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90 results for “Scenario based”
A large ensemble of CMIP6-based transient climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments often require a large ensemble of local-scale transient climate scenarios. Each ensemble member represents plausible long weather series at a local scale. The climate projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. Moreover, very few projections are usually available for each GCM due to a high computational cost. An alternative approach involves employing a stochastic weather generator to produce a large number of transient scenarios based on the climate projections from GCMs. In a current dataset, transient climate scenarios were generated using the LARS-WG weather generator, based on climate projections from GCMs from the CMIP6 ensemble across 26 representative sites throughout the UK. Each transient scenario spans the period from 2020 to 2090. At each site, 100 transient scenarios were generated for two emission scenarios (SSP2-4.5 and SSP5-8.5) and five selected GCMs from CMIP6 (ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR, and MRI-ESM2-0). The choice of GCMs were based on their performance over northern Europe and their climate sensitivity. The use of a subset of GCMs substantially reduces computational time required for impact assessment, while allowing to quantify uncertainties in impacts related to uncertain future climate. The dataset can be used with impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare, when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</p>
CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments require local-scale climate scenarios. The climate change projections from <span>Global Climate Models (GCMs) </span>are difficult to use at local scale due to their <span>coarse spatial and temporal resolution. </span><span>It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of </span>realisations <span>to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A</span><span> <span>dataset of future climate change scenarios was therefore generated at </span></span><span>26 representative sites across the UK</span><span> based on the latest </span><span>CMIP6 multi-model ensemble </span><span>downscaled to local-scale by using a </span><span>stochastic weather generator LARS-WG 7.0. The data set provides </span><span>1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (</span><span>Shared Socioeconomic Pathways - SSPs <em>viz</em>. </span>SSP2-4.5 and <span>SSP5-8.5)</span><span>.</span><span> </span><span>A total of </span>15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. <span>LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. </span>Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, <em>viz</em>.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs.<span> <span>The 1000 years of </span></span>realisations <span>of daily weather for the baseline as well as future climate change scenarios are helpful for estimating </span>seasonality and<span> inter-annual variation, and for detecting short, </span>low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset <span>can be used as an input to climate change impact models in various fields, including, </span><span>land and water resources, agriculture and food production, </span>ecology and epidemiology, and <span>human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</span></p>
Detailed abundances based on different nuclear physics for theoretical r-process scenarios
<p>This data set contains detailed abundances (at a time t=10^6 years after the event) for individual trajectories for seven different simulations of potential r-process sites, and based on nine different combinations of nuclear mass models and fission fragment distribution models. The data have been used and are discussed in Cote, Eichler, Yagüe, et al. (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) to determine the isotopic ratios of I129/Cm247 and compare them to meteoritic data.</p> <p>Furthermore, a code is included which samples a subset of trajectories reproducing the measured meteoritic I129/Cm247 abundance ratio of 438 +- 92. See the README file and the publication (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) for more details.</p>
Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"
<p>This dataset includes the empirical datasets for the manuscript: Andreas Andreou, Panagiotis Fragkos, Faidra Filippidou, Eleftheria Zisarou, Georgios Avgerinopoulos, Robert Pietzcker, Robin Hasse, Ricarda Rosemann, Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe’s residential buildings sector (under review in Environmetal Research Letters). The dataset contains one CSV file with detailed modelling results for the scenarios presented in the manuscript.</p>
Review of existing modelling studies focusing on specific soil-based ecosystem services (SES) and threats (ST) including climate change, management and land use change scenarios.
