis of large datasets

Science.gov (United States)

Modern animal breeding datasets are large and getting larger, due in part to the recent availability of DNA data for many animals. Computational methods for efficiently storing and analyzing those data are under development. The amount of storage space required for such datasets is increasing rapidl...

Public support for river restoration funding in relation to local river ecomorphology, population density, and mean income

Science.gov (United States)

SchläPfer, Felix; Witzig, Pieter-Jan

2006-12-01

In 1997, about 140,000 citizens in 388 voting districts in the Swiss canton of Bern passed a ballot initiative to allocate about 3 million Swiss Francs annually to a canton-wide river restoration program. Using the municipal voting returns and a detailed georeferenced data set on the ecomorphological status of the rivers, we estimate models of voter support in relation to local river ecomorphology, population density, mean income, cultural background, and recent flood damage. Support of the initiative increased with increasing population density and tended to increase with increasing mean income, in spite of progressive taxation. Furthermore, we found evidence that public support increased with decreasing "naturalness" of local rivers. The model estimates may be cautiously used to predict the public acceptance of similar restoration programs in comparable regions. Moreover, the voting-based insights into the distribution of river restoration benefits provide a useful starting point for debates about appropriate financing schemes.

A robust dataset-agnostic heart disease classifier from Phonocardiogram.

Science.gov (United States)

Banerjee, Rohan; Dutta Choudhury, Anirban; Deshpande, Parijat; Bhattacharya, Sakyajit; Pal, Arpan; Mandana, K M

2017-07-01

Automatic classification of normal and abnormal heart sounds is a popular area of research. However, building a robust algorithm unaffected by signal quality and patient demography is a challenge. In this paper we have analysed a wide list of Phonocardiogram (PCG) features in time and frequency domain along with morphological and statistical features to construct a robust and discriminative feature set for dataset-agnostic classification of normal and cardiac patients. The large and open access database, made available in Physionet 2016 challenge was used for feature selection, internal validation and creation of training models. A second dataset of 41 PCG segments, collected using our in-house smart phone based digital stethoscope from an Indian hospital was used for performance evaluation. Our proposed methodology yielded sensitivity and specificity scores of 0.76 and 0.75 respectively on the test dataset in classifying cardiovascular diseases. The methodology also outperformed three popular prior art approaches, when applied on the same dataset.

A Comparative Analysis of Classification Algorithms on Diverse Datasets

Directory of Open Access Journals (Sweden)

M. Alghobiri

2018-04-01

Full Text Available Data mining involves the computational process to find patterns from large data sets. Classification, one of the main domains of data mining, involves known structure generalizing to apply to a new dataset and predict its class. There are various classification algorithms being used to classify various data sets. They are based on different methods such as probability, decision tree, neural network, nearest neighbor, boolean and fuzzy logic, kernel-based etc. In this paper, we apply three diverse classification algorithms on ten datasets. The datasets have been selected based on their size and/or number and nature of attributes. Results have been discussed using some performance evaluation measures like precision, accuracy, F-measure, Kappa statistics, mean absolute error, relative absolute error, ROC Area etc. Comparative analysis has been carried out using the performance evaluation measures of accuracy, precision, and F-measure. We specify features and limitations of the classification algorithms for the diverse nature datasets.

How do you assign persistent identifiers to extracts from large, complex, dynamic data sets that underpin scholarly publications?

Science.gov (United States)

Wyborn, Lesley; Car, Nicholas; Evans, Benjamin; Klump, Jens

2016-04-01

Persistent identifiers in the form of a Digital Object Identifier (DOI) are becoming more mainstream, assigned at both the collection and dataset level. For static datasets, this is a relatively straight-forward matter. However, many new data collections are dynamic, with new data being appended, models and derivative products being revised with new data, or the data itself revised as processing methods are improved. Further, because data collections are becoming accessible as services, researchers can log in and dynamically create user-defined subsets for specific research projects: they also can easily mix and match data from multiple collections, each of which can have a complex history. Inevitably extracts from such dynamic data sets underpin scholarly publications, and this presents new challenges. The National Computational Infrastructure (NCI) has been experiencing and making progress towards addressing these issues. The NCI is large node of the Research Data Services initiative (RDS) of the Australian Government's research infrastructure, which currently makes available over 10 PBytes of priority research collections, ranging from geosciences, geophysics, environment, and climate, through to astronomy, bioinformatics, and social sciences. Data are replicated to, or are produced at, NCI and then processed there to higher-level data products or directly analysed. Individual datasets range from multi-petabyte computational models and large volume raster arrays, down to gigabyte size, ultra-high resolution datasets. To facilitate access, maximise reuse and enable integration across the disciplines, datasets have been organized on a platform called the National Environmental Research Data Interoperability Platform (NERDIP). Combined, the NERDIP data collections form a rich and diverse asset for researchers: their co-location and standardization optimises the value of existing data, and forms a new resource to underpin data-intensive Science. New publication

Data publication activities in the Natural Environment Research Council

Science.gov (United States)

Leadbetter, A.; Callaghan, S.; Lowry, R.; Moncoiffé, G.; Donnegan, S.; Pepler, S.; Cunningham, N.; Kirsch, P.; Ault, L.; Bell, P.; Bowie, R.; Harrison, K.; Smith-Haddon, B.; Wetherby, A.; Wright, D.; Thorley, M.

2012-04-01

The Natural Environment Research Council (NERC) is implementing its Science Information Strategy in order to provide a world class service to deliver integrated data for earth system science. One project within this strategy is Data Citation and Publication, which aims to put the promotion and recognition stages of the data lifecycle into place alongside the traditional data management activities of NERC's Environmental Data Centres (EDCs). The NERC EDCs have made a distinction between the serving of data and its publication. Data serving is defined in this case as the day-to-day data management tasks of: • acquiring data and metadata from the originating scientists; • metadata and format harmonisation prior to database ingestion; • ensuring the metadata is adequate and accurate and that the data are available in appropriate file formats; • and making the data available for interested parties. Whereas publication: • requires the assignment of a digital object identifier to a dataset which guarantees that an EDC has assessed the quality of the metadata and the file format and will maintain an unchanged version of the data for the foreseeable future • requires the peer-review of the scientific quality of the data by a scientist with knowledge of the scientific domain in which the data were collected, using a framework for peer-review of datasets such as that developed by the CLADDIER project. • requires collaboration with journal publishers who have access to a well established peer-review system The first of these requirements can be managed in-house by the EDCs, while the remainder require collaboration with the wider scientific and publishing communities. It is anticipated that a scientist may achieve a lower level of academic credit for a dataset which is assigned a DOI but does not follow through to the scientific peer-review stage, similar to publication in a report or other non-peer reviewed publication normally described as grey literature, or

Multi-source Geospatial Data Analysis with Google Earth Engine

Science.gov (United States)

Erickson, T.

2014-12-01

The Google Earth Engine platform is a cloud computing environment for data analysis that combines a public data catalog with a large-scale computational facility optimized for parallel processing of geospatial data. The data catalog is a multi-petabyte archive of georeferenced datasets that include images from Earth observing satellite and airborne sensors (examples: USGS Landsat, NASA MODIS, USDA NAIP), weather and climate datasets, and digital elevation models. Earth Engine supports both a just-in-time computation model that enables real-time preview and debugging during algorithm development for open-ended data exploration, and a batch computation mode for applying algorithms over large spatial and temporal extents. The platform automatically handles many traditionally-onerous data management tasks, such as data format conversion, reprojection, and resampling, which facilitates writing algorithms that combine data from multiple sensors and/or models. Although the primary use of Earth Engine, to date, has been the analysis of large Earth observing satellite datasets, the computational platform is generally applicable to a wide variety of use cases that require large-scale geospatial data analyses. This presentation will focus on how Earth Engine facilitates the analysis of geospatial data streams that originate from multiple separate sources (and often communities) and how it enables collaboration during algorithm development and data exploration. The talk will highlight current projects/analyses that are enabled by this functionality.https://earthengine.google.org

An assessment of differences in gridded precipitation datasets in complex terrain

Science.gov (United States)

Henn, Brian; Newman, Andrew J.; Livneh, Ben; Daly, Christopher; Lundquist, Jessica D.

2018-01-01

Hydrologic modeling and other geophysical applications are sensitive to precipitation forcing data quality, and there are known challenges in spatially distributing gauge-based precipitation over complex terrain. We conduct a comparison of six high-resolution, daily and monthly gridded precipitation datasets over the Western United States. We compare the long-term average spatial patterns, and interannual variability of water-year total precipitation, as well as multi-year trends in precipitation across the datasets. We find that the greatest absolute differences among datasets occur in high-elevation areas and in the maritime mountain ranges of the Western United States, while the greatest percent differences among datasets relative to annual total precipitation occur in arid and rain-shadowed areas. Differences between datasets in some high-elevation areas exceed 200 mm yr-1 on average, and relative differences range from 5 to 60% across the Western United States. In areas of high topographic relief, true uncertainties and biases are likely higher than the differences among the datasets; we present evidence of this based on streamflow observations. Precipitation trends in the datasets differ in magnitude and sign at smaller scales, and are sensitive to how temporal inhomogeneities in the underlying precipitation gauge data are handled.

Strontium removal jar test dataset for all figures and tables.

Data.gov (United States)

U.S. Environmental Protection Agency — The datasets where used to generate data to demonstrate strontium removal under various water quality and treatment conditions. This dataset is associated with the...

Benchmarking of Typical Meteorological Year datasets dedicated to Concentrated-PV systems

Science.gov (United States)

Realpe, Ana Maria; Vernay, Christophe; Pitaval, Sébastien; Blanc, Philippe; Wald, Lucien; Lenoir, Camille

2016-04-01

Accurate analysis of meteorological and pyranometric data for long-term analysis is the basis of decision-making for banks and investors, regarding solar energy conversion systems. This has led to the development of methodologies for the generation of Typical Meteorological Years (TMY) datasets. The most used method for solar energy conversion systems was proposed in 1978 by the Sandia Laboratory (Hall et al., 1978) considering a specific weighted combination of different meteorological variables with notably global, diffuse horizontal and direct normal irradiances, air temperature, wind speed, relative humidity. In 2012, a new approach was proposed in the framework of the European project FP7 ENDORSE. It introduced the concept of "driver" that is defined by the user as an explicit function of the pyranometric and meteorological relevant variables to improve the representativeness of the TMY datasets with respect the specific solar energy conversion system of interest. The present study aims at comparing and benchmarking different TMY datasets considering a specific Concentrated-PV (CPV) system as the solar energy conversion system of interest. Using long-term (15+ years) time-series of high quality meteorological and pyranometric ground measurements, three types of TMY datasets generated by the following methods: the Sandia method, a simplified driver with DNI as the only representative variable and a more sophisticated driver. The latter takes into account the sensitivities of the CPV system with respect to the spectral distribution of the solar irradiance and wind speed. Different TMY datasets from the three methods have been generated considering different numbers of years in the historical dataset, ranging from 5 to 15 years. The comparisons and benchmarking of these TMY datasets are conducted considering the long-term time series of simulated CPV electric production as a reference. The results of this benchmarking clearly show that the Sandia method is not

Geocam Space: Enhancing Handheld Digital Camera Imagery from the International Space Station for Research and Applications

Science.gov (United States)

Stefanov, William L.; Lee, Yeon Jin; Dille, Michael

2016-01-01

advance in geolocation from the manual feature-matching approach for both nadir and off-nadir viewing imagery. With the initial geolocation estimate, full georeferencing of an image is completed using the rapid tie-pointing interface in GeoRef, and the resulting data is added to the Gateway to Astronaut Photography of Earth online database in both Geotiff and Keyhole Markup Language (kml) formats. The integration of the GeoRef software component of Geocam Space into the CEO image cataloging workflow is complete, and disaster response imagery acquired by the ISS crew is now fully georeferenced as a standard data product. The on-orbit hardware component (GeoSens) is in final prototyping phase, and is on-schedule for launch to the ISS in late 2016. Installation and routine use of the Geocam Space system for handheld digital camera photography from the ISS is expected to significantly improve the usefulness of this unique dataset for a variety of public- and private-sector applications.

