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5,371 results for “Flow”
Geothermal heat source estimations through ice flow modelling at Mýrdalsjökull, Iceland - Datasets
<p>This repository contains data used in the study "<em>Geothermal heat source estimations through ice flow modelling at</em><br><em>Mýrdalsjökull, Iceland", </em>to be published in <strong>The Cryosphere. </strong>A detailed reference will be added after publication.</p> <p>Details on processing of the data and the creation of the simulated data can be found in the aforementioned publication.</p> <p><strong>Data Specifications:</strong></p> <ul> <li>Cartographic projection: ISN93 / Lambert 1993 (EPSG:3057, <a href="http://https/epsg.io/3057">https://epsg.io/3057</a>)</li> <li>Origin of Elevation: meters above GRS80 ellipsoid (WGS84)</li> <li>Raster data format: GeoTIFF</li> <li>Pléiades dataset includes only DEMs because the Pléiades ortho imagery is for licensed use only. Please contact the authors for further information on this.</li> </ul> <p><strong>File descriptions:</strong></p> <ul> <li><em><strong>bedrock_Magnusson_etal_2021.tif: </strong></em>contains bedrock data published by Magnússon et al. 2021 for the simulation domain used in the paper. See reference below.</li> <li><em><strong>surface_27092016_pleiades.tif: </strong></em>contains glacier surface data from September 27th, 2016 which is used as a starting geometry for the simulations described in the paper. This data is based on Pléiades satellite images.</li> <li><em><strong>surface_01092017_pleiades.tif: </strong></em>contains glacier surface data from September 1st, 2017 which is used as a reference target geometry for the simulations described in the paper. This data is based on Pléiades satellite images.</li> <li><em><strong>HM_run04.tif:</strong></em> contains the best fitting simulation based surface which was compared to <em><strong>surface_01092017_pleiades.tif </strong></em>in the paper.</li> <li><em><strong>HM_run04_hillshade.png: </strong></em>a simple hillshade image for preview purposes.</li> </ul> <p> </p>
Sheet Flow Data
<p>The netCDF files "data_expe_mb1.nc" and "data_expe_mb2.nc" contains the experimental results of intense sediment transport experiments (sheet flow) carried out in the LEGI tilting flume. Synchronised and colocated concentration and veclocity (wall-normal and streamwise components) measurements have been obtained by using the Acoustic Concentration and Velocity Profiler (ACVP - Hurther et al., 2011). Details about the experimental protocol can be found in Revil-Baudard et al. (2015) and Revil-Baudard et al. (2016). As there is no sediment recirculation in the flume, the same run has been repeated several times to perform ensemble averages. The results of two-phase flow numerical simulations performed using SedFOAM-2.0 are disseminated in two formats (i) the complete openFOAM case directories can be found in the repository "data_num" (ii) NetCDF files containing the concentration, velocity, shear stress and Turbulent Kinetic Energy profiles. The repository contains different combination of intergranular stress and turbulence models: the mu(I) rheology or the kinetic theory of granular flows and mixing length or k-epsilon turbulence models. All the details concerning the numerical results and the configurations can be found in Chauchat et al. (2017a) The SedFOAM-2.0 source code is distributed under a GNU General Public License v2.0 (GNU GPL v2.0) and is available at https://github.com/SedFoam/sedfoam/releases/tag/v2.0 or on Zenodo at https://zenodo.org/record/836643#.Wc47Yoo690s with the following DOI https://doi.org/10.5281/zenodo.836643 (Chauchat et al.,2017b).</p>
Theory and a heuristic for the minimum path flow decomposition problem
<p>This is the data used in the following paper:</p> <p>Shao, Mingfu, and Carl Kingsford. "Theory and A Heuristic for the Minimum Path Flow Decomposition Problem." <em>IEEE/ACM Transactions on Computational Biology and Bioinformatics </em>(2017).</p>
Dataset for: Statistical properties of meso-scale plasma flows in the nightside high-latitude ionosphere
