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2,260 results for “climate change”
Lake mask and distance to land dataset of 2024 lakes for the European Space Agency Climate Change Initiative Lakes v2
<p>This dataset contains the distance to land and the lake identifiers as a global netcdf file for all the water pixels at 1km (1/120 deg) lat/lon resolution of 2024 lakes distributed globally. It contains also the list of lakes as a csv file with information such as the lake center as defined in [1], and the coordinate of a box to easily locate the like in the global netcd file. The mask excludes islands on lakes and it has been derived from the GloboLakes high resolution limnology dataset [2]. The dateset have been further harmonized with the lake maximum extent lake polygons by PML [3]. The lake list with the plot of the mask and the polygons is available as a html file accessible also from the lake website at the University of Reading: http://www.laketemp.net/home_CCI/LMPolygons.php</p> <p>This dataset accompanies the <strong>ESA CCI Lakes v2 dataset</strong> [4].</p> <p> </p> <p>[1] Carrea, L.; Embury, O.; Merchant, C.J. (2015): High-resolution datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates - Geoscience Data Journal, 2 (2). pp. 83-97. ISSN 2049-6060 doi: https://doi.org/10.1002/gdj3.32</p> <p>[2] Carrea, L.; Embury, O.; Merchant, C.J. (2015): GloboLakes: high-resolution global limnology dataset v1. Centre for Environmental Data Analysis. doi:10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb. <a href="http://dx.doi.org/10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb">http://dx.doi.org/10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb</a></p> <p>[3] Simis, S.; Mata, A.; Selmes, N.; Carrea, L. (2021) Lake polygons dataset accompanying Calimnos v1.4.0 and ESA CCI Lakes Climate Research Data Package v2.0. zenodo https://doi.org/10.5281/zenodo.4899250</p> <p>[4] Carrea, L.; Crétaux, J.-F.; Liu, X.; Wu, Y.; Bergé-Nguyen, M.; Calmettes, B.; Duguay, C.; Jiang, D.; Merchant, C.J.; Mueller, D.; Selmes, N.; Simis, S.; Spyrakos, E.; Stelzer, K.; Warren, M.; Yesou, H.; Zhang, D. (2022): ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0.1. NERC EDS Centre for Environmental Data Analysis <a href="https://catalogue.ceda.ac.uk/uuid/03c935c6890c4b2ebf4aae4d84cd9472">https://catalogue.ceda.ac.uk/uuid/03c935c6890c4b2ebf4aae4d84cd9472</a></p>
Small-scale fisheries adaptations understudied in climate change hotspots - database
<p>Using a systematic review approach, we identified a global dataset of 301 reported adaptation responses of small-scale fishers to climate change. The adaptations were extracted from academic publications and grey literature (reports and Ph.D. theses) published from 2008 to 2020. The database provides coordinates and/or location, climate change hazard identified as motivating the response, small-scale fisher adaptation response, and any other stressor related to the response.</p>
Assessment of vulnerability to climate change of coastal communities in the Gulf of California and the Yucatan Peninsula: vulnerability outputs
<p>The dataset includes the outputs of the project: "Assessment of vulnerability to climate change of coastal communities in the Gulf of California and the Yucatan Peninsula: vulnerability outputs" funded by the David and Lucille Packard Foundation and awarded to H. Reyes-Bonilla (UABCS). </p> <p>This study analyzed vulnerability of fisheries-dependent coastal communities based on three components: a) adaptive capacity (84 indicators), which reflect the ability of a community to respond and recover after adverse events; b) susceptibility (11 indicators) which was determined based on fishing dependence; and c) exposure (31 indicators) that was evaluated with current environmental data. Future vulnerability was determined for a 2050 horizon and based on two climate change scenarios: SSP126, which represents low emissions, and SSP585, which takes into consideration that the amount of greenhouse gases will continue to increase. These data come from the Coupled Model Intercomparison Project 6 (CMIP6), which serves as the basis for the 6th IPCC report. We evaluated vulnerability using indicators what were available at the local scale.</p>
Data from Davison et al. (2024) Changes in Danish bird communities over four decades of climate and land-use change
