Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
98
datasets available to search
ShareScore release 0.9.0
Dataset results
98 results for “pCO2”
Spot and continuous seasonal pCO2 sensor measurements from lagoon and river sites along the Alaska Beaufort Sea coast, 2019-ongoing
pCO2 (partial pressure of carbon dioxide) was measured in multiple seasons and depths in coastal ecosystems as part of the Beaufort Lagoons Ecosystems LTER core sampling program. Sites include several Beaufort Lagoons from across the North Slope of Alaska and several river sites near the lagoons. Data includes averages of sensor data for each site and depth deployment of pCO2 (uatm), CO2 (ppm), water temperature (°C), and absolute pressure (kPa). Standard deviation is included for pCO2 (uatm). Surface measurements were taken either 30 cm below the water surface, or bottom of the ice in ice-cover season, and bottom measurements were taken 30 cm from the seafloor of the lagoons. For lagoon sites, data were logged every one or five minutes; for river sites data were recorded every five minutes. Each site and depth was recorded for at least 30 minutes and up to four hours and taken as spot measurement.
Daily summer riverine and groundwater pCO2, dissolved oxygen, and ecosystem metabolism from sites along the Alaska Beaufort Sea coast, 2019-2022
Three riverine systems near Utqiaġvik, Alaska, are sampled regularly by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program in order to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Sampling includes one intermediate river, Avak Creek, and two smaller streams: the Mayoeak River (thermokarst stream) and a beaded stream. In 2019, 2021, and 2022, we performed additional sampling at these core program sites, with continuous monitoring of the streams between late June and August. Continuous measurements included riverine discharge, pCO₂, dissolved oxygen (DO), and ecosystem production, along with periodic grab samples of dissolved organic carbon (DOC). In 2022, there was also monitoring of groundwater wells at the two streams, capturing continuous hydraulic head and groundwater pCO₂, in addition to grab samples for DOC. Daily averages of continuous data and DOC laboratory results are presented for surface water and groundwater. This dataset is not part of the BLE Core Program and is not currently planned as an ongoing effort.
Daily average pCO2, dissolved oxygen, and water quality in Elson Lagoon: 2019, 2021, 2022
pCO2 (partial pressure of carbon dioxide) and other physicochemical parameters were measured during the open water season (July-August) in three years (2019, 2021, 2022) at five sites in Elson Lagoon, a coastal ecosystem near Utqiagvik, AK, as a supplement to the Beaufort Lagoons Ecosystems LTER core sampling program. Locations include three shallow sites (<1 m water depth) at a depth of 0.5 m and two deep sites (up 2 m depth) at a depth of ~1.7m. Data includes daily averages of sensor data for each site of pCO2 (uatm), water temperature (°C), absolute pressure (kPa), salinity (ppt), dissolved oxygen (DO, mg/L), light extinction (Kd; m-1), and water level difference from mean tidal level (m). Discrete laboratory data for dissolved organic carbon (C; umol/L) and turbidity (NTU) are also presented.
PIE LTER transects of the Parker River Plum Island Sound Estuary, Massachusetts conducted at dawn and dusk, containing dissolved oxygen, conductivity, temperature, percent saturation, pH, DIC and pCO2 data.
Multi-year transects, beginning in 1995, of the Parker River Plum Island Sound Estuary conducted at dawn and dusk, containing dissolved oxygen, conductivity, temperature, percent saturation, pH, salinity, DIC and pCO2 measurements. Differences between dawn and dusk measurements can be used to determine the water metabolism and corresponding estimates of gross primary production, total system respiration and net ecosystem production.
Transects of the Rowley River, Plum Island Sound Estuary, Massachusetts conducted at dawn and dusk, containing dissolved oxygen, conductivity, temperature, percent saturation, pH, DIC and pCO2 data, PIE LTER.
Multi-year transects, beginning in 2016, of the Rowley River Plum Island Sound Estuary, MA conducted at dawn and dusk, containing dissolved oxygen, conductivity, temperature, percent saturation, pH, DIC and pCO2 measurements. Differences between dawn and dusk measurements can be used to determine the water column metabolism and corresponding estimates of gross primary production, total system respiration and net ecosystem production.
