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.
25
datasets available to search
ShareScore release 0.9.0
Dataset results
25 results for “Climate grids”
German weather services (DWD) multi annual meteorological rasters for the climate period 1991-2020 refined to 25m grid
<h1>Overview</h1> <p>These are two multi-annual raster products from the german weather service, that got refined from a 1km grid to a 25m grid, by using a local regression model.</p> <p>The base rasters from DWD are:</p> <ul> <li>HYRAS precipitation</li> <li>REGNIE precipitation</li> <li>DWD-grid (precipitation, potential evapotranspiration and temperature 2m above ground)</li> </ul> <p>To refine the grids the Copernicus DEM with a resolution of 25m got used. For every cell a linear regression model got created, by selecting the multi-annual rasters value and the elevation, from the original digital elevation model that was used by the DWD to create the raster, in a certain window around the cell. This window was at least 2 cells around the considered cell, so 5x5=25 cells. If the standard deviation of the elevation in this window was less than 4m, more neighbooring cells are considered until a maximum of 13x13=169 cells are considered. This widening of the window was necessary for flat regions to get a reasonable regression model.</p> <p>Out of these combinations of elevation and climate parameter a linear regression model was build. These regression models are then applied to the finer digital elevation model with its 25m resolution from Copernicus.</p> <p>The following image illustrates the generation of the refined rasters on a small example window:</p> <p></p>
Climate based seed zones for Mexico: spatial grids to guide reforestation under observed and projected climate change
<p>This database entry provides climate-based seed zone system for Mexico to address climate change observed over the last 30 years and projected climate change for the 2050s. The database corresponds to a journal publication by Castellanos-Acuña et al. (2018), available at https://doi.org/10.1007/s11056-017-9620-6. This seed zone classification is based on bands of two climate variables that have often been shown to drive genetic adaptation of tree species: mean coldest month temperature (MCMT), and an aridity index (AHM). MCMT was divided into ten bands of 3°C intervals, with the limits of these bands being, temperatures below <2°C, 2-5°, 5-8°, 8-11°, 11-14°, 14-17°, 17-20°, 20-23°, 23-26°, >26°C. AHM was divided into seven bands with intervals that are approximately equal width under a log-transformation: <20, 20-30, 30-45, 45-65, 65-95, 95-140, and >140 °C/mm. The gridded files provided in this database entry, the classes are coded as integer numbers, with the last digit representing the AHM class (1-7) and the first or first and second digit representing the MCMT class (1-10).</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>
Downscaled climate grids at 30m for a variety of bioclimatic variables over the San Joaquin Experimental Range, CA: 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
Downscaled climate grids at 30m for a variety of bioclimatic variables over the Teakettle Experimental Forest, 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
Downscaled climate grids at 30m for a variety of bioclimatic variables over the Tejon Ranch, CA: 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
Datasets used for "Heat Pump - Heating Electrification and Climate Change - Grid Impact Studies"
<h2> Summary</h2> <p> </p> <p>In this work, we explore long term patterns in electricity demand driven by the dual effects of space heating electrification and climate change. We use an open source nodal power system model of the Electric Reliability Council of Texas (ERCOT) system to investigate a wide range of future climate and technology scenarios that evolve over time, and report results in terms of market prices, reliability and corresponding relative capacity requirements </p> <h2> About </h2> <p>The technical analysis aimed to:</p> <h3>1) Understand the Long-Term Patterns:</h3> <p>We aim to analyze patterns in peak load, total load, loss of load, and the seasonality of these phenomena, driven by widespread heat pump adoption alongside climate change.</p> <h3>2) Use Extensive Scenario Analysis:</h3> <p>Explore a wide range of future scenarios, including variations in climate pathways, to capture the uncertainty associated with these long-term changes. In total, 1280 simulation years.</p> <h3>3) Use a validated open source DC OPF model(reproducibility)</h3> <p>Use an open-source nodal power system model of the ERCOT system to simulate and understand the potential impacts on market prices, reliability, and relative capacity requirements. Similar models are available for all interconnections of the conterminous US.</p> <h3>4) Assess Grid Vulnerability:</h3> <p>Assess the vulnerability of the grid to these simultaneous changes, identify potential vulnerability.</p> <h3>5) Provide Insights for System Planners:</h3> <p>Offer results that can assist long-term system planners in anticipating and preparing for potential shifts in grid reliability.</p>
Downscaled ERA-Interim gridded historical climate data over China (1980-2010)
