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2,260 results for “Climatic change”
Fig. 4 in Niche overlap and host specificity in parasitic Maculinea butterflies (Lepidoptera: Lycaenidae) as a measure for potential extinction risks under climate change
Fig. 4 Estimated potential distributions of Maculinea butterflies (light grey) and Myrmica ants (dark grey) under the A2a climate change scenario in 2080 show large geographic overlaps of the butterfly
Fig. 1 in Allopatric divergence and secondary contacts in Euphorbia spinosa L: Influence of climatic changes on the split of the species
Fig. 1 Network, distribution of chloroplast DNA haplotypes and range of E. spinosa in the Mediterranean basin. Each line in the network corresponds to a single mutational change, and boxes
Responses of hydrological extremes to future climate change and forest disturbance in snow-dominated watersheds of southern British Columbia
<p>1. Future hydrological predictions in watershed 241, Camp, Greata, and Trepanier watersheds, including daily hydrometeorological predictions and aggregated future hydrometeorological metrics. The future climate data are from six GCMs and under three distinct development pathways.</p> <p>2. Trends of multiple future hydrological signatures (i.e., high and low flows) in watershed 241, Camp, Greata, and Trepanier watersheds.</p>
Fig. 6 in Multigenic resistance to Xylella fastidiosa in wild grapes (Vitis sps.) and its implications within a changing climate
Fig. 6 Climate predictions and projections of the prevalence of X. fastidiosa for focal crops. a The map portrays the number of climate models (out of 54 total) that support movement across the BIO8 = 10 °C threshold. The warmer colors reflect regions that are moving from below (in the present) to above the threshold, while the cooler colors portray areas that are moving from above (in the present) to below the threshold. The intensity of color in the scale bar reflects the number of 54 climate models that agree with the threshold transition. The generation of this map, as well as the maps in Figures S1 and S18, relied on publicly available information from WorldClim2 (https://www.worldclim.org/) and CMIP6 (https://pcmdi.llnl.gov/CMIP6/). b A summary of the percentage of locations associated with movement from above the 8 C °C or 10 °C threshold (in the present) to below that threshold for five crops and for V. arizonica. c. A summary of the percentage of locations associated with movement from above the threshold (in the present) to below the threshold for five crops and V. arizonica. Both b and c are based on 6204 locations for coffee; 3386 locations for almonds, 1111 locations for V. arizonica; 5256 locations for Citrus; 174,713 locations for olives and 33,225 locations for grapevines. In both b and c, each dot represents an estimate based on one of the 54 climate models.
Fig. 3 in Multigenic resistance to Xylella fastidiosa in wild grapes (Vitis sps.) and its implications within a changing climate
Fig. 3 Genetic analyses of the PdR1 region. a A plot of linkage disequilibrium (LD) across chromosome 14, where darker colors represent higher levels of LD. The two dark squares on the diagonal include GWA peak 4 (on the left, from 0 to 7 Mb on the chromosome) and the GWA peak that corresponds to PdR1 (on the right, from 22 to 28 Mb on the chromosome). The two off-diagonal squares reflect LD between these two distinct regions. b A Manhattan plot of chromosome 14 indicating the locations of peak 4 and the PdR1 region. c An expanded representation of the PdR1 region showing the location of significant SNPs (red circles denote SNP significant with two GWA methods), significant kmers (green triangles) and CNVs (blue triangles). The dashed vertical lines represent the 361 kb region defined by SSR markers and the 106 kb region defined by the location of significant markers. The schematics below represent genes in PdR1 and a summary of gene expression results. Genes are denoted by rectangles and colored if they are related to R genes, with the category of R gene indicated by its color. Expression information shows expression, in transcripts per million (TPM) for leaves, for stems during four stages after infection and for mock controls.
Nursing Interventions to Mitigate Climate Change-related Effects on Asthma
ClinicalTrials.gov study NCT07106047. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Awareness of Climate Change Impacts Among University Students
ClinicalTrials.gov study NCT06792006. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Data from: An objective approach to select climate scenarios when projecting species distribution under climate change
Open the record for dataset details and reuse information.
On the role of DOM under varying climate change conditions in Crocosphaera
GEO Series GSE229037. Crocosphaera watsonii WH 8501. 36 samples. Type: Expression profiling by high throughput sequencing.
