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9,786 results for “selection”
ERA5-Land selected indicators daily aggregates for Africa, 1975
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1975.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1976
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1976.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1977
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1977.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1979
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1979.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1978
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1978.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1974
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1974.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1970
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1970.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p> </p>
ERA5-Land selected indicators daily aggregates for Africa, 1973
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1973.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1971
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1971.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1972
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1972.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
CLDF dataset derived from Hale's "Wordlists in Selected Languages of Nepal" from 1973
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hale, Austin (1973): Clause, sentences, and discourse patterns in selected languages of Nepal. Kathmandu: Institute of Nepal and Asiatic Studies.</p> </blockquote>
Accompnaying Dataset for: Chemical Heredity as Group Selection at the Molecular Level
<p>Accompnaying Dataset for: Chemical Heredity as Group Selection at the Molecular Level. File descriptions are provided in the Appendix of [Markovitch, Witkowski and Virgo; Chemical Heredity as Group Selection at the Molecular Level, arXiv (2018)] (https://arxiv.org/abs/1802.08024).</p>
Herschel‐ATLAS/GAMA: a census of dust in optically selected galaxies from stacking at submillimetre wavelengths
<p> </p> <p>Stacked sub-millimetre fluxes, luminosities, and derived dust masses and temperatures for GAMA galaxies...</p> <ul> <li>as a function of stellar mass, optical colour and redshift: StackResults_g-r_Mstar</li> <li>as a function of r-band absolute magnitude, optical colour and redshift: StackResults_g-r_Mr</li> </ul> <p>See readme files for full details.</p>
Computational data for selection of PBE+50HFX functional for studying Ru-Cl/H-PR3 complexes
<p>The dataset provides all relevant computational data for selecting the hybrid GGA functional (PBE) with 50% HF exchange and saturated (def2TZVP) basis set as the most reasonable level of theory that reproduces the current golden standard of CCSD(T) results for Ru-Cl/H-PR3 complexes.<br> The file <strong>inventory</strong> gives you an overview of the data entries provided. <br> The PDF file <strong>labelling.pdf</strong> defines the symbols used for labelling various complexes.<br> In brief, the archive content is as follows:</p> <p><strong>basis sets</strong> - definition of BS1 to BS5 <br> <strong>Z-matrix definitions</strong> - definition of internal coordinates for isomers considered<br> <strong>operational procedures</strong> - operation procedures for how to derive atomic orbital compositions</p> <p><strong>density functionals</strong> - optimized structures calculated using various density functionals<br> <strong>wave functions</strong> - optimized structures calculated at various levels of wave function theory<br> <strong>population analyses</strong> - summary of electronic structure analysis<br> <strong>difference density</strong> - manipulated cube files used for electron density contours</p> <p><strong>CCDB</strong> - optimized structures of XRD characterized Ru-Cl/H-PR3 complexes<br> <strong>chemical speciation</strong> - structural optimization and energetics of reaction pathways</p>
User Study Data from "Point-and-Shake: Selecting from Levitating Object Displays"
<p>This dataset contains anonymous user study data from the two experiments described in the corresponding CHI 2018 publication.</p>
Data from: Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells
<p>Data that was reported in "Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells" by </p> <p>Nathalie R. Reinhard<sup>1</sup>, Sanne van der Niet<sup>1</sup>, Anna Chertkova<sup>1</sup>, Marten Postma<sup>1</sup>, Theodorus W.J. Gadella Jr.<sup>1</sup>, Peter L. Hordijk<sup>1,2</sup>, and Joachim Goedhart<sup>1*</sup><br> </p> <p><strong>Affiliations:</strong></p> <p><sup>1 </sup>University of Amsterdam, Molecular Cytology, Swammerdam Institute for Life Sciences, van Leeuwenhoek Centre for Advanced Microscopy, Amsterdam, the Netherlands</p> <p><sup>2 </sup>Department of Physiology, Free University Medical Center, Amsterdam, The Netherlands</p> <p> </p> <p>*Correspondence to: j.goedhart@uva.nl</p>
