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18,619 results for “development”
Alaska 2020 update for USGS G19AP00019: Initial Development of Alaska Community Seismic Velocity Models
<p>Seismic velocity model AKEP2020 uses earthquake travel-time and ambient noise group velocity data to update Eberhart-Phillips et al. (2006: AKEP2006)</p> <p> </p> <p>The model is provided in a table: vlAKEP2020xyzltlnSFDRE.tbl.txt</p> <p>and in the simul output from velocity inversion: vlAKEP2020.out.txt</p> <p>Map plots of Vp and Vp/Vs are also provided, with lines denoting limits of adequate data.</p> <p> </p> <p>Velocity Inversion Procedure Notes for AKEP2020 model</p> <p> </p> <p>The 2006 AK model and the 2020 updated model both use Transverse Mercator coordinate transformation with central meridian= -150 and counterclockwise rotation of 162.1. Earth-flattening transformation is used for velocity during ray-tracing. The depths are relative to sea-level and station elevations are used. For the group-velocity data, the surface is taken as the 30-km median filtered topography.</p> <p>Velocity with the 3D gridded model is defined by linearly interpolating between nodes.</p> <p>A gradational inversion approach was used with earthquake and shot travel-time data, and group velocity observations for periods 6-15 s. The table provides, from the computed resolution matrix, the diagonal resolution element (DRE), and the spread function (SF), for each Vp and Vp/Vs node.</p> <p> </p> <p>This material is based upon work supported by the U.S. Geological Survey under Grant No. G19AP00019. Note that an earlier model, AKEP2018, from the first year of this funded project was reported in Eberhart-Phillips et al. (2019).<br> <br> The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey</p>
MiRoR15-P2-Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research
<p>Survey questionnaire, anonymised survey data, and codebook related to: Superchi C, Hren D, Blanco D, Rius R, Recchioni A, Boutron I, González JA. Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research. BMJ Open 2020;0:e035604. doi:10.1136/bmjopen-2019-035604</p>
Dataset for paper entitled "Developing Reliable Foam Sensors with Novel Electrodes"
<p>This dataset includes all the experimental results presented in the IEEE Sensors 2019 paper "Developing Reliable Foam Sensors with Novel Electrodes" (DOI: 10.1109/SENSORS43011.2019.8956750).<br> URL of IEEE Xplore:<br> https://ieeexplore.ieee.org/document/8956750</p> <p>List of data in this dataset:<br> Fig-1-Stress-Strain curves of PU foam and Coated foam.xlsx<br> Fig-3-Conductive foam without electrodes.xlsx<br> Fig-4-Foam sensor with Ag electrodes.xlsx<br> Fig-5-Foam sensor-stability-100cycles.xlsx</p> <p>All the data included in this dataset were collected by Dr. Hongbo Wang.</p> <p>Contact person:<br> Dr. Hongbo Wang, ustcwhb@gmail.com</p>
MAGIC Deliverable D6.5: Shale gas development in the EU 10Km radius well grid scenario
<p>Geo data set of escenario of shale gas implementation in Europe. Developed for WP 6 of the <a href="https://magic-nexus.eu/">MAGIC-Nexus project</a>. It derives from a Geomodel of wells and a database of shale gas played developed by the <a href="https://ec.europa.eu/jrc/sites/jrcsh/files/pl1-britze.pdf">EUOGA </a>project. </p> <p><strong>DB Fields------------------------------------------</strong></p> <p>WELLid: Id of the well</p> <p>RBid: Id of the River Basin in which the well is located</p> <p>RBtxtINT: Name of the River Basin - English</p> <p>RBtxt: Name of the River Basin - Country's Name</p> <p>GWid: Groundwater basin ID</p> <p>PADid: ID of the extraction pad</p> <p>Formation: Shale formation</p> <p>Age: of the well </p> <p>Depth_avg: Average depth of the shale (inherited)</p> <p>Mature_avg: Average matureness of