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47 results for “physical processes”
Physical and microbial processing of dissolved organic nitrogen (DON) (Salinity Experiment) along an oligotrophic marsh/mangrove/estuary ecotone (Taylor Slough and Florida Bay) for August 2003 in Everglades National Park (FCE), South Florida, USA
A better understanding of the biogeochemical cycling of nutrients entering Florida Bay is a key issue regarding the restoration of the Everglades. In addition to precipitation, the other major source of freshwater to Florida Bay is from Taylor Slough and the C-111 Basin in the northeast section of the Bay. While it is known that these areas deliver significant amounts of N to the Bay, a significant portion of this is in the form of dissolved organic N (DON). The sources, environmental fate and bioavailability to microorganisms of this DON are however, not known. Should this DON be readily available, any increased load as a function of restoration changes might have an impact on internal phytoplankton bloom dynamics. No significant flocculation or precipitation of DOM occurred with increase in salinity, meaning that terrestrial DOM does not get trapped in the sediments but stays in the water column where it subjected to photolysis and advective transport. Sunlight has a significant effect on the chemical characteristics of DOM. While the DOC levels did not change significantly during photo-exposure, the optical characteristics of the DOM were modified. The environmental implications of this are conflicting: photo-induced polymerization may stabilize the DOM by reducing its bioavailability while photolysis may make the DOM more labile. Overall, DON bioavailability was relatively low in this region. Even though the amount of DON loaded to the bay may be significant, the fraction of DON available for microbial cycling is much smaller. The amount of N supplied by recycling may be a significant portion of the total DIN pool. All this must be considered in context with the proposed CERP modifications to flows. As of the latest initial Comprehensive Everglades Restoration Project (CERP) update, the flows to Taylor Slough and C-111/Panhandle Basis are not predicted to change very much from base conditions. Therefore we do not expect any great increases in TN loading in this
Physical and microbial processing of dissolved organic nitrogen (DON) (Photodegradation Experiment) along an oligotrophic marsh/mangrove/estuary ecotone (Taylor Slough and Florida Bay) for August 2003 in Everglades National Park (FCE), South Florida, USA
A better understanding of the biogeochemical cycling of nutrients entering Florida Bay is a key issue regarding the restoration of the Everglades. In addition to precipitation, the other major source of freshwater to Florida Bay is from Taylor Slough and the C-111 Basin in the northeast section of the Bay. While it is known that these areas deliver significant amounts of N to the Bay, a significant portion of this is in the form of dissolved organic N (DON). The sources, environmental fate and bioavailability to microorganisms of this DON are however, not known. Should this DON be readily available, any increased load as a function of restoration changes might have an impact on internal phytoplankton bloom dynamics. No significant flocculation or precipitation of DOM occurred with increase in salinity, meaning that terrestrial DOM does not get trapped in the sediments but stays in the water column where it subjected to photolysis and advective transport. Sunlight has a significant effect on the chemical characteristics of DOM. While the DOC levels did not change significantly during photo-exposure, the optical characteristics of the DOM were modified. The environmental implications of this are conflicting: photo-induced polymerization may stabilize the DOM by reducing its bioavailability while photolysis may make the DOM more labile. Overall, DON bioavailability was relatively low in this region. Even though the amount of DON loaded to the bay may be significant, the fraction of DON available for microbial cycling is much smaller. The amount of N supplied by recycling may be a significant portion of the total DIN pool. All this must be considered in context with the proposed CERP modifications to flows. As of the latest initial Comprehensive Everglades Restoration Project (CERP) update, the flows to Taylor Slough and C-111/Panhandle Basis are not predicted to change very much from base conditions. Therefore we do not expect any great increases in TN loading in this
Detailed abundances based on different nuclear physics for theoretical r-process scenarios
<p>This data set contains detailed abundances (at a time t=10^6 years after the event) for individual trajectories for seven different simulations of potential r-process sites, and based on nine different combinations of nuclear mass models and fission fragment distribution models. The data have been used and are discussed in Cote, Eichler, Yagüe, et al. (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) to determine the isotopic ratios of I129/Cm247 and compare them to meteoritic data.</p> <p>Furthermore, a code is included which samples a subset of trajectories reproducing the measured meteoritic I129/Cm247 abundance ratio of 438 +- 92. See the README file and the publication (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) for more details.</p>
Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory
<p>This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).</p><p>A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.</p><p>Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:</p><p> - the equipment; the machine used to perform the task,</p><p> - the operator; the person who performed the task.</p><p>In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.</p><p>The dataset includes data from 4 years, specifically from January 2020 to December 2022.</p><p>Finally, the exact column structure is given as follows (python 3.9.5 datatype):</p><ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>
