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942 results for “Scenarios”
USEPE Project Scenarios Dataset
<p><strong>Scenarios</strong></p> <p>Scenario files, with configuration and data, for USEPE simulations, both reference and solution (D2C2) scenarios.</p> <p><strong>Exercises included</strong></p> <p>1. Last Mile Delivery (In directory exercise_1)<br> 2. Emergency Services (In directory exercise_2)<br> 3. Urban Surveillance (In directory exercise_3)</p> <p>Exercise structure:</p> <ul> <li>settings file (.cfg) - This file defines the input data to be loaded in a specific experiment. </li> <li>data folder - the input data required for the experiments. Some data is not included due to large size or property restrictions.</li> <li>output - raw outputs obtained from the experiments in the USEPE project</li> <li>scenario - BlueSky scenario files (.scn) required for the experiment </li> </ul> <p><strong>How to</strong><br> See how_to_setup_for_simulations.pptx for step-by-step explanation on how to use these files to create runnable scenarios for BlueSky with the USEPE plugin.<br> </p>
European migration scenarios with probabilistic uncertainty assessment – Data Description
<p>This open data deposit contains the data and code accompanying used in the report: Bijak (2023): European migration scenarios with probabilistic uncertainty assessment, QuantMig Project Deliverable D9.4. The cover note should be read in conjunction with the report, available via www.quantmig.eu, and with the individual readme files in the data folders that can be found within this Zenodo repository (DOI: 10.5281/zenodo.7954150).</p>
Results of article : "Prospective European District Heating Scenarios based on Geographical Analysis"
<p>Results of the paper "Prospective European District Heating scenarios based on geographical analysis".</p> <p>Three scenarios are generated : Ambitious, Circular, and Conservative. For each scenario, there is a gpkg file and an excel file. The gpkg file is the whole dataset of inputs and results, each row being a European city. The excel summarizes the results for each EU27+UK country.</p> <p> </p>
Dataset on Integrated Urban Scenarios
<p>This dataset supports the original research article Kılkış (2023) on “<strong>Integrated urban scenarios of emissions, land use efficiency and benchmarking for climate neutrality and sustainability</strong>.” <br> <br> The dataset involves a total of 37 datasheets that are organised into three main domains as follows:</p> <ol> <li>The first domain contains 9 datasheets focused on urban emissions scenarios. Analysed and generated data on carbon footprint per capita, SSP1 urban population projections per urban area, mitigation ratio of emissions trends per scenario, and annual urban emissions in 5-year intervals between 2015 and 2050 are given. Such data is followed by annual urban emissions data as a function of the given variables summed for cumulative urban emissions between 2020 and 2050. Related datasheets involve four scenarios SSP1-1.9, SSP1-2.6, SSP1-RE, and SSP1-RE-MC for 45 urban areas. The latter scenario extends local ambition-driven targets for 15 Mission Cities.</li> <li>The second domain with 4 datasheets focuses on the impacts of land use efficiency (LUE). Data contains LUE values per urban area, built-up area in the reference and future years, and the annual CO<sub>2</sub> sequestration penalty as a function of the urban area, its local biome, and LUE scenario in 5-year intervals between 2020 and 2050. These datasheets involve four scenarios LUE 5%, LUE 15%, LUE Av, and LUE Best scenarios.</li> <li>The third domain has 24 datasheets based on the mean, 5<sup>th</sup> percentile, and 95<sup>th</sup> percentile values of 10,000 Monte Carlo simulations for each scenario that summarises the results of 3.6 million cells of data (45 urban areas x 10,000 simulations x 8 scenarios). These later worksheets involve the annual urban emissions per urban area for cumulative urban emissions (SSP1-1.9, SSP1-2.6, SSP1-RE, and SSP1-RE-MC) and annual CO<sub>2</sub> sequestration penalty for cumulative CO<sub>2</sub> sequestration penalties (LUE 5%, LUE 15%, LUE Av, and LUE Best) based on the Monte Carlo simulation results.</li> </ol> <p>The data structure is described in the information sheet and the dataset contains a total of 55,309 cells of data. The method and results based on the data are described in the original article that is published in <em>Energy</em>. The dataset of this original research article should be cited as: Kılkış (2023), Dataset on Integrated Urban Scenarios (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7983041">https://doi.org/10.5281/zenodo.7983041</a></p>
Projecting Residential Energy Consumption across Multiple Income Groups under Decarbonization Scenarios using GCAM-USA
