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Summon Topic Explorer Results by Search Query
<p>8,000 Summon Topic Explorer results from actual user search queries at GVSU using Summon 2.0 with the Topic Explorer fed from Wikipedia and Gale Virtual Reference Library.</p> <p>topics.xlsx is a collection of the 8,000 analyzed search queries with Topic Explorer results.</p> <p>All_Problematic_Searches.xlsx is a sheet of 561 incorrect or biased Topic Explorer results.</p> <p>Results_with_Bias.xlsx contains 54 biased Topic Explorer results.</p>
Survey results from FISC exams
<p>These data are derived from tests for Field Identification Skills Certificates (FISCs) that have been running for eight years under the aegis of the Botanical Society of Britain and Ireland. These certificates are intended to give the participants and potentially their future or current employers a guide to their skill at plant identification in the field. These day-long tests include a field test and it is from this field test that the data in this paper are derived.</p> <p>These files contain 238 surveys from the FISCs conducted in Shropshire, UK. From 2007 to 2014 six sites were used, Sweeney Fen (N 52° 49' 4", W 3° 4' 38"); Ballstone Quarry (N 52° 35' 35", W 2° 34' 35"); Windmill Hill (N 52° 36' 13", W 2° 33' 18"); Aston Locks off-line reserve (N 52° 49' 51", W 2° 59' 18"); The Old River Bed (N 52° 43' 40", W 2° 44' 47"), Shrewsbury and Blakeway Hollow (N 52° 35' 37", W 2° 34' 57"). The sites were chosen to fit the following criteria, which are consistent with the FISC protocols. They are around 2–3 hectares of accessible habitat, which are relatively safe in health and safety terms. They were selected for their habitat, such as unimproved grassland with some scrub areas, or short fen, or not too wet sedge swamp, or broad-leaved woodland. The area to be surveyed was made very clear to the participants – if a smaller area was used within a larger reserve it was fenced or taped off clearly. It was made clear to participants whether or not hedges were to be surveyed.</p> <p>The observations are separated into taxa that are accepted to be present; taxa that are unreasonable or known to be incorrect; ‘mythical’ species which don't exist and ‘cautious’ records, with just a generic name. Each column represents the survey of one surveyor.</p>
Toward Estimating the Rank Correlation between the Test Collection Results and the True System Performance
<p>This archive contains the simulated collections and the estimated correlation coefficients. For the full code and description, please refer to https://github.com/julian-urbano/sigir2016-correlation</p>
Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004
<p>Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)<sup>-1</sup> yr<sup>-1</sup>] or [g N (kg product)<sup>-1</sup> yr<sup>-1</sup>] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. </p> <p>Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector</p>
Data and modeling results for publication: Landscape genetics indicate recently increased habitat fragmentation in African forest-associated chafers
<ul> <li>DNA sequences: <em>cox1</em> and ITS1 alignments</li> <li>spatial records (in hypervolume archive)</li> <li>spatial principal component 1-3 used for <em>hypervolume</em> models (in hypervolume archive)</li> <li>Present and past species distribution models (SDMs): <ul> <li><em>biomod2</em> ensemble SDMs <ul> <li>Present</li> <li>Holocene Altithermal</li> <li>Last Glacial Maximum</li> </ul> </li> <li><em>biomod2</em> SDMs for single PMIP3 models <ul> <li>Present</li> <li>Holocene Altithermal</li> <li>Last Glacial Maximum</li> </ul> </li> <li><em>hypervolume</em> SDMs</li> </ul> </li> <li>landscape connectivity models <ul> <li>circuitscape (for F0, F1, and F2)</li> <li>least cost corridors and paths (for F0, F1, and F2)</li> </ul> </li> </ul>
Drosophila simulans LD results from PLINK for Chromosome X
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Work</strong>: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and dataset</strong>s: Drosophila simulans VCF, LD results from chromosomes 2L, 2R, 3R, 3L, and 4.</p>
Drosophila simulans LD results from PLINK for Chromosome 3R
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Work</strong>: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and datasets</strong>: Drosophila simulans VCF, LD results from chromosomes 2L, 2R, 3L, 4, and X.</p>
Drosophila simulans LD results from PLINK for Chromosome 4
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Wor</strong>k: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and datasets</strong>: Drosophila simulans VCF, LD results from chromosomes 2L, 2R, 3R, 3L, and X.</p>
Drosophila simulans LD results from PLINK for Chromosome 2R
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Wor</strong>k: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and datasets</strong>: Drosophila simulans VCF, LD results from chromosomes 2L, 3L, 3R, 4, and X.</p>
Drosophila simulans LD results from PLINK for Chromosome 3L
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Work</strong>: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and datasets</strong>: Drosophila simulans VCF, LD results from chromosomes 2L, 2R, 3R, 4, and X</p>
Drosophila simulans LD results from PLINK for Chromosome 2L
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Work</strong>: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and datasets</strong>: Drosophila simulans VCF, LD results from chromosomes 2R, 3L, 3R, 4, and X.</p>
Spatial Evolve Algorithm Results for Erdos Renyi Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Erdős Rényi random network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Random Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Results from the RDM Survey - LEARN project (June 2016)
<p>First data obtained from the open survey developed by the LEARN project (http://www.learn-rdm.eu/) as a self-assessment tool to assist institutions discover how ready they are for managing research data. The survey is based on the issues posed to institutions by the LERU Roadmap for Research Data published at the end of 2013, and available at: http://www.learn-rdm.eu/material/leru_roadmap_for_research_data<br /> The survey has thirteen questions addressing the main elements to be taken into account in developing an institutional strategy for research data management. Each question has three possible answers representing green, yellow or red light. The more ‘green light’ responses recorded, the readier an institution probably is for managing its research data.</p> <p>The survey is available in English at http://learn-rdm.eu/en/rdm-readiness-survey/ and in Spanish at http://learn-rdm.eu/encuesta-rdm/</p>
graphANNIS benchmark queries and results
<p>Queries used in the benchmark are included in the file "queries_graphannis_benchmark.zip". Each folder corresponds to one corpus. Each query has an ".aql" file containing the AQL query, a ".json" file with the parsed query.</p> <p>The actual results of the benchmark are in the "journal-2016-07-27.csv " file. Additional the memory usage for each corpus in graphANNIS is given in "memoryusage.csv".</p>
Spatial Evolve Algorithm Results for Watts Strogatz Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Watts Strogatz small world network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Erdős Rényi Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Erdős Rényi random network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the strategies list, deterministic and non and on the sample size. </p>
Spatial Evolve Algorithm Results for Complete Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been a complete network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Random Topology Minimum Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the minimum normalized rank. Two files are contained here based on the sample size. All 132 strategies of the Axelrod have been used. </p>
Comparison results from 'A global comparison of integrated water vapour estimates from WMO radiosondes, AERONET sun photometers and GPS for the 17 year period from 1997 to 2013' by Wilson, Hansen, Bingley and Milton
<p>This dataset contains the raw comparison results from the paper <em>A global comparison of integrated water vapour estimates from WMO radiosondes, AERONET sun photometers and GPS for the 17 year period from 1997 to 2013</em> by Wilson, Hansen, Bingley and Milton.</p> <p>The three files contain comparisons between:</p> <p>a) AERONET and Radiosonde (Validation_AERONET-Radiosonde.csv)</p> <p>b) AERONET and GPS (Validation_AERONET.csv)</p> <p>c) Radiosonde and GPS (Validation_Radiosonde.csv)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.