A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process, and Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform
<p>The data contained in this repository was used in the production of the publication "A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process" (<a href="https://doi.org/10.1088/1367-2630/ac3048">https://doi.org/10.1088/1367-2630/ac3048</a>) and "Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform" (<a href="https://doi.org/10.1016/j.physletb.2025.139247">https://doi.org/10.1016/j.physletb.2025.139247</a>).</p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
r-process abundances in neutron-rich merger ejecta given different theoretical nuclear physics inputs
<p>This data release contains nucleosynthesis predictions for the r-process abundances presented in Côté, Eichler, Yagüe, Vassh et al. (2021) for compact object merger ejecta based on the publicly available simulation trajectories of Rosswog et al. (2013). All ejecta for the merger scenarios considered here are very neutron-rich (Ye ~ 0.016-0.11). Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating (here with a reheating efficiency of 50%). Results are reported for several different theoretical nuclear physics inputs but all calculations make use of the GEF fission yield prescription (see Vassh et al. 2019). All abundances are given at 1 Myr (10^6 years) post-merger. Please see the README file for more details and references.</p> <p>When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh et al. 2021), and refer to Vassh et al. (2019) and Côté, Eichler, Yagüe, Vassh et al. (2021) for further details on the nuclear data applied as well as Rosswog et al. (2013), Piran et al. (2013), and Korobkin et al. (2012) for further details on the merger ejecta trajectories.</p>
Processed data and models in support of manuscript "Deciphering the state of the lower crust and upper mantle with multi-physics inversion"
<p>Data and model files in original format used in the manuscript "Deciphering the state of the lower crust and upper mantle with multi-physics inversion". These files are accompanied by a set of python scripts to reproduce several of the figures in the Manuscript. Please refer to the Manuscript and the included files for further information on data origin and how to use the scripts. A link will be added upon acceptance.</p>
On the Role of Physical Processes in Controlling Equatorial Plasma Bubble Morphology
<p>Dataset for the paper entitled "On the Role of Physical Processes in Controlling Equatorial Plasma Bubble Morphology" submitted to the Journal of Geophysical Research: Space Physics.</p>
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
Climate model (CM2.6) and regional model (ACM) processed output used to investigate the physical drivers and biogeochemical effects of the weakening of the northwest North Atlantic Shelfbreak Jet (Garcia-Suarez & Fennel., 2024; JAMES)
<p>Key processed output from the climate model GFDL CM2.6 and the regional Atlantic Canada model (ACM) used to investigate the physical drivers and the biogeochemical effects of the weakening of the shelfbreak jet in the northwest North Atlantic Ocean. The dataset includes all model variables required to reproduce the key results in <em>Garcia-Suarez & Fennel (2024, JAMES)</em>. See <em>GarciaSuarezandFennel_JAMES_CM26_ACM_data_README_v2.txt</em> for more details.</p>
Results from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>This repository contains results from the paper, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied Statistics</em>. The storage of these results helps facilitate access to and replication of the analysis in the paper. The results include output from the:</p> <ol> <li>Sulfate analysis <ul> <li>tx-2016-100k-int-mx.RDS</li> </ul> </li> <li>Asthma analysis <ul> <li>asthma-pois-cut.RDS</li> <li>asthma-pois-plugin.RDS</li> <li>asthma-bart-cut.RDS</li> <li>asthma-bart-plugin.RDS</li> </ul> </li> <li>Medicare analysis <ul> <li>medicare-pois-cut.RDS</li> <li>medicare-pois-plugin.RDS</li> <li>medicare-bart-cut.RDS</li> <li>medicare-bart-plugin.RDS</li> </ul> </li> <li>Simulation study <ul> <li>simstudy-cm1-lm.RDS</li> <li>simstudy-cm1-bart.RDS</li> <li>simstudy-cm2-lm.RDS</li> <li>simstudy-cm2-bart.RDS</li> <li>simstudy-cm3-lm.RDS</li> <li>simstudy-cm3-bart.RDS</li> <li>simstudy-pm1-pois.RDS</li> <li>simstudy-pm1-bart.RDS</li> <li>simstudy-pm2-pois.RDS</li> <li>simstudy-pm2-bart.RDS</li> <li>simstudy-pm3-pois.RDS</li> <li>simstudy-pm3-bart.RDS</li> </ul> </li> <li>Log-linear BART sensitivity analysis <ul> <li>sensitivity-m100.RDS</li> <li>sensitivity-m200.RDS</li> <li>sensitivity-m300.RDS</li> <li>sensitivity-m400.RDS</li> <li>sensitivity-power05.RDS</li> <li>sensitivity-power1.RDS</li> <li>sensitivity-power15.RDS</li> <li>sensitivity-power2.RDS</li> <li>sensitivity-power25.RDS</li> <li>sensitivity-power3.RDS</li> <li>sensitivity-power4.RDS</li> <li>sensitivity-power5.RDS</li> </ul> </li> </ol> <p>A description of these results can be found at <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a>, along with the R code used to generate these (and other results, such as figures) found in the manuscript and supplementary material.</p>
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