<p>Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL).</p> <p>GCAM-USA operates within the Global Change Analysis Model, which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).</p> <p>GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: <a href="https://jgcri.github.io/gcam-doc/toc.html">https://jgcri.github.io/gcam-doc/toc.html</a></p> <p>The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of May 15, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP <em>per capita</em>) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.</p> <p>Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2045 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the key output variables related to the residential building energy sector under the four scenarios, including:</p> <ul> <li>income shares by consumer groups at each state over 2015-2045 (Casper et al. 2022)</li> <li>residential energy consumption <em>per capita</em> by service and fuel, by state and income group, 2015-2045</li> <li>residential energy service output (energy consumption * technology efficiency) <em>per capita</em> by service, fuel, and technology, by state and income group, 2015-2045</li> <li>estimated energy burden (Eq.1), by state and income group, 2015-2045</li> <li>residential heating service inequality (Eq.2), by state, 2015-2045</li> </ul> <p> </p> <p><strong>Table 1</strong></p> <table> <thead> <tr> <th scope="col">Scenarios</th> <th scope="col">Policies</th> <th scope="col">Climate Change Impacts</th> </tr> </thead> <tbody> <tr> <td>BAU (Business-as-usual)</td> <td>Existing state-level energy and emission policies</td> <td>Constant HDD/CDD (heating degree days / cooling degree days)</td> </tr> <tr> <td>BAU_climate</td> <td>Existing state-level energy and emission policies</td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> <tr> <td>NZnoCCS (Net-Zero by 2050 without CCS)</td> <td> <p>Two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Constant HDD/CDD</td> </tr> <tr> <td>NZnoCCS_climate</td> <td> <p>Two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> </tbody> </table> <p> </p> <p><strong>Eq. 1</strong></p> <p><span class="math-tex">\(Energy\ burden_i = \dfrac{\sum_j (service\ output_{i,j} * service\ cost_j)}{GDP_i}\)</span></p> <p>for income group<em> i</em> and service <em>j</em></p> <p> </p> <p><strong>Eq. 2</strong></p> <p><strong><span class="math-tex">\(Residential\ heating\ service\ inequality = \dfrac{S_{d10}}{(S_{d1} +S_{d2} + S_{d3} + S_{d4})}\)</span></strong></p> <p>where <em>S</em> is the residential heating service output <em>per capita</em> of the highest income group (<em>d10</em>) divided by the sum of that of the lowest four income groups (<em>d1</em>, <em>d2</em>, <em>d3</em>, and <em>d4</em>), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality.</p> <p> </p> <p><strong>Reference</strong></p> <p>Casper, Kelly, Narayan, Kanishka B., O'Neill, Brian C., & Waldhoff, Stephanie. 2022. State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100) (latest version obtained from the authors on April 6, 2023) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7227128">https://doi.org/10.5281/zenodo.7227128</a></p> <p>IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].</p> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p>
Data from: Stream thermalscape scenarios for British Columbia, Canada
<p class="Abstract"><span>Water temperature is a key feature of freshwater ecosystems but comprehensive datasets are severely lacking, a limiting factor in research and management of freshwater species and habitats. An existing statistical stream temperature model developed for British Columbia (BC), Canada, was refit to predict August mean stream temperatures, a common index of stream thermal regime also used in thermalscapes developed for the western United States (US). Thermalscapes of predicted August mean stream temperature were produced for 680,000 km of stream network at approximately 400 m intervals. Temperature predictions were averaged for 20-year periods from 1981–2100 to produce 86 scenarios: one for each historical period (i.e., 1981–2000, 2001–2020), and 21 for each future period (i.e., six global climate models and an ensemble average under three representative concentration pathways). </span><span class="MsoCommentReference"><span>T</span></span><span>he final model performance was consistent with other published regional-scale statistical models (R<sup>2</sup> = 0.79, RMSE = 1.53°C, MAE = 1.18°C), performing well given the relative paucity of data, large geographic extent, and range of climatic and physiographic conditions. Model results suggested an average increase of August mean stream temperature of 2.9 ± 1.0°C (RCP 4.5 ensemble mean ± SD) by end of century, with significant heterogeneity in predicted temperatures and warming rates across the province. Compared to stream temperature predictions from the western US, the predictions for BC showed good agreement at cross-border streams (Pearson's <em>r</em> = 0.91), suggesting the possible integration of both products for a thermalscape covering much of western North America. These stream thermalscapes for BC address a major data deficiency in freshwater ecosystems and have potential applications to stream ecology, species distribution modelling, and evaluation of climate change impacts. </span></p>
