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942 results for “Scenarios”
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>
Climate Solutions Explorer - downscaled country-level IAM scenarios
<p><strong>This is a pre-release dataset and is subject to change.</strong></p> <p>The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. <strong><a href="https://www.climate-solutions-explorer.eu">www.climate-solutions-explorer.eu</a></strong></p> <p>The Mitigation (and Summary) Dashboards present mitigation information, i.e. emissions, energy and carbon sequestration, for over 200 countries and 10 regions. To present data for all countries, Integrated Assessment Model runs from the MESSAGEix-GLOBIOM model have been downscaled by using a methodology described in Sferra et al. 2021 <a href="#_ftn1">[1]</a>. The algorithm produces a range of pathways consistent with the underlying IAM-results, based on criteria such as historical data, planned capacities, country-available resource in the form of supply cost-curves, quality of governance as well as regional benchmarks based on IAM results. The data is provided from 2020 to 2070, for a limited set of variables used on the website.</p> <p>The scenarios included are:</p> <ul> <li><strong>Current Policies:</strong> Current Policies scenarios here are based on the implementation of national mitigation targets implemented by country without any further strengthening of action. Expected to lead to 2.7 °C by 2100. The data is from the MESSAGEix-GLOBIOM_1.1 GP_CurPol_T45 scenario.</li> <li><strong>NDCs Delayed Action to 2030:</strong> Assumes trajectory based on the implemented NDCs until 2030, and then reduces emissions typically in line with a globally 2°C by 2100. The data is from the MESSAGEix-GLOBIOM_1.1 GP_NDC2030_T45 scenario.</li> <li><strong>Glasgow Pledges:</strong> "Glasgow Pledges" scenarios here are based on the pledges made by countries at the 2022 COP26 Glasgow Summit, and represent increased ambition, likely taking the world closer to below 2°C in 2100, but still some distance away from the aspirations of 1.5°C of the Paris Agreement. The data is from the MESSAGEix-GLOBIOM_1.1 GP_Glasgow scenario.</li> <li><strong>Glasgow Pledges+:</strong> "Glasgow Pledges+" scenarios drops the NDC pledges and expands mid-century strategy pledges to net-zero for all countries and regions. The data is from the MESSAGEix-GLOBIOM_1.1 GP_GlasgowP scenario.</li> <li><strong>Glasgow Pledges++:</strong> "Glasgow Pledges++" scenarios here aims at filling the gap between national mid-century strategies and the 1.5/2 °C global scenarios. This scenario builds upon the Glasgow+ scenario and anticipates the action (net-zero target year defined for each region) in 5 or 10 years (depending on the model’s time steps). The data is from the MESSAGEix-GLOBIOM_1.1 GP_GlasgowPP scenario.</li> </ul> <p><a href="#_ftnref1">[1]</a> Sferra, F. et al. 2021. Downscaling IAMs results to the country level – a new algorithm. IIASA Report. IIASA, Laxenburg, Austria. <a href="https://pure.iiasa.ac.at/17501">https://pure.iiasa.ac.at/17501</a>.</p> <p> </p> <p> </p> <p><strong>Release notes (v0.2)</strong></p> <p>This version brings improvements in:</p> <ul> <li>harmonization data source, now done for 2018 using PRIMAP</li> <li>calculation of Kyoto Gases for R10, and Kyoto Gases (incl. indirect AFOLU) for countries</li> <li>addition of R10 and EU27 region data</li> <li>Corrections to variable aggregation</li> </ul> <p> </p>
The short gamma-ray burst population in a quasi-universal jet scenario: MCMC chains
<p>The paper "The short gamma-ray burst population in a quasi-universal jet scenario" (https://arxiv.org/abs/2306.15488) described an effort in modelling the short gamma-ray burst population under the assumption that all jets share the same angular profile.</p> <p>This repository contains <strong>emcee </strong>hdf5 files with the MCMC chains corresponding to the "full sample" and "flux-limited sample" analyses described in the paper.</p>
QuantMig microsimulation population projection model and migration scenarios for 31 European countries