<p>Environmental and biodiversity data associated with the article: <br><strong>Davison, C. W., Rahbek, C., & Morueta-Holme, N. (2024) Changes in Danish bird communities over four decades of climate and land-use change. <em>Oikos. </em></strong>https://doi.org/10.1111/oik.10697</p> <p>Data on local bird species richness, functional diversity, temporal and spatial turnover (beta diversity), abundance, and biomass at volunteer led survey routes across Denmark. Matched habitat data (from volunteers) and historical climate data (E-OBS). Bird observations are a subset of the Common Bird Monitoring programme (DOF – BirdLife Denmark) that include routes surveyed in the summer season, spanning ≥10 years, and with full GPS coordinates. This excel document contains all of the derived (and anomysied) data used in the final analyses and includes metadata describing the variables.</p> <p>Climate and trait data were obtained from open-access databases (see references). Metadata is included in the excel file.</p> <ul> <li><strong>Danish Common Bird Monitoring programme</strong> – Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. <em>Dansk Ornitologisk Forening</em>.</li> <li><strong>E-OBS European gridded climate data</strong> – Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., & New, M. (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. <em>Journal of Geophysical Research Atmospheres</em>, <em>113</em>(20). https://doi.org/10.1029/2008JD010201</li> <li><strong>AVONET bird traits data</strong> – Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. <em>Ecology Letters</em>, <em>25</em>(3), 581–597. https://doi.org/10.1111/ele.13898</li> </ul> <p> </p>
Replication material for paper "Freihardt (2025): Trapped by climate change? (In)voluntary immobility in Bangladesh. Regional Environmental Change. DOI 10.1007/s10113-025-02452-3."
<p>This is the data and replication code underlying the paper:</p> <p>Freihardt, J. Trapped by climate change? (In)voluntary immobility in Bangladesh. <em>Reg Environ Change</em> <strong>25</strong>, 117 (2025). https://doi.org/10.1007/s10113-025-02452-3</p>
Database of indicators to evaluate the contribution of urban nature-based solutions to climate change adaptation, biodiversity conservation, and social justice
<p>Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. <em>Global Environmental Change</em>, <em>89</em>, 102939. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102939">https://doi.org/10.1016/j.gloenvcha.2024.102939</a>. Please also cite this paper when citing this database.</p> <div> <div>Within this database, you can find a list of indicators used to evaluate the contribution of a collection of 74 nature-based solutions (NbS) to climate change adaptation and related biodiversity and social justice challenges in cities. This list of indicators may be useful to those working in cities to provide inspiration for similar indicators they may wish to use to evaluate NbS in their city. This collection of NbS was drawn from previous work published in <em>Nature Sustainability</em> <a href="https://rdcu.be/c4tjk">here</a>.</div> <div> </div> </div> <p><em>The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program. </em></p> <p><em>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</em></p>
Supplementary Data: Global rise in forest fire emissions linked to climate change in the extratropics
<p>Supplementary Data for the paper "Global rise in forest fire emissions linked to climate change in the extratropics" by Jones et al. (2024, <em>Science</em>).</p> <p>The records include mapped pyromes and data and code used to delineate the pyromes.</p> <h3><strong>Mapped Pyromes</strong></h3> <p>The data records include mapped pyromes in three forms:</p> <ol> <li><strong>Shapefile</strong> (Jones_etal_2024_Global_Forest_Pyromes.shp.zip). Vector features in shapefile format containing data fields <em>pyrome ID</em> and <em>pyrome name</em>. The zipped file contains .shp, .dbf, .prj, .shx files.</li> <li><strong>Lower-resolution NetCDF </strong>(Jones_etal_2024_Global_Forest_Pyromes_Qdeg.nc). NetCDF version 4 file containing gridded values of <em>pyrome ID</em> at quarter-degree resolution.</li> <li><strong>Higher-resolution NetCDF</strong> (Jones_etal_2024_Global_Forest_Pyromes_005deg.nc). NetCDF version 4 file containing gridded values of <em>pyrome ID</em> at 0.05 degree resolution.