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210 results for “energy modelling”
Survey Results: LP/MIP Solver Usage for Energy Modeling
<p>This report summarizes the responses to the recent solver benchmark survey conducted by Open Energy Transition. The survey aimed at better understanding energy modelers’ needs and pain points, and to gauge the usefulness of creating a new solver benchmark website for the energy modeling community. </p>
Artifact Description/Artifact Evaluation/Computational Artifact for paper, entitled Analytic Roofline Modeling and Energy Analysis of the LULESH Proxy Application on Multi-Core Clusters
We provide reproducibility initiative dependencies (Artifact Description or Artifact Evaluation or Computational Results Analysis) appendix. To allow a third party to duplicate the findings, this article provides our extensive performance data artifact and describes further details regarding the software environments, experimental design, and methodology employed for the results shown in the paper. The computational artifacts will enable experienced performance engineers to reproduce and interpret the data shown in the paper in the appropriate way and to follow the conclusions we draw from it.
Dataset for simulation of a low-carbon urban energy system using the Backbone model
<p>The dataset contains the input data for cost optimization of an urban energy system. The case study has been described in the article "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system" of Applied Energy.</p> <p>The dataset is in Microsoft Excel format. To make it available for GAMS, one should use e.g. the attached shell script (requires GAMS installation) to convert it to *.gdx file. The generation expansion model is available in the Git repository https://gitlab.vtt.fi/backbone/backbone (under branch projik/planet).</p>
TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling
<p>Data required to rebuild the study: "TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling". In this dataset of New York City, one can find building footprints, monthly energy consumption data for each of these buildings, and matching / cleaned microclimate data from a variety of data sources which are referenced in the work. Among them, thermal infrared measurements may be found, climate models from NOAA and ERA5 may be found, and preprocessed vision systems from Google are used.</p>
Business Models in Energy Communities: an analysis through legal lenses
<p>This research explores energy communities (EC) and their business models’ attributes. We develop a conceptual framework, which combines and extends the social, economic, environmental, and technological dimensions of value generation to include the legal dimension. The latter has been considered only implicitly in previous studies on this sector. Applying this framework to forty business cases of energy communities allows to identify six business model (BM) archetypes representative of ECs. This study can encourage and support new ventures in this sector to model their strategy and comply with the requirements.</p>
Energy spectra and eigenvectors of the sine-Gordon and double sine-Gordon model
<p>This dataset contains low-energy spectra and eigenvectors of two (1+1)-dimensional Quantum Field Theory models, the sine-Gordon (SG) and the double sine-Gordon (DSG) model, for a representative choice of parameter values. The data were computed using the Truncated Conformal Space Approach (TCSA), which is a Hamiltonian truncation method.</p> <p> </p> <p><strong>Parameters:</strong></p> <ul> <li>Cosine frequencies: <em>β</em> = 2.49239 for SG and <em>β</em><sub>1</sub> = 1.01066 and <em>β</em><sub>2</sub> = 2.49239 for DSG</li> <li>Dimensionless (mass)⨉(system size) parameter (<em>m</em>: first SG breather mass, <em>L</em>: system size): <em>mL</em> = 0.01, 0.1, 1, 2, 5</li> </ul> <p><strong>TCSA details: </strong></p> <ul> <li>truncation basis: free massless boson CFT with Dirichlet boundary conditions, restricted to the ground state symmetry sector</li> <li>truncation cutoff (maximum CFT energy shell): 42 </li> <li>basis size: 85674</li> </ul> <p>The spectra correspond to the full list of eigenvalues of the truncated Hamiltonian matrices in increasing order, and the eigenvectors correspond to matrices of dimensions 5173⨉5173, corresponding to the components of the lowest 5173 energy levels in the lowest 5173 basis states (the best convergent part of the eigenvector matrix at the top left corner). Each eigenvector corresponds to a column of the above matrices, in the same order as the eigenvalues.</p> <p><strong>Format:</strong></p> <p>Python NumPy .npy files</p> <p>The filenames are of the form: <em>descriptor</em>_<em>model</em>_mL<em>x</em>.npy</p> <p>where:</p> <p><em>descriptor</em> = "Spectrum" or "Eigenvectors" </p> <p><em>model</em> = "SG" or "DSG"</p> <p><em>x</em> = 0.01, 0.1, 1, 2 or 5 (<em>mL</em> value)</p>
