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.
2,837
datasets available to search
ShareScore release 0.7.1
Dataset results
2,837 results for “climate data”
Data provided for the Preenacting Climate Change Scenarios project 2021
<p>CMIP6 model output data processed using the scripts provided here: https://github.com/lukasbrunner/preenact/</p>
Data for "Phenotypic responses to climate change are significantly dampened in big-brained birds"
<p>Anthropogenic climate change is rapidly altering local environments and threatening biodiversity throughout the world. Although many wildlife responses to this phenomenon appear largely idiosyncratic, a wealth of basic research on this topic is enabling the identification of general patterns across taxa. Here we expand those efforts by investigating how avian responses to climate change are affected by the ability to cope with ecological variation through behavioral flexibility (as measured by relative brain size). After accounting for the effects of phylogenetic uncertainty and interspecific variation in adaptive potential, we confirm that although climate warming is generally correlated with major body size reductions in North American migrants, these responses are significantly weaker in species with larger relative brain sizes. Our findings suggest that cognition can play an important role in organismal responses to global change by actively buffering individuals from the environmental effects of warming temperatures.</p>
Data and code for 'Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Turner, A., Heard, G., Hall, A., Wassens, S. (in review). Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk.</p> <p>The data are provided as a series of .csv files, R script and two zip folders of R packages (Surv_mod and VB_mod)</p> <p>1. <strong>Skeleto_dat_ready_Jan2021.csv</strong> Data from frog surveys conducted by Anna Turner</p> <p>2. <strong>Geoffs_data.csv</strong> Data from frog surveys conducted by Geoff Heard</p> <p>3. <strong>Environmental_variables_skeleto.csv</strong> Environmental data collected during surveys </p> <p>4. <strong>sk.dat_July21.csv</strong> Collated data from Anna and Geoff - created by 'Data_collation_for_analysis_2.R' ready for analysis</p> <p>5. <strong>Variables_that_are_highly_correlated_with_each_other_season_wide.csv</strong> Testing for correlation</p> <p>6. <strong>Model_structure_skeleto_2.csv </strong>creates model structure for analysis</p> <p>7. <strong>Model_selection_statistics_June_21.csv </strong>Output from model</p> <p>R code is provided seperately for each of the following components:</p> <p>1. <strong>Data_collation_for_analysis_2.R</strong> Collating data from Anna and Geoffs datasets</p> <p>2. <strong>Skeleto_analysis_5.R - </strong>First uses regression modelling to explore factors correlated with variation in age</p> <p> - Following Scheele et al. (2016) regression models with a poisson distribution</p> <p> - Use bayesian non-linear regression to fit the Von Bertalanffy growth model to size-at-age data</p> <p> - Plots male and female growth curves</p> <p> - Uses catch curve approach to estimate survival from best fitting regression model following Scroggie (2012) but with bayesian implementation</p>
Supporting data for review article: The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective
<p>Supporting data and code for review article: Sokol N.W., Whalen E.D., Kallenbach C., Pett-Ridge J., Georgiou K. The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective. <em>Functional Ecology, </em>2022.</p> <p>We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415). For this review article, we specifically focused on measurements of bulk and mineral-associated soil organic carbon concentrations (reported in units of gC/kg soil) and the proportion of bulk soil organic carbon that is mineral-associated (reported as a %). This subset also includes auxiliary data regarding climate and biome characteristics extracted from the synthesized papers; for more variables, see the original full dataset. Köppen-Geiger climate zones were extracted from a georeferenced global database (using R package 'kgc' v1.0.0.2) with site coordinates, where available. Three files are provided in this repository: (1) data file, (2) metadata file, and (3) code for manuscript figures and summary statistics.</p>
Data for: A severe landslide event in the Alpine foreland under possible future climate and land-use changes
<p>Data underlying manuscript and supplementary figures of the corresponding publication, as well as the scripts to conduct the final analyses.</p>
Supplementary data for manuscript titled: "Radiolitid Rudists: An Underestimated Archive for Cretaceous Climate Reconstruction"
<p>Supplementary data for manuscript titled: "Radiolitid Rudists: An Underestimated Archive for Cretaceous Climate Reconstruction"</p> <p>Containing raw stable isotope and trace element data used in the study</p>
