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.
269
datasets available to search
ShareScore release 0.9.0
Dataset results
269 results for “Regional climate”
Data for "Africa's Climate Response to Marine Cloud Brightening Strategies is Highly Sensitive to Deployment Region"
<p>The data presented here were used the following study:</p> <p>Africa's Climate Response to Regional Marine Cloud Brightening Strategies.<span> </span></p> <p><span>Romaric C. Odoulami<sup>1</sup>, Haruki Hirasawa<sup>2,3</sup>, Kouakou Kouadio<sup>4</sup>, Trisha D. Patel<sup>1</sup>, Kwesi A. Quagraine<sup>5,6</sup>, Izidine Pinto<sup>6,7</sup>, Temitope S. Egbebiyi<sup>6</sup>, Babatunde J. Abiodun<sup>6</sup>, Christopher Lennard<sup>6</sup>, Mark G. New<sup>1</sup> </span></p> <p><sup><span>1</span></sup><span>African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa.</span></p> <p><sup><span>2</span></sup><span>School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada.</span></p> <p><sup><span>3</span></sup><span>Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA</span></p> <p><sup><span>4</span></sup><span>Laboratory of Atmospheric Physics and Fluid Mechanics, University Felix Houphouet-Boigny, Abidjan, Côte d’Ivoire.</span></p> <p><sup><span>5</span></sup><span>National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA.</span></p> <p><sup><span>6</span></sup><span>Climate System Analysis Group (CSAG), Environmental and Geographical Science Department, University of Cape Town, Cape Town, South Africa.</span></p> <p><sup><span>7</span></sup><span>Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands.</span></p> <p> </p> <p>The datasets contains six folders. Each of them contains two folders: (a) "precipitation_indices" containing all precipitation indices and (b) "temperature_indices" containing all temperature indices for the African domain analysed in this study.</p> <p> (i) "historical"<br> This folder contains all historical precipitation and temperature indices analysed.</p> <p> (ii) "ssp245"<br> This folder contains all ssp245 precipitation and temperature indices analysed.</p> <p>(iii) "ssp245_MCB_R1"<br> This folder contains all ssp245+MCB_R1 (MCB over Northeast Pacific: MCB_NEP) precipitation and temperature indices analysed.</p> <p> (iv) "ssp245_MCB_R2"<br> This folder contains all ssp245+MCB_R2 (MCB over Southeast Pacific: MCB_SEP) precipitation and temperature indices analysed.</p> <p> (v) "ssp245_MCB_R3"<br> This folder contains all ssp245+MCB_R2 (MCB over Southeast Atlantic: MCB_SEA) precipitation and temperature indices analysed.</p> <p> (vi) "ssp245_MCB_R1R2R3"<br> This folder contains all ssp245+R1R2R3 (MCB simultaneously over Northeast Pacific, Southeast Pacific, and Southeast Atlantic: MCB_ALL) precipitation and temperature indices analysed.</p> <p> </p>
A companion dataset to the paper Scenarios of future climate zone changes in Europe based on EURO-CORDEX regional model ensemble by Holtanová et al., to be submitted to Regional Environmental Change
<p>The content of the dataset is described in the metadata.txt file. </p>
Regionalized dynamic climate series for ecological climate impact research in modern controlled environment facilities
<p>Modern controlled environment facilities (CEFs) enable the simulation of dynamic microclimates in controlled ecological experiments through their technical ability to precisely control multiple environmental parameters. However, few CEF studies exploit the technical possibilities of their facilities, as climate change treatments are frequently applied by static manipulation of an inadequate number of climate change drivers, ignoring intra-annual variability and co-variation of multiple meteorological variables. We present so called Test Reference Years (TRY) that consist of typical annual cycles of temperature, relative humidity, air pressure, global radiation, photosynthetically active photon flux density, tropospheric ozone concentration and CO<sub>2</sub>. The TRYs represent possible annual cycles of a reference period (1987-2016) and the two contrasting climate scenarios RCP2.6 and RCP8.5.</p>
Validation Data used for manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model"
<p>those are the processed data that used for model-data comparison in the manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model", including Lake Surface Temperature and Lake Surface Ice Cover from Great Lakes Surface Environmental Analysis (GLSEA), Surface Air temperature and Precipitation from Climatic Research Unit (CRU). </p>