LDEO pCO2-Residual Method
<p>The ocean reduces human impacts on global climate by absorbing and sequestering CO<sub>2</sub> from the atmosphere. To quantify global, time-resolved air-sea CO<sub>2</sub> fluxes, surface ocean pCO<sub>2</sub> is needed. A common approach for estimating full-coverage pCO<sub>2</sub> is to train a machine learning algorithm on sparse in situ pCO<sub>2</sub> data and associated physical and biogeochemical observations. Though these associated variables have understood relationships to pCO<sub>2</sub>, it is often unclear how they drive pCO<sub>2</sub> outputs. Here, we make two advances that enhance connections between physical understanding and reconstructed pCO<sub>2</sub>. First, we apply pre-processing to the pCO<sub>2</sub> data to remove the direct effect of temperature. This enhances the biogeochemical/physical component of pCO<sub>2</sub> in the target variable and reduces the complexity that the machine learning must disentangle. Second, we demonstrate that the resulting algorithm has physically understandable connections between input data and the output biogeochemical/physical component of pCO<sub>2</sub>. The final pCO<sub>2</sub> reconstruction agrees modestly better with independent data than most other approaches. Uncertainties in the reconstructed pCO<sub>2</sub> and impacts on the estimated CO<sub>2</sub> fluxes are quantified. Uncertainty in piston velocity drives substantial flux uncertainties in some regions, but does not increase globally integrated estimates of uncertainty in CO<sub>2</sub> fluxes from observation-based products. Our reconstructed CO<sub>2</sub> fluxes show larger interannual variability than smoother neural network approaches, but a lesser trend since 2005. </p>
Cardiorespiratory metrics of cobia during and following a Ucrit trial upon a 3 wk exposure to either ambient or elevated pCO2
This study examined cardiac and swimming performance during a maximum sustained swimming trial (Ucrit) of cobia (Rachycentron canadum) following a three-week exposure to either elevated (~1,600 µAtm) or ambient (~500 µAtm) pCO₂. This dataset encompasses cardiac variables (stroke volume, heart rate, and cardiac output) during swimming and recovery from a Ucrit trial, hematological variables (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration) following the swimming test and hour long recovery, oxygen consumption variables (MO₂ min, MO₂ max, aerobic scope, and factorial aerobic scope) during and following the swimming test.
LDEO-pCO2 HPD Back in Time
<p>The ocean reduces human impact on the climate by absorbing and sequestering CO2 . From 1950s to the 1980s, observations of pCO2 and related ocean carbon variables were sparse and uncertain. Thus, global ocean biogeochemical models (GOBMs) have been the basis for quantifying the ocean carbon sink. The LDEO-Hybrid Physics Data product (LDEO-HPD) interpolates sparse surface ocean pCO2 data to global coverage by using GOBMs as priors, and applying machine learning to estimate full-coverage corrections.</p> <p>The largest component of the GOBM corrections are climatological. This is consistent with recent findings of large seasonal discrepancies in GOBMs, but contrasts the long-held view that interannual variability is a major source of GOBM error. This supports extension of the LDEO-HPD pCO2 product back to 1959, using a climatology of model-observation misfits prior to 1982. Consistent with previous studies for 1980 onward, air-sea CO2 fluxes demonstrate response to atmospheric pCO2 growth and volcanic eruptions.</p>
SeaFlux v2023: harmonised sea-air CO2 fluxes from surface pCO2 data products using a standardised approach