<p>This open data deposit contains the data and model code of QuantMig-Mic microsimulation population projection model for 31 European countries and accompanies deliverables D8.3: Model outputs for dissemination and D8.1: Microsimulation projection model.</p> <p>This Zenodo deposit contains datasets of model input (baseline population and immigration database) and output data (demography and components output tables) and model code with parameters of the Baseline scenario (termed Default in the model code) deposited in QuantMig_mic.zip file. To view the code the users must first install MODGEN software (or can view code files in Visual Studio). All scenarios share the same parameters except the immigrant population - to change the immigration assumptions the users can import immigration assumptions for any other scenario from the ImmigDataBase.csv and change it in the immigration module using the MODGEN user interface or using Visual Studio.</p> <p><strong>The file structure and codebook for the data files is included in the cover note file "readme_quantmig_datasets.pdf"</strong></p> <p>Detailed <strong>information about the QuantMig-Mic microsimulation model, its modules and parameters</strong>:</p> <p>Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf</p> <p>Instructions how to install MODGEN can be found in:</p> <p>Marois, G. and Potančoková, M. (2022) QuantMig-mic microsimulation tool. QuantMig Project Deliverable D8.1. International Institute for Applied Systems Analysis (IIASA). http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.1%20v1.1.pdf </p> <p>Detailed <strong>information about the QuantMig migration scenarios</strong> can be found in:</p> <p>Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf</p> <p>A <strong>guide to the datasets</strong> and the codebook can be found in: <strong>readme_quantmig_datasets.pdf</strong></p> <p><strong>Countries included in the model: </strong></p> <p>Austria, Belgium, Bulgaria, Croatia, Czechia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom</p> <p> </p>
Data from 1564 earthquake/tsunami scenario simulations targeting the Nankai Trough subduction zone
<p><strong>Summary:</strong></p> <ul> <li>Data from 1564 earthquake/tsunami scenario simulations targeting the Nankai Trough subduction zone</li> <li>Tsunami simulation solver: TUNAMI-N2</li> <li>Fault rupture model: Okada model (Okada, 1985)</li> <li>Each scenario data comprises 247 ASCII files storing the simulated wave sequences at synthetic gauges.</li> <li>Every single scenario is tagged as "JNan_X.X_YYY", where X.X indicates the magnitude and YYY is a serial number.</li> <li>Some synthetic gauges are in identical locations to actual ocean gauges (e.g., DONET2, NOWPHAS) near Shikoku, Japan.</li> </ul> <p><strong>Details of each file:</strong></p> <ul> <li><strong>data.tar.gz:</strong> A series of wave sequences (6-hour wave historical data, recorded every 5 seconds) at the 247 virtual gauges. Note that 52 GB of additional storage would be required to fully unzip this archive file with the following command. <pre><code>tar xzvf data.tar.gz</code></pre> <p>The unzipped directory contains all scenario data as follows:</p> <pre><code>data ├── JNan_7.6_001 ├── JNan_7.6_002 ├── JNan_7.6_003 ├── ├── ├── JNan_8.8_326 ├── JNan_8.8_327 └── JNan_8.8_328</code></pre> <p>Each directory contains 247 files named 'pntX_YY.asc'.</p> <pre><code>JNan_X.X_YYY ├── pnt1_01.asc ├── pnt1_02.asc ├── pnt1_03.asc ├── ├── ├── pnt5_46.asc ├── pnt5_47.asc └── pnt5_48.asc</code></pre> <p>In each ASCII file, the time (minute) elapsed from the fault rupture is aligned in the left column, and the wave displacements (meter) from the original sea level are in the right column.</p> </li> <li> <p><strong>quake_params.csv:</strong> The parameter used to generate 1564 earthquake scenarios caused by the rupture of rectangular fault, by means of the Okada model (Okada 1985)</p> </li> <li> <p><strong>synthetic_gauges.csv:</strong> The locations of the 247 synthetic gauges</p> </li> </ul>
Simulated forest dynamics (2016-2100) for six future climate-fire scenarios and five representative landscapes in Greater Yellowstone, USA
We simulated fire (incorporating fuels feedbacks) and forest dynamics on five landscapes spanning the Greater Yellowstone Ecosystem (GYE) to ask: (1) How and where are forest landscapes likely to change with 21st-century warming and fire activity? (2) Are future forest changes gradual or abrupt, and do forest attributes change synchronously or sequentially? (3) Can forest declines be averted by mid-21st-century stabilization of atmospheric greenhouse gas (GHG) concentrations? We used the spatially explicit individual-based forest model iLand to track multiple attributes (forest extent, stand age, tree density, basal area, aboveground carbon stocks, dominant forest types, species occupancy) through 2100 for six climate scenarios. The five study landscapes are representative of dominant forest types and environmental gradients of the Northern Rockies; collectively, they encompass nearly 300,000 ha, of which 279,488 ha are potentially stockable with trees. This data set contains annual landscape-level output data for simulations to 2100 with 6 climate scenarios (3 general circulation models x 2 representative concentration pathways) x 5 landscapes x 20 iterations of simulated fires. We include the data and R scripts used for the analyses of abrupt change in the publication associated with these data; all other analyses used standard functions in R.
