Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
670
datasets available to search
ShareScore release 0.9.0
Dataset results
670 results for “gridded data”
Globally-gridded data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon
<p>Supporting globally-gridded data products for manuscript: Georgiou K., Jackson R. B., Vindušková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. <em>Nature Communications</em>, 2022.</p> <p>We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415) along with ancillary data on climate, vegetation, and soil characteristics to produce spatially-explicit global estimates of mineral-associated soil organic carbon stocks (MOC) and mineralogical carbon capacity (MOC<sub>max</sub>) in non-permafrost, non-desert mineral soils. Globally-gridded datasets are given in kgC/m<sup>2</sup> for topsoil (0-30cm) and subsoil (30-100cm) at 0.5 degree by 0.5 degree spatial resolution.</p>
Data set for "Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel"
<p>Here we share the relevant data of the manuscript “Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel”.</p> <p>Files:</p> <ul> <li>Opt_Fr_stability_part1.txt</li> <li>Opt_Fr_stability_part2.txt</li> </ul> <p>contain the data used for evaluation of optical frequency transfer stability (Fig. 7 in the paper). The measurements were done with 8-channels K+K phase/frequency recorder. Column 1 contains date, col. 2: time, col. 5: in-loop beatnote phase, col. 6: out-of-loop beatnote phase. The phase is recorded in cycles. In case of out-of-loop beatnote it was divided by factor of two before recording, therefore the data from col. 6 should be multiplied by two to obtain true values of the optical phase fluctuations.</p> <p>File:</p> <ul> <li>RF_stability.txt</li> </ul> <p>contains the data used for evaluation of RF frequency transfer stability (Fig. 8 in the paper). Column 1 contains time in hours, and col. 2 RF phase fluctuations in seconds.</p>
Measurement data of a three-phase grid-side converter with a grid-forming synchronverter-based control method with current limitation
<p>The data set was recorded for a publication currently undergoing the submission process. The published data correspond to the data presented in the figures. The first column represents the time vector. All other columns are linked to the corresponding scenario by an identifier in the column name. The column name also contains the name of the recorded signal and the associated unit. The naming convention is <identifier_to_figure>_<recorded_signal>_<unit>.</p>
Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)
<p><strong>Gridded historical climate </strong><strong>data over China, spanning 1851 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by 20th century reanalysis (20CRv2c, NOAA/ESRL PSD 20th Century Reanalysis version 2c, ensemble member 37).</strong></p> <p>This data has been un-rotated to true latitude longitude coordinates from its original rotate pole frame of reference. For more information on the PRECIS regional climate model, visit <a href="http://www.metoffice.gov.uk/precis">www.metoffice.gov.uk/precis</a>. Data near the boundaries should be used with caution due to model configuration aspects of regional climate modelling, and the interpolation method applied.</p> <p><strong>Domain</strong>: 17N to 58.84N, 73E to 135.7E</p> <p><strong>Countries covered</strong>: China, Nepal, Bhutan, Bangladesh, Taiwan, Mongolia, North Korea, South Korea, Kyrgzstan, and northern parts of India, Myanmar, Lao PDR & Vietnam.</p> <p><strong>Variables</strong>: pr (mean precipitation flux), tm (mean surface temperature), tn (minimum surface temperature) & tx (maximum surface temperature)</p> <p><strong>Time averaging</strong>: monthly</p> <p> </p> <p><em>This data set supplements the equivalent downscaled ERA-Interim data set: <a href="https://zenodo.org/record/2600192#.XJj3uKD7RWE">Downscaled ERA-Interim gridded historical climate data over China (1980-2010)</a> doi: 10.5281/zenodo.2600192</em></p>
CEDS_Gridded_Data_Proxies_v2019_06_18
<p>Spatial proxy data needed to produce gridded historical emission datasets using the <a href="https://github.com/JGCRI/CEDS">Community Emissions Data System</a>. The published reference for this data is <a href="https://www.geosci-model-dev.net/11/369/2018/gmd-11-369-2018.html">Hoesly et al, Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev. 11, 369-408, 2018.</a></p>
Magnetic data grids of the Northern Vosges, surveys EOST2008 and GPR2015.
