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”
Data for: Combined threats of climate change and contaminant exposure through the lens of bioenergetics
<p>This dataset contains a detailed description of studies identified by a review examining interactive effects of climate change-sensitive environmental variables and chemical contaminant exposure.</p>
Data for "The neglected role of abandoned cropland in supporting both food security and climate change mitigation"
<p><strong>Data for "The neglected role of abandoned cropland in supporting both food security and climate change mitigation"</strong></p> <p><strong>All files will be made publicly accessible before publication.</strong></p> <p>Version: 2.0 (Round 2 revision)</p> <p>Content (spatial resolution, data info)</p> <ol> <li>Abandoned cropland map (10arcsec, 1 raster file)</li> <li>Suitabability of abanonded cropland for reforestation and recultivation (5arcmin, 3 raster files)</li> <li>Food production potential of global abandoned cropland (5arcmin,1 raster file)</li> <li>Climate change mitigation potential of global abandoned cropland (5arcmin, 1 raster file)</li> <li>outcomes of key scenarios <ul> <li>4 key Scenarios: Maximizing food production, Maximizing climate change mitigation, Equal Allocation, Maximizing combined potential</li> <li>Data included in the output of each scenario: <ul> <li>Land allocated for reforestation and recultivation (5arcmin/1arcdeg, 5 raster files)</li> <li>Climate change mitigation potential and food production potential (5arcmin/1arcdeg, 5 raster files)</li> <li>Emission for land clearing (5arcmin, 1 raster)</li> <li>Foregone climate change mitigation potential (5arcmin, 1 raster)</li> <li>Summary table</li> </ul> </li> <li>Summary table for 4 key scenarios</li> </ul> </li> <li>Codes for generating 4 key scenarios</li> </ol>
Evolution of butterfly seasonal plasticity driven by climate change varies across life stages
<p>Photoperiod is a common cue for seasonal plasticity and phenology, but climate change can create cue-environment mismatches for organisms that rely on it. Evolution could potentially correct these mismatches, but phenology often depends on multiple plastic decisions made during different life stages and seasons that may evolve separately. For example, <em>Pararge aegeria</em> (Speckled wood butterfly) has photoperiod-cued seasonal life history plasticity in two different life stages: larval development time and pupal diapause. We tested for climate-change-associated evolution of this plasticity by replicating common garden experiments conducted on two Swedish populations 30 years ago. We found evidence for evolutionary change in the contemporary larval reaction norm—although these changes differed between populations—but no evidence for evolution of the pupal reaction norm. This variation in evolution across life stages demonstrates the need to consider how climate change affects the whole life cycle to understand its impacts on phenology.</p>
Morphed extreme weather data for Vantaa and Sodankylä under RCP climate change scenarios by 2030, 2050 and 2080
<p>Morphed extreme weather data for 2 Finnish locations: Vantaa and Sodankylä. Created for "Near-, medium- and long-term impacts of climate change on the thermal energy consumption of buildings in Finland under RCP climate scenarios" publication (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.energy.2024.131636</span></a>). Used climate change scenarios are RPC2.6, RCP4.5 and RCP8.5. Data is created for 2030, 2050 and 2080 and includes 6 extreme weather scenarios: </p> <ul> <li>W1 - Winter with high heating demand</li> <li>W2 - Winter with low heating demand</li> <li>W3 - Winter with the coldest individual day by average temperature</li> <li>S1 - Summer with the lowest cooling demand</li> <li>S2 - Summer with the highest heating demand</li> <li>S3 - Summer with the warmest individual day by average temperature</li> </ul> <p>Selected years and the procedure for their selection are described in <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.energy.2024.131636</span></a>.</p> <p>Original weather data is downloaded for the selected years from Finnish Meteorological Institute's Open data repository: https://www.ilmatieteenlaitos.fi/havaintojen-lataus under CC BY 4.0 licence.</p> <p>Future change in climate is based on Finnish Meteorological Institute's data used in creating Test Reference Year weather files (<a href="https://www.ilmatieteenlaitos.fi/energialaskenta-try2020">https://www.ilmatieteenlaitos.fi/energialaskenta-try2020</a>) for which the climate change data is presented by Ruosteenoja et al. (2016).</p> <p>The data is statistically downscaled through a method called morphing created by Belcher et al. (2005) with some parts using methods from Räisänen & Räty (2013) and Jylhä et al, (2015). Morphing was computationally conducted through created software <a href="https://github.com/japulk/Weather-Morphing-Tool">https://github.com/japulk/Weather-Morphing-Tool</a> For additional information please refer to <a href="https://doi.org/10.1016/j.energy.2024.131636">original article</a> or contact the authors.</p> <p> </p>
Can regime shifts in reproduction be explained by changing climate and food availability?
