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855 results for “model system”
Realistic complex geoelectric model with topography, curved layers with the airborne electromagnetic (AEM) system positions and dBz/dt signals
<p>The uploaded files contain the description of the complex model that is used to provide some computational experiments.It is a realistic complex geoelectric model with topography, curved layers, 3-D objects of complex shape, and a fragment of a real observation system containing several thousand AEM system positions. The observation system file also includes dBz/dt values obtained in the measuring points.</p> <p>The model is described with several archieved text files which format is explained in the "readme.txt" file.</p>
Data set of paper Model-Driven System-Performance Engineering for Cyber-Physical Systems
<p>This data set contains the raw and processed data of the paper <em>Model-Driven System-Performance Engineering for Cyber-Physical Systems</em>, published in the proceedings of ESWEEK’21.</p>
Input data for performing a model evaluation of the sectional aerosol module SALSA embedded to PALM model system 6.0
<p>This dataset includes the input information applied to perform a model evaluation study of the PALM model system together with the sectional aerosol module SALSA. </p> <p>The content:</p> <ul> <li>PIDS_STATIC: building height and leaf area density data</li> <li>PIDS_AERO_<simulation time>_<number of aerosol size bins>: aerosol emission data as size bin specific surface emissions (level of detail 2) and aerosol background concentrations</li> <li>PIDS_CHEM_<simulation time>: emission data and background concentrations of gaseous compounds</li> </ul> <p>PIDS_STATIC contains static data and is therefore the same for all simulations.</p> <p>See the model documentation https://palm.muk.uni-hannover.de/trac/wiki/doc for further details.</p>
Techno-economic dataset for long-term energy systems modelling in Viet Nam
<p>Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam. This includes data on electricity generation and consumption, electricity imports and exports, fuel prices, emissions, refineries, power transmission and distribution, electricity generation technologies, and renewable energy potential and reserves for the years 2015 to 2050.</p>
Dataset: Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System
<p>The dataset provided here is intended for publication - Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System.</p> <p>Direct use of our provided datasets is available from Zenodo, and the source code to generate the datasets is published in <a href="https://gitlab.com/dlr-ve/esy/open-brazilian-energy-data">Gitlab</a>. We describe the data collection process in detail and open source the code for data processing and analysis in our publication.</p> <p><br> The assembled dataset includes the following subcategories, as detailed in the methods section of our publication: i) geospatial data for Brazil, ii) aggregated grid network topology, iii) vRES potentials --- profile and installable generation capacity, iv) geographically installable capacity of biomass thermal plants, v) hydropower plants inflow, vi) existing and planned power generators with their capacity, vii) electricity load profile, viii) scenarios of sectoral energy demand and ix) cross-border electricity exchanges. This dataset is resolved geographically by Brazilian federal states, and time series data are resolved by hours, spanning 2012-2020.</p> <p>The dataset can be used as input to popular open energy system models such as PyPSA and any other modelling framework.</p> <p>We encourage you to contribute to improving the datasets.</p>
Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output
<p>We produce a dataset that uses pattern scaling, a common method of emulating climate models. Our dataset is built on the Pangeo CMIP6 archive, which has the advantage that we don't need to actually download the climate model output. Here we demonstrate the utility of our dataset, called Pangeo-Enabled ESM Pattern Scaling (PEEPS). The dataset, which is encapsulated in a Jupyter notebook (and replicated in a Python file), is flexible and can be extended to multiple scenarios and multiple variables, as long as they are in the Pangeo-accessible archive.</p>
INCA-CH seamless nowcasting system: 1km digital elevation model
<p>Digital elevation model at 1km horizontal resolution used in the INCA-CH seamless nowcasting system (in Swiss coordinates CH03). For INCA-CH parameters see here: <a href="https://zenodo.org/record/6470725">INCA-CH set of data</a></p>
PyPSA-Eur: An Open Optimisation Model of the European Transmission System (Dataset)