Central Baltic EwE scenario forcing
<p>Dataset describes forcing functions for climate and nutrient load scenarios simulated by Central Baltic EwE</p>
Country resolved combined emission and socio-economic pathways based on the RCP and SSP scenarios
<p><strong>Recommended citation</strong></p> <p>Article citation will be added once the article is available.</p> <p><strong>Content</strong></p> <ul> <li><a href="#use-of-the-dataset-and-full-description">Use of the dataset and full description</a></li> <li><a href="#abstract">Abstract</a></li> <li><a href="#support">Support</a></li> <li><a href="#files-included-in-the-dataset">Files included in the dataset</a></li> <li><a href="#notes">Notes</a></li> <li><a href="#data-format-description-columns">Data format description (columns)</a></li> <li><a href="#data-sources">Data sources</a></li> <li><a href="#changelog">Changelog</a></li> <li><a href="#references">References</a></li> </ul> <p><strong>Use of the dataset and full description</strong></p> <p>Before using the dataset, please read this document and the article describing the methodology, especially the "Discussion and limitations" section.</p> <p>The article will be referenced here as soon as it is published.</p> <p>Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.</p> <p>When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the RCP-SSP-dwn dataset. See the full citations in the References section further below.</p> <p><strong>Support</strong></p> <p>If you encounter possible errors or other things that should be noted or need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de.</p> <p><strong>Abstract</strong></p> <p>This dataset provides country scenarios, downscaled from the RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process harmonized to and combined with up to date historical data.</p> <p><strong>Files included in the dataset</strong></p> <p>The repository comprises several datasets. Each dataset comes in a csv file. The file name is constructed from dataset properties as follows: <Source><Bunkers><Downscaling>.csv</p> <p><em><Source></em></p> <p>The "Source" flag indicates which input scenarios were used.</p> <ul> <li><strong>PMRCP:</strong> RCP scenarios downscaled using the SSPs: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.</li> <li><strong>PMSSP:</strong> Downscaled SSP IAM scenarios: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.</li> </ul> <p><em><Bunkers></em></p> <p>the "Bunkers" flag indicates if the input emissions scenarios have been corrected for emissions from international shipping and aviation (bunkers) before downscaling to country level or not. The flag is "B" for scenarios where emissions from bunkers have been removed before downscaling and "" (no flag) where they have not been removed.</p> <p><em><Downscaling></em></p> <p>The "Downscaling" flag indicates the downscaling technique used.</p> <ul> <li><strong>IE:</strong> Convergence downscaling with exponential convergence of emissions intensities and convergence before transition to negative emissions.</li> <li><strong>IC:</strong> Regional emission intensity growth rates for all countries.</li> <li><strong>CS:</strong> Constant emission shares as a reference case independent of the socio-economic scenario.</li> </ul> <p>All files contain data for all countries and variables. For detailed methodology descriptions we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published.</p> <p>Finally the data description including detailed references is included: RCP-SSP-dwn_v1.0_data_description.pdf.</p> <p><strong>Notes</strong></p> <p>If you encounter problems with the size of the csv files please let us know, so we can find solutions for future releases of the data.