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.
72
datasets available to search
ShareScore release 0.9.0
Dataset results
72 results for “Energy Climate”
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Sint-Katelijne-Waver, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
REPEAT Project Section-by-Section Summary of Energy and Climate Policies in the 117th Congress
<p>This public spreadsheet provides a section-by-section summary of climate and energy policy measures contained in major legislation introduced and/or enacted in the 117th United States Congress (sitting January 2021-January 2023)<br> <br> In separate worksheets, the following pieces of legislation are summarized: <br> 1. The Infrastructure Investment and Jobs Act of 2021, H.R. 3684, as passed by Senate 8/10/21 (aka the Bipartisan Infrastructure Law)<br> 2. The first full draft of the House Build Back Better Act of 2021, H.R. 5376, as referred to House Budget Committee, 9/25/21<br> 3. The Infrastructure Investment and Jobs Act of 2021, H.R. 3684, as passed by the House and sent to President Biden on 11/6/21 (aka the Bipartisan Infrastructure Law)<br> 4. The House-passed Build Back Better Act of 2021, H.R. 5376, Managers Amendment to Rules Committee, 11/3/21 (R.C.P. 117-18), as passed by the House on 11/19/21<br> 5. The Inflation Reduction Act of 2022, H.R. 5376, as introduced 7/27/22 and passed by the House and Senate in August 2022</p> <p>The spreadsheets also contain documentation of how policies are modeled or considered in <a href="http://repeatproject.org">REPEAT Project's analysis</a> of each federal legislation.<br> <br> The sheets for each version of the Build Back Better Act and Inflation Reduction Act also attempts to track how each policy changed (or was removed) during the evolution of the FY2022 budget reconciliation bill (H.R. 5376).</p> <p>Caveat emptor: all summaries are compiled by the authors and may contain errors; it is the responsibility of readers/users to verify the accuracy of summaries.<br> <br> This most recently updated live version of this dataset can always be accessed via Google Sheets at <a href="http://bit.ly/REPEAT-Policies">http://bit.ly/REPEAT-Policies</a></p>
Energy Consumption Synthetic Dataset Generation Through Parametric Analysis for Residential Buildings in Hot Climates
Open the record for dataset details and reuse information.
Effects of low-carbon energy adoption on airborne particulate matter concentrations with feedbacks to future climate over California
Open the record for dataset details and reuse information.
Climate-driven changes in electricity demand and energy expenditures
<p>These data are the full model outputs corresponding to the direct electricity demand & energy expenditure impacts reported in Hsiang et al. (2017), "Estimating economic damage from climate change in the United States," DOI 10.1126/science.aal4369. Model documentation can be found in that article and in Houser et al. (2015), "Economic Risks of Climate Change: An American Prospectus," ISBN 9780231174565.</p>
SECURES-Energy: Hourly electricity demand and supply profiles for historical climate and climate change projections in Europe until 2100
<p><strong>SECURES-Energy</strong></p> <p>Weather-dependent renewable electricity systems are vulnerable to climate change impacts. Electricity generation and demand profiles considering weather and climate impacts are needed in energy system modelling. We present a consistent and high-quality energy database in data formats useful for energy system modelling and keeping the high spatiotemporal complexity of climate data. The open-access dataset SECURES-Energy contains all relevant electricity demand and supply components for the EU and several additional European countries in hourly resolution covering the period 1981-2100. It is based on reanalysis data ERA5(-Land) for the historical period and two EURO-CORDEX emission scenarios (RCP 4.5 and RCP 8.5). On the generation side, impacts on onshore and offshore wind power generation, solar PV generation, and hydropower generation (run-of-river and reservoirs) – which is often missing in comparable datasets – are provided. On the demand side, all demand components relevant to future electricity systems including e-heating, e-cooling, e-mobility, and electricity demand in industry, are provided.</p> <p>The detailed methods are described in the final project report (see link below) in Chapter 2.2 and Chapter 4.3 and a related journal publication is currently in preparation.