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942 results for “Scenarios”
Case Study Scenarios (CSS) 9 for Filament for Limb Nadir application
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Case Study Scenarios (CSS) 3 for Biomass Burning
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Case Study Scenarios (CSS) 4 for Volcanic Eruption for Limb Nadir application
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Future simulation of the provision of multiple ecosystem services under scenarios of landscape development in Switzerland until 2060
<p>This dataset is a preliminary product of ongoing research, it was created for the purpose of analysis for the final report of the <a href="https://valpar.ch/index_de.php">ValPar.CH project</a> and should not be relied upon for formal analysis. It will be updated and expanded upon in upcoming publications because of this access is contingent upon request to the data creators. </p> <p>The dataset consists of simulated maps of the provision of 8 ecosystem services for the years 2020 and 2060 under 5 different scenarios of Land Use and Land cover (LULC) change. The maps covering the extent of Switzerland and are presented as 100m resolution raster data. </p> <p>The ecosystem services were calculated using models adpated from the work of Külling et al. 2024: </p> <div> <div>Külling, Nathan, Antoine Adde, Audrey Lambiel, Sergio Wicki, Antoine Guisan, Adrienne Grêt-Regamey, and Anthony Lehmann. 2024. ‘Nature’s Contributions to People and Biodiversity Mapping in Switzerland: Spatial Patterns and Environmental Drivers’. <em>Ecological Indicators</em> 163 (June):112079. <a href="https://doi.org/10.1016/j.ecolind.2024.112079">https://doi.org/10.1016/j.ecolind.2024.112079</a>.<br><br>The scenarios of LULC change were taken from the work of Black et al. 2024: </div> </div> <div> <div>Black, Benjamin, Antoine Adde, Daniel Farinotti, Antoine Guisan, Nathan Külling, Manuel Kurmann, Caroline Martin, et al. 2024. ‘Broadening the Horizon in Land Use Change Modelling: Normative Scenarios for Nature Positive Futures in Switzerland’. <em>Regional Environmental Change</em> 24 (3): 115. <a href="https://doi.org/10.1007/s10113-024-02261-0">https://doi.org/10.1007/s10113-024-02261-0</a>.</div> <div> </div> <div>THe README file provides further details of the ecosystem services and land use scenarios. </div> </div>
Data from: An objective approach to select climate scenarios when projecting species distribution under climate change
[No abstract entered]
FIGURE 25 in DNA barcodes reveal different speciation scenarios in the four North American Anthocharis Boisduval, Rambur, [Duménil] & Graslin, [1833] (Lepidoptera: Pieridae: Pierinae: Anthocharidini) species groups
FIGURE 25. Distribution of "cethura group". Blue-filled square—subspecies morrisoni, red-filled squares—nominotypical subspecies cethura, pink-filled squares—subspecies hadromarmorata, green-filled squares—subspecies mohavensis, orangefilled squares—subspecies pima, yellow-filled square—subspecies catalina, violet-filled squares—subspecies bajacalifornica, pale-blue filled square—un-named population similar to pima from Baja California Cape region.
