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.
80
datasets available to search
ShareScore release 0.9.0
Dataset results
80 results for “RCP”
Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species
<p>Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.</p>
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial 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
<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>
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>
Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations)
<p>This is the outcome data from our study titled "<a href="http://dx.doi.org/10.1016/j.scitotenv.2024.170481" target="_blank" rel="noopener"><em><u>Prediction of global wheat cultivation distribution under climate change and socioeconomic development</u></em></a>" which was published in The Science of The Total Environment. The present study represents a significant extension of our previous research on "<em><a href="http://dx.doi.org/10.1016/j.scitotenv.2019.06.153" target="_blank" rel="noopener">The Potential Distribution and Dynamics of Global Wheat under Multiple Climate Change Scenarios"</a></em>.</p> <p>Socioeconomic and climate change are both critical factors influencing the global distribution of crop cultivation. However, there has been limited exploration of the role of socioeconomic factors in predicting future crop cultivation distribution under climate change.</p> <p>We have proposed the MaxEnt-SPAM approach under the assumption that environmental conditions are the primary determinants of land suitability for cultivating wheat, while socioeconomic factors play a crucial role in influencing farmers' crop choices. In essence, the distribution of wheat cultivation is contingent upon maximizing potential revenue and ensuring suitability for wheat planting.</p> <p>The proposed MaxEnt-SPAM approach was utilized to estimate the distribution of wheat cultivation in three combined Representative Concentration Pathway (RCP) - Shared Socioeconomic Pathway (SSP) scenarios, namely RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3. The methodology involved estimating wheat planting suitability under future RCP scenarios using the MaxEnt model, predicting farmers' crop choices under future SSP scenarios through Time series-Backpropagation (TS-BP) models, and ultimately estimating global wheat cultivation distribution based on the SPAM model. Validation of this approach against major known datasets on the distribution of wheat cultivation demonstrated satisfactory accuracy, with a predictive accuracy exceeding 85% and a significant positive correlation (p < 0.01) between the predicted global wheat cultivation and multiple known datasets.</p> <p>Based on the aforementioned concept and methodology, a global wheat cultivation distribution grid (0.5 degree × 0.5 degree) was projected under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios.</p> <p>The findings suggest that RCP8.5-SSP3 may offer the most favorable conditions for wheat cultivation. Additionally, socioeconomic development significantly constrains the potential distribution of wheat cultivation, with estimated areas accounting for an average of 77% of the potential distribution determined by climatic factors under the selected RCP-SSP scenarios. Socioeconomic development appears to have a positive impact on wheat cultivation in Africa.</p> <p>Our results illustrate the influence of socioeconomic factors on crop distribution within a market economy framework, underscoring the importance of integrating socioeconomic factors and climate change for accurate predictions of crop cultivation distribution.</p> <p>We contend that the global wheat cultivation distribution datasets under future climatic and socio-economic conditions (RCP-SSP combinations) are a valuable addition to existing products. This prediction data is among the few products to consider both climate change and socio-economic development, providing a more comprehensive understanding of crop cultivation distribution dynamics.</p> <p>The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) is expected to enhance our comprehension of the dynamics and distribution of global wheat cultivation under different climate change and socio-economic development paths in the future, potentially supporting research in earth system simulation and agricultural sciences.</p> <p>The dataset for the Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) and the Maxent-SPAM approach code is stored in a zip package named SPAM_MaxEnt.zip, which contains two folders (code and data).</p> <p><strong>code: </strong></p> <p>This sub-folder provides the main program and example data for the MaxEnt-SPAM approach. Codes are written in Matlab language by Puying Zhang. There are also 'read me.txt' files under the code folder to provide the necessary information.</p> <p>The exampleData contains</p> <p>1. h_pri.tif: prior data</p> <p>2. h_res.tif: global C3 crop cultivation proportion</p> <p>Run the main programme: cross_entroy.m</p> <p><strong>data: </strong></p> <p>This sub-folder contains global wheat cultivation distribution stored in GeoTIFF file format.</p> <p><strong>1 Global distribution of the long-term wheat-</strong><strong>c</strong><strong>ultivation area fraction: </strong></p> <p>This sub-folder contains the data for the global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The value of each data ranges from 0 to 1, indicating the long-term wheat-cultivation area fraction in each grid, and the higher the value, the more wheat cultivated.