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57 results for “energy system modelling”
Modelling and Comparing Converter Architectures and Energy Harvesting ICs for Battery-Free Systems
<p>Artifacts containing measurement scripts, data sets, and simulations for the paper "Modelling and Comparing Converter Architectures and Energy Harvesting ICs for Battery-Free Systems" (currently submitted and under review).</p>
Data Bundle for PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System
<p>While small data files used in PyPSA-Eur-Sec are included directly in the git repository, larger ones are collected in this data bundle. The data bundle’s size is around 680 MB.</p> <p><strong>Licenses</strong></p> <p>Different licenses apply to the various components of this data bundle (mostly attribution).</p> <p>For details see <a href="https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements">https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements</a></p> <p><strong>Changelog 0.3.1</strong></p> <ul> <li>Fix IRENASTAT encoding</li> </ul> <p><strong>Changelog 0.3.0</strong></p> <ul> <li>Add <a href="https://pxweb.irena.org/pxweb/en/IRENASTAT">IRENASTAT</a> country-level power generation capacities.</li> </ul> <p><strong>Changelog 0.2.0</strong></p> <ul> <li>add hydrogen salt cavern storage potential (h2_salt_caverns_GWh_per_sqkm.geojson)</li> </ul> <p> </p>
Diurnal rainfall response to the physiological and radiative effects of CO2 in tropical forests in the Energy Exascale Earth System Model v1
<p>Necessary outputs and scripts for recreating the figures for the journal article with the same title.</p>
Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling
<p><strong>egon-data</strong> provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project <strong>eGo<sup>n</sup></strong>. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGo<sup>n</sup> <a href="https://ego-n.org/">project website</a> or its <a href="https://github.com/openego/eGon-data">Github repository.</a></p> <p>egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data.</p> <p>The following data sets are part of the available data bundle:</p> <ol> <li><strong>climate_zones_germany</strong> <ul> <li>Climate zones in Germany</li> <li>source: Own representation based on DWD TRY climate zones</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>emobility</strong> <ul> <li>Data on eMobility mit_trip_data:<br> motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR).</li> <li>Reiner Lemoine Institut, June 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>geothermal_potential</strong> <ul> <li>Spatial distribution of deep geothermal potentials in Germany</li> <li>source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>household_electricity_demand_profiles</strong> <ul> <li>Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children.<br> The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012.<br> The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh.<br> A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-374761.html</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>household_heat_demand_profiles</strong> <ul> <li>Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>hydrogen_storage_potential_saltstructures</strong> <ul> <li>The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..</li> <li>Source: Flach lagernde Salze, (c) BGR Hannover, 2021.<br> Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. &<br> Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., &<br> Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und<br> Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen,<br> Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen<br> zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) –<br> Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht.<br> Hannover: BGR.</li> <li>License: The original data are licensed under the GeoNutzV, see https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</li> </ul> </li> <li><strong>industrial_sites</strong> <ul> <li>Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site.</li> <li>source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>nep2035_version2021</strong> <ul> <li>Data extracted from the German grid development plan - power</li> <li>source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>pipeline_classification_gas</strong> <ul> <li>Parameters for the classification of gas pipelines</li> <li>source: Single parameters extracted from <a href="https://www.econstor.eu/bitstream/10419/173388/1/1011162628.pdf">Electricity, Heat and Gas Sector Data for Modelling the German System</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>pypsa_eur_sec</strong> <ul> <li>Preliminary results from scenario generator pypsa-eur-sec</li> <li>source: own calculation using pypsa-eur-sec fork (https://github.com/openego/pypsa-eur-sec)</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>regions_dynamic_line_rating</strong> <ul> <li>German regions suitable to model dynamic line rating</li> <li>source: Own representation based on <a href="https://www.transnetbw.de/files/pdf/netzentwicklung/netzplanungsgrundsaetze/UENB_PlGrS_Juli2020.pdf">Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>re_potential_areas</strong> <ul> <li>Eligible areas for wind turbines and ground-mounted PV systems.