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557 results for “demand”
Incorporating infrastructure and vehicle technology requirements, changes in demand, and Decarbonization policies’ considerations into freight planning
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Axial Nephron Fate Switching Demonstrates a Plastic System Tunable on Demand [RNA-seq]
GEO Series GSE283868. Homo sapiens. 39 samples. Type: Expression profiling by high throughput sequencing.
Shared and distinct metabolic demands for innate and adaptive cytotoxic lymphocytes during viral infection
GEO Series GSE149447. Mus musculus. 11 samples. Type: Expression profiling by high throughput sequencing.
Single-cell transcriptomic analysis of adult mouse pituitary reveals sexual dimorphism and physiologic demand-induced cellular plasticity
GEO Series GSE146619. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Enhanced glucose metabolism through activation of HIF-1α covers the energy demand in a rat embryonic heart primordium after heartbeat initiation.
GEO Series GSE185702. Rattus norvegicus. 6 samples. Type: Expression profiling by array.
Drop-on-demand single cell isolation and total RNA analysis
GEO Series GSE24330. Mus musculus. 6 samples. Type: Expression profiling by array.
LARP1 senses free ribosomes to coordinate supply and demand of ribosomal proteins
GEO Series GSE246077. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Aldh2 dependent formaldehyde metabolism fuels purine demands in melanocyte stem cells
GEO Series GSE183868. Danio rerio. 2 samples. Type: Expression profiling by high throughput sequencing.
Comparison of RNA Amplification Techniques meeting the demands for the Expression Profiling of Clinical Cancer Samples
GEO Series GSE5588. Homo sapiens. 41 samples. Type: Expression profiling by array.
Modulation of calcium signalling on demand to decipher the molecular mechanisms of primary aldosteronism
GEO Series GSE282358. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
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>
JRC - Raw materials demand for wind and solar PV technologies in the transition towards a decarbonised energy system - Materials demand database
<p>This dataset contains the results of the materials demand scenarios for wind and solar PV technologies developed in the following report by the European Commission's Joint Research Centre (JRC):</p> <p>Carrara S., Alves Dias P., Plazzotta B. and Pavel C., Raw materials demand for wind and solar PV technologies in the transition towards a decarbonised energy system, EUR 30095 EN, Publication Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-16225-4, doi:10.2760/160859, JRC119941</p> <p>The report can be found at the following link:</p> <p><a href="https://ec.europa.eu/jrc/en/publication/raw-materials-demand-wind-and-solar-pv-technologies-transition-towards-decarbonised-energy-system">https://ec.europa.eu/jrc/en/publication/raw-materials-demand-wind-and-solar-pv-technologies-transition-towards-decarbonised-energy-system</a></p>
dsmlib - region4FLEX example (Supplementary Material for the manuscript: Assessment of the regionalised demand response potential in Germany using an open source tool and dataset)
<p>This is the supplementary material for the manuscript:<br> Heitkoetter, Wilko, et al. "Assessment of the regionalised demand response potential in Germany using an open source tool and dataset." <em>Advances in Applied Energy</em> (2020): 100001.<br> Article DOI (open access): <a href="https://doi.org/10.1016/j.adapen.2020.100001">https://doi.org/10.1016/j.adapen.2020.100001</a></p> <p><strong>REPOSITORY CONTENT</strong><br> This repository contains the dsmlib python tool for calculating regionalised load shifting potentials and cost-potential curves.<br> Further, the input data and load shifting potential results of the region4FLEX example are provided (<a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model description</a>). In the region4FLEX example dsmlib is applied to the 401 German administrative districts (NUTS-3 regions) considering multiple demand sectors and technologies (see Metadata).