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2,260 results for “climate change”
Influence of hillslope flow on hydroclimatic evolution under climate change - Dataset
<p>Dataset used for analyses in the paper "<strong>Influence of hillslope flow on hydroclimatic evolution under climate change".</strong></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>
Wikipedia Talk Page 'Climate Change' Sentiment and Toxicity Dataset
<p>The given dataset was prepared as part of a master's research project under the Master's program in Computational Social Systems at RWTH Aachen University.</p> <p>The talk page was parsed using the <a href="https://aclanthology.org/E17-3006/">GraWiTas tool</a> in JSON format. The file <em>Climate_change.comment_list.json </em> is raw export of discussions that needs to be cleaned before using it for calculating sentiment and toxicity scores.</p> <p>The sentiment scores were calculated using VADER and toxicity scores using the Perspective API by Google.</p> <p>Date & time of dump is: 27-06-2022 12:12 UTC+02:00</p>
Arctic Cyclones and Climate Change Cases A-C
<p>Weather Research and Forecasting Model (v3.9.1.1) set up namelists and simulation output data used to analyze the effect of climate change on spring Arctic Cyclone characteristics for cyclone cases A-C. This dataset is made available to accompany the Nature Communications paper "<strong>The Influence of Recent and Future Climate Change on Spring Arctic Cyclones"</strong> by Parker et al. 2022 <a href="https://doi.org/10.1038/s41467-022-34126-7">https://doi.org/10.1038/s41467-022-34126-7</a> . See associated Zenodo datasets for other Cyclone cases in this work: 10.5281/zenodo.7131287; 10.5281/zenodo.7126117.</p>
Arctic Cyclones and Climate Change Cases G-I
<p>Weather Research and Forecasting Model (v3.9.1.1) simulation output data used to analyze the effect of climate change on spring Arctic Cyclone characteristics for cyclone cases G-I. This dataset is made available to accompany the Nature Communications paper "<strong>The Influence of Recent and Future Climate Change on Spring Arctic Cyclones"</strong> by Parker et al. 2022 <a href="https://doi.org/10.1038/s41467-022-34126-7">https://doi.org/10.1038/s41467-022-34126-7</a>. See associated Zenodo datasets for other Cyclone cases in this work: 10.5281/zenodo.7131287; 10.5281/zenodo.7131284.</p>
Arctic Cyclones and Climate Change Cases D-F
<p>Weather Research and Forecasting Model (v3.9.1.1) simulation output data used to analyze the effect of climate change on spring Arctic Cyclone characteristics for cyclone cases D-F. This dataset is made available to accompany the Nature Communications paper "<strong>The Influence of Recent and Future Climate Change on Spring Arctic Cyclones"</strong> by Parker et al. 2022 <a href="https://doi.org/10.1038/s41467-022-34126-7">https://doi.org/10.1038/s41467-022-34126-7</a>. See associated Zenodo datasets for other Cyclone cases in this work: 10.5281/zenodo.7131284; 10.5281/zenodo.7126117</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>
Climate based seed zones for Mexico: spatial grids to guide reforestation under observed and projected climate change
<p>This database entry provides climate-based seed zone system for Mexico to address climate change observed over the last 30 years and projected climate change for the 2050s. The database corresponds to a journal publication by Castellanos-Acuña et al. (2018), available at https://doi.org/10.1007/s11056-017-9620-6. This seed zone classification is based on bands of two climate variables that have often been shown to drive genetic adaptation of tree species: mean coldest month temperature (MCMT), and an aridity index (AHM). MCMT was divided into ten bands of 3°C intervals, with the limits of these bands being, temperatures below <2°C, 2-5°, 5-8°, 8-11°, 11-14°, 14-17°, 17-20°, 20-23°, 23-26°, >26°C. AHM was divided into seven bands with intervals that are approximately equal width under a log-transformation: <20, 20-30, 30-45, 45-65, 65-95, 95-140, and >140 °C/mm. The gridded files provided in this database entry, the classes are coded as integer numbers, with the last digit representing the AHM class (1-7) and the first or first and second digit representing the MCMT class (1-10).</p>
Changes in above- versus belowground biomass distribution in permafrost regions in response to climate warming
<p>Permafrost regions contain approximately half of the carbon stored in land ecosystems and have warmed at least twice as much as any other biome. This warming has influenced vegetation activity, leading to changes in plant composition, physiology, and biomass storage in aboveground and belowground components, ultimately impacting ecosystem carbon balance. Yet, little is known about the causes and magnitude of long-term changes in the above- to belowground biomass ratio of plants (η). Here, we analyzed η values based on 3,013 plots and 26,337 plant-specific measurements representing eight sites across the Tibetan Plateau from 1995 to 2021. Our analysis revealed distinct temporal trends in η for three vegetation types: a 17% increase in alpine wetlands, and a decrease of 26% and 48% in alpine meadows and alpine steppes, respectively. These trends were primarily driven by temperature-induced growth preferences rather than shifts in plant species composition. Our findings indicate that in wetter ecosystems climate warming promotes aboveground plant growth, while in drier ecosystems, such as alpine meadows and alpine steppes, plants allocate more biomass belowground. Four process-based biogeochemical models failed to simulate the observed changes in η, which highlights the importance of improved process understanding of the processes driving the response of biomass distribution to climate warming, which is crucial for predicting the future carbon trajectory of permafrost ecosystems.</p>
Classification and frequency of climate change drivers and responses of small-scale fishers found in literature review
