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”
Figure 6 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 6. Empirical cumulative distribution function (ECDF) of the Predicted error |PE| (cft) in testing period for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
Figure 4 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 4. Box-plots of the Predicted error | PE| (cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
Figure 7 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 7. Taylor diagram showing the correlation coefficient between the predicted and observed yields (Blue pine and Silver fir) (cft) and standard deviation for the RF and KRR models.
Figure 5 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 5. Polar plots show the Predicted error |PE|(cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
Fig. 4 in Changing climate-changing pathogens: Toxoplasma gondii in North-Western Europe
Fig. 4 Expected increases in T. gondii prevalence in NorthWestern Europe towards 2069 based upon the combination of climatic conditions from Figs. 2 and 3. The dotted bright green areas indicate a small increase in T. gondii prevalence as a result of climatic change, pink areas a limited increase, and red areas a substantial increase
Fig. 2 in Changing climate-changing pathogens: Toxoplasma gondii in North-Western Europe
Fig. 2 Total precipitation 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 is the total mean precipitation in period from 1970 to 1999 (a), and the projected total mean precipitation from 2010 to 2039 (b) and 2040–2069 (c). Figures obtained from www.ipcc-data.org
Fig. 3 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 3 | Response of tree species to climate change across biomes. The median absolute latitude and median elevation shift among species, fraction of lost and gained species, and change in taxonomic and phylogenetic composition under climate change were computed for each forest ecoregion. The boxplots show statistics for n = 239 ecoregions for Tropical Moist Broadleaf Forests, n = 14 ecoregions for Tropical Coniferous Forests, n = 55 ecoregions for Tropical Dry Broadleaf Forests, n = 26 ecoregions for Boreal Forests, n = 91 ecoregions for Temperate Broadleaf Forests, n = 49 ecoregions for Temperate Conifer Forests and n = 61 ecoregions for Mediterranean Forests. The center line of the boxplots shows the median, the box limits the quartiles, the whiskers 1.5 times the interquartile range, and the points the outliers.Changes are computed between predicted distributions with climate variables for 1981-2010 and climate projections for 2071-2100 under climate change scenario SSP 5.85. Changes in composition are computed as the Euclidean distance between scaled NMDS and evoPCA values computed at the ecoregion level. Source data are provided as a Source Data file.
Fig. 2 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 2 | Species occupancy range distribution and loss. a Distributions of species occupancy range sizes globally (gray) and constrained to forests (at least 10% tree cover, color) for species in each forest biome. b Boxplot of relative range reduction across species in each forest biome with the center line showing the median, the box limits the quartiles, the whiskers 1.5 times the interquartile range, and the points the outliers. The distributions and boxplots are computed for n = 6810 species for Tropical Moist Broadleaf Forests, n = 588 species for Tropical Coniferous Forests, n = 1101 species for Tropical Dry Broadleaf Forests, n = 54 species for Boreal Forests, n = 1744 species for Temperate Broadleaf Forests, n = 178 species for Temperate Conifer Forests and n = 580 species for Mediterranean Forests. c Global map of median species range size constrained to forests, created with QGIS110. The gray base map corresponds to all areas for which model predictors were available. d Plot of species' median latitude against range size constrained to forests, colored by point density, where red indicates the highest density. Source data are provided as a Source Data file.
Fig. 1 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 1 | Gradients in taxonomic and phylogenetic composition show a near- a, c. Scatter plot of taxonomic and phylogenetic ordinations in environmental unique biodiversity signature of every single location on the planet. Taxonomic space, a 2-dimensional space made up of the 2 first axes of a PCA of the environcomposition is represented by a 3-axis non-metric dimensional scaling (NMDS) and mental variables used for species distribution modeling: mean annual temperature phylogenetic beta-diversity is represented by the 3 first axes of a phylogenetic (MAT), temperature seasonality (T season), annual precipitation (Annual P), preordination (evoPCA). Both the taxonomic and phylogenetic ordinations are com- cipitation seasonality (P season), growing season length (GSL), net primary proputed on the global community matrix derived from the modeled distributions of ductivity (NPP),silt content (Silt),coarse fragments (CF),and soil pH (pH).b, d. Map n = 10,590 tree species sampled at a resolution of 100 km, resulting in n = 12,548 of taxonomic and phylogenetic ordinations in geographical space. Source data are sites. The 3 axes of each ordination are mapped to red, green, and blue with provided as a Source Data file. The maps were created with QGIS110 and the gray minimum and maximum values corresponding to the 10th and 90th percentiles. base map corresponds to all areas for which model predictors were available.