</p> <p><strong>quick_start_dsmlib_region4FLEX.zip:</strong><br> This file contains the python dsmlib source code and the resulting average and maximum load shifting potential values per administrative district for all technologies (e.g.: see "/results/2030/extreme_values/av_values_p_max.csv" for average load increase potentials in 2030). Unless otherwise stated the units of the results are in MW for power and MWh for energy. (For more information on the results, please refer to \dsmlib-zenodo\examples\region4FLEX_dsm_potential\README.md --> section Results). The resulting time series and large-scale input data are not contained, to allow for a fast download. </p> <p><strong>full_download_dsmlib_region4FLEX.zip:</strong><br> This file contains the python dsmlib source code and the full set of input and result data.<br> For more information refer to /dsmlib/examples/region4FLEX_dsm_potential/README.md</p> <p><strong>LICENSES</strong><br> The developed source code is licensed under the GPL v3 License. All result data are licensed under the CC-BY 4.0 License.<br> The input data are licensed under different open licenses. For more information refer to the provided README files, LICENSE files and input_data_overview files.</p> <p><strong>METADATA</strong><br> <strong>Demand sectors:</strong> Residential, commercial trade and services, industry, power-to-heat, power-to-gas, e-mobility<br> <strong>Technologies:</strong> Washing, drying, cooling, ventilation, AC, air separation, cement production, pulping, paper production,<br> recycled paper production, process heat, heat pumps, resistive space heating, resistive DHW heating, power-to-heat in district heating,<br> power-to-methane, power-to-hydrogen, e-mobility <br> <strong>Energy sectors:</strong> Electricity (+ interfaces to heat, gas and transport sector)<br> <strong>Geographical scope:</strong> Germany<br> <strong>Geographical resolution:</strong> Administrative districts (NUTS-3)<br> <strong>Temporal scope:</strong> 2018, 2030<br> <strong>Temporal resolution:</strong> 15min</p> <p><strong>NEWS AND CONTACT</strong><br> This dataset will be used as part of the region4FLEX model. If you wish to receive news or have general questions please contact: wheitkoetter(at)gmail.com</p>
Annual Gas & Electricity demand data for Dublin Emissions Trading System (ETS) industrial buildings as of November 2019
<ul> <li>AER Reports.zip <ul> <li>Pdfs of individual Dublin buildings each containing energy data from <a href="http://epa.ie/licensing/">http://epa.ie/licensing/</a></li> </ul> </li> <li>EPA Reports.xlsx <ul> <li>Extracted energy data from AER Reports into a single spreadsheet</li> </ul> </li> </ul>
Data from: The combined fatigue effects of sequential exposure to seated whole body vibration and physical, mental, or concurrent work demands
Many occupations in agriculture, construction, transportation, and forestry are non-routine, involving non-cyclical tasks, both discretionary and non-discretionary work breaks, and a mix of work activities. Workers in these industries are exposed to seated whole body vibration (WBV) and tasks consisting of physical, mental, or a combination of demands. Risk assessment tools for non-routinized jobs have emerged but there remains a need to understand the combined effects of different work demands to improve risk assessment methods and ultimately inform ergonomists and workers on optimum work arrangement and scheduling strategies. The objective of this study was to investigate fatigue-related human responses of WBV sequentially combined with physical, mental, or concurrent physical and mental demands. Sixteen healthy participants performed four conditions on four separate days: (1) physically demanding work, (2) mentally demanding work, (3) concurrent work, and (4) control quiet sitting. For each condition, participants performed two 15-minute bouts of the experimental task, separated by 30-minutes of simulated WBV based on realistic all-terrain vehicle (ATV) riding data. A test battery of fatigue measures consisting of biomechanical, physiological, cognitive, and sensorimotor measurements were collected at four interval periods: pre-session, after the first bout of the experimental task and before WBV, after WBV and before the second bout of the experimental task, and post-session. Nine measures demonstrated statistically significant time effects during the control condition; 11, 7, and 12 measures were significant in the physical, mental, and concurrent conditions, respectively. Overall, the effects of seated WBV in combination with different tasks are not additive but possibly synergistic or antagonistic. There appears to be a beneficial effect of seated ATV operation as a means of increasing task variation; but since excessive WBV may independently pose a health risk in the longer-term, these beneficial results may not be sensible as a long-term solution.
