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72 results for “Energy Climate”
plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"
<p>The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems.<br> Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3.<br> The present dataset contains all the public data built for this case study.</p> <p>The related documentation is included in plan4res deliverable D4.5 </p> <pre>https://doi.org/10.5281/zenodo.3785010</pre>
Data set for the integrated Climate, Land, Energy and Water systems modelling exercise RCLEWs in OSeMOSYS
<p>This dataset refers to the modelling exercise (version01_210616RCLEWs). The dataset contains the OSeMOSYS code used to run the modelling exercise, the model input data, the scenarios model data files, and the results. The code for the results visualization is available at https://github.com/KTH-dESA/teaching-CLEWs_visualization.</p> <p>This is an update of version 01_210827 available at: https://doi.org/10.5281/zenodo.5293834</p>
National Energy and Climate Plans - Preliminary analysis on Prosumerism for 9 EU Member States
<p>Preliminary check on the provisions on self-consumption and energy communities in the draft National Energy and Climate Plans (NECPs) of nine EU Member States (BE, DE, ES, FR, HR, IT, NL, PT, UK). The main findings are that only France and Spain put a reasonable emphasis on the importance of self-consumption and energy communities as suggested by RED II. Germany and Italy show some efforts while the NECPs of the other five member states contain only weak or no provisions on prosumerism. Most countries don’t define neither targets nor measures.</p>
Regional Aspects of a Climate and Energy Tax Reform in Norway—Exploring Double and Multiple Dividends
<p>Results for the different scenarios described in Table 4.</p>
Zambezi dataset to "WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus"
<p>This is the dataset used in the HESS publication "<a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus</a>"</p> <p>The dataset describes the water-energy-food nexus of the Zambezi River Basin used as input to the <a href="https://github.com/RaphaelPB/WHAT-IF">WHAT-IF model</a>.</p> <p>The file Data_Organization.pdf, summarizes the available data. For more info look at the <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">publication</a> and/or <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.</p>
Climate change impacts on energy demand
<p>Climate change impacts on energy demand by energy carrier (electricity, natural gas, and petroleum) and sector (agriculture, industry, residential, and commercial).</p>
Energy Climate dataset consitent with ENTSO-E TYNDP2020 studies (CSV & NetCDF) for ACDC-ESM
<p><strong>Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format</strong></p> <p><strong>TL;DR</strong>: this is a tidy and friendly version of a recreation of ENTSO-E's PECD 2021.3 data by using ERA5: hourly capacity factors for wind onshore, offshore, solar PV and hourly electricity demand are provided. All the data is provided for 28-71 climatic years (1950-2020 for wind and solar, 1982-2010 for demand).</p> <p><strong>Description</strong><br> Country averages of energy-climate variables generated using the Python scripts, based on the <a href="https://2020.entsos-tyndp-scenarios.eu/">ENTSO-E's TYNDP 2020 study</a>. For the following scenario's data is available</p> <ul> <li>National trends 2025 (NT 2025)</li> <li>National trends 2030 (NT 2030)</li> <li>National trends 2040 (NT 2040)</li> <li>Distributed Energy 2030 (DE 2030)</li> <li>Distributed Energy 2040 (DE 2040)</li> <li>Global Ambitions (GA 2030)</li> <li>Global Ambitions (GA 2040)</li> </ul> <p>The time-series are at hourly resolution and the included variables are:</p> <ul> <li>Generation wind offshore (aggregated for all years per scenario in a .zip)</li> <li>Generation wind onshore (aggregated for all years per scenario in a .zip)</li> <li>Generation solar photovoltaic (aggregated for all years per scenario in a .zip)</li> <li>Total energy demand (all zones combined in single file per scenario)</li> </ul> <p>The Files are provided in CSV (.csv) & NetCDF (.nc). The data is given per ENTSO-E's bidding zone as used within the TYNDP2020.<br> </p> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. </em></p>
Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. Data Set for Final Data Report
<p>The 43 txt-files included in this dataset relate to the report: Ryhl-Svendsen, Jensen, Bøhm, and Klenz Larsen (2012): <em>Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. UMTS Research Project 2007</em>–<em>2011: Final Data Report</em>, Kgs. Lyngby: National Museum of Denmark, 122 pp.</p> <p>The document <a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/00_List-of-data-files.pdf?versionId=a3e9691f-6e73-4a7b-aaab-c8ccee7c419b">00_List-of-data-files.pdf</a> contain a full list of the data files with a description of their structure and content, and is the key to how the individual data files relate to the report. </p> <p>The research project focussed on four modern museum storage facilities in Denmark, for which the indoor climate, air quality, and the energy consumption of the climate control systems was measured at several locations, typically for a period of between two and four years. The storage facilities were Museum of Southwest Jutland’s storage building in Ribe (‘Ribe’), The Shared Storage Facility at The Centre for Preservation of Cultural Heritage in Vejle (‘Vejle’), The Joint Storage Facility for museums in East Jutland/ Museum Østjylland (‘Randers’), and from The National Museum of Denmark the storage building Hall P at the Ørholm Storage Facility (‘Ørholm’). For description of the sites, monitoring campaigns, and graphed data, the report should be consulted.</p> <p>For completeness, the report is included with the dataset (<a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/Report_low-energy-museum-storage-buildings.pdf?versionId=44097d39-775b-4031-9e07-6978c68912a9">Report_low-energy-museum-storage-buildings.pdf</a>).</p>
Data and code in support of "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower"
<p>This dataset contains all the data and processing needed to produce results and figures reported in the manuscript "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower".</p> <p> </p> <p>The README file guides through the material available to support replication of the results and figures.</p>
Scripts and datas for "A unified energy-constrained mesoscale parameterisation for ocean climate models".
