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Dataset results
942 results for “scenarios”
Hourly LC impacts - Acidification - current mix and future scenarios
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Acidifcation, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Primary Non-renewable energy - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Primary Non-renewable Energy, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly generation and supply data - current mix and future scenarios
<p>Dataset on hourly generation, imports and exports of electricity in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030).</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p> <p> </p>
Hourly LC impacts - Particulate Matter - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Particulate Matter, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Ozone Layer Depletion - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Ozone Layer Depletion, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Fresh water Eutrophication - current mix and future scenarios
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Fresh water Eutrophication, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Terrestrial Eutrophication - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Terrestrial Eutrophication, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Marine Eutrophication - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Marine Eutrophication, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Global Warming - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Global Warming, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Resource use - minerals and metals - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Resource use - minerals and metals, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Soil Quality Index - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Soil Quality Index, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
LC impacts -24h profiles - current mix and future scenarios, marginal demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - marginal demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100)
<p>This dataset is documented in this manuscript here- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta</p> <p>Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. We project US state level income distributions using a PCA-based approach, applying a downscaled version of the approach employed by Narayan et al. (2022, in-prep). A state-level dataset had to be synthesized and projected based on existing sources. We apply a PC-based model to our derived state-level dataset, employing projected GINI’s from the SSP scenarios. We produce projected income distribution by income decile for three SSPs to year 2100. For the purpose of the projections, we developed a consistent set of tax adjusted net income deciles for all states from 2011 to 2014. This dataset was used for initialization of the projections and for validation.</p> <p>If/when using this dataset, please cite this paper- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta</p>
Scenario emissions and temperature data for PROVIDE project
<p>Data for tier 1 and tier 2 PROVIDE scenarios. </p> <p>Tier 1 scenarios are mostly from integrated assessment models. Tier 2 scenarios are much more numerous and are kept in a separately zipped folder for temperatures and csv file for emissions data. The temperature folders contains the full set of FaIR runs for scenarios entirely defined by emissions. Summaries are much smaller files containing quantile info for each scenario, including the scenarios defined by combinations of emissions and temperature trends. </p> <ul> <li>10 Tier 1 scenarios until 2100</li> <li>15 Tier 1 scenarios defined until 2300, all of which are variations of the original 10</li> <li>Many Tier 2 scenarios, aiming to completely tile reasonable emissions space parameterised with 4 variables</li> </ul> <p><em>Several objectives of the PROVIDE project depend on a set of scenarios that can be modelled through either a ‘classical’ forward-looking approach or by a novel approach that ‘reverses the impact chain’. These scenarios are also key elements for the integration of PROVIDE findings in the outward-looking stakeholder Dashboard of the project. Here we describe the set of scenarios that has been developed and will be used within PROVIDE. In total, PROVIDE explores <strong>three complementary approaches</strong>:</em></p> <ol> <li><em>10 distinct tier 1 scenarios extending until 2100, mostly based on the existing literature, used for short-term assessments of impacts</em></li> <li><em>15 distinct tier 1 scenarios extending until 2300, based on different extensions of the 10 literature scenarios, used for assessing longer-run impacts and the geophysical impact of significant temperature overshoot</em></li> <li><em>~1350 distinct tier 2 scenarios, exploring several dimensions of emissions space systematically, such as CO<sub>2</sub> net zero date and relative methane intensity. This is used to explore which scenarios are compatible with given climate outcomes. These scenarios can be used to reverse the traditional impact chain, going from acceptable climate risks to descriptions of acceptable emissions. </em></li> </ol>
A set of typical relevant exposure scenarios for nanoparticles in semiconductor industry (dataset)