SIAM 2007 Text Mining Competition dataset

Data.gov (United States)

National Aeronautics and Space Administration — Subject Area: Text Mining Description: This is the dataset used for the SIAM 2007 Text Mining competition. This competition focused on developing text mining...

Environmental Dataset Gateway (EDG) REST Interface

Data.gov (United States)

U.S. Environmental Protection Agency — Use the Environmental Dataset Gateway (EDG) to find and access EPA's environmental resources. Many options are available for easily reusing EDG content in other...

Public Data Archiving in Ecology and Evolution: How Well Are We Doing?

Science.gov (United States)

Roche, Dominique G.; Kruuk, Loeske E. B.; Lanfear, Robert; Binning, Sandra A.

2015-01-01

Policies that mandate public data archiving (PDA) successfully increase accessibility to data underlying scientific publications. However, is the data quality sufficient to allow reuse and reanalysis? We surveyed 100 datasets associated with nonmolecular studies in journals that commonly publish ecological and evolutionary research and have a strong PDA policy. Out of these datasets, 56% were incomplete, and 64% were archived in a way that partially or entirely prevented reuse. We suggest that cultural shifts facilitating clearer benefits to authors are necessary to achieve high-quality PDA and highlight key guidelines to help authors increase their data’s reuse potential and compliance with journal data policies. PMID:26556502

Overview of a public-industry partnership for enhancing corn nitrogen research and datasets

Science.gov (United States)

Due to economic and environmental consequences of nitrogen (N) lost from fertilizer applications in corn (Zea mays L.), considerable public and industry attention has been devoted to development of N decision tools. Now a wide variety of tools are available to farmers for managing N inputs. However,...

Data Publication: A Partnership between Scientists, Data Managers and Librarians

Science.gov (United States)

Raymond, L.; Chandler, C.; Lowry, R.; Urban, E.; Moncoiffe, G.; Pissierssens, P.; Norton, C.; Miller, H.

2012-04-01

Current literature on the topic of data publication suggests that success is best achieved when there is a partnership between scientists, data managers, and librarians. The Marine Biological Laboratory/Woods Hole Oceanographic Institution (MBLWHOI) Library and the Biological and Chemical Oceanography Data Management Office (BCO-DMO) have developed tools and processes to automate the ingestion of metadata from BCO-DMO for deposit with datasets into the Institutional Repository (IR) Woods Hole Open Access Server (WHOAS). The system also incorporates functionality for BCO-DMO to request a Digital Object Identifier (DOI) from the Library. This partnership allows the Library to work with a trusted data repository to ensure high quality data while the data repository utilizes library services and is assured of a permanent archive of the copy of the data extracted from the repository database. The assignment of persistent identifiers enables accurate data citation. The Library can assign a DOI to appropriate datasets deposited in WHOAS. A primary activity is working with authors to deposit datasets associated with published articles. The DOI would ideally be assigned before submission and be included in the published paper so readers can link directly to the dataset, but DOIs are also being assigned to datasets related to articles after publication. WHOAS metadata records link the article to the datasets and the datasets to the article. The assignment of DOIs has enabled another important collaboration with Elsevier, publisher of educational and professional science journals. Elsevier can now link from articles in the Science Direct database to the datasets available from WHOAS that are related to that article. The data associated with the article are freely available from WHOAS and accompanied by a Dublin Core metadata record. In addition, the Library has worked with researchers to deposit datasets in WHOAS that are not appropriate for national, international, or domain

A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery.

Science.gov (United States)

Ahmidi, Narges; Tao, Lingling; Sefati, Shahin; Gao, Yixin; Lea, Colin; Haro, Benjamin Bejar; Zappella, Luca; Khudanpur, Sanjeev; Vidal, Rene; Hager, Gregory D

2017-09-01

State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging. In this paper, we address two major problems for surgical data analysis: First, lack of uniform-shared datasets and benchmarks, and second, lack of consistent validation processes. We address the former by presenting the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a public dataset that we have created to support comparative research benchmarking. JIGSAWS contains synchronized video and kinematic data from multiple performances of robotic surgical tasks by operators of varying skill. We address the latter by presenting a well-documented evaluation methodology and reporting results for six techniques for automated segmentation and classification of time-series data on JIGSAWS. These techniques comprise four temporal approaches for joint segmentation and classification: hidden Markov model, sparse hidden Markov model (HMM), Markov semi-Markov conditional random field, and skip-chain conditional random field; and two feature-based ones that aim to classify fixed segments: bag of spatiotemporal features and linear dynamical systems. Most methods recognize gesture activities with approximately 80% overall accuracy under both leave-one-super-trial-out and leave-one-user-out cross-validation settings. Current methods show promising results on this shared dataset, but room for significant progress remains, particularly for consistent prediction of gesture activities across different surgeons. The results reported in this paper provide the first systematic and uniform evaluation of surgical activity recognition techniques on the benchmark database.

Sensitivity of a numerical wave model on wind re-analysis datasets

Science.gov (United States)

Lavidas, George; Venugopal, Vengatesan; Friedrich, Daniel

2017-03-01

Wind is the dominant process for wave generation. Detailed evaluation of metocean conditions strengthens our understanding of issues concerning potential offshore applications. However, the scarcity of buoys and high cost of monitoring systems pose a barrier to properly defining offshore conditions. Through use of numerical wave models, metocean conditions can be hindcasted and forecasted providing reliable characterisations. This study reports the sensitivity of wind inputs on a numerical wave model for the Scottish region. Two re-analysis wind datasets with different spatio-temporal characteristics are used, the ERA-Interim Re-Analysis and the CFSR-NCEP Re-Analysis dataset. Different wind products alter results, affecting the accuracy obtained. The scope of this study is to assess different available wind databases and provide information concerning the most appropriate wind dataset for the specific region, based on temporal, spatial and geographic terms for wave modelling and offshore applications. Both wind input datasets delivered results from the numerical wave model with good correlation. Wave results by the 1-h dataset have higher peaks and lower biases, in expense of a high scatter index. On the other hand, the 6-h dataset has lower scatter but higher biases. The study shows how wind dataset affects the numerical wave modelling performance, and that depending on location and study needs, different wind inputs should be considered.

Querying Large Biological Network Datasets

Science.gov (United States)

Gulsoy, Gunhan

2013-01-01

New experimental methods has resulted in increasing amount of genetic interaction data to be generated every day. Biological networks are used to store genetic interaction data gathered. Increasing amount of data available requires fast large scale analysis methods. Therefore, we address the problem of querying large biological network datasets.…

Spatial datasets of radionuclide contamination in the Ukrainian Chernobyl Exclusion Zone

Science.gov (United States)

Kashparov, Valery; Levchuk, Sviatoslav; Zhurba, Marina; Protsak, Valentyn; Khomutinin, Yuri; Beresford, Nicholas A.; Chaplow, Jacqueline S.

2018-02-01

The dataset Spatial datasets of radionuclide contamination in the Ukrainian Chernobyl Exclusion Zone was developed to enable data collected between May 1986 (immediately after Chernobyl) and 2014 by the Ukrainian Institute of Agricultural Radiology (UIAR) after the Chernobyl accident to be made publicly available. The dataset includes results from comprehensive soil sampling across the Chernobyl Exclusion Zone (CEZ). Analyses include radiocaesium (134Cs and 134Cs) 90Sr, 154Eu and soil property data; plutonium isotope activity concentrations in soil (including distribution in the soil profile); analyses of hot (or fuel) particles from the CEZ (data from Poland and across Europe are also included); and results of monitoring in the Ivankov district, a region adjacent to the exclusion zone. The purpose of this paper is to describe the available data and methodology used to obtain them. The data will be valuable to those conducting studies within the CEZ in a number of ways, for instance (i) for helping to perform robust exposure estimates to wildlife, (ii) for predicting comparative activity concentrations of different key radionuclides, (iii) for providing a baseline against which future surveys in the CEZ can be compared, (iv) as a source of information on the behaviour of fuel particles (FPs), (v) for performing retrospective dose assessments and (vi) for assessing natural background dose rates in the CEZ. The CEZ has been proposed as a radioecological observatory (i.e. a radioactively contaminated site that will provide a focus for long-term, radioecological collaborative international research). Key to the future success of this concept is open access to data for the CEZ. The data presented here are a first step in this process. The data and supporting documentation are freely available from the Environmental Information Data Centre (EIDC) under the terms and conditions of the Open Government Licence: https://doi.org/10.5285/782ec845-2135-4698-8881-b38823e533bf.

Spatial datasets of radionuclide contamination in the Ukrainian Chernobyl Exclusion Zone

Directory of Open Access Journals (Sweden)

V. Kashparov

2018-02-01

Full Text Available The dataset Spatial datasets of radionuclide contamination in the Ukrainian Chernobyl Exclusion Zone was developed to enable data collected between May 1986 (immediately after Chernobyl and 2014 by the Ukrainian Institute of Agricultural Radiology (UIAR after the Chernobyl accident to be made publicly available. The dataset includes results from comprehensive soil sampling across the Chernobyl Exclusion Zone (CEZ. Analyses include radiocaesium (134Cs and 134Cs 90Sr, 154Eu and soil property data; plutonium isotope activity concentrations in soil (including distribution in the soil profile; analyses of hot (or fuel particles from the CEZ (data from Poland and across Europe are also included; and results of monitoring in the Ivankov district, a region adjacent to the exclusion zone. The purpose of this paper is to describe the available data and methodology used to obtain them. The data will be valuable to those conducting studies within the CEZ in a number of ways, for instance (i for helping to perform robust exposure estimates to wildlife, (ii for predicting comparative activity concentrations of different key radionuclides, (iii for providing a baseline against which future surveys in the CEZ can be compared, (iv as a source of information on the behaviour of fuel particles (FPs, (v for performing retrospective dose assessments and (vi for assessing natural background dose rates in the CEZ. The CEZ has been proposed as a radioecological observatory (i.e. a radioactively contaminated site that will provide a focus for long-term, radioecological collaborative international research. Key to the future success of this concept is open access to data for the CEZ. The data presented here are a first step in this process. The data and supporting documentation are freely available from the Environmental Information Data Centre (EIDC under the terms and conditions of the Open Government Licence: https://doi.org/10.5285/782ec845-2135-4698-8881-b

A dataset of human decision-making in teamwork management

Science.gov (United States)

Yu, Han; Shen, Zhiqi; Miao, Chunyan; Leung, Cyril; Chen, Yiqiang; Fauvel, Simon; Lin, Jun; Cui, Lizhen; Pan, Zhengxiang; Yang, Qiang

2017-01-01

Today, most endeavours require teamwork by people with diverse skills and characteristics. In managing teamwork, decisions are often made under uncertainty and resource constraints. The strategies and the effectiveness of the strategies different people adopt to manage teamwork under different situations have not yet been fully explored, partially due to a lack of detailed large-scale data. In this paper, we describe a multi-faceted large-scale dataset to bridge this gap. It is derived from a game simulating complex project management processes. It presents the participants with different conditions in terms of team members' capabilities and task characteristics for them to exhibit their decision-making strategies. The dataset contains detailed data reflecting the decision situations, decision strategies, decision outcomes, and the emotional responses of 1,144 participants from diverse backgrounds. To our knowledge, this is the first dataset simultaneously covering these four facets of decision-making. With repeated measurements, the dataset may help establish baseline variability of decision-making in teamwork management, leading to more realistic decision theoretic models and more effective decision support approaches.