</li> </ol> <p>Shapefiles are accessible via GIS programmes such as QGIS or ArcGIS. All files .shp, .dbf, .prj, .shx files must be stored in a single directory</p> <p>NetCDF files can be access by a variety of programming languages such as Python and R. For quick visualisations and access to the data structure, we suggest using the Panoply tool https://www.giss.nasa.gov/tools/panoply/.</p> <h3><strong>Correlation Data</strong></h3> <p>The data records (Correlation_Qdeg.zip) include gridded quarter-degree correlations between forest burned area (BA) and each of the following variables:</p> <ul> <li><em><strong>Fire weather index</strong></em></li> <li><em><strong>Atmospheric instability (continuous Haines index)</strong></em></li> <li><em><strong>Lightning flash density</strong></em></li> <li><em><strong>Soil moisture</strong></em></li> <li><em><strong>Vegetation productivity (Normalised Difference Vegetation Index)</strong></em></li> <li><em><strong>Population density</strong></em></li> <li><em><strong>Cropland cover</strong></em></li> <li><em><strong>Pasture cover</strong></em></li> <li><em><strong>Road density</strong></em></li> <li><em><strong>Potential fuel loads - surface fuels</strong></em></li> <li><em><strong>Potential fuel loads - shrub fuels</strong></em></li> <li><em><strong>Potential fuel loads - canopy and ladder fuels</strong></em></li> <li><em><strong>Terrain ruggedness index</strong></em></li> <li><em><strong>Forest area density</strong></em></li> </ul> <p>The BA data derive from MODIS MCD64A1 collection 6.1 (Giglio et al., 2018). BA data for forests is masked using the MODIS MOD44B product (DiMiceli et al., 2021) with a 30% tree cover threshold. The predictor data derive from multiple sources as desribed by Jones et al. (2024). See Supplementary Methods and Materials.</p> <p>The gridded correlations data are provided in Hierarchical Data Format version 5 (.hdf5) files, zipped to Correlation_Qdeg.zip. File names describe the variables used.<em> Cropland_Pasture_Qdeg.hdf5 </em>contains data for both cropland and pasture. Each file contains layers describing the Spearman's rho (ρ) correlation coefficient and the related p-value.</p> <p>As explained and justified by Jones et al. (2024), the correlation structure used depends on the variable (see Supplementary Methods and Materials) as per the following categories:</p> <ul> <li><strong><em>Fire Weather Index, Atmospheric Instability, and Lightning Flash Density:</em></strong> Monthly correlation between forest BA and each variable across all fire season months in the period 2001-2021 at the quarter-degree resolution.</li> <li><strong><em>Soil Moisture:</em></strong> Inter-annual correlation between (i) mean soil moisture during the fire season and (ii) accumulated forest BA during the fire season at quarter-degree resolution across years 2001-2021. </li> <li><strong><em>Vegetation Productivity (NDVI):</em></strong> Inter-annual correlation between (i) mean NDVI during the prior growing season and (ii) accumulated forest BA during the fire season at quarter-degree resolution across years 2001-2021. </li> <li><strong><em>Population Density, Cropland Cover, Pasture Cover, Road Density, T</em></strong><strong><em>errain Ruggedness Index, Forest Area Density: </em></strong>Spatial correlation between mean annual forest BA and each variable across the 0.05° cells within each quarter-degree cell during 2001-2021.</li> </ul> <p>Note that these grids are provided for insights into spatial variation in the input correlation data. Pyromes are defined based on correlations fitted on the spatial scale of Olson ecoregions, not quarter-degree grid cells (see further details below).</p> <h3><strong>Clustering Code</strong></h3> <p>DEMO_Clustering.zip contains R Statistics code for clustering forest ecoregions into pyromes based on correlations observed between forest BA and 14 predictors at regional level. The <em>Input</em> directory contains a .RData data frame with correlations between forest BA and each predictor for ecoregions. For demonstrative purposes the code is applied to cluster forest ecorgions of North America into pyromes. The <em>Regions</em> directory contains ecoregions of North America in shapefile format. The <em>Output</em> directory contains output generated by M. Jones, which can be used for validation purposes once other users have trialled the code.</p>
Review of existing modelling studies focusing on specific soil-based ecosystem services (SES) and threats (ST) including climate change, management and land use change scenarios.