<p>Grid computation and details are explained in Gavazzi et al., 2019 [1] and exploited in Bertrand et al., [2].<br> This file contains non-null grid elements.</p> <p>PARAMETERS<br> Format : ASCII<br> Number of elements : 86902<br> Grid cell size : 125 m</p> <p>FIELDS<br> - LON : grid cell WGS84 longitude (°East);<br> - LAT : grid cell WGS84 latitude (°North );<br> - ALT : grid cell altitude amsl (m) - this grid was computed at a constant altitude of 1400 m above mean sea level;<br> - TMI : total magnetic intensity anomaly (nT);<br> - RTP : (double) reduction to the pole of the TMI (nT) - mean regional field direction Inclination=1.60°, Declination=64.14°;<br> - DV : vertical derivative of the RTP at order 0.5 (nT/m);<br> - DH : horizontal derivative of the RTP at order 1 (nT/m);</p> <p>[1] Gavazzi, B., Bertrand, L., Munschy, M., Mercier de Lépinay, J., Diraison, M. & Géraud, Y. (submitted). On the use of aeromagnetism for geological interpretation part I: comparison of 1 scalar and vector magnetometers for aeromagnetic surveys and an equivalent source 2 interpolator for combining, gridding and transform fixed altitude and draping 3 datasets, Journal of Geophysical Research: Solid Earth (2019).</p> <p>[2] Bertrand, L., Gavazzi, B., Mercier de Lépinay, J., Diraison, M., Géraud, Y. & Munschy, M. (submitted). On the use of aeromagnetism for geological interpretation part II: geological interpretation on outcropping basement rocks for geothermal energy prospection, Journal of Geophysical Research: Solid Earth (2019).</p>
Socio-economic development of global river deltas from gridded data
<p>Crop, population, and GDP values in the world's major river deltas, derived from publicly available gridded datasets. </p> <p>v0: Dec. 2022</p> <p>v1: Jan 2023 (added Metadata)</p>
Gridded Surface Emission Data for Delhi-NCR
<p>The gridded (~400mt) surface chemistry data for Delhi-NCR with domain size 70km X 65 km which includes megacity Delhi and surrounding regions by including 17 different types of Anthropogenic sources of air pollutants like PM<sub>2.5</sub>, PM<sub>10</sub>, CO, NO<sub>x</sub>, VOC, SO<sub>2</sub>, BC and OC for the base year 2020. Unit is Tons/Grid/yr. The files are given in .shp file format suitable for GIS software.</p>
ONFIRE Dataset: Monthly Gridded Burned Area data
<p>The ONFIRE Dataset presents a 1° x 1° lat-long gridded database detailing monthly burned areas (BA) stemming from national fire data across five regions: Australia, Canada, Chile, Europe, and the United States. Each grid cell encapsulates the center's latitude and longitude, coupled with the total square meters burned within the month. The dataset spans varying periods per region, starting from 1950 in Australia, 1959 in Canada, 1985 in Chile, 1980 in Europe, and 1984 in the US, extending up to 2021.The ONFIRE DATASET is available in netCDF4, RData, and ASCII formats for accessibility and ease of integration.</p>
Microclimate data for Saddle grid, 1993, daily.
The objective of this field campaign was to measure microclimatic variables within a mid-latitude alpine tundra zone during the growing season (June - August), and to relate those observations to ecosystem processes and landscape patterns. Microclimatic data were recorded using a Campbell CR21X micrologger on or near the Niwot Ridge Saddle grid. Five sites were chosen (2 of which were snow covered and the remaining 3 being snowfree tundra). Measurements were made at 60-second intervals and averaged on the half hour. These half-hourly data were subsequently averaged for the day. All meteorological instruments on the tower were oriented parallel to the surface at the tower location. The anemometer was located at the top of the meteorological station tower (i.e., 3 m above the surface). The air temperature and relative humidity probe were 2 m above the surface. The pyranometer used for the calculation of incoming solar radiation was 1.5 m above the surface. The pyranometer used for the calculation of outgoing solar radiation was 1.25 m above the surface. The net radiometer was situated between 0.75 and 1 m above the surface. The heat flux transducer was located 5 cm below the surface of the vegetation canopy. The soil temperature probe was located between 0 and 10 cm below the ground surface.
Microclimate data for Saddle grid, 1993, half-hourly.
The objective of this field campaign was to measure microclimatic variables within a mid-latitude alpine tundra zone during the growing season (June - August), and to relate those observations to ecosystem processes and landscape patterns. Microclimatic data were recorded using a Campbell CR21X micrologger on or near the Niwot Ridge Saddle grid. Five sites were chosen (2 of which were snow covered and the remaining 3 being snowfree tundra). Measurements were made at 60-second intervals and averaged on the half hour. These half-hourly data were subsequently averaged for the day. All meteorological instruments on the tower were oriented parallel to the surface at the tower location. The anemometer was located at the top of the meteorological station tower (i.e., 3 m above the surface). The air temperature and relative humidity probe were 2 m above the surface. The pyranometer used for the calculation of incoming solar radiation was 1.5 m above the surface. The pyranometer used for the calculation of outgoing solar radiation was 1.25 m above the surface. The net radiometer was situated between 0.75 and 1 m above the surface. The heat flux transducer was located 5 cm below the surface of the vegetation canopy. The soil temperature probe was located between 0 and 10 cm below the ground surface.