Relate-estimated coalescence rates, allele ages, and selection p-values for the 1000 Genomes Project
<p><strong>Overview</strong></p> <p>Coalescence rates, allele ages, and p-values for evidence of positive selection calculated for 2478 samples of the 1000 Genomes Project using Relate.</p> <p>We estimated the joint genealogy of all 1000 GP populations and then extracted the embedded genealogy for each population.<br> For the genealogy of each population, we jointly estimated the population size history and branch lengths. <br> Variants segregating in more than one population therefore have correlated but different allele ages in each population.</p> <p>Please refer to <a href="https://www.nature.com/articles/s41588-019-0484-x">Speidel et al. Nature Genetics (2019)</a> for more details or email leo.speidel@outlook.com for any queries.</p> <p><strong>Coalescence rates</strong></p> <p>The zipped directory coalescence_rates.zip contains coalescence rates for 26 populations in the 1000 Genomes Project data set.</p> <ul> <li>The .coal files show the haploid coalescence rates, please refer to the <a href="https://myersgroup.github.io/relate/modules.html#PopulationSizeScript_FileFormats">Relate documentation</a> for the file format.</li> <li>The popsize.RData file is an R data frame storing the diploid population sizes (0.5/coalescence rate) calculated using the .coal files. The columns of this data frame, named "pop_size", are <ul> <li>gens_ago: Time in generations at which epoch starts. (To get years from generations, we multiply by 28.)</li> <li>population_size: Diploid population size in this epoch.</li> <li>population: Name of population </li> <li>region: Name of region (AFR, AMR, EAS, EUR, SAS)</li> </ul> </li> </ul> <p><strong>Allele ages and selection p-values</strong></p> <p>The zipped directories allele_ages_*.zip contain R data frames for each 1000GP population storing allele ages and selection p-values.<br> Please note that only mutations that segregate in the population and map to a unique branch in the Relate-estimated marginal trees are included. Selection p-values are only provided for mutations of DAF > 2 that pass quality filters (see Speidel et al., 2019). </p> <p>To get an age estimate for a neutral mutation, use 0.5*(lower_age + upper_age). To get years from generations, we multiply by 28.</p> <p>The columns of these data frames, named "allele_ages", are</p> <ul> <li>CHR: chromosome index</li> <li>BP: base-pair position (GRCh37)</li> <li>ID: id of SNP</li> <li>lower_age: Age in generations of coalescence event at the lower end of the branch onto which the mutation maps</li> <li>upper_age: Age in generations of coalescence event at the upper end of the branch onto which the mutation maps</li> <li>ancestral/derived: Ancestral/derived allele</li> <li>upstream: Upstream (5') allele</li> <li>downstream: Downstream (3') allele</li> <li>DAF: Derived-allele frequency</li> <li>pvalue: log10 p-value for selection evidence</li> </ul>
Nitrogen Input, Nitrogen Surplus, and Nitrogen Use Efficiency Globally and for Selected Regions
<p>Data on nitrogen input, surplus and nitrogen use efficiency are provided globally, and for USA, European Union, China, Brazil and South Asia (i.e. India, Nepal, Bangladesh and Pakistan) for 1995 to 2013. Nitrogen input (in the file ninput_1995_2013.csv) is separated into synthetic fertiliser (FERT), manure (MANURE), biological nitrogen fixation (BNF), and NOx deposition from non-agricultural sources (NOx). These N-inputs are for the whole agricultural system, including crops and grassland. Also included is N-surplus (SURPLUS) for cropping systems, which is calculated as the difference between the total N-input and the N removed through harvest. NUE (in the file nue_1995_2013.csv) is N in harvest relative to the total N-input. </p> <p>Synthetic fertilizer application is based on the FAOSTAT dataset (http://www.fao.org/home/en/) with several inputs from the International Fertilizer Association (<a href="https://www.fertilizer.org/">https://www.fertilizer.org/</a>). Total animal excretion is calculated using the FAOSTAT livestock inventory and dynamic excretion factors, biological N fixation is calculated from crop productivities (Anglade et al., 2015)<sup> </sup>and atmospheric deposition was from Dentener et al. (2006). Grassland nitrogen fixation was based on the grassland production estimated following Lassaletta et al. (2016). N in harvested crops is based on crop productivity and N content of 177 crops, utilizing data from the FAOSTAT database.</p>