</li> <li>Reiner Lemoine Institut, January 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>WZ_definition</strong> <ul> <li>Definitions of industrial and commercial branches</li> <li>source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a></li> <li>Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.</li> </ul> </li> <li><strong>zensus_households</strong> <ul> <li>Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution.</li> <li>source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps: <ul> <li>Search for: "1000A-2029"</li> <li>or choose topic: "Bevölkerung kompakt"</li> <li>Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"</li> <li>Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.</li> </ul> </li> <li>Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.</li> </ul> </li> </ol> <p> </p>
European power system infrastructure in the open energy system model PyPSA-Eur
<p>The image is created using the data and scripts in the European open energy system model <a href="https://github.com/PyPSA/pypsa-eur">PyPSA-Eur.</a></p>
SESMG Model Definitions: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the files is one SESMG model definition used for the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". Further information can be found in this publication. The file names indicate to which sensitivity analysis of the study the individual model definition belongs to. Used acronyms: "ng" = natural gas.</p>
SESMG Model Results: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the folders contains SESMG results for a sensitivity analysis of the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". More information can be found in this publication. Each folder contains two subfolders. The "cost-minimum" subfolder contains the results for financially optimized systems, and the "emission-minimum" subfolder contains the results for GHG emission-optimized systems. Within these subfolders, the results for different gradations of the respective sensitivity parameters are stored in separate sub-subfolders. The 01_reference_total_ghg_emissions folder has a slightly different structure. Since the results are not separated into financially and emissions-optimized scenarios, the results of different gradations are stored directly in the main folder of this sensitivity analysis.</p>
Data and model code: Assessing the Implications of Hydrogen Blending on the European Energy System towards 2050
<p>Dataset for <em>Assessing the Implications of Hydrogen Blending on the European Energy System towards 2050</em></p>
The potential of sector coupling in future European energy systems soft linking between the Dispa-SET and JRC-EU-TIMES models - Dataset
<p>Supporting dataset and Dispa-SET version used within "The potential of sector coupling in future European energy systems soft linking between the Dispa-SET and JRC-EU-TIMES models" paper.</p>
Unstructured global to coastal wave modeling for the Energy Exascale Earth System Model - 1/2 degree WaveWatchIII configuration files
<p>This dataset contains the mesh and model configuration information for a WaveWatchIII run using a 1/2 degree structured grid.</p> <ul> <li>glo_30m.bot <ul> <li>Bottom depth file for a 1/2 degree structured grid</li> </ul> </li> <li>glo_30m.mask <ul> <li>Mask file for a 1/2 degree structured grid</li> </ul> </li> <li>obstructions_local.glo_30m.in <ul> <li>local obstructions file for use with UOST source term switch</li> </ul> </li> <li>obstructions_shadow.glo_30m.in <ul> <li>shadow obstructions file for use with UOST source term switch</li> </ul> </li> <li>ww3_grid.inp <ul> <li>Input file for the ww3_grid pre-processing program. This file specifies many of the model configuration settings.</li> </ul> </li> <li>ww3_shel.inp <ul> <li>Input file for the ww3_shel program.</li> </ul> </li> </ul>
Dataset for Analysis of Various Spatial Resolutions for Modelling Sector-Coupled Energy Systems
<p>Dataset for preprocessing Balmorel data in this Danish case study.</p>
Sector-coupled model for the German energy system in 2019