<p><span>Marine populations often show considerable variation in their productivity, including regime shifts. Of special interest are prolonged shifts to low recruitment and low abundance which occur in many fish populations despite reductions in fishing pressure. One of the possible causes for the lack of recovery has been suggested to be the Allee effect (depensation). Nonetheless, both regime shifts and the Allee effect are empirically emerging patterns but provide no explanation about the underlying mechanisms. Environmental forcing, on the other hand, is known to induce population fluctuations and </span><span>has also been suggested as one of the primary challenges for recovery.</span> <span>Yet, traditional stock-recruitment models used in fisheries management have been time-invariant and considered only density-dependence. </span><span>In the present study, we build upon recently developed Bayesian change-point models to explore the contribution of food and climate as external drivers in recruitment regime shifts, while accounting for density-dependent mechanisms (compensation and depensation). Food availability is approximated by the copepod community. Temperature is included as a climatic driver. Three demersal fish populations in the Irish Sea are studied: Atlantic cod (<em>Gadus morhua</em>), whiting (<em>Merlangius merlangus</em>), and common sole (<em>Solea solea</em>). </span><span>We demonstrate that while spawning stock biomass undoubtedly impacts recruitment, abiotic and biotic drivers can have substantial additional impacts, which can explain regime shifts in recruitment dynamics or low recruitment at low population abundances. Our results stress the fact that traditional abundance-based stock-recruitment models are not sufficient to capture variability in fish recruitment. </span></p>
Widespread changes in Southern Ocean phytoplankton blooms linked to climate drivers
<p>Bloom phenology metrics calculated from OC-CCI v6.0 data. Dataset forms part of a publication in Nature Climate Change.</p>
Life on the edge: A new toolbox for population-level climate change vulnerability assessments
<p>Global change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species' geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes.</p> <p>We developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity and Genotype-Environment Associations) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RAD-seq, ddRAD-seq, whole genome sequencing data) representing environmental variation across the species geographic range.</p> <p>We apply our toolbox to a georeferenced genomic dataset for the East African spiny reed frog (<em>Afrixalus fornasini</em>) to predict population vulnerability, as well as demonstrating that range loss projections based on adaptive variation can be accurately reproduced using data for two European bat species (<em>Myotis escalerai</em>, and <em>M. crypticus</em>).</p> <p>Our framework sets the stage for large scale, multi-species genomic datasets to be leveraged in a novel climate change vulnerability framework to quantify intraspecific differences in genetic diversity, local adaptation, range shifts and population vulnerability based on exposure, sensitivity, and range shift potential.</p>
Holocene climate change in southern Oman deciphered by speleothem records and climate model simulations
<p>8 ka BP (thousand year, before present, 8K) and pre-industrial period (PI) paleoclimate equilibrium experiment, including precipitation (pr), near-surface relative humidity (hurs), evaporation (evspsbl) and 850hPa wind (u850 & v850).</p>
Fig. 1 in Adaptations of tenebrionid beetles to Mediterranean sand dune environments and the impact of climate change (Coleoptera: Tenebrionidae)
Fig. 1 – Relationship between activity and temperature in some tenebrionid species in Palestine investigated by Bodenheimer (1934). Activity intensity is expressed by the following rank scale: (1) cold-torpor, (2) only weak, occasional movements of legs and antennae, (3) crawling with interruptions, (4) normal activity, (5) high activity, (6) excited activity, (1) heat-torpor, (0) heat-death. Redrawn from Fattorini (2008) with corrections. Inset: Zophosis punctata (photo S. Fattorini).
Fig. 2 in Adaptations of tenebrionid beetles to Mediterranean sand dune environments and the impact of climate change (Coleoptera: Tenebrionidae)
Fig. 2 – Diel and monthly activity patterns of tenebrionid beetles of Mediterranean dunes. A, diel activity of Erodius siculus in Latium (Central Italy) in May 1997; B, diel activity of Pimelia bipunctata in Latium (Central Italy) in March 1997; C, diel activity of Pimelia bipunctata in the same locality in May 1997. In these experiments, activity was measured as number of individuals intercepted by pitfall traps per hour in single days. After counting, beetles were immediately released. N: number of trapped individuals per hour. Ta: ambient temperature (°C), Ti: soil internal (3-4 cm depth) temperature (°C), Ts: soil surface temperature (°C). D, Phenological patterns of Erodius siculus in Latium (Central Italy) and Sicily (Southern Italy). Phenologies are expressed as number of locations in which the species has been recorded in each month over a period of a century (from 1897 to 1997). A and D are based on Di Stefano & Fattorini (2002). B and C are based on Fattorini & Di Stefano (2004). Photos: courtesy of L. Di Biase.