EVALUATION OF LAND USE/LAND COVER DATASETS FOR URBAN WATERSHED MODELING

International Nuclear Information System (INIS)

S.J. BURIAN; M.J. BROWN; T.N. MCPHERSON

2001-01-01

Land use/land cover (LULC) data are a vital component for nonpoint source pollution modeling. Most watershed hydrology and pollutant loading models use, in some capacity, LULC information to generate runoff and pollutant loading estimates. Simple equation methods predict runoff and pollutant loads using runoff coefficients or pollutant export coefficients that are often correlated to LULC type. Complex models use input variables and parameters to represent watershed characteristics and pollutant buildup and washoff rates as a function of LULC type. Whether using simple or complex models an accurate LULC dataset with an appropriate spatial resolution and level of detail is paramount for reliable predictions. The study presented in this paper compared and evaluated several LULC dataset sources for application in urban environmental modeling. The commonly used USGS LULC datasets have coarser spatial resolution and lower levels of classification than other LULC datasets. In addition, the USGS datasets do not accurately represent the land use in areas that have undergone significant land use change during the past two decades. We performed a watershed modeling analysis of three urban catchments in Los Angeles, California, USA to investigate the relative difference in average annual runoff volumes and total suspended solids (TSS) loads when using the USGS LULC dataset versus using a more detailed and current LULC dataset. When the two LULC datasets were aggregated to the same land use categories, the relative differences in predicted average annual runoff volumes and TSS loads from the three catchments were 8 to 14% and 13 to 40%, respectively. The relative differences did not have a predictable relationship with catchment size

Taking the Temperature of Pedestrian Movement in Public Spaces

DEFF Research Database (Denmark)

Nielsen, Søren Zebitz; Gade, Rikke; Moeslund, Thomas B.

2014-01-01

independent of light and the technique is non-intrusive and preserves privacy. The approach extends the analysis to the GIS domain by capturing georeferenced tracks. We present a pilot study conducted in Copenhagen in 2013. The tracks retrieved by CV are compared to manually annotated ground truth tracks...

Sharing Video Datasets in Design Research

DEFF Research Database (Denmark)

Christensen, Bo; Abildgaard, Sille Julie Jøhnk

2017-01-01

This paper examines how design researchers, design practitioners and design education can benefit from sharing a dataset. We present the Design Thinking Research Symposium 11 (DTRS11) as an exemplary project that implied sharing video data of design processes and design activity in natural settings...... with a large group of fellow academics from the international community of Design Thinking Research, for the purpose of facilitating research collaboration and communication within the field of Design and Design Thinking. This approach emphasizes the social and collaborative aspects of design research, where...... a multitude of appropriate perspectives and methods may be utilized in analyzing and discussing the singular dataset. The shared data is, from this perspective, understood as a design object in itself, which facilitates new ways of working, collaborating, studying, learning and educating within the expanding...

Interpolation of diffusion weighted imaging datasets

DEFF Research Database (Denmark)

Dyrby, Tim B; Lundell, Henrik; Burke, Mark W

2014-01-01

anatomical details and signal-to-noise-ratio for reliable fibre reconstruction. We assessed the potential benefits of interpolating DWI datasets to a higher image resolution before fibre reconstruction using a diffusion tensor model. Simulations of straight and curved crossing tracts smaller than or equal......Diffusion weighted imaging (DWI) is used to study white-matter fibre organisation, orientation and structural connectivity by means of fibre reconstruction algorithms and tractography. For clinical settings, limited scan time compromises the possibilities to achieve high image resolution for finer...... interpolation methods fail to disentangle fine anatomical details if PVE is too pronounced in the original data. As for validation we used ex-vivo DWI datasets acquired at various image resolutions as well as Nissl-stained sections. Increasing the image resolution by a factor of eight yielded finer geometrical...

Architecture of the local spatial data infrastructure for regional climate change research

Science.gov (United States)

Titov, Alexander; Gordov, Evgeny

2013-04-01

Georeferenced datasets (meteorological databases, modeling and reanalysis results, etc.) are actively used in modeling and analysis of climate change for various spatial and temporal scales. Due to inherent heterogeneity of environmental datasets as well as their size which might constitute up to tens terabytes for a single dataset studies in the area of climate and environmental change require a special software support based on SDI approach. A dedicated architecture of the local spatial data infrastructure aiming at regional climate change analysis using modern web mapping technologies is presented. Geoportal is a key element of any SDI, allowing searching of geoinformation resources (datasets and services) using metadata catalogs, producing geospatial data selections by their parameters (data access functionality) as well as managing services and applications of cartographical visualization. It should be noted that due to objective reasons such as big dataset volume, complexity of data models used, syntactic and semantic differences of various datasets, the development of environmental geodata access, processing and visualization services turns out to be quite a complex task. Those circumstances were taken into account while developing architecture of the local spatial data infrastructure as a universal framework providing geodata services. So that, the architecture presented includes: 1. Effective in terms of search, access, retrieval and subsequent statistical processing, model of storing big sets of regional georeferenced data, allowing in particular to store frequently used values (like monthly and annual climate change indices, etc.), thus providing different temporal views of the datasets 2. General architecture of the corresponding software components handling geospatial datasets within the storage model 3. Metadata catalog describing in detail using ISO 19115 and CF-convention standards datasets used in climate researches as a basic element of the

ClimateNet: A Machine Learning dataset for Climate Science Research

Science.gov (United States)

Prabhat, M.; Biard, J.; Ganguly, S.; Ames, S.; Kashinath, K.; Kim, S. K.; Kahou, S.; Maharaj, T.; Beckham, C.; O'Brien, T. A.; Wehner, M. F.; Williams, D. N.; Kunkel, K.; Collins, W. D.

2017-12-01

Deep Learning techniques have revolutionized commercial applications in Computer vision, speech recognition and control systems. The key for all of these developments was the creation of a curated, labeled dataset ImageNet, for enabling multiple research groups around the world to develop methods, benchmark performance and compete with each other. The success of Deep Learning can be largely attributed to the broad availability of this dataset. Our empirical investigations have revealed that Deep Learning is similarly poised to benefit the task of pattern detection in climate science. Unfortunately, labeled datasets, a key pre-requisite for training, are hard to find. Individual research groups are typically interested in specialized weather patterns, making it hard to unify, and share datasets across groups and institutions. In this work, we are proposing ClimateNet: a labeled dataset that provides labeled instances of extreme weather patterns, as well as associated raw fields in model and observational output. We develop a schema in NetCDF to enumerate weather pattern classes/types, store bounding boxes, and pixel-masks. We are also working on a TensorFlow implementation to natively import such NetCDF datasets, and are providing a reference convolutional architecture for binary classification tasks. Our hope is that researchers in Climate Science, as well as ML/DL, will be able to use (and extend) ClimateNet to make rapid progress in the application of Deep Learning for Climate Science research.

Resampling Methods Improve the Predictive Power of Modeling in Class-Imbalanced Datasets

Directory of Open Access Journals (Sweden)

Paul H. Lee

2014-09-01

Full Text Available In the medical field, many outcome variables are dichotomized, and the two possible values of a dichotomized variable are referred to as classes. A dichotomized dataset is class-imbalanced if it consists mostly of one class, and performance of common classification models on this type of dataset tends to be suboptimal. To tackle such a problem, resampling methods, including oversampling and undersampling can be used. This paper aims at illustrating the effect of resampling methods using the National Health and Nutrition Examination Survey (NHANES wave 2009–2010 dataset. A total of 4677 participants aged ≥20 without self-reported diabetes and with valid blood test results were analyzed. The Classification and Regression Tree (CART procedure was used to build a classification model on undiagnosed diabetes. A participant demonstrated evidence of diabetes according to WHO diabetes criteria. Exposure variables included demographics and socio-economic status. CART models were fitted using a randomly selected 70% of the data (training dataset, and area under the receiver operating characteristic curve (AUC was computed using the remaining 30% of the sample for evaluation (testing dataset. CART models were fitted using the training dataset, the oversampled training dataset, the weighted training dataset, and the undersampled training dataset. In addition, resampling case-to-control ratio of 1:1, 1:2, and 1:4 were examined. Resampling methods on the performance of other extensions of CART (random forests and generalized boosted trees were also examined. CARTs fitted on the oversampled (AUC = 0.70 and undersampled training data (AUC = 0.74 yielded a better classification power than that on the training data (AUC = 0.65. Resampling could also improve the classification power of random forests and generalized boosted trees. To conclude, applying resampling methods in a class-imbalanced dataset improved the classification power of CART, random forests

BASE MAP DATASET, INYO COUNTY, OKLAHOMA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme,...

BASE MAP DATASET, JACKSON COUNTY, OKLAHOMA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme,...

BASE MAP DATASET, KINGFISHER COUNTY, OKLAHOMA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme,...

Image segmentation evaluation for very-large datasets

Science.gov (United States)

Reeves, Anthony P.; Liu, Shuang; Xie, Yiting

2016-03-01

With the advent of modern machine learning methods and fully automated image analysis there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. Current approaches of visual inspection and manual markings do not scale well to big data. We present a new approach that depends on fully automated algorithm outcomes for segmentation documentation, requires no manual marking, and provides quantitative evaluation for computer algorithms. The documentation of new image segmentations and new algorithm outcomes are achieved by visual inspection. The burden of visual inspection on large datasets is minimized by (a) customized visualizations for rapid review and (b) reducing the number of cases to be reviewed through analysis of quantitative segmentation evaluation. This method has been applied to a dataset of 7,440 whole-lung CT images for 6 different segmentation algorithms designed to fully automatically facilitate the measurement of a number of very important quantitative image biomarkers. The results indicate that we could achieve 93% to 99% successful segmentation for these algorithms on this relatively large image database. The presented evaluation method may be scaled to much larger image databases.

A New Dataset Size Reduction Approach for PCA-Based Classification in OCR Application

Directory of Open Access Journals (Sweden)

Mohammad Amin Shayegan

2014-01-01

Full Text Available A major problem of pattern recognition systems is due to the large volume of training datasets including duplicate and similar training samples. In order to overcome this problem, some dataset size reduction and also dimensionality reduction techniques have been introduced. The algorithms presently used for dataset size reduction usually remove samples near to the centers of classes or support vector samples between different classes. However, the samples near to a class center include valuable information about the class characteristics and the support vector is important for evaluating system efficiency. This paper reports on the use of Modified Frequency Diagram technique for dataset size reduction. In this new proposed technique, a training dataset is rearranged and then sieved. The sieved training dataset along with automatic feature extraction/selection operation using Principal Component Analysis is used in an OCR application. The experimental results obtained when using the proposed system on one of the biggest handwritten Farsi/Arabic numeral standard OCR datasets, Hoda, show about 97% accuracy in the recognition rate. The recognition speed increased by 2.28 times, while the accuracy decreased only by 0.7%, when a sieved version of the dataset, which is only as half as the size of the initial training dataset, was used.

The CMS dataset bookkeeping service

Science.gov (United States)

Afaq, A.; Dolgert, A.; Guo, Y.; Jones, C.; Kosyakov, S.; Kuznetsov, V.; Lueking, L.; Riley, D.; Sekhri, V.

2008-07-01

The CMS Dataset Bookkeeping Service (DBS) has been developed to catalog all CMS event data from Monte Carlo and Detector sources. It provides the ability to identify MC or trigger source, track data provenance, construct datasets for analysis, and discover interesting data. CMS requires processing and analysis activities at various service levels and the DBS system provides support for localized processing or private analysis, as well as global access for CMS users at large. Catalog entries can be moved among the various service levels with a simple set of migration tools, thus forming a loose federation of databases. DBS is available to CMS users via a Python API, Command Line, and a Discovery web page interfaces. The system is built as a multi-tier web application with Java servlets running under Tomcat, with connections via JDBC to Oracle or MySQL database backends. Clients connect to the service through HTTP or HTTPS with authentication provided by GRID certificates and authorization through VOMS. DBS is an integral part of the overall CMS Data Management and Workflow Management systems.