<p><span>We </span><span>reviewed existing modelling studies focusing on soil ecosystem services (SES) and soil threats (ST) including climate change, land use change and management scenarios. A publication has been submitted and is currently being reviewed. The title of the manuscript is: </span><span>Assessing and mapping soil ecosystem services and soil threats changes in agroecosystems through scenario-based approaches – a systematic review. </span></p> <p><span>Work was split between various authors. All Co-authors were working on either one or more SES or one ST. Excel sheets were prepared by INRA and BFW to ensure the comparability of results that members extracted from the papers found. Literature search was done in Scopus and Web of Science. The final list of related publications is reported here. <br></span></p>
Projection of temperature-related mortality in 854 European cities under climate change and adaptation scenarios
<p>This repository contains the data and results from the paper <strong>Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities</strong> published in <em>Nature Medicine</em> (<a href="https://doi.org/10.1038/s41591-024-03452-2">https://doi.org/10.1038/s41591-024-03452-2</a>).</p> <p>It provides projections of excess death rates and burden for the period 2015-2099 for five age groups in 854 cities across 30 countries, under three Shared Socioeconomic Pathway (SSP) scenarios, and four adaptation scenarios. The results include point estimates for five-year periods and four global warming levels, along with 95% empirical confidence intervals. </p> <p>The fully reproducible analysis code using the data and producing the results included in this repository is provided in <a href="https://github.com/PierreMasselot/EUcityProj" target="_blank" rel="noopener">GitHub</a>. The results can be visualised and explored in a dedicated <a href="https://ehm-lab.shinyapps.io/vistemphip/">Shiny app</a>.</p> <h3>Content</h3> <p>This repository contains three zip files, each with an internal codebook:</p> <ul> <li><em>data.zip</em>: contains the input data necessary to run the analysis. It includes historical and projected daily temperature at the city level, age-group specific projections of population and survival rates at the country level, and exposure-response functions extracted from another Zenodo repository (<a href="https://doi.org/10.5281/zenodo.10288665" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10288665</a>). This file also include a script showing how each dataset was extracted for the purpose of this projection study.</li> <li><em>results_csv.zip</em>: contains the full results from the health impact projections. It includes one file for each combination of geographical level (city, country, region or European wide) and scale of reporting (five year periods or global warming levels). </li> <li><em>results_parquet.zip</em>: contains the same information as the <em>results_csv.zip</em> but in a parquet format. This allows for more efficient storage and data reading.</li> </ul> <p>It is recommended to only download <em>results_csv.zip</em> for a quick exploration of the results, or only <em>results_parquet.zip</em> when the results are to be loaded into a software for deeper analysis.</p> <p> </p>
Replication data for "Climate change may induce connectivity loss and mountaintop extinction in Central American forests"
<p>Model code and predictor data underlying the publication "<strong>Climate change may induce connectivity loss and mountaintop extinction in Central American forests</strong>".</p>
Associating Land Cover Changes with Climate Sensitive Infection in Fennoscandia, as part of the CLINF project: Example on Tick-Borne Diseases
<p>The data was used as part of the IJERPH article below. The GeoJSON and shapefile ZIP archive are two versions of the same geometries to represent geographically the districts whole of Fennoscandia and the Russian districts of Leningrad, St Petersburg, Vologda, Arkhangelsk, Nenetsia, Murmansk, Karelia, and Komi, making up 69 districts used for the analysis.</p> <p>Leibovici DG, Bylund H, Björkman C, Tokarevich N, Thierfelder T, Evengård B, Quegan S (2021). Associating Land Cover Changes with Patterns of Incidences of Climate Sensitive Infections: An Example on Tick-Borne Diseases in the Nordic Area. <strong><em>International Journal of Environmental Research and Public Health, 18(20):10963. <a href="https://doi.org/10.3390/ijerph182010963">doi:10.3390/ijerph182010963</a></em></strong></p> <p>Special Issue: <a href="https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects">https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects</a></p> <p> </p>
PALEODEM/ What burned the forest? Wildfires, climate change and human activity during the Mesolithic – Neolithic transition in SE Iberian Peninsula
