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4,230 results for “Energie”
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>
Modelled data - Energy Transition in Bolivia
<p>Model structure, input data and output files from MoManI </p>
WEEE, A Multi-Device and Multi-Modal Dataset for Wearable Human Energy Expenditure Estimation
<p>We present WEEE, a multi-device and multi-modal dataset collected from 17 participants under different physical activities.<br> WEEE contains: 1) sensor data collected using 7 wearable devices placed on 4 body locations - head, ear, chest, and wrist<br> -, 2) respiratory data collected with an indirect calorimeter serving as ground-truth information, 3) demographics and body<br> composition data (e.g., muscle or fat percentage), 4) activity type - and their corresponding metabolic equivalent of task (MET) values - and intensity level, and 5) answers to questionnaires related to physical activity level, diet, stress and sleep. Thanks to the diversity of sensors and body locations of the WEEE dataset, we envision that this dataset will enable the development of novel human energy expenditure estimation techniques for a diverse set of application scenarios. Energy expenditure (EE) refers to the amount of energy an individual uses to maintain body functions and as a result of physical activity. The ability to estimate EE allows computing systems obtaining valuable insights regarding people’s physical activity and providing personalized recommendations for promoting a healthier and more active lifestyle.</p>
Comparability of skeletal fibulae surfaces generated by different source scanning (dual-energy CT scan vs. high resolution laser scanning) and 3D geometric morphometric validation
<p><strong>SI_Appendix 1.</strong> Matrix of Cartesian coordinates of analyzed specimens.</p> <p><strong>SI_Appendix 2</strong>. Sample list and acquisition methods. </p>
The global energy balance of the ASDEX Upgrade tokamak determined with the revised cooling water calorimetry
<p>Provided data includes data sets required for running the AUG calorimetry. The database consists of the flow rate, time correction and the distance between the temperature measuring points of inlet and outlet cooling water of each cooling unit. </p>
Potential energy surfaces for HCCNCS and DCCNCS
<p>Molpro restart files (ASCII compressed) for the XSURF program of the potential energy surfaces for HCCNCS AND DCCNCS. The expansion point of these surfaces is the 2nd order transition state of the linear structure. Vibrational structure calculations based on these surfaces are reported in "A combined computational and experimental study on the vibrational structure of ethynyl isothiocyanate, HCCNCS, a molecule with a Champagne bottle potential" (https://doi.org/10.1016/j.jms.2022.111626).</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>
example perturbation free energy data (GROMOS format)
<p>example perturbation free energy data used to show the usage of the simulation update scheme feature of the SMArt toolkit - available at https://github.com/drazen-petrov/SMArt</p> <p>data contains:</p> <p>GROMOS format: perturbation of a histone tail H3K4 lysine trimethylation mark bound to the Sp100C PHD finger</p>
Raw data on mortality, feeding rates, swimming behavior and energy reserves: Goetz et al 2022
<p><span>The abundance and persistence of plastic nanoparticles in aquatic habitats are considered a threat to marine and freshwater biota. However, the risk assessment of plastic particles is complicated due to various factors that need to be considered, including composition, size and environmental abundance.</span><span><br></span></p> <p><span>This study investigated the behavioural response of a key river species, <em>Gammarus roeseli</em>, to dietary exposure of plain biodegradable and non-biodegradable plastic as well as to natural small micro- and nanoparticles. Mortality, feeding, swimming velocity and energy assimilation endpoints were examined by considering four particles sizes ranging from 30 to 1000 nm in two concentrations.</span><span><br></span></p> <p><span>Contrary to our expectations, neither decreasing size nor increasing abundance of each tested particle impacted any of the examined endpoints. Likewise, dietary exposition with biodegradable plain polylactide did not induce other or stronger effects than non-biodegradable plain polystyrene or natural silica micro- and nanoparticles, as all three particle types did not lead to adverse effects on <em>G. roeseli</em>. These findings also suggest that the functional role of <em>Gammarus roeseli</em> as a shredder is not impaired due to particle occurrence within the exposure range of this study.</span></p>