Viskari et al. (2019) The influence of canopy radiation parameter uncertainty on model projections of terrestrial carbon and energy cycling
<p>Zenodo DOI release for permanent archiving outside of GitHub</p>
Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling - Supplementary Material
<p>Supplementary material for the manuscript "Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling".</p> <p>This deposit contains all data and visualization scripts needed to replicate results in the manuscript.This includes user created figures, model input files, model output files, configuration files for running the workflow, and all scripts needed to process results.</p> <p>In addition to the European Commission, we acknowledge that Trevor Barnes' contribution to this paper was funded via a Mitacs Globalink Research Award, grant number IT2569</p>
Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members' active contributions
<p>This dataset was used in the case study of the following publication:</p> <p> - Luis Gomes, Zita Vale, "Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members’ active contributions," Sustainable Cities and Society, Volume 101, 2024, 105060, ISSN 2210-6707, <a href="https://doi.org/10.1016/j.scs.2023.105060">https://doi.org/10.1016/j.scs.2023.105060</a> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>The dataset is composed by energy generation, consumption, and forecast (for generation, and for consumption) expressed in Wh. The data considers an energy community of 10 prosumers in 30 days.</p> <p>The dataset also has energy prices that have been collected from MIBEL (Iberian Electricity Market).</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
Pre-built Swiss-Calliope sector-coupled energy model
<div> <h3>Swiss-Calliope prebuilt model</h3> <div>The model consists of Switzerland and its neighbours, as described in <a href="https://doi.org/10.1016/j.enconman.2024.118426" target="_blank" rel="noopener">Mellot et al., 2024</a>. Switzerland's heating and transport sectors are modelled on top of its electricity sector.</div> <br> <div>The model is ready to be loaded into Calliope, for 2016--2018. This prebuilt was specifically designed for the study of Switzerland's winter deficit, but is easily modifiable for any other analysis. Refer to Calliope's <a href="https://calliope.readthedocs.io/en/stable/" target="_blank" rel="noopener">documentation</a> for information on how to do this.</div> <br> <div>To run the same scenarios as for the Swiss winter deficit analysis, you need to do the following steps. Note that these scenarios were ran on ETH's Euler cluster which uses the slurm batch system. You can otherwise just adapt the following shell scripts to run the scenarios on other systems.</div> <div>1. Set up the conda environment with the correct version of calliope <code>conda env create -f environment.yaml</code>. On slurm systems you may also need to load gurobi <code>module load new gurobi/9.0.0</code>.</div> <div>2. Run the baseline scenarios, i.e. those corresponding to the EP2050+ configuration, by running <code>sh run_baselines 4</code>, where 4 corresponds to the time resolution.</div> <div>3. Once these runs are finished, run the python file <code>python read_baselines_and_fix_neighbours.py</code>. This will fix Switzerland's neighbouring countries' installed capacities for the next scenarios.</div> <div>4. Then you may run the study's scenarios by running <code>sh run_initial_scenarios.sh 4</code>, and the sensitivity analysis scenarios by running <code>sh run_sensitivies.sh 4</code>.</div> <div> </div> <div>The model's units are GW, GWh, Million euros, and Million kilometers.</div> </div>
Socio- and Techno-Economic Dataset for Energy Modelling in Sierra Leone
<p>This repositary contains a Reference Energy Syatem (RES) and dataset containing the raw data used in the Sierra Leone energy models created by CCG and the Ministry of Energy in Sierra Leone including scenario-specific constraints used in the modelling. The models used were MAED and OSeMOSYS. Full information regarding data sources and assumptions used can be found in the corresponding Data in Brief.</p> <p>This work was supported by the Climate Compatible Growth Programme (#CCG) of the UK's Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government's official policies.</p>
Scripts and datas for "A unified energy-constrained mesoscale parameterisation for ocean climate models".