<p><span>We </span><span>reviewed existing modelling studies focusing on soil ecosystem services (SES) and soil threats (ST) including climate change, land use change and management scenarios. A publication has been submitted and is currently being reviewed. The title of the manuscript is: </span><span>Assessing and mapping soil ecosystem services and soil threats changes in agroecosystems through scenario-based approaches – a systematic review. </span></p> <p><span>Work was split between various authors. All Co-authors were working on either one or more SES or one ST. Excel sheets were prepared by INRA and BFW to ensure the comparability of results that members extracted from the papers found. Literature search was done in Scopus and Web of Science. The final list of related publications is reported here. <br></span></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>
3DO Dataset | On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios
<p><strong>On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios</strong></p> <p>This repository contains the <strong>3DO dataset</strong> proposed in <a href="https://doi.org/10.1007/978-3-031-78354-8_13">[1]</a>.</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the 3DO dataset is provided at: <a href="https://github.com/StrohmayerJ/3DO/tree/main">https://github.com/StrohmayerJ/3DO</a></p> <p><strong>Dataset Description</strong></p> <p>The 3DO dataset comprises 42 five-minute recordings (~1.25M WiFi packets) of three human activities performed by a single person, captured in a WiFi through-wall sensing scenario over three consecutive days. Each WiFi packet is annotated with a 3D trajectory label and a class label for the activities: no person/background (0), walking (1), sitting (2), and lying (3). (<strong>Note:</strong> The labels returned in our dataloader example are walking (0), sitting (1), and lying (2), because background sequences are not used.)</p> <p>The directories <code>3DO/d1/</code>, <code>3DO/d2/</code>, and <code>3DO/d3/</code> contain the sequences from days 1, 2, and 3, respectively. Furthermore, each sequence directory (e.g., <code>3DO/d1/w1/</code>) contains a <code>csiposreg.csv</code> file storing the raw WiFi packet time series and a <code>csiposreg_complex.npy</code> cache file, which stores the complex Channel State Information (CSI) of the WiFi packet time series. (If missing, <code>csiposreg_complex.npy</code> is automatically generated by the provided dataloader.)</p> <p>Dataset Structure:</p> <p>/3DO</p> <p>├── d1 <em><-- day 1 subdirectory</em></p> <p> └── w1 <em><-- sequence subdirectory</em></p> <p> └── csiposreg.csv <em><-- raw WiFi packet time series</em></p> <p> └── csiposreg_complex.npy <em><-- CSI time series cache</em></p> <p>├── d2 <-- day 2 subdirectory</p> <p>├── d3 <-- day 3 subdirectory</p> <p> </p> <p>In [1], we use the following training, validation, and test split:</p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Day</strong></td> <td><strong>Sequences </strong></td> </tr> <tr> <td>Train</td> <td>1</td> <td>w1, w2, w3, s1, s2, s3, l1, l2, l3</td> </tr> <tr> <td>Val</td> <td>1</td> <td>w4, s4, l4</td> </tr> <tr> <td>Test</td> <td>1</td> <td>w5 , s5, l5</td> </tr> <tr> <td>Test</td> <td>2</td> <td>w1, w2, w3, w4, w5, s1, s2, s3, s4, s5, l1, l2, l3, l4, l5</td> </tr> <tr> <td>Test</td> <td>3</td> <td>w1, w2, w4, w5, s1, s2, s3, s4, s5, l1, l2, l4</td> </tr> </tbody> </table> <p><em>w = walking, s = sitting and l= lying</em></p> <p><strong>Note: </strong>On each day, we additionally recorded three ten-minute background sequences (b1, b2, b3), which are provided as well.</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a>.</p> <p><a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a> Strohmayer, J., Kampel, M. (2025). On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios. In: Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15315. Springer, Cham. <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-78354-8_13</a></p> <p>BibTeX citation:</p> <pre>@inproceedings{strohmayerOn2025, author="Strohmayer, Julian and Kampel, Martin",<br> title="On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios",<br> booktitle="Pattern Recognition",<br> year="2025",<br> publisher="Springer Nature Switzerland",<br> address="Cham",<br> pages="194--211",<br> isbn="978-3-031-78354-8" }</pre>
