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.
36
datasets available to search
ShareScore release 0.9.0
Dataset results
36 results for “emission scenarios”
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>
Prospects for Neotropical forest birds and their habitats under contrasting emissions scenarios
Open the record for dataset details and reuse information.
Impacts of anthropogenic emission change scenarios on U.S. water and carbon balances at national and state scales in a changing climate
Open the record for dataset details and reuse information.
Dataset on Scenarios for Urban Emissions and LUE with Increments of Global Warming
<p>This dataset supports the original research article on “<strong>Framework for comparing urban emissions and land use efficiency scenarios to avoid increments of global warming</strong>” (Kılkış, forthcoming). <br><br>A total of 28 datasheets are involved in this dataset that is organised into four main domains as follows: </p> <ol> <li>The first domain with 3 datasheets contains urban emissions data for three urban emissions scenarios (SSP1-1.9, SSP1-2.6, and SSP1-RE) on an annual basis. The data is summed to obtain cumulative urban emissions between 2020 and 2050 per urban area for 465 urban areas.</li> <li>The second domain with 9 datasheets provides the quantification of the original parameter on contributions to increments of global warming based on cumulative urban emissions data for three urban emissions scenarios (SSP1-1.9, SSP1-2.6, and SSP1-RE) between 2020 and 2050. The best estimate of the transient climate response to cumulative carbon dioxide (CO<sub>2</sub>) emissions (TCRE) and its 5-95th percentile range are considered within the quantifications of this analysis.</li> <li>The third domain with 4 datasheets has the annual CO<sub>2</sub> sequestration penalties of four land use efficiency scenarios (LUE 5%, LUE 15%, LUE Av, and LUE Best). Data is summed for cumulative CO<sub>2</sub> sequestration penalties between 2020 and 2050 per urban area for 135 urban areas.</li> <li>The fourth domain with 12 datasheets contains the quantification of the original parameter on contributions to increments of global warming based on CO<sub>2</sub> sequestration penalties for four land use efficiency scenarios (LUE 5%, LUE 15%, LUE Av, and LUE Best) between 2020 and 2050. The analysis is based on the best estimate of TCRE as well as its 5-95th percentile range.</li> </ol> <p>The data structure is described in the information sheet and the 28 datasheets contain 292,100 cells in total. The method and results that utilise the data are described in the original article submitted to <em>Energy</em>. The dataset of this research article should be cited as: Kılkış (2023), Dataset on Scenarios for Urban Emissions and LUE with Increments of Global Warming (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.10407848">https://doi.org/10.5281/zenodo.10407848</a></p>
Dataset for "Extended scenarios for solar radio emissions with downshifted electron beam plasma excitations"
<p>Input data for the PIC simulation</p>
Dataset - modelled CO2 emissions from tropical peat-draining rivers and coastal waters based on enhanced weathering scenarios.
<p>Dataset related to the manuscript "Destabilization of carbon in tropical peatlands by enhanced weathering" (DOI: <a href="https://doi.org/10.1038/s43247-022-00544-0">10.1038/s43247-022-00544-0</a>).</p> <p>Model runs for enhanced leaching of dissolved inorganic carbon (DIC) and of dissolved organic carbon (DOC) were conducted.</p> <p>Results include in-river carbon dioxide (CO2), DIC, DOC, oxygen (O2) and pH as well as CO2 emissions from rivers and from coastal waters.</p> <p>Main results are in "River_And_Coastal_Response_To_Enhanced_Weathering.xlsx".<br> Results for uncertainty study are in "River_And_Coastal_Response_To_Enhanced_Weathering_Uncertainty_Scenarios.xlsx".</p>
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>
Dataset for Nigeria cement sector emissions scenarios in Yetano Roche (2022)
<p>Dataset and tool for "Built for net-zero: analysis of long-term greenhouse gas (GHG) emission pathways for the Nigerian cement sector", Journal of Cleaner Production (2022)</p>
Determinants of energy futures - a scenario discovery method applied to cost and carbon emission futures for South American electricity infrastructure
<p>Scenario discovery SAMBA data files:</p> <p>1) The folder SAMBA_324_datafiles.zip contains all 324 data files for the OSeMOSYS run.<br> Each of these files has a code on top referring to the combination that it represents.<br> The key to the levers is in the Excel file "Metafile". There the naming convention of technologies as well as corresponding combination for scenario are also available.<br> 2) The Access database Scenario_discovery_database.mbd contans results from the 324 runs.<br> The key to the scenarios are in the Excel file "Metafile" tab "Scenario_key".<br> 3) The file OSeMOSYS_SAMBA_161130.txt is the version OSeMOSYS that was used to run all scenarios.<br> 4) The PRIM analysis is available on the GitHub repository: https://github.com/NMoksnes/Scenario_discovery</p>
IPCC Special Report on Emissions Scenarios (SRES) Emissions Scenarios Dataset Version 1.1
The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) Emissions Scenarios Dataset Version 1.1 consists of 40 global and regional greenhouse gases (GHGs) and sulfur emissions scenarios projected every 10 years beginning in 1990 through 2100. The scenarios are based on extensive assessment of driving forces and emissions in the scenario literature, alternative modeling approaches, and an open process that solicited wide participation and feedback. The scenarios provide the basis for future assessments of climate change and possible response strategies. This data set is produced by the IPCC and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
IPCC IS92 Emissions Scenarios (A, B, C, D, E, F) Dataset Version 1.1