<p>Scripts and datasets used for creating the results of a submitted work :</p> <p><strong>R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak</strong>: <em>A unified energy-constrained mesoscale parameterisation for ocean climate models. </em>(submitted in JAMES).<em><br></em></p> <p>Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017").</p> <p>The reference EKE of <a href="https://doi.org/10.1029/2023gl104688">Torres et al. (2023)</a> is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. <a href="https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18">World Ocean Atlas 2018</a>, <a href="https://gmd.copernicus.org/articles/13/3643/2020/">Tsujino et al. (2020)</a> and <a href="https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/04c79ece-3186-349a-e063-6c86abc0158c/">RAPID</a>)</p> <p>IPython notebooks for computing and plotting metrics are provided :</p> <ul> <li><em>james-eke-heat_budget.ipynb</em> : plots for heat transport and global heat storage (section 4.1)</li> <li><em>james-eke-southern_ocean.ipynb</em> : plots for Southern Ocean (section 4.2) analysis</li> <li><em>james-eke-north_atlantic.ipynb</em> : plots for North Atlantic and Labrador Sea (section 4.3) analysis</li> <li><em>james-eke-timeseries.ipynb</em> : plot 0D metric timeseries for simulations (including spin-up)</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
Biomass availability at NUTS3 level for modelling European energy system with 3 future scenario
<p>The database is built over three main sources</p> <ul> <li>S2Biom database from where most of the numbers come from <a title="S2Biom" href="https://s2biom.wenr.wur.nl/home" target="_blank" rel="noopener">(S2Biom original repo)</a></li> <li>ENSPRESO database that we use for few energy sources that are not part of s2biom (<a title="JRC" href="https://data.jrc.ec.europa.eu/collection/id-00138" target="_blank" rel="noopener">ENSPRESO</a>)</li> <li>National data for Switzerland (<a href="https://www.envidat.ch/dataset/swiss-biomass-potentials" target="_blank" rel="noopener">FoReMA Forest Resources Management Insititute</a></li> </ul> <p>Data processing is done with Julia code that has short documentation and additional databasePipeline.pdf to understand how the dataset was built. To rebuild the dataset, refer to the github repository linked to this dataset.</p> <p>Data are available as a csv file and as a sqlite database. Data query methods are available from the Github repository linked to this dataset.</p> <p>The dataset includes biomass energy availability, expressed in PJ, at nuts 0-3 (NUTS 2013), and ENTSOE bidding zones aggregation. For each biomass source, the roadsidecost of each source is associated. While the biomass data is varied, large, and detailed following standards (ISO 17225-1:2021, ISO 17225-2:2021, ISO 17225-3:2021, ISO 17225-4:2021, ISO 17225-5:2021, ISO 17225-6:2021, ISO 17225-7:2021, ISO 18125:2017, EN 13556), biomass sources have been aggregated into three categories: Forestry, Agriculture, Organic waste. There are 3 bioenergy potential, low, medium, and high. These were based on the available data listed above. </p> <p>Note: Technical availability of biomass is often much higher than the current use. Check comparison_biofuel_amounts.xlsx to compare the potentials to actual use in Eurostat and IEA data. Full potential should often not be used, because of possible issues with biodiversity and land use emissions.</p> <p> </p>
Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power »
<p>This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power" by Sterl et al. (2022).</p> <p>It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.</p> <p>Five separate datasets are included:</p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 2, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Algeria<br>Angola<br>Benin<br>Botswana<br>Burkina Faso<br>Burundi<br>Cameroon<br>Central African Republic<br>Chad<br>Congo Republic<br>Democratic Republic of the Congo<br>Djibouti<br>Egypt<br>Equatorial Guinea<br>Eritrea<br>Eswatini<br>Ethiopia<br>Gabon<br>The Gambia<br>Ghana<br>Guinea<br>Guiné-Bissau<br>Côte d'Ivoire<br>Kenya<br>Lesotho<br>Liberia<br>Libya<br>Madagascar<br>Malawi<br>Mali<br>Mauritania<br>Morocco<br>Mozambique<br>Namibia<br>Niger<br>Nigeria<br>Rwanda<br>Senegal<br>Sierra Leone<br>Somalia<br>South Africa<br>South Sudan<br>Sudan<br>Togo<br>Tunisia<br>Uganda<br>Tanzania<br>Zambia<br>Zimbabwe</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <span><a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></span></p> <p><strong>See also</strong></p> <p>Sterl, S. (2024). Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10650822" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10650822</a></p>
WP2: Photochemical model of planetary atmospheres driven by high-energy stellar irradiation
<p>Modeling results of irradiated planetary atmospheres (Locci et al. 2021, <em>Extreme Ultraviolet and X-ray Driven Photochemistry of Gaseous Exoplanets</em>, PSJ, submitted). See <em>Introduction</em> and <em>Readme_Reference_model</em> for more details.</p>
Two Source Energy Balance Model Inputs and Outputs from Drone Surveys at Majadas de Tietar in May 2021