the shale (inherited)</p> <p>TOC_avg: Average Organic content of the shale (inherited)</p> <p>ThickGross: Gross Thickness of the shale play in meters (inherited)</p> <p>ThickNet_m: Net Thickness of the shale play in meters (inherited)</p> <p>EUOGA_Basi: Basin of the well according ot the EUOGA project database (inherited)</p> <p>Basin_inde: Id of the shale basin (inherited)</p> <p>NGS_Basin: Id of the BAsin as stated by the national geological service</p> <p>Shale_CP: Shale country </p> <p>RF_Maturit: Reference Maturity</p> <p>RF_Depth: Reference Depth</p> <p>CNTR_CODE, Country code</p> <p>NUTS_NAME: Name of the NUTS region</p> <p>NUTid: ID of the NUTS region</p> <p>x,y Coordinates of the well</p>
Neurothreads: development of supportive carriers for mature dopaminergic neuron differentiation and implantation
<p>Raw data for the publication:</p> <p><strong>Neurothreads: development of supportive carriers for mature dopaminergic neuron differentiation and implantation</strong></p>
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <p> </p>
Database of water, agriculture and economic development in Huang-Huai-ai region of China
<p>The database of water, agriculture and economic development contains 61 prefecture-level cities in the Huang-Huai-Hai region from 2010 to 2019.</p> <p>Firstly, we summarize the city-level agricultural dataset from the Provincial Bureau of Statistics, which contains the annual agricultural output, total planting area, labor, fertilizer, and machinery of each prefecture-level city. </p> <p>Secondly, we collect agricultural output (total land value per hectare) as the output and four main types of inputs: labor, fertilizer, machinery, and agricultural water consumption.</p> <p>Thirdly, we also collect city-level unbalanced panel data from the Water Resources Bulletin database, which contains annual data on agricultural water consumption, groundwater supply, precipitation, and groundwater resources.</p>
Co-design – Part 1: Workshops to explore current imaginaries behind smart home technologies development and use
<h3>Description</h3> <p>This qualitative dataset is the <strong>first part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a series of independent <strong>in-person workshops</strong> with professionals developing smart technology, its early-adopters, and late/non-adopters. The data collected during the subsequent parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from workshop with professionals</h3> <ul> <li><strong>P1_WSP-PRO-TRANSCR_R02.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-PRO-VIS_000 </strong>till _<strong>013.jpg</strong> (participant-generated visual data)</li> </ul> <p> </p> <h3>Documents from workshop with early-adopters</h3> <ul> <li><strong>P1_WSP-EA-TRANSCR_R01.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-EA-VIS_000 </strong>till _<strong>011.jpg</strong> (participant-generated visual data)</li> </ul> <p> </p> <h3>Documents from workshop with late/non-adopters</h3> <ul> <li><strong>P1_WSP-LN-TRANSCR_R00.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-LN-VIS_000 </strong>till _<strong>012.jpg</strong> (participant-generated visual data)</li> </ul> <h3> </h3> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
Development of piezocapacitive pressure sensor by DLP
<p>This data set corresponds to the analyses carried out in the following article: Arias‐Ferreiro, G., Ares‐Pernas, A., Lasagabáster‐Latorre, A., Dopico‐García, M. S., Ligero, P., Pereira, N., ... & Abad, M. J. (2022). <br>Photocurable printed piezocapacitive pressure sensor based on an acrylic resin modified with polyaniline and lignin. Advanced Materials Technologies, 7(8), 2101503.<br>DOI: 10.1002/admt.202101503</p>
Database from: Developing a lateral topographic density model for Brazil.