WRF model configuration and data used for the NHESS manuscript "Droughts in Germany: Performance of Regional Climate Models in reproducing observed characteristics"
<p>The file contains:</p> <ul> <li>the namelist.input document with the description of the WRF model configuration used in Warscher et al. (2019)</li> <li>WRF simulation outputs from the reanalysis run: monthly values for the time period 1980-2009 of precipitation, maximum and minimum temperature (needed for the SPEI calculation) from the innermost (5 km grid resolution) and second innermost (15 km) domain; from both domains the same section, relevant for the study, was taken; the data was bilineraily interpolated to 12.5 km horizontal grid resolution to match the EUR-11 CORDEX format</li> </ul> <p> </p>
Supporting Information: Vortex streets to the lee of Madeira in a km-resolution regional climate model
<p>This is the Supporting Information for the manuscript <em>Vortex streets to the lee of Madeira in a km-resolution regional climate model</em>.</p> <p>It is posted as a preprint on EGUsphere for the journal Weather and Climate Dynamics: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-965/.</p>
Supplementary material 2 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
Combined AS-ISK report for the 24 non-native aquatic plant species screened for their potential risk of invasiveness in the Pannonian and Mediterranean regions of Croatia.
Kilometre-scale regional climate model simulations of two atmospheric river case studies in West Antarctica
<p>Regional climate model simulations produced using the MetUM, HCLIM and Polar-WRF models at 1 km horizontal grid spacing. The data span two case studies in which an atmospheric river made landfall over the Amundsen Sea Embayment and Thwaites / Pine Island ice shelves. The first is a winter case (23-30 June 2020) and the second a summer case (3-9 February 2020). </p> <p>Data are gridded, in native model coordinates, and saved as netcdf.</p> <p>Data produced by:</p> <p>HCLIM: José Abraham Torres</p> <p>MetUM: Ella Gilbert</p> <p>Polar-WRF: Denys Pishniak</p> <p>Data were produced to support the analysis presented in Gilbert et al. (2024) [preprint] . The research was funded by the PolarRES project, which is funded under the EU's Horizon 2020 programme call H2020-LC-CLA-2018-2019-2020 under grant agreement 101003590. MetUM simulations were performed on the ARCHER2 UK National Supercomputer. </p>
SDM results for 10,590 tree species from "Regional uniqueness of tree species composition and response to forest loss and climate change"
<p>Output from species distribution models (SDMs) with geographic constraints to estimate the spatial distribution of tree species at the global level at a 30-arc second resolution, presented in the publication "Regional uniqueness of tree species composition and response to forest loss and climate change". </p> <h2>Data</h2> <p>This file contains the results for 10,590 tree species. The results for each species are contained in a directory with the species name connected by an underscore. For most species, the directory contains several .tif files that make up the tiles of the distribution maps for that species and a metadata file. The .tif files can be merged with the gdal_merge.py function to obtain a single .tif file per species (see example below). For some species, the directory contains a single .tif file which does not require merging. In all cases, the .tif files contain 9 bands that correspond to the predicted species distribution using climatic variables corresponding to various climate projections from Chelsa 2.1.</p> <h3>Band order</h3> <ol> <li>covariates_1981_2010: average of historical climate measurements from 1981 to 2010</li> <li>covariates_2011_2040_ssp126: average future climate projection for 2011-2040 under shared socioeconomic pathway (SSP) 1.26</li> <li>covariates_2011_2040_ssp370: average future climate projection for 2011-2040 under SSP 3.70</li> <li>covariates_2011_2040_ssp585: average future climate projection for 2011-2040 under SSP 5.85</li> <li>covariates_2041_2070_ssp126: average future climate projection for 2041-2070 under SSP 1.26</li> <li>covariates_2041_2070_ssp370: average future climate projection for 2041-2070 under SSP 3.70</li> <li>covariates_2041_2070_ssp585: average future climate projection for 2041-2070 under SSP 5.85</li> <li>covariates_2071_2100_ssp126: average future climate projection for 2071-2100 under SSP 