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. The software pipeline to assemble the model is developed at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a> and documentation is available at <a href="http://pypsa-eur.readthedocs.io">pypsa-eur.readthedocs.io.</a></p> <p><strong>This repository provides pre-built PyPSA networks resulting from corresponding PyPSA-Eur Releases using the default configuration!</strong></p> <p>The model alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p>It only includes freely available and open data. It provides a fully automated free software pipeline to assemble the load-flow-ready model from the original datasets, which enables easy configuration, replacement and<br> improvement of the individual parts.</p> <p>The model is suitable both for operational studies and generation and transmission expansion planning studies.</p> <p>Some basic validation is provided in a paper describing the dataset:</p> <ul> <li>Jonas Hörsch, Fabian Hofmann, David Schlachtberger, and Tom Brown. PyPSA-Eur: An open optimisation model of the European transmission system. Energy Strategy Reviews, 22:207-215, 2018. <a href="https://arxiv.org/abs/1806.01613">https://arxiv.org/abs/1806.01613</a>, <a href="http://https://doi.org/10.1016/j.esr.2018.08.012">https://doi.org/10.1016/j.esr.2018.08.012</a>.</li> </ul>
Analyzing the Impact of Workloads on Modeling the Performance of Configurable Software Systems (Supplementary Material)
<p>This repository provides supplementary material to the ICSE 2023 paper "Analyzing the Impact of Workloads on Modeling the Performance of Configurable Software Systems". We provide the following material:</p> <p>- The experimental setup, including the performance and measurement scripts.</p> <p>- (Aggregated) measurement data and configurations used in our analysis as well as the raw code coverage measurements.</p> <p>- An interactive dashboard to reproduce and reenact our analyses/findings, re-create all visualizations used in the original paper and those omitted due to space limitations.<br> <br> The repository is structured as follows:<br> <br> - accepted_paper.pdf: Camera-ready version of the original paper for reference.</p> <p>- coverages_raw.tar.gz: Raw coverage reports as compressed CSV files (uncrompressed: ~60 GB)</p> <p>- artifacts_excluding_raw_coverage.zip: aggregated measurement data, interactive dashboard, and experimental setup</p> <p>- README.md: A detailed documentation of all the material provided.</p>
Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data."
<p>Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data." Additional information can be found at https://github.com/amtseismo/hrgnss_denoising.</p>
Insights into the Magmatic Feeding System of the 2021 Eruption at Cumbre Vieja (La Palma, Canary Islands) Inferred from Gravity Data Modeling. Remote Sens. 2023, 15, 1936. https://doi.org/10.3390/rs15071936
<p>Paper: Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling <br> F. G. Montesinos1,7, S. Sainz-Maza2,7, D. Gómez-Ortiz3, J. Arnoso4,7, I. Blanco-Montenegro5,7, M. Benavent1,7 E. Vélez4,7, N. Sánchez6 and T. Martín-Crespo3</p> <p>1 Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Plaza de Ciencias 3, 28040 Madrid, Spain.<br> 2 Observatorio Geofísico Central (IGN). C/ Alfonso XII, 3. 28014 Madrid, Spain.<br> 3 Dpt. Biología y Geología, Física y Química Inorgánica, ESCET, Universidad Rey Juan Carlos. C/Tulipán s/n, 28933 Móstoles, Madrid, Spain.<br> 4 Instituto de Geociencias (IGEO), CSIC-UCM. C/ Doctor Severo Ochoa, 7. 28040 Madrid, Spain.<br> 5 Departamento de Física, Escuela Politécnica Superior, Universidad de Burgos. Avda. de Cantabria s/n, 09006 Burgos, Spain.<br> 6 Instituto Geológico y Minero de España (IGME, CSIC), Unidad Territorial de Canarias, Alonso Alvarado, 43, 2A, 35003 Las Palmas de Gran Canaria, Spain.<br> 7 Research Group ‘Geodesia’, Universidad Complutense de Madrid, Spain.</p> <p><br> Corresponding author: Fuensanta G. Montesinos (fuensant@ucm.es)</p> <p>This research is supported by the project PID2019-104726GB-I00/AEI/10.13039/501100011033 funded by the Spanish Research Agency. Further, the University Complutense of Madrid (grants Financiación Grupos 2021, UCM 2022-GRFN14/22) and the Spanish Ministry of Science and Innovation (RD 1078/2021, funding for research activities of the CSIC-PIE project CSIC-LAPALMA-07) supported this research.