</p> <p><strong>r2s1f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1 scenario</p> <p><strong>r4s2f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP4.5-SSP2 scenario</p> <p><strong>r8s3f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP8.5-SSP3 scenario</p> <p><strong>2 </strong><strong>S</strong><strong>patial overlap between the long-term period of land suitability for wheat </strong><strong>planting </strong><strong>and wheat cultivation distribution: </strong></p> <p>This sub-folder contains the data for Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution in multi-scenarios. The value of each data contains three values:<strong>{1, 2, 3}</strong>, <strong>1</strong> wheat cultivation existed but was predicted to be unsuitable to plant wheat; <strong>2 </strong>presented a reduction in the wheat cultivation area compared to the land's suitability; <strong>3</strong> presented the region that wheat cultivation existed and was predicted to be suitable to plant wheat.</p> <p><strong>com_suit_fra126.tif: </strong>the spatial overlap between the long-term period land suitability for wheat planting and wheat cultivation distribution in (a) RCP2.6-SSP1 scenario and RCP2.6</p> <p><strong>com_suit_fra245.tif: </strong>the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (b) RCP4.5-SSP2 scenario and RCP4.5</p> <p><strong>com_suit_fra385.tif:</strong> the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (c) RCP8.5-SSP3 scenario and RCP8.5</p> <p><strong>3 Differences in the proportion of long-term wheat </strong><strong>c</strong><strong>ultivation: </strong></p> <p>This sub-folder contains the data for the difference in the proportion of long-term wheat cultivation under the RCP-SSP scenarios and the distribution of long-term wheat planting suitability under the same RCP scenarios. The value of each data ranges from -1 to 1, This data is obtained by using the wheat-cultivation area fraction minus planting suitability grid to grid. the negative value indicates that the proportion of wheat cultivation is lower than the wheat planting suitability, while this positive value indicates that the proportion of wheat cultivation is higher than the wheat planting suitability.</p> <p><strong>r2s1_f.tif:</strong> Difference in the proportion of long-term wheat cultivation under the RCP2.6-SSP1 scenario and the distribution of long-term wheat planting suitability under the RCP2.6 scenario</p> <p><strong>r4s2_f.tif:</strong> Differences between the proportion of long-term wheat cultivation in RCP4.5-SSP2 and the suitability of long-term wheat planting under the RCP4.5 scenario</p> <p><strong>r8s3_f.tif:</strong> Differences between the proportion of long-term wheat cultivation in RCP8.5-SSP3 and the suitability of long-term wheat planting under the RCP8.5 scenario </p> <p><strong>References:</strong></p> <p>Yaojie Yue, Puying Zhang, Yanrui Shang. The Potential Distribution and Dynamic of Global Wheat under Multiple Climate Change Scenarios. Science of the Total Environment, 2019, 688: 1308-1318.</p> <p>Xi Guo, Puying Zhang, Yaojie Yue. Prediction of global wheat cultivation distribution under climate change and socio-economic development. Science of the Total Environment, 2024, 919: 170481.</p> <p>For more details on the MaxEnt (Maximum entropy) model, please refer to (Phillips et al., 2006; Elith et al., 2011). SPAM (spatial production allocation model) refers to (You et al., 2009; You et al., 2014).</p> <p>Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of maxent for ecologists. Divers Distrib 17 (1), 43-57. https://coi.org/10.1111/j.1472-4642.2010.00725.x.</p> <p>Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190 (3-4), 231-259. https://coi.org/10.1016/j.ecolmodel.2005.03.026.</p> <p>You, L.Z., Wood, S., Wood-Sichra, U., 2009. Generating plausible crop distribution maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agr Syst 99 (2-3), 126-140. https://coi.org/10.1016/j.agsy.2008.11.003.</p> <p>You, L.Z., Wood, S., Wood-Sichra, U., Wu, W.B., 2014. Generating global crop distribution maps: from census to grid. Agr Syst 127, 53-60. https://coi.org/10.1016/j.agsy.2014.01.002</p>
Time projections of Sea Surface Temperature, for RCP 4.5 and RCP 8.5, for decades 2020, 2030, 2040 and 2040
<p>The CMIP5 Sea Surface Temperature models projections correspond to the Coupled atmosphere-ocean general circulation models’ output named ‘tos’ (Temperature Of Surface) with a monthly time-step (12 values per year, from 2006 to 2100), for RCP 4.5 and RCP 8.5. For a given RCP, some models can have different sets of input parameters (called input ensemble), numbered r1i1p1, r1i1p2, etc., corresponding to different settings, resulting is an output for each rXiYpZ input. Variable ‘tos’ is provided by 86 combinations of models and input ensembles (see list in Annex). To compute an ensemble mean with equal weight for each model, the different outputs of a single model are first averaged. The resulting averaged models outputs, 1 average per model, are then regridded to a common grid, defined as a regular grid, with a spatial resolution of ½ ° in latitude per ½ ° in longitude, from 0° to 360° in longitude, and -85° to 85° in latitude. Then, the regridded averages are averaged all together with the same weight.</p> <p>The averaging operations are grid-cell and time independent, which means that the averaging operator is not applied along the space and time dimensions, only in-between the different models values for the same place and time.</p> <p><br /> The result of the operation is a time series of ocean surface temperature, from 2020 to 2050, at a grid resolution of 0.5°. Because of the difference in the spatial gridding, and difference in the land mass representation, some grid points did not used the same number of models averages to compute the final average: the number of model averages per grid cell is given in the final product, as well as the min-max amplitude between model averages.</p>
Morphed extreme weather data for Vantaa and Sodankylä under RCP climate change scenarios by 2030, 2050 and 2080
