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942 results for “Scenarios”
SeisSol dynamic rupture model setup of the Mw 7.5 Palu earthquake scenario published in Ulrich et al. (2019)
<p>All data required to run the dynamic rupture model of the Palu earthquake presented in:</p> <p>Ulrich, T., Vater, S., Madden, E. H., Behrens, J., van Dinther, Y., van Zelst, I., Fielding, E. J., Liang, C. & Gabriel, A. A. (2019). Coupled, Physics-based Modeling Reveals Earthquake Displacements are Critical to the 2018 Palu, Sulawesi Tsunami. doi: 10.31223/osf.io/3bwqa.</p> <p>A detailed readme file summarizing the data and data formats is also provided.</p>
Data for scenario extraction ESR 12
<p>Data for scenario extraction by Mobileye camera.</p>
Emissions-based MCMC chains for Hector emissions scenario paper
<p>These csvs contain MCMC chains and sampled subsets for emissions-based calibration of the Hector simple climate model (<a href="https://github.com/JGCRI/hector">https://github.com/JGCRI/hector</a>, DOI:10.5194/gmd-8-939-2015).</p> <p>The calibrations use a version of Hector that includes the BRICK sea-level module (<a href="https://github.com/scrim-network/BRICK">https://github.com/scrim-network/BRICK</a>, DOI:10.5194/gmd-10-2741-2017). Hector with BRICK is available on my fork of the Hector model (https://github.com/bvegawe/hector/tree/dev_slr). The calibration process is also adapted from BRICK. The code used to produce these chains can be found at https://github.com/bvegawe/hector_probabilistic, DOI:10.5281/zenodo.3236411.</p> <p>These four sets of MCMC chains were produced using hector_calib_driver.R. Inputs used to create each calibration are specified below: </p> <p>emissions_05.csv: Rscript hector_calib_driver.wideDiff.R -f *output folder* -n 1000000 --endyear 2005 --np 10</p> <p>emissions_09.csv: Rscript hector_calib_driver.wideDiff.R -f *output folder* -n 1000000 --endyear 2009 --np 10</p> <p>emissions_ohc_05.csv: Rscript hector_calib_driver.wideDiff.R -f *output folder* -n 1000000 --endyear 2005 --np 10 --obs_set noTE_obs --model_set noTE_model</p> <p>emissions_ohc_09.csv: Rscript hector_calib_driver.wideDiff.R -f *output folder* -n 1000000 --endyear 2009 --np 10 --obs_set noTE_obs --model_set noTE_model</p> <p> </p>
Laboratory modeling of gap-leaping and intruding western boundary currents under different climate change scenarios
<p>Western boundary currents (WBCs), such as, the Kuroshio and the Gulf Stream, are very intense currents flowing along the western boundaries of the oceans.<br>WBCs -and their respective extensions- have an important effect on climate because of their huge heat transports, the corresponding air–sea interactions and the role they play in sustaining the global conveyor belt. It is therefore very relevant to analyze WBC dynamics not only through observations and numerical modelling, but also by means of laboratory experiments; to this respect several rotating tank experiments have been performed in recent years.<br>The new laboratory experiments proposed here for the Hydralab+ 19GAPWEBS project are aimed at analyzing the interactions of a WBC with gaps located along the western coast. Examples of such processes include the Gulf Stream leaping from the Yucatan to Florida and the Kuroshio leaping, and partly penetrating, through the South and East China Seas and through the wider gap separating Taiwan to Japan. In the experiments the WBC is produced by a horizontally unsheared current flowing over a topographic beta slope; along the western lateral boundary a sequence of gaps of different widths simulate the openings present in the above mentioned locations.</p>
Home Assistant - Automation Scenarios Poll
<p>The poll showed in this image was used to know how frequently the listed domotics scenarios appear in automations of members of this community. </p> <p>Source: <a href="https://community.home-assistant.io/t/help-automation-scenarios-poll/132914">https://community.home-assistant.io/t/help-automation-scenarios-poll/132914</a> </p>
Code and data to reproduce the results of the paper: "Land Use Patterns and Climate Change---A Modeled Scenario of the Late Bronze Age in Southern Greece"
<p>Code and data to reproduce the results of Knitter et al. (2019): Land Use Patterns and Climate Change---A Modeled Scenario of the Late Bronze Age in Southern Greece. ERL.</p>
Simulation data of European seabass and meagre growth in Greece (C12A) under climate change scenarios