<p><strong>BE SURE TO DOWNLOAD 2023.02</strong></p> <p>See the additional notes for updates on the products. </p> <p>Fluxes calculated using the standardized approach:</p> <p> \(F\text{CO}_2=K_0 \cdot K_w \cdot (p\text{CO}_2^\text{sea} - p\text{CO}_2^\text{atm})\ \cdot (1 - [ice])\).</p> <p>We provide each of the components to this equation to reduce the potential for errors in fluxes due to methodological differences.</p> <p>The netCDF files contain the following data (<strong>note that only bold names have been updated in v2023</strong>): </p> <ul> <li>fgco2_all_winds_products: the sea-air CO2 flux for all spCO2 products (6) and <em>kw</em> from all wind products (5). </li> <li>fgco2_global:<strong> </strong>the globally integrated sea-air CO2 fluxes for all spCO2 products (6) and <em>kw</em> from all wind products (6)</li> <li><strong>sol:</strong> \(K_0\) is calculated using the Weiss (1974) parameterization with EN4 salinity and OISST temperatures </li> <li><strong>kw:</strong> \(k_w\) is calculated for winds with each being scaled independently to a 14-C bomb flux estimate of 16.5 cm/hr using the quadratic formulation by Wanninkhof (1992). <ul> <li>CCMPv2</li> <li>ERA5</li> <li>JRA55</li> <li>NCEP1</li> <li>NCEP2</li> </ul> </li> <li>spco2_SOCOM_unfilled<em>: </em>\(p\text{CO}_2^\text{sea}\) downloaded from various sources contains the following products: <ul> <li>CMEMS_FFNN</li> <li>CSIR_ML6</li> <li>JENA_MLS</li> <li>JMA_MLR</li> <li>MPI_SOMFFN</li> <li>NIES_FNN</li> </ul> </li> <li>spco2_filler<em>: </em>scaled version of the Landschützer et al. (2020) climatology used to fill missing regions of <em>spco2_SOCOM_unfilled</em></li> <li><strong>fco2atm: </strong>\(p\text{CO}_2^\text{atm}\) is calculated from NOAA's marine boundary layer product with ERA5 mean sea level pressure corrected for pH2O. The virial coefficient is then applied to pCO2atm</li> <li><strong>ice: </strong>\([ice]\) is the ice fraction from the OISST product</li> <li><strong>area_ocean:</strong><em> </em>the surface area of the ocean including the fractional area of the coastal regions</li> <li><strong>seafrac: </strong>the fraction of a pixel that is ocean</li> </ul> <p><strong><em>Units are listed in the metadata of each of the netCDF variables. </em></strong></p>
MCR LTER: Coral Reef: Coral size, temperature, and pCO2 data in support of Edmunds 2016 J Exp Biology
These coral size, temperature and pCO2 data support the publication Edmunds and Burgess, Size-dependent physiological responses of the branching coral Pocillopora verrucosa to elevated teperature and pCO2, J Exp Biology, 2016. Additional tank conditions data are provided. This study focuses on the response of a branching coral to high temperature and PCO2, and explores the implications for this response of metabolic scalingin a colonial modular design. For the manuscript published in the Journal of Experimental Biology (219, 3896-3906 doi:10.1242/jeb.146381), an incorrect Supplementary Information Table S1 was submitted for publication. A revised table has now been published and contains changes associated with correctly assigning conditions to treatment tanks and values averaged over the number of days that accurately reflects the duration of each trial. This correction does not affect the conclusions of the experimental work. The change only affects the data describing tank conditions in Trial 1 and 2.
OSSE dataset for assessing the sensitivity of pCO2 reconstructions to sampling scales across a Southern Ocean sub-domain
<p>The data stored in this repository are part of the manuscript entitled "The sensitivity of pCO<sub>2</sub> reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach" submitted in consideration for publication for in European Geosciences Union: Biogeosciences.</p> <p> </p> <p>The netcdf file includes oceanographic (physical + biogeochemical) data, namely the partial pressure of carbon dioxide (pCO<sub>2</sub>) data at the surface ocean from the high-resolution (±10km) forced NEMO-PISCES coupled ocean model (BIOPERIANT12) and from the semi-idealized observing system simulation experiments (OSSEs) we performed.</p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)
<p>Based on <em>in situ</em> seawater <em>p</em>CO<sub>2</sub> data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater <em>p</em>CO<sub>2</sub> was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2,therm</sub> ) and upwelling index (<em>UI<sub>SST</sub></em>), to characterise the combined effect of atmospheric CO<sub>2</sub> forcing, thermodynamic effects, and multiple mixing processes on seawater <em>p</em>CO<sub>2</sub>. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived <em>p</em>CO<sub>2</sub>, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater <em>p</em>CO<sub>2</sub>. </p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the South China Sea (2003-2019)