<p>This dataset is a compilation of statistical results from Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). If you would like to use the dataset, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com). Depending on how the results are used, the main authors request co-authorship on publications. </p> <p>The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt <br> Files named ***_FLOW-DATA-PCvsAO_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> FWHMavg_AO [degrees]<br> FWHMkmavg_AO=[km]<br> longtestranges=[ignore]<br> Velmaxavg_AO=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]<br> FWHMavg_PC=[degrees]<br> FWHMkmavg_PC=[km]<br> Velmaxavg_PC=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]<br> ;;For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not<br> ;;those used in the paper. They were not found with the strict selection criteria. Please do not use.<br> mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] <br> gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> ;;;;;;;;;;;;;;;<br> minlatAO=[degrees, min geographic latitude of the flow]<br> maxlatAO=[degrees, max geographic latitude of the flow]<br> minlatPC=[degrees, min geographic latitude of the flow]<br> maxlatPC=[degrees, max geographic latitude of the flow]<br> mltAO=[degrees (MLT)]<br> mltPC=[degrees (MLT)]<br> AE=[nT]<br> AL=[nT]<br> SYMH=[nT]<br> IMFBz=[nT]<br> IMFBy=[nT]<br> F107=[sfu]</p> <p>The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt <br> Files named ***_orientation_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. <br> https://doi.org/10.1029/2018JA025440 <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> mbearingAO [degrees clockwise from magnetic North]<br> gbearingAO [degrees clockwise from geographic North]<br> mbearingPC [degrees clockwise from magnetic North]<br> gbearingPC [degrees clockwise from geographic North]</p> <p>The following list describes the columns in each data file labeled, ***_SPEC_TEST_***_noRG1-2.txt</p> <p> time [YYYYMMDDhhmmss]<br> RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]</p>
Multilingual Wikidata Property Translation Flow Dataset
<p>Multilingual Wikidata property translation Flow dataset contains the translation flow of Wikidata properties as collected on July 7, 2019. It contains four columns: timestamp, property, language, type. Every line in the dataset corresponds to the <strong>first-time action</strong> related to a language, i.e., the time at which the first translation of a label, description or an alias was made in a given language for a given property.</p> <p>Taking an example line from this dataset,</p> <p><em>2013-09-10T22:43:54Z,P856,en,label </em></p> <p>corresponds to the action that an <em>English</em> <em>label</em> of Property <em>P856</em> was added for the first time at <em>2013-09-10T22:43:54Z</em>.</p> <p>Following is a description of each column.</p> <p>1. timestamp: the time at which an action was made. For example, <em>2013-09-10T22:43:54Z</em></p> <p>2. property: Wikidata property identifier. It uses the P-number, For example, <em>P856</em></p> <p>3. language: the language in which a label/description/alias was first translated</p> <p>4. type: It could be one of the following values: label, description and alias</p>
DPMFA_EU_ENM_2000-2020: Dynamic Probabilistic Material Flows of Engineered Nanomaterials from 2000 to 2020 - Raw results
<p>This dataset is related to the following publication:</p> <p>Title: Dynamic probabilistic material flow analysis of engineered nanomaterials in European waste treatment systems</p> <p>Authors: Sana Rajkovic, Nikolaus A. Bornhöf<span>t</span>, Renata van der Weijden, Bernd Nowack, Véronique Adam</p> <p>Submitted to the journal Waste Management in September 2019.</p> <p>The files contain key values of probability distributions associated with the emissions of selected engineered nanomaterials to the environment.</p>
Creative Flow in Dance: Consensual Assessment of Creative Outcomes of Dance Group Improvisation
<p>Group flow refers to peak experience when a group is performing at its highest level of abilities. This study examined the relationship between the group flow experience of an improvising group of contemporary dancers and their creative outcomes evaluated by the consensual assessment method (CAT). The dataset consists of the data and analysis used in the "Creative Flow in Dance: Consensual Assessment of Creative Outcomes of Dance Group Improvisation (in press)."</p> <p>First, eight groups of four dancers were recruited to perform improvised dance scores for video recordings. The low and high-flow improvisations videoclips were selected based on dancers’ reports in the video-stimulated recall method, Flow (Łucznik, May, 2021). Subsequently, these videoclips were rated by 77 experts and 126 nonexperts using CAT on five dimensions: aesthetic appeal, technique demands, meaningfulness, coherence /collaboration of the group, and creativity.</p> <p>Experts rated high-flow improvisations as more creative than the low-flow ones. In comparison, nonexperts’ ratings of high and low-flow improvisation did not differ, although the reliability measures of nonexperts creativity judgments were noticeably lower than in the expert group. Regardless of expertise, creativity judgments were significantly related to all other CAT factors: aesthetic appeal, technique, meaningfulness and coherence of improvisation. The results provide evidence that group flow leads to higher creativity. Moreover, they suggest that subjective judgments of creativity of performance arts cannot be easily separated from judgments of their technical level, aesthetic appeal, meaningfulness or coherence of the group.</p>