</p> <p><strong>Further information:</strong></p> <ul> <li>Project website SECURES: https://www.secures.at/</li> <li>All project-related publications: https://www.secures.at/publications</li> <li>Final SECURES project report: https://www.secures.at/fileadmin/cmc/Final_Report_SECURES.pdf and https://www.klimafonds.gv.at/wp-content/uploads/sites/16/C061007-ACRP12-SECURES-KR19AC0K17532-EB.pdf</li> </ul> <p>The SECURES-Energy dataset provides variables visible in the table.</p> <ol> <li>Hourly profiles ERA5-Land 1981-2010</li> <li>Hourly profiles RCP 4.5/RCP 8.5 2011-2100</li> </ol> <p> </p> <p><strong>Production profiles:</strong></p> <table> <tbody> <tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Temporal resolution</th> </tr> <tr> <th>Photovoltaics</th> <td>pv</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Wind onshore</th> <td>wind</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Wind offshore</th> <td>wind_offshore</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Hydro run-of-river</th> <td>hydro_ror</td> <td>-</td> <td>hourly</td> </tr> </tbody> </table> <p> </p> <p><strong>Demand profiles:</strong></p> <table> <tbody> <tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Explanation</th> </tr> <tr> <th>Temperature</th> <td>temperature</td> <td> <p>°C</p> </td> <td> <p>Population-weighted mean temperature (2 m)</p> </td> </tr> <tr> <th> <p>Rounded temperature</p> </th> <td>rounded_temperature</td> <td>°C</td> <td>Temperature values rounded to zero decimal places</td> </tr> <tr> <th>Daytype</th> <td>day type</td> <td>-</td> <td> <p>weekdays = typeday 0; Saturday or day before a holiday = typeday 1; Sunday or holiday = typeday 2</p> </td> </tr> <tr> <th>Month<strong><br></strong></th> <td> <p>month</p> </td> <td> <p>-</p> </td> <td> <p> The column “month” refers to the month of the year. 1 = January, 2 = February etc.</p> </td> </tr> <tr> <th> Season</th> <td>season</td> <td>-</td> <td> <p>0 = Summer (15/05 - 14/09)</p> <p>1 = Winter (1/11 - 20/3)</p> <p>2 = Transition (21/3 - 14/5 & 15/9 - 31/10)</p> </td> </tr> <tr> <th>Load e-mobilty</th> <td> <p>load_emobility</p> </td> <td> <p>-</p> </td> <td> <p>E-mobility electricity demand profile, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Non-metallic minerals</th> <td> <p>non_metallic_minerals</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector non-metallic minerals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Paper</th> <td> <p>paper</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector paper, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Iron and steel</th> <td> <p>iron_and_steel</p> </td> <td> <p>-</p> </td> <td>Electricity demand profile of the industrial sector iron and steel, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</td> </tr> <tr> <th>Chemicals and petrochemicals</th> <td> <p>chemicals_and_petrochemicals</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector chemicals and petrochemicals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Food and tobacco</th> <td> <p>food_and_tobacco</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector food and tobacco, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>SHW residential</th> <td> <p>shw_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for sanitary hot water in the residential sector, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>SHW tertiary<strong><br></strong></th> <td> <p>shw_tertiary</p> </td> <td> <p> </p> </td> <td> <p>Electricity demand profile for sanitary hot water in the tertiary sector, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Cooling residential<strong><br></strong></th> <td> <p>cooling_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for cooling in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Heating residential<strong><br></strong></th> <td> <p>heating_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for heating in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Cooling tertiary</th> <td> <p>cooling_tertiary</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for cooling in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Heating tertiary<strong><br></strong></th> <td> <p>heating_tertiary</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for heating in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Rest<strong><br></strong></th> <td> <p>rest</p> </td> <td> <p>-</p> </td> <td> <p>Rest electricity demand profile, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Exogenous