<p>This repository contains new XRD data from the Villena paleolake, archaeological radiocarbon evidence from the Villena area and the R code used to produce Summed Probability distribution analyses. </p> <p>They correspond to the following reference: </p> <p>Sánchez-García, C., Revelles, J., Burjachs, F., Euba, I., Expósito, I., Ibáñez, J., Schulte, L., Fernández-López de Pablo, J. What burned the forest? Wildfires, climate change and human activity during the Mesolithic – Neolithic transition in SE Iberian Peninsula (submitted to Catena). </p> <p>We specify the content of file further down:</p> <ul> <li>Vinalopo.csv: the list of radiocarbon dates from Villena spanning ca.9500-5500 cal BP from the following sites: Arenal de la Virgen, Cueva del Lagrimal and Casa Corona. </li> <li>ngrip.csv: NGRIP GICC05 paleotemperature record based on oxygen isotope series from Rasmussen SO <em>et al.</em>2006 A new Greenland ice core chronology for the last glacial termination. <em>J. Geophys. Res. Atmos.</em><strong>111</strong>. (doi:10.1029/2005JD006079) and Andersen KK <em>et al.</em>2006 The Greenland Ice Core Chronology 2005, 15–42ka. Part 1: constructing the time scale. <em>Quat. Sci. Rev.</em>25, 3246–3257.</li> <li>Char.csv: Sedimentary charcoal data set from the Villena Paleolake (VL3 core) published by Jones, S.E., Burjachs, F., Fernández-López de Pablo (2018) DOI/10.5281/zenodo.1244003, according to the new Bacon chronological model of the Villena paleolake (Fernández-López de Pablo et al., 2022 . Impacts of Early Holocene environmental dynamics on open-air occupation patterns in the Western Mediterranean: insights from El Arenal de la Virgen (Alicante, Spain). <a href="https://doi.org/10.31235/osf.io/5yqsr">https://doi.org/10.31235/osf.io/5yqsr</a>)</li> <li>SPD_analysis.R: R script with the code to reproduce the SPD analysis presented in the manuscript. </li> <li>SupplMat1xlsl: an excel file This file is composed by 8 spreadsheets:</li> </ul> <ol> <li>‘Selected variables 12.6-5.5’: all the data included in the time frame 12600-5500 cal BP, interpolated to 50 yr time windows. These data have been used for the Spearmans’rs correlation analysis (see spreadsheet ‘Spearmans’rs 12.6-5.5’ to track the results), Detrended Correspondence Analysis (see spreadsheet ‘Figure 5_DCA 12.6-5.5’ to track the results) and have been plotted in Figure 3 and 7. </li> <li>'Selected variables 9.1-5.5’: data included in the analysis focused on the time period 9.1-5.5 cal BP, interpolated to 50 yr time windows. These data have been used for the Spearmans’rs correlation analysis (see spreadsheet ‘Spearmans’rs 9.1-5.5’ to track the results), Detrended Correspondence Analysis (see spreadsheet ‘Figure 6_DCA 9.1-5.5’ to track the results) and have been plotted in Figure 8.</li> <li>‘Spearmans’rs 12.6-5.5’: Spearmans’rs correlation analysis applied to the 12600-5500 cal BP dataset (data from ‘Selected variables 12.6-5.5’).</li> <li>‘Spearmans’rs 9.1-5.5 cal BP’ Spearmans’rs correlation analysis applied to the 9100-5500 cal BP dataset, including here high-resolution XRD data (data from ‘Selected variables 9.1-5.5’).</li> <li>‘Figure 2 charcoal results’: original sedimentary charcoal results provided in this work. Data plotted in Figure 2. </li> <li>‘Figure 4 XRD results’: original XRD results provided in this work. Data plotted in Figure 4.</li> <li>‘Figure 5 DCA 12.6-5.5’: results of Detrended Correspondence analysis focused on the time period from 12600 to 5500 cal BP. Data plotted in Figure 5.</li> <li>‘Figure 6 DCA 9.1-5.5’ results of Detrended Correspondence analysis focused on the time period from 9100 to 5500 cal BP, including here high-resolution XRD data. Data plotted in Figure 6.</li> </ol>
Tweets containing "climate change" with topic annotations
<p>This dataset contains the Twitter IDs of all ~20M tweets containing the phrase "climate change" 2018-2021. Additionally, it contains the topical annotations and 2D semantic representation of our thematic analysis based on ~980 topic clusters that are grouped by hand into seven themes (COVID-19, Politics, Contrarian, Movements, Solutions, Impacts, Causes) as well as "non-relevant/spam", "others", and highlighting of potentially interesting topics.