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.

2,260

datasets available to search

ShareScore release 0.7.1

Dataset results

2,260 results for “climate change”

Learn how ShareScore rates datasets ↗

zenodo44/100

Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence

This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication: Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. GeoHealth.  The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario.  Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3.

opencc-by-4.0Aug 2019View details →

zenodo44/100

Climate change impact and mitigation cost data - The economically optimal warming limit of the planet

This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper: Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. <a href="https://doi.org/10.5194/esd-10-741-2019">https://doi.org/10.5194/esd-10-741-2019</a> Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).   Climate change impact data File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.   File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).   File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action). In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, <a href="https://tntcat.iiasa.ac.at/SspDb/">https://tntcat.iiasa.ac.at/SspDb/</a>, last accessed 15Nov 2019).   Climate change mitigation cost data The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2]. File 4: REMIND_scenario_results_economic_data.csv File 5: REMIND_scenarios_climate_data.csv Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature. In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios. The first dimension specifies the climate policy regime (delayed action, baseline scenarios): 1xx: climate action from 2010 5xx: climate action from 2015 2xx climate action from 2020 (used in this study) 3xx climate action from 2030 4x1 weak policy baseline (before Paris agreement) The second dimension specifies the technology portfolio and assumptions: x1x Full technology portfolio (used in this study) x2x noCCS: unavailability   of   CCS x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed x4x NucPO: phase out of investments into nuclear energy x5x Limited SW: penetration of   solar   and   wind   power   limited x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases) x6x noBECCS: unavailability   of   CCS   in   combination   with   bioenergy The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.). xx1   0$/tCO2   (baseline) xx2   10$/tCO2 xx3   30$/tCO2 xx4   50$/tCO2  xx5   100$/tCO2 xx6   200$/tCO2 xx7   500$/tCO2 xx8   40$/tCO2 xx9   20$/tCO2 xx0   5$/tCO2 For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).   [1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a. [2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

opencc-by-4.0Nov 2019View details →

zenodo44/100

Dataset for: Infectious disease responses to human climate change adaptations

Original and derived data products referenced in the original manuscript are provided in the data package. <h3>Description of the data and file structure</h3> Original data: <code>Table_1_source_papers.csv</code>: Papers that met review criteria and which are summarized in Table 1 of the manuscript. <ol> <li>ID: The paper identification number</li> <li>Topic: The broad topic (i.e., each row of Table 1)</li> <li>Authors: The names of the authors of the paper</li> <li>Article Title: The title of the paper</li> <li>Source Title: The name of the journal in which the paper was published</li> <li>Abstract: The paper's abstract, retrieved from the Web of Science search</li> <li>study_type: Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.</li> <li>pathogen_broad: Broad classification of the type of pathogen discussed in the paper.</li> <li>transmission_type: Categorization of indirect, direct, sexual, vector, or other transmission modes.</li> <li>pathogen_type: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.</li> <li>country: Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).</li> </ol> Derived data: <code>change_livestock_country.csv:</code> A dataframe containing values used to generate Figure 4a in the manuscript. <ol> <li>County Name: The name of the county in Kenya</li> <li>Sheep and goats 1980: The estimated number of sheep and goats in 1980</li> <li>Sheep and goats 2016: The estimated number of sheep and goats in 2016</li> <li>pct_change_shoat: The percent change in sheep and goat numbers from 1980 to 2016</li> <li>Cattle 1980: The estimated number of cattle in 1980</li> <li>Cattle 2016: The estimated number of cattle in 2016</li> <li>pct_change_cattle: The percent change in cattle numbers from 1980 to 2016</li> <li>Camel 1980: The estimated number of camels in 1980</li> <li>Camel 2016: The estimated number of camels in 2016</li> <li>pct_change_camel: The percent change in camel numbers from 1980 to 2016</li> <li>human_pop 1980: The estimated human population in the county in 1980</li> <li>human_pop 2016: The estimated human population in the county in 1980</li> <li>pct_change_human: The percent change in the human population from 1980 to 2016</li> <li>area_sq_km: The land area of the county</li> <li>change_ind_per_sq_km_shoat: Absolute change in number of sheep and goats from 1980 to 2016</li> <li>change_ind_per_sq_km_cattle: Absolute change in number of cattle from 1980 to 2016</li> <li>change_ind_per_sq_km_camel: Absolute change in number of camels from 1980 to 2016</li> </ol> <code>country_avg_schist_wormy_world.csv</code>: A dataframe containing values used to generate Figure 3 in the manuscript. <ul> <li>Country: The country in which the schistosome prevalence studies were performed.</li> <li>Latitude: The latitute in decimal degrees</li> <li>Longitude: The longitute in decimal degrees</li> <li>Maximum.prevalence: The mean maximum schistosomiasis prevalence of studies conducted within each country.</li> </ul> <code>kenya_precip_change_1951_2020.csv</code>: A dataframe containing values used to generate Figure 4b in the manuscript. <ul> <li>Precipitation (mm): Binned annual precipitation values</li> <li>1951-1980: The density of observations for each annual precipitation value for the 1951-1980 period</li> <li>1971-2000: The density of observations for each annual precipitation value for the 1971-2000 period</li> <li>1991-2020: The density of observations for each annual precipitation value for the 1991-2020 period</li> </ul> <h3>Sharing/Access information</h3> Data were derived from the following sources: <ul> <li> Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? PloS ONE, 11(9), e0163249. https://doi.org/10.1371/journal.pone.0163249 </li> <li> London Applied & Spatial Epidemiology Research Group (LASER). (2023). Global Atlas of Helminth Infections: STH and Schistosomiasis [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0 </li> <li> World Bank Group. (2023). Climate Data & Projections—Kenya. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections </li> </ul>