Fig. 2 in Retrieving climate change dependent Sea Surface Temperature (SST) in Southern Turkey by using Landsat thermal imagery
Fig. 2 — The four sections of the sampling site Table 1 — Landsat images features (29) Product Type Pixel size (collected) Pixel size (resampled) Thermal band Landsat 4-5 TM L1 120-meters 30-meters Band 6 Landsat 7 ETM+ L1 60-meters 30-meters Band 6 Landsat 8 OLI/TIRS L1 100-meters 30-meters Band 10/ Band11
Fig. 3 in Retrieving climate change dependent Sea Surface Temperature (SST) in Southern Turkey by using Landsat thermal imagery
Fig. 3 — SST anomalies: a) T1 Cross-section, b) T2 Cross-section, c) T3 Cross-section, and d) T4 Cross-section
Data from: Reconstructing 120 years of climate change impacts on Joshua tree flowering
<p>Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (<em>Yucca brevifolia</em> and <em>Y. jaegeriana</em>), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo-referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records, and reveals slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years — but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity.</p>
FIGURE 2 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 2 Distributions (left), maximum likelihood (ML) phylogenetic trees (middle), principal component analysis (PCA) ordination plots from cranial measurements, photographs or drawings of the baculum and sonograms of echolocation calls (right) of selected groups of paramontane southern African bats having ranges categorized as arid (red symbols), Mediterranean (turquoise symbols), temperate-montane (blue), savanna-montane (orange), and tropical rain forest (green; see Table S1 for classification): horseshoe bats (Rhinolophus) of the R. capensis (a), R. darlingi (b), R. ferrumequinum (c), R. fumigatus (d) groups, wing-gland bats (Family Cistugidae, genus Cistugo (e), and long-eared serotine bats of the genus Laephotis (f)). Distribution maps were based on IUCN Redlist maps (open polygons), correctly identified vouchers from molecular studies (colored symbols; this study; GenBank; Curran et al., 2022; Demos et al., 2019; Dool et al., 2016; Taylor et al., 2018) and skulls measured in this study (crosses). In a few cases (see legends), GBIF records were indicated for the Angolan range of species. Gray shading indicates elevations over 1200 m a.s.l. Phylogenetic trees are shown for sub-clades (i.e., excluding outgroups) of three separate ML analyses undertaken with IQTREE of Rhinolophus, Cistugo, and Laephotis (Figures S2–S4). Values above nodes (in bold) represent median dates obtained for corresponding nodes from separate BEAST analyses in Figures S5–S7 (see text for details). Node support values for ML trees, obtained by the IQTREE program, are given below the nodes for SH-like approximate likelihood ratio tests (SH-aLRT), aBayes posterior probabilities, and ultra-fast bootstrap values (UFBS) respectively (see text for details). Tip labels marked in bold represent new sequences from this study. Underlined tip labels represent two instances of mtDNA introgression where morphologically distinct taxa from different biomes have near-identical cyt-b sequences. Species ranges of echolocation call peak frequencies were obtained from the literature for Rhinolophidae (Adams & Kwiecinski, 2018; Curran et al., 2022; Jacobs et al., 2013; Jacobs et al., 2017; Laverty & Berger, 2020; Monadjem et al., 2020; Mutumi et al., 2016; Odendaal & Jacobs, 2011; Odendaal et al., 2014; Schoeman & Jacobs, 2008), Cistugo (Monadjem et al., 2020; Schoeman & Jacobs, 2003, 2008), and long-eared Laephotis (Adams & Kwiecinski, 2018; Jacobs et al., 2005; Monadjem et al., 2020; Pierce et al., 2011). Bacula photographs and drawings were obtained from this study as well as Benda and Vallo (2012), Taylor et al. (2018), Curran et al. (2022). Abbreviation of South African province names: EC, Eastern Cape; FS, Free State; GP, Gauteng; KZN, KwaZulu-Natal; LP, Limpopo; MP, Mpumalanga; NC, Northern Cape; WC, Western Cape. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