</p> <p><strong>Data format description (columns)</strong></p> <p><em>"source"</em></p> <p>For <em>PMRCP</em> files source values are</p> <ul> <li>RCPSSP<Bunkers><Downscaling>: unharmonized downscaled RCP SSP scenarios</li> <li>PMRCP<Bunkers><Downscaling>: downscaled RCP SSP scenarios harmonized to and combined with historical data</li> <li>PMRCPMISC<Bunkers><Downscaling>: GDP and population data harmonized to and combined with historical data</li> </ul> <p>For <em>PMSSP</em> files source values are</p> <ul> <li>SSPIAM<Bunkers><Downscaling>: unharmonized downscaled SSP IAM scenarios</li> <li>PMSSP<Bunkers><Downscaling>: downscaled SSP IAM scenarios harmonized to and combined with historical data</li> <li>PMSSPMISC<Bunkers><Downscaling>: GDP and population data harmonized to and combined with historical data</li> </ul> <p>For possible values of <Bunkers> and <Downscaling> please see section <a href="#files-included-in-the-dataset">Files included in the dataset</a> above.</p> <p><em>"scenario"</em></p> <p>For <em>PMRCP</em> files the scenarios have the format <RCP><SSP><group>, where</p> <ul> <li><RCP> denotes the RCP scenario. Values are RCP3PD, RCP45, RCP6, and RCP85.</li> <li><SSP> denotes the SSP scenario. Values are SSP1, SSP2, SSP3, SSP4, and SSP5.</li> <li><groups> denotes the SSP basic elements GDP modeling group. Values are IIASA, OECD, and PIK. Not all RCP SSP combinations exist as some SSP storylines are not compatible with all RCP emissions scenarios. For details we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published.</li> </ul> <p>For <em>PMSSP</em> files the scenarios have the format <SSP><forcing><model> where</p> <ul> <li><SSP> denotes the SSP scenario. Values are SSP1, SSP2, SSP3, SSP4, and SSP5.</li> <li><forcing> denotes the radiative forcing level of the scenario. Values are 19, 26, 34, 45, 60, 85, and BL where 19 stands for 1.9W/m<sup>2</sup> etc. and BL stands for baseline.</li> <li><model> denotes the Integrated Assessment Model (IAM) used to generate the scenario. Values can be found below</li> </ul> <p>Model codes in scenario names</p> <ul> <li>AIMCGE: AIM-CGE</li> <li>IMAGE: IMAGE</li> <li>GCAM4: GCAM</li> <li>MESGB: MESSAGE-GLOBIOM</li> <li>REMMP: REMIND-MAGPIE</li> <li>WITGB: WITCH-GLOBIOM</li> </ul> <p><em>"country"</em></p> <p>ISO 3166 three-letter country codes or custom codes for groups:</p> <p>Additional "country" codes for country groups.</p> <ul> <li>EARTH: Aggregated emissions for all countries</li> <li>ANNEXI: Annex I Parties to the UNFCCC</li> <li>NONANNEXI: Non-Annex I Parties to the UNFCCC</li> <li>AOSIS: Alliance of Small Island States</li> <li>BASIC: BASIC countries (Brazil, South Africa, India and China)</li> <li>EU28: European Union (still including the UK)</li> <li>LDC: Least Developed Countries</li> <li>UMBRELLA: Umbrella Group</li> </ul> <p><em>"category"</em></p> <p>Category descriptions.</p> <ul> <li>IPCM0EL: Emissions: National Total excluding LULUCF</li> <li>ECO: Economical data</li> <li>DEMOGR: Demographical data</li> </ul> <p><em>"entity"</em></p> <p>Gases and gas baskets using global warming potentials (GWP) from either Second Assessment Report (SAR) or Fourth Assessment Report (AR4).</p> <p>Gases / gas baskets and underlying global warming potentials</p> <ul> <li>CH4: Methane (CH<sub>4</sub>)</li> <li>CO2: Carbon Dioxide (CO<sub>2</sub>)</li> <li>N2O: Nitrous Oxide (N<sub>2</sub>O)</li> <li>FGASES: Fluorinated Gases (SAR): HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub></li> <li>FGASESAR4: Fluorinated Gases (AR4): HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub></li> <li>KYOTOGHG: Kyoto greenhouse gases (SAR)</li> <li>KYOTOGHGAR4: Kyoto greenhouse gases (AR4)</li> </ul> <p><em>"unit"</em></p> <p>The following units are used:</p> <ul> <li>Million2011GKD: Million 2011 international dollars</li> <li>ThousandPers: Thousand persons</li> <li>kt: kilotonnes</li> <li>Mt: Megatonnes</li> <li>Gg: Gigagrams</li> <li>MtCO2eq: Megatonnes of CO<sub>2</sub> equivalents using the GWPs defined by "entity"</li> <li>GgCO2eq: Gigagrams of CO<sub>2</sub> equivalents using the GWPs defined by "entity"</li> </ul> <p><em>Remaining columns</em></p> <p>Years from 1850-2100.