opencc-by-4.0Aug 2024View details →

zenodo44/100

Projected distribution of invasive plant species in the tropical Andes under climate change

Distribution maps of 11 invasive species now and in the future (2040-70). The projections were the result of the assembly of three algorithms: Adaptive Boosting (AdaBoost), Boosted Regression Trees (BRT), and Extreme Gradient Boosting (XGBoost). Future projections were made for three global circulation models and three climate change scenarios, each with low (SSP126), medium (SSP370), and high (SSP585) levels of carbon emission. Habitat suitability and presence/absence maps are also included. The threshold for establishing a species as present was determined to be the value that maximized the TSS.  For more information, see the article accompanying the dataset by González-Trujillo et al. Mapping the threat: Projecting invasive plant distribution in the tropical Andes under climate change List of modeled invasive plant species and their known impacts in the tropics. <table> <tbody> <tr> <td> Species </td> <td> Biogeographic origin </td> <td> Impacts </td> <td> References </td> <td> GBIF data (DOIs) </td> </tr> <tr> <td> Acacia decurrens </td> <td> Australian </td> <td> Create regular layers of litter on the ground, inhibit or redirect successional processes, inhibit the expression of seed banks, and limit resource supply, leading to displacement of native plants and animals and increasing the frequency of fires. </td> <td>  (Cárdenas López et al., 2017; Le Maitre et al., 2011) </td> <td> https://doi.org/10.15468/dl.mjyxhw </td> </tr> <tr> <td> Acacia melanoxylon </td> <td> Australian </td> <td> Alter the structure and function of their ecosystems, thereby displacing their native flora. It also causes soil erosion and alters hydrological cycles, negatively affecting agriculture. </td> <td> (Kumschick and Jansen, 2023; Le Maitre et al., 2011)   </td> <td> https://doi.org/10.15468/dl.4cugnk </td> </tr> <tr> <td> Arundo donax   </td> <td> Holarctic </td> <td> Alter the natural vegetation structure, outcompete native plant species and diminish the diversity and abundance of animals such as arthropods and birds. It also drives out soil, fuels forest fires, displaces native species, and increases the invasion of ticks that affect livestock. </td> <td> (Cárdenas López et al., 2017; Girotto et al., 2021; Lambert et al., 2010) </td> <td> https://doi.org/10.15468/dl.bfep4t </td> </tr> <tr> <td> Genista monspessulana </td> <td> Holarctic </td> <td> Alter fire regime and nutrient cycling displace native species and decrease native diversity by forming dense monospecific stands. It also facilitates the establishment of other invasive species and produces seeds that are toxic to livestock and humans. </td> <td> (Cárdenas López et al., 2017; Herrera et al., 2016; Pauchard et al., 2008) </td> <td> https://doi.org/10.15468/dl.gyhnxh </td> </tr> <tr> <td> Hedychium coronarium </td> <td> Indo-Malesian </td> <td> Alter hydrological and nutrient cycles in soil. It forms thickets that suppress the successional and regeneration processes of native species, thus affecting the native flora and crops. </td> <td> (Cárdenas López et al., 2017; Costa et al., 2019) </td> <td> https://doi.org/10.15468/dl.6z2jgb </td> </tr> <tr> <td> Melinis minutiflora </td> <td> African </td> <td> Increases the occurrence of fires, displaces native species, and alters soil properties and decomposition. It also inhibits the growth of native species. </td> <td> (Cárdenas López et al., 2017; Nogueira et al., 2019; Sandoval et al., 2022) </td> <td> https://doi.org/10.15468/dl.fsqwsv </td> </tr> <tr> <td> Pteridium aquilinum </td> <td> Holarctic </td> <td> Alter vegetation success processes affect crops and cause livestock poisoning.  It also produces acids that inhibit root growth in native and cultivated species. </td> <td>  (Berget et al., 2015; Cárdenas López et al., 2017; Valdez-Ramírez et al., 2020)   </td> <td> https://doi.org/10.15468/dl.sp4uuv </td> </tr> <tr> <td> Ricinus communis </td> <td> African </td> <td> Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species. </td> <td> (Cárdenas López et al., 2017; Sandoval et al., 2022; Silva and Fabricante, 2022) </td> <td> https://doi.org/10.15468/dl.dhbphb </td> </tr> <tr> <td> Senecio madagascariensis </td> <td> African </td> <td> Alter soil nutrient cycles, damage to agricultural crops, and outcompete native species. It also contains substances that are toxic to both animals and humans.  </td> <td> (Wijayabandara et al., 2021) </td> <td> https://doi.org/10.15468/dl.7e8eyx </td> </tr> <tr> <td> Thunbergia alata </td> <td> African </td> <td> Displace native species and reduce habitat heterogeneity, thereby affecting the structure and function of native ecosystems. </td> <td> (Cárdenas López et al., 2017; Quijano-Abril et al., 2021) </td> <td> https://doi.org/10.15468/dl.g9zybc </td> </tr> <tr> <td> Ulex europeaus </td> <td> Holarctic </td> <td> Dry soil and increase the occurrence of fires. Inhibits vegetative growth, including pastures in agricultural and livestock lands. </td> <td> (Anderson and Anderson, 2009; Cárdenas López et al., 2017) </td> <td> https://doi.org/10.15468/dl.6642q9 </td> </tr> </tbody> </table>