</p> <p>Code and additional notes are available on GitHub: https://github.com/TimRepke/twitter-climate</p> <p>The topics, including statistics and the annotator labels for broader themes (aka "super topics") are contained in the spreadsheet. This data is extrapolated to the tweets contained in the share.jsonl file containing one json object per line with the following fields:</p> <ul> <li><strong>'rel':</strong> true iff Tweet is contained in analysis</li> <li><strong>'filters':</strong> null if Tweet is not included, otherwise contains an object with "reasons" why this tweet was excluded <ul> <li><strong>'dup'</strong>: 1 iff this is a duplicate (excl first)</li> <li><strong>'lan':</strong> 1 iff language is English (and not None)</li> <li><strong>'txt': </strong>1 iff status text is not None</li> <li><strong>'mit': </strong>1 iff text has minimum number of tokens (>=4)</li> <li><strong>'mah'</strong>: 1 iff text has less than maximum number of hashtags (<=5),</li> <li><strong>'pfd'</strong>: 1 iff tweet was posted after 01.01.2018</li> <li><strong>'ptd'</strong>: 1 iff tweet was posted before 31.12.2021</li> <li><strong>'cli':</strong> 1 iff tweet actually contains "climate change" (API matches some false positives)</li> </ul> </li> <li><strong>'ann'</strong>: null if Tweet is not included, otherwise contains an object with topic annotations <ul> <li><strong>'t_km': </strong> topic (based on "keep & majority vote" strategy)</li> <li><strong>'t_kp': </strong> topic (based on "keep & closest topic centroid [proximity]" strategy)</li> <li><strong>'t_fm': </strong> topic (based on "drop sample topic [fresh] & majority vote" strategy)</li> <li><strong>'t_fp':</strong> topic (based on "drop sample topic [fresh] & closest topic centroid [proximity]")</li> <li><strong>'st_int':</strong> theme annotation "Interesting"</li> <li><strong>'st_nr': </strong> theme annotation "Non-relevant / spam"</li> <li><strong>'st_cov':</strong> theme annotation "COVID"</li> <li><strong>'st_pol': </strong> theme annotation "Politics"</li> <li><strong>'st_mov': </strong> theme annotation "Movements"</li> <li><strong>'st_imp':</strong> theme annotation "Impacts"</li> <li><strong>'st_cau': </strong> theme annotation "Causes"</li> <li><strong>'st_sol': </strong> theme annotation "Solutions"</li> <li><strong>'st_con': </strong>theme annotation "Contrarian"</li> <li><strong>'st_oth': </strong> theme annotation "Other"</li> <li><strong>'x': </strong> x position in 2D representation</li> <li><strong>'y': </strong> x position in 2D representation</li> <li><strong>'sample':</strong> true iff this tweet was in the original topic model sample</li> </ul> </li> </ul>
Dataset regarding the « Reasons for concern » about climate change from figures in IPCC and related publications
<p>This data corresponds to the 'burning ember' diagrams from IPCC reports and related publications (IPCC TAR, Smith et al. 2009 for AR4-related embers, AR5 and SR15). It was used to build figure 3 of Zommers et al. 2020 (<em>Burning Embers: Towards more transparent and robust climate change risk assessments</em>. Accepted for publication in Nature Reviews Earth & Environment). The data provided here is the result of extraction of information from the original figures, as presented in the related technical document <a href="https://doi.org/10.5281/zenodo.3992856">10.5281/zenodo.3992856</a>. As explained in the Supplementary Information of Zommers et al. 2020 and the technical document, this is not data from the IPCC. The provided values are approximations of the global mean temperature increase corresponding to each change in risk in the original diagrams. The rigour of the preparation process and the limitations of the dataset are explained in the technical document.</p>
Current and future global distribution of potential biomes under climate change scenarios
<p>Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080.</p> <p>Files are named according to the following naming convention, e.g.:</p> <ul> <li>biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410</li> </ul> <p>with the following fields:</p> <ul> <li>generic theme: <strong>biomes</strong>,</li> <li>variable name: <strong>graminoid.and.forb.tundra.rcp85</strong>,</li> <li>variable type, e.g. probability ("<strong>p</strong>"), hard class ("<strong>c</strong>"), model deviation ("<strong>md</strong>")</li> <li>spatial resolution: <strong>1km</strong>,</li> <li>depth reference, e.g. below ("<strong>b</strong>"), above ("<strong>a</strong>") ground or at surface ("<strong>s</strong>"),</li> <li>begin time (YYYYMMDD): <strong>20610101</strong>,</li> <li>end time: <strong>20801231</strong>,</li> <li>bounding box, e.g. global land without Antarctica ("<strong>go</strong>"),</li> <li>EPSG code: <strong>epsg.4326</strong>,</li> <li>version code, e.g. creation date: <strong>v20230410</strong>.</li> </ul> <p>We provide probability and hard class layers using a revised classification system of the <a href="https://www.jstor.org/stable/2846196">BIOME 6000 project</a> explained in the work of <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a>. The 20 classes from this classification system have then been aggregated in 6 biome classes following the <a href="https://global-ecosystems.org/page/typology">IUCN Global Ecosystem Typology</a> classification system.