The CMS dataset bookkeeping service

Energy Technology Data Exchange (ETDEWEB)

Afaq, A; Guo, Y; Kosyakov, S; Lueking, L; Sekhri, V [Fermilab, Batavia, Illinois 60510 (United States); Dolgert, A; Jones, C; Kuznetsov, V; Riley, D [Cornell University, Ithaca, New York 14850 (United States)

2008-07-15

The CMS Dataset Bookkeeping Service (DBS) has been developed to catalog all CMS event data from Monte Carlo and Detector sources. It provides the ability to identify MC or trigger source, track data provenance, construct datasets for analysis, and discover interesting data. CMS requires processing and analysis activities at various service levels and the DBS system provides support for localized processing or private analysis, as well as global access for CMS users at large. Catalog entries can be moved among the various service levels with a simple set of migration tools, thus forming a loose federation of databases. DBS is available to CMS users via a Python API, Command Line, and a Discovery web page interfaces. The system is built as a multi-tier web application with Java servlets running under Tomcat, with connections via JDBC to Oracle or MySQL database backends. Clients connect to the service through HTTP or HTTPS with authentication provided by GRID certificates and authorization through VOMS. DBS is an integral part of the overall CMS Data Management and Workflow Management systems.

The CMS dataset bookkeeping service

International Nuclear Information System (INIS)

Afaq, A; Guo, Y; Kosyakov, S; Lueking, L; Sekhri, V; Dolgert, A; Jones, C; Kuznetsov, V; Riley, D

2008-01-01

The CMS Dataset Bookkeeping Service (DBS) has been developed to catalog all CMS event data from Monte Carlo and Detector sources. It provides the ability to identify MC or trigger source, track data provenance, construct datasets for analysis, and discover interesting data. CMS requires processing and analysis activities at various service levels and the DBS system provides support for localized processing or private analysis, as well as global access for CMS users at large. Catalog entries can be moved among the various service levels with a simple set of migration tools, thus forming a loose federation of databases. DBS is available to CMS users via a Python API, Command Line, and a Discovery web page interfaces. The system is built as a multi-tier web application with Java servlets running under Tomcat, with connections via JDBC to Oracle or MySQL database backends. Clients connect to the service through HTTP or HTTPS with authentication provided by GRID certificates and authorization through VOMS. DBS is an integral part of the overall CMS Data Management and Workflow Management systems

The CMS dataset bookkeeping service

International Nuclear Information System (INIS)

Afaq, Anzar; Dolgert, Andrew; Guo, Yuyi; Jones, Chris; Kosyakov, Sergey; Kuznetsov, Valentin; Lueking, Lee; Riley, Dan; Sekhri, Vijay

2007-01-01

The CMS Dataset Bookkeeping Service (DBS) has been developed to catalog all CMS event data from Monte Carlo and Detector sources. It provides the ability to identify MC or trigger source, track data provenance, construct datasets for analysis, and discover interesting data. CMS requires processing and analysis activities at various service levels and the DBS system provides support for localized processing or private analysis, as well as global access for CMS users at large. Catalog entries can be moved among the various service levels with a simple set of migration tools, thus forming a loose federation of databases. DBS is available to CMS users via a Python API, Command Line, and a Discovery web page interfaces. The system is built as a multi-tier web application with Java servlets running under Tomcat, with connections via JDBC to Oracle or MySQL database backends. Clients connect to the service through HTTP or HTTPS with authentication provided by GRID certificates and authorization through VOMS. DBS is an integral part of the overall CMS Data Management and Workflow Management systems

Low aerial imagery – an assessment of georeferencing errors and the potential for use in environmental inventory

Directory of Open Access Journals (Sweden)

Smaczyński Maciej

2017-06-01

Full Text Available Unmanned aerial vehicles are increasingly being used in close range photogrammetry. Real-time observation of the Earth’s surface and the photogrammetric images obtained are used as material for surveying and environmental inventory. The following study was conducted on a small area (approximately 1 ha. In such cases, the classical method of topographic mapping is not accurate enough. The geodetic method of topographic surveying, on the other hand, is an overly precise measurement technique for the purpose of inventorying the natural environment components. The author of the following study has proposed using the unmanned aerial vehicle technology and tying in the obtained images to the control point network established with the aid of GNSS technology. Georeferencing the acquired images and using them to create a photogrammetric model of the studied area enabled the researcher to perform calculations, which yielded a total root mean square error below 9 cm. The performed comparison of the real lengths of the vectors connecting the control points and their lengths calculated on the basis of the photogrammetric model made it possible to fully confirm the RMSE calculated and prove the usefulness of the UAV technology in observing terrain components for the purpose of environmental inventory. Such environmental components include, among others, elements of road infrastructure, green areas, but also changes in the location of moving pedestrians and vehicles, as well as other changes in the natural environment that are not registered on classical base maps or topographic maps.

A cross-country Exchange Market Pressure (EMP dataset

Directory of Open Access Journals (Sweden)

Mohit Desai

2017-06-01

Full Text Available The data presented in this article are related to the research article titled - “An exchange market pressure measure for cross country analysis” (Patnaik et al. [1]. In this article, we present the dataset for Exchange Market Pressure values (EMP for 139 countries along with their conversion factors, ρ (rho. Exchange Market Pressure, expressed in percentage change in exchange rate, measures the change in exchange rate that would have taken place had the central bank not intervened. The conversion factor ρ can interpreted as the change in exchange rate associated with $1 billion of intervention. Estimates of conversion factor ρ allow us to calculate a monthly time series of EMP for 139 countries. Additionally, the dataset contains the 68% confidence interval (high and low values for the point estimates of ρ’s. Using the standard errors of estimates of ρ’s, we obtain one sigma intervals around mean estimates of EMP values. These values are also reported in the dataset.

A cross-country Exchange Market Pressure (EMP) dataset.

Science.gov (United States)

Desai, Mohit; Patnaik, Ila; Felman, Joshua; Shah, Ajay

2017-06-01

The data presented in this article are related to the research article titled - "An exchange market pressure measure for cross country analysis" (Patnaik et al. [1]). In this article, we present the dataset for Exchange Market Pressure values (EMP) for 139 countries along with their conversion factors, ρ (rho). Exchange Market Pressure, expressed in percentage change in exchange rate, measures the change in exchange rate that would have taken place had the central bank not intervened. The conversion factor ρ can interpreted as the change in exchange rate associated with $1 billion of intervention. Estimates of conversion factor ρ allow us to calculate a monthly time series of EMP for 139 countries. Additionally, the dataset contains the 68% confidence interval (high and low values) for the point estimates of ρ 's. Using the standard errors of estimates of ρ 's, we obtain one sigma intervals around mean estimates of EMP values. These values are also reported in the dataset.

Data publication and sharing using the SciDrive service

Science.gov (United States)

Mishin, Dmitry; Medvedev, D.; Szalay, A. S.; Plante, R. L.

2014-01-01

Despite the last years progress in scientific data storage, still remains the problem of public data storage and sharing system for relatively small scientific datasets. These are collections forming the “long tail” of power log datasets distribution. The aggregated size of the long tail data is comparable to the size of all data collections from large archives, and the value of data is significant. The SciDrive project's main goal is providing the scientific community with a place to reliably and freely store such data and provide access to it to broad scientific community. The primary target audience of the project is astoromy community, and it will be extended to other fields. We're aiming to create a simple way of publishing a dataset, which can be then shared with other people. Data owner controls the permissions to modify and access the data and can assign a group of users or open the access to everyone. The data contained in the dataset will be automaticaly recognized by a background process. Known data formats will be extracted according to the user's settings. Currently tabular data can be automatically extracted to the user's MyDB table where user can make SQL queries to the dataset and merge it with other public CasJobs resources. Other data formats can be processed using a set of plugins that upload the data or metadata to user-defined side services. The current implementation targets some of the data formats commonly used by the astronomy communities, including FITS, ASCII and Excel tables, TIFF images, and YT simulations data archives. Along with generic metadata, format-specific metadata is also processed. For example, basic information about celestial objects is extracted from FITS files and TIFF images, if present. A 100TB implementation has just been put into production at Johns Hopkins University. The system features public data storage REST service supporting VOSpace 2.0 and Dropbox protocols, HTML5 web portal, command-line client and Java

The NASA Subsonic Jet Particle Image Velocimetry (PIV) Dataset

Science.gov (United States)

Bridges, James; Wernet, Mark P.

2011-01-01

Many tasks in fluids engineering require prediction of turbulence of jet flows. The present document documents the single-point statistics of velocity, mean and variance, of cold and hot jet flows. The jet velocities ranged from 0.5 to 1.4 times the ambient speed of sound, and temperatures ranged from unheated to static temperature ratio 2.7. Further, the report assesses the accuracies of the data, e.g., establish uncertainties for the data. This paper covers the following five tasks: (1) Document acquisition and processing procedures used to create the particle image velocimetry (PIV) datasets. (2) Compare PIV data with hotwire and laser Doppler velocimetry (LDV) data published in the open literature. (3) Compare different datasets acquired at the same flow conditions in multiple tests to establish uncertainties. (4) Create a consensus dataset for a range of hot jet flows, including uncertainty bands. (5) Analyze this consensus dataset for self-consistency and compare jet characteristics to those of the open literature. The final objective was fulfilled by using the potential core length and the spread rate of the half-velocity radius to collapse of the mean and turbulent velocity fields over the first 20 jet diameters.

Herbarium collection of the Rio de Janeiro Botanical Garden (RB), Brazil.

Science.gov (United States)

Lanna, João M; da Silva, Luís Alexandre E; Morim, Marli P; Leitman, Paula M; Queiroz, Natália O; Filardi, Fabiana L R; Dalcin, Eduardo C; Oliveira, Felipe A; Forzza, Rafaela C

2018-01-01

This paper provides a quantitative and general description of the Rio de Janeiro Botanical Garden herbarium (RB) dataset. Created over a century ago, the RB currently comprises ca. 750,000 mounted specimens, with a strong representation of Brazilian flora, mainly from the Atlantic and Amazon forests. Nearly 100% of these specimens have been entered into the database and imaged and, at present, about 17% have been geo-referenced. This data paper is focused exclusively on RB's exsiccatae collection of land plants and algae, which is currently increasing by about twenty to thirty thousand specimens per year thanks to fieldwork, exchange and donations. Since 2005, many national and international projects have been implemented, improving the quality and accessibility of the collection. The most important facilitating factor in this process was the creation of the institutional system for plants collection and management, named JABOT. Since the RB is continuously growing, the dataset is updated weekly on SiBBr and GBIF portals. The most represented environments are the Atlantic and Amazon forests, a biodiversity hotspot and the world's largest rain forest, respectively. The dataset described in this article contains the data and metadata of plants and algae specimens in the RB collection and the link to access the respective images. Currently, the RB data is publicly available online at several biodiversity portals, such as our institutional database JABOT, the Reflora Virtual Herbarium, the SiBBr and the GBIF portal. However, a description of the RB dataset as a whole is not available in the literature.

Herbarium collection of the Rio de Janeiro Botanical Garden (RB), Brazil

Science.gov (United States)

da Silva, Luís Alexandre E; Morim, Marli P.; Leitman, Paula M.; Queiroz, Natália O.; Filardi, Fabiana L. R.; Dalcin, Eduardo C.; Oliveira, Felipe A.