The Intergovernmental Panel on Climate Change (IPCC) IS92 Emissions Scenarios (A, B, C, D, E, F) Dataset Version 1.1 consists of six global and regional greenhouse gases (GHGs) emissions scenarios projected from 1990 through 2100. The six alternative IPCC scenarios (IS92 A to F) were published in the 1992 Supplementary Report to the IPCC Assessment. These scenarios embodied a wide array of assumptions affecting how future greenhouse gas emissions might evolve in the absence of climate policies beyond those already adopted. The data set was originally produced by IPCC in 1992, and the digital version was re-edited in 2005 to resolve the discrepancies among versions of the data over years. The definitive version of this data set is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
IPCC Special Report on Emissions Scenarios (SRES) 1x1 Degree Gridded Emissions Dataset
The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) 1x1 Degree Gridded Emissions Dataset consists of global gridded emissions for greenhouse gases (GHGs) projected every 10 years beginning in 1990 through 2100. The grids are produced for reactive gases Methane (CH4), Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Non-Methane Volatile Organic Compounds (NMVOC), along with Sulfur Dioxide (SO2), based on the IPCC SRES Emissions Scenarios Data Set Version 1.1. This data set is produced by the IPCC and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
IPCC Special Report on Emissions Scenarios (SRES) Fluor-Gases Emissions Dataset
The Intergovernmental Panel on Climate Change (IPCC) Special Report Emissions Scenarios (SRES) Fluor-Gases Emissions Dataset consists of global and regional emissions of Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs), Sulfur Hexafluoride (SF6), Cholorfluorocarbons (CFCs) and Hydrochlorofluorocarbons (HCFCs) projected every 10 years beginning in 1990 through 2100. This data set is produced by the IPCC and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios
The Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios is an update to a major crop modeling study by the NASA Goddard Institute for Space Studies (GISS). The initial study was published in 1997, based on output of HadCM2 model forced with greenhouse gas concentration from the IS95 emission scenarios in 1997. Results of the initial study are presented at SEDAC's Potential Impacts of Climate Change on World Food Supply: Data Sets from a Major Crop Modeling Study, released in 2001. The co-authors developed and tested a method for investigating the spatial implications of climate change on crop production. The Decision Support System for Agrotechnology Transfer (DSSAT) dynamic process crop growth models, are specified and validated for one hundred and twenty seven sites in the major world agricultural regions. Results from the crop models, calibrated and validated in the major crop-growing regions, are then used to test functional forms describing the response of yield changes in the climate and environmental conditions. This updated version is based on HadCM3 model output along with GHG concentrations from the Special Report on Emissions Scenarios (SRES). The crop yield estimates incorporate some major improvements: 1) consistent crop simulation methodology and climate change scenarios; 2) weighting of model site results by contribution to regional and national, and rainfed and irrigated production; 3) quantitative foundation for estimation of physiological CO2 effects on crop yields; 4) Adaptation is explicitly considered; and 5) results are reported by country rather than by Basic Linked System region. The data are produced by A. Iglesias and C. Rosenzweig and the maps are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
A land use scenario of SSP1-Low emissions scenario for Scotland
<p>This dataset contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emissions scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production. The 2019 baseline land use map at 100m resolution, was created by combining the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). The land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility among others that meets all the constraints stipulated for the scenario.</p> <p>For a detailed description of the scenario refer to the following web storymap : https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</p> <p>This analysis was conducted as part of the Land use Transformations (https://landusetransformations.hutton.ac.uk/) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.</p> <p>The exact licence for this dataset is currently being finalised. When ready, the dataset will be uploaded as a new version.<br> </p>
Emissions scenario database of the European Scientific Advisory Board on Climate Change, hosted by IIASA
<p>This scenario ensemble collects emissions pathways quantitative, model-based scenarios related to the mitigation of climate change.</p> <p>The ensemble was compiled from the energy and integrated-assessment modelling community in response to a call by the European Scientific Advisory Board on Climate Change, see <a href="https://www.eea.europa.eu/about-us/climate-advisory-board/call-for-scenario-data-contributions">https://www.eea.europa.eu/about-us/climate-advisory-board/call-for-scenario-data-contributions</a>.</p> <p>The scenario ensemble can be accessed via the <strong>EU Climate Advisory Board Scenario Explorer</strong> hosted by IIASA at <a href="https://data.ece.iiasa.ac.at/eu-climate-advisory-board">https://data.ece.iiasa.ac.at/eu-climate-advisory-board</a>. The data can be downloaded and re-used for analysis and data visualization, but re-publication of a substantial portion is prohibited. </p> <p>The reason for the restriction is that we anticipate updates/extensions and (possibly) error corrections of this scenario ensemble.<br> We want to avoid a situation where multiple inconsistent versions of the scenario database are in wide circulation, which can lead to confusion for users. Therefore, please refer to the IIASA Scenario Explorer for the most-up-to-date version of the database.</p> <p>Further guidance and the full license text is available at <a href="https://data.ece.iiasa.ac.at/eu-climate-advisory-board/#/license">https://data.ece.iiasa.ac.at/eu-climate-advisory-board/#/license</a>.</p>
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.