opencc-by-4.0Apr 2024View details →

zenodo44/100

Reddit Climate Change Debate Dataset

This dataset contains pairwise interactions between Reddit users debating climate change on general-purpose subreddits. Each account is enriched with information about the stance concerning climate change (e.g., whether one denies or believes climate change exists) estimated by a deep neural model.  All data is anonymized, and no personally identifiable information is released. <h2>Dataset</h2> Interactions are stored in four files, each encompassing 3 months of interactions in 2022. Each interaction file climatechange-X.csv contains three columns identifying source, target, and weight, respectively. The resulting graphs are directed. The climatechange-opinions.csv file contains three columns identifying node, opinion, and time window. Opinions are stored as floats in [-1,1] such that 1 implies maximum adherence with deniers, -1 implies maximum adherence with supporters, and 0 implies neutrality. Thus, a line like 42,0.99,2 should be read as "node 42 is a climate change denier in the second quarter of 2022". Code to reproduce the experiments in the paper is released in a jupyter notebook. For further information on fields and volumes, please refer to the data paper. <h3>Citation</h3> If used for research purposes, please cite the following paper describing the dataset details: TBD <h3>Acknowledgements</h3> This work is supported by: <ul> <li>the European Union – Horizon 2020 Program under the scheme “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” (http://www.sobigdata.eu); </li> <li>SoBigData.it which receives funding from the European Union – NextGenerationEU – National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: “SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021;</li> <li>EU NextGenerationEU programme under the funding schemes PNRR-PE-AI FAIR (Future Artificial Intelligence Research). </li> </ul>

opencc-by-4.0Aug 2024View details →

zenodo44/100

Two-wave Post-Disaster Survey on Climate Change Attitudes: Texas after Hurricane Harvey and the 2021 North American Winter Storms