<p><strong>Introduction</strong></p> <p>The Robot Inverse Dynamics Dataset is a collection of trajectories and joint torque measurements of two robotic manipulators, a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL. Additionally, the dataset contains the inverse dynamical models and other useful quantities learned to reproduce the results reported on our reference paper "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification". The proposed model relies on a novel multidimensional kernel, called Lagrangian Inspired Polynomial (LIP) kernel.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~700MB.</li> <li>The dataset contains</li> <ul> <li>collections of joint trajectories and joint torque measurements of two robot manipulators: a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL.</li> <li>models of the inverse dynamics of the two manipulators learned on the datasets</li> </ul> <li>The main directories are</li> <ul> <li>Simulated_PANDA/ contains the trajectories, models and results obtained on different configurations of a Franka Emika PANDA robot, simulated in sympybotics.</li> <li>Robots/ contains the data, models and results obtained on two real robots, a Franka Emika PANDA and a Mitsubishi Electric MELFA RV4FRL</li> </ul> <li>See the README.md file for a detailed description of the directories.</li> </ul> <p><strong>Other Resources</strong></p> <p>Python code to train the models and reproduce the results in the paper are available <a href="https://github.com/merlresearch/LIP4RobotInverseDynamics">here</a>.</p> <p><strong>Citation</strong></p> <p>If you use the Robot Inverse Dynamics dataset in your research, please cite our contribution:</p> <pre><code>@InProceedings{ title={A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification}, author={Giacomuzzo, G., Dalla Libera, A., Romeres, D.,}, booktitle={IEEE Transaction on Robotics}, year={2024} } </code></pre> <p><strong>License</strong></p> <p>The Robot Inverse Dynamics dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
Quantitative interpretation of the physical processes associated with the interannual variation of South Asian summer monsoon
<p>This is the CFRAM-A data of "Quantitative interpretation of the physical processes associated with the interannual variation of South Asian summer monsoon". The data includes 10 years: 1997, 1999, 2000, 2001, 2008, 2009, 2013, 2015, 2018, and 2019. Each year includes two "nc" data, one is the partial temperature, the other is the total temperature.</p>
Physical processes controlling the rifting of Larsen C Ice Shelf, Antarctica, prior to the calving of iceberg A68 in 2017
Open the record for dataset details and reuse information.
Ionospheric Electron Densities, Neutral Temperature, Winds and Post-processed Output from TIEGCM Simulations in Support of Publication "Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill"
This dataset is associated with the publication "Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill". In particular, the NCAR- Community model: high-resolution thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM) has been used to investigate the response of ionospheric F2-region electron density (Ne) at Millstone Hill (42.610N, 71.480W, maximum obscuration: 63%) to the Great American Solar Eclipse on 21 August 2017. Two sets of model runs were done, one with the eclipse and the other without. Model outputs of winds, temperatures and electron densities, as well as diagnostic variables of key chemical and physical processes that determined the ionosphere responses to the eclipse, were analyzed and used in producing a paper: Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill.
Data for "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes"
<p>This is sampling data of "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes".</p> <p>'qv_nn_in' for the large scale specific humidity, [kg/kg]<br> 'T_nn_in' for the large scale temperature, [K]<br> 'dqvls_nn_in' for the large scale moisture advection, [kg/kg/s]<br> 'dTls_nn_in' for the large scale moisture advection, [K/s]<br> 'qtend_check' for the moistening rate by CRM, [kg/kg/s]<br> 'stend_check' for the heating rate by CRM, [K/s]<br> 'SOLS' for direct shorwave solar radiation down to surface, [W/m2]<br> 'SOLSD' for diffusive shortwave solar radiation down to surface, [W/m2]<br> 'SOLL' for direct near infrared solar radiation down to surface, [W/m2]<br> 'SOLLD' for diffusive near infrared solar radiation down to surface, [W/m2]<br> 'SOLIN' for insolation at model top, [W/m2]<br> 'FSNS' for net shortwave radiation at model surface, [W/m2]<br> 'FSNT' for net shortwave radiation at model top, [W/m2]<br> 'FLNS' for net longwave radiation at model surface, [W/m2]<br> 'FLNT' for net longwave radiation at model top, [W/m2]<br> 'SPPS' for surface pressure, [Pa]</p> <p>To download the full dataset of the SPCAM simulation in 1998. Please click the dropbox link: https://www.dropbox.com/s/p841v1tw00rokdy/SPCAM_VAR_1998.tar.gz?dl=0</p>
Simulation output of the reference setup in "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses"
<p>This supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses" contains the simulation output of the 20 reference runs. Additional data is available upon request from the corresponding author.</p>
Dataset for the plots in paper: 'A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation' in 'Journal of Computational Physics'
<p>The .xlsx file contains the data used for the plots Fig. 3, 4, 7, 8 and 9 in the paper 'Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation'.</p>
Data of: Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing
<p>This dataset contains the data and models used in the research journal publication: "Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing " which was funded from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 101001567).</p> <p> </p>
ScienceDex guides
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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.