1.26</li> <li>covariates_2071_2100_ssp370: average future climate projection for 2071-2100 under SSP 3.70</li> <li>covariates_2071_2100_ssp585: average future climate projection for 2071-2100 under SSP 5.85</li> </ol> <h3>Metadata</h3> <p>The metadata contains more information about the bands, as well as the following species-level properties:</p> <ul> <li>nobs: number of spatially distinct occurrence records used in model training</li> <li>precision: precision of binarised model output computed through 3-fold cross-validation</li> <li>threshold: threshold used to binarise probabilistic model output, determined as the threshold maximizing the true skill statistic (TSS) during 3-fold cross-validation</li> <li>f1: F1 score of binarised model output computed through 3-fold cross-validation</li> <li>auc: area under the ROC curve (AUC) of model output computed through 3-fold cross-validation</li> <li>prevalence: prevalence of presences (ie. occurrences records) throughout the training data which consisted of occurrence records and pseudo-absences</li> <li>tss: TSS of binarised model output computed through 3-fold cross-validation</li> <li>recall: recall of binarised model output computed through 3-fold cross-validation</li> <li>nativeness_info: indicates whether reported native countries were available for this species (possible values: "yes" or "no", should be "yes" for all species included)</li> <li>npa: number of pseudo-absences used in model training</li> <li>system:index: species name </li> </ul> <h3>Merging example</h3> <p>For example, the directory Abarema_barbouriana contains files Abarema_barbouriana_0.tif, Abarema_barbouriana_2.tif, ..., Abarema_barbouriana_9.tif and metadata.json. The tiles can be merged with the command "gdal_merge.py -o Abarema_barbouriana_merged.tif Abarema_barbouriana/Abarema_barbouriana_*.tif".</p>
Investigating the "Too Bright" Issue Pertaining to Non-PBL Clouds over the South Pacific Trade-Wind Region in CMIP6 Global Climate Models
<p><a href="../api/records/13314147/draft/files/f09.C6.B-hist.SON_ANN.tar.gz/content" target="_blank" rel="noopener noreferrer">f09.C6.B-hist.SON_ANN.tar.g</a>z</p> <p>CESM2-CAM6 with falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.</p> <p> </p> <p>The data includes with netcdf self description.</p> <p>f09.C6.B-hist.h01_AWNC_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLDHGH_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLDLOW_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDMED_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDTOT_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLOUD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CONCLD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_FREQL_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_ICWMR_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_NUMLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_OMEGA_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_PRECC_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_PRECL_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_SST_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_tauy_ANN_climo-CDO.nc</p> <p><a href="../api/records/13314147/draft/files/f09.C6.B-hist.SON_ANN.tar.gz/content" target="_blank" rel="noopener noreferrer">f09.C6.B-hist.NOS_ANN.tar.g</a>z</p> <p>CESM2-CAM6 without falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.</p> <p><br>f09.C6.B-hist.nos81_AWNC_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CDNUMC_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDHGH_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CLDLOW_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDMED_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDTOT_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLOUD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CONCLD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_FREQL_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_ICWMR_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_NUMLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_OMEGA_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_PRECC_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_PRECL_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_SST_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_taux_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_tauy_ANN_climo-CDO.nc</p>
Tree growth responses to climate in the Macaronesian region
Open the record for dataset details and reuse information.
