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608 results for “ensembles”
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Large Ensemble Dataset for Discovering Global Peak Water Limit of Future Groundwater Withdrawals Using 900 GCAM Runs
<h2><strong>Global Groundwater Withdrawals Peak Over the 21st Century </strong></h2> <p>The large ensemble dataset contains groundwater related model outputs from 900 scenarios modeled using <a href="http://jgcri.github.io/gcam-doc/toc.html">Global Change Analysis Model (GCAM)</a>. The scenario ensemble members include five Shared Socioeconomic Pathways (SSPs), four Representative Concentration Pathways (RCPs), five global climate model outputs, three groundwater depletion limits, two surface water storage expansion regimes, and two historical groundwater depletion trends.</p> <h3><strong>Journal Article</strong></h3> <p>Niazi, H., Wild, T.B., Turner, S.W.D., Graham, N.T., Hejazi, M., Msangi, S., Kim, S., Lamontagne, J.R., & Zhao, M. (2024). <a href="https://rdcu.be/dFpb5">Global peak water limit of future groundwater withdrawals</a>. <em>Nature Sustainability, 7</em>(4), 413–422. <a href="https://doi.org/10.1038/s41893-024-01306-w" rel="nofollow">https://doi.org/10.1038/s41893-024-01306-w</a></p> <p>Read full-text here: <a href="https://rdcu.be/dFpb5">https://rdcu.be/dFpb5</a> </p> <h3><strong>Data Repository </strong></h3> <p>This <em><strong>data</strong></em> repository is to be used in combination with the <em><strong>main</strong></em> <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a> containing all scripts and files for reproducing the experiment as well as the analysis and post-processing of the model outputs.</p> <p>Scripts and smaller files are provided in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz">GitHub meta-repository</a> whereas larger files are provided in this data repository. Please complete the repository by placing the files as described hereunder. Please find the GitHub meta-repository here: <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">https://github.com/JGCRI/niazi-etal_2024_nature-sustainability</a></p> <p>Descriptions of files:</p> <ol> <li><em><strong>gcam-5.7z</strong></em> contains the GCAM version used to simulate 900 scenarios of plausible futures. The model folder contains all necessary input files to reproduce the simulations. <ul> <li>The model is to be used in combination with the <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a> to setup batch runs on cluster.</li> <li>Please navigate to <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model">model/</a> folder for other scenario-specific and model setup folders and files. <em><strong>gcam-5</strong></em> is to be extracted in the same directory (./<em>model/gcam-5/</em>). </li> <li>For the first-time users of GCAM, please follow guidance on <a href="http://jgcri.github.io/gcam-doc/toc.html">GCAM wiki</a> to setup GCAM or for background knowledge. </li> </ul> </li> <li><em><strong>crop_yeild.7z</strong></em>: This file contains inputs related to climate impacts on crop yields. This is to be downloaded and extracted in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model/combined_impacts">model/combined_impacts/</a> folder. </li> <li><em><strong>outputs-all.7z: </strong></em>Key model outputs queried and collated from 900 GCAM runs are explained hereunder. The files could be downloaded individually (.csv files) or all at once in .7z format (<a href="../api/files/80b237d3-b22f-499f-8b8e-76c3846720a0/outputs-all.7z">outputs-all.7z</a>). These files are to be placed in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model/outputs">model/outputs</a> folder of the <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a>. <ul> <li><em><strong>ag_prod_all_GW_scenarios.csv</strong></em> - Agricultural production across all scenario for 2050 and 2100 (tonnes)</li> <li><em><strong>prices_water_withdrawal_all.csv</strong> - </em>Water prices across all scenarios and years ($/km<sup>3</sup>)</li> <li><em><strong>global_irrigated_prod_by_crop.csv</strong></em> - All irrigated agricultural production for each crop across and scenarios all years (tonnes)</li> <li><em><strong>surface_water_production_all.csv</strong></em> - Runoff across all scenarios and years (km<sup>3</sup>)</li> <li><em><strong>groundwater_production_FINAL.csv</strong></em> - Groundwater withdrawals across all scenarios and years (km<sup>3</sup>)</li> <li><em><strong>water_withdrawals_desal_all.csv</strong></em> - Water withdrawals from desalination plants across all scenarios and years (km<sup>3</sup>)</li> </ul> </li> </ol> <h3><strong>Short introduction to the study</strong></h3> <p>Using 900 GCAM runs, this study finds that global groundwater withdrawals are expected to peak around mid-century, followed by a decline through 21st century, exposing about half of the population living in one-third of basins to groundwater stress, with cost and availability of surface water storage being the most significant driver of future groundwater withdrawals. This first-ever robust, quantitative confirmation of the peak-and-decline pattern for groundwater, previously only known for fossil fuels and minerals, raises concerns for basins heavily dependent on groundwater.</p> <p>Niazi, H., Wild, T.B., Turner, S.W.D., Graham, N.T., Hejazi, M., Msangi, S., Kim, S., Lamontagne, J.R., & Zhao, M. (2024). <a href="https://rdcu.be/dFpb5">Global peak water limit of future groundwater withdrawals</a>. <em>Nature Sustainability, 7</em>(4), 413–422. <a href="https://doi.org/10.1038/s41893-024-01306-w" rel="nofollow">https://doi.org/10.1038/s41893-024-01306-w</a></p> <p>Read full-text here: <a href="https://rdcu.be/dFpb5">https://rdcu.be/dFpb5</a></p> <h3><strong>Contact </strong></h3> <p>Please reach out to Hassan Niazi at <a href="mailto:hassan.niazi@pnnl.gov">hassan.niazi@pnnl.gov</a> for any questions. </p>