<p>Climate change hazards were classified into resource availability and fishing operations or both following the framework proposed by Cheung et al (2012). Response units were firstly classified into overarching responses and then categorized as suggested by the adaptive-transformative framework of Barnes et al.<sup> </sup>(2020). Adaptation units that did not represent an active adaptation response were classified as remaining. More than one hazard could be attributed to each fishers' response.</p>
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Electric power outages from 900k simulated hurricanes in a changing climate, for the United States and Puerto Rico
<p>This dataset is described and explored in Rice et al. 2025, "<a href="https://doi.org/10.1088/1748-9326/adad85">Projected Increases in Tropical Cyclone-induced U.S. Electric Power Outage Risk</a>", published in Environmental Research Letters.</p> <p>This dataset collects peak outage levels modeled for 900,000 synthetic tropical cyclones (TCs; also commonly known as hurricanes) representative of a modeled historical (1980-2015) and future (2066-2100) period under SSP5-8.5 warming. Synthetic TCs are generated with the Risk Analysis Framework for Tropical Cyclones (RAFT; see Xu et al. 2024 and Balaguru et al. 2023), forced by climate simulation data from the Coupled Model Intercomparison Project phase 6 (CMIP6; see Eyring et al. 2016). Outages are modeled with the newly introduced Electric Power Outages from Cyclone Hazards (EPOCH) model, which was trained on county-level outage data from 23 historical TC events in the EAGLE-I dataset (Brelsford et al. 2024). </p> <p>The EPOCH model predicts outages based on county population and the maximum wind speed and rainfall rate experienced during the TC. Predicted outage levels are provided in the form of peak outage fraction: the maximum fraction of electricity customers expected to experience an outage at any one time during the storm's lifetime. Although we do not model outage duration, other research suggests peak outage level is strongly correlated with duration (Jamal and Hasan, 2023).</p> <p><strong>Data Format</strong></p> <p>The data is provided in NetCDF4 files, one for each CMIP6 model and time period. Each NetCDF4 files has the following:</p> <p>Dimensions:</p> <ul> <li>ncounties = 2715. The counties in the study domain</li> <li>ntracks = 50000. The number of storms</li> </ul> <p>Variables:</p> <ul> <li>int pseudofips(ncounties). The FIPS code for each county. Puerto Rico data is not available at county level, but instead for six utility-defined regions. We assign "pseudo-FIPS" codes to these region starting at 100000</li> <li>double centroid_lons(ncounties). Longitude of approximate center of county, in the range [-180, 0].</li> <li>double centroid_lats(ncounties). Latitude of approximate center of county, in the range [0, 90].</li> <li>float outage_prediction(ntracks, ncounties). The predicted peak outage fraction for each county, for each storm. Due to the particularities of ensemble models, some predictions may be slightly below zero or above one; we clip these values to the range [0,1] before any analysis in our study.</li> <li>ubyte prediction_complete_flag(ntracks). A verification flag used during dataset generation. This flag should equal 1 everywhere for complete data.</li> </ul> <p>Each file also contains the raw predictors at a county level for every storm, inside the 'predictors' group, for feature analysis.</p> <p>Also provided for convenience is 'counties_pseudofips.csv', which maps the pseudo-FIPS codes to the the name and spatial extent (WKT format) of each county. It can be read easily by Python GeoPandas, or other software.</p>
Supplementary data: Rain-on-snow events in mountainous catchments under climate change
<p>The file in this record represents supplementary data for the journal paper Hotovy, O., Nedelcev, O., Seibert, J., Jenicek, M. (2024): Rain-on-snow events in mountainous catchments under climate change submitted to Hydrology and Earth System Sciences.<br>The presented files contain daily simulations of the HBV rainfall-runoff model for 93 mountain catchments in Czechia, Germany and Switzerland. The model simulated different water balance components, such as runoff, base flow, snow water equivalent, evapotranspiration, and soil and groundwater storages for the study period 1980-2010 as well as hydrological projections assuming different increases in air temperature and precipitation.</p>
Archetypes of climate change adaptation among large-scale arable farmers in southern Romania
<p>Supplementary material belonging to the publication.</p> <p>Two files:</p> <p>1. Excel file with database containing raw data and information resulted from surveying a sample of 30 farmers/farm managers in southern lowlands of Romania between April and June 2020.</p> <p>2. PDF with interview guideline</p>
Simulated climate change reduced the capacity of lichen-dominated biocrusts to act as carbon sinks in two semi-arid Mediterranean ecosystems
<p> Biocrust gas exchange measurements used as input data for this study. The methods are described in detail in the related identifier paper.</p>
Resource heterogeneity leads to unjust effort distribution in climate change mitigation
<p>Climate change mitigation is a shared global challenge that involves the collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together toward a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer - sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two "generous clusters'" and the richest into a "greedy cluster''. Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences.</p> <p>Vicens J, Bueno-Guerra N, Gutiérrez-Roig M, Gracia-Lázaro C, Gómez-Gardeñes J, Perelló J, et al. (2018) Resource heterogeneity leads to unjust effort distribution in climate change mitigation. PLoS ONE 13(10): e0204369. https://doi.org/10.1371/journal.pone.0204369</p>
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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.