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620 results for “sector”
Reference Data Set: Electricity, Heat, and Gas Sector Data for Modeling the German System
<p>This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector).</p> <p>While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references).</p> <p>The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D.</p>
Electricity consumption and real vaue-added data for the Swiss and Genevan secondary and tertiary sectors.
<p>This file contains datasets of electricity consumption and real value added (base year 2000) in the secondary and tertiary sector for Switzerland and Geneva for the years between 2000 and 2015. The structure of the Swiss and Genevan datasets has been matched to ensure comparability of results from index decomposition analyses conducted on each region. It also contains heating and cooling degrees for both Switzerland and Geneva.</p>
Dataset of FEUTURE Online Paper No. 8 "Understanding the EU-Turkey Sectoral Trade Flows During 1990-2016: a Trade Gravity Approach"
<p>The dataset provides the following variables for 1990-2016 for Austria Belgium, Bulgaria, China, Denmark, France, Germany, Greece, Hungary, Iran, Ireland, Italy, Japan, Netherlands, Poland, Russia, Spain, Sweden, UK, and USA: </p> <p>- Total export and import (in USD and %)</p> <p>- Intermediate goods exports and imports (in USD and %)</p> <p>- Household goods exports and imports (in USD and %)</p> <p>- Capital goods exports and imports (in USD and %)</p> <p>- Mixed-end exports and imports (in USD and %)</p> <p>- Miscellaneous exports and imports (in USD and %)</p> <p> </p>
GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"
<p>GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"</p>
Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean
<p>These are the Wave glider data used in the analysis and creation of figures in Edholm et al. 2022: <em>Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean</em> in support of open-code, transparency, and repeatability.</p> <p>Abstract:</p> <p>Atmospheric rivers (ARs) dominate moisture transport globally; however, it is unknown what impact ARs have on surface ocean buoyancy. This study explores the surface buoyancy gained by ARs using high-resolution surface observations from a Wave Glider deployed in the subpolar Southern Ocean (54°S, 0°E) between 19 December 2018 and 12 February 2019 (55 days). When ARs combine with storms, the associated precipitation is significantly enhanced (189%). In addition, the daily accumulation of AR-induced precipitation provides a buoyancy gain to the surface ocean equivalent to warming by surface heat fluxes. Over the 55 days, ARs accounted for 47% of the total precipitation equating to 10% of the summer surface ocean buoyancy gain. This study indicates that ARs play an important role in the summer precipitation over the subpolar Southern Ocean and that they can alter the upper-ocean buoyancy budget from synoptic to seasonal timescales.</p>
Survey of Electrical & Electronic Equipment (EEE) Sector Stakeholders & End Users of EEE regarding Circular Economy in the EEE Sector
<p>The data is the full results of surveys of stakeholders in the electrical and electronic equipment (EEE) value chain conducted as part of the Horizon 2020 funded <a href="https://c-serveesproject.eu/index.php">C-SERVEES project</a>. </p> <p>The purpose of the survey was to provide guidance for the successful implementation of circularity in the EEE sector, specifically to develop circular economy business plans for four items of equipment: printers (inc. toner cartridges); washing machines; televisions; and network monitoring equipment. The survey aimed to capture data on:</p> <ul> <li>Circular economy awareness;</li> <li>Current practices relating to circular economy; and</li> <li>Circular economy opportunities, barriers and enablers.</li> </ul> <p>Investigation of opportunities, barriers and enablers focused on technical, economic, socio-cultural, regulatory and environmental themes. In addition, background information was also collected to enable some of the attributes of the respondent to be recorded. This included: country; city, years of experience in the industry as well as, in the household users’ survey, age and education level. </p> <p>The stakeholder groups surveyed are noted below, these being the key EEE supply chain actors. A separate dataset is available for each.</p> <ol> <li>Designers of EEE</li> <li>Suppliers to manufacturers of EEE</li> <li>Manufacturers of EEE</li> <li>EEE Retailers</li> <li>Business users of EEE</li> <li>Household users of EEE</li> <li>Waste EEE handlers</li> </ol> <p>A project report analysing the results of the survey is available: <em><a href="https://c-serveesproject.eu/project_activities.php?op=5">Guidelines for circular economic models in the E&E sector</a> . </em>A journal article outlining the subsequent development of the circular economy business models is also available: <a href="https://www.sciencedirect.com/science/article/abs/pii/S095965262101430X"><em>A circular economy business model innovation process for the electrical and electronic equipment sector</em></a>. </p>
Model run and scenario data for study "Bioenergy-induced land-use change emissions with sectorally fragmented policies"