Estimation of crop yield and profit of agrivoltaics under both half-density and full-density PV system scenarios
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IAMC 1.5°C Scenario Explorer and Data hosted by IIASA
<p><strong>Deprecation warning</strong></p> <p>This is no longer the latest release of the IPCC Assessment Report.</p> <p>For the latest version please visit <a href="https://doi.org/10.5281/zenodo.5886911">https://doi.org/10.5281/zenodo.5886911</a> and <a href="https://data.ece.iiasa.ac.at/ar6">https://data.ece.iiasa.ac.at/ar6</a> for the AR6 Scenario Explorer where you can download the data set.</p> <p> </p> <p>As part of the IPCC's <a href="https://www.ipcc.ch/report/sr15/"><em>Special Report on Global Warming of 1.5°C</em> (SR15)</a>, an assessment of quantitative, model-based climate change mitigation pathways was conducted. To support the assessment, the Integrated Assessment Modeling Consortium (IAMC) facilitated a coordinated and systematic community effort by inviting modelling teams to submit their available 1.5°C and related scenarios to a curated database. The compilation and assessment of the scenario ensemble was conducted by authors of the IPCC SR15, and the resource is hosted by the International Institute for Applied Systems Analysis (IIASA) as part of a cooperation agreement with Working Group III of the IPCC. The scenario ensemble contains more than 400 emissions pathways with underlying socio-economic development, energy system transformations and land use change until the end of the century, submitted by over a dozen research teams from around the world. The criteria for submission included that the scenario is presented in a peer-reviewed journal accepted for publication no later than May 15, 2018, or published in a report determined by the IPCC to be eligible grey literature by the same date.</p> <p>This release extends the original scenario ensemble with additional timeseries data on prices related to agiculture and food supply, which was used in the IPCC's <a href="https://www.ipcc.ch/report/srccl/"><em>Special Report on Climate Change and Land</em> (SRCCL)</a>.</p> <p>The data is available for download at the <a href="https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/downloads"><em>IAMC 1.5°C Scenario Explorer hosted by IIASA</em></a>. The license permits use of the scenario ensemble for scientific research and science communication, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and <a href="https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/license">legal code</a> for more information.</p>
Fig. 4 in Cryptic diversity, sympatry, and other integrative taxonomy scenarios in the Mexican Ceratozamia miqueliana complex (Zamiaceae)
Fig. 4 Variation of glaucous leaflets: A. Ceratozamia zoquorum; B. C. santillanii, C. C. becerrae; D. C. euryphyllidia; E. C. miqueliana; F. C. subroseophylla
GCAM Reference Scenario All Land Transitions
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Fig. 1 Three early deuterostome cladograms and a consistent evolutionary scenario. a in The phylogeny, evolutionary developmental biology, and paleobiology of the Deuterostomia: 25 years of new techniques, new discoveries, and new ideas
Fig. 1 Three early deuterostome cladograms and a consistent evolutionary scenario. a The cladogram of Brusca and Brusca (1990). Characters are as follows: 1 Complete gut with mouth not arising from blastopore. 2 Mesoderm derived directly from archenteron. 3 Body cavity (coelom) tripartite and derived by enterocoely. 4 Sheets of subepidermal muscles derived, at least in part, from archenteric mesoderm. 5 Longitudinal nerve cords not ladder-like in arrangement and not emphasized ventrally. 6 Ciliated feeding tentacles derived from mesosome and containing extensions of the mesocoel. 7 Circulatory system derived, at least in part, from archenteric mesoderm (varies among taxa). 8 Pharyngeal gill slits. 9 Dorsal hollow nerve cord. 10 Loss of mesosomal tentacles. 11 Notochord. 12 Muscular, locomotor, postanal tail. 13 Endostyle. 14 Tadpole larva. b The cladogram of Schram (1991). Characters are as follows: 1 Loss of spiral quartet cleavage. 2 Loss of 4d mesoderm. 3 Upstream particle capture in adults. 4 Upstream particle capture in larvae. 5 Tornaria/bipinaria larva. 6 Loss of coiled/looped gut. 7 Loss of lophophore. 8 Loss of upstream
FIG. 1 in Deciphering Geographical Affinity and Reconstructing Invasion Scenarios of Boa imperator on the Caribbean Island of Cozumel
FIG. 1. Map depicting sampling localities for Boa imperator from (A) continental mainland and (B) Cozumel Island. Squares indicate individuals from the Gulf of Mexico clade (n ¼ 14; see Results and Fig. 2), circles from the Yucatán Peninsula clade (n ¼ 16), and diamonds from Cozumel (n ¼ 16). Colors follow the ODC haplotype network population nomenclature (Fig. 3).
FIG. 4 in Deciphering Geographical Affinity and Reconstructing Invasion Scenarios of Boa imperator on the Caribbean Island of Cozumel
FIG. 4. Neighbor-net haplotype network based on 41 unique nuclear ODC haplotypes within Boa imperator.