Overview This repository contains data needed to reproduce the analysis results from Chen et al. 2024. "Disaster Experience Mitigates the Partisan Divide on Climate Change: Evidence from Texas," Global Environmental Change. It is a study about climate change attitudes and experience with climate disasters across U.S. partisan groups. For details about the data, please see the published paper. Results reproduction code is available at <a href="https://github.com/tedhchen/floodStorm" target="_blank" rel="noopener">https://github.com/tedhchen/floodStorm</a>.   Data Set Details `texas_climate_attitudes.csv` contains data from two waves of surveys of Democrats and Republicans living in Texas, with the following groups of variables. <ul> <li>climate change attitudes</li> <li>self-reported exposure to climate disasters</li> <li>scientific information treatment condition and checks</li> <li>political leaning</li> <li>sociodemographics and residential location</li> <li>survey administration details</li> </ul> `outage2021_data.RData` contains power outage data for counties and cities in Texas during Feb. 2020 and Feb. 2021. `outage2021_data_multithreshold.RData` contains power outage data for counties and cities in Texas during Feb. 2020 and Feb. 2021, aggregated to the county level based on different thresholds of uncertainty about which cities people live in. `gtrends_archive.RData` contains Google Trends data for "hurricane", "astros", and "power", in Texas between 2017 and 2021.   References Please reference the original study when using this data set. Ted Hsuan Yun Chen, Christopher J. Fariss, Hwayong Shin, Xu Xu. 2024. "Disaster Experience Mitigates the Partisan Divide on Climate Change: Evidence from Texas." Global Environmental Change. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102918" target="_blank" rel="noopener">doi:10.1016/j.gloenvcha.2024.102918</a>.

opencc-by-4.0Aug 2024View details →

zenodo44/100

Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context - Supporting Information S2 and S3

The data contains the databases used to calculate the climate change impacts of second-life batteries including full Life Cycle Inventory data published in the article entitled "Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context". The second file S3 contains the economic data and the climate change impacts of the same article. In version 2.0 of S2, a sensitivity analysis and more detail is added in the results.

opencc-by-4.0Jul 2024View details →

zenodo44/100

Coral growth data for the research article "Reconstruction of long-term sub-lethal effects of warming on a temperate coral in a climate change hotspot" in Journal of Animal Ecology

This repository contains the coral growth data files used to generate the results for the following article:   MJ. Vergotti, JP. D’Olivo, T. Brachert, P. Capdevila, J. Garrabou, C. Linares, P. Spreter, DK. Kersting (2024) Reconstruction of long-term sub-lethal effects of warming on a temperate coral in a climate change hot-spot. Journal of Animal Ecology. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.14225   Abstract: The impact of warming on zooxanthellate corals is widespread, from tropical to temperate seas, with their associated mortalities causing global concern. The temperate coral Cladocora caespitosa is the only zooxanthellate coral with reef-building capacity in the Mediterranean Sea, a climate change hotspot with warming rates triple the global average. Over the past two decades, C. caespitosa populations have suffered severe mortality events associated with marine heatwaves (MHWs). However, with monitoring efforts beginning, at best, in the 2000s, the occurrence of MHWs before to that period, as well as the sub-lethal effects of these events remain poorly understood. Here we use sclerochronology to reconstruct the histories of past stress events and long-term sub-lethal effects on C. caespitosa in three locations within the NW Mediterranean Sea, each with different environmental conditions. Skeletal extension, density and calcification rates were compared to the in situ seawater temperature of each site to assess their relationship. Additionally, we assessed the occurrence of skeletal growth anomalies to reconstruct stress events between 1991 and 2021, a period that encompasses the onset and evolution of warming-related mass mortality events in the NW Mediterranean Sea. Our results reveal a positive association between calcification and temperature, following a latitudinal temperature gradient. However, the evolution of the likelihood distribution of growth rates in the warmest site (Columbretes Islands) since the 1990s indicates a decrease in linear extension and calcification rates during the most recent years. With the increase in the frequency of MHWs and growth anomalies during the last decade, this decline suggests a recurrence in physiological stress events. These results unravel information on the long-term impacts of warming on coral growth and highlight the potential of applying sclerochronology to reconstruct sub-lethal effects of warming using C. caespitosa.      Funding: This research is supported by the Horizon 2020 program of research and innovation of the European Union under the MaCoBioS grant agreement, by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project no. 401447620) and by the Spanish Ministry of Science, Innovation and Universities under the project UndResCoral (project no. PID2022-137539OA-C22). D.K.K. was supported by a Ramon y Cajal postdoctoral grant funded by the Ministry of Science and Innovation (PEICTI 2021–2023; grant no. RYC2021-033576-I).  C.L. acknowledges the support by ICREA Academia. J.G. acknowledges the grant “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S) funded by AEI 10.13039/501100011033.

