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858 results for “Energy Data”
Data for A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis
<p>Representative frames, topology for MD simulation with NAMD and GROMACS, data and notebook to reproduce analyses in the paper "A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis" (Blanc, Houdusse, Cecchini, PLOS Computational Biology 2024).</p>
Application of JumpCount on free energy profiles (preliminary data)
<p>JumpCount method applied on free energy profiles from [1].</p> <p>Ref:<br>1 - Science 2011, 334, 517-520.</p> <p> </p>
Data related to "Energie- und Klimapolitik: Effekte eines europ¨ aischen Ansatzes auf Preise, Versorgungssicherheit und CO2-Emissionen in Deutschland"
<p>This is the database necessary to run the underlying model codes in GAMS. See <a href="../records/10928291">Code related to "Energie- und Klimapolitik: Effekte eines europäischen Ansatzes auf Preise, Versorgungssicherheit und CO2-Emissionen in Deutschland" (zenodo.org)</a> for codes. The data needs to get placed into the database-folder.</p>
AWESOME Energy System Model data and model
<p><span>This repository collects all the necessary items defined to setup and to run the Energy System optimization model for the AWESOME project.</span></p> <p><span>Detailed specifications of the adopted model (OSeMOSYS) and data are descripted in Deliverable D2.4 document: "</span><span>Future Energy Scenarios".</span></p> <p><span>In particular, the repository provides all essential data and scripts to define the energy model defined for projecting the energy scenarios developed for the AWESOME project. The document reports the development of an open-source energy system optimization model of the energy supply chain for the spatial domain useful for the AWESOME project (i.e. including Egypt, Ethiopia, and Sudan). The model is then used to explore different pathways of future energy scenarios in terms of energy demand and infrastructure evolution and their economic and environmental impacts. The future sectoral energy demand scenarios are developed based on the Socio-economic Pathways (SSPs) and the outcomes of D2.1 (Demographic projections), using a multi-sectoral optimal resource allocation economic model. </span></p> <p>This record contains:</p> <p>- The Deliverable D2.4, where the optimization model and the calculation of the energy system scenarios under different climatic scenarios are presented.</p> <p>- The results for each implemented scenario, in terms of installed capacity and energy generation of energy technologies (.tif files and excel files), for the Nile River Basin and at the national level for each focus country (Ethiopia, Sudan and Egypt).</p> <p>- Description of the data (excel file and pdf file)</p>
Source Data for the publication "Sub-100-fs energy transfer in coenzyme NADH is a coherent process assisted by a charge-transfer state"
<p>Molecular Structures for solvated NADH. </p> <p>The folder "QMMM_OPTIMIZED_STRUCTS" contains the pdb files of the six representatives for the three conformational clusters obtained after REMD used in the Supplementary Information.</p> <p>The folder "SOLVENT_ENSEMBLE_AROUND_FIXED_SOLUTE" conatins AMBER RESTART files for 200 solvent configurations around two cluster reps displayed in Figure 2 of main manuscript. </p> <p>The folder "PARAMETERS_FOR_MLMCTDH" contains the input file, operator file and parameters for ML-MCTDH dynamics for the structures shown in Main Manuscript and Supplementary. </p>
Data: relation between soft tissue energy dissipation and leg stiffness in running at different step frequencies
<p>Data set - paper: <span>Relation between soft tissue energy dissipation and leg stiffness in running at different step frequencies </span></p>
Data and GDS file for "Methods to achieve near-millisecond energy relaxation and dephasing times for a superconducting transmon qubit"
<p>Data and GDS files for "Methods to achieve near-millisecond energy relaxation and dephasing times for a superconducting transmon qubit"</p>
Data for "Thermal Desorption Kinetics, Binding Energies, and Entrapment of Methyl Mercaptan Ices"
<p>This data includes infrared and mass spectra for thermal desorption experiments of methyl mercaptan and methanol ices. There are also Gaussian 16 log files that were used for complementary analysis of the data (i.e. single/dimer molecule optimization+frequency and harmonic/anharmonic band strength calculations) using the M062X/cc-pVDZ or M062X/cc-pVQZ or B3LYP/cc-pVDZ levels of theory. </p>
Large-eddy simulation of airborne wind energy farms: AWES virtual flight data
<p>Large-eddy simulation of airborne wind energy farms: AWES virtual flight data.</p> <p>This dataset contains virtual flight data collected from individual systems in airborne wind energy parks obtained by means of large-eddy simulations. All data are stored as Python dictionary objects in the Pickle format. Additional Python scripts are provided to visualize the data. </p> <p> </p>
Energy Scenarios Tool - JRC - data behind the tool
<p>This dataset includes the data behind the JRC Energy Scenarios Tool that is available online on <a href="https://visitors-centre.jrc.ec.europa.eu/en/media/tools/energy-scenarios-explore-future-european-energy">https://visitors-centre.jrc.ec.europa.eu/en/media/tools/energy-scenarios-explore-future-european-energy</a> The aim of this tool is to explain how energy is produced and used in the EU, today and in the future. The tool helps to understand the magnitude and speed of the projected changes, in other words, to understand the energy transition.</p>