Indicative distribution map for Ecosystem Functional Group T6.2 Polar/alpine cliffs, screes, outcrops and lava flows
<p>This archive contains indicative distribution maps and profiles for <strong>T6.2 Polar/alpine cliffs, screes, outcrops and lava flows</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
COVID19 Flow-Maps Daily Cases Reports
<p>This repository contains COVID-19 data for Spain, including daily cases at the level of autonomous communities as well as provinces, and higher spatial resolution for several autonomous communities (eight out of the nineteen autonomous communities publish reports with local daily COVID-19 cases at the level of municipalities or Basic Health Areas).</p> <p>Each record has an identifier, the associated date, the corresponding identifier of the layer and code of the region and a set of COVID-19 related fields, which include the number of new cases (daily incidence) and total cases.</p> <ul> <li> <p><strong>Autonomous Communities</strong>: ES.covid_cca</p> </li> <li> <p><strong>Provinces</strong>: ES.covid_cpro</p> </li> <li> <p>Higher spatial resolution:</p> <ul> <li> <p><strong>Principado de Asturias</strong>: 03.covid_cumun</p> </li> <li> <p><strong>Cantabria</strong>: 06.covid_cumun</p> </li> <li> <p><strong>Castilla y Leon</strong>: 07.covid_abs</p> </li> <li> <p><strong>Cataluña/Catalunya</strong>: 09.covid_abs</p> </li> <li> <p><strong>Comunitat Valenciana</strong>: 10.covid_cumun</p> </li> <li> <p><strong>Comunidad de Madrid</strong>: 13.covid_abs</p> </li> <li> <p><strong>Comunidad Foral de Navarra</strong>: 15.covid_abs</p> </li> <li> <p><strong>País Vasco/Euskadi</strong>: 16.covid_abs</p> </li> </ul> </li> </ul> <p>For information about data sources, visit: <a href="https://flowmaps.life.bsc.es/flowboard/data">https://flowmaps.life.bsc.es/flowboard/data</a></p>
COVID19 Flow-Maps Mobility-Associated-Risk
<p><strong>The Mobility Associated Risk</strong></p> <p>The Mobility Associated Risk is a risk score combines mobility and COVID-19 incidence to estimate how many cases could theoretically be exported/imported between different origin-destination pairs of regions.</p> <p>For more information about how the MAR is calculated visit: <a href="https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk">https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk</a></p> <p>Dashboard The Mobility Associated Risk combines mobility and COVID-19 incidence to estimate how many cases could theoretically be exported/imported between different origin-destination pairs of regions.</p> <p>For more information about how the MAR is calculated visit: <a href="https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk">https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk</a></p> <p>Dashboard <a href="https://flowmaps.life.bsc.es/flowboard/">https://flowmaps.life.bsc.es/flowboard/</a></p>
COVID19 Flow-Maps Population data
<p><strong>Daily population and trips per person data from Spain 2020-2021</strong></p> <p>This repository contains daily population records based on a study conducted by the MITMA, that analysed the mobility and distribution of the population in Spain from February 14th 2020 to May 9th 2021. The study is based on a sample of more than 13 million anonymised mobile phone lines provided by a single mobile operator whose subscribers are evenly distributed.</p> <p>For more information on the data visit: <a href="https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data">https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data</a></p> <p>Data provided by MITMA is related to the layer mitma_mov. For the rest of the layers, the population was estimated using the population grid from GEOSTAT: <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat</a></p>
Data: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
<p>Network parameters of continuous normalizing flows trained for the <span class="math-tex">\(\varphi^4\)</span> theory.</p> <p>Corresponding article: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows [<a href="https://arxiv.org/abs/2207.00283">2207.00283</a>]</p> <p>Abstract: We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem. We test our model on the <span class="math-tex">\(\varphi^4\)</span> theory, showing that it systematically outperforms previously proposed flow-based methods in sampling efficiency, and the improvement is especially pronounced for larger lattices. Furthermore, we demonstrate that our model can learn a continuous family of theories at once, and the results of learning can be transferred to larger lattices. Such generalizations further accentuate the advantages of machine learning methods.</p>