Raw data for the article "Games on Climate Change: Identifying Development Potentials through Advanced Classification and Game Characteristics Mapping"
<p>Raw data used for the article "Gerber, Andreas, Markus Ulrich, Flurin X. Wäger, Marta Roca-Puigròs, João S.V. Gonçalves, and Patrick Wäger. 2021. "Games on Climate Change: Identifying Development Potentials through Advanced Classification and Game Characteristics Mapping" <em>Sustainability</em> 13, no. 4: 1997. <a href="https://doi.org/10.3390/su13041997">https://doi.org/10.3390/su13041997</a>"</p> <p>The documents include the raw data (both as .csv and .xlsx files with the same content), as well as the publication (.pdf file). The data collection process and the data itself are described in the publication. The data is published as "supplementary material" on the publisher's homepage.</p>
Raw data for the book chapter "Review of Haptic and Computerized (Simulation) Games on Climate Change"
<p>Raw data used for the book chapter "Gerber, A., Ulrich, M., Wäger, P. (2021). Review of Haptic and Computerized (Simulation) Games on Climate Change. In: Wardaszko, M., Meijer, S., Lukosch, H., Kanegae, H., Kriz, W.C., Grzybowska-Brzezińska, M. (eds) Simulation Gaming Through Times and Disciplines. ISAGA 2019. Lecture Notes in Computer Science(), vol 11988. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-72132-9_24">https://doi.org/10.1007/978-3-030-72132-9_24</a>"</p> <p>The documents include the raw data (both as .csv and .xlsx files with the same content), as well as the publication (.pdf file). The data collection process and the data itself are described in the publication. The data is published as "supplementary material" on the publisher's homepage.</p>
Numerical data analysed to produce Figure 3a of Nature Climate Change submission "Five challenges for subseasonal to decadal prediction research " by Merryfield et al.
<p>NetCDF4-formatted files containing daily sea ice concentration data from Environment and Climate Change Canada's CanSIPSv2 seasonal forecasting system described in Lin et al. (2020) https://doi.org/10.1175/WAF-D-19-0259.1 </p> <ul> <li>2 models, CanCM4i and GEM-NEMO</li> <li>10 ensemble members for each model, each in separate files as indicated by suffixes _1 to _10</li> <li>initialized May 1, 1980 to 2021</li> <li>840 files total (42 predicted years x 10 ensemble members x 2 models)</li> <li>model outputs interpolated to common 1-degree grid</li> </ul> <p>The calibrated probabilistic forecast map shown in Figure 3a is based on the nonhomogeneous censored Gaussian regression (NCGR) method described in Dirkson et al, (2021) https://doi.org/10.1175/WAF-D-20-0066.1 and produced using scripts available at https://github.com/adirkson/sea-ice-timing </p> <p>The procedure uses as inputs</p> <ul> <li>freeze-up dates calculated from the provided model outputs as described in Sigmond et al. (2016) https://doi.org/10.1002/2016GL071396</li> <li> <p>NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 3: https://nsidc.org/data/G02202/versions/3</p> </li> </ul>
Data used in the article: "Climate change impacts the vertical structure of marine ecosystem thermal ranges"
<p>This dataset is used in the manuscript "Climate change impacts the vertical structure of marine ecosystem thermal ranges" accepted in Nature Climate Change 2022.</p>
Modern pollen data from the East Asian Pollen Database (EAPD): pollen, vegetation and climate relationship
<p>This is a modern pollen dataset of eastern Asia, in which a total of 1756 sample sites is selected from the original database EAPD (East Asian Pollen Database) which consists of 2858 samples. The sample types are mainly surface soil, moss, sediment top (lake, delta, peatland, river basin, reservoir and so on), and dust capture. The pollen data are mostly count numbers, but a few was originally given in percentage (marked with TRUE for proportion or percentage). We have checked pollen taxonomic nomenclature and combined some synonym pollen types from different original sources.</p> <p>This dataset includes only the samples collected in the areas under natural vegetation or land cover with low human disturbance, that the sites located in the agriculture areas or strong human intervention have been excluded. This screening procedure makes the pollen data readily available for biome and climate reconstructions. The contributors' original research concerning pollen-climate relationship from EAPD sources have been published in Zheng, et al. (2014 and 2008), which have revealed that pollen taxa in the dataset have significant relationship with climate variables. This dataset is potentially useful for multiscale paleovegetation and paleoclimate reconstruction studies in Asia. </p> <p>EAPD is developed and maintained by the Laboratory of Quaternary Science and Palynology in the School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai, China.</p>
Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"
<p>This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld, M. Finkbeiner, J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”<strong><em>,</em></strong> <em>Sustainable Energy Fuels</em>, <strong>2022</strong>, <strong>6</strong>, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>.</p> <p>It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.</p>
Climatological data from mechanistic model experiments of Boljka and Birner (2022/3; npj Climate and Atmospheric Science)
<p>Some climatological output data from mechanistic dry dynamical core model experiments used for the paper of Boljka and Birner (2022/3): "Potential impact of tropopause sharpness on the structure and strength of the general circulation", npj Climate and Atmospheric Science. For more details see the manuscript. </p>
Sea ice proxy data from "Sea ice fluctuations in the Baffin Bay and the Labrador Sea during glacial abrupt climate changes"
<p>Dataset s1: Sub-decadal sodium, bromine, and bromine enrichment data from NEEM ice core between 34-42 ka b2k.</p> <p>Dataset s2: Magnetic susceptibility, total organic carbon (TOC) and biomarkers data (IP25, brassicasterol, HBI-III) from the Eirik Drift core GS16-204-23CC, covering 31-42 ka b2k.</p>
Data and code for: "Improving the relevance of paleontology to climate change policy"
<p>Data and code for the article: "Improving the relevance of paleontology to climate change policy". [Link here]</p>
Residual emissions in long-term national climate strategies show limited climate ambition - Supplementary Data
<p>This supplementary data file contains the strategy data required to produce all figures in 'Residual emissions in long-term national climate strategies show limited climate ambition', in addition to tables presented in Supplemental Information.</p> <p>See 'Title' tab for contents. </p>
Scripts and datas for "Climate-driven projections of future global wetlands extent"
<p>Computations scripts (1, 2), associated input dataset (3), and output datasets for wetland fractions (4, 5) used and presented in the study:</p> <p><em><strong>L. Hardouin, B. Decharme, J. Colin, C. Delire: </strong>Climate-driven projections of future global wetlands extent.</em></p> <p>The calculation and input scripts include:<br><em>1_var_comput </em>: Calculation of the main variables used to diagnose wetlands: depth of the "active" layer d_wtl, liquid water content w_l, ice content and maximum content in the layer d_wtl.</p> <p><em>2_TOPMODEL </em>: The scripts used to diagnose the wetland fraction and to calibrate the models using the TOPMODEL approach. In this folder, the mean, maximum, minimum, standard deviation and skewness datasets of the topographic indices at the grid-cell level are also included.</p> <p><em>3_alpha_and_beta </em>: Calibrated alpha and beta parameters used to obtain the historical and projected wetland fractions with the calibrated version.</p> <p>The outputs datasets contain:</p> <p><em>4_fwtl_model_period </em>: The fraction of wetlands computed from each model in the calibrated version, for the historical period and the 4 SSPs scenarios presented in the submitted work.</p> <p><em>5_not_calibrated_fwtl_model_period </em>: The fraction of wetlands computed from each model in the uncalibrated version with alpha=0.65, for the historical period and the 4 SSPs scenarios, where only the historical period is used in the submitted work.</p> <p> </p> <p>Additional data not created by the authors are needed to reproduce the study (see the Open research section in the submitted article). Feel free to contact the authors (lucas.hardouin@meteo.fr) for any help or questions.</p>
Data and code from: Climate-based prediction of carbon fluxes from deadwood in Australia
This repository contains the code for the publication 'Climate-based prediction of carbon fluxes from deadwood in Australia'.
nextGEMS cycle3 datasets: statistical summaries for streamed data from climate simulations
<p>This Zenodo holds the datasets used in the paper "Statistical summaries for streamed data from climate simulations" by Katherine Grayson. All the data comes from the nextGEMS cycle 3 and has been regridded for plotting purposes with resolution given in the title of each data set. The wind speed data set has been made by taking the square root of the squared and summed v10 and u10 components respectively. All data has been retrieved and regridded through the AQUA reader on the Levante supercomputer, developed as part of the Destination Earth initative. The source code to create all the figures using this data can be found in https://github.com/kat-grayson/one_pass_algorithms_paper/tree/main </p>
Data for Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty
<p>Data from the paper "Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty"</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.