2018-01-01

Abstract Background This paper provides a quantitative and general description of the Rio de Janeiro Botanical Garden herbarium (RB) dataset. Created over a century ago, the RB currently comprises ca. 750,000 mounted specimens, with a strong representation of Brazilian flora, mainly from the Atlantic and Amazon forests. Nearly 100% of these specimens have been entered into the database and imaged and, at present, about 17% have been geo-referenced. This data paper is focused exclusively on RB's exsiccatae collection of land plants and algae, which is currently increasing by about twenty to thirty thousand specimens per year thanks to fieldwork, exchange and donations. Since 2005, many national and international projects have been implemented, improving the quality and accessibility of the collection. The most important facilitating factor in this process was the creation of the institutional system for plants collection and management, named JABOT. Since the RB is continuously growing, the dataset is updated weekly on SiBBr and GBIF portals. New information The most represented environments are the Atlantic and Amazon forests, a biodiversity hotspot and the world's largest rain forest, respectively. The dataset described in this article contains the data and metadata of plants and algae specimens in the RB collection and the link to access the respective images. Currently, the RB data is publicly available online at several biodiversity portals, such as our institutional database JABOT, the Reflora Virtual Herbarium, the SiBBr and the GBIF portal. However, a description of the RB dataset as a whole is not available in the literature. PMID:29674937

Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network

Directory of Open Access Journals (Sweden)

Kindie Biredagn Nahato

2015-01-01

Full Text Available The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets.

Enhanced Publications: Data Models and Information Systems

Directory of Open Access Journals (Sweden)

Alessia Bardi

2014-04-01

Full Text Available “Enhanced publications” are commonly intended as digital publications that consist of a mandatory narrative part (the description of the research conducted plus related “parts”, such as datasets, other publications, images, tables, workflows, devices. The state-of-the-art on information systems for enhanced publications has today reached the point where some kind of common understanding is required, in order to provide the methodology and language for scientists to compare, analyse, or simply discuss the multitude of solutions in the field. In this paper, we thoroughly examined the literature with a two-fold aim: firstly, introducing the terminology required to describe and compare structural and semantic features of existing enhanced publication data models; secondly, proposing a classification of enhanced publication information systems based on their main functional goals.

Feature-Based Approach for the Registration of Pushbroom Imagery with Existing Orthophotos

Science.gov (United States)

Xiong, Weifeng

Low-cost Unmanned Airborne Vehicles (UAVs) are rapidly becoming suitable platforms for acquiring remote sensing data for a wide range of applications. For example, a UAV-based mobile mapping system (MMS) is emerging as a novel phenotyping tool that delivers several advantages to alleviate the drawbacks of conventional manual plant trait measurements. Moreover, UAVs equipped with direct geo-referenced frame cameras and pushbroom scanners can acquire geospatial data for comprehensive high-throughput phenotyping. UAVs for mobile mapping platforms are low-cost and easy to use, can fly closer to the objects, and are filling an important gap between ground wheel-based and traditional manned-airborne platforms. However, consumer-grade UAVs are capable of carrying only equipment with a relatively light payload and their flying time is determined by a limited battery life. These restrictions of UAVs unfortunately force potential users to adopt lower-quality direct geo-referencing and imaging systems that may negatively impact the quality of the deliverables. Recent advances in sensor calibration and automated triangulation have made it feasible to obtain accurate mapping using low-cost camera systems equipped with consumer-grade GNSS/INS units. However, ortho-rectification of the data from a linear-array scanner is challenging for low-cost UAV systems, because the derived geo-location information from pushbroom sensors is quite sensitive to the performance of the implemented direct geo-referencing unit. This thesis presents a novel approach for improving the ortho-rectification of hyperspectral pushbroom scanner imagery with the aid of orthophotos generated from frame cameras through the identification of conjugate features while modeling the impact of residual artifacts in the direct geo-referencing information. The experimental results qualitatively and quantitatively proved the feasibility of the proposed methodology in improving the geo-referencing accuracy of real

A dataset of multiresolution functional brain parcellations in an elderly population with no or mild cognitive impairment

Directory of Open Access Journals (Sweden)

Angela Tam

2016-12-01

Full Text Available We present group eight resolutions of brain parcellations for clusters generated from resting-state functional magnetic resonance images for 99 cognitively normal elderly persons and 129 patients with mild cognitive impairment, pooled from four independent datasets. This dataset was generated as part of the following study: Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies (Tam et al., 2015 [1]. The brain parcellations have been registered to both symmetric and asymmetric MNI brain templates and generated using a method called bootstrap analysis of stable clusters (BASC (Bellec et al., 2010 [2]. We present two variants of these parcellations. One variant contains bihemisphereic parcels (4, 6, 12, 22, 33, 65, 111, and 208 total parcels across eight resolutions. The second variant contains spatially connected regions of interest (ROIs that span only one hemisphere (10, 17, 30, 51, 77, 199, and 322 total ROIs across eight resolutions. We also present maps illustrating functional connectivity differences between patients and controls for four regions of interest (striatum, dorsal prefrontal cortex, middle temporal lobe, and medial frontal cortex. The brain parcels and associated statistical maps have been publicly released as 3D volumes, available in .mnc and .nii file formats on figshare and on Neurovault. Finally, the code used to generate this dataset is available on Github.

Spatially-explicit estimation of geographical representation in large-scale species distribution datasets.

Science.gov (United States)

Kalwij, Jesse M; Robertson, Mark P; Ronk, Argo; Zobel, Martin; Pärtel, Meelis

2014-01-01

Much ecological research relies on existing multispecies distribution datasets. Such datasets, however, can vary considerably in quality, extent, resolution or taxonomic coverage. We provide a framework for a spatially-explicit evaluation of geographical representation within large-scale species distribution datasets, using the comparison of an occurrence atlas with a range atlas dataset as a working example. Specifically, we compared occurrence maps for 3773 taxa from the widely-used Atlas Florae Europaeae (AFE) with digitised range maps for 2049 taxa of the lesser-known Atlas of North European Vascular Plants. We calculated the level of agreement at a 50-km spatial resolution using average latitudinal and longitudinal species range, and area of occupancy. Agreement in species distribution was calculated and mapped using Jaccard similarity index and a reduced major axis (RMA) regression analysis of species richness between the entire atlases (5221 taxa in total) and between co-occurring species (601 taxa). We found no difference in distribution ranges or in the area of occupancy frequency distribution, indicating that atlases were sufficiently overlapping for a valid comparison. The similarity index map showed high levels of agreement for central, western, and northern Europe. The RMA regression confirmed that geographical representation of AFE was low in areas with a sparse data recording history (e.g., Russia, Belarus and the Ukraine). For co-occurring species in south-eastern Europe, however, the Atlas of North European Vascular Plants showed remarkably higher richness estimations. Geographical representation of atlas data can be much more heterogeneous than often assumed. Level of agreement between datasets can be used to evaluate geographical representation within datasets. Merging atlases into a single dataset is worthwhile in spite of methodological differences, and helps to fill gaps in our knowledge of species distribution ranges. Species distribution

The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset

Science.gov (United States)

Huffman, George J.; Adler, Robert F.; Arkin, Philip; Chang, Alfred; Ferraro, Ralph; Gruber, Arnold; Janowiak, John; McNab, Alan; Rudolf, Bruno; Schneider, Udo

1997-01-01

The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit -satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5 deg x 2.5 deg latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino-Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.

A new dataset and algorithm evaluation for mood estimation in music

OpenAIRE

Godec, Primož

2014-01-01

This thesis presents a new dataset of perceived and induced emotions for 200 audio clips. The gathered dataset provides users' perceived and induced emotions for each clip, the association of color, along with demographic and personal data, such as user's emotion state and emotion ratings, genre preference, music experience, among others. With an online survey we collected more than 7000 responses for a dataset of 200 audio excerpts, thus providing about 37 user responses per clip. The foc...

A Large-Scale 3D Object Recognition dataset

DEFF Research Database (Denmark)

Sølund, Thomas; Glent Buch, Anders; Krüger, Norbert

2016-01-01

geometric groups; concave, convex, cylindrical and flat 3D object models. The object models have varying amount of local geometric features to challenge existing local shape feature descriptors in terms of descriptiveness and robustness. The dataset is validated in a benchmark which evaluates the matching...... performance of 7 different state-of-the-art local shape descriptors. Further, we validate the dataset in a 3D object recognition pipeline. Our benchmark shows as expected that local shape feature descriptors without any global point relation across the surface have a poor matching performance with flat...

The Wind Integration National Dataset (WIND) toolkit (Presentation)

Energy Technology Data Exchange (ETDEWEB)

Caroline Draxl: NREL

2014-01-01

Regional wind integration studies require detailed wind power output data at many locations to perform simulations of how the power system will operate under high penetration scenarios. The wind datasets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as being time synchronized with available load profiles.As described in this presentation, the WIND Toolkit fulfills these requirements by providing a state-of-the-art national (US) wind resource, power production and forecast dataset.

An integrated pan-tropical biomass map using multiple reference datasets

NARCIS (Netherlands)

Avitabile, V.; Herold, M.; Heuvelink, G.B.M.; Lewis, S.L.; Phillips, O.L.; Asner, G.P.; Armston, J.; Asthon, P.; Banin, L.F.; Bayol, N.; Berry, N.; Boeckx, P.; Jong, De B.; Devries, B.; Girardin, C.; Kearsley, E.; Lindsell, J.A.; Lopez-gonzalez, G.; Lucas, R.; Malhi, Y.; Morel, A.; Mitchard, E.; Nagy, L.; Qie, L.; Quinones, M.; Ryan, C.M.; Slik, F.; Sunderland, T.; Vaglio Laurin, G.; Valentini, R.; Verbeeck, H.; Wijaya, A.; Willcock, S.

2016-01-01

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of

Researcher perspectives on publication and peer review of data.

Directory of Open Access Journals (Sweden)

John Ernest Kratz

Full Text Available Data "publication" seeks to appropriate the prestige of authorship in the peer-reviewed literature to reward researchers who create useful and well-documented datasets. The scholarly communication community has embraced data publication as an incentive to document and share data. But, numerous new and ongoing experiments in implementation have not yet resolved what a data publication should be, when data should be peer-reviewed, or how data peer review should work. While researchers have been surveyed extensively regarding data management and sharing, their perceptions and expectations of data publication are largely unknown. To bring this important yet neglected perspective into the conversation, we surveyed ∼ 250 researchers across the sciences and social sciences- asking what expectations"data publication" raises and what features would be useful to evaluate the trustworthiness, evaluate the impact, and enhance the prestige of a data publication. We found that researcher expectations of data publication center on availability, generally through an open database or repository. Few respondents expected published data to be peer-reviewed, but peer-reviewed data enjoyed much greater trust and prestige. The importance of adequate metadata was acknowledged, in that almost all respondents expected data peer review to include evaluation of the data's documentation. Formal citation in the reference list was affirmed by most respondents as the proper way to credit dataset creators. Citation count was viewed as the most useful measure of impact, but download count was seen as nearly as valuable. These results offer practical guidance for data publishers seeking to meet researcher expectations and enhance the value of published data.

Provenance of Earth Science Datasets - How Deep Should One Go?

Science.gov (United States)

Ramapriyan, H.; Manipon, G. J. M.; Aulenbach, S.; Duggan, B.; Goldstein, J.; Hua, H.; Tan, D.; Tilmes, C.; Wilson, B. D.; Wolfe, R.; Zednik, S.