Future land use/cover change (LUCC) simulated by FLUS and SD model under four SSP-RCP scenarios
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Future forest age distribution after land use/cover change under four SSP-RCP scenarios
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The EMSx dataset: historical photovoltaic and load scenarios and forecasts for 70 industrial sites
<p>The EMSx dataset gathers a large collection of photovoltaic and load profiles collected by the company Schneider Electric on 70 anonymized industrial sites. This dataset is released as a component of the EMSx benchmark for energy management systems introduced in a sister publication DOI: <a href="https://doi.org/10.1007/s12667-020-00417-5"> 10.1007/s12667-020-00417-5 </a>. Please refer to this related journal article for a detailed description of the dataset and if mentioning the EMSx dataset in your work.</p> <p>This dataset contains compressed files with a site ID ranging from 1 to 70. For these files, each line represents a timestep, gathering historical photovoltaic energy production and energy consumption data over the last 15 minutes, together with historical forecasts computed by Schneider Electric. The column naming follows the rules: </p> <ul> <li><code>load_XX</code> in kWh: For XX from 00 to 95, a forecast for the consumption where each subsequent value is 15 minutes later. <code>load_00</code> is a forecast for the load in the next 15 minutes. These values are forecasts, and so will not exactly match the actual consumption at that timestep.</li> </ul> <ul> <li><code>pv_XX</code> in kWh: For XX from 00 to 95, a forecast for the pv produced on site where each subsequent value is 15 minutes later. <code>pv_00</code> is a forecast for the pv production during the next 15 minutes. These values are forecasts, and so will not exactly match the actual_pv at that timestep.</li> </ul> <p>Besides files per sites, one file contains the unique historical photovoltaic production profile and forecasts employed in the data of all sites, after being rescaled appropriately. In this file, we have rescaled power values to [0,1] so that this data can serve the modeling of a photovoltaic unit beyond the scope of the EMSx benchmark. Finally, this dataset contains a metadata file providing battery storage capacities in kWh, battery flow capacities for 15 minutes in kWh, and battery efficiency coefficients, designed for the EMSx benchmark.</p> <p>This dataset contains information from Schneider's <a href="https://shop.exchange.se.com/en-US/apps/52535/microgrid-energy-management-benchmark">Microgrid Energy Management Benchmark</a>, which is made available under the Open Database License (ODbL).</p>
AR6 Scenarios Database
<h2><strong>The data is available for download at the <a href="https://data.ece.iiasa.ac.at/ar6" target="_blank" rel="noopener">AR6 Scenario Explorer hosted by IIASA</a>.<<< click here.</strong></h2> <p>As part of the <a href="https://www.ipcc.ch/report/sixth-assessment-report-working-group-3/">IPCC's 6th Assessment Report (AR6)</a>, authors from Working Group III on Mitigation of Climate Change undertook a comprehensive exercise to collect and assess quantitative, model-based scenarios related to the mitigation of climate change.</p> <p>Building on previous assessments, such as those undertaken for the 5th Assessment Report (AR5) and the Special Report on Global Warming of 1.5°C (SR15), the calls for AR6 for scenarios have been expanded and includes economy-wide GHG emissions, energy, and sectoral scenarios from global to national scales, thus more broadly supporting the assessment across multiple chapters (see Annex III, Part 2 of the WGIII report for more details).</p> <p>The compilation and assessment of the scenario ensemble was conducted by authors of the IPCC AR6 report, and the resource is hosted by the International Institute for Applied Systems Analysis (IIASA) as part of a cooperation agreement with Working Group III of the IPCC. The scenario ensemble contains 3,131 quantitative scenarios with data on socio-economic development, greenhouse gas emissions, and sectoral transformations across energy, land use, transportation, buildings and industry. These scenarios derive from 191 unique modelling frameworks, 95+ model families that are either globally comprehensive, national, multi-regional or sectoral.