Spectral radiation data for Saddle grid, 1994.
During the summer of 1992 10-m transects were established at each of the Niwot Ridge Saddle grid points, with the exception of grid points 1, 13, 30A, 37, 41, 42, 63, and 71. No transects were set up at those locations because they were essentially barren of vegetation or, in the case of grid point 37, were covered with a willow shrub thicket. Transect locations were chosen so that the vegetation along the transect would be consistent with that in the 1x1-m plot used to characterize the species composition at the corresponding Saddle grid point. These transects were established as part of a study designed to quantify any relationship between spectral emissivity and net primary productivity. Spectral measurements were made, using a PS-II (Analytical Spectral Devices) spectrometer, of approximately 50- x 20-cm plots along a transect immediately prior to it being clip harvested for aboveground biomass determinations. Photographs were taken at the same time that the spectral measurements were made in 1994, but at different times in 1993. The photographs were subsequently used to quantify various cover classes for the harvested portion of the transect. The relationships between these cover classes and vegetation indices derived from the spectral data were explored.
Cover class data for Saddle grid, 1993 - 1994.
During the summer of 1992 10-m transects were established at each of the Niwot Ridge Saddle grid points, except grid points 1, 5, 13, 15, 25, 30A, 35, 37, 41, 42, 45, 46, 55, 63, 65, and 71. No transects were set up at locations 1, 13, 30A, 41, 42, 63, and 71 because they were essentially barren of vegetation or, in the case of grid point 37, were covered with a willow shrub thicket. Transect locations were chosen so that the vegetation along the transect would be consistent with that in the 1x1-m plot used to characterize the species composition at the corresponding Saddle grid point. These transects were established as part of a study designed to quantify any relationship between spectral emissivity and net primary productivity. Spectral measurements were made, using a PS-II (Analytical Spectral Devices) spectrometer, of approximately 50- x 20-cm plots along a transect immediately prior to it being clip harvested for aboveground biomass determinations. Photographs were taken at the same time that the spectral measurements were made in 1994, but at different times in 1993. The photographs were subsequently used to quantify various cover classes for the harvested portion of the transect.
Snow depth data for saddle grid, 1982 - 1990.
The depth of snow was measured at 88 points on the Saddle grid. Depths were measured form fixed extendable poles on the western or accumulation portion of the 350 x 500 m grid, while depths were measured by probing on the eastern portion. Measurements during winter months varied from every 2 weeks to about monthly depending on weather conditions. Measurements during the summer were approximately weekly until all snow disappeared or snow accumulation began for the next winter. Meltout date can be determined from the last date snow was present at a grid point. The area of the study was 19.7 ha.
CICE gx1 Grid and Initial Condition Data - 2020.03.20
<p>This file contains the gx1 grid and initial condition data for CICE. </p> <p>The latest information about CICE forcing data and files can be found at the GitHub Resource Index: <a href="https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index">https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index</a> under the "Input Data" link.</p> <p>These data are provided by the CICE Consortium (<a href="https://github.com/CICE-Consortium">https://github.com/CICE-Consortium</a>).</p>
CICE gx3 Grid and Initial Condition Data - 2020.03.20
<p>This file contains the gx3 grid and initial condition files for CICE. </p> <p>The latest information about CICE forcing data and files can be found at the GitHub Resource Index: <a href="https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index">https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index</a> under the "Input Data" link.</p> <p>These data are provided by the CICE Consortium (<a href="https://github.com/CICE-Consortium">https://github.com/CICE-Consortium</a>).</p>
Data for: Downscaled gridded global dataset for Gross Domestic Product (GDP) per capita at purchasing power parity (PPP) over 1990-2022