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change
<p><strong>Title</strong>: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>This repository contains</strong>:</p> <ol> <li>Zipped folder:<strong> Fig1_data_input.zip</strong> - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1_analysis_all_variables_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<strong><driver>_assessment_v2.csv</strong>". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. </li> <li>Zipped folder:<strong> Fig1_confidence_intervals.zip</strong> - all the files need to produce the confidence intervals seen in Figure 1a-e of the main paper: units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limit</li> <li>Zipped folder:<strong> Fig2_regions_outline.zip</strong> - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. </li> <li>Zipped folder: <strong>Fig1g_2b_e_variable_importance.zip</strong> - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_variable_importance.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files include the sample data used to build the random forest model at each iteration - as a .csv file and the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. </li> <li>Zipped folder: <strong>Fig3_data_input.zip</strong> - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_analysis_byAllRegions_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<strong><REGION>-Group.csv</strong>". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The folder also contains another set of files "<strong><REGION>_whole_class.csv" </strong>these files do not distinguish disturbance and can be used to model the regrowth for the whole region (this is not shown in any of the Figures). The code takes data in AGB and converts to AGC.</li> <li>Zipped folder:<strong> Fig3_confidence_intervals.zip</strong> - all the files needed to produce the confidence intervals seen in Figure 3a-d of the main paper. These filenames are in the format <region>_number of the region_<number referring to the disturbance combination>_confidence_interval_asAGC.csv. Where the number of the region: 1 - SW; 2 - SE; 3 - NW; 4 - NE. Where the disturbance combination: 1 - No disturbance; 2 - Only fire disturbance; 3 - Only deforestation disturbance; 4 - Both disturbances. so the file NE_4_1_confidence_interval_as_AGC.csv, contains the confidence intervals of the regrowth model in the NE sector of the Amazon under No disturbance. " Units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limit. </li> <li> Zipped folder: <strong>Fig4_5_carbon_sink_2017.zip </strong>- Contains two subfolders: a) <strong>Map_aggre_0.1deg</strong> -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) <strong>secondary_forest_by_region_and_disturbance </strong>- this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_analysis.R" in the code repository (see below). </li> </ol> <p><strong>Code: </strong>The corresponding code mentioned here can be access here: <a href="https://github.com/heinrichTrees/secondary-forest-amazonia-regrowth">heinrichTrees/secondary-forest-amazonia-regrowth: This repository contains the code used to produce data shown in Heinrich et al. (github.com)</a> </p> <p><strong>Data usage: </strong>When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper. </p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Responding flexibly to climate: behavioural thermoregulation compensates for changes in insolation in a wild insect
<p>Data and code to support manuscript: Responding flexibly to climate: behavioural thermoregulation compensates for changes in insolation in a wild insect</p>
Empirical Data, Survey and Letter of Consent of the Study: Brouillet C. et al. "Soil extraction as an adaptation strategy to climate change - a focus on urban ecosystem services"
<ul> <li>Empirical Data (quantitative part of the results), Survey and Letter of Consent</li> <li>From the study entitled "Soil extraction as an adaptation strategy to climate change - a focus on urban ecosystem services" Brouillet C. et al. </li> <li>All documents are in French.</li> </ul>
Climate Change Survey Data (United States)
<p>This dataset is a comprehensive collection of survey responses aimed at understanding the role of media in shaping public beliefs about climate change issues in the United States. The survey was conducted between February 15, 2024 and February 28, 2024, targeting a diverse demographic across the country. The dataset comprises detailed information on media consumption habits, perceptions of climate change, trust in various media sources, political affiliations, and engagement in pro-environmental behaviors.</p>
Ecological and morphological traits determine community-wide responses of birds to climate change in a tropical dry forest
<p><strong><span>Description</span></strong></p> <p><span>Raw data and species distribution maps for conducting work on bird community changes caused by climate change in the largest block of tropical dry forests in South America. In addition to the R scripts for the climate modeling analyses and the subsequent analyses in the work. </span></p> <p> </p> <p><strong><span>File contents</span></strong></p> <p><span>Centoids.zip: Data with the centroids of the distributions of each species in the current scenario and the six future climate scenarios.</span></p> <p><span>Climate_valeus_scenrios.zip: Climate variable values for all the cells in the Caatinga grid. </span></p> <p><span>Correlation_species_variable.zip: Correlation for selecting the climate variables used for each species. </span></p> <p><span>Dataset_traits.csv: Species traits used in the analyses. </span></p> <p><span>Maps_species_distributions.zip: Distribution maps for all species, in the current climate scenario and the six future scenarios.</span></p> <p><span>occurrence_birds.zip: Occurrence data used to build the models for each species<br><br>PGLS.html: PGLS analysis correlating the percentage of change in the distribution area of </span><span>each species and species traits.</span></p> <p><span>Species_tree.zip: Species phylogeny built from BirdTree and used as input in PGLS</span></p> <p><span>Species_variables.csv: Individual variables used to build climate models for each species</span></p>