</p> <p>The criteria for submission included that the scenario is presented in a peer-reviewed journal accepted for publication no later than October 11th, 2021, or published in a report determined by the IPCC WG III Bureau to be eligible grey literature by the same date. The AR6 scenario database is documented in Annex III.2 of the Sixth Assessment Report of Working Group III. For the purpose of the assessment, scenarios have been grouped in various categories relating to, among other things, climate outcomes, overshoot, technology availability and policy assumptions.</p> <p>The AR6 Scenarios Database is jointly published by the <a href="https://www.iamconsortium.org/" target="_blank" rel="noopener">Integrated Assessment Modeling Consortium</a> & <a href="https://data.ece.iiasa.ac.at/ar6" target="_blank" rel="noopener">International Institute for Applied Systems Analysis</a>.</p> <h3><strong>The data is available for download at the <a href="https://data.ece.iiasa.ac.at/ar6" target="_blank" rel="noopener">AR6 Scenario Explorer hosted by IIASA</a>.<<< click here.</strong></h3> <p>For ease of use, the database is provided as multiple files:</p> <table> <tbody> <tr> <th><strong>Filename</strong></th> <th><strong>Description</strong></th> <th><strong>Region coverage</strong></th> <th><strong>Uncompressed Size (MB)</strong></th> </tr> </tbody> <tbody> <tr> <td><em><strong>Standard files for assessment</strong></em></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>AR6_Scenarios_Database_World_v1.1.csv</td> <td>All data reported for the World region, primarily from integrated assessment models (IAMs), as well as variables from the climate assessment </td> <td>World only</td> <td>353</td> </tr> <tr> <td>AR6_Scenarios_Database_R5_regions_v1.1.csv</td> <td>All data reported and aggregated to R5 regions, primarily from IAMs.</td> <td>5 global regions</td> <td>847</td> </tr> <tr> <td>AR6_Scenarios_Database_R6_regions_v1.1.csv</td> <td>All data reported and aggregated to R6 regions (as preferred by IPCC), primarily from IAMs.</td> <td>6 global regions</td> <td>408</td> </tr> <tr> <td>AR6_Scenarios_Database_R10_regions_v1.1.csv</td> <td>All data reported and aggregated to R10 regions, primarily from IAMs.</td> <td>10 global regions</td> <td>1,266</td> </tr> <tr> <td>AR6_Scenarios_Database_ISO3_v1.1.csv</td> <td>Ass data reported at the country level, primarily from national integrated assessment and energy systems models, but also IAMs for major countries.</td> <td>Country level</td> <td>1,155</td> </tr> <tr> <td>AR6_Scenarios_Database_metadata_indicators_v1.1.xlsx</td> <td>Wide range of categorical and numerical indicators calculated for each model-scenario.</td> <td>Primarily world data</td> <td>3</td> </tr> <tr> <td><strong><em>Additional "climate assessment" files</em></strong></td> <td><em><strong>New in v1.1</strong></em></td> <td> </td> <td> </td> </tr> <tr> <td>AR6_Scenarios_Database_World_ALL_CLIMATE_v1.1.csv</td> <td>Same as World snapshot above, but with all the climate assessment data for MAGICC and FaIR models included</td> <td>World only</td> <td>3,006</td> </tr> <tr> <td>AR6_Climate_Diagnostics_CICERO-SCM_v1.1.csv</td> <td>Climate assessment data for the CICERO-SCM model</td> <td>World only</td> <td>743</td> </tr> <tr> <td>AR6_Climate_Diagnostics_metadata_indicators_v1.1.xlsx</td> <td>Full set of categorical and numerical indicators relating to the climate assessment, calculated for each model-scenario</td> <td>World only</td> <td>2</td> </tr> <tr> <td>AR6_historical_emissions.csv</td> <td>Historical CO2 and GHGs for world region used in climate assessment</td> <td>World only</td> <td>0.01</td> </tr> </tbody> </table> <p>The data is available for download at the <a href="https://data.ece.iiasa.ac.at/ar6">AR6 Scenario Explorer hosted by IIASA</a>. The license permits use of the scenario ensemble for scientific research and science communication, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and <a href="https://data.ece.iiasa.ac.at/ar6/#/license">legal code</a> for more information.</p> <p>In addition to the data you may find more relevant information and cite one of the relevant chapters of the WG III report.