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.
5,155
datasets available to search
ShareScore release 0.9.0
Dataset results
5,155 results for “resolution”
Assessment of the Effectiveness of Coarse Resolution Fire Products in Monitoring Long-term Changes in Fire Regime within Protected Areas in South Africa
<p>The dataset includes fire histrory (shapefiles with data perimeters for 2003-2020) within 3 protected areas in South Africa, Blyde, Manyeleti, and Songimvelo.</p>
GOES-16 Derived 1-km resolution Daily Solar Insolation (2022)
<p><strong><span>Dataset Description</span></strong></p> <p><span>This is the University of Alabama in Huntsville (UAH) GOES satellite-derived solar insolation dataset. The dataset and method have been described in previous documents, including Jacobs et al. (2008), Paech et al. (2009), Mecikalski et al. (2011, 2018), Diak (2017) and Cheng et al. (2020). The data are in separate files for Julian days 1-365 (or 366). The dataset extends for the period 1 January 1985 through 31 December 2023.</span></p> <p><span>Prior to 2022, the dataset domain was on a 474 x 407 grid at 2 km resolution centered on the State of Florida. From 2022 onward the data domain is on a 1668 x 1668 grid of 1 km resolution that includes several Southeastern U. S. states in addition to Florida. The daily solar insolation is in units of MegaJoules per square meter (MJ/m^2/day), and the data themselves are in compressed ASCII format with the latitude (degrees) and longitude (degrees) of a given point listed. These solar insolation data are used to produce an evapotranspiration (ET) product that covers all watersheds that flow onto the extent of the Floridan aquifer system, which will allow water managers in north Florida to use the dataset when conducting water budget analyses that necessarily cross state borders. The ET data are subsequently used for climatological studies and water resources management.</span></p> <p><span>From 1985 to 2021, the solar insolation data was developed using native 1-km resolution GOES-East visible channel data. For each grid point, an averaging of 4 x 4 1-km resolution pixels was done to define a 2 km resolution product. For the 2022 to current datasets, native 500-meter, 10-minute resolution channel 2 (0.64 µm) data are used rom the GOES-16/-18 satellite's Advanced Baseline Imager. For each grid point from 2022 onward, an averaging of 2 x 2 500-meter resolution pixels occurred to define a 1 km resolution product. The GOES data were gathered from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).</span></p> <p><span>Prior to 2022, a simple climatological or daily model reanalysis based total precipitable water (TPW) correction was implemented to the solar insolation data. From 2022 onward, the TPW correction for solar insolation is performed using the NESDIS Blended Hydrometeorological Product suite (BLENDHYDRO) “blended total precipitable water” data. These data, as obtained from NOAA CLASS, represents a merging of microwave derived TPW products from multiple polar-orbiting and geostationary satellite sensors including: AMU/MHS onboard the NOAA and MetOp satellite series; SSMIS onboard the DMSP satellite series; ATMS onboard S-NPP and NOAA-20; Sounder onboard the GOES satellite series; and GPS Met onboard Orbview-1, and have a spatial resolution of 16 km at the Equator. From 24 hourly files per day, a daily average TPW grid was created and then mapped onto the 1 km x 1 km grid that spans the domain of solar insolation coverage.</span></p> <p><strong><span>Data Quality</span></strong></p> <p><span>For the solar insolation dataset, between 15 and 22 pyranometer stations from across the state of Florida are utilized, with ~30%<span> </span>used for calibration of the satellite-estimated model product and the remaining used for validation of model performance. Every effort was made to screen for data quality, with the highest quality data being reserved for calibration. These data were provided by three State of Florida Water Management District (WMD) weather station networks (South Florida (SF), Saint John’s River (SJR) and Southwest Florida (SWF), the University of Florida (UF) Institute of Food and Agricultural Sciences (IFAS) Florida Automated Weather Network (FAWN), and the United States Geological Survey (USGS) network.</span></p> <p><span>The following uncalibrated and calibrated pyranometer station-averaged statistics were developed for comparison of satellite-estimated and pyranometer-measured daily-integrated insolation at the ten verification pyranometer station locations: mean bias error (MBE), root mean square error (RMSE, and as a percentage of the mean pyranometer-measured value given in parentheses), and coefficient of determination (R2). As an example for 2023, the uncalibrated values are: MBE = –0.59 MJ/m^2/day, RMSE = 1.38 MJ/m^2/day (8%), and R2 = 0.96. The calibrated values are: MBE = 0.19 MJ/m^2/day, RMSE = 1.20 MJ/m^2/day (7%), and R2 = 0.97.