<p>Morphed extreme weather data for 2 Finnish locations: Vantaa and Sodankylä. Created for "Near-, medium- and long-term impacts of climate change on the thermal energy consumption of buildings in Finland under RCP climate scenarios" publication (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.energy.2024.131636</span></a>). Used climate change scenarios are RPC2.6, RCP4.5 and RCP8.5. Data is created for 2030, 2050 and 2080 and includes 6 extreme weather scenarios: </p> <ul> <li>W1 - Winter with high heating demand</li> <li>W2 - Winter with low heating demand</li> <li>W3 - Winter with the coldest individual day by average temperature</li> <li>S1 - Summer with the lowest cooling demand</li> <li>S2 - Summer with the highest heating demand</li> <li>S3 - Summer with the warmest individual day by average temperature</li> </ul> <p>Selected years and the procedure for their selection are described in <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.energy.2024.131636</span></a>.</p> <p>Original weather data is downloaded for the selected years from Finnish Meteorological Institute's Open data repository: https://www.ilmatieteenlaitos.fi/havaintojen-lataus under CC BY 4.0 licence.</p> <p>Future change in climate is based on Finnish Meteorological Institute's data used in creating Test Reference Year weather files (<a href="https://www.ilmatieteenlaitos.fi/energialaskenta-try2020">https://www.ilmatieteenlaitos.fi/energialaskenta-try2020</a>) for which the climate change data is presented by Ruosteenoja et al. (2016).</p> <p>The data is statistically downscaled through a method called morphing created by Belcher et al. (2005) with some parts using methods from Räisänen & Räty (2013) and Jylhä et al, (2015). Morphing was computationally conducted through created software <a href="https://github.com/japulk/Weather-Morphing-Tool">https://github.com/japulk/Weather-Morphing-Tool</a> For additional information please refer to <a href="https://doi.org/10.1016/j.energy.2024.131636">original article</a> or contact the authors.</p> <p> </p>
Necessitats hídriques netes de la vinya a la D.O. Empordà (Alt Empordà) a llarg del segle XXI a l'escenari de canvi climàtic RCP 8.5 (format TIFF)
<p>Distribució espacial del valor mitjà de les necessitats hídriques netes de la vinya (dia de l’any) a la D.O. Alt Empordà (Alt Empordà) per l’escenari de canvi climàtic intensiu RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Sint-Katelijne-Waver, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Uccle KMI, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N, 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven City centre, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Leuven City Centre (50°52'48"N 4°42'0" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven Casa Blanca, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Casa Blanca neighbourhood Leuven (50°52'48"N, 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Nombre de nits tropicals a la fase de maduració de la vinya al territori de la D.O. Penedès al llarg del segle XXI a l'escenari de canvi climàtic RCP 4.5 (format TIFF)
<p>Distribució espacial del valor mitjà del nombre de nits tropicals a la fase de maduració de la vinya al territori de la D.O. Penedès per l’escenari de canvi climàtic moderat RCP 4.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005. </p>
Nombre de nits tropicals a la fase de maduració de la vinya al territori de la D.O. Penedès al llarg del segle XXI a l'escenari de canvi climàtic RCP 8.5 (format TIFF)
<p>Distribució espacial del valor mitjà del nombre de nits tropicals a la fase de maduració de la vinya al territori de la D.O. Penedès per l’escenari de canvi climàtic moderat RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005. </p> <p> </p>
Necessitats hídriques netes de la vinya al territori de la D.O. Penedès al llarg del segle XXI a l'escenari de canvi climàtic RCP 8.5 (format TIFF)
<p>Distribució espacial del valor mitjà de les necessitats hídriques netes de la vinya al territori de la D.O. Penedès per l’escenari de canvi climàtic moderat RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005. </p>
Necessitats hídriques netes de la vinya al territori de la D.O. Penedès al llarg del segle XXI a l'escenari de canvi climàtic RCP 4.5 (format TIFF)
<p>Distribució espacial del valor mitjà de les necessitats hídriques netes de la vinya al territori de la D.O. Penedès per l’escenari de canvi climàtic moderat RCP 4.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005. </p>
Data de verema de la vinya al territori de la D.O. Penedès al llarg del segle XXI a l'escenari de canvi climàtic RCP 8.5 (format TIFF)
<p>Distribució espacial del valor mitjà de la data de verema de la vinya al territori de la D.O. Penedès per l’escenari de canvi climàtic moderat RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005. </p>
Data de floració de la vinya al territori de la D.O. Penedès al llarg del segle XXI a l'escenari de canvi climàtic RCP 8.5 (format TIFF)
<p>Distribució espacial del valor mitjà de la data de floració de la vinya al territori de la D.O. Penedès per l’escenari de canvi climàtic moderat RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005. </p>
Data de verema de la vinya al territori de la D.O. Penedès al llarg del segle XXI a l'escenari de canvi climàtic RCP 4.5 (format TIFF)
<p>Distribució espacial del valor mitjà de la data de verema de la vinya al territori de la D.O. Penedès per l’escenari de canvi climàtic moderat RCP 4.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005. </p>
Data de floració de la vinya al territori de la D.O. Penedès al llarg del segle XXI a l'escenari de canvi climàtic RCP 4.5 (format TIFF)
<p>Distribució espacial del valor mitjà de la data de floració de la vinya al territori de la D.O. Penedès per l’escenari de canvi climàtic moderat RCP 4.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005. </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.