</p> <p><strong>Data Sources</strong></p> <p>The following data sources were used during the generation of this dataset:</p> <p><em>Scenario data</em></p> <ul> <li><strong>RCP scenarios</strong> <a href="https://tntcat.iiasa.ac.at/RcpDb/">website/data</a></li> <li><strong>SSP basic elements</strong> <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> <li><strong>SSP IAM scenarios</strong> <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> <li><strong>SSP CMIP6 scenarios</strong> <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> </ul> <p><em>Historical data</em></p> <ul> <li><strong>CDIAC</strong> <a href="http://doi.org/10.3334/CDIAC/00001_V2017">data</a></li> <li><strong>CEDS CMIP6 data</strong> <a href="https://www.geosci-model-dev.net/11/369/2018/">paper/data</a></li> <li><strong>EDGAR version 4.3.2:</strong> <a href="http://doi.org/10.2904/JRC_DATASET_EDGAR">data</a>, <a href="https://doi.org/10.5194/essd-2017-79">paper</a></li> <li><strong>IMO GHG report</strong> <a href="http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution/Documents/Third%20Greenhouse%20Gas%20Study/GHG3%20Executive%20Summary%20and%20Report.pdf">report</a></li> <li><strong>PRIMAP-hist v2.1</strong> <a href="http://www.earth-syst-sci-data.net/8/571/2016/">paper</a>, <a href="https://www.pik-potsdam.de/primap-live/primap-hist/">website</a>, <a href="https://doi.org/10.5880/PIK.2019.018">data</a></li> <li><strong>PRIMAP-hist SocioEco v2.1</strong> <a href="https://doi.org/10.5880/PIK.2019.019">data</a></li> </ul> <p><strong>Changelog</strong></p> <p>For future versions</p> <p><strong>References</strong></p> <p>For full references we refer to the pdf version of the data description available in this repository and the list of related identifiers.</p>
CanESM5 data for CCCma COVID-19 climate scenarios
<p>This data is associated with the publication:<br> <br> <strong> Quantifying the Influence of COVID-19 Emission Reductions on Climate</strong></p> <p><br> John C. Fyfe, Viatcheslav V. Kharin, Neil Swart, Gregory M. Flato, Michael<br> Sigmond and Nathan Gillett<br> <br> Canadian Centre for Climate Modelling and Analysis, Environment and Climate<br> Change Canada, Victoria, British Columbia, V8W 2Y2, Canada.<br> <br> The data includes the monthly CO2 emissions used to drive CanESM5, and also the<br> monthly CO2 concentrations, and Global Mean Screen Temperatures resulting from<br> the model simulations. Using this data, Figure 1 of the paper can be completely<br> reproduced.</p> <p> </p> <p>The organisation of the data is described in the readme.txt file. All contents are</p> <p>collected into a tar archive.<br> <br> </p>
ERCOT Reservoir Watershed Delineations and Inflow Scenarios
<p>This dataset includes daily runoff totals for historical (reanalysis) and future (GCM simulations) climate. The scenarios are generated for upstream watersheds of 44 reservoirs that supply water for thermal plant cooling throughout the Electric Reliability Council of Texas (ERCOT) power grid region. These data are derived from 1/24 degree gridded CONUS-scale hydrological simulations. Runoff projections are masked to delineated watershed areas (included as shape files) and summed across grid cells within the target watershed. These data may be aggregated to monthly scale to produce unregulated inflow scenarios for ERCOT reservoirs. Reservoir attributes and storage records are included in a supplementary excel spreadsheet. Please see README.txt for further details.</p>
Water availability and temperature scenarios for water-dependent power plants in the Danube river basin and the Iberian Peninsula
<p>The dataset is composed by 12 files reporting the water availability and temperature scenarios for 167 water-dependent power plants in the Danube river basin and the Iberian Peninsula.</p> <p>The dataset is split into multiple files by region (Danube river basin (Danube) or Iberian Peninsula (IP)), variable (discharge or river temperature) and scenario (baseline, RCP26 or RCP85) considered.</p> <p>The title of each file is composed by the variable reported (discharge or river temperature) and the scenario considered (baseline: 1951-2004, RCP26: 2006-2100, RCP85: 2006-2100). The first row is used to report the fields considered: the first three columns report the day, the month and the year. The remaining columns report the name of the power plant considered in each region (57 for the Daube river basin and 110 for the Iberian Peninsula). In each row day, month, year and streamflow or river temperature values are reported for every water-dependent power plant examined in the study.</p> <p>Temperature is reported as daily average temperature in degrees Celsius (°C) while water availability is reported as daily average streamflow in cubic meters per second (m^3/s).</p> <p>For a description on how these files were obtained, please refer to <a href="https://doi.org/10.2777/135510">https://doi.org/10.2777/135510</a>.</p>
