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 0.9.0
Dataset results
2,260 results for “Climatic change”
Supplementary material 1 from: Piria M, Radočaj T, Vilizzi L, Britvec M (2022) Climate change may exacerbate the risk of invasiveness of non-native aquatic plants: the case of the Pannonian and Mediterranean regions of Croatia. In: Giannetto D, Piria M, Tarkan AS, Zięba G (Eds) Recent advancements in the risk screening of freshwater and terrestrial non-native species. NeoBiota 76: 25-52. https://doi.org/10.3897/neobiota.76.83320
Table S1
MAgPIE model input data sets: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>These MAgPIE input data sets include harmonized crop yield projections from several crop models (9 crop models and 5 climate models). Additionally, regional, validation, and calibration data sets are also reported.</p>
Supplementary material 2 from: Duquesne E, Fournier D (2024) Connectivity and climate change drive the global distribution of highly invasive termites. NeoBiota 92: 281-314. https://doi.org/10.3897/neobiota.92.115411
Occurrences of the 22 invasive termites as well as their source
Supplementary material 1 from: Duquesne E, Fournier D (2024) Connectivity and climate change drive the global distribution of highly invasive termites. NeoBiota 92: 281-314. https://doi.org/10.3897/neobiota.92.115411
Supplementary tables and figures (S1 to S7)
An Investigation of Climate Change Effects on Design Wind Speeds along the US East and Gulf Coasts
Open the record for dataset details and reuse information.
Attribution of daily ocean temperatures to climate change data
Open the record for dataset details and reuse information.
climetrics: an R package to quantify multiple dimensions of climate change
Open the record for dataset details and reuse information.
Fig. 1 in Changing climate-changing pathogens: Toxoplasma gondii in North-Western Europe
Fig. 1 Projected changes in annual precipitation in case of a B2 scenario. Source: HadCM3 model, Hadley Centre, United Kingdom
Fig. 3 in Changing climate-changing pathogens: Toxoplasma gondii in North-Western Europe
Fig. 3 Mean temperatures in North-Western Europe as calculated by the CCSR (Center for Climate System Research, University of Tokyo) and NIES (National Institute for Environmental Studies) model under a SRES A1 scenario. Presented are mean temperatures in period from 1970 to 1999 (a), and the projected mean temperatures from 2010 to 2039 (b) and 2040–2069 (c). Figures obtained from www.ipcc-data.org
Impacts of anthropogenic management legacies on forest dynamics of the Tibetan Plateau transition region under changing climates
Open the record for dataset details and reuse information.
Primary Dataset on Farmers' Adaptation Strategies to Climate Change and Sustainable Development Goals in Tanzania
Open the record for dataset details and reuse information.
Figure 1 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 1. Current potential geographic distribution of Costalimaita ferruginea determined by the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Accompanying data to "Could an extremely cold central European winter such as 1963 happen again despite climate change?"
<h2>Accompanying data to "Could an extremely cold central European winter such as 1963 happen again despite climate change?"</h2> <div> </div> <div><strong>16.07.2024 This repository contains data that underlies the following publication:</strong></div> <div>Sippel, S., Barnes, C., Cadiou, C., Fischer, E., Kew, S., Kretschmer, M., Philip, S., Shepherd, T. G., Singh, J., Vautard, R., and Yiou, P.: Could an extremely cold central European winter such as 1963 happen again despite climate change? <em>Weather and Climate Dynamics</em> (accepted), 2024. Preprint: https://doi.org/10.5194/egusphere-2023-2523.</div> <div> </div> <div>This repository is a data collection, which contains simulated extremely cold Central European winter storylines. Climate model simulations use the technique of climate model boosting, and statistical generation using stochastic weather generators (SWG) empirical importance sampling. The repository contains the following data files:</div> <div> </div> <h3>(1) Climate model ensemble boosting for extremely cold winter storylines. </h3> <div> <ul> <li>Zip file BSSP370cmip6.0000013.zip: Contains all 750 files of the first-order boosting. First order boosting is based on ensemble member 21 in the CESM2-ETH ensemble, and with restart dates between 01.12 and 15.12.2022 (SSP3-70 scenario), with 50 members for each starting date. Example file: BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc</li> </ul> </div> <div>The boosting files follow a naming convention: </div> <div> <ul> <li> <ul> <li>BSSP370cmip6 all files based on CMIP6 SSP3-70 forcing.