Data for climate mitigation scenarios with persistent COVID-19 related energy demand changes
<p>This repository contains data for the main text figures plus some supplementary figures in the article:<br> Kikstra et al 2021 Nat. Energy. DOI: <a href="https://doi.org/10.1038/s41560-021-00904-8">10.1038/s41560-021-00904-8</a></p> <p>This dataset should be cited as: Kikstra et al. (2021). Data for climate mitigation scenarios with persistent COVID-19 related energy demand changes. DOI: <a href="https://doi.org/10.5281/zenodo.5211169">10.5281/zenodo.5211169</a></p> <p>In order to reproduce the figures, one needs to use the script that is available on GitHub at:<br> <a href="https://github.com/iiasa/covid-energy-demand-scenarios">https://github.com/iiasa/covid-energy-demand-scenarios</a></p> <p>The most accessible way of exploring the scenario data behind this article would be to go to <a href="https://data.ece.iiasa.ac.at/engage/#/workspaces/60">https://data.ece.iiasa.ac.at/engage/#/workspaces/60</a>.<br> This goes to a web tool hosted by the International Institute of Applied Systems Analysis (IIASA) which provides access to a database of these and more variables of interest, defined for each scenario on the detail of MESSAGE regions, with a few example workspaces available within the ENGAGE Scenario Explorer.<br> The Scenario Explorer is a versatile open access tool to browse, visualize and download data and results. Users can freely create a private workspace where customized plots can be saved and shared.<br> For tutorials on how to use the Scenario Explorer, please visit <a href="https://software.ece.iiasa.ac.at/ixmp-server/tutorials.html">https://software.ece.iiasa.ac.at/ixmp-server/tutorials.html</a>.</p> <p>The scenarios that were used for the IPCC Special Report on 1.5C warming (SR1.5) have been made available at <a href="https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/">https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/</a>.</p> <p>The data is available for download at the <a href="https://data.ece.iiasa.ac.at/engage/">ENGAGE Scenario Explorer</a>. The license permits use of the scenario ensemble for scientific research and science communication, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and <a href="https://data.ece.iiasa.ac.at/engage/#/license">legal code</a> for more information.</p>
Future distribution patterns of cuttlefishes under climate change
<p>Dataset and scripts for Article "Future distribution patterns of cuttlefishes under climate change"</p>
Dataset of "Impact of climate change on the distribution and habitat suitability of the world's main commercial squids"
<p>Data of the manuscript "Impact of climate change on the distribution and habitat suitability of the world’s main commercial squids"</p>
Emissions scenario database of the European Scientific Advisory Board on Climate Change, hosted by IIASA
<p>This scenario ensemble collects emissions pathways quantitative, model-based scenarios related to the mitigation of climate change.</p> <p>The ensemble was compiled from the energy and integrated-assessment modelling community in response to a call by the European Scientific Advisory Board on Climate Change, see <a href="https://www.eea.europa.eu/about-us/climate-advisory-board/call-for-scenario-data-contributions">https://www.eea.europa.eu/about-us/climate-advisory-board/call-for-scenario-data-contributions</a>.</p> <p>The scenario ensemble can be accessed via the <strong>EU Climate Advisory Board Scenario Explorer</strong> hosted by IIASA at <a href="https://data.ece.iiasa.ac.at/eu-climate-advisory-board">https://data.ece.iiasa.ac.at/eu-climate-advisory-board</a>. The data can be downloaded and re-used for analysis and data visualization, but re-publication of a substantial portion is prohibited. </p> <p>The reason for the restriction is that we anticipate updates/extensions and (possibly) error corrections of this scenario ensemble.<br> We want to avoid a situation where multiple inconsistent versions of the scenario database are in wide circulation, which can lead to confusion for users. Therefore, please refer to the IIASA Scenario Explorer for the most-up-to-date version of the database.</p> <p>Further guidance and the full license text is available at <a href="https://data.ece.iiasa.ac.at/eu-climate-advisory-board/#/license">https://data.ece.iiasa.ac.at/eu-climate-advisory-board/#/license</a>.</p>
Social Media Big Dataset for Research, Analytics, Prediction, and Understanding the Global Climate Change Trends
<p>Yuriy Syerov, October 6, 2023, "Social Media Big Dataset for Research, Analytics, Prediction, and Understanding the Global Climate Change Trends", IEEE Dataport, doi: https://dx.doi.org/10.21227/71ms-8v86</p> <p>https://ieee-dataport.org/documents/social-media-big-dataset-research-analytics-prediction-and-understanding-global-climate</p>
A methodological approach to identify priority zones for monitoring and assessment of wild bee species under climate change.
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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.