<p>The dataset contains excel files with the biological predictions for European seabass and meagre generated within ClimeFish C12A. Simulations are done for climate scenarios RCP45 and RCP85 and at three time scales denoting short (2015-2025)-, mid (2025-2035)- and long (2045-2055)- term projections. The temperature data used for the simulations are also included as well as a file containing metadata.</p>
Datas for Leveraging ecosystems responses to enhanced rock weathering in mitigation scenarios
<p>This dataset contains the jupyter notebook calibrating the P-cycle emulator based on the ORCHIDEE-CNP outputs produced by Daniel S. Goll. The zip file contains a set of results from the simulations. </p>
Moreno_et_al_2024_Biodiversity impacts of Paris-compliant land-based mitigation scenarios
<p>Land cover areas in 2020 and 2050, charecterisation factors and PSL impacts in 2050 by land cover type and by ecoregion under the five mitigation scenarios modelled.</p>
Eutrophication Risk Index (ERI) for the Cerrado and Caatinga: Modeling Scenarios for 2030 and 2040
<p><span>To assist in mapping Water Pollution Risk (WPR), we developed the Eutrophication Risk Index (ERI). This index is designed to assess and predict the vulnerability of water bodies to eutrophication, a process driven by excessive nutrient accumulation—particularly nitrogen and phosphorus—resulting in uncontrolled algal growth. The ERI helps identify at-risk areas and supports the development of more effective mitigation strategies aimed at preserving water quality and sustaining aquatic ecosystems.</span></p> <p><span>The proposed Eutrophication Risk Index (ERI) specifically accounts for human pressures on aquatic ecosystems. The ERI is determined by the phosphorus contribution to aquatic environments, derived from urban effluents and the excess nutrients (phosphorus and nitrogen) applied to the soil, measured in tons per hectare per year (Ton ha⁻¹ year⁻¹). To calculate the ERI for the Cerrado and Caatinga, we employed an equation with two main components: one concerning nutrient loss from agricultural systems and the other related to nutrient loss in wastewater.</span></p> <p><strong><span>Nutrient Loss in Agricultural Areas:</span></strong><span><br>Nutrient loss from agricultural areas was estimated using a spatially explicit soil nutrient balance model, incorporating secondary data sources and land use and land cover maps of the study area. For this analysis, we assumed that the nutrient balance in the soil is the difference between total inputs (IN) and total outputs (OUT), where IN includes chemical and organic fertilizers and OUT represents agricultural products. A positive nutrient balance, or surplus, indicates potential nutrient loss that could impact adjacent ecosystems. In our calculations, we also considered phosphorus saturation levels as a risk factor for phosphorus loss, alongside soil types.</span></p> <p><strong><span>Nutrient Loss in Wastewater:</span></strong><span><br>Nutrient loss in wastewater was based on data from the National Water and Sanitation Agency. This method considers the nutrient content in untreated wastewater and in effluents from wastewater treatment plants. We assumed a constant treatment efficiency of 30%, although this value may be optimistic given the primary effluent treatment processes in Brazil. For future assessments, local data on sewage treatment plants could be incorporated into the calculations if available during the project's execution.</span></p> <p><span>This dataset includes empirical data and model simulations developed under the NEXUS project (</span><a href="https://nexus.ccst.inpe.br/" target="_new"><span>https://nexus.ccst.inpe.br/</span></a><span>), which analyzed the interrelationship and challenges of agricultural production, energy, and water resource use in the Caatinga and Cerrado regions. Conducted between 2018 and 2024, the NEXUS project employed a participatory multiscale approach, combining qualitative and quantitative methods from natural and social sciences. Over its six-year duration, the project engaged more than one hundred stakeholders from various sectors, producing diagnostics and scenarios for sustainable futures in these biomes.</span></p> <p><strong><span>Scenarios Descriptions:</span></strong><span><br>The “Green Transition” scenario aligns with the dominant sustainability narrative in the business sector, focusing on efficiency gains and technological solutions (e.g., low-carbon agriculture, energy transition led by large corporations) to address environmental challenges. This scenario envisions agricultural production concentrated in highly productive areas, facilitating the restoration of natural vegetation and fostering an increasingly urban future.