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.
942
datasets available to search
ShareScore release 0.9.0
Dataset results
942 results for “scenario”
FIG. 5 in Deciphering Geographical Affinity and Reconstructing Invasion Scenarios of Boa imperator on the Caribbean Island of Cozumel
FIG. 5. (A–C) Main demographic scenarios modeled with DIYABC (Cournet et al., 2014) based on two molecular datasets and the Constrained Optimization scheme of Boa imperator from Cozumel, the Yucatán Peninsula, and the Gulf of Mexico. Note that time is not to scale. (D–E) Prior distribution of parameters corresponding to the three invasion scenarios tested, where the first two axes are shown. (F–G) Posterior predictive test of model fit corresponding to Scenario 1. (H–I) Direct and (J–K) logistic approaches of the three scenario model comparisons. In all cases, left panels (D, F, H, J) and right panels (E, G, I, K) show results from the Original Reduced Datasets (ORD) and the Polymorphic Microsatellite-based Dataset (PMD), respectively.
Datafiles supporting the article: Impact of methane and other precursor emission reductions on surface ozone in Europe: Scenario analysis using the EMEP MSC-W model
<p>This repository contains the input and output files of the EMEP model related to the article "Impact of methane and other precursor emission reductions on surface ozone in Europe: Scenario analysis using the EMEP MSC-W model" submitted to ACP.</p> <p>The folder Python_scripts includes the .py files used to generate the MAGICC7 model input files and simulation results, as well as the files used to create the figures in both the manuscript and supplementary information. For users, the paths in these scripts will have to be adjusted to point to the data files included in the EMEP_output folder. The latter folder contains the 5-year average scenario simulations as discussed in the main body of the text, as well as the ozone sensitivity to methane concentrations.</p> <p>The EMEP_input folder contains the IIASA scenario emission files for the baseline, 2050 CLE, 2050 MFR, and 2050 LOW scenarios, including separate international shipping files. The regional 0.1 by 0.1 degree emission files are a combination of IIASA files for EECCA countries and EMEP reported emissions scaled by the included 'femis' files. The EMEP model code and reported emissions are available from the open-source release, available here: https://zenodo.org/record/8431553.</p>
Carbon storage response to land use/land cover changes and SSP-RCP scenarios simulation: A case study in Yunnan Province, China
Open the record for dataset details and reuse information.
Data for manuscript "Substantial Contraction of Dense Shelf Water in the Ross Sea under Future Climate Scenarios"
<p>Relevant data shown in figures of the manuscript "Substantial Contraction of Dense Shelf Water in the Ross Sea under Future Climate Scenarios"</p>
Supplementary materials for the Hausfather 2024 review of current policy scenarios
<p>This file contains four tabs:</p> <ul> <li>Review of literature estimates includes the climate uncertainty, emissions uncertainty, and central estimate (when relevant) for various publications examining current policies, 2030 NDCs, other constraints, and net-zero commitments.</li> <li>Summary statistics includes a copy of the first tab with a central estimate calculated in cases where it was not provided by the original source, as well as summary statistics (median, means, p5, p95) each type of scenario.</li> <li>Figure 2 data includes the data plotted in Figure 2 of the paper, which was adapted from Hausfather and Moore 2022</li> <li>Figure 3 data includes the data plotted in Figure 3 of the paper, which was adapted from Venmans and Carr 2023</li> </ul>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 9: Operational and damaged scenarios in regular waves
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 9. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
The simulation data for paper: Permafrost Response in Northern High-Latitude Regions to 1.5 °C Warming and Overshoot Scenarios Achieved via Solar Radiation Modification
Open the record for dataset details and reuse information.