Dataset corresponding to the « Reasons for concern » about climate change impacts from all IPCC reports (TAR to AR6)
<p>This data corresponds to all the "burning embers" diagrams for the "Reasons for Concern" published in IPCC reports (and the related paper Smith et al. 2009 for AR4) until AR6 (thus including TAR, AR4, AR5, SR1.5 and AR6). For TAR to SR1.5, the data is the result of extracting 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 also explained in the Supplementary Information of Zommers et al. (2020), the data does not come directly from the IPCC, although it is based on the assessment provided in the IPCC reports listed in the references. For IPCC AR6, the source is the supplementary material of chapter 16. Details regarding specific values provided in the dataset are explained alongside the values in the main file: "RFCs-ALL-2023_05_12.xlsx". </p> <p>The main file includes the parameters needed to produce a diagram that supplements figure 3 from Zommers et al. (2020) with AR6 data and the confidence levels from previous reports when available. The Excel files in RFCs-2023-UsageExamples.zip contain the same data with different parameters, so that uploading these files to the Ember Factory (<a href="https://climrisk.org/emberfactory">https://climrisk.org/emberfactory</a>) produces different figures - including a comparison between AR5 and AR6 (as in IPCC AR6 Synthesis Report, but with AR5 confidence levels included). The resulting diagrams are also provided.</p>
Fig. 1 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 1. Camera-trap image of mainland serow (Capricornis sumatraensis) from the lowlands of the Pasoh Forest Reserve in Peninsular Malaysia at an elevation of ~100 m.
Fig. 4 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 4. Regional-scale relationships between serow captures and covariates. Displayed are the variables within the top-performing multivariate model as assessed by lower AICc scores. All covariates are averaged at a 20-km radius around the study area.
Fig. 5 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 5. The relationships between serow predicted abundance and habitat variables at the local scale from Royle-Nichols hierarchical models. Oil palm, roughness and Human Footprint Index were in the top performing multivariate model.
Fig. 3 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 3. Presence of the serow within its Southeast Asian range. Panel a) shows the IUCN Red List range extent of occurrence (EOO; shaded orange), and the occurrence records coloured by the data source. Panel b) shows the jackknife-based assessment of variable importance. The blue bars showing the explanatory power in the model using only the denoted variable, while the teal bars show the predictive power of the full model without the denoted variable, highlighting whether the variable captures unique information. Panel c) shows the probability of presence of the serow from Maxent modelling mapped within the Southeast Asian region covered by this study. Panel d) shows the forest cover in 2015 that is potentially occupied within the EOO. Panel e) is the Maxent probability of presence of serow inside the remaining forested areas within Southeast Asia. Original artwork courtesy of Tamzin Barber (https://www.talkinganimals.com.au/).