Mapping of aridity and its connections with climate classes and climate desertification in future scenarios – Brazilian semi-arid region
<p>This database comes from the article entitled ''Mapping of aridity and its connections with climate classes and climate desertification in future scenarios –Brazilian semi-arid region'' (https://seer.ufu.br/index.php/sociedadenatureza /article/view/67666/36193). We provide data on aridity and desertification index for the current scenario and future projections considering changes in climate.</p>
Prediction of potential suitable areas for Phoebe zhennan in future different climate scenarios
<p>This dataset includes sample collection data of existing <em>Phoebe zhennan</em> in China, as well as historical climate data and future climate data (with a resolution of 2.5 minutes and using the BCC-CSM2-MR GCM model) collected by Worldclim, along with geographical elevation data. These data are used to predict the potential distribution range of <em>Phoebe zhennan</em> in the future.</p>
The Optimal Climate Intervention Scenario for Crop Production Varies by Nation
<p><strong>Abstract. </strong>Stratospheric aerosol intervention (SAI) is a proposed strategy to reduce the effects of anthropogenic climate change. As it would not directly counteract the increased forcing from CO<sub>2</sub>, its impacts on national crop production need to be analyzed, as SAI regionally modifies variables such as surface temperature, precipitation, humidity, total solar radiation, diffuse radiation, ultraviolet radiation, and surface ozone. In this work, we analyze impacts to crop production by looking at output from 11 different SAI scenarios carried out with a fully coupled Earth System Model coupled to a crop model. Higher latitude nations tend to produce the most calories under unabated climate change, while midlatitude nations maximize calories under moderate temperature limitation, and equatorial nations prefer large levels of climate intervention to produce the most calories from crops. Our results highlight the challenges in defining “globally optimal” SAI strategies, even if such definitions are based on just one metric.</p>
WILIAM Data Dictionary and Database of selected simulated scenarios and results
<p>The shared database and open database management system developed for the Integrated Assessment Model WILIAM in the LOCOMOTION project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 821105.</p> <p>The shared database and open database management system consists of:</p> <ul> <li>The Data Dictionary, which includes information on the Symbols developed within the project (variables, historical data, constants, parameters, scenario parameters, etc.) and used in the WILIAM model, and information related to their metadata, as well as acronyms, semantic rules, etc. A specific protocol has been defined for the Data Dictionary in order to validate modifications based on the authorised roles of users. The purpose of the Data Dictionary is to enhance the transparency of the WILIAM model by disclosing all the Symbols and data sources used.</li> <li>The WILIAM Database of selected simulated scenarios and results, includes information on the narrative description of selected storylines and selected simulated scenarios and the necessary details to access the inputs used for these scenarios, as well as the future projections of the main output variables of the WILIAM model for selected simulated scenarios. The purpose of the WILIAM Database of selected simulated scenarios and results is to be used as an instrument for dissemination of the results of WILIAM.</li> </ul> <p> </p>
Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios
<p>All total 152 datasets are collected in the benchmarking study.</p> <p>Every dataset contains two parts: the gene expression matrix (or well-established model by dynwrap for trajectory) and the data information including the data id, repository, accession number, URL, technology platform, species, organ (source), cell number, gene number, data type, ERCC spike-in, dilution factor, volume, group condition, treatment, batch information and cluster labels.</p> <p>There are 23 datasets (data79-data101) for evaluating the simulation ability for cell trajectories which are derived from another Zenodo repository (https://zenodo.org/record/1443566).</p>
Data for the publication "Sensitivity of Banner Cloud Formation to Orography and the Ambient Atmosphere: Transition From Idealized to More Realistic Scenarios"