FIGURE B in An Annotated Checklist to Vascular Flora of the Ica Region, Peru-with notes on endemic species, habitat, climate and agrobiodiversity
FIGURE B. (+DELWDWV: 4XHEUDGD, &DFWXV VORSHV, $QGHDQ /RPDV) 1, 2. /RZHU VORSH EDMDGDV ZLWK [HURSK\WLF YHJHWDWLRQ DIWHU IUHDN ƻol7 (162 UDLQV ZLWK SUHGRPLQDQFH RI Aristida adscensionis ± 4XHEUDGD &DQVDV, /D 7LQJXLxD (l4oo P); 3.;HULF VORSH DQG ORHVV VXEVWUDWH; HSKHPHUDO $QGHDQ ORPDV YHJHWDWLRQ ZLWK SUHGRPLQDQFH RI Pyrolirion albicans DQG Orthopterygium huaucui WUHHV ± /D &DQWHUD, 4XHEUDGD 7LQJXH (ƻƻoo P); 4. +HUEDFHRXV $QGHDQ ORPDV ZLWK Nasa urens, Nolana humifusa DERYH RUJDQLF DJULFXOWXUH YDOOH\ ± +XDTXLQD, 7RSDUi, &KLQFKD (8oo P); 5. &DFWXV VORSHV DIWHU IUHDN UDLQ ZLWK Neoraimondia arequipensis ± 4XHEUDGD 3DPSDKXDVL, <DXFD GHO 5RVDULR (8oo P); 6. &DFWXV VORSHV ZLWK Cnidoscolus pavonianus, Neoraimondia arequipensis, Orthopterygium huaucui, Weberbauerocereus rauhii ± 6RO GH 2UR, 1DVFD (l7oo P); 7. 0LG±TXHEUDGD ZLWK Acacia macracantha, Maytenus octogona, Scutia spicata, Tecoma fulva ± DERYH 0ROOHWDPER (l³ƽo P); 8. &DFWXV VFUXE ZLWK Neoraimondia arequipensis ± (O,QJHQLR, 1DVFD (9oo P). 3KRWRV: /& (l), +<(ƻ). $2 (4, 6), &3 (ƽ), (5 (8).
FIGURE E in An Annotated Checklist to Vascular Flora of the Ica Region, Peru-with notes on endemic species, habitat, climate and agrobiodiversity
FIGURE E (Flora of Ica): 1. Grindelia glutinosa, 2. Guadua superba, 3. Haageocereus decumbens, 4. Haageocereus pseudomelanostele, 5. Haageocereus aff. tenuis, 6. Heliotropium krauseanum, 7. Indigofera truxillensis, 8-9. Inga feuillei, 10. Ipomoea dubia, 11. Jacquemontia unilateralis, 12. Jarava pachypus, 13. Junellia aff. juniperina, 14. Krameria lappacea, 15. Lepidium raimondii. Pictures: AO (2); OP (8, 12).
FIGURE J in An Annotated Checklist to Vascular Flora of the Ica Region, Peru-with notes on endemic species, habitat, climate and agrobiodiversity
FIGURE J (Flora of Ica): 1. Scutia spicata, 2. Senecio calcicola, 3. Solanum corymbosum, 4-5. Solanum edmondstonei, 6-7. Solanum montanum, 8. Solanum paposanum, 9. Spergularia congestifolia, 10. Spondias purpurea, 11. Suaeda foliosa, 12-13. Tecoma fulva subsp. guarume, 14. Tessaria integrifolia, 15. Tetragonia microcarpa. Pictures: AO (13); CP (15).
FIGURE B in An Annotated Checklist to Vascular Flora of the Ica Region, Peru-with notes on endemic species, habitat, climate and agrobiodiversity
FIGURE B (Flora of Ica): 1. Baccharis salicifolia, 2. Bixa orellana, 3. Boerhavia verbenacea, 4. Bolboschoenus maritimus, 5. Buddleja americana, 6. Bulnesia retama, 7. Browningia candelaris subsp. icaensis, 8. Calliandra aff. taxifolia, 9. Capparis avicennifolia, 10. Carica candicans, 11. Cenchrus aff. echinatus, 12. Chenopodium petiolare, 13. Cistanthe paniculata, 14. Cleistocactus acanthurus. Pictures: CP (1); HY (3, 7); AO (8).