A Practical Tool-Chain for the Development of Coordination Scenarios - Graphical Modeler, DSL, Code Generators and Automaton-Based Simulator
<p>The Peer Model is a modeling tool for coordination based on blackboard-based collaboration. </p> <p>The tool-chain consists of a modeler, translator and simulator.</p> <p>Its goal is to help developers of distributed and concurrent coordination software better understand algorithms and identify deficiencies from the beginning.</p> <p><br> </p>
Scenario delay times regarding the expansion of the transmission system in Germany based on social acceptance
<p>The data is closely related to Mester et al. 2017. Integrating Social Acceptance of Electricity Grid Expansion into Energy System Modeling: A Methodological Approach for Germany. In Wohlgemuth, V., Fuchs-Kittowski, F. and Wittmann, J. (eds.) <em>Advances and New Trends in Environmental Informatics: Stability, Continuity, Innovation</em>. Cham: Springer International Publishing, pp. 115 - 129. doi: 10.1007/978-3-319-44711-7_10.</p> <p>Each dataset contains the assumed delays in commissioning of German transmission grid projects given in years influenced by social acceptance based on three different scenarios - low, mid and high. In the attached file <em>VerNetzen-Verzoegerungszeiten-Kreise.csv </em>these<em> </em>delay times are given per district, which can be identified by their key, "Regionalschlüssel", and corresponding geo data (EPSG: 25832) of the administrative area provided by the Federal Agency for Cartography and Geodesy. © GeoBasis-DE / BKG 2014 (data was changed) Additionally other files contain scenario data for each transmission grid project. Geo data in these files is provided by the Bundesnetzagentur (data was changed).</p> <p>The dataset was created in the context of the interdisciplinary research project VerNetzen and is described in detail in the final project report: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., pp. 72-92, 102-105, 135-142.</p> <p><em><strong>Deutsch:</strong></em></p> <p>Die Daten stehen in engem Zusammenhang mit Mester et al. 2017. Integrating Social Acceptance of Electricity Grid Expansion into Energy System Modeling: A Methodological Approach for Germany. In Wohlgemuth, V., Fuchs-Kittowski, F. and Wittmann, J. (eds.) <em>Advances and New Trends in Environmental Informatics: Stability, Continuity, Innovation</em>. Cham: Springer International Publishing, pp. 115 - 129. doi: 10.1007/978-3-319-44711-7_10.</p> <p>Je Datensatz ist angegeben, welche akzeptanz-bedingten Verzögerungen in Jahren für die Inbetriebnahme von Übertragungsnetzausbauvorhaben in den drei Szenarien - low, mid und high - zu erwarten sind. In der Datei <em>VerNetzen-Verzoegerungszeiten-Kreise.csv </em>werden Verzögerungen je Landkreis aufgeführt. Diese können durch den Regionalschlüssel oder durch Geodaten (EPSG: 25832) des Bundesamtes für Kartographie und Geodäsie zugeordnet werden. © GeoBasis-DE / BKG 2014 (Daten geändert) Darüber hinaus enthalten die anderen Dateien Daten je Vorhaben. Die entsprechenden Geodaten in diesen Dateien wurden von der Bundesnetzagentur bereitgestellt (Daten geändert).</p> <p>Der Datensatz ist im Kontext des interdisziplinären Forschungsprojekts VerNetzen entstanden und ist ausführlich im Projektabschlussbericht beschrieben: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., S.72-92, S.102-105, S.135-142.</p>
Fig. 4 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 4. Distribution of pairwise values of genetic distances amongst species with allopatric areas: 1 — for the Western Palearctic genus Sylvaemus; 2 — for the Eastern Palearctic genera Apodemus and Alsomys; 3 — for the Palearctic Muridae as a whole, including species of genera Micromys and Mus.
Fig. 3 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 3. Distribution of pairwise intraspecies genetic distances within: 1 — the Western Palearctic genus Sylvaemus; 2 — the Eastern Palearctic genera Apodemus and Alsomys; 3 — in general for the Palearctic Muridae, including Micromys and Mus.