</span></p> <p><span>Conversely, the “Lives in Balance” scenario reflects the aspirations and struggles of social movements and traditional communities for recognition and the coexistence of diverse ways of life. It advocates transforming production systems, particularly through decentralized food and energy production, and emphasizes strengthening family farming and agroecological systems.</span></p> <p> </p> <p><strong><span>Acknowledgements</span></strong><span><br>The authors would like to thank the NEXUS Project, funded by the São Paulo Research Foundation – FAPESP (grants 2022/00917-0 and 2017/22269-2), and the Coordination for the Improvement of Higher Education Personnel (CAPES) for their support to Marcela Miranda through the National Postdoctoral Program (grants 88882.317530/2019-1 and 1732909/2017-2).</span></p>
Anthropogenic pressure index on biomes (APIB): Nexus scenarios for Brazil 2040
<p>These new scenarios were created within the scope of the Thematic Project “Nexus – Transition to sustainability and the water-agriculture-energy nexus: exploring an integrative approach with case studies in the Cerrado and Caatinga biomes” (FAPESP - 2022/05856-0 and 2017/22269-2). For more information, visit: https://nexus.ccst.inpe.br/publicacao/ and https://zenodo.org/records/10197094.</p> <p> </p> <p><strong>Data</strong></p> <p>Anthropogenic Pressure Index on Biomes (APIB) value.</p> <p> </p> <p><strong>Spatial resolution</strong></p> <p>The data is available at a spatial resolution of 100 km² and covers the entire Brazilian territory.</p> <p> </p> <p><strong>Temporal resolution </strong></p> <p>Period of observed data: 2020</p> <p>Scenario Period: 2025, 2030, 2035 and 2040</p> <p> </p> <p><strong>Coordinate reference system</strong> </p> <p>Geographic Coordinate System with Datum SIRGAS 2000 (EPSG:5880)</p> <p> </p> <p><strong>Data format</strong></p> <p>Data is provided as Shapefile.</p> <p> </p> <p><strong>Dataset usage</strong> </p> <p>It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.</p> <p>F. G. S. Bezerra, <em>et al.</em>, Spatio-temporal analysis of dynamics and future scenarios of anthropic pressure on biomes in Brazil. <em>Ecol Indic</em> <strong>137</strong> (2022). https://doi.org/10.1016/j.ecolind.2022.108749</p> <p> </p> <p><strong>Publication & further information</strong></p> <p>For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The authors would like to thank the São Paulo Research Foundation (FAPESP, project number 2022/05856-0, 2017/22269-2 and Nexus Project) for their support in the development of this study.</p>
Global gridded scenarios of residential cooling energy demand to 2050
<p># ggACene (global gridded Air Conditioning energy) projections</p> <p>### Output AC and AC electricity gridded data</p> <p>This repository hosts output data for SSPs126, 245, 370 and 585 on the estimated and future projected ownership of residential air conditioning (% of households), the related energy consumption (TWh/yr.), and the underlying population counts (useful to quantify the per-capita average consumption or the headcount of people affected by the cooling gap). These data are contained in the multi-layer .nc (NCDF) files, which can be opened and processed in any GIS software/library, or visualised in softwares such as Panoply.</p> <p>### Input data and analysis replication</p> <p>The repository also hosts input data to replicate the entire data generating process. A twin Github repository hosts code (<a href="https://github.com/giacfalk/ggACene">https://github.com/giacfalk/ggACene</a>) to run the model generating the ggACene (global gridded Air Conditioning energy) projections dataset.</p> <p>## Instructions<br>To reproduce the model and generate the dataset from scratch, please refer to the following steps:<br>- Download input data "replication_package_input_data.7z" by cloning the repository<br>- Decompress the folder using 7-Zip (https://www.7-zip.org/download.html)<br>- Open RStudio and adjust the path folder in the sourcer.R script<br>- Run the sourcer.R script to train the ML model, make projections, and represent result files<br><br></p> <p>### Figures replication package</p> <p>Finally, the source_code_data_replication_figures.zip archive contains an R script and processed input data to replicate all the figures contained in the manuscript.<br><br></p> <p>### Reference<br><br>Falchetta, G., De Cian, E., Pavanello, F., & Wing, I. S. Inequalities in global residential cooling energy use to 2050. Nature Communications. https://www.nature.com/articles/s41467-024-52028-8</p>
Salt marsh litter quality and decomposition under sea-level rise scenarios: from leaves to fine absorptive roots
Open the record for dataset details and reuse information.