</p> <p>------------------------------------------------------------------------------------------------</p> <p>Responsible Researchers:<br> - Fuensanta González Montesinos, Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Spain<br> fuensant@ucm.esResponsible Researchers: </p> <p>- José Arnoso Sampedro, Instituto de Geociencias (CSIC-UCM), Spain<br> jose_arnoso@csic.es</p> <p> </p> <p><br> >> The use of this data set is limited to academic or research purposes and it have to be referenced</p> <p><br> Zone:Cumbre Vieja (La Palma Island, Spain)<br> Geodetic Coordinates Datum WGS84<br> Gravity(mGal) and Bouguer Gravity anomaly GRS80 (mGal)(Terrain density 2450 kg/m3)</p> <p>The file GravityCumbreVieja_FGMontesinos_et_al.dat includes the values of gravity and complete Bouguer gravity anomaly (GRS80) calculated for the land gravity stations at the Cumbre Vieja area (La Palma Island, Spain). The gravity values were observed in 142 land gravity stations (Figure 3 in the manuscript) by our group in 2005 and 2021 surveys The positions of the stations were selected to cover most of the Cumbre Vieja area, and the coordinates were obtained by differential GPS (WGS84 Datum). The gravity observations were processed taking into account the usual corrections (instrument height, drift, jumps, etc.). The tidal correction was calculated from gravity tide measurements made in several islands of the Canary Archipelago. All the gravity values referred to absolute gravity stations (Table S1). The procedure to obtain the terrain correction and the Bouguer anomaly map is explained in the manuscript and in the supporting information.</p>
Business process models for a ride fulfilment process in a ride-hailing company enabled by an autonomous driving system
<p>The repository contains the business process models of a ride fulfilment process in a ride-hailing company enabled by an autonomous driving system. The models are depicted using BPMN.2.0 language and are extended with annotations for usage in the <a href="https://dpotool.cs.ut.ee/">DPO tool</a>. The models, which contain "Pleak" as a part of the title, are created using PE-BPMN language and some of them are annotated for usage in the <a href="https://pleak.io/">Pleak</a> tool set. </p> <p>The repository also contains two .csv files which contain the results of the simple disclosure analysis ("<em>6_RideFulfillment_PK_SecSharing_combined-Simple-Disclosure-results.csv</em>") and the results of the leak-when analysis ("<em>7_Pleak_RideFulfillment_PK_SecrSharing_BPMN_Leak_When-results.csv</em>") in the ride fulfilment process. Both analyses have been conducted using the <a href="https://pleak.io/">Pleak</a> tool set and the business process models with respective names from this repository.</p>
Deepwater Horizon Oil Spill Simulations using COAWST Modeling System
<p>There are four netcdf files which are 6-hr model outputs from the Coupled Ocean-Atmosphere-Wave-Sediment-Transport (COAWST) modeling system. The simulations cover the period 21-04-2010 12:00:00:00 to 05-05-2010 00:00:00 UTC. The datasets are described below:</p> <p>DWH_No-oil_romsout.nc: surface temperature from ROMS for no oil simulation</p> <p>DWH_No-oil_wrfout.nc: selected variables from WRF for no oil simulation</p> <p>DWH_Oil_romsout.nc: surface temperature from ROMS for oil simulation</p> <p>DWH_Oil_wrfout.nc: selected variables from WRF for oil simulation</p> <p>These netcdf files can be read by matlab, ferret, and any other softwares which can read netcdf datasets.</p> <p> </p>
Files associated with Christopher Holder and Anand Gnanadesikan, How well do Earth System Models capture apparent relationships between phytoplankton biomass and environmental variables? [Version 1]
<p><strong>1. process_cmip_rf.m</strong> is a matlab script that reads a single file, generates a random forest using the parameters in the associated paper and computes permutation importance and sensitivities. Note- in order to get process_cmip_rf.m to work as written you must have the Statistics and Machine Learning toolbox installed on Matlab and download the table_modis.asc file below. </p> <p>Files 2-16 are tabular filew containing all datapoints used in Random Forest analysis for the NCAR CESM2 model. Columns are</p> <p> 1. Index of point, enabling a mapping back to the model grid if the resolution is known.</p> <p> 2. Longitude</p> <p> 3. Latitude</p> <p> 4. Month</p> <p> 5. Iron in mol/m<sup>3</sup>.</p> <p> 6. Mixed layer in m.</p> <p> 7. Ammonia in mol/m<sup>3</sup></p> <p> 8. Nitrate in mol/m<sup>3</sup>.</p> <p> 9. Phytoplankton carbon in mol/m<sup>3</sup>.</p> <p> 10. Phosphate in mol/m<sup>3</sup>.</p> <p> 11. Shortwave radiation (net solar radiation at ocean surface in W/m<sup>2</sup>).</p> <p> 12. Silicate in mol/m<sup>3</sup>.</p> <p> 13. Salinity in PSU</p> <p> 14. Temperature in C.</p> <p> 15. Upwelling velocity in m/s.</p> <p>If variable is not included in the dataset, the column will be filled with zeros.