</span></p> <p><strong><span>References</span></strong></p> <p><span>Cheng, P., A. Pour-Biazar, R. T. McNider, and J. R. Mecikalski, 2020: Validation of GOES-based surface insolation retrievals and its utility for model evaluation. <em>J. Atmos. Ocean Tech</em>., <strong>37</strong>, 553–571.</span></p> <p><span>Diak, G. R., 2017: Investigations of improvements to an operational GOES-satellite-data-based insolation system using pyranometer data from the U. S. Climate Reference Network (USCRN). <em>Remote Sens. Environ</em>., <strong>195</strong>, 79–95, doi:10.1016/j.rse. 2017.04.002.</span></p> <p><span>Jacobs, J., J. Mecikalski, and S. Paech, 2008: Satellite-based solar radiation, net radiation, and potential and reference evapotranspiration estimates over Florida. Technical Report. July 2008, 138 pp. http://fl.water.usgs.gov/et/publications/GOES_FinalReport.pdf.</span></p> <p><span>Mecikalski, J. R., W. B. Shoemaker, Q. Wu, M. A. Holmes, S. J. Paech, and D. M. Sumner, 2018: A 20-Year high-resolution GOES insolation–evapotranspiration dataset for water resource management over the State of Florida. <em>J. Irrig. Drain. Eng</em>., <strong>144</strong>(9): 04018025.</span></p> <p><span>Mecikalski, J. R., D. M. Sumner, J. M. Jacobs, C. S. Pathak, S. J. Paech, and E. M. Douglas, 2011: Use of visible Geostationary Operational Meteorological Satellite imagery in mapping reference and potential evapotranspiration over Florida. <em>Evapotranspiration</em>. ISBN 978-953-307-251-7, Editor Leszek Labedzki, Chapter 10, pgs. 229-254.</span></p> <p><span>Paech, S. J., J. R. Mecikalski, D. M. Sumner, C. S. Pathak, Q. Wu, S. Islam, and T. Sangoyomi, 2009: A calibrated, high-resolution GOES satellite solar insolation product for a climatology of Florida evapotranspiration. <em>J. Amer. Water Resources Assoc.</em>, <strong>45</strong>, 1328-1342.</span></p>
Global Daily River Discharge product at 0.5° resolution from ISBA-CTRIP simulations based on ERA5-GPCC forcing from 1950 to 2023
<p>This product provides <strong>global daily river discharge at a resolution of 0.5 degree from 1950 to 2023</strong>. It is derived from an offline simulation conducted with the <a href="http://www.umr-cnrm.fr/spip.php?article1092&lang=en" target="_blank" rel="noopener">ISBA-CTRIP</a> global land surface modeling system (<a href="https://doi.org/10.1029/2018MS001545" target="_blank" rel="noopener"><em>Decharme et al. </em>2019</a>) embedded in the <a href="http://www.umr-cnrm.fr/spip.php?article145&lang=en" target="_blank" rel="noopener">SURFEX</a> version 8 modeling platform. It was designed for use in large-scale hydrological applications, as well as in the the <a href="http://www.umr-cnrm.fr/cmip6/spip.php?rubrique8" target="_blank" rel="noopener">CNRM climate models</a> that participate in <a href="https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6" target="_blank" rel="noopener">CMIP6</a> but also in large scale hydrological applications. The model is driven by meteorological forcing data (temperature, precipitation, humidity, winds, etc) derived from the <a href="https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5" target="_blank" rel="noopener">ERA5</a> global atmospheric reanalysis, but where montly precipitation is corrected to align with the <a href="https://www.dwd.de/EN/ourservices/gpcc/gpcc.html" target="_blank" rel="noopener">GPPC</a> observed precipitation product. GPCC <a href="https://opendata.dwd.de/climate_environment/GPCC/html/fulldata-monthly_v2022_doi_download.html">Full Data Monthly Product Version 2022</a> at 0.5 degree resolution are used from 1950 to 2020. For the last three years (2021 to 2023), we used the GPCC <a href="https://opendata.dwd.de/climate_environment/GPCC/html/gpcc_monitoring_v2022_doi_download.html" target="_blank" rel="noopener">Monitoring Product Version 2022</a>, which is only available at 1 degree resolution, and then interpolated at 0.5 degree using a conservative remapping.</p>
Daily streamflow at 0.5 deg resolution generated using 0.22 deg runoff from the historical (1986-2005) and two future scenarios' (RCP 4.5 and 8.5, 2081-2100) simulations of CanRCM4 for its North American domain
