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.
269
datasets available to search
ShareScore release 0.9.0
Dataset results
269 results for “Regional climate”
Fig. 3 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 3 | Response of tree species to climate change across biomes. The median absolute latitude and median elevation shift among species, fraction of lost and gained species, and change in taxonomic and phylogenetic composition under climate change were computed for each forest ecoregion. The boxplots show statistics for n = 239 ecoregions for Tropical Moist Broadleaf Forests, n = 14 ecoregions for Tropical Coniferous Forests, n = 55 ecoregions for Tropical Dry Broadleaf Forests, n = 26 ecoregions for Boreal Forests, n = 91 ecoregions for Temperate Broadleaf Forests, n = 49 ecoregions for Temperate Conifer Forests and n = 61 ecoregions for Mediterranean Forests. The center line of the boxplots shows the median, the box limits the quartiles, the whiskers 1.5 times the interquartile range, and the points the outliers.Changes are computed between predicted distributions with climate variables for 1981-2010 and climate projections for 2071-2100 under climate change scenario SSP 5.85. Changes in composition are computed as the Euclidean distance between scaled NMDS and evoPCA values computed at the ecoregion level. Source data are provided as a Source Data file.
Fig. 2 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 2 | Species occupancy range distribution and loss. a Distributions of species occupancy range sizes globally (gray) and constrained to forests (at least 10% tree cover, color) for species in each forest biome. b Boxplot of relative range reduction across species in each forest biome with the center line showing the median, the box limits the quartiles, the whiskers 1.5 times the interquartile range, and the points the outliers. The distributions and boxplots are computed for n = 6810 species for Tropical Moist Broadleaf Forests, n = 588 species for Tropical Coniferous Forests, n = 1101 species for Tropical Dry Broadleaf Forests, n = 54 species for Boreal Forests, n = 1744 species for Temperate Broadleaf Forests, n = 178 species for Temperate Conifer Forests and n = 580 species for Mediterranean Forests. c Global map of median species range size constrained to forests, created with QGIS110. The gray base map corresponds to all areas for which model predictors were available. d Plot of species' median latitude against range size constrained to forests, colored by point density, where red indicates the highest density. Source data are provided as a Source Data file.
Fig. 1 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 1 | Gradients in taxonomic and phylogenetic composition show a near- a, c. Scatter plot of taxonomic and phylogenetic ordinations in environmental unique biodiversity signature of every single location on the planet. Taxonomic space, a 2-dimensional space made up of the 2 first axes of a PCA of the environcomposition is represented by a 3-axis non-metric dimensional scaling (NMDS) and mental variables used for species distribution modeling: mean annual temperature phylogenetic beta-diversity is represented by the 3 first axes of a phylogenetic (MAT), temperature seasonality (T season), annual precipitation (Annual P), preordination (evoPCA). Both the taxonomic and phylogenetic ordinations are com- cipitation seasonality (P season), growing season length (GSL), net primary proputed on the global community matrix derived from the modeled distributions of ductivity (NPP),silt content (Silt),coarse fragments (CF),and soil pH (pH).b, d. Map n = 10,590 tree species sampled at a resolution of 100 km, resulting in n = 12,548 of taxonomic and phylogenetic ordinations in geographical space. Source data are sites. The 3 axes of each ordination are mapped to red, green, and blue with provided as a Source Data file. The maps were created with QGIS110 and the gray minimum and maximum values corresponding to the 10th and 90th percentiles. base map corresponds to all areas for which model predictors were available.
Fig 1 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
Fig 1: Relative gene expression in tambaqui juveniles farmed in two Brazilian regions: Northern (Balbina; BA) and Southeast (Brumado; BRU). Different letters represent statistical differences between populations. The graphs show expression of A) hif-1α (p = 0.137), B) hsp-70 (p = 0.465), C) mstn (p = 0.907), D) ube3a (p = 0.205), E) ras (p = 0.041), F) cry-1 (p = 0.001), G) per-1 (p = 0.001), H) ogt (p = 0.001) and I) acly (p = 0.025).
Fig 3 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
Fig 3: IBR analyses of relative gene expression in Balbina (BA) and Brumado (BRU) populations. The IBR values are 42.7 (Balbina) and 6.79 (Brumado).
Fig 2 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
Fig 2: Heatmap of relative expression in Balbina (BA) and Brumado (BRU) populations. The colour scale ranges from blue (low transcript levels) to red (high transcript levels).