Effect of the Increased Nursing Attrition Rate on Nursing Administration Process during the Covid-19 Pandemic in a Selected Tertiary Care Hospital
<p><span>During<span> </span>the<span> </span>COVID-19<span> </span>outbreak,<span> </span>healthcare<span> </span>professionals,<span> </span>particularly<span> </span>nurses,<span> </span>were<span> </span>more<span> </span>prone to<span> </span>diseases.<span> </span>Globally<span> </span>attrition<span> </span>rate<span> </span>was<span> </span>high<span> </span>among<span> </span>nurses<span> </span>and<span> </span>during<span> </span>the<span> </span>pandemic,<span> </span>it<span> </span>increased because<span> </span>of<span> </span>various<span> </span>reasons<span> </span>such<span> </span>as<span> </span>the<span> </span>risk<span> </span>of<span> </span>infection,<span> </span>occupational<span> </span>and<span> </span>psychological<span> </span>stress, causing risk to their loved ones. This led to a chaotic situation where nurse managers were forced to implement specific strategic plans to deal with increased nurse attrition. This study aims<span> </span>to<span> </span>describe<span> </span>the<span> </span>impact<span> </span>of<span> </span>nurse<span> </span>attrition<span> </span>rate<span> </span>on<span> </span>nursing<span> </span>administration<span> </span>during<span> </span>COVID-19 at a selected tertiary care hospital. The research approach adopted in this study is descriptive cross-sectional. A total sample of 66 nurses involved in nursing administration. The data is collected through a structured questionnaire and the nurse attrition data during the COVID-19 pandemic period was collected from the interview method during the survey. Statistical tests used were frequency, percentage, mean, Standard Deviation (S.D). The study showed that there is a moderate impact of increased nurse attrition on nursing administration during the COVID-19 pandemic. The study led to the identification of gaps that need to be addressed in a similar crisis.</span></p>
Displacements on selected rock glaciers in the United States
<p>Lateral displacements between two years on selected rock glaciers in the United States (Rocky Mountains and westwards). Displacements were measured using image correlation on repeat orthoimages from the United States Geological Survey archive. Correlation software used: CIAS (http://mn.uio.no/icemass).</p> <p><em><strong>The data are the raw data behind the following publication. This publication contains more details about the measurements. </strong></em></p> <p><a href="https://www.nature.com/articles/s41467-024-52093-z">Kääb, A., Røste, J. Rock glaciers across the United States predominantly accelerate coincident with rise in air temperatures. Nat Commun 15, 7581 (2024). https://doi.org/10.1038/s41467-024-52093-z</a></p> <h3>Detailed rock glacier positions [lat°, lon°]:</h3> <p>Star Peak [44.253,-120.417]<br>Galena creek [44.645,-109.791]<br>Sulphur creek [44.617,-109.756]<br>Crater Mtn [44.023,-109.627]<br>Old Hyndman [43.743, -114.106]<br>Ferguson ranch [39.270, -107.194]<br>Thomas lake [39.270,-107.155]<br>Arapaho [40.020,-105.641]<br>Mt Mears [38.018,-107.871]<br>Mt Sneffels [38.010,-107.781]<br>Teakettle Mtn [38.011,-107.768]<br>Twin Sisters [37.762,-107.803]<br>Pine creek [37.078,-118.450]<br>Birch creek [37.065,-118.431]<br>Cardinal Mtn N [37.011,-118.414]<br>Cardinal Mtn S [37.008,-118.409].</p> <h3>Names of individual displacement files:</h3> <p>Short-name-of-rock-glacier_year1_year2_correlation-window-size_search-window-size_xxx.txt/.csv<br>xxx is either 'helm' indicating that the two orthoimages have been coregistered using Helmert transformation, or 'filt' indicating Helmert transformation and filtering of grid-based measurements for outliers. </p> <h3>Data columns of each file:</h3> <p>X: UTM coordinate, Easting of displacement measurement point <br>Y: UTM coordinate, Northing of displacement measurement point<br>dx: displacement component in east<br>dy: displacement component in north<br>length: pythagoras of dx and dy (vector length)<br>direction: azimuth of vector (from North, clock-wise)<br>max_corrcoeff: correlation coefficient of displacement match<br>avg_corrcoeff: background correlation coefficent at a matching poistion. <br><em><strong> </strong></em></p> <p> </p>
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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.