<p>This repository contains input data for the open-source Python tool <a href="https://github.com/openego/eTraGo">eTraGo</a> (<strong>e</strong>lectricity <strong>Tra</strong>nsmission <strong>G</strong>rid <strong>o</strong>ptimization) version 0.10.0.<br>This data will be uploaded to the <a href="https://openenergy-platform.org/">OpenEnergy Platform</a> which can be accessed by eTraGo. This dataset is an intermediate solution until the data is uploaded.</p> <p>The published data includes the sector-coupled transmission grid data for the scenario <em>status2019</em>. It was created with the open-source tool <a href="https://github.com/openego/powerd-data">powerd-data</a> within the research project <a href="https://h2-powerd.de/">PoWerD</a>. All input data sets as well as the code are available under open source licenses.</p> <p>We thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project PoWerD (grant number: 03EI1042C)</p> <p>The data is stored as a PostgreSQL database in the attached backup file. First, the required schemas and extensions have to be created within the database by running the following SQL statements:</p> <p><code>CREATE EXTENSION postgis;</code></p> <p>Afterwards, the data can be restored by using e.g. pgAdmin or via PostgreSQL's <a href="https://www.postgresql.org/docs/current/app-pgrestore.html">pg_restore</a> command (replace <code>HOST</code>, <code>DATABASE_NAME</code>, <code>PORT</code> and <code>USER</code> by your settings):</p> <p><code>pg_restore --host HOST --port PORT --username USER --no-password --dbname </code><code>DATABASE_NAME --no-owner --no-privileges --verbose "PoWerD_status2019_v3.backup"</code></p>
Ocean Dynamics in the DOE Energy, Exascale, Earth System Model (E3SM)
<p>Climate research at the U.S. Department of Energy (DOE) includes the development of ocean, sea-ice, atmosphere, land-vegetation and land-ice models. The ability to run high-resolution global simulations efficiently on the world’s largest computers is a priority for the DOE. This movie shows simulations from the variable-resolution ocean model, the Model for Prediction Across Scales (MPAS-Ocean), which is developed at Los Alamos National Laboratory. MPAS-Ocean is a component of the DOE’s newly released Energy, Exascale, Earth System Model (E3SM). Applications of E3SM include the simulation of 20th-century and future climate scenarios, as well as special configurations where model resolution is enhanced in regions of particular interest, like coastal areas, the Arctic, or below Antarctic ice shelves.</p> <p>Website: <a href="https://e3sm.org">https://e3sm.org</a>. </p>
A systems biology approach reveals a link between systemic cytokines and skeletal muscle energy metabolism in a rodent smoking model and human COPD
GEO Series GSE56099. Cavia porcellus. 49 samples. Type: Expression profiling by array; Expression profiling by high throughput sequencing.
The effects of fair allocation principles on energy system model designs
<p>This is the associated dataset to <a href="https://github.com/OskarVagero/highRES-Europe-WF/tree/MENOFS">https://github.com/OskarVagero/highRES-Europe-WF/tree/MENOFS </a>, which contains the data necessary to replicate the study. </p> <p>In addition to the data required to replicate the study, we also include six pre-generated model results (.db), which represent the "top performers", as well as the cost-optimal model run. </p> <ul> <li>Weather data is based on ERA5, from ECMWF (https://doi.org/10.1002/qj.3803) </li> <li>Demand data is based on the Interannual Electricity Demand Calculator (https://zenodo.org/records/10820928)</li> <li>Existing hydropower capacity is based on the JRC Hydropower database (https://zenodo.org/records/5215920)</li> <li>Historic electricity generation from hydropower is based on the U.S. Energy Information Administration (https://www.eia.gov/international/data/world/electricity/electricity-generation)</li> </ul> <p>More details on how to use the data can be found in the GitHub repository. </p>
RAB38 Facilitates Energy Metabolism and Counteracts Cell Death in Glioblastoma Model Systems
GEO Series GSE162444. Homo sapiens. 4 samples. Type: Expression profiling by array.
Italy's energy system model (Electricity, Heat and Hydrogen), with 6 region resolution simulation under PNIEC2019 scenario. (April 2024)
<p>Parameters and Sets files for an updated model built for Italy's energy system model (Electricity, Heat and Hydrogen), with 6 region resolution simulation under PNIEC2019 scenario. (April 2024)</p> <p>* Data are designed as an input for Hypatia Modelling Framework</p> <p>** Model was develeped for master thesis study " Investigating the regional contributions to the Italian decarbonization: an Energy Modelling multi-regional approach." C. Lo Guidice, F. Cruz, K. Gad, E. Colombo</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.