H2<strong><br></strong></th> <td> <p>exogenous_H2</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for electrolysis (flat profile), normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Total<strong><br></strong></th> <td> <p>total</p> </td> <td> <p>-</p> </td> <td> <p>Total electricity demand profile containing all components above (e-mobility, industry, residential heating, residential sanitary hot water, residential cooling, tertiary heating, tertiary sanitary hot water, tertiary cooling, rest, and exogenous H2 electricity demand), normalized to an annual demand of 10,000,000 in the reference year 2010</p> </td> </tr> </tbody> </table> <p>Electricity supply profiles for wind (onshore and offshore), hydro (run-of-river), and solar generation are provided for almost all European countries, namely: Andorra (AD), Albania (AL), Austria (AT), Bosnia and Herzegovina (BA), Belgium (BE), Bulgaria (BG), Switzerland (CH), Czech Republic (CZ), Germany (DE), Denmark (DK), Estonia (EE), Spain (ES), Finland (FI), France (FR), United Kingdom of Great Britain and Northern Ireland (GB), Greece (GR), Croatia (HR), Hungary (HU), Republic of Ireland (IE), Italy (IT), Liechtenstein (LI), Lithuania (LT), Luxembourg (LU), Latvia (LV), Montenegro (ME), North Macedonia (MK), Malta (MT), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Sweden (SE), Slovenia (SI), Slovakia (SK), San Marino (SM), Ukraine (UA), Vatican (VA), and Kosovo (XK). The countries covered by the electricity demand profiles are the EU27 countries (except for Cyprus), CH, GB, and NO.</p> <p>Industrial, heating, and cooling demand profiles are based on regressions developed in the H2020 Hotmaps project [1] [2]. </p> <p>SECURES-Energy is available in a tabular csv format for the historical period (1981-2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 2011-2100) created from one CMIP5 EURO-CORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E) on the<strong> </strong>spatial aggregation level<strong> NUTS0 </strong>(country-wide).</p> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shapefiles of the different NUTS levels.</p> <p>Hydro reservoir profiles are also published and can be found in the related dataset SECURES-Met: https://zenodo.org/records/7907883.</p> <p>The project SECURES and corresponding publications are funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p> <p>[1] Fallahnejad M. Hotmaps-data-repository-structure 2019. https://wiki.hotmaps.eu/en/Hotmaps-open-data-repositories.</p> <p>[2] Pezzutto S, Zambotti S, Croce S, Zambelli P, Garegnani G, Scaramuzzino C, et al. HOTMAPS - D2.3 WP2 Report – Open Data Set for the EU28. 2019.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Antwerp Berchem, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem (51°12'00"N 4°26'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Fish shrinking, energy balance and climate change
<p><strong>'1_Daily_oxygen_consumption.R</strong> to<strong> 5_Feeding_duration.R': </strong>R scripts used to investigate effect of temperature and feeding treatments on oxygen consumption of sardines</p> <p><strong>'6_Figure 2.R': </strong>script of Figure 2</p> <p><strong>'Daily_consumption_mgO2_rel2'</strong>: Dataset of daily oxygen consumption (AUC) per tank, date, feeding treatment and temperature</p> <p><strong>'Duration_feeding_activity'</strong>: Dataset of feeding duration calculated per tank, date, feeding treatment and temperature. 'Fin_repas=999999' means no end was detected.</p> <p><strong>'Feeding_slope'</strong>: Dataset of oxygen consumption slopes during feeding period.</p> <p><strong>'Fiche_suivi_bassins'</strong>: Experiemental monitoring dataset</p> <p><strong>'Slope_segmented_end_feeding_manip1_2_and_3_and_fas'</strong>: Dataset of slopes estimated during feeding using moving window (per date, tank, feeding treatment and temperature).</p> <p><strong>'Slopes_part1_V2', 'Slopes_part2_V2', 'Slopes_part3_V2' = </strong>Datasets including slopes per feeding treatment, date, tank and temperature</p> <p><strong>'TABLEAU_mgO2_rel2': </strong>Dataset of oxygen consumption estimations per feeding treatment, date, tank and temperature</p> <p><strong>'Manip_.txt'</strong> : Raw data of oxygen concentration in tanks acquired during this study</p>
Seasonal and interannual variability of the wave climate at a wave energy hotspot off the southwestern coast of Australia