DETERMINING THE DEMAND FOR MINERAL FERTILIZERS BASED ON THE STATISTICAL ANALYSIS OF WINTER WHEAT VARIETIES' AGRONOMIC INDICATORS
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GIS Data and Analysis for Cooling Demand and Environmental Impact in The Hague
<p>This dataset contains raw GIS data sourced from the BAG (<i>Basisregistratie Adressen en Gebouwen</i>; Registry of Addresses and Buildings). It provides comprehensive information on buildings, including advanced height data and administrative details. It also contains geographic divisions within The Hague. Additionally, the dataset incorporates energy label data, offering insights into the energy efficiency and performance of these buildings. This combined dataset serves as the backbone of a Master's thesis in Industrial Ecology, analysing residential and office cooling and its environmental impacts in The Hague, Netherlands. The codebase of this analysis can be found in this Github repository: <a href="https://github.com/simonvanlierde/msc-thesis-ie"><strong>https://github.com/simonvanlierde/msc-thesis-ie</strong></a></p><p>The dataset includes a background research spreadsheet containing supporting calculations. It also presents geopackages with results from the cooling demand model (CDM) for various scenarios: Status quo (SQ), 2030, and 2050 scenarios (Low, Medium, and High)</p><h3>Background research data</h3><p>The <i>background_research_data.xlsx</i><strong> </strong>spreadsheet contains comprehensive background research calculations supporting the shaping of input parameters used in the model. It contains several sheets:</p><ul><li><strong>Cooling Technologies</strong>: Details the various cooling technologies examined in the study, summarizing their characteristics and the market penetration mixes used in the analysis.</li><li><strong>LCA Results of Ventilation Systems</strong>: Provides an overview of the ecoinvent processes serving as proxies for the life-cycle impacts of cooling equipment, along with calculations of the weight of cooling systems and contribution tables from the LCA-based assessment.</li><li><strong>Material Scarcity</strong>: A detailed examination of the critical raw material content in the material footprint of ecoinvent processes, representing cooling equipment.</li><li><strong>Heat Plans per Neighbourhood</strong>: Forecasts of future heating solutions for each neighbourhood in The Hague.</li><li><strong>Building Stock</strong>: Analysis of the projected growth trends in residential and office building stocks in The Hague. AC Market: Market analysis covering air conditioner sales in the Netherlands from 2002 to 2022.</li><li><strong>Climate Change</strong>: Computations of climate-related parameters based on KNMI climate scenarios.</li><li><strong>Electricity Mix Analysis</strong>: Analysis of future projections for the Dutch electricity grid and calculations of life-cycle carbon intensities of the grid.</li></ul><h3>Input data</h3><p><strong>Geographic divisions</strong></p><ul><li>The outline of The Hague municipality through the Municipal boundaries (<i>Gemeenten</i>) layer, sourced from the <a href="http://www.pdok.nl/geo-services/-/article/bestuurlijke-gebieden">Administrative boundaries (<i>Bestuurlijke Gemeenten</i>) dataset</a> on the PDOK WFS service.</li><li>District (<i>Wijken</i>) and Neighbourhood (<i>Buurten</i>) layers were downloaded from the PDOK WFS service (from the <a href="https://www.pdok.nl/geo-services/-/article/cbs-wijken-en-buurten#df0df8fa1c3bab1a71a2f09d990abd7e"><i>CBS Wijken en Buurten 2022</i></a><i> </i>data package) and clipped to the outline of The Hague.</li><li>The 4-digit postcodes layer was downloaded from PDOK WFS service (<a href="http://www.pdok.nl/ogc-webservices/-/article/cbs-postcode4"><i>CBS Postcode4 statistieken 2020</i></a>) and clipped to The Hague's outline. The postcodes within The Hague were subsequently stored in a csv file.</li><li>The census block layer was downloaded from the PDOK WFS service (from the <a href="http://www.pdok.nl/introductie/-/article/cbs-vierkantstatistieken-100m"><i>CBS Vierkantstatistieken 100m 2021</i></a> data package) and also clipped to the outline of The Hague.</li><li>These layers have been combined in the <i>GeographicDivisions_TheHague</i> GeoPackage.</li></ul><p><strong>BAG data</strong></p><ul><li>BAG data was acquired through the download of a BAG GeoPackage from the BAG <a href="https://www.pdok.nl/downloads/-/article/basisregistratie-adressen-en-gebouwen-ba-1">ATOM download page</a>.</li><li>In the resulting GeoPackage, the Residences (<i>Verblijfsobject</i>) and Building (<i>Pand</i>) layers were clipped to match The Hague's outline.