</li> <li><span>2022-12-06 starting date of the respective ensemble member.</span></li> <li><span>0000013 Ensemble member of CESM2-ETH that was used for boosting (i.e. member 13 of CESM2-ETH).</span></li> <li><span>ens023 Ensemble member of the boosted ensemble (i.e. member 23 with starting date 06.12.2022).</span></li> </ul> </li> </ul> </div> <div>The second-order boosting was branched off from first-order boosting file BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc.</div> <div> </div> <div> <ul> <li>Zip file BSSP370cmip6.0230013.zip: Contains all 750 files of the first set of second-order boosting simulations. All these simulations are based on first-order boosting file BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc. That is, the first-order boosting file started from ensemble member 13 of CESM2-ETH, starting date 06.12.2022 and ensemble member 23 of the first-order boosted ensemble. The second-order boosting file shown in Figs. 5-6 is the file BSSP370cmip6.0230013.2023-01-08.ens047.cam.h1.2023-01-09-00000.nc. That is, ensemble member 47 in second-order boosting ensemble from starting date 08.01.2023. </li> </ul> </div> <div> </div> <div> <ul> <li>Zip file BSSP370cmip6.0480013.zip: Contains all 750 files of the second set of second-order boosting simulations. All these simulations are based on first-order boosting file BSSP370cmip6.0000013.2022-12-15.ens048.cam.h1.2022-12-16-00000.nc. That is, the first-order boosting file started from ensemble member 13 of CESM2-ETH, starting date 15.12.2022 and ensemble member 48 of the first-order boosted ensemble. The second-order boosting file shown in Figs. 5-6 is the file BSSP370cmip6.0480013.2023-01-08.ens032.cam.h1.2023-01-09-00000.nc. That is, ensemble member 32 in second-order boosting ensemble from starting date 08.01.2023. </li> </ul> </div> <div> </div> <div> </div> <h3>(2) CESM2 maps of extremely cold winters (to generate Fig. 5)</h3> <div>* Zip file cesm2_maps.zip. Contains the following entries, all for DJF average anomalies (relative to the ensemble average climatology):</div> <div>- tas_ssp370_r2i1p1.2005-2035_anom.nc</div> <div>- tas_ssp370_r12i1p1.2005-2035_anom.nc</div> <div>Two members (r2i1p1 in 2008, r12i1p1 in 2007) from the CESM2-ETH ensemble, which produce very cold winters. Variables tas (surface air temperature), Z500 (geopotential height at 500 hPa), FSDS (surface downwelling shortwave radiation), and FSNS (surface net shortwave radiation) are available (FSDS and FSNS to calculate albedo). </div> <div>- tas_ssp370_0230013.2023-01-08.ens047_anom.nc</div> <div>- tas_ssp370_0480013.2023-01-08.ens032_anom.nc</div> <div>The two extremely cold boosted winters as described above, concatenated with their parent files from boosting. </div> <div> </div> <div> </div> <h3>(3) Storylines of extremely cold winters generated via Stochastic weather generator (SWG) empirical importance sampling</h3> <div>SWG-empirical-importance-sampling.zip Storylines of extremely cold winters generated via Stochastic weather generator (SWG) empirical importance sampling (Yiou and Jézéquel, 2020, https://doi.org/10.5194/gmd-13-763-2020). The available maps are seasonal average anomalies resampled from ERA5 (to generate Fig. 5):</div> <div> <ul> <li>Surface air temperature: t2m_WEGE_germany_1963_1972-2021_DJFmean.nc</li> <li><span>Albedo: fal_WEGE_germany_1963_1972-2021_DJFmean.nc</span></li> <li><span>z500: z500_WEGE_germany_1963_1972-2021_DJFmean.nc</span></li> </ul> </div> <div> </div>
Alpine viper in changing climate: thermal ecology and prospects of a cold-adapted reptile in the warming Mediterranean