FIGURE F in An Annotated Checklist to Vascular Flora of the Ica Region, Peru-with notes on endemic species, habitat, climate and agrobiodiversity
FIGURE F (Flora of Ica): 1. Leptoglossis ferreyraei, 2. Leptoglossis lomana, 3. Lomanthus icaensis, 4. Loxanthocereus aff. hystrix, 5. Lycium americanum, 6. Maytenus octogona, 7. Melocactus peruvianus, 8. Myrcianthes ferreyrae, 9. Nasa urens, 10. Neoraimondia arequipensis, 11. Neoraimondia arequipensis subsp. roseiflora, 12. Nicotiana paniculata, 13. Nolana adansonii, 14. Nolana chancoana, 15. Nolana humifusa. Pictures: AO (4, 10, 15); RS (7), ER (11); DG (14).
FIGURE 11 in An Annotated Checklist to Vascular Flora of the Ica Region, Peru-with notes on endemic species, habitat, climate and agrobiodiversity
FIGURE 11. Hoffmannseggia (Leguminosae) species of Ica. (A) Hoffmannseggia miranda (lomas rocky substrate); (B) Hoffmannseggia miranda (lomas sandy substrate); (C) Hoffmannseggia viscosa (lower valley alluvial silt); (D) Hoffmannseggia viscosa (lower valley alluvial clay); (E, F) Hoffmannseggia prostrata (huayco alluvial clay); (G) Hoffmannseggia aff. ternata (lower valley alluvial clay); (H) Hoffmannseggia viscosa (lower valley alluvial silt) with andrenid bee pollinator. (photos: OW).
FIGURE 12 in An Annotated Checklist to Vascular Flora of the Ica Region, Peru-with notes on endemic species, habitat, climate and agrobiodiversity
FIGURE 12. Huarango of Ica (Prosopis limensis Benth.) showing: (A) typical reclining habit in valley margin trees (Copara, Valle Las Trancas, Nazca, Ica); (B) characteristic brachyblasts and leaf clumping in mature branches (Mancha verde, Nasca); (C) yellow flowers; (D) ripe pods noting width (pale yellow, curved and straight); (E) fissured reddish trunk; (F) flowers, leaf pinna and red stems; (G) harvested pods drying; (H) the oldest Prosopis tree on south coast Peru 'Huarango Milenario' de Huayurí † (died 2016); (I) ancient P. limensis in Laguna San Pedro Cachiche, Ica. (photos: OW).
FIGURE 7 in An Annotated Checklist to Vascular Flora of the Ica Region, Peru-with notes on endemic species, habitat, climate and agrobiodiversity
FIGURE 7. The Guanaco and Andean Condor 'flagship' species for Ica with LSF logo. Flagship animals and plant dispersal agents photographed in Lomas San Fernando / Marcona: top and centre right: rare images of Peruvian south coast guanaco subspecies (Lama guanicoe cacsilensis Lönnberg 1913), browsing on the inflorescences of Tillandsia latifolia (photos: Alfonso Orellana, 15 May 2016); centre left: Andean Fox (Lycalopex culpaeus Molina, 1782) (OW); bottom left: Andean condor (Vultur gryphus Linnaeus 1758) (photo Justin Moat); bottom right: logo of Reserva Nacional San Fernando (RNSF) with condor and guanaco. RNSF is the most significant coastal habitat for the threatened Andean condor in Peru, a species once frequent on the south coast of Peru (see Murphy 1925), where they depend on carrion from breeding sea mammals (Stucchi 2009, Vásquez 2015).
FIGURE 6 in An Annotated Checklist to Vascular Flora of the Ica Region, Peru-with notes on endemic species, habitat, climate and agrobiodiversity
FIGURE 6. Climate of lomas. Data derived from calibrated datalogger readings from herbaceous lomas vegetation (May 2013—January 2015) at 435 m elev. in Lomas San Fernando, showing: Annual maximum and minimum relative humidity (%RH) and temperature (˚C). The temperature spike (red) is thought to be anomalous and associated with the year prior to ENSO. The phenology is indicative and derived from monitoring and collection vouchers. (illustration: OW).
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.