<p>These data are provided in support of the following manuscript which is currently (July 2024) under revision for the <strong>Hydrology and Earth System Science</strong> journal. The details about how these data were generated can be found in this manuscript (or its final accepted version, hoping our manuscript will be accepted). <br><br><a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/">https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/</a><br><br>The effect of climate change on the simulated streamflow of six Canadian rivers based on the CanRCM4 regional climate model<br>Vivek K. Arora, Aranildo Lima, and Rajesh Shrestha <br><br>The netcdf files provide here contain two variables.<br><br>1) streamflow, variable name is fout_land, units are m3/s<br>2) flow velocity, variale name is velocity, units are m/s<br><br><br></p>
Data used in manuscript "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning"
<p>Data used in the study titled "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning" published in Environmental Research Letters</p>
Implications of Lateral Groundwater Flow Across Varying Spatial Resolutions in Global Land Surface Modeling
<p>This folder contains the data used for plotting and analysis in the manuscript.</p>
EARice10: A 10 m Resolution Annual Rice Distribution Map of East Asia for 2023
<p>This dataset provides a 10 m <span>Resolution </span><span>Annual Rice </span><span>Distribution </span><span>Map of East Asia </span><span>for </span><span>2023 </span>(<strong>EARice10</strong>). The accuracy of EARice10 <span>is </span>evaluated using more than 90,000 validation samples and achieves an overall accuracy of 90.48%, with both user’<span>s</span> and producer’<span>s</span> <span>accuracies</span> exceeding 90%. The reliability of the dataset is verified by an R<sup>2</sup> <span>values ranging</span> between 0.94 and 0.98 with respect to official statistics<span>,</span> and between 0.79 and 0.98 with respect to previous rice mapping datasets.</p> <p><strong>File format:</strong> tif</p> <p><strong>Spatial extent:</strong> East Asia</p> <p><strong>Pixel size:</strong> 10 m</p> <p><strong>Projection information:</strong> EPSG: 4326 (WGS84)</p> <p><strong>Crop type: </strong>Paddy rice</p> <p><strong>Year:</strong> 2023</p>
High-resolution topography data and fault trace along the Southern Riyueshan fault, NE margin of Tibetan Plateau, China
<p>High-resolution digital elevation models (DEM) topography data extracted from the uncrewed aerial vehicle (UAV) of a DJI (Dajiang Innovations Science and Technology Co., Ltd.) Phantom 4 RTK, and GF-7 satellite stereo imagery. The trace of the Riyueshan fault is interpreted based on these high-resolution topography data.</p>
Near real-time ultrahigh-resolution imaging from unmanned aerial vehicles for sustainable land use management and biodiversity conservation in semi-arid savanna under regional and global change (SAVMAP)
<p>To prevent aggravation of existing poverty in semi-arid savannas, a comprehensive concept for the sustainable adaptive management and use of these ecosystems under unprecedented conditions is needed. SAVMAP is an innovative, trans-, and inter-disciplinary initiative whose goal is to develop a valuable monitoring tool for both sustainable land-use management and rare species conservation (black rhinoceros) in semi-arid savanna in Namibia. SAVMAP uses near real-time ultrahigh-resolution photographic imaging (NURI) facilitated by unmanned aerial vehicles (UAVs) designed at EPFL.</p>
High temporal resolution microclimate records
<b>Description: </b><p>Microclimate records collected at very high temporal resolution (10 sec) at a small number of sites</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/111"><b>Microclimate stratification in modified forests</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=52">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>High temporal resolution microclimate records</b> (Worksheet Data)</p><p>Dimensions: 229330 rows by 7 columns</p><p>Description: Microclimate records collected at very high temporal resolution (10 sec) at a small number of sites</p><p>Fields: </p><ul><li><b>Plot</b>: Location of record (Field type: Location)</li><li><b>time</b>: Date and time of record (Field type: Datetime)</li><li><b>Temp</b>: Air temperature 1 m above ground (Field type: Numeric)</li><li><b>RH</b>: Relative humidity. (Field type: Numeric)</li><li><b>LoggerType</b>: Make of datalogger that was used (Lascar or iButton) (Field type: ID)</li><li><b>LoggerID</b>: Unique reference number for the datalogger (Field type: ID)</li></ul><br></li></ol><p><b>Date range: </b>2015-12-11 to 2016-12-07</p><p><b>Latitudinal extent: </b>4.7104 to 4.7523</p><p><b>Longitudinal extent: </b>116.9484 to 117.6278</p>
Land facet data for North America at 100m resolution.