<p>Scripts and datasets used for creating the results of a submitted work :</p> <p><strong>R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak</strong>: <em>A unified energy-constrained mesoscale parameterisation for ocean climate models. </em>(submitted in JAMES).<em><br></em></p> <p>Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017").</p> <p>The reference EKE of <a href="https://doi.org/10.1029/2023gl104688">Torres et al. (2023)</a> is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. <a href="https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18">World Ocean Atlas 2018</a>, <a href="https://gmd.copernicus.org/articles/13/3643/2020/">Tsujino et al. (2020)</a> and <a href="https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/04c79ece-3186-349a-e063-6c86abc0158c/">RAPID</a>)</p> <p>IPython notebooks for computing and plotting metrics are provided :</p> <ul> <li><em>james-eke-heat_budget.ipynb</em> : plots for heat transport and global heat storage (section 4.1)</li> <li><em>james-eke-southern_ocean.ipynb</em> : plots for Southern Ocean (section 4.2) analysis</li> <li><em>james-eke-north_atlantic.ipynb</em> : plots for North Atlantic and Labrador Sea (section 4.3) analysis</li> <li><em>james-eke-timeseries.ipynb</em> : plot 0D metric timeseries for simulations (including spin-up)</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
Climate model output for "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"
<p>Climate model output associated with the manuscript "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"</p>
Data and code: Climate policy accelerates structural changes in energy employment
<p>The file contains code to create the figures used in main text and supplementary information of the paper <strong>Climate policy accelerates structural changes in energy employment</strong>.</p> <p>To run the RMD file and see the resulting figures, press Knit on R studio (requires the package knitr), or else see the attached HTML file, already created through such a process.</p>
Seasonal analysis comparison of three air-cooling systems in terms of thermal comfort, air quality and energy consumption for school buildings in Mediterranean climates
<p>Efficient air-cooling systems for hot climatic conditions, such as Southern Europe, are required in the context of nearly Zero Energy Buildings, nZEB. Innovative air-cooling systems such as regenerative indirect evaporative coolers, RIEC and desiccant regenerative indirect evaporative coolers, DRIEC, can be considered an interesting alternative to direct expansion air-cooling systems, DX. The main aim of the present work was to evaluate the seasonal performance of three air-cooling systems in terms of air quality, thermal comfort and energy consumption in a standard classroom. Several annual energy simulations were carried out to evaluate these indexes for four different climate zones in the Mediterranean area. The simulations were carried out with empirically validated models. The results showed that DRIEC and DX improved by 29.8% and 14.6% over RIEC regarding thermal comfort, for the warmest climatic conditions, Lampedusa and Seville. However, DX showed an energy consumption three and four times higher than DRIEC for these climatic conditions, respectively. RIEC provided the highest percentage of hours with favorable indoor air quality for all climate zones, between 46.3% and 67.5%. Therefore, the air-cooling systems DRIEC and RIEC have a significant potential to reduce energy consumption, achieving the user’s thermal comfort and improving indoor air quality.</p>
Climate Change and 2030 Cooling Demand in Ahmedabad, India: Opportunities for Expansion of Renewable Energy and Cool Roofs (Supplemental Information)
<p>Supplemental information and analysis files for article, "Climate change and 2030 cooling demand in Ahmedabad, India: opportunities for expansion of renewable energy and cool roofs" (Original article available at: https://doi.org/10.1007/s11027-022-10019-4)</p>
Data files for "Quantifying the global climate feedback from energy-based adaptation"
<p>Data files for "Quantifying the global climate feedback from energy-based adaptation".</p> <p>Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/ACDM_Climate_Adaptation_Feedback.</p> <p>Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.</p> <p> </p> <p><strong>Attribution:</strong></p> <p><br>Some processed data contain excerpts of Non-Creative Commons Material as defined by the International Energy Agency (IEA -- see their terms of use at `https://www.iea.org/terms/terms-of-use-for-non-cc-material'). The emissions factors we use in our analysis are generated using IEA datasets. These data are aggregates of the underlying country-by-fuel level emissions factors and as presented contain only insubstantial amounts of the Non-CC Material. We attest they cannot be used to reconstruct individual data points in the original dataset. The factors we produce are attributable to the following two sources: </p> <p>IEA. Emissions factors. Tech. Rep., International Energy Agency (IEA 2021). URL https://www.iea.org/data-and-statistics/data-product/910emissions-factors-2021. All Rights Reserved.</p> <p>IEA. World energy balances 2021. Tech. Rep., International Energy Agency (IEA) (2022). URL https://www.iea.org/data-and-statistics/data-product/world-energy-balances. All Rights Reserved.</p> <p> </p>