Vibration-based smart sensor for high flow dust measurement
<p><strong>Abstract:</strong> Drying process of aggregates needed for asphalt manufacturing involves a high quantity of dust or filler that needs to be heated and extracted with the aid of a baghouse. A sensor that is able to measure the amount of filler aspirated will be a relevant innovation as the current state of the art for drying of aggregates involves a high amount of energy to heat all the aggregates so the highest amount of dust or filler is extracted. The final step of asphalt production is to mix all the components like bitumen, aggregates and cold filler itself. In the context of European project CAPRI [1,2], it is presented a prototype for measurement of filler flow based on vibration analysis, inside the pipe with an accelerometer in the insulator of an existing thermocouple subjected to the hard conditions of temperature and pressure. The paper shows the laboratory prototype results together with preliminary onsite evaluation previously to final demonstration. The paper provides also open access to all the data and results used as part of the commitment of CAPRI project with open science.</p> <p><strong>Keywords:</strong> Sensors, Innovation, Process Industry, Automation, Industry 4.0, Digital Transformation, Industrial Plants, Filler, Dust, Vibration, Signal processing, Smart sensing.</p>
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Imaging Flow Citometry Data
<p>Imaging flow citometry (IFC) datasets analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision).</p> <p>The folders contain acquisitions of giant unilamellar vesicles (GUVs) for lipid exchange and content exchange controls, with file naming convention DATE_SAMPLE_REPLICATE.rif, content exchange is indicated by CE samples in the 20230228_CE.zip folder, lipid exchange by LE samples in the 20221222_LE.zip folder. 24 samples per set are included, triplicates of isolated P1 (DOPE Af488 0.6% in LE; Dex-Af488 40 uM for CE), P2 (DOPE Cy50.6% in LE; Dex-Af647 10 uM for CE), NC (P1 + P2 1:1), PC (DOPE Af488 0.3% + DOPE Cy5 0.3 in LE; Dex-Af488 20 uM + Dex-Af647 5 uM for CE), and M samples numbered 1 to 4, prepared by mixing P1, P2 and PC in different ratios (M1= 1:1:1; M2= 1:1:0.5; M3= 1:1:0.1; M4= 1:1:0.05).</p> <p>Only .rif files are provided, they have to be elaborated via compensation and application of an analysis template using the Amnis IDEAS software. Compensation matrices for lipid exchange (20230217_LEcom.ctm) and content exchange (20230217_CEcomp.ctm) are included, as well as the analysis template (Lipid_exchange_analysis_6.2.ast). Gating in the latter may have to be adjusted to analyse LE and CE experiments.</p> <p>10000 objects in the GUV population or 50000 objects in total were acquired in each file. The files were elaborated in batch mode, outputting the statistic reports (Statistics report CE.txt for CE; Statistics report LE.txt for LE) that were elaborated using an R scirpt (included, IFC_analysis.R) </p>
Dataset for Min-Deviation-Flow in Bi-directed Graphs for T-Mesh Quantization
<p>Dataset for the paper <a href="https://www.algohex.eu/publications/bimdf-quantization/"><strong>Min-Deviation-Flow in Bi-directed Graphs for T-Mesh Quantization</strong></a>, <em>Martin Heistermann, Jethro Warnett, David Bommes</em>, ACM Transactions on Graphics, Volume 42, Issue 4 (2023), <a href="https://doi.org/10.1145/3592437">DOI 10.1145/3592437</a></p> <p>Both archive files (.zip and .tar.zst) have identical contents.</p> <p>Contents:</p> <ul> <li>Results of running various algorithmus variants on the "300" dataset <ul> <li>Intermediate data (e.g., patch decompositions)</li> <li>Final meshes</li> <li>Logfiles</li> <li>Metadata in json format (e.g., utilized settings, achieved energies, detailed runtimes)</li> </ul> </li> <li>Meshes used in all figures</li> </ul> <p> </p>
2021-2022 West False River Emergency Drought Barrier water quality, flow, and fish monitoring
To manage the critically low 2021 water supply for beneficial uses, DWR installed the temporary emergency drought barrier (EDB) on West False River in the Sacramento–San Joaquin Delta (Delta), approximately 5 miles south of Rio Vista, California, in Contra Costa County in June 2021. To monitor the effectiveness and impacts of the EBD, a monitoring program was initiated to track changes in hydrodynamics, water quality, fish, harmful algal blooms, and aquatic weeds in the vicinity of the EDB. The EDB was left in place during the winter of 2021-2022 and removed in fall of 2022. This data set includes all data collected as part of that monitoring program and subsets of ongoing monitoring programs that were used in the final effectiveness report for the EDB.