opencc-by-4.0Oct 2024View details →

zenodo44/100

Twiter Dataset on climate change discussions: COP27, IPCC, climate refugees and Doñana - Clint project

CLINT Data This repository contains the date used in the project CLINT and the paper  "<a href="https://arxiv.org/abs/2410.21187">A cross-platform analysis of polarization and echo chambers in climate change discussions</a>"   Open Twitter Data We used the Twitter’s search to gather historical tweets and the streaming API to follow specified accounts and also collect in real-time tweets that mention specific keywords. To comply with <a href="https://developer.twitter.com/en/developer-terms/agreement-and-policy">Twitter’s Terms of Service</a>, we are only publicly releasing the tweet IDs of the collected tweets. The data is released for non-commercial research use.  With Twitter's changes to its Academic API policies, it’s no longer possible to collect or rehydrate tweets as we usually did, however we open data in case at some point it will become feasible to do it. <table> <tbody> <tr> <td> </td> <td>IPCC</td> <td>Doñana</td> <td>Climate Refugees</td> <td>COP27</td> </tr> <tr> <td>Number of tweets</td> <td>352,723 </td> <td>1,487,425</td> <td>1,938,932</td> <td>6,225,508 </td> </tr> <tr> <td>Number of authors</td> <td>157,056</td> <td>290,782</td> <td>841,454 </td> <td>1,351,903 </td> </tr> <tr> <td>First tweet date</td> <td>2023-03-18</td> <td>2019-01-01</td> <td>2008-03-10 </td> <td>2022-09-01 </td> </tr> <tr> <td>Last tweet date</td> <td>2023-03-26</td> <td>2023-04-30</td> <td>2022-12-31 </td> <td>2022-11-27</td> </tr> </tbody> </table>

opencc-by-4.0Nov 2024View details →

zenodo44/100

Supplementary Data for "Climate change may induce connectivity loss and mountaintop extinction in Central American forests"

Supplementary data underlying the main figures presented in the publication "Climate change may induce connectivity loss and mountaintop extinction in Central American forests"

opencc-by-4.0May 2021View details →

zenodo44/100

Model projection of the effect of climate change and fishing pressure on key species of the South East Asia Seas

The dataset contain Projection from the Size-Spectra Bioclimatic Envelop Model (SS-DBEM), this work was part of the GCRF Blue communities Programme (www.blue-communities.org). The model provides distribution and abundance and/or biomass of fish and other species of commercial interest under climate change and fishing pressure. The model outputs are yearly abundance/biomass on a 0.5-by-0.5 degree grid, covering the period from 2000 to 2098. Further description of the model and relevant references are listed in the following file: Guide-fish-model-output-use.docx The model was run under two climate scenario: RCP4.5 and RCP8.5, with different combinations of fishing pressure expressed as the Maximum Sustainable Yield (MSY) for the following values: 0 (no fishing, climate change alone will cause variation in fish biomass), 1 (sustainable fishing), 2, 3 (overfishing), and, 4 (overfishing with destructive practice). The intent is not to reproduce current fishing level but to provide a range of scenarios with which the future of fisheries can be explored. We projected fish species that were identified as key in the South East Asia seas region by our regional partners.The full list is provided in document: Fish-list-modelguide.xlsx There are 4 zip files that contain the model outputs of in either abundance (number of fish) or biomass grams of fish) for the two climate scenario. For example Biomass-RCP45.zip will contain model outputs in biomass for projections under RCP4.5 and all MSY. within the zip files are .csv files of the outputs for each species under the 5 MSY (0 to 4), the individual file names identify the species (identified by a 6digit code), the output provided (abundance or biomass), the RCP (8.5 or 4.5), and the MSY (0, 1, 2, 3, or 4). For example the file labelled 600107-Abundance-rcp85-msy4.csv contains the outputs for species 600107 (Skipjack tuna, Katsuwonnus pelamis), as abundance, under RCP8.5 with MSY4. Headers indicate what is in each column (latitude, longitude and year).   Note: some knowledge of Python, R, or a similar software is recommended to ensure easy of use.