Data for: Environment-dependent relationships between corticosterone and energy expenditure during reproduction: insights from seabirds in the context of climate change
<p>We studied the relationship between baseline levels of the steroid hormone corticosterone and daily energy expenditure (DEE) in the little auk (<em>Alle alle</em>), an Arctic sea bird that is experiencing mounting energetic challenges due to climate change. We specifically investigated the hypothesis that there might be environment-dependent relationships between baseline corticosterone, DEE, time activity budgets, diving behavior and fitness-related traits (chick provisioning rate, adult body condition). Furthermore, we also examined whether mercury (Hg) contamination might interfere with corticosterone production, and hence potentially the capacity to upregulate DEE. In addition, we performed a phylogenetically controlled analysis across breeding seabird species to assess the relationship between baseline corticosterone and DEE, which we estimated via <span>a model derived from a phylogenetically controlled meta-analysis, </span><span>available within a <span>web-based app (‘Seabird FMR Calculator’, </span></span><span><a href="https://ruthedunn.shinyapps.io/seabird_fmr_calculator/"><span>https://ruthedunn.shinyapps.io/seabird_fmr_calculator/</span></a></span><span>) (Dunn et al. 2018). These datasets contain information on corticosterone levels, DEE, TABs and Hg in little auks, and the data used in our phylogenetically controlled analysis. Please see the READ me file for details.</span></p>
Long-term Performance and Life Cycle Assessment of Energy Piles in three Different Climatic Conditions_Dataset
<p>In this file it is possible to find the dataset linked to the related pubblication. In the file each spreadsheet corresponf to a picture of the paper.</p>
Dataset: Energy services' access deprivation in Mexico: A geographic, climatic and social perspective
<p>This dataset contains all the information at the municipal level from the publication "Energy services' access deprivation in Mexico: A geographic, climatic and social perspective" published in Energy Policy (DOI: <a href="https://doi.org/10.1016/j.enpol.2022.112822">10.1016/j.enpol.2022.112822</a>).</p> <p>The information contains key categorizations on energy services access at the municipal level in Mexico, classified per climatic zone. It is complemented with key information on population and households at the municipal level.</p> <p>The raw data sources used to produce this secondary data are listed below. A detailed methodological description is available in the primary article (DOI: <a href="https://doi.org/10.1016/j.enpol.2022.112822">10.1016/j.enpol.2022.112822</a>) and the article "Dataset of household energy services access and socioeconomic variables in Mexico" to be published in Data in Brief. </p> <p>Raw data:</p> <ul> <li>2015 Intercensal Survey: <a href="https://www.inegi.org.mx/programas/intercensal/2015/">https://www.inegi.org.mx/programas/intercensal/2015/</a></li> <li>Poverty Index by Municipality in Mexico 2015: <a href="https://www.coneval.org.mx/Medicion/Paginas/PobrezaInicio.aspx">https://www.coneval.org.mx/Medicion/Paginas/PobrezaInicio.aspx</a></li> <li>Raster Map of Climates: <a href="https://www.inegi.org.mx/temas/climatologia/#Mapa">https://www.inegi.org.mx/temas/climatologia/#Mapa</a></li> </ul> <p>The dataset's geographic scope is as follows:</p> <ul> <li>City/Town/Region: All municipalities</li> <li>Country: Mexico</li> </ul>
The cost of movement: assessing energy expenditure in a long-distant ectothermic migrant under climate change
<p>Functions to simulate monarch migration under set weather conditions. Data for repsirometry measurements and weather stations are also included in ZIP folders. Functions include working example of movement based on literature values for thresholds. Functions can be modified for other species as needed. Weather station data were collected from NOAA LCD stations. Alternative data sources include Wunderground Personal Weather Station datasets. However, Wunderground requires an API to access their data unless you have a PWS in their system. Connecting a PWS to wunderground provides you an API key for accessing data. </p>
The potential impact of climate change on European renewable energy droughts
<p>The file contains a supplementary material for an article entitled <em>The potential impact of climate change on European renewable energy droughts</em> (currently under review):</p> <p>The projected change of the total number of drought days for a wind, solar and hybrid generator in relation to the reference period as predicted by the models considered</p> <p> </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.