</li><li>The resulting residence data can be found in the <i>BAG_buildings_TheHague</i> GeoPackage.</li></ul><p><strong>3D BAG </strong></p><ul><li>Due to limitations imposed by the PDOK WFS service, which restricts the number of downloadable buildings to 10,000, it was necessary to acquire 145 individual GeoPackages for tiles covering The Hague from the <a href="http://3dbag.nl/nl/download">3D BAG website</a>.</li><li>These GeoPackages were merged using the <i>ogr2ogr</i> <i>append</i> function from the <a href="http://gdal.org/index.html">GDAL library </a>in bash.</li><li>Roof elevation data was extracted from the <i>LoD 1.2 2D</i> layer from the resulting GeoPackage.</li><li>Ground elevation data was obtained from the <i>Pand</i> layer.</li><li>Both of these layers were clipped to match The Hague's outline.</li><li>Roof and ground elevation data from the <i>LoD 1.2 2D</i> and <i>Pand</i> layers were joined to the <i>Pand</i> layer in the BAG dataset using the <i>BAG ID</i> of each building.</li><li>The resulting data can be found in the <i>BAG_buildings_TheHague</i> GeoPackage.</li></ul><p><strong>Energy labels</strong></p><ul><li>Energy labels were downloaded from the <a href="http://www.ep-online.nl/PublicData">Energy label registry</a> (<i>EP-online</i>) and stored in <i>energy_labels_TheNetherlands</i>.<i>csv</i>.</li></ul><p><strong>UHI effect data</strong></p><ul><li>A bitmap with the UHI effect intensity in The Hague was retrieved from the from the <a href="https://www.atlasnatuurlijkkapitaal.nl/kaarten?config=58bf95bc-67bf-402d-a355-af211ad33949&gm-x=121187.11870218973&gm-y=467370.5793842884&gm-z=3.1666666666666665&gm-b=1544180834512,true,1;1554714019959,true,0.8;&activateOnStart=layermanager&deactivateOnStart=layercollection">Dutch Natural Capital Atlas</a> (<i>Atlas Natuurlijk Kapitaal</i>) and stored in <i>UHI_effect_TheHague.tiff</i>.</li></ul><h3>Output data</h3><ul><li>The residence-level data joined to the building layer is contained in the <i>BAG_buildings_with_residence_data_full</i> GeoPackage.</li><li>The results for each building, according to different scenarios, are compiled in the <strong>buildings_with_CDM_results_[scenario]_full</strong> GeoPackages. The scenarios are abbreviated as follows:<ul><li><strong>SQ</strong>: Status Quo, covering the 2018-2022 reference period.</li><li><strong>2030</strong>: An average scenario projected for the year 2030.</li><li><strong>2050_L</strong>: A low-impact, best-case scenario for 2050.</li><li><strong>2050_M</strong>: A medium-impact, moderate scenario for 2050.</li><li><strong>2050_H</strong>: A high-impact, worst-case scenario for 2050.</li></ul></li></ul><p> </p>
Tourism demand and predictors
<p>This database contains predictor variables for tourism demand in Spain and data on tourism demand in the Valencian Community. </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>
Outputs of the "Passenger demand mobility model" developed for South Asia
<p>This dataset comprises outputs and intermediate calculations of the passenger mobility demand model developed for South Asian regions. The dataset includes the following: </p> <ul> <li>'Total_PDT.zip' folder contains files of the type <em><strong>total_pdt_scename_j_m.csv</strong></em>: <ul> <li> These CSV files are the model output that contain passenger distance travelled (PDT) by mode in each area type and distance category for daily travel; and PDT by mode for long distance travel in different scenarios (scen_name), type of urbanisation (j), and mode share trajectory (m).</li> <li><strong>scename</strong>- This refers to the scenarios considered which are as follows - 'Base', 'NP', 'BP', 'TOD and 'TECH'</li> <li><strong>j</strong>- This refers to the type of urbanisation trajectories considered and are defined as follows: <ul> <li>1 - <a href="../api/records/10303828/draft/files/Urbanisation_type_trajectory_1.csv/content">Urbanisation_type_trajectory_1.csv</a></li> <li>2 - <a href="../api/records/10303828/draft/files/Urbanisation_type_trajectory_2.csv/content">Urbanisation_type_trajectory_2.csv</a></li> </ul> </li> <li><strong>m</strong>- This refers to the mode share trajectories considered in this study and are defined as follows: <ul> <li>1- Business as Usual (BaU)</li> <li>2- Car-oriented future (COF)</li> <li>3- Sustainable future (SF)</li> </ul> </li> </ul> </li> </ul> <ul> <li>Trip rates calculated for SSP 2- <a href="../api/records/10303828/draft/files/trip_rate_2.csv/content">trip_rate_2.csv</a></li> </ul> <p> </p>
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.