<p><strong>Gridded historical climate data over China, spanning 1981 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF).</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. Data created as part of the Met Office Climate Science for Service Partnership China (<a href="https://www.metoffice.gov.uk/research/collaboration/cssp-china">CSSP China</a>), work package 1 output, supported by the Newton Fund and the Department for Business, Energy & Industrial Strategy (BEIS) <a href="https://www.gov.uk/government/publications/newton-fund-building-science-and-innovation-capacity-in-developing-countries/newton-fund-building-science-and-innovation-capacity-in-developing-countries">UK-China Research Innovation Partnership Fund</a>.</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 20CRv2c data set: <a href="https://zenodo.org/record/2558135#.XJj2uaD7RWE">Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)</a> doi: 1</em>0.5281/zenodo.2558135</p>
Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations
<p>This dataset contains NetCDF files necessary to replicate results from the 2024 paper "<em>Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations</em>"</p> <p>The dataset contains NetCDF files with 1 year of zonally-averaged NASA Ames Mars Global Climate Model (MGCM) fields with 5-sol binning for each of the simulations presented in the paper: </p> <ul> <li>a "low-resolution" simulation with no parameterization for gravity waves</li> <li>a "high-resolution" simulation with no parameterization for gravity waves</li> <li>a "low-resolution" simulation with parameterizations for orographic and non-orographic gravity waves</li> </ul> <p>Also included are:</p> <ul> <li>a file describing the coordinates for the MGCM's vertical grids used in the study </li> <li> atmospheric fields not provided in the other NetCDF files and necessary to replicate figures 3 and supplemental figure FS2 from the paper.</li> <li>a README.txt detailing the content of each file in the dataset</li> </ul>
Downscaled climate grids of California at 90m for a variety of bioclimatic variables from 1971-2000, derived from historical climate grids
This dataset is comprised of 90 Geotiff images of selected bioclimatic variables for the state of California (extended past state lines to river basin boundaries). Originally created to model plant species distributions in California (Franklin et al. 2013. Modeling plant species distributions under future climates: how fine-scale do climate projections need to be? Global Change Biology 19: 473-483).
Supporting data for: Development of alternate climate divisions for Colorado based on gridded data
<p>The official climate divisions for the contiguous United States are used for a wide range of purposes, including ongoing climate monitoring, and through NOAA's long-standing nClimDiv dataset. In Colorado, the climate divisions are based around the basins of the large rivers that flow out of the state. However, considering the complex topography and climate of the state, these divisions do not always represent key climate variations and changes. This study builds upon an approach first developed by Wolter and Allured to establish alternate climate divisions that more closely reflect observed climate variability across Colorado. Hierarchical cluster analysis is applied to gridded temperature and precipitation data (NOAA's nClimGrid) from 1950–2021 to identify areas with similar climate variability, then manual inspection is used to establish 11 divisions. These resulting divisions are being used in an updated state-level climate change assessment. The method is flexible and uses open-source tools that could be extended to other regions or datasets.</p>
Climate Energy Dataset For Off Grid Electricity Infrastructure
<p>The dataset comprises real-time electrical measurements—including voltages, currents, and power factors—for three-phase and single-phase systems across generation, distribution, and consumption stages showcasing the energy generation and demand within an off-grid electricity infrastructure located in the Kalam Region, a specific climate zone in Pakistan. Collected every five minutes from March 6, 2023, to October 24, 2024, it includes over 45 million instances covering data from four micro-hydropower generators, 26 transformers, and 585 end-users. Additionally, the dataset incorporates climate data—such as temperature, dew point, wind components, precipitation, snowfall, and snow cover—from the ERA5 dataset. This comprehensive collection enhances its utility for research in energy systems analysis, climate change studies, electrical engineering, and artificial intelligence applications.</p> <p>This dataset was originally created with support from Lacuna Fund, the world’s first collaborative effort to provide data scientists, researchers, and social entrepreneurs in low- and middle-income contexts globally with the resources they need to produce labeled datasets that address urgent problems in their communities. Lacuna Fund is a funder collaborative that includes The Rockefeller Foundation, Google.org, Canada’s International Development Research Centre, the German Federal Ministry for Economic Cooperation and Development (BMZ) with GIZ as implementing agency, Wellcome Trust, Gordon and Betty Moore Foundation, Patrick J. McGovern Foundation, and The Robert Wood Johnson Foundation. See https://lacunafund.org/about/ for more information.</p>
Coarsened fine-grid model data for: A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation
Open the record for dataset details and reuse information.
Supporting data for: Development of alternate climate divisions for Colorado based on gridded data
Open the record for dataset details and reuse information.