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Bioenergy-induced land-use change emissions with sectorally fragmented policies</strong></p> <p>by <em>Leon Merfort, Nico Bauer, Florian Humpenöder, David Klein, Jessica Strefler, Alexander Popp, Gunnar Luderer, Elmar Kriegler</em></p> <p>published in <em>Nature Climate Change </em>(2023).</p> <p><em><strong>ModelRuns_remind </strong></em>(directory) contains all REMIND model runs of the scenarios underlying the paper.</p> <p><em><strong>ModelRuns_magpie </strong></em>(directory) contains all MAgPIE model runs of the scenarios underlying the paper.</p> <p><em><strong>DataAnalysis </strong></em>(directory) contains an RStudio Project that was used for the data analysis and the generation of the figures of the paper. It additionally contains all figures and figure data that are shown in the paper.</p> <p><em><strong>ScenarioMapping.pdf</strong></em> contains the mapping from scenario names used in the paper to the model experiment names (in the model run directories).</p>
Result data related to "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors"
<p>Parameter estimations from the conjoint experiments performed in "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors". Parameter estimations are given for different:</p> <p>* estimands: average marginal component effects (amce) or marginal means,</p> <p>* variables: choice and rating,</p> <p>* sectors: buildings (heat) and transport sector,</p> <p>* subgroups: by-<subgroupname>.</p> <p>Filenames accordingly are: <estimand>-<variable>-<sector>.csv or <estimand>-<variable>-<sector>-by-<subgroup>.csv</p>
Research generated data supporting the article manuscript "Setting Grounds for Data Literacy in the Sector of Agriculture: Learning About and with Open Data"
<p>In the research 345 MS courses and 216 MS courses data from the ECTS catalogue (2019) of University of Zagreb Faculty of Agriculture were mapped onto the data literacy competence areas (theme) and DL competence areas sub-themes adapted ODI Data Skills Framework (2020) expanding the term “skill” to “competence” to include knowledge and attitudes. Teaching staff was interviewed in semi-structured interviews on the data literacy competences covered in their courses and open data use and teaching in their courses as well as their perceived importance for the sector of the course.</p> <p>The upload consists of the following .csv files:</p> <table> <tbody> <tr> <td>readme_DL_OD_Salamonetal.csv</td> </tr> <tr> <td>01DL_OD_Salamonetal.csv</td> </tr> <tr> <td>02DL_OD_Salamonetal.csv</td> </tr> <tr> <td>03DL_OD_Salamonetal.csv</td> </tr> <tr> <td>04DL_OD_Salamonetal.csv</td> </tr> <tr> <td>05DL_OD_Salamonetal.csv</td> </tr> <tr> <td>06DL_OD_Salamonetal.csv</td> </tr> <tr> <td>07DL_OD_Salamonetal.csv</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Variability of OB stars from TESS southern Sectors 1-13 and high-resolution IACOB and OWN spectroscopy
<p>Typical MESA and GYRE inlists associated with <a href="https://arxiv.org/abs/2005.09658">Burssens et al. 2020</a>. MESA v. 12155, GYRE version v. 5.2.</p> <p><em>Context:</em> Lack of high-precision long-term continuous photometric data for large samples of stars has prevented the large-scale exploration of pulsational variability in the OB star regime. As a result, the candidates for in-depth asteroseismic modelling remained limited to a few tens of dwarfs. The TESS nominal space mission has surveyed the southern sky, including parts of the galactic plane, yielding continuous data of at least 27 d for hundreds of OB stars.<br> <em>Aims:</em> We aim to couple TESS data in the southern sky with ground-based spectroscopy to study the variability in two dimensions, mass and evolution. We focus mainly on the presence of coherent pulsation modes that may or may not be present in the predicted theoretical instability domains and unravel all frequency behaviour in the amplitude spectra of the TESS data.<br> <em>Methods: </em>We compose a sample of 98 OB-type stars observed by TESS in Sectors 1-13 and with available multi-epoch, high-resolution spectroscopy gathered by the IACOB and OWN surveys. We present the short-cadence 2-min light curves of dozens of OB-type stars, that have one or more spectra in the IACOB or OWN database. Based on these light curves and their Lomb-Scargle periodograms we perform variability classification and frequency analysis. We place the stars in the spectroscopic Hertzsprung-Russell diagram to interpret the variability in an evolutionary context.<br> <em>Results:</em> We deduce diverse origins of the mmag-level variability found in all of the 98 OB stars in the TESS data. We find among the sample several new variable stars, including three hybrid pulsators, three eclipsing binaries, high frequency modes in a Be star, and potential heat-driven pulsations in two Oe stars. <br> <em>Conclusions:</em> We identify stars for which future asteroseismic modelling is possible, provided mode identification is achieved. By comparing the position of the variables to theoretical instability strips we discuss the current shortcomings in non-adiabatic pulsation theory, and the distribution of pulsators in the upper Hertzsprung-Russell diagram.</p> <p> </p> <p> </p>
Projected freshwater needs of the energy sector in the European Union and the UK
<p>This dataset contains the projections of future freshwater demands of the energy sector (primary energy supply and transformation in power plants and oil refineries) in the EU and the United Kingdom for the period 2015-2050, in 5-year steps. The projections are estimated by the combination of water withdrawal and consumption factors for different energy technologies under six energy scenarios, at national and NUTS2 level, as described in the JRC technical report: Hidalgo Gonzalez, I., Medarac, H. and Magagna, D., Projected freshwater needs of the energy sector in the European Union and the UK, EUR 30266 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-19829-1 (online), doi:10.2760/796885 (online), JRC121030.</p>
Daily aerosol emissions changes in 2020 due to Covid19: modified SSP2-4.5 to account for sector activity level