Heat scenarios over Stockholm
<p>A number of heat scenarios studying how green infrastructure would affect the future climate of Stockholm. The dataset consist of a number of scenarios which simulates how building plans could affect heat exposure in Stockholm. The scenarios are:</p> <ul> <li>Stockholm 2014: Baseline scenario simulating the heatwave during the summer of 2014 in Stockholm</li> <li>Stockholm 2030: Simulating the effects of the heatwave 2014 with new building according to plans for city expansion 2030.</li> <li>Stockholm 2050: Simulating the effects of the heatwave 2014 with new buildings according to available plans for city expansion 2050.</li> <li>Grey Scenario: Simulating the effects of the heatwave 2014 in a city where green infrastructure has been minimized.</li> </ul>
Projected Global Area Equipped for Irrigation Datasets during 2020-2100 under SSP scenarios
<h1><strong>1. Background</strong></h1> <p>Accurately predicting the global area equipped for irrigation in the future is crucial for providing essential datasets relevant to fields such as earth system simulation, agricultural water resource management, climate change adaptation, and environmental conservation. However, the predictive datasets of the area equipped for irrigation are still lacking. To address this gap, we provide the <strong>Projected Global Area Equipped for Irrigation Datasets (PGAEID)</strong>, which provide spatially explicit estimates of Area Equipped for Irrigation (AEI) from 2020 to 2100 under three Shared Socioeconomic Pathway (SSP) scenarios: <strong>SSP1</strong> (sustainable development), <strong>SSP2</strong> (intermediate development), and <strong>SSP3</strong> (regional rivalry), <strong>SSP4</strong> (unequal development), and <strong>SSP5</strong> (fossil-fueled development).</p> <h1><strong>2. Methodology</strong></h1> <h3><strong>2.1 Ensemble Machine Learning (EML) Framework</strong></h3> <ul> <li><strong>Algorithms</strong>: Integrated six machine learning models: <ul> <li>Multiple Linear Regression (MLR)</li> <li>Decision Trees (DT)</li> <li>Autoregressive Integrated Moving Average (ARIMA)</li> <li>Multi-Layer Perceptron (MLP)</li> <li>Radial Basis Function (RBF)</li> <li>Random Forests (RF)</li> </ul> </li> <li><strong>Training Data</strong>: Historical national irrigation records (FAO AQUASTAT, 1961–2015).</li> <li><strong>Validation Metrics</strong>: <ul> <li>Nash-Sutcliffe Efficiency (NSE): <strong>0.9</strong><strong>8</strong></li> <li>Kling-Gupta Efficiency (KGE): <strong>0.</strong><strong>97</strong></li> <li>Mean Absolute Percentage Error (MAPE): <strong>1</strong><strong>.</strong><strong>7%</strong></li> </ul> </li> </ul> <h3><strong>2.2 Spatial Downscaling</strong></h3> <ul> <li><strong>Baseline</strong>: FAO 2005 irrigation data combined with GMIA2005 gridded agricultural intensity maps.</li> <li><strong>Dynamic Projection</strong>: Annual change rates applied to 5′ × 5′ grids under SSP-specific socioeconomic drivers.</li> </ul> <h1><strong>3. Dataset Overview</strong></h1> <h3><strong>3.1 Key Features</strong></h3> <ul> <li><strong>Temporal Coverage</strong>: 2020–2100 (10-year intervals).</li> <li><strong>Spatial Resolution</strong>: 5-arcminute (≈10 km at the equator).</li> <li><strong>Scenarios</strong>: SSP1, SSP2, SSP3, SSP4, SSP5.</li> <li><strong>Variables</strong>: area equipped for irrigation (10<sup>3</sup> ha/year).</li> </ul> <h3><strong>3.2 Dataset Structure</strong></h3> <p>The dataset is provided as a compressed archive (PGAEID_Ver3.0.rar), containing:</p> <p>1.<strong>Global_Area_Equipped_for_Irrigation_GeoTiff</strong><strong>/</strong></p> <ul> <li><strong>Subfolders</strong>: <ul> <li>SSP1</li> <li>SSP2</li> <li>SSP3</li> <li>SSP4</li> <li>SSP5</li> </ul> </li> <li><strong>File Format</strong>: GeoTIFF (45 files total).