Data for "Africa's Climate Response to Marine Cloud Brightening Strategies is Highly Sensitive to Deployment Region"
<p>Community Earth System Model 2 simulation data with marine cloud brightening perturbations used to compute climate impact metrics in "Africa's Climate Response to Marine Cloud Brightening Strategies is Highly Sensitive to Deployment Region" by Romaric C. Odoulami, Haruki Hirasawa, Kouakou Kouadio, Trisha D. Patel, Kwesi A. Quagraine, Izidine Pinto, Temitope S. Egbebiyi, Babatunde J. Abiodun, Christopher Lennard, and Mark G. New. Simulation descriptions can be found in Hirasawa et al., 2023 <em>Geophysical Research Letters</em> doi.org/10.1029/2023GL104314.</p>
Antarctic surface mass balance with the regional climate model MAR (1979–2015)
<p>Outputs of the regional climate model MAR v3.6.41 for Antarctica, resolution 35km + source code</p> <p>===========================================</p> <p>Cécile Agosta, 23 Jan 2019 </p> <p>cecile.agosta@gmail.com</p> <p>===========================================</p> <p>Grid specifications are given in MAR-ant35km-grid.nc (projection : EPSG 3031).</p> <p>* State variables are averages of daily means:</p> <p> TT > temperature (°C)</p> <p> ZZ > height above sea level (m)</p> <p> UU, VV > x-wind and y-wind in the stereographic grid (m s-1)</p> <p> UV > wind speed (m s-1)</p> <p>State variables ending with z (e.g. UUz) are interpolated on fixed altitude levels above the ground.</p> <p>State variables ending with p (e.g. UUp) are interpolated on fixed pressure levels.</p> <p>* SMB components are summed: kg m-2 month-1 for montly files, kg m-2 year-1 for annual files, kg m-2 year-1 for clim files</p> <p> snf > snowfall</p> <p> rnf > rainfall</p> <p> rof > run-off</p> <p> sbl > sublimation/condensation</p> <p> smb = snf + rnf - sbl - rof</p> <p> mlt > snowmelt</p> <p> rfz > refreezing</p> <p>If you use this data, please cite the final accepted version of this article:</p> <p>Agosta C., Amory C., Kittel C., Orsi A., Favier V., Gallée H., van den Broeke M.R., Lenaerts J.T., van Wessem J.M., & Fettweis X. (in review, 2018). Estimation of the Antarctic surface mass balance using MAR (1979-2015) and identification of dominant processes. <em>The Cryosphere Discussions</em>, 1–22, <a href="https://doi.org/10.5194/tc-2018-76">doi:10.5194/tc-2018-76</a>.</p> <p>Please contact me if you need other outputs (variables/daily or hourly time steps)</p>
Figure 1. A map showing the three climatic regions from which the 162 in Patterns of skull variation in relation to some geoclimatic conditions in the greater jerboa Jaculus orientalis (Rodentia, Dipodidae) from Tunisia
Figure 1. A map showing the three climatic regions from which the 162 samples of Jaculus orientalis were collected in Tunisia, with isohyets indicating the average annual rainfall measured in millimeters (mm year–1). Main climates: B- arid, C- warm temperate. Precipitation: W- desert, S- steppe, s- summer dry. Temperature: a- hot summer, h- hot arid.
Atmospheric moisture recycling in Mediterranean-type climate regions across the world
<p>Please cite the corresponding manuscript when using this data:</p> <p>... (information will follow as soon as the manuscript is published)</p> <p>This dataset includes the local precipitation recycling ratios and the regional moisture recycling ratios for five major Mediterranean-type climate regions across the globe. Below we list these five regions and explain the concepts of local precipitation recycling and regional moisture recycling. </p> <p> </p> <p><strong>Mediterranean-type climate regions</strong></p> <p>Region 1: South West Australia</p> <p>Region 2: West coast of the US (California)</p> <p>Region 3: Central Chile</p> <p>Region 4: Mediterranean Basin (region around the Mediterranean Sea)</p> <p>Region 5: The Cape region of South Africa</p> <p> </p> <p><strong>Local precipitation recycling ratio</strong></p> <p>The local precipitation recycling ratio is the fraction of precipitation that originated within approximately 50 km from where it rains out, i.e., it evaporated from the grid cell where it rains out and the 8 surrounding grid cells. The grid cells have a resolution of 0.5DEGx0.5DEG. A more detailed explanation is provided in the journal article Theeuwen et al. (2024). </p> <p>The files that include local precipitation recycling ratios are:</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Study region</strong></td> <td><strong>Time dimension (month)</strong></td> <td><strong>Latitude range</strong></td> <td><strong>Longitude range</strong></td> </tr> <tr> <td>PLMR_SWAustralia.nc</td> <td>South West Australia</td> <td>January-December</td> <td>-15:-48 DEGN</td> <td>106:154 