Results and plotting scripts for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'
<p><br>This archives the results for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'</p> <p>For the model version used to create these results, please see: https://doi.org/10.5281/zenodo.13981520</p> <p>Files to reproduce the figures, in R language:<br>SuCCESs validation.R - Reads GDX files and produces plots for energy and emissions.<br>SuCCESs validation MC.R - The same, but with the Monte Carlo GDXs.</p> <p>External data sources:</p> <p>***<br>GHG emissions are from IGCC and PRIMAP</p> <p>IGCC: https://climatechangetracker.org/igcc (CC-BY license)</p> <p>PRIMAP:<br>Gütschow, Johannes; Jeffery, M. Louise; Gieseke, Robert; Gebel, Ronja; Stevens, David; Krapp, Mario; Rocha, Marcia (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, https://doi.org/10.5194/essd-8-571-2016<br>Gütschow, Johannes ; Busch, Daniel ; Pflüger, Mika (2024): The PRIMAP-hist national historical emissions time series (1750-2023) v2.6. Zenodo. https://doi.org/10.5281/zenodo.13752654<br>https://primap.org/primap-hist/ (CC-BY-4.0 license)</p> <p>***<br>Historical energy production and use data are from IEA Energy Statistics Data Browser (CC BY 4.0 licence).<br>https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=CO2%20emissions&indicator=CO2BySource</p> <p>***<br>IAM results are from the SSP database: https://tntcat.iiasa.ac.at/SspDb </p> <p>Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlík, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni.<br>The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environmental Change, Volume 42, Pages 153-168, 2017,<br>DOI:110.1016/j.gloenvcha.2016.05.009</p> <p>Rogelj, J., Popp, A., Calvin, K.V., Luderer, G., Emmerling, J., Gernaat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G., Krey, V., Kriegler, E., Riahi, K., van Vuuren, D.P., Doelman, J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlik, P., Humpenöder, F., Stehfest, E., Tavoni, M., Scenarios towards limiting global mean temperature increase below 1.5 °C. Nature Climate Change 8, 2018, 325-332.<br>DOI:10.1038/s41558-018-0091-3</p>
TSN Scheduler Benchmarking: Scenarios v2.0.0
<p><strong>TSN Scheduler Benchmarking Scenarios </strong></p> <p>This dataset provides scenarios for testing Time-Sensitive Networking (TSN) schedulers, originally developed as part of the following PhD thesis (to be published):</p> <ul> <li>E. Schweissguth, "<strong>Routing and Scheduling of Time-Triggered Ethernet Networks: ILP Models and Scheduler Benchmarking.</strong>". University of Rostock.</li> </ul> <p>An earlier version (v1.0.0) of benchmarking scenarios was published alongside the following publication:</p> <ul> <li>E. Schweissguth, S. Mehner, D. Hellmanns, J. Falk, H. Parzyjegla, P. Danielis, O. Hohlfeld, G. Muehl, and D. Timmermann, "<strong>TSN Scheduler Benchmarking.</strong>" in <em>WFCS 2023</em>. IEEE.</li> </ul> <p>Scenarios of this earlier version are considered deprecated, but may still be useful for testing schedulers which do not support heterogenous cycle times. They are still available in the history (see releases). Compared to v1.0.0, this version contains scenarios with heterogeneous cycle times and frame sizes, reworked scenario parameters, and additional multicast scenarios. </p> <p>Please refer to the thesis for detailed background information. Refer to "<strong>TSN Scheduler Benchmarking: Results</strong>" (<a href="https://github.com/EikeSG/TSNBenchResults">https://github.com/EikeSG/TSNBenchResults</a> or linked projects on Zenodo) for a comprehensive comparison of ILP-based scheduling models based on this scenario dataset. When accessing this dataset through Zenodo, also check the corresponding GitHub repository (<a href="https://github.com/EikeSG/TSNBenchScenarios">https://github.com/EikeSG/TSNBenchScenarios</a>) for updates and for a good in-browser view (especially for markdown files).</p> <p>The scenarios are input files for scheduler benchmarking, intended for re-use by other researchers. For further details about the dataset as well as licensing and citation please see README.md.</p>
Design scenarios of time representation in online meetings
<p>The videos show four design scenarios related to the management of temporality in online meeting platforms.<br>The scenarios has been developed from an analysis of the most popular platforms in order to represent some UI design solutions for visualizing the temporal dimension in these contexts.</p>
Urban pluvial flood maps under different green cover scenarios
<p>This dataset provides pluvial flood water depth maps for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece as a part of the REACHOUT project. The maps are generated using a Pluvial Flood Tool for different return periods estimated based on observations and EURO-CORDEX future climate change scenarios (Logroño only) under different nature-based green cover scenarios, depending on the city.</p> <p>Technical Info</p> <p>The pluvial flood hazard maps are generated for each event using rainfall intensity as input for the hydrostatic inundation model SaferRAIN (Samela et al., 2020). This is a simplified raster-based model based on a hierarchical filling and spilling algorithm, identifying inundated areas on the basis of high-resolution digital elevation model. It accounts for spatially distributed rainfall input and infiltration, building upon the pixel-based Green-Ampt model (Green and Ampt, 1911). It is suitable for applications over large urban areas.