</li> <li><strong>Naming Convention</strong>:<br>AEI_[SSP]_[Year].tif <ul> <li>Example: AEI_SSP1_2020.tif</li> </ul> </li> </ul> <p>2. <strong>National & Regional_AEI</strong><strong>/</strong></p> <ul> <li><strong>Shapefiles</strong>: National/regional area equipped for irrigation for 26 prediction units (2020–2100).</li> <li><strong>Excel File</strong>: Global Area Equipped for Irrigation (2020-2100).xlsx.</li> </ul> <p>3. <strong>Technical Annex.docx</strong></p> <ul> <li>Detailed methodology, validation, and workflow documentation.</li> </ul> <h1><strong>4. Applications</strong></h1> <p>This dataset supports:</p> <ul> <li><strong>Earth System Simulation: </strong>Supporting irrigation parameterization in global climate and hydrological models.</li> <li><strong>Water Resource Management: </strong>Assisting decision-makers in sustainable irrigation planning.</li> <li><strong>Climate Change Adaptation: </strong>Providing insights into how irrigation practices evolve under different socioeconomic pathways.</li> <li><strong>Environmental Conservation: </strong>Assessing the impact of irrigation on regional ecosystems.</li> </ul> <p><strong>Note:</strong></p> <p>Global aggregated totals of area equipped for irrigation derived from the 26 prediction units (country/regional scale) may exhibit minor discrepancies compared to sums calculated from the 5-arcminute gridded data (≈10 km resolution). Such differences stem from variations in spatial aggregation methods, file formats (vector vs. raster), and underlying data processing frameworks. Users may select the dataset best aligned with their analytical objectives:</p> <ul> <li>The <strong>country/region-based data (26 units)</strong> is recommended for national-scale analyses or policy evaluations requiring administrative boundaries.</li> <li>The <strong>5-arcminute gridded data</strong> is preferable for spatially explicit modeling or subnational assessments.</li> </ul> <p>Both datasets maintain equivalent quality and methodological rigor; the choice depends on the desired spatial granularity and application context.</p>
Projected Global Fertilizers Consumption Datasets during 2020-2100 under SSP scenarios
<h1><strong>1. Background</strong></h1> <p>Accurate projections of future global fertilizer consumption are critical for advancing research in earth system modeling, agricultural sustainability, and fertilizer industry planning. However, existing datasets often lack long-term temporal coverage and high spatial resolution. To address this gap, we present the <strong>Projected Global Fertilizers Consumption Datasets (PGFCD)</strong>, which provide spatially explicit estimates of nitrogen (N), phosphorus (P), and potassium (K) fertilizer consumption from 2020 to 2100 under three Shared Socioeconomic Pathway (SSP) scenarios: <strong>SSP1</strong> (sustainable development), <strong>SSP2</strong> (intermediate development), and <strong>SSP3</strong> (regional rivalry), <strong>SSP4</strong> (unequal development), and <strong>SSP5</strong> (fossil-fueled development).</p> <p> </p> <h1><strong>2. Methodology</strong></h1> <h2><strong>2.1 Ensemble Machine Learning (EML) Framework</strong></h2> <ul> <li><strong>Algorithms</strong>: Integrated six machine learning models: <ul> <li>Multiple Linear Regression (MLR)</li> <li>Decision Trees (DT)</li> <li>Autoregressive Integrated Moving Average (ARIMA)</li> <li>Multi-Layer Perceptron (MLP)</li> <li>Radial Basis Function (RBF)</li> <li>Random Forests (RF)</li> </ul> </li> <li><strong>Training Data</strong>: Historical national/regional fertilizer consumption (FAOSTAT, 1961–2015).</li> <li><strong>Validation Metrics</strong>: <ul> <li>Nash-Sutcliffe Efficiency (NSE): <strong>0.93</strong></li> <li>Kling-Gupta Efficiency (KGE): <strong>0.89</strong></li> <li>Mean Absolute Percentage Error (MAPE): <strong>10.97%</strong></li> </ul> </li> </ul> <h2><strong>2.2 Spatial Downscaling</strong></h2> <ul> <li><strong>Baseline</strong>: FAO 2000 fertilizer data combined with gridded nutrient application maps for major crops in 2000.</li> <li><strong>Dynamic Projection</strong>: Annual change rates applied to 5′ × 5′ grids under SSP-specific socioeconomic drivers.</li> </ul> <p> </p> <h1><strong>3. Dataset Overview</strong></h1> <h2><strong>3.1 Key Features</strong></h2> <ul> <li><strong>Temporal Coverage</strong>: 2020–2100 (10-year intervals).