</p> <p><strong>2.table_cesm2.asc:</strong> Data created from Danabasoglu, G., 2019, NCAR CESM model output prepared for CMIP6 CMIP esm-pi-control <a href="http://doi.org/10.22033/ESGF/CMIP6.7579">http://doi.org/10.22033/ESGF/CMIP6.7579</a>. Grid is 360x180x12</p> <p><strong>3.table_cems2_fv2.asc:</strong> Data created from Danabasoglu, G., 2019, NCAR CESM-FV2 model output prepared for CMIP6 CMIP pi-control <a href="http://doi.org/10.22033/ESGF/CMIP6.11301">http://doi.org/10.22033/ESGF/CMIP6.11301</a>. Grid is 360x180x12</p> <p><strong>4. table_cesm2_waccm.asc: </strong>Data created from Danabasoglu, G., 2019, NCAR CESM2-WACCM model output prepared for CMIP6 CMIP piControl <a href="http://doi.org/10.22033/ESGF/CMIP6.10094">http://doi.org/10.22033/ESGF/CMIP6.10094</a>. Grid is 360x180x12</p> <p><strong>5. table_cesm2_waccm_fv2.asc</strong>: Data created from Danabasoglu, G., 2019, NCAR CESM-WACCM-FV2 model output prepared for CMIP CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.11302">http://doi.org/10.22033/ESGF/CMIP6.11302</a>. Grid is 360x180x12</p> <p><strong>6. table_gfdl_cm4.asc</strong>: Data created from Guo, Huan; John, Jasmin G; Blanton, Chris et al,2018, NOAA-GFDL GFDL-CM4 model output piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8666">http://doi.org/10.22033/ESGF/CMIP6.8666</a>. Grid is 360x180x12</p> <p><strong>7.table_gfdl_esm4.asc</strong> Data created from Krasting, John P.; John, Jasmin G; Blanton, Chris et al., 2018, NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8669">http://doi.org/10.22033/ESGF/CMIP6.8669</a>. Grid 360x180x12</p> <p><strong>8. table_ipsl_cm5a2_inca.asc:</strong> Data created from Boucher, Olivier; Denvil, Sébastien; Levavasseur, Guillaume et al.: 2021, IPSL IPSL-CM5A2-INCA model output prepared for CMIP6 CMIP piControl <a href="http://doi.org/10.22033/ESGF/CMIP6.13683">http://doi.org/10.22033/ESGF/CMIP6.13683</a>. Grid is 182x149x12</p> <p><strong>9.</strong> <strong>table_ipsl_cm6a_lr.asc:</strong> Data created from Boucher, Olivier; Denvil, Sébastien; Levavasseur, Guillaume et al., 2018:, IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.5251">http://doi.org/10.22033/ESGF/CMIP6.5251</a>. Grid is 362x332x12.</p> <p><strong>10</strong>. <strong>table_mpi_esm1-2-ham.asc:</strong> Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe et al., 2019: HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.5037">http://doi.org/10.22033/ESGF/CMIP6.5037</a>. Grid is 256x220x12.</p> <p><strong>11</strong>. <strong>table_mpi_esm1-2-hr.asc:</strong> Data created from Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann et al., 2019: MPI-M MPI-ESM1.2-HR model output prepared for CMIP6 CMIP piControl <a href="http://doi.org/10.22033/ESGF/CMIP6.6674">http://doi.org/10.22033/ESGF/CMIP6.6674</a>. Grid is 802x404x12.</p> <p><strong>12</strong>. <strong>table_mpi_esm1-2-lr.asc:</strong> Data created from Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann et al. 2019:MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP piControl</p> <p> <a href="http://doi.org/10.22033/ESGF/CMIP6.6675">http://doi.org/10.22033/ESGF/CMIP6.6675</a>. Grid is 256x220x12.</p> <p><strong>13. </strong><strong>table_noresm2-lm.asc: </strong>Seland, Øyvind; Bentsen, Mats; Oliviè, Dirk Jan Leo et al.,2019 NCC NorESM2-LM model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8217">http://doi.org/10.22033/ESGF/CMIP6.8217</a>. Grid is 360x385x12</p> <p><strong>14.</strong><strong> table_noresm2-mm.asc</strong>: Data created from Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind et al.,2019 <strong>:</strong> NCC NorESM2-MM model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8221">http://doi.org/10.22033/ESGF/CMIP6.8221</a>. Grid is 360x385x12.</p> <p>15-16. <strong>table_kostadinov.asc, </strong><strong>table_modis.asc</strong> Data is a merger of observational products and model output Observational climatologies for temperature, salinity, mixed layer depth, silicate, phosphate, and nitrate were downloaded from the World Ocean Atlas (WOA) 2018 (Garcia et al., 2019; Locarnini et al., 2019; Zweng et al., 2019). MODIS-POC was downloaded from oceancolor.nasa.gov. Kostadinov POC is taken from <a href="https://doi.pangaea.de/10.1594/PANGAEA.859005">https://doi.org/10.1594/PANGAEA.859005</a> Grid is 360x180x12.</p>
The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections
<p>These files are associated with the article "The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections". </p> <p>1. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/soil_hyper_albedo_RF_int.nc">soil_hyper_albedo_RF_int.nc</a> - hyperspectral soil albedo </p> <p>2. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/lai_hyper_albedo_RF_int.nc">lai_hyper_albedo_RF_int.nc</a> - hyperspectral surface albedo</p> <p>3. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/atmos_F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_diff_red_dir.21_50.tgz">atmos_F2000climo_clm5sp.21_50-F2000climo_clm5sp_bl ...</a> - diagnostic results of the atmospheric model CAM between broadband and hyperspectral simulations. </p> <p>4. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_diff_red_dir.21_50.tgz">F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_dif ...</a> - diagnostic results of the land model CLM between broadband and hyperspectral simulations. </p>
Naturally segregating variants contributing to thermal tolerance in a D. melanogaster model system.