Fig. 2 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 2. Phenogram of genetic distances (Tamura, Nei, 1993) calculated from cytb sequences amongst representatives of the genera/subgenera Alsomys, Apodemus and genera Micromys, Mus, Rattus, constructed using the UPGMA algorithm. Representatives of the Arvicolidae and Cricetidae as well as S. s. dichrurus, S. flavicollis, S. (K.) mystacinus and S. (K.) epimelas were taken as outgroups.
Fig. 5 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 5. Distribution of pairwise genetic distances amongst taxa: 1 — Western Palearctic genus Sylvaemus, 2 — Eastern Palearctic genera Apodemus, Alsomys, 3 — Western Palearctic genus Sylvaemus and contrarily Eastern Palearctic genera Apodemus, Alsomys.
Fig. 1 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 1. Phenogram of genetic distances calculated from cytb sequences amongst representatives of the genera Sylvaemus, Rattus, constructed using the UPGMA algorithm, as mentioned above. Microtus arvalis (Arvicolidae) and Cricetus cricetus (Cricetidae) are used as outgroups.
A SSP1-Low emission land use scenario based on LCM2019 for Scotland - Land Use Change only - baseline 2019 and scenario 2050 (nov22)
<p>This set of datasets contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emission scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production.</p> <p>The baseline dataset is based on the Land Cover Map 2019 (Morton et al, 2020) aggregated at 100m resolution. Grazing intensity was added to it by using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). From the baseline dataset, the land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility that meets all the constraints stipulated for the scenario.</p> <p><strong>For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a></strong></p> <p>This analysis was conducted as part of the Land use Transformations (<a href="https://landusetransformations.hutton.ac.uk/">https://landusetransformations.hutton.ac.uk/</a>) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.</p> <p> </p> <p><strong>This version of the datasets only includes 100m cells with land use change (14% of Scotland). The full dataset has a non-commercial version of the licence (<a href="https://doi.org/10.5281/zenodo.10927157">https://doi.org/10.5281/zenodo.10927157</a>).</strong></p> <p> </p> <p><strong>-------------------------</strong></p> <p><strong>Datasets accessible here : <a href="https://openscience.hutton.ac.uk/dataset/low-emission-land-use-scenarios-land-use-change">SSP1-Low Emission Land Use Scenarios - land use change - Dataset - Natural Asset Register Data Portal (hutton.ac.uk)</a></strong></p> <p><strong>License</strong>: CC-BY-4.0 namely “Creative Commons Attribution 4.0 International“ <a name="_Hlk161153952"></a>(https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>Copyright to display of the datasets</strong>: <br>“Contains Data owned by UK Centre for Ecology & Hydrology © Database Right/Copyright UKCEH. Based on Data from LPIS and JAC (Scottish Government, 2019).”</p> <p><strong>2 Main files :</strong></p> <ul> <li><strong>SSP1LEonLCM19_LUC_2019.tif</strong> : original land uses (2019) on which the scenario is based on. This land use map, of a resolution of 100m, is based on the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions </strong>to the baseline dataset (SSP1LEonLCM19_LUC_2019.tif) : <ul> <li>100% of 100m cells: Land Cover Map 2019 (Morton et al, 2020)</li> <li>93.88% of 100m cells: the LCM 2019 was subdivided by grazing intensity using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This impacts the grasslands, heathers, bogs and arable classes.</li> <li>Estimated overall contributions: 65% UKCEH, 35% JHI</li> </ul> </li> </ul> <ul> <li><strong>SSP1LEonLCM19_LUC_2050.tif :</strong> land use scenario (2050), which is within the scope of a SSP1 - Low emissions scenario (Shared Scocio-Economic Pathways). The scenario was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a><u>. </u>This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions</strong> to the scenario dataset (SSP1LEonLCM19_LUC_2050.tif) : <ul> <li>cf. contribution to the baseline (above)</li> <li>100% of 100m cells: modelled land use change</li> <li>Estimated overall contributions: 50% UKCEH, 50% JHI</li> </ul> </li> </ul> <p> </p> <p><strong>Main references:</strong></p> <p>Morton, R. D., Marston, C. G., O’Neil, A. W., & Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB) [Data set]. NERC Environmental Information Data Centre. <a href="https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC">https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC</a></p> <p>Wardell-Johnson, D. (2022) Stocking rates derived from IACS 2019 version 4. <br>Based on data from Land Parcel Information System (2019) courtesy of Rural Payments and Inspections Division, Scottish Government.