2015-12-01

For credibility of scientific research, transparency and reproducibility are essential. This fundamental tenet has been emphasized for centuries, and has been receiving increased attention in recent years. The Office of Management and Budget (2002) addressed reproducibility and other aspects of quality and utility of information from federal agencies. Specific guidelines from NASA (2002) are derived from the above. According to these guidelines, "NASA requires a higher standard of quality for information that is considered influential. Influential scientific, financial, or statistical information is defined as NASA information that, when disseminated, will have or does have clear and substantial impact on important public policies or important private sector decisions." For information to be compliant, "the information must be transparent and reproducible to the greatest possible extent." We present how the principles of transparency and reproducibility have been applied to NASA data supporting the Third National Climate Assessment (NCA3). The depth of trace needed of provenance of data used to derive conclusions in NCA3 depends on how the data were used (e.g., qualitatively or quantitatively). Given that the information is diligently maintained in the agency archives, it is possible to trace from a figure in the publication through the datasets, specific files, algorithm versions, instruments used for data collection, and satellites, as well as the individuals and organizations involved in each step. Such trace back permits transparency and reproducibility.

Connecting the Public to Scientific Research Data - Science On a Sphere°

Science.gov (United States)

Henderson, M. A.; Russell, E. L.; Science on a Sphere Datasets

2011-12-01

Connecting the Public to Scientific Research Data - Science On a Sphere° Maurice Henderson, NASA Goddard Space Flight Center Elizabeth Russell, NOAA Earth System Research Laboratory, University of Colorado Cooperative Institute for Research in Environmental Sciences Science On a Sphere° is a six foot animated globe developed by the National Ocean and Atmospheric Administration, NOAA, as a means to display global scientific research data in an intuitive, engaging format in public forums. With over 70 permanent installations of SOS around the world in science museums, visitor's centers and universities, the audience that enjoys SOS yearly is substantial, wide-ranging, and diverse. Through partnerships with the National Aeronautics and Space Administration, NASA, the SOS Data Catalog (http://sos.noaa.gov/datasets/) has grown to a collection of over 350 datasets from NOAA, NASA, and many others. Using an external projection system, these datasets are displayed onto the sphere creating a seamless global image. In a cross-site evaluation of Science On a Sphere°, 82% of participants said yes, seeing information displayed on a sphere changed their understanding of the information. This unique technology captivates viewers and exposes them to scientific research data in a way that is accessible, presentable, and understandable. The datasets that comprise the SOS Data Catalog are scientific research data that have been formatted for display on SOS. By formatting research data into visualizations that can be used on SOS, NOAA and NASA are able to turn research data into educational materials that are easily accessible for users. In many cases, visualizations do not need to be modified because SOS uses a common map projection. The SOS Data Catalog has become a "one-stop shop" for a broad range of global datasets from across NOAA and NASA, and as a result, the traffic on the site is more than just SOS users. While the target audience for this site is SOS users, many

Dataset on daytime outdoor thermal comfort for Belo Horizonte, Brazil.

Science.gov (United States)

Hirashima, Simone Queiroz da Silveira; Assis, Eleonora Sad de; Nikolopoulou, Marialena

2016-12-01

This dataset describe microclimatic parameters of two urban open public spaces in the city of Belo Horizonte, Brazil; physiological equivalent temperature (PET) index values and the related subjective responses of interviewees regarding thermal sensation perception and preference and thermal comfort evaluation. Individuals and behavioral characteristics of respondents were also presented. Data were collected at daytime, in summer and winter, 2013. Statistical treatment of this data was firstly presented in a PhD Thesis ("Percepção sonora e térmica e avaliação de conforto em espaços urbanos abertos do município de Belo Horizonte - MG, Brasil" (Hirashima, 2014) [1]), providing relevant information on thermal conditions in these locations and on thermal comfort assessment. Up to now, this data was also explored in the article "Daytime Thermal Comfort in Urban Spaces: A Field Study in Brazil" (Hirashima et al., in press) [2]. These references are recommended for further interpretation and discussion.

Comparison of global 3-D aviation emissions datasets

Directory of Open Access Journals (Sweden)

S. C. Olsen

2013-01-01

Full Text Available Aviation emissions are unique from other transportation emissions, e.g., from road transportation and shipping, in that they occur at higher altitudes as well as at the surface. Aviation emissions of carbon dioxide, soot, and water vapor have direct radiative impacts on the Earth's climate system while emissions of nitrogen oxides (NO_x, sulfur oxides, carbon monoxide (CO, and hydrocarbons (HC impact air quality and climate through their effects on ozone, methane, and clouds. The most accurate estimates of the impact of aviation on air quality and climate utilize three-dimensional chemistry-climate models and gridded four dimensional (space and time aviation emissions datasets. We compare five available aviation emissions datasets currently and historically used to evaluate the impact of aviation on climate and air quality: NASA-Boeing 1992, NASA-Boeing 1999, QUANTIFY 2000, Aero2k 2002, and AEDT 2006 and aviation fuel usage estimates from the International Energy Agency. Roughly 90% of all aviation emissions are in the Northern Hemisphere and nearly 60% of all fuelburn and NO_x emissions occur at cruise altitudes in the Northern Hemisphere. While these datasets were created by independent methods and are thus not strictly suitable for analyzing trends they suggest that commercial aviation fuelburn and NO_x emissions increased over the last two decades while HC emissions likely decreased and CO emissions did not change significantly. The bottom-up estimates compared here are consistently lower than International Energy Agency fuelburn statistics although the gap is significantly smaller in the more recent datasets. Overall the emissions distributions are quite similar for fuelburn and NO_x with regional peaks over the populated land masses of North America, Europe, and East Asia. For CO and HC there are relatively larger differences. There are however some distinct differences in the altitude distribution

Integration of public procurement data using linked data

Directory of Open Access Journals (Sweden)

Jindrich Mynarz

2014-10-01

Full Text Available Linked data is frequently casted as a technology for performing integration of distributed datasets on the Web. In this paper, we propose a generic workflow for data integration based on linked data and semantic web technologies. The workflow comes out of an analysis of the application of linked data to integration of public procurement data. It organizes common data integration tasks, including schema alignment, data translation, entity reconciliation, and data fusion, into a sequence of repeatable steps towards achieving a unified view of the combined datasets. We propose this workflow with the main goal of improving the usability of data and its value for data analyses. Specifically, we target the domain of public procurement and its characteristic challenges this application area poses to data integration. As the paper progresses through the individual steps of the integration workflow, it highlights the key concerns and proposes solutions based on linked data to address these concerns. The discussed data integration methods are illustrated and evaluated on a running example describing integration of public procurement data from the Czech Republic. Using a motivating example of an analytical query on public procurement data we demonstrate how data integration enables to reveal a likely case of clientelism between a contracting authority and its supplier, which is validated by previous coverage in the media.

Glyco-centric lectin magnetic bead array (LeMBA − proteomics dataset of human serum samples from healthy, Barrett׳s esophagus and esophageal adenocarcinoma individuals

Directory of Open Access Journals (Sweden)

Alok K. Shah

2016-06-01

Full Text Available This data article describes serum glycoprotein biomarker discovery and qualification datasets generated using lectin magnetic bead array (LeMBA – mass spectrometry techniques, “Serum glycoprotein biomarker discovery and qualification pipeline reveals novel diagnostic biomarker candidates for esophageal adenocarcinoma” [1]. Serum samples collected from healthy, metaplastic Barrett׳s esophagus (BE and esophageal adenocarcinoma (EAC individuals were profiled for glycoprotein subsets via differential lectin binding. The biomarker discovery proteomics dataset consisting of 20 individual lectin pull-downs for 29 serum samples with a spiked-in internal standard chicken ovalbumin protein has been deposited in the PRIDE partner repository of the ProteomeXchange Consortium with the data set identifier PRIDE: http://www.ebi.ac.uk/pride/archive/projects/PXD002442. Annotated MS/MS spectra for the peptide identifications can be viewed using MS-Viewer (〈http://prospector2.ucsf.edu/prospector/cgi-bin/msform.cgi?form=msviewer〉 using search key “jn7qafftux”. The qualification dataset contained 6-lectin pulldown-coupled multiple reaction monitoring-mass spectrometry (MRM-MS data for 41 protein candidates, from 60 serum samples. This dataset is available as a supplemental files with the original publication [1].

Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat

Data.gov (United States)

National Aeronautics and Space Administration — The Global Human Built-up And Settlement Extent (HBASE) Dataset from Landsat is a global map of HBASE derived from the Global Land Survey (GLS) Landsat dataset for...

The Lunar Source Disk: Old Lunar Datasets on a New CD-ROM

Science.gov (United States)

Hiesinger, H.

1998-01-01

A compilation of previously published datasets on CD-ROM is presented. This Lunar Source Disk is intended to be a first step in the improvement/expansion of the Lunar Consortium Disk, in order to create an "image-cube"-like data pool that can be easily accessed and might be useful for a variety of future lunar investigations. All datasets were transformed to a standard map projection that allows direct comparison of different types of information on a pixel-by pixel basis. Lunar observations have a long history and have been important to mankind for centuries, notably since the work of Plutarch and Galileo. As a consequence of centuries of lunar investigations, knowledge of the characteristics and properties of the Moon has accumulated over time. However, a side effect of this accumulation is that it has become more and more complicated for scientists to review all the datasets obtained through different techniques, to interpret them properly, to recognize their weaknesses and strengths in detail, and to combine them synoptically in geologic interpretations. Such synoptic geologic interpretations are crucial for the study of planetary bodies through remote-sensing data in order to avoid misinterpretation. In addition, many of the modem datasets, derived from Earth-based telescopes as well as from spacecraft missions, are acquired at different geometric and radiometric conditions. These differences make it challenging to compare or combine datasets directly or to extract information from different datasets on a pixel-by-pixel basis. Also, as there is no convention for the presentation of lunar datasets, different authors choose different map projections, depending on the location of the investigated areas and their personal interests. Insufficient or incomplete information on the map parameters used by different authors further complicates the reprojection of these datasets to a standard geometry. The goal of our efforts was to transfer previously published lunar

Geoinformation web-system for processing and visualization of large archives of geo-referenced data

Science.gov (United States)

Gordov, E. P.; Okladnikov, I. G.; Titov, A. G.; Shulgina, T. M.

2010-12-01

Developed working model of information-computational system aimed at scientific research in area of climate change is presented. The system will allow processing and analysis of large archives of geophysical data obtained both from observations and modeling. Accumulated experience of developing information-computational web-systems providing computational processing and visualization of large archives of geo-referenced data was used during the implementation (Gordov et al, 2007; Okladnikov et al, 2008; Titov et al, 2009). Functional capabilities of the system comprise a set of procedures for mathematical and statistical analysis, processing and visualization of data. At present five archives of data are available for processing: 1st and 2nd editions of NCEP/NCAR Reanalysis, ECMWF ERA-40 Reanalysis, JMA/CRIEPI JRA-25 Reanalysis, and NOAA-CIRES XX Century Global Reanalysis Version I. To provide data processing functionality a computational modular kernel and class library providing data access for computational modules were developed. Currently a set of computational modules for climate change indices approved by WMO is available. Also a special module providing visualization of results and writing to Encapsulated Postscript, GeoTIFF and ESRI shape files was developed. As a technological basis for representation of cartographical information in Internet the GeoServer software conforming to OpenGIS standards is used. Integration of GIS-functionality with web-portal software to provide a basis for web-portal’s development as a part of geoinformation web-system is performed. Such geoinformation web-system is a next step in development of applied information-telecommunication systems offering to specialists from various scientific fields unique opportunities of performing reliable analysis of heterogeneous geophysical data using approved computational algorithms. It will allow a wide range of researchers to work with geophysical data without specific programming

Proposed structure of a data paper structure as scientific publication

Directory of Open Access Journals (Sweden)

Sandra M. Roa-Martínez

2017-03-01

Full Text Available This paper presents a review of the main motivations and paths for publishing datasets that are generated and managed during the research process. The Data Paper is considered as a form of scientific publication with the same recognition, acceptance and scientific rigor as conventional research articles. Therefore we propose a common structure defined by elements based mainly on dataset metadata. This will enable creators, publishers, consumers and expert peer reviewers to recognise, share, evaluate and facilitate data reuse. Doing so will facilitate information reproducibility, validation of results, and rapid new research generation.