<p>Main_Incapacitation.zip and Incapacitation_founders.zip contain raw thermal tolerance scores for individuals measured within the heat box. Each folder is labeled with the RIL or founder ID and replicates within each file are labeled with group numbers. </p> <p>RNAi_files_to_tar.txt contains the metadata for the Combined_tracks_RNAi_1.Rds.zip and Combined_tracks_RNAi_2.Rds.zip.</p> <p>Combined_tracks_RNAi_1.Rds.zip and Combined_tracks_RNAi_2.Rds.zip. contains raw data for RNAi lines measured on the heat plate. </p> <p>plate_finder-kinglab-2021-05-02.zip contains the DeepLabCut model used for finding the corners of aluminum mounting plate used to hold the fly vials for thermal sensitivity testing. This directory contains the training data as well as the trained and evaluated model. No retraining should be necessary for use.</p> <p>fly_tracker_2-king-2021-09-27.zip contains the DeepLabCut model used for tracking individual flies during thermal sensitivity testing. This directory contains the training data as well as the trained and evaluated model. No retraining should be necessary for use.</p> <p>fly_tracker_batch.py is a python (>= 3.0) script that processes the raw movie files collected via the Raspberry Pi. This script uses the plate finder DeepLabCut model to find the corners of the plate, rotate and crop the images, and output movie files for individual flies. It then uses the fly tracker DeepLabCut model to track the flies and output the data for subsequent processing in R.</p>
PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</p>
Three-dimensional CAD model of the robotic system used for acquiring samples from bacterial swarms
<p>This CAD model shows the robotic sampling system that was used in the scientific article "Simultaneous spatiotemporal transcriptomics and microscopy of <em>Bacillus subtilis</em> swarm development reveal cooperation across generations" by the following authors: Hannah Jeckel*, Kazuki Nosho*, Konstantin Neuhaus, Alasdair D. Hastewell, Dominic J. Skinner, Dibya Saha, Niklas Netter, Nicole Paczia, Jörn Dunkel, Knut Drescher. The symbol "*" indicates an equal contribution. </p> <p>The CAD model consists of 81 individual files in IPT or IAM format, which need to be loaded together into a AutoDesk Inventor to be viewed. We used AutoDesk Inventor 2021 to create and view this CAD model. </p>
Figures: Vortex model of the aerodynamic wake of airborne wind energy systems
<p>Figures in .pdf, .png and .fig format.</p><p>Figures in .fig format can be opened with MATLAB or other open source programming languages (e.g., Python thought the command scipy.io.loadmat or Octave)</p><p>Figures were updated after: Trevisi, F., Croce, A., and Riboldi, C. E. D.: Corrigendum to "Vortex model of the aerodynamic wake of airborne wind energy systems", published in Wind Energ. Sci., 8, 999–1016, 2023, https://doi.org/10.5194/wes-8-999-2023-corrigendum"</p>
High-temperature stress induces bacteria-specific adverse and reversible effects on Ulva (Chlorophyta) growth and its chemosphere in a reductionist model system
<p>This dataset contains raw files from a mass spectrometric analysis of the exo-metabolome of the green macroalga <em>Ulva mutabilis</em> (Chlorophyta).</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.