<p>Data accompanying the paper titled: "Sensitivity of Banner Cloud Formation to Orography and the Ambient Atmosphere: Transition From Idealized to More Realistic Scenarios"</p> <p>This repository contains output statistics for thirty simulations run with the EULAG model in LES mode (Prusa et al., 2008).</p> <p>We use three distinct configurations of the model domain and its orography, ranging from highly idealized to fully realistic. The configuration with realistic orography is referred to as “Matt_Ref” and covers a domain of 12.8 x 9.6 x 7 km. As our intermediate configuration, we use the realistic orography from the Matterhorn summit protruding from a flat plain, referred to as "Matt_Iso". As our highly idealized configuration we consider a Pyramid protruding from a flat plain, referred to as "Pyr". In both configurations the model domain extends 11.6 x 9.2 x 4.5 km. The simulations of all three configurations are performed with an isotropic grid spacing of 25 m.<br> <br> Each zip-file contains the time-averaged fields of six simulations with inflow conditions of a sheared or constant wind profile with 5, 10 and 20 m/s, and neutral stratification up to the height of the mountain summit. For the "Matt_Ref" configuration exists two additional zip-files containing data of a neutral stratification up to 500 meter and 800 meter below the mountain summit. Moreover, for the "Matt_Ref" configuration exists three additional zip-files containing the time-averaged turbulent fields. For more details have a look at the paper.</p>
Updated Projections of Residential Energy Consumption across Multiple Income Groups under Decarbonization Scenarios using GCAM-USA
<p>Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL). Compared to the first version of the dataset (<a href="https://zenodo.org/record/79880387">https://zenodo.org/record/79880387</a>), this updated dataset is based on model runs where the Inflation Reduction Act (IRA) are implemented in the model scenarios. In addition to the queried and post-processed key output variables related to residential energy sector in .csv tables, we also upload the full model output databases in this repository, so that users can query their desired model outputs.</p> <p>GCAM-USA operates within the Global Change Analysis Model (GCAM), which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).</p> <p>GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: <a href="https://jgcri.github.io/gcam-doc/toc.html">https://jgcri.github.io/gcam-doc/toc.html</a>.</p> <p>The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of September 24, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP <em>per capita</em>) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.</p> <p>Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2050 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the full model output databases and key output variables related to the residential energy sector under the four scenarios, including:</p> <ul> <li>income shares by consumer groups at each state over 2015-2050 (Casper et al., 2023)</li> <li>residential energy consumption <em>per capita</em> by service, fuel, state, and income group, 2015-2050</li> <li>residential energy service output (energy consumption * technology efficiency) <em>per capita </em>by service, fuel, state, and income group, 2015-2050</li> <li>estimated energy burden (Eq.1), by state and income group, 2015-2050</li> <li>estimated satiation gap (Eq.2), by service, state, and income group, 2015-2050</li> <li>residential heating service inequality (Eq.3), by state, 2015-2050</li> </ul> <p> </p> <p><strong>Table 1</strong></p> <table> <thead> <tr> <th scope="col">Scenarios</th> <th scope="col">Policies</th> <th scope="col">Climate Change Impacts</th> </tr> </thead> <tbody> <tr> <td>BAU (Business-as-usual)</td> <td>Existing state-level energy and emission policies (including IRA)</td> <td>Constant HDD/CDD (heating degree days / cooling degree days)</td> </tr> <tr> <td>BAU_climate</td> <td>Existing state-level energy and emission policies (including IRA)</td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> <tr> <td>NZ (Net-Zero by 2050)</td> <td> <p>In addition to BAU, two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Constant HDD/CDD</td> </tr> <tr> <td>NZ_climate</td> <td> <p>In addition to BAU, two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> </tbody> </table> <p> </p> <p><strong>Eq. 1</strong></p> <p><span class="math-tex">\(Energy\ burden_{i,k} = \dfrac{\sum_j (service\ output_{i,j,k} * service\ cost_{j,k})}{GDP_{i,k}}\)</span></p> <p>for income group <em>i </em>and state <em>k</em>, that sums over all residential energy services <em>j</em>.</p> <p> </p> <p><strong>Eq. 2</strong></p> <p><span class="math-tex">\(Satiation\ Gap_{i,j,k} = \dfrac{satiation\ level_{j,k} - service\ output_{i,j,k}} {satiation\ level_{j,k}}\)</span></p> <p>for service <em>j</em>, income group <em>i</em>, and state <em>k</em>. Note that the satiation level and service output are per unit of floorspace.</p> <p> </p> <p><strong>Eq. 3</strong></p> <p><strong><span class="math-tex">\(Residential\ heating\ service\ inequality_j = \dfrac{S_j^{d10}}{(S_j^{d1} +S_j^{d2} + S_j^{d3} + S_j^{d4})}\)</span></strong></p> <p>for service <em>j </em>where <em>S</em> is the residential heating service output <em>per capita</em> of the highest income group (<em>d10</em>) divided by the sum of that of the lowest four income groups (<em>d1</em>, <em>d2</em>, <em>d3</em>, and <em>d4</em>), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality. Among the key output variables in this repository, we provide the residential <em>heating</em> service inequality output table as an example.</p> <p> </p> <p><strong>Reference</strong></p> <p>Casper, K. C., Narayan, K. B., O'Neill, B. C., Waldhoff, S. T., Zhang, Y., & Wejnert-Depue, C. (2023). Non-parametric projections of the net-income distribution for all U.S. states for the shared socioeconomic pathways. <em>Environmental Research Letters</em>. http://iopscience.iop.org/article/10.1088/1748-9326/acf9b8.</p> <p>IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].</p> <p> </p> <p><strong>Acknowledgement</strong></p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Surface temperature pattern scenarios suggest larger rates of warming than projected [DATA]