<p>This dataset provides a gridded dataset for GDP per capita at purchasing power parity (PPP) downscaled to an admin 2 level (43,501 admin units). The dataset is based on reported subnational admin data (from 89 countries and 2,708 subnational units) and spans three decades from 1990 to 2022. </p> <p>The dataset is presented in details in the following publication. <strong><em>Please cite this paper when using data. </em></strong></p> <p>Kummu, M., Kosonen, M. & Masoumzadeh Sayyar, S. 2025. Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022. Scientific Data 12: 178. <a href="https://doi.org/10.1038/s41597-025-04487-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-04487-x</a></p> <p><strong>Code is available</strong> at: <a href="https://github.com/mattikummu/griddedGDPpc" target="_blank" rel="noopener">https://github.com/mattikummu/griddedGDPpc </a></p> <p> </p> <p><strong>The following data is given (formats in brackets)</strong></p> <ul> <li>GDP per capita (PPP) at admin 0 level (national) (GeoTIFF, gpkg, csv)</li> <li>GDP per capita (PPP) at admin 1 level (at the level of reporting, either admin 1 level or admin 0 level) (GeoTIFF, gpkg, csv)</li> <li>GDP per capita (PPP) at admin 2 level (downscaled from admin 1 level) (GeoTIFF, gpkg, csv)</li> <li>Total GDP (PPP), downscaled admin 2 level GDP per capita (PPP) multiplied by gridded population count, with three resolutions: 30 arc-sec, 5 arc-min, and 30 arc-min (GeoTIFF) </li> <li>Input data for the script that was used to generate the data above (code_input_data.zip). Code available at https://github.com/mattikummu/griddedGDPpc </li> </ul> <p><strong>Files are named as follows</strong><br><em>Format</em>: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*. <br><em>Admin levels:</em> adm0 for admin 0 level, adm1 for admin 1 level, and adm2 for admin 2 level<br><em>Product type:</em> GDP per capita at purchasing power parity (PPP): _gdp_perCapita_; and total GDP at purchasing power parity (PPP): _gdp_tot_</p> <p> </p> <p><strong>Metadata </strong></p> <p><em>Grids for GDP per capita data:</em></p> <p>Resolution: 5 arc-min (0.083333333 degrees) (for admin 2 level also 30 arc-min, 0.5 degree, resolution is provided)</p> <p>Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: Multiband geotiff; each band for each year over 1990-2022 </p> <p>Unit: USD in 2017 international dollars</p> <p> </p> <p><em>Grids for total GDP:</em></p> <p>Resolution: 30 arc-sec, 5 arc-min or 30 arc-min</p> <p>Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: Multiband geotiff; each band for each year over 1990-2022 (5 arc-min, 30 arc-min) or for each five years 1990, 1995, ... 2015, 2020 (30 arc-sec)</p> <p>Unit: USD in 2017 international dollars</p> <p> </p> <p><em>Geospatial polygon (gpkg) files: </em></p> <p>Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax) </p> <p>Temporal extent: annual over 1990-2022</p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: gkpk </p> <p>Unit: USD in 2017 international dollars</p>
Supplementary data: "Influence of flexibility options on the German transmission grid — A sector-coupled mid-term scenario"
<p>This repository contains result data for the paper <i> "Influence of flexibility options on the German transmission grid — A sector-coupled mid-term scenario"</i>.</p><p>The published data includes optimization results of the three main scenarios in the mentioned publication. </p><p>The data for each scenario is stored as csv-files, which allows analysing it with many different tools. In addition, the data can be imported in Python as a network object of the open-source tool PyPSA by using the function <a href="https://pypsa.readthedocs.io/en/latest/api_reference.html#pypsa.Network.import_from_csv_folder">import from csv folder </a>. </p><p> </p><p>The authors thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project eGon (funding code: 03EI1002).</p>
Distribution grid data generated by ding0
<p>Distribution grid data generated with ding0 in the <a href="https://ego-n.org/" target="_blank" rel="noopener">eGo^n project</a>.<br>Data from pre-release v0.3.0-alpha using branch <em>ding0_run/2023_04_06</em>, head: <a href="https://github.com/openego/ding0/tree/9fe5f1c3785ccb2f5afa56fe25e4f2290df76e5c" target="_blank" rel="noopener">9fe5f1c3785ccb2f5afa56fe25e4f2290df76e5c</a>.</p> <p>Input data from eGon-data, branch <a href="https://github.com/openego/eGon-data/tree/continuous-integration/run-everything-2022-11-10" target="_blank" rel="noopener">run-everything-2022-11-10</a>.</p> <p>See <code>README.md</code> for details.</p> <p> </p> <p> </p>
Data Grids for examples in Probe Particle Atomic Force Microscopy simulation program (ppafm)
<p>These files are used for running the examples for [ppafm](https://github.com/Probe-Particle/ppafm/) program.</p> <p>The grids are stored in in [.xsf](http://www.xcrysden.org/doc/XSF.html) and [.cube](https://paulbourke.net/dataformats/cube/) format.</p> <p>The data set compiles both the new examples used in paper [Advancing scanning probe microscopy simulations: A decade of development in probe-particle models](https://www.sciencedirect.com/science/article/pii/S0010465524002649) as well as older examples.</p> <p>Notice that the structure does not exactly reflect the directory structure in the [example folder of ppafm](https://github.com/Probe-Particle/ppafm/tree/main/examples) to prevent possible redudancy, but is instead flatenized and sorted by molecules.</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.