<br>Based on data from the June Agricultural Census (2019) courtesy of Rural and Environment Science and Analytical Services, Agricultural Statistics team, Scottish Government.</p> <p>Chapman, P. (2007) Conservation Grazing of Semi-natural Habitats. Technical note TN586. SAC tn586-conservation.pdf (sruc.ac.uk)</p> <p>FAS (2021) Practical Guide: Managing Peatlands and Upland Habitats. <a href="https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/">https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/ </a>(author: Paul Chapman)</p> <p>Castellazzi, M.S.; Gimona, A. (2021) SLM-OptionsTool, a land use change tool for Ecosystem Services (arcgis toolbox and user manual included, part of RESAS Deliverable-O1.4.2ciiD27).</p> <p>Castellazzi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown I., Conrad, K.F., Perry J.N. (2010). Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale . Environmental Modelling and Software 25, 1881-1889. <a href="https://doi.org/10.1016/j.envsoft.2010.04.006">https://doi.org/10.1016/j.envsoft.2010.04.006</a> </p> <p><a href="https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts">https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts</a></p> <p> </p> <p> </p>
Data Sources for Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis
<p><strong>Dataset Name:</strong><br><em>Literature Data, Archetype Parameter Sheets, and Schedules for the publication, named Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis</em>.</p> <p><strong>Description:</strong><br>This dataset includes Excel sheets containing literature sources, archetypal data, and schedules for Nigerian residential dwelling typologies.</p> <p><strong>Files:</strong><br>The following files are included in the dataset:</p> <ul> <li> Nigeria<em>_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> Nigeria<em>_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li> Nigeria<em>_Schedules.xlsx:</em> Excel sheet containing the operation schedules compiled from literature sources and reorganized by expert consensus and given in Designbuilder input format.</li> </ul> <p><strong>Usage:</strong><br>The dataset is intended for researching and analyzing the Nigerian residential buildings. The literature sources included in the Nigeria_LiteratureSources.xlsx and Nigeria_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br>The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br>If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Chibuikem Chrysogonus Nwagwu, Sahin Akin, and Edgar G. Hertwich. 2024. “Data Sources for Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis” https://doi.org/10.5281/zenodo.10995123</p> <p><strong>Contact:</strong><br>The archetypes' energy models (DesignBuilder or IDF files) as well as full material and energy use result sheets can be provided on request. If you have any questions or comments about the dataset, please contact <strong>chibuikem.nwagwu@sintef.no, the corresponding author.</strong></p>
Life cycle-based environmental impacts of energy scenarios - additional data
<p>This data set documents additional results of the paper "Life cycle-based environmental impacts of energy system transformation strategies for Germany: Are climate and environmental protection conflicting goals?" (Tobias Naegler and co-authors, published in Energy Reports (2020), https://doi.org/10.1016/j.egyr.2022.03.143). It shows life cycle-based environmental impacts for 10 different transformation strategies for the German energy and transport system.</p>
An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data
<p>This repository contains the data used in "An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data" by Oettli and Kotsuki (submitted to Journal of Geophysical Research: Atmospheres).</p>
Data study "The Impact of Augmented Reality on Biodiversity Learning in a Pedagogical Scenario Based on Analogical Reasoning: An Experimental Study"
<p>This data was collected in 2023 as part of a study on the impact of location-based AR on biodiversity education. </p>
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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)
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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.