<p>Daily aerosol emissions estimates for 2020, modified by the country-specific impacts of COVID-19 lockdown. </p> <p>This repository holds the netcdf files for aerosol and precursor emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020, with observation-based data up until the 5th of July and a fixed estimate thereafter. This is the daily equivalent of <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a>. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details.</p>
Relevance of national policy in the provision of open data on South African higher education sector
<p>Spreadsheet providing breakdown of the national Acts, Bills and Standards relevant to sharing open data on the governance of higher education in South Africa. Data generated as a component of the situational analysis conducted for the 'Use of open data in the governance of South African higher education' research project, in the IDRC/WWWF 'Exploring Emerging Impacts of Open Data in the South' initiative.</p>
Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004
<p>Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)<sup>-1</sup> yr<sup>-1</sup>] or [g N (kg product)<sup>-1</sup> yr<sup>-1</sup>] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. </p> <p>Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector</p>
Data on digital perfomance of public sector in South-East Asia
<p>Data on digital perfomance of public sector in South-East Asia includes variables affecting process of digitalization in public sector in South-East Asian countries. </p>
Supplementary data: "Influence of flexibility options on the German transmission grid — A sector-coupled mid-term scenario"
<p>This repository contains result data for the paper <i> "Influence of flexibility options on the German transmission grid — A sector-coupled mid-term scenario"</i>.</p><p>The published data includes optimization results of the three main scenarios in the mentioned publication. </p><p>The data for each scenario is stored as csv-files, which allows analysing it with many different tools. In addition, the data can be imported in Python as a network object of the open-source tool PyPSA by using the function <a href="https://pypsa.readthedocs.io/en/latest/api_reference.html#pypsa.Network.import_from_csv_folder">import from csv folder </a>. </p><p> </p><p>The authors thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project eGon (funding code: 03EI1002).</p>
From fork to farm: Impacts of more sustainable diets in the EU-27 on the agricultural sector
<p>This work was supported by the European Union's Horizon 2020 project Nutri2Cycle (Grant agreement No. 773682) </p><p>These datasets are supplementary information to the manuscript titled "<a href="https://onlinelibrary.wiley.com/doi/10.1111/1477-9552.12530">From fork to farm: Impacts of more sustainable diets in the EU-27 on the agricultural sector</a>"</p>
Sensitivity of the global agricultural sector to changes in climate policy - EU countries compared to the rest of the world
<p>The files contain data from the FAOSTAT database used in the article: DOI:10.2478/oszn-2023-0012</p> <p>File content:<br>Agricultural emissions data for the period 1961-2020<br>Population data for 1950-2020<br>Production value from agriculture for the period 1961-2020<br>Agricultural area for the period 1961-2020</p> <p>The layout of the tables and the description of the columns is the same as the FAOSTAT database methodology</p>
Pre-built Swiss-Calliope sector-coupled energy model
<div> <h3>Swiss-Calliope prebuilt model</h3> <div>The model consists of Switzerland and its neighbours, as described in <a href="https://doi.org/10.1016/j.enconman.2024.118426" target="_blank" rel="noopener">Mellot et al., 2024</a>. Switzerland's heating and transport sectors are modelled on top of its electricity sector.</div> <br> <div>The model is ready to be loaded into Calliope, for 2016--2018. This prebuilt was specifically designed for the study of Switzerland's winter deficit, but is easily modifiable for any other analysis. Refer to Calliope's <a href="https://calliope.readthedocs.io/en/stable/" target="_blank" rel="noopener">documentation</a> for information on how to do this.</div> <br> <div>To run the same scenarios as for the Swiss winter deficit analysis, you need to do the following steps. Note that these scenarios were ran on ETH's Euler cluster which uses the slurm batch system. You can otherwise just adapt the following shell scripts to run the scenarios on other systems.</div> <div>1. Set up the conda environment with the correct version of calliope <code>conda env create -f environment.yaml</code>. On slurm systems you may also need to load gurobi <code>module load new gurobi/9.0.0</code>.</div> <div>2. Run the baseline scenarios, i.e. those corresponding to the EP2050+ configuration, by running <code>sh run_baselines 4</code>, where 4 corresponds to the time resolution.</div> <div>3. Once these runs are finished, run the python file <code>python read_baselines_and_fix_neighbours.py</code>. This will fix Switzerland's neighbouring countries' installed capacities for the next scenarios.</div> <div>4. Then you may run the study's scenarios by running <code>sh run_initial_scenarios.sh 4</code>, and the sensitivity analysis scenarios by running <code>sh run_sensitivies.sh 4</code>.</div> <div> </div> <div>The model's units are GW, GWh, Million euros, and Million kilometers.</div> </div>
Monthly Mean Equatorial Electrojet variations over the Indian sector for the years 2001 to 2022
<p>This database contain the monthly mean Equatorial Electrojet variations over the Indian sector for a period from 2001 to 2022. This data has been mainly used in building the Indian Equatorial Electrojet (IEEJ) Model and manuscript is submitted for possible publication in JGR-Space Physics. </p>
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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.