Gridded 5km GHCN-Daily Temperature and Precipitation Dataset, Version 1

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Gridded 5km GHCN-Daily Temperature and Precipitation Dataset (nClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature,...

ENHANCED DATA DISCOVERABILITY FOR IN SITU HYPERSPECTRAL DATASETS

Directory of Open Access Journals (Sweden)

B. Rasaiah

2016-06-01

Full Text Available Field spectroscopic metadata is a central component in the quality assurance, reliability, and discoverability of hyperspectral data and the products derived from it. Cataloguing, mining, and interoperability of these datasets rely upon the robustness of metadata protocols for field spectroscopy, and on the software architecture to support the exchange of these datasets. Currently no standard for in situ spectroscopy data or metadata protocols exist. This inhibits the effective sharing of growing volumes of in situ spectroscopy datasets, to exploit the benefits of integrating with the evolving range of data sharing platforms. A core metadataset for field spectroscopy was introduced by Rasaiah et al., (2011-2015 with extended support for specific applications. This paper presents a prototype model for an OGC and ISO compliant platform-independent metadata discovery service aligned to the specific requirements of field spectroscopy. In this study, a proof-of-concept metadata catalogue has been described and deployed in a cloud-based architecture as a demonstration of an operationalized field spectroscopy metadata standard and web-based discovery service.

Environmental Dataset Gateway (EDG) CS-W Interface

Data.gov (United States)

U.S. Environmental Protection Agency — Use the Environmental Dataset Gateway (EDG) to find and access EPA's environmental resources. Many options are available for easily reusing EDG content in other...

Annotating spatio-temporal datasets for meaningful analysis in the Web

Science.gov (United States)

Stasch, Christoph; Pebesma, Edzer; Scheider, Simon

2014-05-01

More and more environmental datasets that vary in space and time are available in the Web. This comes along with an advantage of using the data for other purposes than originally foreseen, but also with the danger that users may apply inappropriate analysis procedures due to lack of important assumptions made during the data collection process. In order to guide towards a meaningful (statistical) analysis of spatio-temporal datasets available in the Web, we have developed a Higher-Order-Logic formalism that captures some relevant assumptions in our previous work [1]. It allows to proof on meaningful spatial prediction and aggregation in a semi-automated fashion. In this poster presentation, we will present a concept for annotating spatio-temporal datasets available in the Web with concepts defined in our formalism. Therefore, we have defined a subset of the formalism as a Web Ontology Language (OWL) pattern. It allows capturing the distinction between the different spatio-temporal variable types, i.e. point patterns, fields, lattices and trajectories, that in turn determine whether a particular dataset can be interpolated or aggregated in a meaningful way using a certain procedure. The actual annotations that link spatio-temporal datasets with the concepts in the ontology pattern are provided as Linked Data. In order to allow data producers to add the annotations to their datasets, we have implemented a Web portal that uses a triple store at the backend to store the annotations and to make them available in the Linked Data cloud. Furthermore, we have implemented functions in the statistical environment R to retrieve the RDF annotations and, based on these annotations, to support a stronger typing of spatio-temporal datatypes guiding towards a meaningful analysis in R. [1] Stasch, C., Scheider, S., Pebesma, E., Kuhn, W. (2014): "Meaningful spatial prediction and aggregation", Environmental Modelling & Software, 51, 149-165.

Evolving hard problems: Generating human genetics datasets with a complex etiology

Directory of Open Access Journals (Sweden)

Himmelstein Daniel S

2011-07-01

Full Text Available Abstract Background A goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models. Results Here we develop and evaluate a model free evolution strategy to generate datasets which display a complex relationship between individual genotype and disease susceptibility. We show that this model free approach is capable of generating a diverse array of datasets with distinct gene-disease relationships for an arbitrary interaction order and sample size. We specifically generate eight-hundred Pareto fronts; one for each independent run of our algorithm. In each run the predictiveness of single genetic variation and pairs of genetic variants have been minimized, while the predictiveness of third, fourth, or fifth-order combinations is maximized. Two hundred runs of the algorithm are further dedicated to creating datasets with predictive four or five order interactions and minimized lower-level effects. Conclusions This method and the resulting datasets will allow the capabilities of novel methods to be tested without pre-specified genetic models. This allows researchers to evaluate which methods will succeed on human genetics problems where the model is not known in advance. We further make freely available to the community the entire Pareto-optimal front of datasets from each run so that novel methods may be rigorously evaluated. These 76,600 datasets are available from http://discovery.dartmouth.edu/model_free_data/.

A Dataset from TIMSS to Examine the Relationship between Computer Use and Mathematics Achievement

Science.gov (United States)

Kadijevich, Djordje M.

2015-01-01

Because the relationship between computer use and achievement is still puzzling, there is a need to prepare and analyze good quality datasets on computer use and achievement. Such a dataset can be derived from TIMSS data. This paper describes how this dataset can be prepared. It also gives an example of how the dataset may be analyzed. The…

Ontology-based meta-analysis of global collections of high-throughput public data.

Directory of Open Access Journals (Sweden)

Ilya Kupershmidt

2010-09-01

Full Text Available The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.

Ontology-based meta-analysis of global collections of high-throughput public data.

Science.gov (United States)

Kupershmidt, Ilya; Su, Qiaojuan Jane; Grewal, Anoop; Sundaresh, Suman; Halperin, Inbal; Flynn, James; Shekar, Mamatha; Wang, Helen; Park, Jenny; Cui, Wenwu; Wall, Gregory D; Wisotzkey, Robert; Alag, Satnam; Akhtari, Saeid; Ronaghi, Mostafa

2010-09-29

The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today. We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets. Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.

Developing Verification Systems for Building Information Models of Heritage Buildings with Heterogeneous Datasets

Science.gov (United States)

Chow, L.; Fai, S.

2017-08-01

The digitization and abstraction of existing buildings into building information models requires the translation of heterogeneous datasets that may include CAD, technical reports, historic texts, archival drawings, terrestrial laser scanning, and photogrammetry into model elements. In this paper, we discuss a project undertaken by the Carleton Immersive Media Studio (CIMS) that explored the synthesis of heterogeneous datasets for the development of a building information model (BIM) for one of Canada's most significant heritage assets - the Centre Block of the Parliament Hill National Historic Site. The scope of the project included the development of an as-found model of the century-old, six-story building in anticipation of specific model uses for an extensive rehabilitation program. The as-found Centre Block model was developed in Revit using primarily point cloud data from terrestrial laser scanning. The data was captured by CIMS in partnership with Heritage Conservation Services (HCS), Public Services and Procurement Canada (PSPC), using a Leica C10 and P40 (exterior and large interior spaces) and a Faro Focus (small to mid-sized interior spaces). Secondary sources such as archival drawings, photographs, and technical reports were referenced in cases where point cloud data was not available. As a result of working with heterogeneous data sets, a verification system was introduced in order to communicate to model users/viewers the source of information for each building element within the model.

Learning analytics: Dataset for empirical evaluation of entry requirements into engineering undergraduate programs in a Nigerian university.

Science.gov (United States)

Odukoya, Jonathan A; Popoola, Segun I; Atayero, Aderemi A; Omole, David O; Badejo, Joke A; John, Temitope M; Olowo, Olalekan O

2018-04-01

In Nigerian universities, enrolment into any engineering undergraduate program requires that the minimum entry criteria established by the National Universities Commission (NUC) must be satisfied. Candidates seeking admission to study engineering discipline must have reached a predetermined entry age and met the cut-off marks set for Senior School Certificate Examination (SSCE), Unified Tertiary Matriculation Examination (UTME), and the post-UTME screening. However, limited effort has been made to show that these entry requirements eventually guarantee successful academic performance in engineering programs because the data required for such validation are not readily available. In this data article, a comprehensive dataset for empirical evaluation of entry requirements into engineering undergraduate programs in a Nigerian university is presented and carefully analyzed. A total sample of 1445 undergraduates that were admitted between 2005 and 2009 to study Chemical Engineering (CHE), Civil Engineering (CVE), Computer Engineering (CEN), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mechanical Engineering (MEE), and Petroleum Engineering (PET) at Covenant University, Nigeria were randomly selected. Entry age, SSCE aggregate, UTME score, Covenant University Scholastic Aptitude Screening (CUSAS) score, and the Cumulative Grade Point Average (CGPA) of the undergraduates were obtained from the Student Records and Academic Affairs unit. In order to facilitate evidence-based evaluation, the robust dataset is made publicly available in a Microsoft Excel spreadsheet file. On yearly basis, first-order descriptive statistics of the dataset are presented in tables. Box plot representations, frequency distribution plots, and scatter plots of the dataset are provided to enrich its value. Furthermore, correlation and linear regression analyses are performed to understand the relationship between the entry requirements and the

MOBBED: a computational data infrastructure for handling large collections of event-rich time series datasets in MATLAB.

Science.gov (United States)

Cockfield, Jeremy; Su, Kyungmin; Robbins, Kay A

2013-01-01

Experiments to monitor human brain activity during active behavior record a variety of modalities (e.g., EEG, eye tracking, motion capture, respiration monitoring) and capture a complex environmental context leading to large, event-rich time series datasets. The considerable variability of responses within and among subjects in more realistic behavioral scenarios requires experiments to assess many more subjects over longer periods of time. This explosion of data requires better computational infrastructure to more systematically explore and process these collections. MOBBED is a lightweight, easy-to-use, extensible toolkit that allows users to incorporate a computational database into their normal MATLAB workflow. Although capable of storing quite general types of annotated data, MOBBED is particularly oriented to multichannel time series such as EEG that have event streams overlaid with sensor data. MOBBED directly supports access to individual events, data frames, and time-stamped feature vectors, allowing users to ask questions such as what types of events or features co-occur under various experimental conditions. A database provides several advantages not available to users who process one dataset at a time from the local file system. In addition to archiving primary data in a central place to save space and avoid inconsistencies, such a database allows users to manage, search, and retrieve events across multiple datasets without reading the entire dataset. The database also provides infrastructure for handling more complex event patterns that include environmental and contextual conditions. The database can also be used as a cache for expensive intermediate results that are reused in such activities as cross-validation of machine learning algorithms. MOBBED is implemented over PostgreSQL, a widely used open source database, and is freely available under the GNU general public license at http://visual.cs.utsa.edu/mobbed. Source and issue reports for MOBBED

A new dataset validation system for the Planetary Science Archive

Science.gov (United States)

Manaud, N.; Zender, J.; Heather, D.; Martinez, S.

2007-08-01

The Planetary Science Archive is the official archive for the Mars Express mission. It has received its first data by the end of 2004. These data are delivered by the PI teams to the PSA team as datasets, which are formatted conform to the Planetary Data System (PDS). The PI teams are responsible for analyzing and calibrating the instrument data as well as the production of reduced and calibrated data. They are also responsible of the scientific validation of these data. ESA is responsible of the long-term data archiving and distribution to the scientific community and must ensure, in this regard, that all archived products meet quality. To do so, an archive peer-review is used to control the quality of the Mars Express science data archiving process. However a full validation of its content is missing. An independent review board recently recommended that the completeness of the archive as well as the consistency of the delivered data should be validated following well-defined procedures. A new validation software tool is being developed to complete the overall data quality control system functionality. This new tool aims to improve the quality of data and services provided to the scientific community through the PSA, and shall allow to track anomalies in and to control the completeness of datasets. It shall ensure that the PSA end-users: (1) can rely on the result of their queries, (2) will get data products that are suitable for scientific analysis, (3) can find all science data acquired during a mission. We defined dataset validation as the verification and assessment process to check the dataset content against pre-defined top-level criteria, which represent the general characteristics of good quality datasets. The dataset content that is checked includes the data and all types of information that are essential in the process of deriving scientific results and those interfacing with the PSA database. The validation software tool is a multi-mission tool that

Data Recommender: An Alternative Way to Discover Open Scientific Datasets

Science.gov (United States)

Klump, J. F.; Devaraju, A.; Williams, G.; Hogan, D.; Davy, R.; Page, J.; Singh, D.; Peterson, N.

