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1,221 results for “Aggregators”
ERA5-Land selected indicators daily aggregates for Africa, 1961
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1961.</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>
ERA5-Land selected indicators daily aggregates for Africa, 1958
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1958.</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>
ERA5-Land selected indicators daily aggregates for Africa, 1953
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1953.</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>
ERA5-Land selected indicators daily aggregates for Africa, 1954
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1954.</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>
ERA5-Land selected indicators daily aggregates for Africa, 1957
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1957.</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>
ERA5-Land selected indicators daily aggregates for Africa, 1955
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1955.</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>
ERA5-Land selected indicators daily aggregates for Africa, 1950
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1950.</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>
ERA5-Land selected indicators daily aggregates for Europe, 2022
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Europe for 2022.</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>
ERA5-Land selected indicators daily aggregates for Europe, 2023
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Europe for 2023.</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>
Daily time series of 12 human thermal stress indices in Greece aggregated at commune level (1998-2022)
<p>The overview table of the dataset containing 12<strong> </strong>Human Thermal Stress Indices in Greece (<strong>HTSI-GR</strong>):</p> <div> <table> <tbody> <tr> <th> <p>Heat indices names</p> </th> <th> <p>Description</p> </th> <th> <p>Units</p> </th> <th> <p>Reference</p> </th> <th> <p>Dataset file names</p> </th> </tr> <tr> <td> <p><strong>AT</strong></p> </td> <td> <p>Apparent Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Steadman, R. G. Norms of apparent temperature in Australia. Aust. Met. Mag. 43, 1–16 (1994).</p> </td> <td> <p>AT_min_1998-01-01_2022-12-31.csv, AT_mean_1998-01-01_2022-12-31.csv, AT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>HI</strong></p> </td> <td> <p>Heat Index</p> </td> <td> <p>°C</p> </td> <td> <p>Rothfusz, L.P. Te heat index equation. National Weather Service Technical Attachment. Report No. SR 90–23 (1990).</p> </td> <td> <p>HI_min_1998-01-01_2022-12-31.csv, HI_mean_1998-01-01_2022-12-31.csv, HI_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>Humidex</strong></p> </td> <td> <p>Humidity Index</p> </td> <td> <p>°C</p> </td> <td> <p>Masterson, J. & Richardson, F.A. Humidex: a method of quantifying human discomfort due to excessive heat and humidity (Environment Canada, 1979).</p> </td> <td> <p>Humidex_min_1998-01-01_2022-12-31.csv, Humidex_mean_1998-01-01_2022-12-31.csv, Humidex_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>NET</strong></p> </td> <td> <p>Normal Effective Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Landsberg HE. The assessment of human bioclimate: a limited review of physical parameters. Technical Note No. 123, WMO-No. 331 (World Meteorological Organization, 1972).</p> </td> <td> <p>NET_min_1998-01-01_2022-12-31.csv, NET_mean_1998-01-01_2022-12-31.csv, NET_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WBGT</strong></p> </td> <td> <p>Wet Bulb Globe Temperature (simple)</p> </td> <td> <p>°C</p> </td> <td> <p>Australian Bureau of Meteorology. Thermal comfort observations http://bom.gov.au/info/thermal_stress/ (2020).</p> </td> <td> <p>WBGT_min_1998-01-01_2022-12-31.csv, WBGT_mean_1998-01-01_2022-12-31.csv, WBGT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>thermofeelWBGT</strong></p> </td> <td> <p>Wet Bulb Globe Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011).