opencc-by-4.0Jul 2021View details →

zenodo44/100

Tree mortality risks under climate change in Europe: assessment of silviculture practices and genetic conservation networks

General context: Climate change can positively or negatively affect abiotic and biotic drivers of tree mortality. Process-based models integrating these climatic effects are only seldom used at species distribution scale. Objective: The main objective of this study was to investigate the multi-causal mortality risk of five major European forest tree species across their distribution range from an ecophysiological perspective, to quantify the impact of forest management practices on this risk and to identify threats on the genetic conservation network. Methods: We used the process-based ecophysiological model CASTANEA to simulate the mortality risk of \textit{Fagus sylvatica}, \textit{Quercus petraea}, \textit{Pinus sylvestris}, \textit{Pinus pinaster} and \textit{Picea abies} under current and future climate conditions, while considering local silviculture practices. The mortality risk was assessed by a composite risk index \textit{(CRIM)} integrating the risks of carbon starvation, hydraulic failure and frost damage. We took into account extreme climatic events with the \textit{CRIM$_{max}$}, computed as the maximum annual value of the \textit{CRIM}. Results: The physiological processes' contributions to \textit{CRIM} differed among species: it was mainly driven by hydraulic failure for \textit{P. sylvestris} and \textit{Q. petraea}, by frost damage for \textit{P. abies}, by carbon starvation for \textit{P. pinaster}, and by a combination of hydraulic failure and frost damage for \textit{F. sylvatica}. Under future climate, projection showed an increase of \textit{CRIM} for \textit{P. pinaster} but a decrease for \textit{P. abies}, \textit{Q. petraea} and \textit{F. sylvatica}, and little variation for \textit{P. sylvestris}. Under the harshest future climatic scenario, forest management decreased the mean \textit{CRIM} for \textit{P. sylvestris}, increased it for \textit{P. abies} and \textit{P. pinaster} and had no major impact for the two broadleaved species. By the year 2100, 38\% to 90\% of the conservation units are at extinction threat (\textit{CRIM$_{max}$}=1), depending on the species. Conclusions: Using a process-based ecophysiological model allowed us to disentangle the multiple drivers of tree mortality under current and future climate. Taking into account the positive effect of increased CO$_2$ on fertilization and water use efficiency, the average risks may increase or decrease in the future depending on species and sites. However, considering extreme climatic events, future projections are as pessimistic than those obtained with bioclimatic niche models.   Abbreviation for column: X                                    Longitude Y                                    Latitude LAImax                            Leaf area index max reach Nha                                Density per hectar Vha                                Volume per hectar NEE                                Net ecosystem exchange NPP                                net primary production Reco                                Respiration ecosystem GPP                                Gross primary production Etveg                            Evapotranspiration canopy Etsol                                Evapotranspiration sol TR                                tree transpiration ETP                                evapotranspiration potentiel BiomassOfReserves        Biomass of reserve rw                                    ring width dbh                                diameter at breast heast height                            height BBday                            Budburst date rFD                                risk of frost CRIM_max                        Maximum combined risk index of mortality reach rNSC                                risk of carbon starvation rPLC                                risk of embolism rPLC_max                        Maximum risk of embolism reach CRIM                            combined risk index of mortality Climate                            Climatic model rNSC_max                        maximum risk of carbon starvation reach rFD_max                        Maximum risk of frost  reach Scenario_Sylvicol            null means no silvulcture simulated species                            species Country                            Country alt_watch                        altitude of climate simulated grid_watch                    number of the pixel point of WATCH grid_eurocordex                number of the pixel point of Eurocordex Pinus_sylvestris                0 abscence ; 1 presence Fagus_sylvatica                0 abscence ; 1 presence Quercus_petraea            0 abscence ; 1 presence Picea_abies                    0 abscence ; 1 presence Pinus_pinaster                0 abscence ; 1 presence

opencc-by-4.0Dec 2020View details →

zenodo44/100

Time of emergence of climate change impacts

Expected year in which climate impacts would exceed an extreme past economic shock value (95th percentile). Model used: CLIMRISK Scale: 0.5 degrees * 0.5 degrees Shock database consists of changes in annual GDP between 1950 - 2016. Citation: Ignjacevic, Predrag, Francisco Estrada Porrua, and Willem Jan Wouter Botzen. "Time of emergence of economic impacts of climate change." Environmental Research Letters (2021).