<p>The South China Sea (SCS) is one of the largest marginal seas worldwide. It includes a river-dominated, highly productive marginal sea on the north shelf and a wide, oligotrophic ocean-dominated basin with various dynamic sub-regions. Based on an <em>in situ</em> seawater partial pressure of CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>) datasets of 44 cruises/legs collected for the last two decades in the SCS, we proposed a seawater <em>p</em>CO<sub>2</sub> retrieval algorithm by combining the semi-mechanistic and machine learning (ML) methods (MeSAA-ML). The parameter selection strategy was based on the mechanistic analysis of <em>p</em>CO<sub>2</sub> variation, separating impacts of thermodynamics, biological activities, water mixing, and the atmospheric CO<sub>2</sub> forcing. We set a few semi-analytical parameters: <em>p</em>CO<sub>2</sub><sub>_<em>therm</em></sub>, which was a proxy for the combined effect of thermodynamics and the atmospheric CO<sub>2</sub> forcing on seawater <em>p</em>CO<sub>2</sub>; an upwelling index (UI<em><sub>SST</sub></em>) and mixing layer depth (MLD) to characterize the multiple mixing processes; chlorophyll-a concentration (Chl-a) with remote sensing reflectance at 443 and 555 nm (Rrs(443) and Rrs(555)), which were the inputs to proxy the biological effect and other characteristics for distinguishing shelf, basin, and sub-regions. As the seawater <em>p</em>CO<sub>2 </sub>and atmospheric <em>p</em>CO<sub>2</sub> ( <em>p</em>CO<sub>2</sub><sup>air</sup>) have similar data values and characteristics in the vast SCS oligotrophic basin, it will cause instability of the model if one is input and the other is output; thus the difference between them (<em>Δp</em>CO<sub>2</sub><sup>sea-air</sup>) was set as the output, and the seawater <em>p</em>CO<sub>2</sub> was obtained finally by summing <em>p</em>CO<sub>2</sub><sup>air </sup>and <em>Δp</em>CO<sub>2</sub><sup>sea-air</sup>. We compared several ML models, and the XGBoost model was confirmed as the best model. Completely independent cruise-based and observed datasets from Southeastern Asia Time-series Study (SEATS) were used to validate the satellite products, with low root mean square error (RMSE = 11.69 μatm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of satellite-derived <em>p</em>CO<sub>2</sub> (2.44 ± 0.24 μatm/yr) at the location of SEATS was found to be consistent with observed data. We presented that the SCS as a whole is a source of atmospheric CO<sub>2</sub>, releasing an average of 11.00 ± 2.45 Tg C/yr from a total area of 3.32 × 10<sup>6</sup> km<sup>2,</sup> and the northern shelf is a sink (1.69 ± 0.53 Tg C/yr). The area-integrated CO<sub>2</sub> efflux over the entire SCS may decrease with a rate of 0.34 Tg C/yr during 2003–2019. This high-accuracy dataset with 1 km resolution provides a refined understanding of the air-sea CO<sub>2</sub> exchange dynamics in the SCS during 2003–2019.</p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)
<p>Based on <em>in situ</em> seawater <em>p</em>CO<sub>2</sub> data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater <em>p</em>CO<sub>2</sub> was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2,therm</sub> ) and upwelling index (<em>UI<sub>SST</sub></em>), to characterise the combined effect of atmospheric CO<sub>2</sub> forcing, thermodynamic effects, and multiple mixing processes on seawater <em>p</em>CO<sub>2</sub>. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived <em>p</em>CO<sub>2</sub>, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater <em>p</em>CO<sub>2</sub>. </p>
Allequash Creek Wetland Half-Hourly Soil Porewater PCO2 2021
Dissolved CO2, also known as PCO2, was continuously measured in parts per million in soil porewater in a riverine fen in Northern Wisconsin near the eddy covariance flux tower positioned there, named the Allequash Creek Wetland Site (US-ALQ) on AmeriFlux. The site is also a North Temperate Lakes (NTL) LTER study site. This data package contains half-hourly averages of PCO2 from March 24th to October 25th, 2021. This data package is complete but data collection may be reinstated in the future.