Data for "Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa"
<p>This is the data used for "Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa using the improved SSiB4/TRIFFID-Fire model". The data includes two folders: fireon and fireoff representing the scenarios with the fire model turned on and off. Each folder includes 14 years of data from 2000-2013.</p>
Data from: The active mouse rests within: Energy management among and within individuals
<p>1. The relationship between daily energy expenditure (DEE) and resting metabolic rate (RMR) provides insight into how organisms allocate energy to maintenance <i>versus</i> energetically expensive activities such as locomotor activity.</p> <p>2. Three models have been devised to describe energy management: the allocation, independent, and performance models, which respectively predict a DEE-RMR slope of <i>b</i><1, <i>b</i>=1, and <i>b</i>>1.</p> <p>3. Here, we took paired repeated metabolic and behavioural measurements in 51 female white-footed mice to 1) evaluate which energy management models apply at the among- and within-individual levels, and to 2) quantify the relationship between metabolic traits and two energetically expensive behaviours.</p> <p>4. The DEE-RMR slope was different at the among- <i>versus</i> within-individual levels, with values supporting the performance and allocation models at the among- and within-individual levels, respectively. Accordingly, the relationship between voluntary wheel running and RMR was positive at the among-individual level (<i>r</i><sub> </sub>= 0.40±0.21), but negative at the within-individual level (<i>r </i>= -0.23±0.10).</p> <p>5. To our knowledge, this is the first study to simultaneously partition the relationship between RMR and behaviour at the among- <i>versus</i> within-individuals levels while determining which energy management models apply at each of these levels. In doing so, we have identified a mechanism through which compensation occurs at the within-individual level.</p>
Data for Structure of Energy Precipitation Induced by Superbolt-Lightning Generated Whistler Waves
<p>This is the dataset for the publication <strong>Structure of Energy Precipitation Induced by Superbolt-Lightning Generated Whistler Waves. </strong>Detailed instruction attached within.</p>
Data from: Energy and physiological tolerance explain multi-trophic soil diversity in temperate mountains
<p><span><strong>Aim </strong>–</span><span> Although soil biodiversity is extremely rich and spatially variable, both in terms of species and trophic groups, we still know little about its main drivers. Here, we contrast four long-standing hypotheses to explain the spatial variation of soil multi-trophic diversity: energy, physiological tolerance, habitat heterogeneity, and resource heterogeneity.</span></p> <p><span><strong>Location </strong>–</span><span> French Alps</span></p> <p><span><strong>Methods </strong>–</span><span> We built on a large-scale observatory across the French Alps (Orchamp) made of seventeen elevational gradients (~90 plots) ranging from low to very high altitude (280 - 3160 m),</span> <span>and encompassing large variations in </span><span>climate, vegetation and pedological conditions. Biodiversity measurements of 36 soil trophic groups were obtained through environmental DNA metabarcoding. Using a machine learning approach, we assessed 1) the relative importance of predictors linked to different ecological hypotheses in explaining overall multi-trophic soil biodiversity, and 2) the consistency of the response curves across trophic groups. </span></p> <p><span><strong>Results </strong>–</span><span> We showed that predictors associated with the four hypotheses had a statistically significant influence on soil multi-trophic diversity, with the strongest support for the energy and physiological tolerance hypotheses. Physiological tolerance explained spatial variation in soil diversity consistently across trophic groups, and was an especially strong predictor for bacteria, protists and microfauna. The effect of energy was more group-specific, with energy input through soil organic matter strongly affecting groups related to the detritus channel. Habitat and resource heterogeneity had overall weaker and more specific impacts on biodiversity with habitat heterogeneity affecting mostly autotrophs, and resource heterogeneity affecting bacterivores, phytophagous insects, enchytraeids and saprotrophic fungi.</span></p> <p><span><strong>Main Conclusions</strong> –</span><span> Despite the variability of responses to the environmental drivers found across soil trophic groups, major commonalities on the ecological processes structuring soil biodiversity emerged. We conclude that among the major ecological hypotheses traditionally applied to aboveground organisms, some are particularly relevant to predict the spatial variation in soil biodiversity across the major soil trophic groups.</span></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>
Modelled data - Energy Transition in Bolivia
<p>Model structure, input data and output files from MoManI </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>
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>
ScienceDex guides
Understand access before you commit
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.