opencc-by-4.0Sep 2021View details →

zenodo44/100

Riverine Flood Insurance assessment indicators under climate and socio-economic change

Expected annual river flood damages, flood insurance premiums, and insurance penetration rates, for EU-regions (NUTS2) and under future climatic and socio-economic conditions (RCP-SSP combinations).

opencc-by-4.0Sep 2021View details →

zenodo44/100

Climate change impacts on energy demand

Climate change impacts on energy demand by energy carrier (electricity, natural gas, and petroleum) and sector (agriculture, industry, residential, and commercial).

opencc-by-4.0Sep 2021View details →

zenodo44/100

The convergence of an ensemble to an attractor during climate change

Time series of the annual mean near-surface air temperature of a grid point in the Southern Pacific in 192 members in each of two ensembles simulated by the Planet Simulator (Fraedrich et al., 2005) as described in Drotos et al. (2017) in relation to Fig. 2. One ensemble was initialised in year 0 (but note that the time series are available only from year 500 on) and the other in year 610 (the temperature of the last unperturbed year, year 609, is also included in the first lines of the corresponding files). The latter date is during a simulated climate change. G. Drótos, T. Bódai and T. Tél (2017), "On the importance of the convergence to climate attractors". Eur. Phys. J. Spec. Top. 226, 2031–2038. https://doi.org/10.1140/epjst/e2017-70045-7 K. Fraedrich, H. Jansen, E. Kirk, U. Luksch, F. Lunkeit (2005), "The Planet Simulator: Towards a user friendly model". Meteorol. Z. 14, 299–304. https://doi.org/10.1127/0941-2948/2005/0043

opencc-by-4.0Nov 2022View details →

zenodo44/100

Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America.

Köppen - Geiger scripts and resulting datasets for the publication entitled "Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America." This scripts can be adapted to any geographic scale and region. Works with climate change scenarios. Original publication: <a href="https://doi.org/10.1016/j.gloplacha.2023.104155">https://doi.org/10.1016/j.gloplacha.2023.104155</a> Dataset description Scripts.rar: R Scripts used in this publication, as well they are reproducible Readme_Köppen.txt: README file that explain the requisites and data formatting to run the scripts Output datasets.zip: Output GIS datasets of this publication. Coordinate system GCS WGS 1984

opencc-by-4.0Jul 2021View details →

zenodo44/100

Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"

This dataset is associated with the following publication: Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, Astronomy and Astrophysics, vol. 665, 2022. doi:10.1051/0004-6361/202142493. In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with: <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations: - 'ds_sel': contains monthly timeseries of selected intersecting data - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10) - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10) - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10)  The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2. The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error. The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures. Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a>

opencc-by-4.0Jan 2023View details →

zenodo44/100

Supplementary material for "Increased sensitivity of marine invertebrates to metal toxicity in the past two decades linked to Climate Change and Ocean Acidification: revelations from a natural population of sea urchins in the Mediterranean Sea." by "Davide Sartori, Guido Scatena, Cristina Vrinceanu, Andrea Gaion".

Satellite observations of environmental factors and effect concentration 50 for copper to sea urchin, from 2003 to 2022.

opencc-by-4.0Dec 2022View details →

zenodo44/100

Data for: Mercury contamination challenges the behavioral response of a keystone species to Arctic climate change

Combined effects of multiple, climate change-associated stressors are of mounting concern, especially in Artic ecosystems. Elevated mercury (Hg) exposure in Arctic animals could affect behavioural responses to changes in foraging landscapes linked to climate change, generating interactive effects on behaviour and population resilience. We investigated this hypothesis in the little auk (Alle alle), a keystone Artic seabird. We compiled behavioural data using accelerometers, and quantified blood mercury and environmental conditions (sea surface temperature (SST), sea ice coverage (SIC)) across multiple years. These datasets contain the behavioral, blood Hg and environmental data (SST, SIC) used in our analyses. Details about the datasets are found in the accompanying word document.

opencc-by-4.0Oct 2022View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record