Data and code for: High light alongside elevated pCO2 alleviates thermal depression of photosynthesis in a hard coral (Pocillopora acuta)
<p>Data and R scripts of analyses performed for the manuscript "<strong>High light alongside elevated pCO<sub>2</sub> alleviates thermal depression of photosynthesis in a hard coral (<em>Pocillopora acuta</em>)</strong>"</p>
Sea-surface pCO2 maps for the Bay of Bengal based on machine learning algorithms
<p>The dataset contains two products, the first being sea-surface <em>p</em>CO<sub>2</sub> and the second being air-sea CO<sub>2</sub> flux for the Bay of Bengal region. The data is climatological data, with 12 months of the climatological year. Each of these data has a spatial resolution of 1/12<sup>o</sup>. The positive value of CO<sub>2</sub> flux indicates the outgassing of CO<sub>2</sub>, and the negative value shows the uptake of atmospheric CO<sub>2</sub>. This version contains an elaborate description in the attribute section of the NC files, which is missing from the previous versions.</p>
SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed
<p>Here we provide sampling masks used in the study "Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling" (Heimdal et al., 2023, https://doi.org/10.5194/bg-2023-160). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 11 different sampling masks that correspond to the experiments presented in Heimdal et al. (2023), which include different sampling patterns of USV Saildrones in the Southern Ocean (SOCAT+USV sampling).</p>
RFR-CCS: A monthly surface pCO2 product for the California Current Large Marine Ecosystem
<p><strong>Description</strong></p> <p>RFR-CCS is a monthly data product of surface pCO2 for the California Current Large Marine Ecosystem. It was built using pCO2 observations from the Surface Ocean CO2 Atlas, fit against a variety of driver variables (sources of which are listed below) using a Random Forest Regression, and provided on a 0.25 degree resolution grid from January 1998 to December 2020.</p> <p><strong>Citation</strong></p> <p>If using this product, please also cite the original manuscript:</p> <p>Sharp, J.D., Fassbender, A.J., Carter, B.C., Lavin, P.D., and Sutton, A.J. <a href="https://doi.org/10.5194/essd-14-2081-2022">A monthly surface pCO2 product for the California Current Large Marine Ecosystem</a>. Earth System Science Data, 14, 2081–2108, 2022. doi: 10.5194/essd-14-2081-2022.</p> <p><strong>Driver Variables (<a href="https://figshare.com/articles/dataset/RFR-CCS/15152013/2">figshare</a>)</strong></p> <p>1. Sea Surface Temperature: <a href="https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html">OISSTv2</a></p> <p>2. Sea Surface Salinity: <a href="https://www.ecco-group.org/products.htm">ECCO2</a></p> <p>3. Chlorophyll-a: <a href="http://sites.science.oregonstate.edu/ocean.productivity/1080.by.2160.monthly.inputData.php">Oregon State (via NASA)</a></p> <p>4. Wind Speed: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5">ERA5</a></p> <p>5. Atmospheric pCO2: <a href="https://gml.noaa.gov/ccgg/mbl/">NOAA MBL</a></p> <p>6. Mixed Layer Depth: <a href="https://www.hycom.org/hycom/">HYCOM</a></p> <p>7. Distance from Shore: Calculated from latitude and longitude (<a href="https://www.chadagreene.com/CDT/CDT_Contents.html">dist2coast, Climate Data Toolbox</a>)</p> <p>8. Year</p> <p>9. Month of Year</p>
Meta-analysis suggests variable, but pCO2-specific, effects of ocean acidification on crustacean biomaterials
Crustaceans comprise an ecologically and morphologically diverse taxonomic group. They are typically considered resilient to many environmental perturbations found in marine and coastal environments, due to effective physiological regulation of ions and hemolymph pH, and a robust exoskeleton. Ocean acidification can affect the ability of marine calcifying organisms to build and maintain mineralized tissue and poses a threat for all marine calcifying taxa. Currently, there is no consensus on how ocean acidification will alter the ecologically-relevant exoskeletal properties of crustaceans. Here, we present a systematic review and meta-analysis on the effects of ocean acidification on the crustacean exoskeleton, assessing both exoskeletal ion content (calcium and magnesium) and functional properties (biomechanical resistance and cuticle thickness). Our results suggest that the effect of ocean acidification on crustacean exoskeletal properties varies based upon seawater <i>p</i>CO<sub>2</sub> and species identity, with significant levels of heterogeneity for all analyses. Calcium and magnesium content were significantly lower in animals held at <i>p</i>CO<sub>2</sub> levels of 1500-1999 μatm as compared to those under ambient <i>p</i>CO<sub>2</sub>. At lower <i>p</i>CO<sub>2</sub> levels, however, statistically significant relationships between changes in calcium and magnesium content within the same experiment were observed: a negative relationship between calcium and magnesium content at <i>p</i>CO<sub>2</sub> of 500-999 μatm and a positive relationship at 1000-1499 μatm. Exoskeleton biomechanics, such as resistance to deformation (microhardness) and shell strength, also significantly decreased under <i>p</i>CO<sub>2</sub> regimes of 500-999 μatm and 1500-1999 μatm, indicating functional exoskeletal change coincident with decreases in calcification. Overall, these results suggest that the crustacean exoskeleton can be susceptible to ocean acidification at the biomechanical level, potentially predicated on changes in ion content, when exposed to high influxes of CO<sub>2</sub>. Future studies will need to accommodate the high variability of crustacean responses to ocean acidification, as well as ecologically-relevant ranges of <i>p</i>CO<sub>2</sub> conditions, when designing experiments with conservation-level endpoints. --
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.