</p> <p>For probability layers, the uncertainty (model deviation: <strong>md</strong>) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel.</p> <p>For hard class layers the uncertainty is calculated using the margin of victory (<a href="https://doi.org/10.1016/j.rse.2020.112148">Calderón-Loor et al., 2021</a>) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the <strong>md </strong>layers to properly interpret the results of the map.</p> <p>Styling files are provided in both <em><strong>.SLD</strong></em> and <em><strong>.QML</strong></em> format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics.</p> <p>The R scripts and a tutorial will be uploaded to the <a href="https://github.com/Envirometrix/PNVmaps">PNVmaps Github repository</a>, where previous versions of the biomes maps from <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a> is currently hosted. To cite the maps and the methodology, it is possible to refer to the scientific publication:</p> <p>Bonannella C, Hengl T, Parente L, de Bruin S. 2023. Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ 11:e15593 <a href="https://doi.org/10.7717/peerj.15593">https://doi.org/10.7717/peerj.15593</a></p>
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (<a href="https://doi.org/10.1034/j.1600-0870.2001.00241.x" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the article "<em>The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example</em>", published by Chaos (<a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a>.</p> <p>All files uploaded were generated from simulations run by the authors.</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p> <p><strong>Note:</strong> This version (v1.1) is the same version as v1.0 but with the correct README file.</p>
Soybean data for paper: "Increase of simultaneous soybean failures due to climate change"
<p>Input and Output data used in the paper: ""Increase of simultaneous soybean failures due to climate change""</p> <p>Upon use of part of this dataset, please cite authors and paper related.</p> <p>Input:</p> <p>Observed soybean data obtained from official authorities pre-processed and regularised at 0.5 x 0.5 spatial resolution:</p> soy_yield_1975_2016_05x05_1prc.nc 43.6 MB soy_yield_arg_1974_2019_05x05.nc 44.6 MB soy_yields_US_all_1975_2020_05x05.nc 47.7 MB soybean_harvest_area_calculated_americas_hg.nc soybean_yields_america_detrended_1978_2016.nc 78.8 MB <p> </p> <p>Outputs:</p> <p>Hybrid model outputs for soybean yield from 2015-2100l with trends at 0.5 x 0.5 spatial resolution:</p> hybrid_trend_gfdl-esm4_ssp126_default_yield_soybea ... 14.6 MB hybrid_trend_gfdl-esm4_ssp585_default_yield_soybea ... 14.6 MB hybrid_trend_ipsl-cm6a-lr_ssp126_default_yield_soy ... 14.6 MB hybrid_trend_ipsl-cm6a-lr_ssp585_default_yield_soy ... 14.6 MB hybrid_trend_ukesm1-0-ll_ssp126_default_yield_soyb ... 14.6 MB hybrid_trend_ukesm1-0-ll_ssp585_default_yield_soyb ... 14.6 MB <p>Hybrid model outputs for soybean yield from 2015-2100 without trends at 0.5 x 0.5 spatial resolution:</p> hybrid_gfdl-esm4_ssp126_default_yield_soybean_2015 ... 7.3 MB hybrid_gfdl-esm4_ssp585_default_yield_soybean_2015 ... 7.3 MB hybrid_ipsl-cm6a-lr_ssp126_default_yield_soybean_2 ... 7.3 MB hybrid_ipsl-cm6a-lr_ssp585_default_yield_soybean_2 ... 7.3 MB hybrid_ukesm1-0-ll_ssp126_default_yield_soybean_20 ... 7.3 MB hybrid_ukesm1-0-ll_ssp585_default_yield_soybean_20 ... 7.3 MB
CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia: Dataset
<p>This repository is linked to the paper "CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia" submitted to Annals of Forest Science and written by Louis DE WERGIFOSSE (corresponding author), Frédéric ANDRE, Hugues GOOSSE, Steven CALUWAERTS, Lesley DE CRUZ, Rozemien DE TROCH, Bert VAN SCHAEYBROECK and Mathieu JONARD.</p> <p>The files stored in the repository are the input files that should be used in the model HETEROFOR to retrieve the results displayed in the study and the corresponding results themselves. The source code of the model HETEROFOR can be freely accessed and downloaded (https://doi.org/10.5281/zenodo.3591348). Additional information on the model can be found in the following description papers: Jonard et al., 2020 (https://doi.org/10.5194/gmd-13-905-2020) and de Wergifosse et al., 2020 (https://doi.org/10.5194/gmd-13-1459-2020).</p> <p>The repository contains three directories. The first (HETEROFOR_input_files) comprises the additional files to those in the model repository presented in the previous paragraph needed to run the model for the purpose of this study. The second directory (Simulation_outputs_raw) contains the data directly provided by the model without any processing. The third directory (Simulation_outputs_raw) includes the model outputs after processing.