DEGE</td> </tr> <tr> <td>PLMR_California.nc</td> <td>West coast of the US (California)</td> <td>January-December</td> <td>52:20 DEGN</td> <td>-131:-105 DEGE</td> </tr> <tr> <td>PLMR_CentralChile.nc</td> <td>Centra Chile</td> <td>January-December</td> <td>-10:-54 DEGN</td> <td>-80:-60 DEGE</td> </tr> <tr> <td>PLMR_Med_Basin.nc</td> <td>Mediterranean Basin</td> <td>January-December</td> <td>48:23 DEGN</td> <td>-20:-45 DEGE</td> </tr> <tr> <td>PLMR_SWCapeSA.nc</td> <td>The Cape region of South Africa</td> <td>January-December</td> <td>-26:-40 DEGN</td> <td>10:38 DEGE</td> </tr> </tbody> </table> <p> </p> <p><strong>Regional moisture recycling ratio</strong></p> <p>The regional moisture recycling ratio data includes both regional evaporation recycling ratios as well as regional precipitation recycling ratios. </p> <p>The regional evaporation recycling ratio is the fraction of evaporated water that rains out within the Mediterranean region it evaporated from. </p> <p>The regional precipitation recycling ratio is the fraction of precipitation that originated from the Mediterranean region it rains out in. </p> <p>This data has a resolution of 0.5DEGx0.5DEG and is a multi-year average (years: 2008-2017). A more detailed description is provided in the journal article Theeuwen et al. (2024). </p> <table> <tbody> <tr> <td><strong>Filename</strong></td> <td><strong>Type of recycling</strong></td> <td><strong>Study region</strong></td> </tr> <tr> <td>ERMR_SWAustralia.nc</td> <td>Regional evaporation recycling</td> <td>South West Australia </td> </tr> <tr> <td>ERMR_California.nc</td> <td>Regional evaporation recycling</td> <td>West coast of the US (California)</td> </tr> <tr> <td>ERMR_CentralChile.nc</td> <td>Regional evaporation recycling</td> <td>Centra Chile</td> </tr> <tr> <td>ERMR_Med-Basin.nc</td> <td>Regional evaporation recycling</td> <td>Mediterranean Basin</td> </tr> <tr> <td>ERMR_CapeSA.nc</td> <td>Regional evaporation recycling</td> <td>The Cape region of South Africa</td> </tr> <tr> <td>PRMR_SWAustralia.nc</td> <td>Regional precipitation recycling</td> <td>South West Australia </td> </tr> <tr> <td>PRMR_California.nc</td> <td>Regional precipitation recycling</td> <td>West coast of the US (California)</td> </tr> <tr> <td>PRMR_CentralChile.nc</td> <td>Regional precipitation recycling</td> <td>Centra Chile</td> </tr> <tr> <td>PRMR_Med-Basin.nc</td> <td>Regional precipitation recycling</td> <td>Mediterranean Basin</td> </tr> <tr> <td>PRMR_CapeSA.nc</td> <td>Regional precipitation recycling</td> <td>The Cape region of South Africa</td> </tr> </tbody> </table>
Data and analysis and plotting scripts for Swaminathan et al., "Regional Impacts Poorly Constrained by Climate Sensitivity"
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Regional Impacts Poorly Constrained by Climate Sensitivity", by Ranjini Swaminathan, Jacob Schewe, Jeremy Walton, Klaus Zimmermann, Colin Jones, Richard A. Betts, Chantelle Burton, Chris D. Jones, Matthias Mengel, Christopher Reyer, Andrew G. Turner & Katja Weigel, submitted for publication in Earth's Futures. Scripts used for plotting and analysis are also included.</p>
Data from: Genetic and functional variation across regional and local scales is associated with climate in a foundational prairie grass
<ul> <li>Global change forecasts in ecosystems require knowledge of within species diversity, particularly of dominant species within communities. We assessed site-level diversity and capacity for adaptation of the dominant species of the shortgrass steppe biome of the Central US, Bouteloua gracilis.</li> <li>We quantified genetic diversity from 17 sites across regional scales, north-south from New Mexico to South Dakota, and local scales in Northern Colorado. We also quantified phenotype and plasticity within and among sites and determined the extent to which phenotypic diversity in B. gracilis was related to climate.</li> <li>Genome sequencing indicated pronounced population structure at the regional scale, and local differences indicated gene flow and/or dispersal may also be limited. Within a common environment, we found evidence for genetic divergence in biomass-related phenotypes, plasticity, and phenotypic variance, indicating functional divergence and different adaptive potential. Phenotypes differentiated according to climate, chiefly median Palmer Hydrological Drought Index and other aridity metrics.</li> <li>Our results indicate conclusive differences in genetic variation, phenotype, and plasticity in this species and suggest a mechanism explaining variation in shortgrass steppe community responses to global change. This analysis of B. gracilis intraspecific diversity across spatial scales will improve conservation and management of the shortgrass steppe ecosystem moving forward.</li> </ul>