<p><span><span>In a rapidly changing thermal environment, reptiles are primarily dependent on in situ adaptation because of their limited ability to disperse and the restricted opportunity to shift their ranges or evolve. However, the rapid pace of climate change may surpass these adaptation capabilities or elevate energy expenditures. Therefore, understanding the variability in thermal traits at both individual and population scales is crucial, offering insights into reptiles' </span></span><span><span>vulnerability</span></span><span><span> to climate change. We studied the thermal ecology of the endangered Greek meadow viper (</span></span><span><span><em>Vipera graeca</em></span></span><span><span>), an endemic venomous snake of fragmented alpine-subalpine meadows above 1600 m of the Pindos mountain range in Greece and Albania,</span></span><span><span> </span></span><span><span>to assess its susceptibility to anticipated changes in </span></span><span><span><span>the</span></span></span><span><span> </span></span><span><span><span>alpine </span></span></span><span><span>thermal environment. We measured preferred body temperature in artificial thermal gradient, field body temperatures and the availability of environmental temperatures in five populations encompassing the entire geographic range of the species. </span></span><span><span>We found that the preferred body temperature (</span></span><span><span><em>T</em></span></span><sub><span><span><em>p</em></span></span></sub><span><span>) differed</span></span><span><span> </span></span><span><span><span>only</span></span></span><span><span> </span></span><span><span>between the northernmost and the southernmost populations </span></span><span><span>and increased with female body size but did not depend on sex or the gravidity status of females. </span></span><span><span><em>T</em></span></span><sub><span><span><em>p</em></span></span></sub><span><span> increased with latitude but was unaffected by the phylogenetic position of the populations. We also found high accuracy of thermoregulation in </span></span><span><span><em>V. graeca</em></span></span><span><span> populations and variation in the thermal quality of habitats throughout the range. The overall effectiveness of thermoregulation was high, indicating that </span></span><span><span><em>V. graeca</em></span></span><span><span> successfully achieves its target temperatures and exploits the thermal landscape. Current climatic conditions limit the activity period by an estimated 1278 hours per year, which is expected to increase considerably under future climate change. Restricted time available for thermoregulation, foraging and reproduction will represent a serious threat to the fitness of individuals and the persistence of populations in addition to habitat loss due to mining, tourism or skiing and habitat degradation due to overgrazing in the shrinking mountaintop habitats of</span></span><span><span><em> </em></span></span><span><span><em>V</em></span></span><span><span><em>. graeca</em></span></span><span><span>.</span></span></p>
Dancing in Unpredictable Times: Cashinahua celebrations for life proliferation and climate change
Open the record for dataset details and reuse information.
Dataset article "Methane emissions may counteract the carbon fixation capacity for climate change mitigation by Mediterranean inland freshwater shallow lakes and ponds"
Open the record for dataset details and reuse information.
Fig. 4 in Predicting the potential distribution of the subalpine broad-leaved tree species, Betula ermanii Cham. under climate change in South Korea
Fig. 4. The response curves for the two largest contributing variables for Betula ermanii under current climate conditions. Response curves indicate the correlation between the climatic variables and the probability of B. ermanii presence.
Fig. 5 in Predicting the potential distribution of the subalpine broad-leaved tree species, Betula ermanii Cham. under climate change in South Korea
Fig. 5. The potential distribution of Betula ermanii under current and future climatic conditions (2050s and 2070s) based on RCPs4.5 and 8.5 scenarios. A. Current. B. 2050s-RCP4.5. C. 2050s-RCP8.5. D. 2070s-RCP4.5. E. 2070s-RCP8.5. Blue dotted lines indicate the locations of potential habitat.
Supplementary material 1 from: Hong Qu H, Wang C-J, Zhang Z-X (2018) Planning priority conservation areas under climate change for six plant species with extremely small populations in China. Nature Conservation 25: 89-106. https://doi.org/10.3897/natureconservation.25.20063
Table S1, S2; Figure S1, S2 : Explanation note:
Open Data and Climate Change Resilience for US cities
<p>Open Data and Climate Change Resilience data in US cities, with 50 cities and 20 states.</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.