</p> </td> <td> <p>thermofeelWBGT_min_1998-01-01_2022-12-31.csv, thermofeelWBGT_mean_1998-01-01_2022-12-31.csv, thermofeelWBGT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WBT</strong></p> </td> <td> <p>Wet Bulb Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011).</p> </td> <td> <p>WBT_min_1998-01-01_2022-12-31.csv, WBT_mean_1998-01-01_2022-12-31.csv, WBT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WCT</strong></p> </td> <td> <p>Wind Chill Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Office of the Federal Coordinator for Meteorological services and supporting research (OFCM). Report on Wind Chill Temperature and extreme heat indices: evaluation and improvement projects. Report No. FCM-R19-2003 (U.S. Office of the Federal Coordinator for Meteorological Services and Supporting Research, 2003).</p> </td> <td> <p>WCT_min_1998-01-01_2022-12-31.csv, WCT_mean_1998-01-01_2022-12-31.csv, WCT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>MRT</strong></p> </td> <td> <p>Mean Radiant Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Weihs, P. et al. The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from measured and observed meteorological data. Int. J. Biometeorol. 56, 537–555 (2012).</p> </td> <td> <p>MRT_min_1998-01-01_2022-12-31.csv, MRT_mean_1998-01-01_2022-12-31.csv, MRT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI</strong></p> </td> <td> <p>Universal Thermal Climate Index (UTCI)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI_min_1998-01-01_2022-12-31.csv, UTCI_mean_1998-01-01_2022-12-31.csv, UTCI_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI2</strong></p> </td> <td> <p>Indoor environment UTCI with 2 parameters (air temperature and humidity)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI2_min_1998-01-01_2022-12-31.csv, UTCI2_mean_1998-01-01_2022-12-31.csv, UTCI2_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI3</strong></p> </td> <td> <p>Outdoor shaded space environment UTCI with 3 parameters (air temperature, humidity, and wind speed)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI3_min_1998-01-01_2022-12-31.csv, UTCI3_mean_1998-01-01_2022-12-31.csv, UTCI3_max_1998-01-01_2022-12-31.csv</p> </td> </tr> </tbody> </table> </div> <p> </p> <p>The overview table of the <strong>HTSI-GR</strong> additional resources folder containing support files and instructions for dataset replication:</p> <table> <tbody> <tr> <th> <p><strong>File names</strong></p> </th> <th> <p>Description</p> </th> </tr> <tr> <td> <p><strong>0. Calculate thermofeelWBGT.py</strong></p> </td> <td> <p>A python script that calculates the Wet Bulb Globe Temperature (WBGT) using the Thermofeel library. Processes NetCDF files containing daily meteorological data and outputs WBGT values in new NetCDF files for each day.</p> </td> </tr> <tr> <td> <p><strong>1. Merge_HI_by_max-mean-min.py</strong></p> </td> <td> <p>A python script that merges daily NetCDF files containing heat index (HI) data into three separate files based on mean, maximum and minimum values for further processing.</p> </td> </tr> <tr> <td> <p><strong>2. QGIS_zonal_statistics.py</strong></p> </td> <td> <p>A python script that calculates zonal statistics for heat indices using QGIS python console. Uses a shapefile of Greek communes and a raster NetCDF file containing daily index values, and outputs daily CSV files with computed statistics.</p> </td> </tr> <tr> <td> <p><strong>3. Zonal_format.py</strong></p> </td> <td> <p>A python script that formats the zonal statistics results into a comprehensive dataset. Combines daily CSV files into a single CSV, fills in missing data using nearest neighbour values, and produces a final formatted dataset.</p> </td> </tr> <tr> <td> <p><strong>Greek Communes.ZIP</strong></p> </td> <td> <p>Contains the shapefile of Greek communes derived from the Hellenic Statistical Authority (ELSTAT) required for zonal statistics calculations. KALCODE and Commune names are linked in the .shp.</p> </td> </tr> <tr> <td> <p><strong>Nearest Neighbour data table.csv</strong></p> </td> <td> <p>A support table to script <strong>3.Zonal</strong><strong>_format.py</strong> that lists communes with missing data and their nearest neighbour with data.</p> </td> </tr> <tr> <td> <p><strong>Read me.txt</strong></p> </td> <td> <p>Provides an overview and instructions for using the scripts. Describes the purpose of each script, lists prerequisites, and provides step-by-step instructions for replicating the dataset.</p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Aggregate dataset on descriptive representation in the Hellenic Parliament (2019-2023)