</p> <p>Rainfall input for the pluvial flood model is computed for return periods (RPs) of 2-, 5-, 10-, 25-, 50-, 100-, 200-years based on the historical rainfall data. Different datasets have been utilized in various cities to tailor the analysis to their specific needs. More specifically:</p> <ul> <li> <p>In the city of Gdynia, historical local station data (Climate data IMGW 1960-2021: https://danepubliczne.imgw.pl/) are used to estimate RPs and assess different precipitation events. </p> </li> </ul> <ul> <li> <p>For the cities of Milan and Athens, 2.2-km ERA5 downscaled data are employed to assess historical precipitation events under different RPs (Essenfelder et al., 2021). </p> </li> <li> <p>In the city of Logroño, historical local station data (SOS-Logroño precipitation data 1999-2022: https://www.larioja.org/emergencias-112/es/meteorologia/datos-actuales-rioja/detalle-estacion?homepage=9&cod_muni=89) are used to estimate RPs and assess different precipitation events. Additionally, here, future climate change projections have been analyzed. These projections are based on the precipitation Intensity-Duration-Frequency (IDF) curves computed from the EURO-CORDEX data (REF to zenodo dataset, Pal J et al., 2024). Observations are then scaled according to the changes simulated between future and historical scenarios, using the median and 90th percentile values estimated from the EURO-CORDEX ensemble.</p> </li> </ul> <p>Different urban green cover maps are used as input for the model to simulate the pluvial flood maps under the current land cover conditions and for different nature-based adaptation scenarios for each city to estimate their benefits. Nature-based adaptation scenarios are the result of codesign processes carried out within REACHOUT, involving local stakeholders, experts and representatives of local administrations. Urban green cover scenarios were identified based on areas that could be converted from built-up areas and concrete surfaces (no water infiltration) to green areas allowing for rainwater infiltration. In addition, during this process, local station precipitation, high-resolution digital elevation model and high-resolution land cover data were collected to configure and run the pluvial flood model.</p> <p>Description of the datase: This dataset contains urban pluvial flood maps for return periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-years for hourly and 15-minute events for different urban green cover scenarios and climate change scenarios depending on the city.</p> <p>Format:</p> <p>The format of this dataset is organized in a ZIP file: PluvialFloodMap_{Cityname}.zip. The zip file is organised into sub-folders, one for each urban green cover scenario, including raster (Tiff) files for the rainfall event associated with each return period.</p> <p>Logrono:</p> <ul> <li> <p>Precipitation events historical: 15-minute events – 9.79 mm (RP2), 13.51mm (RP5), 16.27 mm (RP10), 20.15 mm (RP25), 23.33 mm (RP50), 26.77 mm (RP100), 30.50 mm (RP200)</p> </li> <li> <p>Precipitation events climate change: 15-minute events – CC_Q50 (median): 10.49 mm (RP2), 14.91 mm (RP5), 18.32 mm (RP10), 23.18 mm (RP25), 26.61 mm (RP50), 31.25 mm (RP100), 35.40 mm (RP200): CC_Q90 (90th percentile): 11.83 mm (RP2), 16.76 mm (RP5), 20.96 mm (RP10), 26.87 mm (RP25), 32.27 mm (RP50), 38.99 mm (RP100), 46.65 mm (RP200)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS planned: baseline + additional 4 bioswales/ponds (= 29,850 m3) and a green corridor (5.3 km x 5 m) in the southern part of the city.</p> </li> <li> <p>NBS planned plus: NBS planned scenarios + additional small ponds/rain gardens (depth 0.5 m, 13,500 m3)</p> </li> <li> <p>All Green: baseline + all open spaces converted to green</p> </li> </ul> <p>Milan</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 33.36 mm (RP5), 38.52 mm (RP10), 45.04 mm (RP25), 49.88 mm (RP50), 54.68 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>DMG_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise economic damage reduction</p> </li> <li> <p>POP_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise exposed population reduction</p> </li> </ul> <p>* Each green conversion scenario considers four different incremental conversion percentages: 25%, 50%, 75%, and 100% of all potential green areas.</p> <p>Gdynia</p> <ul> <li> <p>Precipitation events historical: 6-hours events – 24.89 mm (RP2), 35.93 mm (RP5), 43.55 mm (RP10), 53.19 mm (RP25), 60.60 mm (RP100), 75.74 mm (RP200) </p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + bioswales/ponds (+ 50,000 m3)</p> </li> <li> <p>All green: baseline + all open spaces converted to green</p> </li> <li> <p>NBS All green: all green + NBS</p> </li> </ul> <p>Athens</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 28.05 mm (RP5), 34.08 mm (RP10), 42.28 mm (RP25), 48.83 mm (RP50), 55.74 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + ponds/rain gardens in existing green spaces (depth 1m) in the northern district of the city</p> </li> <li> <p>All green: baseline + all open spaces (>100 m2) converted to green</p> </li> </ul>
Maps for Soil loss by water from climate change scenarios for Austria
<p><span>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</span></p> <p><span>This dataset contains the change of modelled annual soil loss rates for changing R-factor according to RCP4.5 and RPC8.5 climate scenarios, relative to modelled soil loss in the base scenario, using R-factor calculated for the 1990-2021 period. For each climate scenario, four periods were considered: 1991-2020, 2021-2040, 2041-2060 and 2061-2080. The RUSLE-based soil loss calculations were done according to the SERENA/EJP-Soil soil erosion cookbook and are described in the respective project deliverables D3.3 and D3.4.</span></p>
Data and code of "Unified percolation scenario for the α and β processes in simple glass formers
<p>Molecular dynamics simulation data and post-processing code for "Unified percolation scenario for the α and β processes in simple glass formers"</p>
Using participatory scenario planning to explore the synergies and trade-offs from upland treescape expansion.