<p>This dataset is part of the article entitled "DEVELOPING A LATERAL TOPOGRAPHIC DENSITY MODEL FOR BRAZIL".</p> <p>This dataset includes the topographic Lateral Topographic Density model for Brazil (LTDBrasil) and standard deviations (sdLTDBrasil), in Kg/m³, with 30 arc-seconds grid spacing.</p> <p>The files are in *tif and *.tfw format.</p> <p>Reference: Medeiros D.F., Marotta G.S., Yokoyama E., Franz I.B., Fuck R.A. 2021. Developing a lateral topographic density model for Brazil. Journal of South American Earth Sciences, v. 110, p. 103425. https://doi.org/10.1016/j.jsames.2021.103425</p>
Code Analysis Tables for Developers Interviews on Dependencies Paper
<p>Code Analysis Tables for the ACM CCS 2020 paper "A qualitative study of dependency management and its security implications"</p>
Dataset for the study Late development of audio-visual integration in the vertical plane
<p>It is not clear how multisensory skills develop and how visual experience impacts on multisensory spatial development. Conflicting results show that visual calibration precedes multisensory integration for the audio-visual spatial bisection task (Gori et al., 2012a, 2012b) while in other tasks such as spatial localization, visual calibration occurs after multisensory development (Rohlf et al., 2020). Results in blind individuals can say something about the role of vision on perceptual development. Scientific evidences show that blind individuals have impairments in bisecting the auditory space (Gori et al., 2014) but not in localizing auditory sources (Lessard et al., 1998). Such results suggest that sensory calibration and impairment are linked. We studied the development of audio-visual multisensory localization in the vertical plane in sighted individuals from 5 years to adulthood to address this hypothesis. We hypothesize that typical children would show late audio-visual integration for the vertical plane, preceded by visual dominance. Unimodal and bimodal audio-visual thresholds and PSEs were measured and compared with the Bayesian optimal-integration model (maximum likelihood estimation). Results show that the development of multisensory integration in the vertical plane is not evident at 5 years, suggesting visual dominance for vertical audio-visual localization. These results support the idea that multisensory perception in the vertical domain depends on sensory calibration. We discuss these scientific results proposing that the process of cross-sensory calibration is task-specific and highlighting the importance of linking the impairment and development to better determine how our brain works.</p> <p>Data are in textual tab delimited format. Columns report for each subject: age, age_bin, condition, jnd.</p> <p> </p>
Mechanical characterisation of the developing cell wall layers of tension wood fibres by Atomic Force Microscopy
<p>This dataset corresponds to the Arnould et al. (2022) paper (available at https://www.biorxiv.org/content/10.1101/2021.09.23.461481v1.full) on the mechanical characterization of developing cell wall layers of tension wood fibers by Atomic Force Microscopy. It contains all raw AFM files (Bruker format .spm, readable by the free software Gwyddion for example) corresponding to mechanical measurements of poplar reaction wood cells (clone 717-1B4) along 3 radial lines/rows, starting from the cambium. Each cell is identified by its "macroscopic" distance from the cambium (value in µm in the name of each file corresponding to the displacement of the sample in the AFM) which was corrected after using the AFM optical image captures. Some files, with a -z extension after the distance value, correspond to a zoom into the cell wall. The data also contain measurements made for mechanical calibration on epoxy embedded Kevlar fibers, controlled measurements in the embedding resin between each radial line and measurements in normal wood cells. Two csv files containing final data extracted from AFM measurements that give the value of the indentation modulus and the relative thickness to cell diameter ratio (by AFM and by phase contrast optical microscopy) in each cell wall layer as a function of cambium distance are also provided.</p>
Thermophysical properties for the published article "Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments"
<p>In order to model the macroscopic metallic melt motion realized in the poor-versus-efficient thermionic emitter leading edge exposures in the ASDEX-Upgrade outer divertor [1], the material library of the MEMENTO melt dynamics code, that previously only concerned tungsten [2] and beryllium [3], had to be extended to iridium and niobium. </p> <p>Reliable experimental data have been analyzed for the latent heats, specific isobaric heat capacity, electrical resistivity, thermal conductivity, mass density, vapor pressure, work function, total hemispherical emissivity and absolute thermoelectric power from the room temperature up to the normal boiling point of iridium and niobium as well as for the surface tension and the dynamic viscosity across the liquid state. Analytical expressions are recommended for the temperature dependence of these thermophysical properties, which involve high temperature extrapolations given the absence of extended liquid iridium and liquid niobium measurements. The analytical expressions, the details of their construction and the main references are included in the accompanying pdf.</p> <p>[1] S. Ratynskaia, K. Paschalidis, P. Tolias, K. Krieger, Y. Corre, M. Balden, M. Faitsch, A. Grosjean, Q. Tichit, R.A. Pitts, the ASDEX-Upgrade team, the WEST team and the Eurofusion MST1 team, "Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments", Nucl. Mater. Energy 33 (2022) 101303.<br> [2] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid tungsten relevant for fusion applications", Nucl. Mater. Energy 13 (2017) 42.<br> [3] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid beryllium relevant for fusion applications", Nucl. Mater. Energy 31 (2022) 101195.</p>
An External Replication on the Effects of Test-driven Development Using a Multi-site Blind Analysis Approach
<p>This dataset contains the <strong>unblinded </strong>version of the data collected and analyzed for the experiment reported in the paper. </p> <p>The semantics of the data can be found in the spreadsheet. For the formulas on how to obtain this data from the raw data, please see the paper. </p>
Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."