FIGURE 1 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 1 Maps of southern, central, and eastern Africa showing (a) topographical features referred to in this study (see text for details), and (b) the extent of minimum monthly temperatures (bioclim6) <0°C from present and past (last glacial maximum [LGM]) models (from Worldclim; https://www.worldclim.com/; see Methods for more details). Gray or darker shading in both maps indicates mountains>1200 m in elevation. In (a), the acronym HEAN stands for the Highlands and Escarpments of Angola and Namibia (Mendelsohn et al., 2023); SEAMA stands for the South-East African Montane Archipelago (Bayliss et al., 2024); LMEE stands for the Limpopo–Mpumalanga– Eswatini Escarpment (Clark et al., 2022). The map in (b) shows distribution points of horseshoe bats, Rhinolophus (crosses), wing-gland bats, Cistugo (open triangles) and long-eared bats, Laephotis (open squares) based on morphological and molecular results from this study and from published a GenBank cyt-b sequences. In (b), minimum monthly temperatures <0°C indicated for the present (blue) and LGM (red), approximating the extent of frost (and hence temperate grasslands) currently and during the LGM (idea from Brain, 1985). Map lines delineate study areas and do not necessarily depict accepted national boundaries.
FIGURE 3 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 3 Map of southern, central, and eastern Africa showing major geographic features (as in Figure 1a) but with biogeographical barriers elucidated by this study indicated as red dashed lines, labelled as (i) to (vii) (see Discussion), and taxa specific to different ranges indicated according to the predominant biomes (green = tropical; red = arid, turquoise = Mediterranean, blue = temperate, orange = savanna). Note that only one savanna lineage is here indicated for ease of visualization. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.
FIGURE 4 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 4 Maps of south-central Africa showing the distribution of Köppen–Geiger climate zones for the present (a) and projected future (2070) (b), as well as past (last glacial maximum: left panel), present (right panel), and projected future (2070; right panel) Maxent distribution models for five species groups of bats; Rhinolophus capensis group (c–e: green = R. swinnyi; blue = R. rhodesiae; orange = R. simulator; turquoise = R. capensis; red = R. denti); R. darlingi group (f–h: blue = R. cervenyi; orange = R. darlingi; red = R. damarensis), R. ferruquinum group, in part (i–k: blue = R. acrotis), Laephotis spp (l–n: blue = L. cf. botswanae; orange = L. angolensis), Cistugo spp (o–q: blue = C. lesueuri; red = C. seabrae). Details of Maxent models given in text. Ranges of species above indicated by colors corresponding to biomes recognized in this study (Tables S1 and S2) as follows: blue or green = temperate; orange = savanna; turquoise = Mediterranean; red = arid. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
Future-ready Nordic homes: Responding to the Changing Climate with Free-running Buildings; Digital Repository of Obtained Results
<p>The figures provided here show complete results of the parametric building performance simulation for each studied location, which was a part of the Master thesis in Energy-efficient and Environmental Building Design (Faculty of Engineering, Lund University, Sweden) by Marko Ljubas.</p> <p>The simulations were performed using software IDA ICE 4.8 by EQUA.</p> <p>The weather files were generated using software Meteonorm 8.2 by Meteotest.</p>
Figure 5 in The range dynamics of a cactophilic Drosophila species under climate change scenarios
Figure 5. Last Interglacial, Last Glacial Maximum, Present (1960–1990), and the Future (2050 and 2070) predictions of the potential distribution of two cacti species (C. hildmannianus and P. machrisii) based on 10% thresholding approaches. The abbreviations are defined as follows: LGM-Last Glacial Maximum, LIG-Last Interglacial.
Figure 2 in The range dynamics of a cactophilic Drosophila species under climate change scenarios
Figure 2. Occurrence points used for ecological niche modeling are shown in red. Squares equal approximately 2 decimal degrees and the background image on thmap shows the elevational structure of Brazil.
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.