<p>R scripts and summary data to accompany a manuscript understanding the synergies and trade-offs of upland treescape expansion using participatory scenarios.</p>
Architectural Uncertainty Analysis for Access Control Scenarios in Industry 4.0 - Data Set
<p>This data set contains additional information to the master's thesis of Nicolas Boltz. Included are the implementation, tests, and model instances of sample scenarios.</p>
Scenario-based Resilience Evaluation and Improvement ofMicroservice Architectures: An Experience Report - Supplementary Material
<p>Supplementary material for:</p> <p>Sebastian Frank, Alireza Hakamian, Lion Wagner, Dominik Kesim, Jóakim vonKistowski, and André van Hoorn: <em>Scenario-based Resilience Evaluation and Improvement of Microservice Architectures: An Experience Report. </em>In ECSA 2021 Companion Volume,<br> Växjö Sweden, 13-17 September, 2021. IEEE, 2021. </p> <p>This artifact includes the details of the scenarios described in the paper.</p> <p>The software artifacts are provided in a separate Code Ocean capsule: <a href="https://doi.org/10.24433/CO.0520280.v1">https://doi.org/10.24433/CO.0520280.v1</a></p>
Restart data for the v1309 scenario
<p>Restart files for the v1309 scenario used in the performance study</p>
Prosopis invasion and management scenarios for Baringo County, Kenya
<p class="MsoBodyText">Climate change, land degradation and invasive alien species (IAS) threaten grassland ecosystems worldwide. IAS clearing and grassland restoration would help to reduce the negative effects of IAS, restore the original vegetation cover, and sustain livelihoods while contributing to climate change mitigation, but uncertain financial benefits to local stakeholders hamper such efforts. This study assessed where and when net financial benefit could be realised from <i>Prosopis juliflora</i> management and subsequent grassland restoration by combining ecological, social and financial information. Impacts of <i>Prosopis </i>invasion and grassland degradation on soil organic carbon (SOC) in nine sublocations in Baringo County, Kenya, were evaluated. Then the financial impacts of <i>Prosopis</i> removal and grassland restoration in the area were calculated and spatially explicit management scenarios for each sublocation modelled, combining geographic information derived from satellite images taken in different years of the invasion with SOC data and socio-economic data collected in the sublocations.The available budget, based on Baringo households' average willingness to pay, would enable removal, on average, of one fifth of <i>Prosopis</i> per sublocation in a single year. A larger area can be cleared if <i>Prosopis</i> is sparse than if it is dense. The analyses show that in some sublocations, households' annual investments could result in restoration of all former grassland areas.</p> <p class="MsoBodyText">This dataset contains shapefiles of the evolution of Prosopis from 1995-2016 and shapefiles containing three management scenarios for four sublocations in Baringo County, Kenya.</p>
Copilot CWE Scenarios Dataset
<p>The dataset and source code and result generation framework used in the paper 'Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions'. It includes 89 different scenarios.</p>
Figure 99. A biogeographic scenario for all 11 in The bumblebees of the subgenus Subterraneobombus: integrating evidence from morphology and DNA barcodes (Hymenoptera, Apidae, Bombus)
Figure 99. A biogeographic scenario for all 11 species of Subterraneobombus by dispersal–vicariance analysis with DIVA using the tree from Figure 5 as an estimate of the phylogeny. Shaded branches above show simplified reconstructions of the ancestral distributions for each of the nodes that they precede (where area reconstructions are ambiguous, the more inclusive/widespread solution is accepted). See the text for details of the area units.
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.