<p>We developed a dataset categorizing the North American continent into physical habitat types at 100m resolution. The input data used included elevation and soil type. Further details on methodology are available <a href="http://www.cacpd.org/Facets/landfacetmethods.pdf">here</a>. The data is provided via a link to a zipfile containing TIFF (.tif) format files that can be imported into ArcGIS or other GIS applications. Projection information is available <a href="http://www.cacpd.org/Facets/lazea.prj">here</a>.</p> <p>One potential strategy for protecting biodiversity in a changing climate is based on the idea of protecting the diversity of abiotic conditions that influence patterns of biodiversity. In this strategy, conservation features (the “targets” considered in the conservation planning process) are derived from data on physical features such as topography, soils, and geology. The approach has been described as “conserving the ecological stage” or protecting “land facets” or “enduring features”. Species distributions, communities, ecosystems, and broader patterns of biodiversity are clearly influenced by abiotic drivers such as soils, geology, topography, and climate. Although climates will change relatively rapidly over the coming century, soils, geology, and topography will not. Thus, local, and some regional, climate patterns and gradients influenced by topography will persist (e.g., higher elevations will still be cooler than lower elevations, although both will likely be warmer) as climates change. The hypothesis underlying use of land facets in climate adaptation planning is that by protecting a diversity of land facets, it may be possible to protect areas that will foster a diversity of biota in the future, albeit different biota than those areas would protect today. Although land facets are clearly an imperfect coarse-filter surrogate for biodiversity, physical habitat diversity may still represent a useful additional source of data that can augment biodiversity data in conservation planning processes.</p>
Data and scripts for the paper: Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations
<p>This is a collection of the processed data, plotting scripts, and processing scrips used in the making of the publication: "Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations" By Griewank, Schemann, and Neggers. </p> <p>WARNING: These files were used exclusively used by Philipp Griewank on his local work station, and the main purpose of uploading them is for reasons of scientific transparency. They are not highly optimized tools intended for general usage, so comments are hit and miss. Feel free to contact Philipp Griewank (currently philipp.griewank@uni-koeln.de) with any questions. </p>
High resolution annual average air pollution concentration maps for the Netherlands
<p>Raster-based air pollution concentration maps for the Netherlands. The dataset consists of air pollution concentration maps for six pollutants (NO2, NO2background, NOx, PM2.5, PM2.5absorbance, PM10), covering the land mass of the Netherlands at 5m spatial resolution. The maps were calculated using the Land Use Regression models from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Several Python scripts used for data preparation and the model scripts creating the datasets are included. Use the free 7-Zip to uncompress. Uncompressed size: 78 GiB.</p> <p>A description of concepts, datasets and scripts is given in the manuscript "High resolution annual average air pollution concentration maps for the Netherlands" by Oliver Schmitz, Rob Beelen, Maciej Strak, Gerard Hoek, Ivan Soenario, Bert Brunekreef, Ilonca Vaartjes, Martin J. Dijst, Diederick E. Grobbee, and Derek Karssenberg. <em>Scientific Data</em> 6:190035 (2019). <a href="https://doi.org/10.1038/sdata.2019.35">https://doi.org/10.1038/sdata.2019.35</a></p> <p>The datasets are licensed under a Creative Commons license (CC-BY 4.0). The Python scripts are licensed under the MIT License.</p> <p>Contact: o.schmitz@uu.nl</p>
High-resolution cone-beam scan of an apple and pebbles with two dosage levels
<p>We release two tomographic scans with two levels of radiation dosage of two measured objects for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The objects are referred to as apple and pebbles (more specific, hydrograins), respectively. The dataset collected with higher dosage is referred to as the "<em><strong>good</strong></em>" <strong>dataset</strong>; and the other as the "<em><strong>noisy</strong></em>" <strong>dataset</strong>, as a way to distinguish between the two dosage levels.</p> <p>The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel. </p> <p> </p> <p>Both dataset were collected over a 360 degrees in circular and continuous motion with 2001 projections distributed evenly over the full circle for the good dataset and 501 projections distributed evenly over the full circle for the noisy dataset. The uploaded dataset are not binned or normalized; a single dark and two (pre- and post-) flat fields are included for each scan. Projections for both sets were collected with 100 ms exposure time with the good data projections averaged over 5 takes, and no averaging was made for the noisy data. The tube settings for the good and noisy dataset were 70kV, 45W and 70kV, 20W, respectively. The total scanning time were 20 minutes for the good; 3 minutes for the noisy scan. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings.</p> <p> </p> <p>These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl or</li> <li>m.j.lagerwerf [at] cwi.nl</li> </ul>