<p>Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."</p>
Beyond the Digital Divide: Sharing Research Data across Developing and Developed Countries
<p>The primary data collection element of this project related to observational based fieldwork at four universities in Kenya and South Africa undertaken by Louise Bezuidenhout (hereafter ‘LB’) as the award researcher. The award team selected fieldsites through a series of strategic decisions. First, it was decided that all fieldsites would be in Africa, as this continent is largely missing from discussions about Open Science. Second, two countries were selected – one in southern (South Africa) and one in eastern Africa (Kenya) – based on the existence of the robust national research programs in these countries compared to elsewhere on the continent. As country background, Kenya has 22 public universities, many of whom conduct research. It also has a robust history of international research collaboration – a prime example being the long-standing KEMRI-Wellcome Trust partnership. While the government encourages research, financial support for it remains limited and the focus of national universities is primarily on undergraduate teaching. South Africa has 25 public universities, all of whom conduct research. As a country, South Africa has a long history of academic research, one which continues to be actively supported by the government. </p> <p>Third, in order to speak to conditions of research in Africa, we sought examples of vibrant, “homegrown” research. While some of the researchers at the sites visited collaborated with others in Europe and North America, by design none of the fieldsites were formally affiliated to large internationally funded research consortia or networks. Fourth, within these two countries four departments or research groups in academic institutions were selected for inclusion based on their common discipline (chemistry/biochemistry) and research interests (medicinal chemistry). These decisions were to ensure that the differences in data sharing practices and perceptions between disciplines noted in previous studies would be minimized. </p> <p>Within Kenya, site 1 (KY1) and Site 2 (KY2) were both chemistry departments of well-established universities. Both departments had over 15 full time faculty members, however faculty to student ratios were high and the teaching loads considerable. KY1 had a large number of MSc and PhD candidates, the majority of whom were full-time and a number of whom had financial assistance. In contrast, KY2 had a very high number of MSc students, the majority of whom were self-funded and part-time (and thus conducted their laboratory work during holidays). In both departments space in laboratories was at a premium and students shared space and equipment. Neither department had any postdoctoral researchers. </p> <p>Within South Africa, site 1 (SA1) was a research group within the large chemistry department of a well-established and comparatively well-resourced university with a tradition of research. Site 2 (SA2) was the chemistry/biochemistry department of a university that had previously been designated a university for marginalized population groups under the Apartheid system. Both sites were the recipients of numerous national and international grants. SA2 had one postdoctoral researcher at the time, while SA1 had none.</p> <p>Empirical data was gathered using a combination of qualitative methods including embedded laboratory observations and semi-structured interviews. Each site visit took between three and six weeks, during which time LB participated in departmental activities, interviewed faculty and postgraduate students, and observed social and physical working environments in the departments and laboratories. Data collection was undertaken over a period of five months between November 2014 and March 2015, with 56 semi-structured interviews in total conducted with faculty and graduate students. Follow-on visits to each site were made in late 2015 by LB and Brian Rappert to solicit feedback on our analysis. </p>
Alaska 2018 update for USGSG18AP00017: Initial Development of Alaska Community Seismic Velocity Models