</li> <li><strong>Spatial Resolution</strong>: 5-arcminute (≈10 km at the equator).</li> <li><strong>Scenarios</strong>: SSP1, SSP2, SSP3, SSP4, SSP5. </li> <li><strong>Variables</strong>: N, P, and K fertilizer consumption (tonnes/year).</li> </ul> <h2><strong>3.2 Dataset Structure</strong></h2> <p>The dataset is provided as a compressed archive (PGFCD_Ver6.0.rar), containing:</p> <p><strong>1. Fertilization_Consumption_GeoTiff</strong><strong>/</strong></p> <ul> <li><strong>Subfolders</strong>: <ul> <li>N_fer/: Nitrogen fertilizer projections</li> <li>P_fer/: Phosphorus fertilizer projections</li> <li>K_fer/: Potassium fertilizer projections</li> </ul> </li> <li><strong>File Format</strong>: GeoTIFF (135 files total). <ul> <li><strong>Naming Convention</strong>:<br>[FertilizerType]_fer_con_[SSP]_[Year].tif <ul> <li>Example: K_fer_con_SSP1_2020.tif</li> </ul> </li> </ul> </li> </ul> <p><strong>2. Country_region_based/</strong></p> <ul> <li><strong>Shapefiles</strong>: National/regional fertilizer consumption for 26 prediction units (2020–2100).</li> <li><strong>Excel File</strong>: Global Fertilizer Consumption (2020-2100).xlsx.</li> </ul> <p><strong>3. Technical Annex.docx</strong></p> <ul> <li>Detailed methodology, validation, and workflow documentation.</li> </ul> <p> </p> <h1><strong>4. Applications</strong></h1> <p>This dataset supports:</p> <ul> <li><strong>Earth System Modeling</strong>: Improved parameterization of fertilization impacts on biogeochemical cycles.</li> <li><strong>Agricultural Policy</strong>: Scenario-based planning for sustainable fertilizer use.</li> <li><strong>Industry Strategy</strong>: Long-term market analysis under diverse socioeconomic pathways.</li> </ul> <p> </p> <h1><strong>Note:</strong></h1> <p>Global aggregated totals of fertilizer consumption derived from the 26 prediction units (country/regional scale) may exhibit minor discrepancies compared to sums calculated from the 5-arcminute gridded data (≈10 km resolution). Such differences stem from variations in spatial aggregation methods, file formats (vector vs. raster), and underlying data processing frameworks. Users may select the dataset best aligned with their analytical objectives:</p> <ul> <li>The <strong>country/region-based data (26 units)</strong> is recommended for national-scale analyses or policy evaluations requiring administrative boundaries.</li> <li>The <strong>5-arcminute gridded data</strong> is preferable for spatially explicit modeling or subnational assessments.</li> </ul> <p>Both datasets maintain equivalent quality and methodological rigor; the choice depends on the desired spatial granularity and application context.</p> <p>We are profoundly indebted to <strong>Dr. Andreas Gericke</strong> at Section II 2.3 Protection of the Seas and Polar Regions, German Environment Agency, and <strong>Dr. Veronika Schlosser</strong> at Chair of Sustainability Assessment of Food and Agricultural Systems, Technical University of Munich for their diligent review and insightful feedback on the previously submitted data. Their expertise has enabled us to thoroughly correct the identified inaccuracies, strengthening the integrity of our research.</p> <p>We hold <strong>Dr. Andreas Gericke</strong> and <strong>Dr. Veronika Schlosser</strong> in the highest esteem and sincerely apologize for any oversights that may have marred our work.</p>
ECEMF Diagnostic Scenarios, version 2.0
<p>This dataset compiles diagnostic scenarios from several Integrated Assessment Models (IAM) and Energy System Models (ESM) to facilitate systematic comparison of a broad range of results across these models.</p> <p>These diagnostic scenarios were developed in the Horizon 2020 project ECEMF (https://ecemf.eu).</p> <p>Visit the ECEMF Scenario Explorer hosted by IIASA at https://ecemf.apps.ece.iiasa.ac.at/ for more information and interactive user interface to work with the scenario data.</p>
SCENARIOS vermichart 2023
<p>Diagramas and draws done by Juan C. Sanchez-Hernandez from University of Castilla-La Mancha explaining vermicomposting.</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.