Ensemble monthly evapotranspiration over italy 1991-2020
<p>Six open access actual ET datasets are merged using an expert-based multiple collocation (MC) approach, with the aim of reconstructing a spatiotemporal consistent monthly dataset for the climatological period 1991-2020 over Italy at a spatial resolution of 1-km.</p> <p>The merged products include: three water balance datasets (BIG BANG, LSA SAF, and LISFLOOD), two residual surface energy balance models (SSEBop, and ALEXI) and the MODIS standard product.</p> <p>More details can be found in Cammalleri et al. (2023, under review)</p>
Underlying data for "Interpretation of Hydrogen-Deuterium Exchange Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles"
<p>This dataset contains code, data, and figures used in the article "Interpretation of Hydrogen-Deuterium Exchange Data<br> by Maximum-Entropy Reweighting of Simulated Structural Ensembles".</p> <p>Contents:</p> <p>code/* - Underlying code used to analyze molecular dynamics trajectories and calculate predicted HDX-MS data, used to reweight structural ensembles to best fit target HDX-MS data, and used to structurally cluster simulation frames after reweighting</p> <p>data/* - Simulation trajectories of the TeaA protein, along with two sub-trajectories corresponding to only 'closed' or 'open' TeaA frames, and predicted HDX-MS deuterated fractions used as target data in simulation reweighting. Also simulation trajectories of the LeuT protein, in either 'outward-facing' or 'inward-facing' conformational states embedded in a DMPC bilayer, and experimental HDX-MS deuterated fractions used as target data in simulation reweighting</p> <p>figures/* - Underlying data and scripts used to create all figures and movies used in the article.</p> <p>Where appropriate, README files include instructions for regenerating data used in the article, and details of the Python packages used to run Python scripts are available in conda_environment.yml</p>
Ensemble calculations of "Tn10p" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>Tn10p</p> <p><strong>Definition:</strong> Average number of days that the daily minimum temperature is below the 10th percentile of daily minimum temperatures of a five day window.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p> <p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The bias-corrected EURO-CORDEX climate model simulations used are:</p> <ul> <li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li> <li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li> </ul>
Ensemble calculations of "Ice Days" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>Ice days</p> <p><strong>Definition:</strong> Number of days with daily maximum temperature below 0°C.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p> <p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The bias-corrected EURO-CORDEX climate model simulations used are:</p> <ul> <li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li> <li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li> </ul>
Ensemble calculations of "RX1day" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>RX1day</p> <p><strong>Definition:</strong> Greatest one-day precipitation amount.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p> <p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The bias-corrected EURO-CORDEX climate model simulations used are:</p> <ul> <li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li> <li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li> </ul>
Example Perturbed Parameter Ensemble (Black Carbon)
<p>This dataset contains the parameter design and example ECHAM-HAM output from the AeroCom Black Carbon (BC) multi-model Perturbed Parameter Ensemble (PPE) experiment described here: https://wiki.met.no/aerocom/phase3-experiments#multi-model_ppe_bc_experiment</p>
The datasets used in the manuscript named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"
<p>The hindcast and real-time prediction output of FGOALS-f2 V1.0 used in the study named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"</p>
AgMIP-Wheat multi-model ensemble simulations on climate change impact and adaptation for 60 representative global locations
<p>This is model output from the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat) dataset of multi-model ensemble simulations for 60 representative global locations under different climate scenarios.</p> <p>The data have been generated following the modeling protocol of Asseng et al. (2019) and Liu et al. (2019).</p> <p>References</p> <p>Asseng, S. et al. (2019). Climate change impact and adaptation for wheat protein. Glob Chang Biol 25, 155-173, doi:10.1111/gcb.14481</p> <p>Liu, B. et al. (2019). Global wheat production with 1.5 and 2.0°C above pre-industrial warming. Global Change Biol 25, 1428-1444, doi:10.1111/gcb.14542</p> <p> </p>
Raw and processed GO term data to support running GCEA analyses using ensemble-based nulls, as described in the manuscript, 'Overcoming bias in gene category enrichment analyses of brain-wide transcriptomic data'.