<p>This is the dataset provided by the AUTH team on descriptive representation in the Hellenic Parliament for WP4 of the ActEU project. </p>
Aggregate Dataset on Descriptive Representation in the Spanish Parliament (2016-2023)
<p>This is the dataset aggregated at the legislative/party level by the CSIC team from the individual-level data provided by the Sciences Po and CSIC teams on descriptive representation in the Spanish lower chamber of Parliament for WP4 of the ActEU project. </p>
PhasAGE Training School 1-Protein aggregation prediction-PRACTICAL
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
Daily weather data averages for Germany aggregated over official weather stations
<p>Daily data averaged across Germany for the period of 2016-01-01 till 2021-06-27:</p> <p><strong>temperature_mean: </strong>mean daily temperature in degree Celsius averaged across all weather stations in Germany.</p> <p><strong>temperature_max:</strong> maximum daily temperature in degree Celsius averaged across all weather stations in Germany.</p> <p><strong>precipitation:</strong> daily precipitation sum in millimeter (equals liter per square meter) averaged across all weather stations in Germany.</p> <p><strong>sunshine: </strong>sunshine duration per day averaged across all weather stations in Germany.</p> <p> </p> <p> </p> <p>gemittelte Werte basierende auf Daten des Deutschen Wetterdiensts, Vermessungsverwaltungen der Länder und BKG (https://gdz.bkg.bund.de/)</p>
Alpine ice sheet erosion potential aggregated variables
<p>These data contain domain-integrated and time-integrated model output variables presented in the reference below or otherwise relevant to last glacial cycle glacier erosion in the Alps.</p> <p><strong>Reference:</strong></p> <ul> <li>J. Seguinot and I. Delanay. Last glacial cycle glacier erosion potential in the Alps, <em>submitted to Earth Surface Dynamics Discussions</em>, 2021.</li> </ul> <p><strong>File names:</strong></p> <pre><code>alpero.{1km|2km}.{epic|grip|md01}.{cp|pp}.agg.nc</code></pre> <ul> <li>Horizontal resolution: <ul> <li><em>1km</em>: 1 km horizontal resolution</li> <li><em>2km</em>: 2 km horizontal resolution</li> </ul> </li> <li>Temperature forcing: <ul> <li><em>epic</em>: EPICA ice core temperature forcing</li> <li><em>grip</em>: GRIP ice core temperature forcing</li> <li><em>md01</em>: MD01-2444 core temperature forcing</li> </ul> </li> <li>Precipitation forcing: <ul> <li><em>cp</em>: constant precipitation</li> <li><em>pp</em>: palaeo-precipitation reduction</li> </ul> </li> </ul> <p><strong>Variables:</strong></p> <ul> <li>Coordinate variables: <ul> <li><em>x</em>: X-coordinate in Cartesian system</li> <li><em>y</em>: Y-coordinate in Cartesian system</li> <li><em>lon</em>: longitude</li> <li><em>lat</em>: latitude</li> <li><em>time</em>: time</li> <li><em>age</em>: model age</li> <li><em>z</em>: elevation band midpoints</li> <li><em>d</em>: distance along transect</li> </ul> </li> <li>Glacier erosion variables: <ul> <li><em>coo2020_cumu</em>: Cook et al. (2020) cumulative glacial erosion potential</li> <li><em>coo2020_rate</em>: Cook et al. (2020) domain total volumic erosion rate</li> <li><em>coo2020_hyps</em>: Cook et al. (2020) erosion rate geometric mean</li> <li><em>coo2020_rhin</em>: Cook et al. (2020) rhine transect erosion rate</li> <li><em>her2015_cumu</em>: Herman et al. (2015) cumulative glacial erosion potential</li> <li><em>her2015_rate</em>: Herman et al. (2015) domain total volumic erosion rate</li> <li><em>her2015_hyps</em>: Herman et al. (2015) erosion rate geometric mean</li> <li><em>her2015_rhin</em>: Herman et al. (2015) rhine transect erosion rate</li> <li><em>hum1994_cumu</em>: Humphrey and Raymond (1994) cumulative glacial erosion