Using weather radar to help minimize wind energy impacts on nocturnally migrating birds
<p>As wind energy rapidly expands worldwide, information to minimize impacts of this development on biodiversity is urgently needed. Here we demonstrate how data collected by weather radar networks can inform placement and operation of wind facilities to reduce collisions and minimize habitat-related impacts for nocturnally migrating birds. We found over a third of nocturnal migrants flew through altitudes within the rotor-swept zone surrounding the North American Great Lakes, a continentally important migration corridor. Migrating birds concentrated in terrestrial stopover habitats within 20-km from shorelines, a distance well beyond the current guidelines for construction of new land-based facilities, and their distributions varied seasonally and at local and regional scales, creating predictable opportunities to minimize impacts from wind energy development and operation. Networked radar data are available across the U.S. and other countries and broad application of this approach could provide information critical to bird-friendly expansion of this globally important energy source.</p>
Calculating bond dissociation energies of X-H (X = C, N, O, S) bonds of aromatic systems via DFT: A detailed comparison of methods
<p>In this study, the bond dissociation energy (BDE) values of X-H (X = C, N, O, S) bonds of aromatic compounds were computed by using 17 different DFT functionals, namely M08-HX, M06-2X, M05-2X, M06, M05, BMK, MPW1B95, B1B95, B98, B97-2, LC-wPBE, B3LYP, cam-B3LYP, B2PLYP, MPWB1K, BB1K, BB95, within the basis set range 6-31G(d), 6-31+G(d), 6-31+G(d,p), 6-311G(d,p) and 6-311++G(d,p). The results show that the 6-31G(d) is the most convenient basis set to perform the BDE calculations with sufficient accuracy compared to the relevant experimental BDEs. The M06-2X, M05-2X, and M08-HX functionals gave highly accurate BDE values (with the average mean unsigned error MUE = 1.2-1.5 kcal/mol), performing better than the other functionals. The results suggest that the M06-2X, M05-2X, and M08-HX density functionals in the combined DFT/6-311+G(3df,2p)//B3LYP/6-31G(d) model chemistry offer the best method for calculating BDEs of ArX-H (X = C, N, O, S) bonds.</p>
Database of social innovation in energy initiatives
<p>A database of n = 500 social innovation in energy initiatives.</p>
Script and data of "Role of Frictional Processes in Mesoscale Eddy Available Potential Energy Budget in the Global Ocean"
<p>% File description:</p> <p>1. Cal_conversions.m: a set of functions calculating the EAPE-EKE and EAPE-EKE conversion terms with CESM output data in B-grid</p> <p>2. smooth2a.m: function of boxcar filtering</p> <p>3. CONV_u100_2d.mat: data of the global distribution of upper 100 m averaged conversion terms used in Figure 2 of the manuscript<br> % Variables inside the file:<br> CONVa_H_u100: MAPE-EAPE conversion driven by frictional process<br> CONVo_H_u100: MAPE-EAPE conversion driven by non-frictional process<br> CONVa_V_u100: EAPE-EKE conversion driven by frictional process<br> CONVo_V_u100: EAPE-EKE conversion driven by non-frictional process</p> <p>4. CONV_profile.mat: data of the vertical profiles of global and regional averaged EAPE-EKE conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_profile: driven by frictional process<br> CONVo_V_GLO_profile: driven by non-frictional process<br> CONVttw_V_GLO_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_profile: driven by frictional process<br> CONVo_V_WBCE_profile: driven by non-frictional process<br> CONVttw_V_WBCE_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_profile: driven by frictional process<br> CONVo_V_STG_profile: driven by non-frictional process<br> CONVttw_V_STG_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_profile: driven by frictional process<br> CONVo_V_SPG_profile: driven by non-frictional process<br> CONVttw_V_SPG_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_profile: driven by frictional process<br> CONVo_V_SO_profile: driven by non-frictional process<br> CONVttw_V_SO_profile: reproduced by TTW balance </p> <p>5. CONV_SeasDiff.mat: data of the seasonal difference (winter minus summer) of global and regional averaged conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of the seasonal difference of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_SeasDiff: driven by frictional process<br> CONVo_V_GLO_SeasDiff: driven by non-frictional process<br> CONVttw_V_GLO_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_SeasDiff: driven by frictional process<br> CONVo_V_WBCE_SeasDiff: driven by non-frictional process<br> CONVttw_V_WBCE_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_SeasDiff: driven by frictional process<br> CONVo_V_STG_SeasDiff: driven by non-frictional process<br> CONVttw_V_STG_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_SeasDiff: driven by frictional process<br> CONVo_V_SPG_SeasDiff: driven by non-frictional process<br> CONVttw_V_SPG_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_SeasDiff: driven by frictional process<br> CONVo_V_SO_SeasDiff: driven by non-frictional process<br> CONVttw_V_SO_SeasDiff: reproduced by TTW balance </p> <p>6. Coord_lon_lat_zw.mat: coordinate information for the variables in "CONV_u100_2d.mat", "CONV_profile.mat"and "CONV_SeasDiff.mat"<br> % Variables inside the file:<br> lon: longitude for the global distributions of the conversion terms<br> lat: latitude for the global distributions of the conversion terms<br> z_w: depth of each vertical level for vertical profiles of conversion terms</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>
Diet and temperature modify the relationship between energy use and ATP production to influence behaviour in zebrafish (Danio rerio)
<p>Food availability and temperature influence energetics of animals, and can alter behavioural responses such as foraging and spontaneous activity. Food availability, however, is not necessarily a good indictator of energy (ATP) available for cellular processes. The efficiency of energy transduction from food-derived substrate to ATP in mitochondria can change with environmental context. Our aim was to determine whether the interaction between food availability and temperature affects mitochondrial efficiency and behaviour in zebrafish (Danio rerio). We conducted a fully factorial experiment to test the effects of feeding frequency, acclimation temperature (three weeks to 18 or 28°C), and acute test temperature (18 and 28°C) on whole-animal oxygen consumption, mitochondrial bioenergetics and efficiency (ADP consumed per oxygen atom; P:O ratio), and behaviour (boldness and exploration). We show that infrequently fed (once per day on four days per week) zebrafish have greater mitochondrial efficiency than frequently fed (three times per day on five days er week) animals, particularly when warm-acclimated. The interaction between temperature and feeding frequency influenced exploration of a novel environment, but not boldness. Both resting rate of producing ATP and scope for increasing it were positively correlated with time spent exploring and distance moved in standardised trials. In contrast, behaviour was not associated with whole-animal aerobic (oxygen consumption) scope, but exploration was positively correlated with resting oxygen consumption rates. We highlight the importance of variation in both metabolic (oxygen consumption) rate and efficiency of producing ATP in determining animal performance and behaviour. Oxygen consumption represents energy use, and P:O ratio is a variable that determines how much of that energy is allocated to ATP production. Our results emphasise the need to integrate whole-animal responses with subcellular traits to evaluate the impact of environmental conditions on behaviour and movement. --</p>
Comprehensive Evaluation of End-Point Free Energy Techniques in Carboxylated-Pillar[6]arene Host-guest Binding: I. Standard Procedure
<p>All the initial structures as well as the Autodock docked ligand poses used and the computational results.</p>
Turbulent Kinetic Energy Production in a Far-Field River Plume under Upwelling-Favorable Winds
<p>Data for submitted paper "Turbulent Kinetic Energy Production in a Far-Field River Plume under Upwelling-Favorable Winds"</p>
Mongird et al. (2022), interconnection vulnerability, water, and energy interdependency data
<p>Data files supporting Mongird et al. (2022).</p>
Energy System Analysis Dataset_HS Offenburg
<p>Energy System Analysis Dataset_HS Offenburg</p>
Biochar Technologies at the Energy-Food nexus in sub-Saharan Africa: A unique window for Climate-Smart Agriculture
<p>Dataset to support the publication</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.