Human Decision-Making Through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications: Example Scenario with Comments
<p>This example scenario with comments has been created in conjunction with the report “A Framework for Human Decision-Making Through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications” to be published by the IEEE Standards Association (IEEE SA) Research Group on Issues of AI and Autonomy in Defense Systems (the Research Group). It provides insights into the Research Group's working methods and process.</p>
Supplementary Material for the paper entitled "Identifying Difficult Environmental Conditions with Scenario-based Hazard and Fault Analysis"
<p>This dataset is a supplementary material for the paper entitled "Identifying Difficult Environmental Conditions with Scenario-based Hazard and Fault Analysis", accepted by SafeComp Workshop SASSUR 2024.</p> <p>The file will be uploaded after a publication process is accomplished.</p> <p>Update: list of triggering conditions is uploaded on 14.10.2024</p>
An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology
<div><strong>Overview</strong>:</div> <div>The dataset contains measurements of Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) collected from Long-Range (LoRa) devices in avalanche Search and Rescue (SAR) scenarios. Data were collected on a plateau located in Col de Mez (Falcade, Italy) at 1870 m in the Italian Dolomites, at two different times of the year: March and April 2024. The depth and conditions of the snow are different: in March, the snow is mostly dry and over one meter deep, while in April, the snow is wetter, with a greater presence of liquid water, and approximately 55 centimeters deep.</div> <div> </div> <div>The dataset includes three test typologies:</div> <div> <ol> <li>Cross test: 1 buried transmitter, at different depths, and 4 receivers on a tripod, positioned at 10 different distances from the burial point along 4 orientations: North, South, East, West. Distances are: 0.6 m, 1.2 m, 1.8 m, 3 m, 5 m, 10 m, 20 m, 30 m, 40 m, 50 m.</li> <li>Maximum Distance test: 1 buried transmitter and 1 receiver, held in hand and moved away from the burial point until the signal is completely lost. The receiver stops periodically, collecting 2 minutes data in specific markers.</li> <li>Drone Flyover test: 1 buried transmitter and 1 receiver mounted on the bottom of a quadcopter professional drone. The drone stands on 121 measurement points, creating a precise grid covering an area of 100 square meters, with the burial location at the center.</li> </ol> </div> <div>All the tests include precise Ground Truth (GT) annotations, indicating the exact positions of the receivers and the burial depth of the transmitter. The dataset is organized in three folders, one for each test: cross, max_dist and drone. In a separate folder, the snow profiles for the two data collection periods, march and april 2024, are also included, according to the AINEVA Model 4.</div> <div> </div> <div>The dataset aims to assess the ability to locate a victim in an avalanche scenario. The collected data allow for the evaluation of the quality of the LoRa signal in various environmental conditions, as well as the snow depth and snowpack profile. By using precise Ground Truth annotations, it is possible to assess the potential performance of a localization system.</div> <div> </div> <div><strong>How to use the dataset</strong>:</div> <div>Please, read the README file detailing the dataset's format and the data collection campaign. In summary, collected data include:</div> <div> </div> <div>1. Cross test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>rx_pos</li> <li>distance</li> <li>depth</li> <li>polarization</li> </ul> </div> <div>2. Maximum Distance test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>depth</li> <li>id_marker</li> <li>longitude</li> <li>latitude</li> </ul> </div> <div>3. Drone Flyover test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>longitude</li> <li>latitude</li> <li>x</li> <li>y</li> <li>depth</li> </ul> </div> <div><strong>How to cite this dataset</strong>:</div> <div>- DOI number of this datsaset: 10.5281/zenodo.12750580</div> <div>- M. Girolami, F. Mavilia, A. Berton, G. Marrocco and G. Maria Bianco, "An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology," in <em>IEEE Access</em>, vol. 12, pp. 171015-171035, 2024, doi: 10.1109/ACCESS.2024.3497654</div>
Scenario-tree model to estimate the sensitivity of a surveillance system for classical scrapie
<p>The supplementary files to the EFSA's scientific report on the "Evaluation of the application of Slovenia to be recognised as having a negligible risk of classical scrapie" include:</p> <p>R code of the scenario-tree model to estimate the sensitivity of the surveillance system of sheep and goats for scrapie in Slovenia.</p> <p>Read-me file with information and instructions on how to rum the model </p> <p>Input data from Slovenia. Surveillance data to be analysed usgin the model.</p>
Species functional data and species distribution model projections for future land-use and fire management scenarios in the Transboundary Biosphere Reserve Gerês-Xurés
<p>The data includes nine functional traits and species distribution model projections for 102 species of vertebrates (amphibians, birds, and reptiles) in the Transboundary Biosphere Reserve Gerês-Xurés. The model projections are available for 2050 under six different land-use and fire management scenarios, namely two land-use scenarios of “business-as-usual” (BAU; ongoing trends of land abandonment) and “High Nature Value farmlands” (HNV), each under three fire management scenarios (low suppression - LS, current fire suppression - CS, and high fire suppression - HS). The species distribution projections for each scenario are presented as matrices of species presences/absences, obtained after reclassifying consensus predictions of species distribution models.</p>
A Factor Two Difference in 21st-Century Greenland Ice Sheet Surface Mass Balance Projections from Three Regional Climate Models for a Strong Warming Scenario (SSP5-8.5)
<p>1km regridded Greenland Ice Sheet SMB / Runoff / Melt projection until 2100. Projections from MAR, RACMO, HIRHAM forced by CESM2 (SSP5-8.5).</p>
Time-Dependent Probabilistic Tsunami Inundation Assessment Using Mode Decomposition to Assess Uncertainty for an Earthquake Scenario
<p>This is the dataset of the manuscript submitted to JGR Ocean (Feb 2021).</p>
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.