Worldclim 2.1 versus Worldclim 1.4: climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe
<p>The influence of climate on the distribution of taxa has been extensively investigated in the last two decades through Habitat Suitability Models (HSMs). In this context, the Worldclim database represents an invaluable data source as it provides worldwide climate surfaces for both historical and future time horizons. Thousands of HSMs-based papers have been published taking advantage of Worldclim 1.4, the first online version of this repository. In 2017, Worldclim 2.1 was released. Here, we evaluated spatially explicit prediction mismatch at continental scale, focusing on Europe, between HSMs fitted using climate surfaces from the two Worldclim versions (between-version differences). To this aim, we simulated occurrence probability and presence-absence across Europe of four virtual species (VS) with differing climate-occurrence relationships. For each VS, we fitted HSMs upon uncorrelated bioclimatic variables derived from each Worldclim version at three grid resolutions. For each factor combination, HSMs attaining sufficient discrimination performance on spatially independent test data were projected across Europe under current conditions and various future scenarios, and importance scores of the single variables were computed. HSMs failed in accurately retrieving the simulated climate-occurrence relationships for the climate-tolerant VS and the one occurring under a narrow combination of climatic conditions. Under current climate, noticeable between-version prediction mismatch emerged across most of Europe for these two VSs, whose simulated suitability mainly depended upon diurnal or yearly variability in temperature; differently, between-version differences were more clustered toward areas showing extreme values, like mountainous massifs or southern regions, for VSs responding to average temperature and precipitation trends. Under future climate, the chosen emission scenarios and Global Climate Models did not evidently influence between-version prediction discrepancies, while grid resolution synergistically interacted with VSs' niche characteristics in determining extent of such differences. Our findings could help in re-evaluating previous biodiversity-related works relying on geographical predictions from Worldclim-based HSMs.</p>
Data for "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"
<p>Data employed for the paper "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"<br><br>Abstract<br>This study explores compounding impacts of climate change on power system's load and generation, emphasising the need to integrate adaptation and mitigation strategies into investment planning. We combine existing and novel empirical evidence to model impacts on: i) air-conditioning demand; ii) thermal power outages; iii) hydro-power generation shortages. Using a power dispatch and capacity expansion model, we analyse the Italian power system's response to these climate impacts in 2030, integrating mitigation targets and optimising for cost-efficiency at an hourly resolution. We outline different meteorological scenarios to explore the impacts of both average climatic changes and the intensification of extreme weather events. We find that addressing extreme weather in power system planning will require an extra 5-8 GW of photovoltaic (PV) capacity, on top of the 50 GW of the additional solar PV capacity required by the mitigation target alone. Despite the higher initial investments, we find that the adoption of renewable technologies, especially PV, alleviates the power system's vulnerability to climate change and extreme weather events. In fact, renewable energy sources are generally less vulnerable to the impacts of climate change, such as rising temperatures and shifting precipitation patterns, compared to thermal power and hydropower generation. Furthermore, enhancing short-term storage with lithium-ion batteries is crucial to counterbalance the reduced availability of dispatchable hydro generation.</p>
Data and code: High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method
<p>Data and code supporting the research article:High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method - <br> Fasil M. Rettie, Sebastian Gayler, Tobias KD Weber, Kindie Tesfaye, Thilo Streck. Please, find detail description of the codes and datasets in readme file.</p>
Data from: Evaluation of downscaled, gridded climate data for the conterminous United States
Open the record for dataset details and reuse information.
Worldclim 2.1 versus Worldclim 1.4: climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe
Open the record for dataset details and reuse information.
High-resolution gridded climate data for Europe based on bias-corrected EURO-CORDEX: the ECLIPS-2.0 dataset
<p>We developed a new climate dataset for Europe referred to as ECLIPS (European CLimate Index ProjectionS), which contains gridded data for 80 annual, seasonal, and monthly climate variables for two past (1961-1990, 1991-2010) and five future periods (2011-2020, 2021-2140, 2041-2060, 2061-2080, 2081-2100). The future data are based on five Regional Climate Models (RCMs)driven by two greenhouse gas concentration scenarios, RCP 4.5 and 8.5.</p> <p>The ECLIPS dataset has two versions; ECLIPS 1.1 contains data with spatial resolution of 0.11° × 0.11°, which is the resolution of underlying RCMs. ECLIPS1.1 is available at <a href="https://doi.org/10.5281/zenodo.1181780">https://doi.org/10.5281/zenodo.1181780</a>.</p> <p>The ECLIPS 2.0 presented here contains a subset of climate indices of ECLIPS 1.1, downscaled to the resolution of 30 arcsec by means of the delta correction approach. Both ECLIPS versions were evaluated by testing their relationship with independent station data from the European Climate Assessment (ECA) dataset. Correlations of the empirical testing data to ECLIPS 1.1 ranged from 0.63 to 0.78,and to ECLIPS 2.0 from 0.78 to 0.93. suggesting substantial improvement due to downscaling. A large number of climate projections, time periods and indices as well as the availability of these data at two different spatial resolutions can support diverse studies across a range of disciplines and thus extend our understanding of climate-sensitive dynamics of many social-ecological systems</p> <p>The zipfile ECLIPS2.0 contains 5 folders with subfolders</p> <p>File naming system for the subfolders / folder are as follows</p> <p>ECLIPS2.0_196191: past climate 1961-1990: < climate index><period></p> <p>ECLIPS2.0_199110: past climate 1991-2010 < climate index><period></p> <p>ECLIPS2.0_45 : future climate RCP4.5 <subfolder-Model name> < climate index><period></p> <p>ECLIPS2.0_85 : future climate RCP8.5 <subfolder-Model name> < climate index><period></p> <p>Incase zpfile reader 7zip is not available, please install from here: <a href="https://www.7-zip.org/">https://www.7-zip.org/</a></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.