<p>Seismic velocity model AKEP2018 uses earthquake travel-time and ambient noise group velocity data to update the Alaska 3-D model of Eberhart-Phillips et al. (2006: AK2006), for the USGS project on developing Alaska Community Seismic Velocity Models . This 2018 model will be expanded with additional data in 2019 in the second year of the funded project.</p> <p>Velocity within the 3D gridded model is defined by linearly interpolating between nodes. The inversion solved for Vp and Vp/Vs. The model is provided in a table: vlAKEP2018xyzltlnSFDRE.tbl.txt, with velocity at inversion nodes in cartesian and latitude-longitude coordinates. The table provides, from the computed resolution matrix, the diagonal resolution element (DRE), and the spread function (SF), for each Vp and Vp/Vs node.</p> <p>This material is based upon work supported by the U.S. Geological Survey under Grant No. G18AP00017. <br> <br> The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey.</p>
A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods
<p><strong>Description</strong><br> This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. <br> The goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. <br> The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. <br> In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. <br> We also include sample codes of baseline models for benchmarking of more elaborated models.</p> <p><strong>Data usage</strong><br> The usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper:<br> <em>Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494</em></p> <p>Although every effort was made to ensure the quality of the data, no guarantees or liabilities are implied by the authors or publishers of the data.</p> <p><strong>Sample code</strong><br> As part of the data release, we are also including the sample code written in Python 3. <br> The preprocessed data used in the scripts are also provided. <br> The code can be used to reproduce the results presented in this work and as a starting point for future studies. <br> Besides the standard scientific Python packages (numpy, scipy, and matplotlib), the code depends on pandas for time-series operations, pvlib for common solar-related tasks, and scikit-learn for Machine Learning models. <br> All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip. </p> <p><strong>Units</strong><br> All time stamps are in UTC (YYYY-MM-DD HH:MM:SS).<br> All irradiance and weather data are in SI units.<br> Sky image features are derived from 8-bit RGB (256 color levels) data.<br> Satellite images are derived from 8-bit gray-scale (256 color levels) data.</p> <p><strong>Missing data</strong><br> The string "NAN" indicates missing data</p> <p><strong>File formats</strong><br> All time series data files as in CSV (comma separated values)<br> Images are given in tar.bz2 files</p> <p><strong>Files </strong></p> <ul> <li><em>Folsom_irradiance.csv</em> Primary One-minute GHI, DNI, and DHI data.</li> <li><em>Folsom_weather.csv </em> Primary One-minute weather data.</li> <li><em>Folsom_sky_images_{YEAR}.tar.bz2</em> Primary Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2.</li> <li><em>Folsom_NAM_lat{LAT}_lon{LON}.csv </em> Primary NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node’s coordinates listed in Table I in the paper. </li> <li><em>Folsom_sky_image_features.csv </em> Secondary Features derived from the sky images.</li> <li><em>Folsom_satellite.csv </em> Secondary 10 pixel by 10 pixel GOES-15 images centered in the target location. </li> <li><em>Irradiance_features_{horizon}.csv</em> Secondary Irradiance features for the different forecasting horizons ({horizon} 1⁄4 {intra-hour, intra-day, day-ahead}). </li> <li><em>Sky_image_features_intra-hour.csv</em> Secondary Sky image features for the intra-hour forecasting issuing times. </li> <li><em>Sat_image_features_intra-day.csv</em> Secondary Satellite image features for the intra-day forecasting issuing times. </li> <li><em>NAM_nearest_node_day-ahead.csv </em> Secondary NAM forecasts (GHI, DNI computed with the DISC algorithm, and total cloud cover) for the nearest node to the target location prepared for day-ahead forecasting.</li> <li><em>Target_{horizon}.csv</em> Secondary Target data for the different forecasting horizons.</li> <li>F<em>orecast_{horizon}.py </em> Code Python script used to create the forecasts for the different horizons. </li> <li><em>Postprocess.py</em> Code Python script used to compute the error metric for all the forecasts.</li> </ul> <p> </p>
Open Access in developing countries – attitudes and experiences of researchers Dataset
<p>A survey was conducted of 507 researchers from the developing world and connected to INASP’s AuthorAID project to ascertain experiences and attitudes to Open Access publishing. This file is the raw output from the survey, with names and email addresses removed to preserve anonymity. </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.