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21 results for “energy supply”
A diet containing mango peel silage impacts upon feed intake, energy supply and growth performances of dairy male calves
<p>The major challenges for disposal of waste from fruit processing factories are high transportation costs, limited landfill availability and environmental pollution. Therefore, developing efficient waste management techniques to reduce transportation costs and environment pollution is important. Mango peels (MP) are abundant during the mango season and high in fermentable carbohydrate, which can easily breakdown and pollute the environment if a proper waste management method is not implemented. Thus, in this study, fresh MP were ensiled after sun-dried for one day and then fed to dairy male calves as the roughage source to evaluate its effect on feed intake, digestibility, energy balance, body weight gain, feed efficiency and blood metabolites. Eight growing crossbred dairy male calves (Holstein Friesians × Zebu) were allocated into two groups [Control (n = 4) and mango peel silage (MPS, n = 4)]. This experiment lasted for 12 weeks and daily feed offered and refusal were recorded to determine the daily feed intake. Digestion trial was performed at the last five days of experiment. Body weight and measurement were recorded every two weeks interval to determine the weight gain and body physical improvement. Blood was collected at the end of experiment to analyze the serum biochemical parameters. Ensiling improved the energy and protein contents and decreased fibre content of MP, thereby improving the forage quality. Feeding MPS to calves increased (<i>P</i> < 0.05) feed intake, energy supply and energy balance, changes in body measurements, weight gain, feed efficiency, and glucose concentration, as well as lowered (<i>P</i> < 0.05) the urea nitrogen concentration. Ensiling fresh MP after sun-drying for one day improved silage quality, and feeding MPS to dairy male calves as a roughage source improved feed intake, energy supply and growth performances. Therefore, ensiling fresh MP could improve the feed supply for ruminant production and be an effective waste management strategy for fruit processing businesses. </p>
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Result data related to Tröndle et al (2024): Rebuilding Ukraine's energy supply in a secure, economic, and decarbonised way
<p>This dataset contains the result data of all the scenarios ran in the scientific article "Rebuilding Ukraine’s energy supply in a secure, economic, and decarbonised way".</p> <p>The results of the main five scenarios of the study are available as PyPSA result files:</p> <ul> <li>nuclear-and-renewables-high.nc: A scenario with nuclear in the mix and high economic growth assumption.</li> <li>nuclear-and-renewables-low.nc: A scenario with nuclear in the mix and low economic growth assumption.</li> <li>only-renewables-high-low-bio.nc: A scenario with only renewables, high economic growth assumption, and only 10% of assumed biomass potential.</li> <li>only-renewables-high.nc: A scenario with only renewables and high economic growth assumption.</li> <li>only-renewables-low.nc: A scenario with only renewables and low economic growth assumption.</li> </ul> <p>See the PyPSA documentation for more information: <a href="https://pypsa.readthedocs.io" target="_blank" rel="noopener">https://pypsa.readthedocs.io</a>.</p> <p>The results of the 330 global sensitivity analysis runs are available as summary files in CSV format:</p> <ul> <li>gsa-capacities-energy-gwh.csv: The installed energy storage capacities for each scenario.</li> <li>gsa-capacities-power-gw.csv: The installed generation capacities for each scenario.</li> <li>gsa-lcoe.csv: The levelised cost of electricity for each scenario.</li> </ul>
Supplying renewable energy to Central European research facilities: A techno-economic comparison of electricity and hydrogen (Dataset)
<p>This dataset contains central input assumptions and results related to the publication "Supplying renewable energy to Central European research facilities: A techno-economic comparison of electricity and hydrogen".</p> <p>Result files are contained in the <strong> results.zip</strong> archive file. The file contains for each scenario, as indicated by the folder structure, the following files:</p> <ul> <li><strong>results.csv</strong>: Central scenario results exported as <em>character separated value</em> <em>(csv)</em> file, with a semicolon (<strong>;</strong>) as field separator. All fields are quoted using double quotation marks <strong>"..."</strong>. Can be explored using standard office software like Microsoft Excel/Libre Office or other tools.</li> <li><strong>network.nc</strong>: PyPSA network file containing the optimized scenario with all input and unprocessed outputs (results). Can be explored using the <a href="https://pypsa.readthedocs.io">PyPSA software package</a>.</li> <li><strong>lcoes.csv</strong>: Levelised Cost of Electricity used to construct the renewable energy source (RES) based supply curve for each scenario.</li> </ul> <p>The dataset further contains the following files which represent central input assumptions to the model and scenarios, both as <em>CSV</em> files:</p> <ul> <li><strong>efficiencies.csv</strong>: Technology process and conversion efficiencies<em> </em>including more details on the assumptions and information on which references the assumptions are based.</li> <li><strong>costs_2030.csv</strong>: Technology cost assumptions for 2030 including more details on the assumptions and information on which references the assumptions are based. This data is based on this <a href="https://github.com/pypsa/technology-data">Technology Data repository</a> on GitHub.</li> </ul>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power »