potential</li> <li><em>hum1994_rate</em>: Humphrey and Raymond (1994) domain total volumic erosion rate</li> <li><em>hum1994_hyps</em>: Humphrey and Raymond (1994) erosion rate geometric mean</li> <li><em>hum1994_rhin</em>: Humphrey and Raymond (1994) rhine transect erosion rate</li> <li><em>kop2015_cumu</em>: Koppes et al. (2015) cumulative glacial erosion potential</li> <li><em>kop2015_rate</em>: Koppes et al. (2015) domain total volumic erosion rate</li> <li><em>kop2015_hyps</em>: Koppes et al. (2015) erosion rate geometric mean</li> <li><em>kop2015_rhin</em>: Koppes et al. (2015) rhine transect erosion rate</li> </ul> </li> <li>Other variables: <ul> <li><em>cumu_sliding</em>: cumulative basal motion</li> <li><em>glacier_time</em>: total ice cover duration</li> <li><em>warmbed_time</em>: temperate-based ice cover duration</li> <li><em>glacier_area</em>: glacierized area</li> <li><em>volumic_lift</em>: volumic bedrock uplift</li> <li><em>warmbed_area</em>: temperate-based ice cover area</li> </ul> </li> </ul> <p><strong>Data format:</strong></p> <p>The data use compressed netCDF format. For quick inspection I recommend ncview. Conversion to GeoTIFF (and other GIS formats) can be achieved with e.g. GDAL::</p> <pre><code>gdal_translate NETCDF:filename.nc:variable filename.variable.tif</code></pre> <p>The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. To convert all variables to separate files use:</p> <pre><code>gdalinfo $filename | grep NETCDF | cut -d '=' -f 2 | egrep -v '(lat|lon|time_bounds)' | while read sub do gdal_translate $sub ${filename%.nc}.${sub##*:}.tif done</code></pre> <p>Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata. Also see glacial cycle <a href="https://doi.org/10.5281/zenodo.1423160">aggregated</a> and <a href="https://doi.org/10.5281/zenodo.1423175">continuous</a> variables.</p> <p><strong>Changes:</strong></p> <ul> <li>Version 2: <ul> <li>Add variable for glacierized area within 100-m elevation band.</li> <li>Use 100-m instead of 10-m elevation bands for erosion rate.</li> </ul> </li> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>
Aggregated data of abundance indices
<p>For the reconstruction of flight peaks, an abundance index was calculated by relating records to search efforts based on the evidence of field activities left in the database by proficient observers. For this search-effort correction, we selected observers with at least 50 records of at least 10 butterfly species each year. As a measure of their collective search effort, we calculated the sum for each day of all the 100 × 100 m grid cells from which an observer had reported records (of any species, including non-butterflies; also including absence records). This method of ‘proven day-grid-visits’ has become the standard proxy for search effort when analyzing incidental observations of the portal waarnemingen.be. Day-grid-visits do not cover search effort completely, because records are not submitted from every visited hectare grid cell, but strongly correlates with it.</p> <p>We provide the raw data containing day (2009-2020), the X and Y coordinate of the centroid of the 100x100 m grid cell (In Lambert 72, EPSG:<em>31370</em> Projected coordinate system for Belgium), the number of peacock butterflies reported, and the number of hectare day grid visits. </p>
Raw and aggregated data for the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors"
<p>This dataset contains all the raw data and aggregated data subject of the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors". The study is based on the bibliographic and citation data contained in 729 articles published in 147 journals in 27 subject areas. The articles contained a total amount of 34,140 bibliographic references and 55,100 mentions and quotations overall.</p> <p>The dataset is composed of a series of files:</p> <ul> <li>the files "subject_area_<discipline-name>.csv" contain the raw data of the articles published in the journals of all the disciplines considered in the study;</li> <li>the file "article_data_summary.csv" contains the aggregated data created considering the raw data in the previous files, which have been used to creating all the tables and figures in the article;</li> <li>the file "starred_metadata_set.csv" contains information about the most used subset of bibliographic metadata;</li> <li>the file "journals_selection.csv" contains information about all the journals selected for the study.</li> </ul>