<p>This is an Excel database part of Deliverable 1.3 "A set of typical relevant exposure scenarios for NP’s in semiconductor industry"</p> <p>https://www.zenodo.org/record/2538388</p> <p> </p>
Supplementary data to "Three scenarios for coal power in Vietnam"
<p>This dataset includes the history of coal power generation in Vietnam, listing generation units capacity, creation date, and current status up to March 2019.</p> <p>It defines three scenarios for the future. “Blazing up” corresponds to the power development plan 7 revised and updated as of March 2019. “Closed window” tells what we think would happen under pure market forces. “Coal peak” tells what could happen if the State continues to steer the electricity system into the energy transition, decisively and without imposing high costs to stakeholders.</p> <p>Corresponds to Table 2 and 3 in the manuscript.</p> <p>#VIETSE</p>
HydroGeoSphere Model Input Files and Results for Validation of Pesticide Leaching to Groundwater for Nine EU FOCUS Scenarios
<p>This dataset includes HydroGeoSphere (HGS) model (Aquanty, 2024) input and output files for nine Forum for the Co-ordination of Pesticide Models and their Use (FOCUS) scenarios (EC, 2014) for simulation of leaching of four test contaminants to groundwater. It is recommended that users are familiar with HGS software in order to best make use of the available files. Scenarios are included in separate subfolders named using the first four letters of the FOCUS scenario location name, e.g. folder "chat" contains the model run for the "Chateaudun" scenario. It is recommended that users familiarize themselves with the (EC, 2014) groundwater scenarios. HGS model inputs are specified in the *.grok ASCII text file for each scenario in each subfolder. Soil material properties and evapotranspiration properties are included in HGS input files in ASCII text format in the "material_properties" subfolder. Solute application timing for each scenario are include in the "solute_app" subfolder. And climate times series inputs are included in the "weather" subfolder.</p>
SERENA Task 1.4 – Definition of scenarios - Survey results
<p>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.</p>
KNMI'23 sea-level scenarios
<p>Reference data for the KNMI'23 sea-level scenarios for the Netherlands, Bonaire and Saba.</p> <p>The scenarios are height anomalies compared to the reference period 1995-2014. They are not referenced to a vertical reference system.</p> <p>The three files "<a href="https://zenodo.org/api/records/14047707/draft/files/DataKNMI23_SeaLevel_2100_ref_period_1995_2014.csv/content" target="_blank" rel="noopener noreferrer">DataKNMI23_SeaLevel_2100_ref_period_1995_2014.csv</a>", "<a href="https://zenodo.org/api/records/14047707/draft/files/DataKNMI23_Saba_SeaLevel_2100_ref_period_1995_2014.csv/content" target="_blank" rel="noopener noreferrer">DataKNMI23_Saba_SeaLevel_2100_ref_period_1995_2014.csv</a>" and "<a href="https://zenodo.org/api/records/14047707/draft/files/DataKNMI23_Bonaire_SeaLevel_2100_ref_period_1995_2014.csv/content" target="_blank" rel="noopener noreferrer">DataKNMI23_Bonaire_SeaLevel_2100_ref_period_1995_2014.csv</a>" are time series up to 2100 with yearly mean data for the Netherlands, Saba and Bonaire. Multiple percentiles are provided.</p> <p>The three files "<a href="https://zenodo.org/api/records/14047707/draft/files/knmi23_long_term_projections.csv/content" target="_blank" rel="noopener noreferrer">knmi23_long_term_projections.csv</a>", "<span><a href="https://zenodo.org/api/records/14047707/draft/files/knmi23_long_term_projections_Saba.csv/content" target="_blank" rel="noopener noreferrer">knmi23_long_term_projections_Saba.csv</a></span>" and "<span><a href="https://zenodo.org/api/records/14047707/draft/files/knmi23_long_term_projections_Bonaire.csv/content" target="_blank" rel="noopener noreferrer">knmi23_long_term_projections_Bonaire.csv</a></span>" are time series up to 2300 for the Netherlands, Saba and Bonaire.</p> <p>Time series of the low-likelihood high-impact scenarios are also provided for the same three regions (called lphi).</p> <p>The file "<a href="https://zenodo.org/api/records/14047707/draft/files/DataKNMI23_SeaLevel_ref_period_1995_2014_DateToReachHeight.csv/content" target="_blank" rel="noopener noreferrer">DataKNMI23_SeaLevel_ref_period_1995_2014_DateToReachHeight.csv</a>" provides the date to reach a given height rather than the height at a given date.</p>
3D geological models of dolomitized clinoforms and flow simulation results: scenario 2 in Teoh, C.P. et al (2021)
<p>3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 2 in Teoh, C.P. et al (2021) doi:<a href="http://doi.org/10.1016/j.marpetgeo.2021.105344">10.1016/j.marpetgeo.2021.105344</a>.<br> Models are built using surface-based modelling approach (doi:<a href="https://doi.org/10.1007/s11004-018-9764-8">10.1007/s11004-018-9764-8</a>). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance.</p> <p>For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (<a href="http://multifluids.github.io/">multifluids.github.io</a>)<br> Flow simulation results are provided as:</p> <ul> <li> 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month.</li> <li> In- and outflow rates and volumetric proportions per phase in .csv</li> </ul> <p>Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem</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.