2017-12-01

Over the past few years, institutions and government agencies have adopted policies to openly release their data, which has resulted in huge amounts of open data becoming available on the web. When trying to discover the data, users face two challenges: an overload of choice and the limitations of the existing data search tools. On the one hand, there are too many datasets to choose from, and therefore, users need to spend considerable effort to find the datasets most relevant to their research. On the other hand, data portals commonly offer keyword and faceted search, which depend fully on the user queries to search and rank relevant datasets. Consequently, keyword and faceted search may return loosely related or irrelevant results, although the results may contain the same query. They may also return highly specific results that depend more on how well metadata was authored. They do not account well for variance in metadata due to variance in author styles and preferences. The top-ranked results may also come from the same data collection, and users are unlikely to discover new and interesting datasets. These search modes mainly suits users who can express their information needs in terms of the structure and terminology of the data portals, but may pose a challenge otherwise. The above challenges reflect that we need a solution that delivers the most relevant (i.e., similar and serendipitous) datasets to users, beyond the existing search functionalities on the portals. A recommender system is an information filtering system that presents users with relevant and interesting contents based on users' context and preferences. Delivering data recommendations to users can make data discovery easier, and as a result may enhance user engagement with the portal. We developed a hybrid data recommendation approach for the CSIRO Data Access Portal. The approach leverages existing recommendation techniques (e.g., content-based filtering and item co-occurrence) to produce

Projected outcomes of a public-industry partnership for enhancing corn nitrogen research and datasets

Science.gov (United States)

Research is needed over a wide geographic range of soil and weather scenarios to evaluate methods and tools for corn N fertilizer applications. The objectives of this research were to conduct standardized corn N rate response field studies to evaluate the performance of multiple public-domain N deci...

Data assimilation and model evaluation experiment datasets

Science.gov (United States)

Lai, Chung-Cheng A.; Qian, Wen; Glenn, Scott M.

1994-01-01

The Institute for Naval Oceanography, in cooperation with Naval Research Laboratories and universities, executed the Data Assimilation and Model Evaluation Experiment (DAMEE) for the Gulf Stream region during fiscal years 1991-1993. Enormous effort has gone into the preparation of several high-quality and consistent datasets for model initialization and verification. This paper describes the preparation process, the temporal and spatial scopes, the contents, the structure, etc., of these datasets. The goal of DAMEE and the need of data for the four phases of experiment are briefly stated. The preparation of DAMEE datasets consisted of a series of processes: (1) collection of observational data; (2) analysis and interpretation; (3) interpolation using the Optimum Thermal Interpolation System package; (4) quality control and re-analysis; and (5) data archiving and software documentation. The data products from these processes included a time series of 3D fields of temperature and salinity, 2D fields of surface dynamic height and mixed-layer depth, analysis of the Gulf Stream and rings system, and bathythermograph profiles. To date, these are the most detailed and high-quality data for mesoscale ocean modeling, data assimilation, and forecasting research. Feedback from ocean modeling groups who tested this data was incorporated into its refinement. Suggestions for DAMEE data usages include (1) ocean modeling and data assimilation studies, (2) diagnosis and theoretical studies, and (3) comparisons with locally detailed observations.

Artificial intelligence (AI) systems for interpreting complex medical datasets.

Science.gov (United States)

Altman, R B

2017-05-01

Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, classify objects based on their measured characteristics, and associate similar patients/diseases/drugs based on common features. However, artificial intelligence (AI) applications in medical data have several technical challenges: complex and heterogeneous datasets, noisy medical datasets, and explaining their output to users. There are also social challenges related to intellectual property, data provenance, regulatory issues, economics, and liability. © 2017 ASCPT.

Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

KAUST Repository

Zhang, Bohai; Sang, Huiyan; Huang, Jianhua Z.

2014-01-01

of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov

PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

Directory of Open Access Journals (Sweden)

S. Devaraju

2014-04-01

Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

Recent Development on the NOAA's Global Surface Temperature Dataset

Science.gov (United States)

Zhang, H. M.; Huang, B.; Boyer, T.; Lawrimore, J. H.; Menne, M. J.; Rennie, J.

2016-12-01

Global Surface Temperature (GST) is one of the most widely used indicators for climate trend and extreme analyses. A widely used GST dataset is the NOAA merged land-ocean surface temperature dataset known as NOAAGlobalTemp (formerly MLOST). The NOAAGlobalTemp had recently been updated from version 3.5.4 to version 4. The update includes a significant improvement in the ocean surface component (Extended Reconstructed Sea Surface Temperature or ERSST, from version 3b to version 4) which resulted in an increased temperature trends in recent decades. Since then, advancements in both the ocean component (ERSST) and land component (GHCN-Monthly) have been made, including the inclusion of Argo float SSTs and expanded EOT modes in ERSST, and the use of ISTI databank in GHCN-Monthly. In this presentation, we describe the impact of those improvements on the merged global temperature dataset, in terms of global trends and other aspects.

CONTEXT-BASED URBAN TERRAIN RECONSTRUCTION FROM UAV-VIDEOS FOR GEOINFORMATION APPLICATIONS

Directory of Open Access Journals (Sweden)

D. Bulatov

2012-09-01

Full Text Available Urban terrain reconstruction has many applications in areas of civil engineering, urban planning, surveillance and defense research. Therefore the needs of covering ad-hoc demand and performing a close-range urban terrain reconstruction with miniaturized and relatively inexpensive sensor platforms are constantly growing. Using (miniaturized unmanned aerial vehicles, (MUAVs, represents one of the most attractive alternatives to conventional large-scale aerial imagery. We cover in this paper a four-step procedure of obtaining georeferenced 3D urban models from video sequences. The four steps of the procedure – orientation, dense reconstruction, urban terrain modeling and geo-referencing – are robust, straight-forward, and nearly fully-automatic. The two last steps – namely, urban terrain modeling from almost-nadir videos and co-registration of models 6ndash; represent the main contribution of this work and will therefore be covered with more detail. The essential substeps of the third step include digital terrain model (DTM extraction, segregation of buildings from vegetation, as well as instantiation of building and tree models. The last step is subdivided into quasi- intrasensorial registration of Euclidean reconstructions and intersensorial registration with a geo-referenced orthophoto. Finally, we present reconstruction results from a real data-set and outline ideas for future work.

Developing a Data-Set for Stereopsis

Directory of Open Access Journals (Sweden)

D.W Hunter

2014-08-01

Full Text Available Current research on binocular stereopsis in humans and non-human primates has been limited by a lack of available data-sets. Current data-sets fall into two categories; stereo-image sets with vergence but no ranging information (Hibbard, 2008, Vision Research, 48(12, 1427-1439 or combinations of depth information with binocular images and video taken from cameras in fixed fronto-parallel configurations exhibiting neither vergence or focus effects (Hirschmuller & Scharstein, 2007, IEEE Conf. Computer Vision and Pattern Recognition. The techniques for generating depth information are also imperfect. Depth information is normally inaccurate or simply missing near edges and on partially occluded surfaces. For many areas of vision research these are the most interesting parts of the image (Goutcher, Hunter, Hibbard, 2013, i-Perception, 4(7, 484; Scarfe & Hibbard, 2013, Vision Research. Using state-of-the-art open-source ray-tracing software (PBRT as a back-end, our intention is to release a set of tools that will allow researchers in this field to generate artificial binocular stereoscopic data-sets. Although not as realistic as photographs, computer generated images have significant advantages in terms of control over the final output and ground-truth information about scene depth is easily calculated at all points in the scene, even partially occluded areas. While individual researchers have been developing similar stimuli by hand for many decades, we hope that our software will greatly reduce the time and difficulty of creating naturalistic binocular stimuli. Our intension in making this presentation is to elicit feedback from the vision community about what sort of features would be desirable in such software.

BASE MAP DATASET, MAYES COUNTY, OKLAHOMA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications: cadastral, geodetic control,...

Data publication, documentation and user friendly landing pages - improving data discovery and reuse

Science.gov (United States)

Elger, Kirsten; Ulbricht, Damian; Bertelmann, Roland

2016-04-01

Research data are the basis for scientific research and often irreplaceable (e.g. observational data). Storage of such data in appropriate, theme specific or institutional repositories is an essential part of ensuring their long term preservation and access. The free and open access to research data for reuse and scrutiny has been identified as a key issue by the scientific community as well as by research agencies and the public. To ensure the datasets to intelligible and usable for others they must be accompanied by comprehensive data description and standardized metadata for data discovery, and ideally should be published using digital object identifier (DOI). These make datasets citable and ensure their long-term accessibility and are accepted in reference lists of journal articles (http://www.copdess.org/statement-of-commitment/). The GFZ German Research Centre for Geosciences is the national laboratory for Geosciences in Germany and part of the Helmholtz Association, Germany's largest scientific organization. The development and maintenance of data systems is a key component of 'GFZ Data Services' to support state-of-the-art research. The datasets, archived in and published by the GFZ Data Repository cover all geoscientific disciplines and range from large dynamic datasets deriving from global monitoring seismic or geodetic networks with real-time data acquisition, to remotely sensed satellite products, to automatically generated data publications from a database for data from micro meteorological stations, to various model results, to geochemical and rock mechanical analyses from various labs, and field observations. The user-friendly presentation of published datasets via a DOI landing page is as important for reuse as the storage itself, and the required information is highly specific for each scientific discipline. If dataset descriptions are too general, or require the download of a dataset before knowing its suitability, many researchers often decide

PENERAPAN TEKNIK BAGGING PADA ALGORITMA KLASIFIKASI UNTUK MENGATASI KETIDAKSEIMBANGAN KELAS DATASET MEDIS

Directory of Open Access Journals (Sweden)

Rizki Tri Prasetio

2016-03-01

Full Text Available ABSTRACT – The class imbalance problems have been reported to severely hinder classification performance of many standard learning algorithms, and have attracted a great deal of attention from researchers of different fields. Therefore, a number of methods, such as sampling methods, cost-sensitive learning methods, and bagging and boosting based ensemble methods, have been proposed to solve these problems. Some medical dataset has two classes has two classes or binominal experiencing an imbalance that causes lack of accuracy in classification. This research proposed a combination technique of bagging and algorithms of classification to improve the accuracy of medical datasets. Bagging technique used to solve the problem of imbalanced class. The proposed method is applied on three classifier algorithm i.e., naïve bayes, decision tree and k-nearest neighbor. This research uses five medical datasets obtained from UCI Machine Learning i.e.., breast-cancer, liver-disorder, heart-disease, pima-diabetes and vertebral column. Results of this research indicate that the proposed method makes a significant improvement on two algorithms of classification i.e. decision tree with p value of t-Test 0.0184 and k-nearest neighbor with p value of t-Test 0.0292, but not significant in naïve bayes with p value of t-Test 0.9236. After bagging technique applied at five medical datasets, naïve bayes has the highest accuracy for breast-cancer dataset of 96.14% with AUC of 0.984, heart-disease of 84.44% with AUC of 0.911 and pima-diabetes of 74.73% with AUC of 0.806. While the k-nearest neighbor has the best accuracy for dataset liver-disorder of 62.03% with AUC of 0.632 and vertebral-column of 82.26% with the AUC of 0.867. Keywords: ensemble technique, bagging, imbalanced class, medical dataset. ABSTRAKSI – Masalah ketidakseimbangan kelas telah dilaporkan sangat menghambat kinerja klasifikasi banyak algoritma klasifikasi dan telah menarik banyak perhatian dari