<p>This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power" by Sterl et al. (2022).</p> <p>It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.</p> <p>Five separate datasets are included:</p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 2, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Algeria<br>Angola<br>Benin<br>Botswana<br>Burkina Faso<br>Burundi<br>Cameroon<br>Central African Republic<br>Chad<br>Congo Republic<br>Democratic Republic of the Congo<br>Djibouti<br>Egypt<br>Equatorial Guinea<br>Eritrea<br>Eswatini<br>Ethiopia<br>Gabon<br>The Gambia<br>Ghana<br>Guinea<br>Guiné-Bissau<br>Côte d'Ivoire<br>Kenya<br>Lesotho<br>Liberia<br>Libya<br>Madagascar<br>Malawi<br>Mali<br>Mauritania<br>Morocco<br>Mozambique<br>Namibia<br>Niger<br>Nigeria<br>Rwanda<br>Senegal<br>Sierra Leone<br>Somalia<br>South Africa<br>South Sudan<br>Sudan<br>Togo<br>Tunisia<br>Uganda<br>Tanzania<br>Zambia<br>Zimbabwe</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <span><a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></span></p> <p><strong>See also</strong></p> <p>Sterl, S. (2024). Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10650822" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10650822</a></p>
Scenario data for article: Effects of the energy transition on environmental impacts of cobalt supply: A prospective Life Cycle Assessment study on future supply of cobalt
<p>This dataset contains the background data for the paper '<a href="https://onlinelibrary.wiley.com/doi/10.1111/jiec.13258">Effects of the energy transition on environmental impacts of the cobalt supply: A prospective Life Cycle Assessment study on the future cobalt supply</a>' as published in the Journal of Industrial Ecology.</p> <p><strong>Please note that an easier to use version of this data for LCA is available through the Premise (<a href="https://www.sciencedirect.com/science/article/pii/S136403212200226X">Sacchi et al. 2022</a>) Community Scenarios <a href="https://github.com/premise-community-scenarios/cobalt-perspective-2050">here</a>.</strong> This version is slightly adapted to fit into the Premise architecture and is compatible with ecoinvent v3.8 cutoff.</p> <p>This repository contains:</p> <ul> <li>Python code + readme to model the variables, generate presamples packages and generate LCA results based on those. (code folder)</li> <li>Input and output data for Variables 1-3 (files 1&2)</li> <li>Presamples excel sheets for each variable/scenario combination (file 3)</li> <li>Summarized LCA results (the full results can be generated through running the code provided) (file 4)</li> <li>Full LCA results used for the contribution analysis (file 5)</li> <li>Underlying data for each of the figures (file 6)</li> </ul> <p>We refer to the paper (linked above) for more information on the study.<br> </p> <p><strong>License: </strong>The metal supply scenario data is licensed under the CC-BY 4.0 license.</p> <p><strong>Access: </strong>Open access</p> <p> </p> <p>[Changelog]</p> <p>2023-03-23 - 1.3.1: Add link to Premise Community scenario page.<br> 2022-05-18 - 1.3.0: Fix minor error in data files '4 - LCA results' and '6 - Figure data' in demand amounts for total impacts.<br> 2022-04-06 - 1.2.1: Included link to article after publication<br> 2022-03-30 - 1.2.0: Included underlying figure data<br> 2022-01-24 - 1.1.1: Opened repository after paper acceptance<br> 2021-11-26 - 1.1.0: Update of code to comply with peer-review<br> 2021-07-12 - 1.0.0: Set-up of repository</p>
[Dataset] Green hydrogen exports in New Zealand and Chile can improve electricity supply security if configured as local energy insurance
<p>This file contains the main outputs of the preprint: "Green hydrogen exports in New Zealand and Chile can improve electricity supply security if configured as local energy insurance"</p>
Energy allocation explains how protozoan phenotypic traits change in response to temperature and resource supply
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The dataset for the research "Evaluation of Digital Supply Chain Technology’s Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain"
In recent years, the topic of digitalisation and sustainability of supply chains has become increasingly important. In addition, as the environmental dynamism becomes more complex, it is essential to explore how technologies impacts on sustainability under the supply chain dynamism. Hence, there is a study to explore the relationship between technologies and sustainability under the supply chain dynamism in the energy supply chain. In this study, the author collects quantitative data from two Chinese companies, including China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd. This is a questionnaire survey and it has 24 questions, including 3 general questions, 5 technologies dimension questions, 12 sustainability dimension questions and 4 supply chain dynamism questions. The author collected data from 30 May 2024 to 6 June May 2024, and there are totally 316 answers.