<p><strong>MONSOON PROJECT SURVEY DATA OUTPUTS: Majadas de Tietar Tree-Grass Savanna Ecosystem 05/05/2021-20/05/2021</strong></p> <p>Here we make available high resolution (0.82 cm) energy and water flux maps from unmanned aerial system (UAS) data collected using a Micasense Altum in May 2021. We use the Two Source Energy Balance Model (via pyTSEB) and include model inputs and outputs. We use the Priestley Taylor (TSEB hereafter) and Dual Time Difference (DTD hereafter) methods in pyTSEB, the details of which can be found here pyTSEB https://pytseb.readthedocs.io/en/latest/index.html. The data collection method largely follows https://www.mdpi.com/2072-4292/13/7/1286, however a new paper detailing these surveys in Majadas is under review (as of November 2021). </p> <p>This upload includes the following gridded datasets:</p> <p><strong>Model inputs</strong></p> <p>Zipfiles are named according to their collection date (<strong>DDMMYYYY.7z</strong>). Within each zipfile are the datasets corresponding to different flight times UTC +2 (<strong>hhmm_DDMMYY</strong>). Within each survey folder are rasters with descriptive filenames using the following format:</p> <p><em>Product type_Resolution_survey area_date_flight time.tif</em></p> <p>The following prefixes denote the Product types:</p> <ul> <li>CHM_... = Canopy Height Model (m)</li> <li>GFrac2_... = Green Fraction (0-1)</li> <li>MSpec_... = Raw multispectral dataset from Altum (Blue, Green, Red, NIR, Rededge, LWIR)</li> <li>TEmpK_... = Radiometric Surface Temperature (empirical calibration, K)</li> <li>TRawK_... = Radiometric Surface Temperature (no calibration, K)</li> <li>LST2_... = Radiometric Surface Temperature (calibrated using methods outlined here https://www.mdpi.com/2072-4292/12/7/1075, K)</li> <li>Grass_... = grass vegetation mask</li> <li>Tree_... = tree vegetation mask</li> </ul> <p>We also supply the config files used to generate TSEB and DTD. To run these you will need to edit the filepaths according to your own system. </p> <p><strong>Model Outputs</strong></p> <p><strong>Majadas_TSEB_EMP_outputs.7z</strong> = Two Source Energy Balance (pyTSEB) model outputs (using the Priestley-Taylor method), using radiometric temperature datasets calibrated empirically. </p> <p><strong>Majadas_DTD_EMP_outputs.7z</strong> = TSEB Dual Time Difference model outputs (from pyTSEB) using radiometric temperature datasets calibrated empirically. </p> <p><strong>DTD_ET.7z</strong> = Evapotranspiration rasters (calculated using DTD latent heat data) in g m<sup>-2</sup> s<sup>-1</sup></p> <p><strong>File names are descriptive</strong>:</p> <p><em>Model type_radiometric temperature method_vegetation type_survey area_date_flighttime.tif</em></p> <p>Model type = DTD or TSEB</p> <ul> <li>Radiometric temperature method = always empirical calibration here</li> <li>vegetation type = grass, tree, or merge (which is both tree and grass)</li> <li>Survey area = N (north, or Nitrogen fertiliser treatment), C (central, or Control fertiliser treatment), S (south, or Nitrogen and Phosphorus fertiliser treatment)</li> <li>date = in DDMMYY format</li> <li>flight time = takeoff time for the drone (hhmm) (UTC+2)</li> </ul> <p>To find the exact local time of survey times, please see the table in flight_data3.csv</p>
Low carbon energy R&D portfolios that are robust when models and experts disagree
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Low carbon energy R&D portfolios that are robust when models and experts disagree</strong></p> <p>by</p> <p>Franklyn Kanyako, Erin Baker, David Anthoff</p> <p> </p> <p><strong>All Model output and Non-Dominated Portfolios</strong>: This contains all expected values of all model outputs, used to determine the non-dominated portfolios under each policy.</p> <p><strong>Large Scale Expert Elicitation of R&D Investment</strong>: Contains samples of expert elicitation from each elicitation team.</p> <p> </p> <p> </p>
Climate model output for "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"
<p>Climate model output associated with the manuscript "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"</p>
Capturing features of hourly-resolution energy models through statistical annual indicators
<p>Dear colleagues,</p> <p>This is the official repository of the Task 7.4 of H2020 Locomotion project. Feel free to use our data by citing this work and comment about our work by referencing the main authors of it. The article explaining this work is under revision. it will be referenced as soon as posible.</p> <p><strong>Python scripts </strong>("create_inputs.txt" and "run_simulations.txt") creates the input files for EnergyPLAN. The second one runs iteratively EnergyPLAN to generate the outputs of combinations (which are saved in the "EU_Iterate_case.xlsx" file). Hourly distributions of demands and supply technologies are contained in the RAR file ("EUdist.rar") and "EU_start_v2_noFlex.txt" initialize the starting configuration of the European energy system. Those files are required to run EnergyPLAN. The <strong>PowerPoint file</strong> ("EnergyPLAN_instructions.pptx") explains the procedure to carry out the runs of combinations in Python/Excel.</p> <p>In case you couldn't properly do the combinations, the<strong> Excel file</strong> ("EU.xlsx") saves this information, so the steps of the approach could be followed from this point with the Excel file. We have used Power Query (Excel) to prepare the data for the next step of building the regression models.</p> <p>The <strong>Matlab file (</strong>"CreateRegressionModels.m"<strong>)</strong> automatically generates the regression models for the European region of WILIAM (official model of the Locomotion project).</p> <p>Best regards,</p> <p>Gonzalo.</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.