NEMO-FABM-ERSEM-MIZER climate change simulation data
<p>This dataset corresponds to data produced from running NEMO-FABM-ERSEM MIZER on the AMM7 domain as part of the COMFORT project and used in the Deliverable 3.3 (see related identifiers). A scientific manuscript is in preparation. The climate simulation was run from 1981-2100 forced by atmospheric and lateral boundary condition from the IPSL-CMR5a-MR (RCP 8.5) climate simulation (Dufresne et al., 2013; WCRP, 2016) . Data reported here are from 1990-2100, unless otherwise stated. </p> <p>Data has been post-processed to convert it to depth independent (2D), monthly averaged variables. Consequently all data are either depth-integrated, mean across depth (temperature, salinity) or surface data (nutrients).</p> <p>For access to the raw data or more details, please contact Helen Powley (hpo@pml.ac.uk).</p> <p> </p> <p> </p> <p> </p> <p> </p>
Forests on the move: Tracking climate-related treeline changes in mountains of the northeastern United States
<div><em>Aim</em></div> <div>Alpine treeline ecotones are influenced by environmental drivers and are anticipated to shift their locations in response to changing climate. Our goal was to determine the extent of recent climate-induced treeline advance in the northeastern United States, and we hypothesized that treelines have advanced upslope in complex ways depending on treeline structure and environmental conditions.</div> <div> </div> <div><em>Location</em></div> <div>White Mountain National Forest (New Hampshire) and Baxter State Park (Maine), USA.</div> <div> </div> <div><em>Taxon</em></div> <div>High-elevation trees – <em>Abies balsamea, Picea mariana, and Betula cordata</em>. </div> <div> </div> <div><em>Methods</em></div> <div>We compared current and historical high-resolution aerial imagery to quantify the advance of treelines over the last four decades, and link treeline changes to treeline form (demography) and environmental drivers. Spatial analyses were coupled with ground surveys of forest vegetation and topographical features to ground-truth treeline classification and provide information on treeline demography and additional potential drivers of treeline locations. We used multiple linear regression models to examine the importance of both topographic and climatic variables on treeline advance.</div> <div> </div> <div><em>Results</em></div> <div>Regional treelines have significantly shifted upslope over the past several decades (on average by 3 m/decade). Diffuse treelines (low tree densities and temperature limited) experienced significantly greater upslope shifts (5 m/decade) compared to other treeline forms, suggesting that both climate warming and treeline demography are important drivers of treeline shifts. Topographical features (slope, aspect) as well as climate (accumulated growing degree days, AGDD) explained significant variation in the magnitude of treeline advance (R<sup>2</sup> = 0.32).</div> <div> </div> <div><em>Main conclusions</em></div> <div>The observed advance of regional treelines suggests that climate warming induces upslope treeline shifts particularly at higher elevations where greater upslope shifts occurred in areas with lower AGDD. Overall, our findings suggest that diffuse treelines at high-elevations are more a of a result of climate warming than other alpine treeline ecotones and thus they can serve as key indicators of ongoing climatic changes.</div>
Outputs of the Jupyter Notebook - Deep learning and variational inversion to quantify and attribute climate change (CIRC23)
<p>The dataset contains the outputs of the notebook "Deep learning and variational inversion to quantify and attribute climate change (CIRC23)" published in The Environmental Data Science Book.</p>
Urban resilience to extreme natural events and climate change: from Brasil to Europe
<p>Originally published at this link: <a href="https://www.iai.it/en/eventi/urban-resilience-extreme-natural-events-and-climate-change-brasil-europe">Urban resilience to extreme natural events and climate change: from Brasil to Europe | IAI Istituto Affari Internazionali</a></p> <p>The workshop will discuss best practices and innovative methodologies for urban resilience building to extreme natural events and climate change. The focus will be on participatory processes and citizen engagement practices, looking in particular at their relevance for physically and socially vulnerable urban areas. The event is designed as a knowledge-sharing opportunity for cities and will include the participation of researchers and experts working on the field with municipalities.</p> <p>The project “Waterproofing Data: Engaging Stakeholders in Sustainable Flood Risk Governance for Urban Resilience” will be discussed as a successful case study, focusing on data co-production practices and the role of digital tools. The project has been implemented in several Brazilian cities to build resilience to flooding in vulnerable communities by engaging citizens in data generation processes critical to design effective early-warning systems and strategies to reduce the risk of flood-related events. Professor João Porto De Albuquerque (University of Glasgow, UK) and Professor Maria Alexandra Viegas da Cunha (Fundação Getulio Vargas, São Paolo, Brasil) will illustrate the results of Waterproofing Data project and its innovative methodology, discussing opportunities for its implementation in cities both in Brasil and Europe and application to a broad spectrum of extreme events, from flooding to heatwaves and drought.</p>
Data from: Accelerating local extinction associated with very recent climate change
<p>Climate change has already caused local extinction in many plants and animals, based on surveys spanning many decades. As climate change accelerates, the pace of these extinctions may also accelerate, potentially leading to large-scale, species-level extinctions. We tested this hypothesis in a montane lizard. We resurveyed 18 mountain ranges in 2021–2022 after only ~7 years. We found rates of local extinction among the fastest ever recorded, which have tripled in the past ~7 years relative to the preceding ~42 years. Further, climate change generated local extinction in ~7 years similar to that seen in other organisms over ~70 years. Yet, contrary to expectations, populations at two of the hottest sites survived. We found that genomic data helped predict which populations survived and which went extinct. Overall, we show the increasing risk to biodiversity posed by accelerating climate change, and the opportunity to study its effects over surprisingly brief timescales.</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.