Fish and crayfish density and count data for Peeks Creek, Macon County, NC, USA 2005-2014, 2019, and 2022 following a catastrophic debris flow, as well as six reference streams
We followed the process of recovery of the fish and crayfish assemblage in Peeks Creek, a high-gradient second order stream in the Little Tennessee River watershed of North Carolina, after a debris flow devastated the channel and its riparian zone. After 15 years, the fish assemblage had recovered, and the channel and riparian zone had stabilized. Of the three major components of the fish assemblage, Rainbow Trout (Oncorhynchus mykiss (Walbaum)), a strong swimmer, reappeared in year 1. Longnose Dace (Rhinichthys cataractae (Valenciennes in Cuvier and Valenciennes)) reappeared in year 3. Mottled Sculpin (Cottus bairdii Girard), a weak swimmer, did not become established until year 6 and only resumed expected abundance in year 9. Appalachian Brook Crayfish (Cambarus bartonii cavatus Hay) numbers recovered quickly, though only one individual was found the year following the debris flow. Unassisted natural recovery occurred after a costly engineered restoration project had been rejected and arguably represents the preferable solution. However, recovery of the fish assemblage may not have been achieved if the stream flowed directly into an impoundment or low gradient river that lacked the source of species for recolonization, or if the stream had been located above a barrier to upstream movement.
Advanced Resolution Canopy FLOw (ARCFLO) experiment employing the SUbcanopy Sonic Anemometer Network (SUSAN) in WS01 of the HJ Andrews Experimental Forest, July-September 2012
This dataset was collected during one of the ARCFLO (Advanced Resolution Canopy FLOw) experiment series’ field campaigns. This field campaign was carried out in WS1 of the HJ Andrews Experimental forest during July-September 2012 by the biomicrometeorology group, PI Christoph Thomas. The ARCFLO experiment series spanned a wide range of topographic conditions (flat, sloped, mountainous) and canopy architectures (grassland, orchard, open forest, dense forest) and was carried out between 2011 and 2014. It was funded through the NSF Career Award in Physical and Dynamical Meteorology to PI Christoph Thomas. The main goal of this project was to develop a novel improved framework to describe the airflow and its transport under weak-wind conditions for a continuous variation of overstory density and stratification. The objective is to i) identify forcing mechanisms of submeso motions, ii) evaluate the impact of plant canopies of different overstory density on the wind, temperature, and humidity fields, and iii) improve predictors for mixing in plant canopies that incorporate the important physical mechanisms. Observations were be made with a unique combination of new and standard techniques including optical fiber measurement of temperature structure, acoustic remote sensing, ultrasonic anemometers, and laser-illuminated flow visualizations.
Water flow velocity data, Shark River Slough (SRS) near Black Hammock island, Everglades National Park (FCE LTER), South Florida from October 2003 to August 2005
Water velocity data measured every 5 or 15 minutes in Shark River Slough beside Black Hammock tree island, Everglades National Park using Sontek Agronaut water flow sampler.
Water flow velocity data, Shark River Slough (SRS) near Chekika tree island, Everglades National Park (FCE LTER) from January 2006 to March 2021
Water velocity data measured every 5 or 15 minutes in Shark River Slough beside Chekika tree island, Everglades National Park, using Sontek Agronaut water flow sampler. Data collection is complete.
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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.