</p> <p>If working with global or regional (R6, R10) data:</p> <ul> <li>Keywan Riahi, Roberto Schaeffer, et al. Mitigation Pathways Compatible with Long-Term Goals, in "Mitigation of Climate Change". Intergovernmental Panel on Climate Change, Geneva, 2022. url:<a href="http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/%20"> http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/</a></li> </ul> <p>If working with national data (ISO region data):</p> <ul> <li>Franck Lecocq, Harald Winkler, et al. Mitigation and development pathways in the near- to mid-term, in "Mitigation of Climate Change". Intergovernmental Panel on Climate Change, Geneva, 2022. url:<a href="http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/%20"> http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/</a></li> </ul> <p>If you find the metadata files particularly useful:</p> <ul> <li>Celine Guivarch, Elmar Kriegler, Joana Portugal Pereira, et al. Annex III: Scenarios and Modelling Methods, in "Mitigation of Climate Change". Intergovernmental Panel on Climate Change, Geneva, 2022. url:<a href="http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/%20"> http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/</a></li> </ul> <p>Scenarios data also supports analysis in the Summary for Policy Makers, Technical Summary and Chapters 2, 5, 6, 7, 9, 10, 12 and 15.</p> <p><strong>Climate assessment of global emissions pathways</strong></p> <p>The climate assessment of the long-term global emissions scenarios was undertaken as part of the Chapter 3 assessment. The workflow is available at <a href="https://github.com/iiasa/climate-assessment">https://github.com/iiasa/climate-assessment</a> and published in Kikstra et al. 2022. <em>The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures.</em> Geoscientific Model Development <a href="https://doi.org/10.5194/egusphere-2022-471">https://doi.org/10.5194/egusphere-2022-471</a>. Scripts for this assessment are at <a href="https://doi.org/10.5281/zenodo.7304736">https://doi.org/10.5281/zenodo.7304736</a> </p> <p>For these purposes, the full climate assessment data is provided, as documented in the table above.</p> <p><strong>Release notes for v1.1</strong></p> <p>Following feedback and identification of some issues between the versions available to authors in preparation of the published report and the v1.0 public release, updates are made to v1.1.<br>Changes made here are made with the intention of facilitating and improving the reproducibility of the IPCC report. There are no resulting corrections to the report and its findings, as these issues were identified by authors and manually addressed. Full list of release notes is published on the Downloads page <a href="https://data.ene.iiasa.ac.at/ar6/#/downloads">https://data.ene.iiasa.ac.at/ar6/#/downloads</a> </p> <h3><strong>The data is available for download at the <a href="https://data.ece.iiasa.ac.at/ar6">AR6 Scenario Explorer hosted by IIASA</a>.<<< click here.</strong></h3>
FIGURE 11 in Revising the taxonomy of Darevskia valentini (Boettger, 1892) and Darevskia rudis (Bedriaga, 1886) (Squamata, Lacertidae): a Morpho-Phylogenetic integrated study in a complex Anatolian scenario
FIGURE 11. Escalation of the calf area in the rudis and valentini complexes. Scale size (great, medium, small) and keeling development (weak keeling, medium keeling, strong keeling). The new nomenclature proposed in the text is used. D. r. lantzicyreni comb. nov. (medium scale size, medium keeling); D. josefschmidtleri sp. nov. (small scale size, weak keeling); D. s. spitzenbergerae stat. et comb. nov. (small scale size, weak keeling); D. s. wernermayeri ssp. nov. (medium scale size, weak keeling); D. valentini (medium scale size, weak keeling); D. b. bithynica (small scale size, weak keeling); D. b. tristis (medium scale size, weak keeling); D. o. bischoffi comb. nov. (great scale size, strongly keeling); D. o. obscura stat. et comb. nov. (great scale size, strongly keeling); D. o. macromaculata comb. nov. (great scale size, strongly keeling); D. r. bolkardaghica (medium scale size, medium keeling); D. rudis (medium scale size, medium keeling); D. mirabilis stat. nov. (medium scale size, medium keeling.
Microbiota-enabled Forecast of Global Grassland pH Dynamics under Future Climate Warming Scenarios
<p>Affilted dataset for analysis and scripts for modelling.</p>
Clinical Benefit and Safety of Raxibacumab in Patients With Symptomatic Inhalational Anthrax in a Mass Exposure Scenario
ClinicalTrials.gov study NCT02177721. IPD Sharing: NO. Countries: 0. Publications: 0.
ScienceDex guides
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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)
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.