</p> <p>The directory "HETEROFOR_input_files" is constituted of two directories called "Climate_files" and "Stand_files". "Climate_files" is subdivided in three sub-directories. Sub-directory "Original_downscaled_CORDEX_timeseries" contains the climate projections of the four sites and scenarios described in the study. These downscaled timeseries have been produced by the Royal Meteorological Institute of Belgium under the program CORDEX.be, which is part of EURO-CORDEX. A bias correction has been further applied to these climate timeseries that are stored in the "Bias_corrected_timeseries" sub-directory. The files of these two sub-directories should be used in HETEROFOR as "Meteorological data" input files. The "CO2_concentrations" sub-directory includes the yearly averaged projected concentrations for the three RCP scenarios described in the paper. In HETEROFOR, they should be put as input in the "Atmospheric CO2 concentration" part after selecting the option "Variable over time". The second directory called "Stand files" contain the six inventory files described in the study for which a thinning has been applied. They should be used in HETEROFOR as "Inventory data" input files.</p> <p>The directory "Simulation_outputs_raw" is divided similarly to the study into two simulation experiments. The "First simulation experiment" directory is further subdivided into constant and time-dependent CO2 concentrations like in the study and contains one file for the regular modality and one for the thinning modality. All the files are constructed the same way with, for each tree and site (or stand, soil and climate), annual values of Net Primary Production (NPP) in kg of carbon, transpiration and potential transpiration in L under the different climate scenarios. In addition, the "Phenology" directory contains, for each day and under all climate scenarios, the green proportion (proportion of green leaves comprised between 0 and 1) for the two tree species considered in the study (Common oak and European beech).</p> <p>Finally, the directory "Simulation_outputs_processed" is constructed similarly to "Simulation_outputs_raw" but all the data are integrated in one file at the yearly time step. However, the units change with the NPP expressed in gC/m2 and transpiration and potential transpiration in mm (or L/m2) while the vegetation period is averaged according to the percentage of species occurrence<br> in the different stands.</p> <p><br> For more information concerning this repository or the study, please do not hesitate to contact Louis DE WERGIFOSSE (louis.dewergifosse@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).</p>
AgMIP-Wheat multi-model ensemble simulations on climate change impact and adaptation for 60 representative global locations
<p>This is model output from the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat) dataset of multi-model ensemble simulations for 60 representative global locations under different climate scenarios.</p> <p>The data have been generated following the modeling protocol of Asseng et al. (2019) and Liu et al. (2019).</p> <p>References</p> <p>Asseng, S. et al. (2019). Climate change impact and adaptation for wheat protein. Glob Chang Biol 25, 155-173, doi:10.1111/gcb.14481</p> <p>Liu, B. et al. (2019). Global wheat production with 1.5 and 2.0°C above pre-industrial warming. Global Change Biol 25, 1428-1444, doi:10.1111/gcb.14542</p> <p> </p>
Data for: "Climatic drivers of (changes in) bat migration phenology at Bracken Cave (USA)"
<p>This dataset contains the spring and autumn migration phenology dataset used in Haest <em>et al.</em> (2020) to determine the drivers of migration phenology of Brazilian free-tailed bats at Bracken Cave (USA) over the period 1995-2017. The phenology dataset was derived from nightly colony population sizes estimated using weather radar data (Stepanian <em>et al.</em>, 2018). See the Materials and Methods section in Haest <em>et al.</em> (2020) for more details on the dataset. </p> <p>References:</p> <p>Haest, B., Stepanian, P. M., Wainwright, C. E., Liechti, F., & Bauer, S. (2021). Climatic drivers of (changes in) bat migration phenology at Bracken Cave (USA). <em>Global Change Biology</em>, 27(4), 768-780. <a href="https://doi.org/10.1111/gcb.15433">https://doi.org/10.1111/gcb.15433</a></p> <p>Stepanian, P. M., & Wainwright, C. E. (2018). Ongoing changes in migration phenology and winter residency at Bracken Bat Cave. <em>Global Change Biology</em>, <em>24</em>(7), 3266–3275. <a href="https://doi.org/10.1111/gcb.14051">https://doi.org/10.1111/gcb.14051</a></p> <p> </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.