<p>Data to support a toolbox for performing gene category enrichment analyses, including against ensembles of null phenotypes.</p> <p>Descriptions of how these data files can be used for this purpose are in the documentation for the toolbox, at https://github.com/benfulcher/GCEA_FalsePositives</p>
Storage enhanced nonlinearities in a cold atomic Rydberg ensemble: experimental data
<p>The data show number of input/output photons under different conditions when coherent pulses of light undergo electromagnetically induced transparency (EIT) in a cold cloud of Rubidium 87 atoms via a ladder system connecting the ground state of 87-Rubidium and different Rydberg levels via (see more details in Distante et al. Phys. Rev. Lett. <strong>117</strong>, 113001 (2016) or in the preprint https://arxiv.org/abs/1605.07478)</p> <p>This is the pre-analysed data from which the results in the paper are derived.</p> <p> </p> <ul> <li>The ODS file contains different sheets which correspond to Rydberg states with different principal quantum numbers</li> <li>The PDF contains useful information regarding the conditions of the experiment under which the data was obtained, such as the optical depth (OD) of the cloud, its dimensions, and the Rabi frequency of the coupling beam.</li> </ul>
Indian Precipitation Ensemble Dataset (IPED)
<p>The<strong> Indian Precipitation Ensemble Dataset (IPED)</strong> is the first observation-based ensemble gridded precipitation dataset for India. It includes the mean and standard deviation of 30 ensembles daily from 1991 to 2023 at a resolution of 0.1°.</p> <p>The dataset contains two folders:</p> <ol> <li>IPED Ensemble's Mean</li> <li>IPED Ensemble's Standard Deviation</li> </ol> <p>For detailed information about this dataset and its development, please refer to the original research article published in the <em>Scientific Data:</em></p> <p><em>Peringiyil, A., Saharia, M., O. P., S. <em>et al.</em> A station-based 0.1-degree daily gridded ensemble precipitation dataset for India. <em>Sci Data</em> <strong>12</strong>, 333 (2025). <a href="https://doi.org/10.1038/s41597-025-04474-2">https://doi.org/10.1038/s41597-025-04474-2</a></em></p> <p><strong>Disclaimer</strong></p> <p>When using the IPED dataset, users must cite it along with the associated research article published in "Scientific Data". </p> <p><strong>To Be Cited:</strong></p> <ol> <li>Peringiyil, A., Saharia, M., O. P., S. <em>et al.</em> A station-based 0.1-degree daily gridded ensemble precipitation dataset for India. <em>Sci Data</em> <strong>12</strong>, 333 (2025). <a href="https://doi.org/10.1038/s41597-025-04474-2">https://doi.org/10.1038/s41597-025-04474-2</a></li> <li>Anagha P, & Manabendra Saharia. (2025). Indian Precipitation Ensemble Dataset (IPED) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8199138">https://doi.org/10.5281/zenodo.8199138</a></li> </ol>
Audiomitschnitte des Symposiumskonzerts «Rubebe, rubechette e rubecone – Alte und Neue Musik für fellbespannte Streichinstrumente, Gesang, Harfe, Laute und Perkussion» mit dem ensemble arcimboldo (29.4.2023)
<p>Das mit einem Tierfell als Decke bespannte Rabab zählte neben der Fidel – und dem erst ab dem 14. Jahrhundert belegten Rebec – zu den wichtigsten Streichinstrumenten des Mittelalters und der frühen Renaissance. Umso erstaunlicher ist die Diskrepanz zwischen den zahlreichen historischen Quellen und ihrer fehlenden ‹Resonanz› in Musikwissenschaft und Musikpraxis. Aus diesen Gründen wurden Rabab und Rebec von 2019–2023 in einem Forschungsprojekt des Schweizerischen Nationalfonds an der Hochschule der Künste Bern HKB interdisziplinär untersucht und die Ergebnisse im April 2023 an einem internationalen Symposium präsentiert. Ein wichtiger Meilenstein des Forschungsprojekts ist das Symposiumskonzert des ensemble arcimboldo. Hier erklingen erstmals die im Forschungsprojekt rekonstruierten Rabab-Prototypen.