<p>Data for global-mean temperature projections and global-mean radiative feedback projections. See PDF for file description.</p>
Dataset used for New_Scenarios Framework_NIAS_India
<p>Data set is used for country classification and QP models used in manuscript titled "A New Scenario Framework for Equitable and Climate-Compatible Futures". Pre-print uploaded at <a href="https://doi.org/10.31219/osf.io/ge92t">10.31219/osf.io/ge92t</a></p>
Dataset used in the study "Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence"
<p>This dataset repository includes input and output spatial data of urban microclimate simulations performed through QGIS and ENVI-met software used in the study "Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence", published in the Building and Environment Journal, <a href="https://doi.org/10.1016/j.buildenv.2023.110854">https://doi.org/10.1016/j.buildenv.2023.110854</a>.</p>
Summary dataset for Indonesia Net-Zero electricity scenarios
<p>Dataset of “Net-Zero Electricity” study as visualized in <a href="https://masadepanenergi.id">https://masadepanenergi.id</a>. This data is generated for electricity sector analysis using Spatially-explicit Energy and LAnd system InfrastRUcture (SELARU) modelling framework.</p> <p>Full documentation and scientific publication of the study is still in preparation. However, technical details, including input datasets and basic assumptions of SELARU modelling framework, can be found in the model’s public repository (<a href="https://github.com/iiasa/selaru">https://github.com/iiasa/selaru</a>).</p> <p>This Upload contains the summary datasets for generation capacity and output of Net-Zero electricity scenarios for each spatial unit (spatial unit GIS file provided in “spatial_unit_nze_2023.gpkg”), as well as provincial and national summary for investment requirements and carbon emissions.</p> <p>Dataset of “Net-Zero Electricity” study as visualized in <a href="https://masadepanenergi.id">https://masadepanenergi.id</a>. This data is generated for electricity sector analysis using Spatially-explicit Energy and LAnd system InfrastRUcture (SELARU) modelling framework.</p> <p>Full documentation and scientific publication of the study is still in preparation. However, technical details, including input datasets and basic assumptions of SELARU modelling framework, can be found in the model’s public repository (<a href="https://github.com/iiasa/selaru">https://github.com/iiasa/selaru</a>).</p> <p>This Upload contains the summary datasets for generation capacity and output of Net-Zero electricity scenarios for each spatial unit (spatial unit GIS file provided in “spatial_unit_nze_2023.gpkg”), as well as provincial and national summary for investment requirements and carbon emissions.</p>
Future land cover and land cover dynamic trajectories in China under anthropogenic and climate forcing in 8 SSP-RCP scenarios
<p>The projection of future land cover in 21st century in China and land cover dynamic trajectories in 8 SSP-RCP scenarios</p>
Extracting Educational Code Scenarios from Python Textbooks
<p><strong>Dataset Overview:</strong> This dataset complements the research project titled "Extracting Learning Scenarios from Python Textbooks." It consists of 1,017 chapter titles collected from 76 Python textbooks.</p><p><strong>Research Findings:</strong> Our analysis revealed that learning scenarios (referred to as "Scenarios") are a prevalent theme, constituting approximately 39.5% of the total chapter titles in comparison to other content categories. We further categorized these scenarios into four types, including Application Programming Interfaces, Data and Processing, Graphical User Interfaces, and other scenarios. Additionally, we identified a list of 19 Python modules commonly used within these scenarios.</p><p><strong>Purpose:</strong> We envision that this work and its insights can serve as stepping stones and lay the groundwork for further extraction and the effective application of how Python can be utilized for its diverse audience.</p>
Suicide Prevention in Rural Veterans During High-risk Care Transition Scenarios
ClinicalTrials.gov study NCT04054947. IPD Sharing: NO. Countries: 1. Publications: 1.
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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.