<p>This dataset contains the output from a 38-year wave hindcast for the Albany region of Western Australia. The methods and analysis are contained within the publication "Seasonal and interannual variability of the wave climate at a wave energy hotspot off the southwestern coast of Australia" by Cuttler et al.</p> <p>Please see publication for full details: Cuttler, MVW, Hansen, JE, and Lowe RJ (2019) Seasonal and interannual variability of the wave climate at a wave energy hotspot off the southwestern coast of Australia. <em>Renewable Energy</em>, 146, 2337-2350.Cuttler, MVW, Hansen, JE, and Lowe RJ (2019) Seasonal and interannual variability of the wave climate at a wave energy hotspot off the southwestern coast of Australia. <em>Renewable Energy</em>, 146, 2337-2350.</p> <p>Files contained within this dataset include:</p> <ul> <li>Hourly spectral output files from the 50m-resolution domains at the proposed development site (30 m depth) in Torbay, Western Australia </li> <li>Hourly spectral output files from the 165-m resolution domain at the WA Dept.of Transport wave buoy (60 m depth) and proposed development site in Torbay, Western Australia</li> <li>Example Matlab scripts for reading 2D spectral data from the 50m- and 165m-resolution grids</li> </ul> <p>For other data requests, comments, or questions please contact Michael Cuttler at michael.cuttler@uwa.edu.au</p>
Energy Consumption Reduction in Historic Urban Residential Sectors: A Case Study of Kyoto City Using Bottom-Up Modeling and Future Climate Scenarios
<p>This dataset is a detailed simulation result of 21 scenarios in the paper. The results include:</p> <ol> <li>Annual daily energy consumption data divided by energy source and residential type.</li> <li>Photovoltaic power generation data, direct photovoltaic use, battery use, and photovoltaic power generation consumed by apartment houses and Kyomachiya through P2C systems. </li> <li>Annual daily net energy consumption data divided by energy source and residential type.</li> </ol>
Data for climate mitigation scenarios with persistent COVID-19 related energy demand changes
<p>This repository contains data for the main text figures plus some supplementary figures in the article:<br> Kikstra et al 2021 Nat. Energy. DOI: <a href="https://doi.org/10.1038/s41560-021-00904-8">10.1038/s41560-021-00904-8</a></p> <p>This dataset should be cited as: Kikstra et al. (2021). Data for climate mitigation scenarios with persistent COVID-19 related energy demand changes. DOI: <a href="https://doi.org/10.5281/zenodo.5211169">10.5281/zenodo.5211169</a></p> <p>In order to reproduce the figures, one needs to use the script that is available on GitHub at:<br> <a href="https://github.com/iiasa/covid-energy-demand-scenarios">https://github.com/iiasa/covid-energy-demand-scenarios</a></p> <p>The most accessible way of exploring the scenario data behind this article would be to go to <a href="https://data.ece.iiasa.ac.at/engage/#/workspaces/60">https://data.ece.iiasa.ac.at/engage/#/workspaces/60</a>.<br> This goes to a web tool hosted by the International Institute of Applied Systems Analysis (IIASA) which provides access to a database of these and more variables of interest, defined for each scenario on the detail of MESSAGE regions, with a few example workspaces available within the ENGAGE Scenario Explorer.<br> The Scenario Explorer is a versatile open access tool to browse, visualize and download data and results. Users can freely create a private workspace where customized plots can be saved and shared.<br> For tutorials on how to use the Scenario Explorer, please visit <a href="https://software.ece.iiasa.ac.at/ixmp-server/tutorials.html">https://software.ece.iiasa.ac.at/ixmp-server/tutorials.html</a>.</p> <p>The scenarios that were used for the IPCC Special Report on 1.5C warming (SR1.5) have been made available at <a href="https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/">https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/</a>.</p> <p>The data is available for download at the <a href="https://data.ece.iiasa.ac.at/engage/">ENGAGE Scenario Explorer</a>. The license permits use of the scenario ensemble for scientific research and science communication, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and <a href="https://data.ece.iiasa.ac.at/engage/#/license">legal code</a> for more information.</p>
Environmental sustainability assessment of cleanrooms under changing climate and energy scenarios
<p>This is the data for <em>Environmental sustainability assessment of cleanrooms under changing climate and energy scenarios.</em></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.