Spanish energy supply prices
<p>This dataset collects information about energy supply prices by days and hours since June 1st 2021, when prices were changed by the government, which also carried to an inflation of the prices. This dataset pretends to be a reference when studying this inflation, by comparing, for example, hour prices with the performance of energy suppliers' stations, determining whether this inflation is justified or not.</p> <p>Dataset structure goes as follows:</p> <ul> <li><strong>Tarifa</strong>. There are two tax types identified by a three-letter code: "Península, Baleares y Canarias" (pbc) and "Ceuta y Melilla" (cym).</li> <li><strong>Fecha</strong>. Date of the extracted data in DD-MM-YYYY format.</li> <li><strong>Hora</strong>. Hour of the extracted data (int type from 0 to 23).</li> <li><strong>Precio total</strong>. Total tax price in kWh/€.</li> <li><strong>Mercado diario e intradiario</strong>. Price breakdown corresponding to diary and intraday markets in kWh/€.</li> <li><strong>Servicios de ajuste</strong>. Price breakdown corresponding to adjust services in kWh/€.</li> <li><strong>Financiación OS</strong>. Price breakdown corresponding to OS financing in kWh/€.</li> <li><strong>Financiación OM</strong>. Price breakdown corresponding to OM financing in kWh/€.</li> <li><strong>Coste Comercialización variable</strong>. Price breakdown corresponding to variable commercialization cost in kWh/€.</li> <li><strong>Peajes y cargos</strong>. Price breakdown corresponding to tolls and charges in kWh/€.</li> <li><strong>Pago por capacidad. </strong>Price breakdown corresponding to capacity payment in kWh/€.</li> <li><strong>Excedente o deficit subastas renovables</strong>. Price breakdown corresponding to surplus or deficit of renewable energy auctions in kWh/€.</li> <li><strong>Servicio de interrumpibilidad</strong>. Price breakdown corresponding to interruptibility service in kWh/€.</li> </ul>
The dataset of "Evaluation of Digital Supply Chain Technology's Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain"
<p>This dataset involves the data from the questionnaire, which come from the project "Evaluation of Digital Supply Chain Technology’s Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain". It comprises three dimensions questions, technology, sustainability and supply chain dynamism. The datas come from two Chinese energy firms, <span>China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd.</span></p>
Effects of embryo energy, egg size and larval food supply on the development of asteroid echinoderms
Organisms have limited resources available to invest in reproduction, causing a tradeoff between the number and size of offspring. One consequence of this tradeoff is the evolution of disparate egg sizes and, by extension, developmental modes. In particular, echinoid echinoderms (sea urchins and sand dollars) have been widely used to experimentally manipulate how changes in egg size affect development. Here we test the generality of the echinoid results by 1) using laser ablations of blastomeres to experimentally reduce embryo energy in the asteroid echinoderms (sea stars), Pisaster ochraceus and Asterias forbesi and 2) comparing naturally produced, variably-sized eggs (1.7 fold volume difference between large and small eggs) in A. forbesi. In P. ochraceus and A. forbesi there were no significant differences between juveniles from both experimentally reduced embryos and naturally produced eggs of variable size. However, in both embryo reduction and egg size variation experiments, simultaneous reductions in larval food had a significant and large effect on larval and juvenile development. These results indicate that 1) food levels are more important than embryo energy or egg size in determining larval and juvenile quality in sea stars and 2) the relative importance of embryo energy or egg size to fundamental life history parameters (time-to and size-at metamorphosis), does not appear to be consistent within echinoderms.
Research on the Enhancement of Aerobic Metabolic Energy Supply Capacity by Ischemic Preconditioning.
ClinicalTrials.gov study NCT07170774. IPD Sharing: NO. Countries: 1. Publications: 4.
Effects of embryo energy, egg size and larval food supply on the development of asteroid echinoderms
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Energy Supply in Athletes and Untrained Persons With Bronchopulmonary Diseases
ClinicalTrials.gov study NCT04415827. IPD Sharing: NO. Countries: 1. Publications: 0.
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>
E-Drone: Transforming the Energy Demand of Supply Chains Through Integrated UAV-to-land Logistics for 2030
ClinicalTrials.gov study NCT04990843. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Endocrine Regulation of Energy and Fluid Supplies in Alcoholic Patients
ClinicalTrials.gov study NCT00447785. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Maladaptive upregulation of aminoprocalcitonin fuels seizure self-sustaining by boosting energy supply in refractory status epilepticus
GEO Series GSE217955. Rattus norvegicus. 9 samples. Type: Expression profiling by high throughput sequencing.
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.