</p><p>Der besondere Klang des fellbespannten mittelalterlichen Streichinstruments Rabab wird beim ersten Hören oft als ‹orientalisch› bezeichnet. Obwohl der Ursprung des Instruments in al Andalus, jenem ab dem 8. Jahrhundert von Muslimen besetzten Teil der Iberischen Halbinsel lag, verbreitete es sich vom 13. bis zum 15. von der Iberischen Halbinsel aus nach Frankreich und Italien. Diesen Weg des Rabab als klanglicher ‹Botschafter› zwischen den Kulturen und musikalischen Stilen spiegelt das Konzertprogramm wider: mit Cantigas de Santa Maria, marokkanischer andalusi-Musik, Werken aus dem Squarcialupi-Codex, Improvisationen sowie der Uraufführung zweier zeitgenössischer Kompositionen von Eleni Ralli (*1984) und Abril Padilla (*1970).</p><p>ensemble arcimboldo, Basel <br>Grace Newcombe – Sopran, Harfe <br>Félix Verry – Alt-Rabab, Fidel <br>Thilo Hirsch – Tenor-Rabab, Bass-Rabab, Tenor <br>Leonardo Bortolotto – Bass-Rabab <br>Peppe Frana – Plektrumlaute <br>Titus Bellwald – Tar</p><p>Wir danken dem Bernischen Historischen Museum als Gastgeber sowie folgenden Stiftungen für die grosszügige Unterstützung der Kompositionsaufträge, des Konzerts und der Erstellung der Audio- und Videomitschnitte: Schweizerischer Nationalfonds, Fachausschuss Musik BS/BL, Gesellschaft zu Schuhmachern Bern, Burgergemeinde Bern, Schweizerische Interpretenstiftung SIS.</p><p>Live-Aufnahme am 29.4.2023 im Orientalischen Saal des Bernischen Historischen Museums, Audio- und Videoproduktion: Oren Kirschenbaum.</p><p>Siehe auch: <a href="https://youtube.com/playlist?list=PL5J-BZoNMhGL2qSFYgLQ_8oMwkjSRIc3v&si=-T_SakuAKNOilc6u">https://youtube.com/playlist?list=PL5J-BZoNMhGL2qSFYgLQ_8oMwkjSRIc3v&si=-T_SakuAKNOilc6u</a></p>
Age-depth model ensembles for SISAL v3 speleothem records
<p>Depth-age model ensembles created for the SISAL database v3 (version for publication), in supplement to <strong><a href="https://essd.copernicus.org/preprints/essd-2023-364/" target="_blank" rel="noopener">Kaushal et al., 2024</a></strong> and building on <a href="https://www.earth-syst-sci-data-discuss.net/essd-2020-39/">Comas-Bru, Rehfeld, Roesch et al., 2020</a>.</p> <p>This upload includes ensemble data for 5 methods (interpolation, linear regression, copRa, Bchron and Bacon) created following the protocol in previous versions but for newly included entities in the database.</p> <p>Each file contains a matrix with the first column giving the row number, the second the SISAL v3 sample ID, the third the depth in the speleothem (in mm), and the fourth to 2003rd column contains the 2000 age model ensemble members.</p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the region of the Yucatán Peninsula
<p>The ensemble provides future projections of key marine variables under climate change for the region of the Yucatán Peninsula. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).<br> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the Chilean coast, see “Related identifiers”.</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Chilean coast
<p>The ensemble provides future projections of key marine variables under climate change for the Chilean coast. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and three different variables (potential temperature, dissolved oxygen, and pH) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Bay of Biscay
<p>The ensemble provides future projections of key marine variables under climate change for the Bay of Biscay region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the North Sea
<p>The ensemble provides future projections of key marine variables under climate change for the North Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></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.