Elucidating the present-day chemical composition, seasonality and source regions of climate-relevant aerosols across the Arctic land surface
<p>Data presented in the figures of the journal article "Elucidating the present-day chemical composition, seasonality and source regions of climate-relevant aerosols across the Arctic land surface" by Moschos et al.</p>
Regional climate model simulations (CCLM 15km) of profiles for the MOSAiC period
<p>The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic. The data set includes simulation data of profiles and derived data for the MOSAiC period (Oct. 2019-Sept.2020). The regional climate model CCLM was used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data (AMSR2) with around 6 km resolution, and sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead data with 1 km resolution for the winter period (Nov. 2019-April 2020). Model output is available every 1h. In the vertical, the model extends up to 22 km with 60 vertical levels. On data below 10km are used. In addition to profiles, integrated water vapour and temperature for the lowest 2km were calculated. Values are grid-box averages at the ship position. Geostrophic wind was computed from the pressure gradient of the four surrounding grid points.</p> <p>Reference: Heinemann, G., Schefczyk, L., Willmes, S., Shupe, M., 2022: Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period. Elem. Sci. Anth., 10 (1). DOI: 10.1525/elementa.2022.00033.</p> <p><strong>Project: </strong> Modelling the impact of sea-ice leads on the atmospheric boundary layer during MOSAiC (MISLAM)</p> <p><strong>Funding: </strong>Federal Ministry of Education and Research (BMBF), grant 03F0887A</p>
MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region. Article data
<p>Regional coupled and Earth System models are fundamental numerical tools for climate investigations, downscaling of predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR (Mediterranean Earth System model at ISMAR) version 1, which is composed of the WRF atmospheric model, the NEMO oceanic 15 model, and the HD hydrological discharge model, coupled via the OASIS coupler. The model is implemented at moderate resolution (about 1/12° for the ocean and river routing, while twice coarser for the atmosphere) for long-term investigations.</p> <p>The gzipped tarball contains data files contained in the manuscript associated with the MESMARv1 description and submitted to Geoscientific Model Developments:</p> <p>MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region</p> <p>by Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino,<br> Rosalia Santoleri, Chunxue Yang</p>
Replication data for: Effect of Regional Marine Cloud Brightening Interventions on Climate Tipping Points
<p>Data for reproduction of Hirasawa, H., Hingmire, D., Singh, H., Rasch, P. J., & Mitra, P. (2023). Effect of regional marine cloud brightening interventions on climate tipping elements. Geophysical Research Letters, 50, e2023GL104314. https://doi.org/10.1029/2023GL104314</p> <p>Includes:</p> <ul> <li>Raw monthly 2m temperature (TREFHT) and precipitation (PRECT) data from CESM2 MCB simulations.</li> <li>Ensemble mean data from CESM2 Large Ensemble</li> <li>Tipping point metric timeseries from CESM2 LE Historical and SSP2-4.5 and CESM2 MCB simulations.</li> <li>Jupyter notebook displaying plotting script</li> <li>Scripts showing procedure for computing tipping point metrics</li> <li>CAM6 SourceMod changes to apply cloud droplet number concentration perturbations</li> <li>Top of atmosphere long and shortwave anomalies from fixed sea surface temperature simulations for computing effective radiative forcing</li> </ul>
Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
<p>This is the preprocessed remote sensing dataset used in the paper<strong> "Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation"</strong>. It contains the preprocessed NOAA data along with the additional files necessary for the TSMP simulation.</p>
Climate model experiments of regional-scale tree die-off replaced by shrubs (all monthly data fields): Part 3
Open the record for dataset details and reuse information.
Neutral processes related to regional bee commonness and dispersal distances are important predictors of plant-pollinator networks along gradients of climate and landscape conditions
Open the record for dataset details and reuse information.
Random forest climatic modeling of agricultural insurance loss across the inland Pacific Northwest region of the United States
Open the record for dataset details and reuse information.
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.