Sentinel-5P Tropospheric Nitrogen Dioxide Density at 2 km from 2018-05 to 2022-11 Monthly Aggregation
<p>Layers include: Tropospheric Nitrogen Dioxide Density monthly median value May 2018 – November 2022. Derived using the <a href="https://eumap.readthedocs.io/en/latest/index.html#">eumap package in Python</a>. We derived three standard statistics: (1) 10th percentile (p10), median (m), and 90th percentile (p10).</p> <p>Band info</p> <table> <tbody> <tr> <td>Name</td> <td>Units</td> <td>Scale</td> <td> <p>Description</p> </td> </tr> <tr> <td>NO<sub>2</sub></td> <td>(µmol m<sup>-</sup><sup>2</sup>)</td> <td>0.1</td> <td>tropospheric nitrogen dioxide density</td> </tr> </tbody> </table> <p>Warning:</p> <p>Original data have the different range of latitude among months. In December, there are no data above N 58°, where is approximately between Iceland and Scotland. Therefore, when it comes to monthly aggregation, there is a strip across N 58° as an artifact. It is not suggested to use this dataset in the region above N 58°.</p> <p>For more info about the s5p NO<sub>2</sub> product see: <a href="https://maps.s5p-pal.com"><strong>https://maps.s5p-pal.com/</strong></a>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL in Cloud Optimised GeoTiff (COG). File naming convention:</p> <ul> <li>no2 = variable: nitrogen dioxide (µmol m<sup>-</sup><sup>2</sup>),</li> <li>s5p.l3.trop.tmwm= determination method: Copernicus Sentinel-5P product, level 3, tropospheric, temporal moving window median</li> <li>p10/p50/p90 = aggregation/statistics method: 10th/50th/90th percentile,</li> <li>2km = spatial resolution / block support: 2 km,</li> <li>a = vertical reference: above ground,</li> <li>start date_end date (i.e. 20180501_20180531) = time reference: from start date to end date</li> <li>go = bounding box: global land without Antarctica</li> <li>epsg.4326 = ESPG code: epsg.4326</li> <li>v20221219 = version code: creation date 20221219</li> </ul>
Aggregated number of officially recorded COVID-19 deaths in 2020 in Poland by county (powiat) with indication of sources
<p>This is a complimentary dataset for the article "Deaths during the first year of the COVID‑19 pandemic: insights from regional patterns in Germany and Poland" by Myck, Oczkowska, Garten, Krol, Brandt in BMC Public Health (DOI: 10.1186/s12889-022-14909-9).</p>
Microwave Single Scattering Properties Database (Horizontally Aligned Aggregates of Dendrites)
<p>The database contains physical and microwave single scattering properties of horizontally aligned frozen hydrometeors as large as 11 cm in diameter. </p> <p>A description of the aggregation model used for particle generation can be found in:<br> Leinonen, J., and Szyrmer, W. (2015), Radar signatures of snowflake riming: A modeling study, <em>Earth and Space Science</em>, 2, 346– 358, doi:<a href="https://doi.org/10.1002/2015EA000102">10.1002/2015EA000102</a>.<br> The code used for particle generation is freely available at: <a href="https://github.com/jleinonen/aggregation">https://github.com/jleinonen/aggregation</a></p> <p>The scattering properties of particles were computed using discrete dipole approximation using ADDA software package (<a href="https://github.com/adda-team/adda">https://github.com/adda-team/adda</a>)</p> <p>Terminal velocity of snowflakes was computed using 4 hydrodynamical models that were implemented as a part of snowScat library (<a href="https://github.com/OPTIMICe-team/snowScatt">https://github.com/OPTIMICe-team/snowScatt</a>)</p> <p>Approximately one half of the snowflake structure files and one quarter of scattering properties (for X, Ku, Ka and W band) were generated for the publication of Leinonen and Szyrmer (2015). The remaining part of the dataset was generated using the ALICE High Performance Computing Facility at the University of Leicester.</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.