High-resolution cone-beam scan of two pomegranates with two dosage levels
<p>We release tomographic scans of two pomegranates with two levels of radiation dosage of two measured objects for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The dataset collected with higher dosage is referred to as the "<em><strong>good</strong></em>" <strong>dataset</strong>; and the other as the "<em><strong>noisy</strong></em>" <strong>dataset</strong>, as a way to distinguish between the two dosage levels.</p> <p>The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel. </p> <p> </p> <p>Both dataset were collected over a 360 degrees in circular and continuous motion with 2001 projections distributed evenly over the full circle for the good dataset and 501 projections distributed evenly over the full circle for the noisy dataset. The uploaded dataset are not binned or normalized; a single dark and two (pre- and post-) flat fields are included for each scan. Projections for both sets were collected with 100 ms exposure time with the good data projections averaged over 5 takes, and no averaging was made for the noisy data. The tube settings for the good and noisy dataset were 70kV, 45W and 70kV, 20W, respectively. The total scanning time were 20 minutes for the good; 3 minutes for the noisy scan. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings.</p> <p> </p> <p>These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl or</li> <li>m.j.lagerwerf [at] cwi.nl</li> </ul>
Dakar population estimates at 100x100m spatial resolution - grid layer - Dasymetric mapping
<p>This dataset contains the a raster layer with the population estimates obtained using a dasymetric mapping procedure (top-down approach). For a detailed description of the methodology, please refer to the following paper:</p> <p>Grippa, Taïs, Catherine Linard, Moritz Lennert, Stefanos Georganos, Nicholus Mboga, Sabine Vanhuysse, Assane Gadiaga, and Eléonore Wolff. 2019. “Improving Urban Population Distribution Models with Very-High Resolution Satellite Information.” <em>Data</em> 4 (1): 13. <a href="https://doi.org/10.3390/data4010013">https://doi.org/10.3390/data4010013</a>.</p> <p>Funding and aknowledgement: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be/">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be/">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p> <p>The authors gratefully thanks the \href{http://assess-sn.org/}{ASSESS project}, funded by the \href{https://www.ares-ac.be}{ARES-CDD}, that provided the access to the census data.</p>
Revisiting the impacts of Stochastic Multicloud model on the MJO using low-resolution ECHAM6.3 atmosphere model
<p>There are the corresponding source codes and input data used to run the numerical experiments. Analysis scripts and model results are also included.</p>
PTB 2D GRAPPA Acquisitions of ACR Resolution Phantom
<p>ACR resolution phantom data acquired on the Siemens Verio 3T MR scanner using a FLASH sequence.<br> Three datasets were acquired with a fully sampled k-space, GRAPPA 2 and GRAPPA 4 acceleration.</p> <p>The data is available in ISMRMRD format.</p> <p> </p>
3-km high resolution model outputs using the WRF and WRF-Hydro model for HRB
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Heihe river basin (HRB). Model results are used for investigating the effect of lateral terrestrial water flow on regional climate modeling. The analysis results were published in a peer-reviewed journal at https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018JD030174</p> <p>Two experiments use the following model configuration: 3km horizontal resolution with 350*350 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Leibniz Supercomputing Center (LRZ) SuperMUC. </p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrfout_selvar_P_ET_R_DRA" provides P, ET, surface runoff, drainage, and filename with "wrfout_selvar_T_Q2_SM_SH2O_AWS_CONV" provides T, specific humidity, soil moisture, and liquid water, atmosphere water storage and convergence. Tagged precipitation from upper HRB is also provided.</p>
Atomic-resolution imaging of surface and core melting in individual size-selected Au nanoclusters on carbon
<p>Experimental and theoretical data sets for the Nature Communications paper "Atomic-resolution imaging of surface and core melting in individual size-selected Au nanoclusters on carbon". Data structure and formats are described in the README.docx file.</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.