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162 results for “Water quality data”
High-frequency water quality data for water temperature, dissolved oxygen, specific conductivity, pH, phycocyanin fluorescence and chlorophyll a fluorescence at Lake Lillinonah, Connecticut, USA, 2011-2017.
From 2011 to 2017, an instrumented buoy maintained by Friends of the Lake (FOTL, friendsofthelake.org) and Fairfield University was deployed on Lake Lillinonah (Connecticut, USA) during the summer months to collect high-frequency water quality data. The buoy was deployed early each summer and remained in the lake until late October. The instrumented buoy was equipped with a YSI 6600 sonde at a depth of 1 meter. The YSI 6600 collected water temperature, dissolved oxygen, specific conductivity, pH, chlorophyll a fluorescence and phycocyanin fluorescence data. Additionally, the instrumented buoy is equipped with an In-Situ RDO sensor which collects water temperature and dissolved oxygen readings at a depth of 15 meters. Included in this data package are datasets containing 15-minute data as well as daily averages from all sensors. Following the summer of 2017, the YSI 6600 sonde was replaced with a YSI EXO2 sonde which collected data on the same variables. Data from the YSIEXO2 sonde, as well as data from the In-Situ RDO sensor during the same period, are available in the EDI data package EDI560. Additionally, the instrumented buoy at Lake Lillinonah is equipped with a water temperature thermistor chain . These data can be found in data packages EDI557 (2011-2015) and EDI558 (2018-current).
High-frequency water quality data for water temperature, dissolved oxygen, specific conductivity, pH, phycocyanin fluorescence and chlorophyll a fluorescence at Lake Lillinonah, Connecticut, USA, 2018-current.
Since 2011, an instrumented buoy maintained by Friends of the Lake (FOTL, friendsofthelake.org) and Fairfield University has been deployed on Lake Lillinonah (Connecticut, USA) during the summer months to collect high-frequency water quality data. The buoy is deployed early each summer and remains in the lake until late October. Since 2018, the instrumented buoy has been equipped with a YSI EXO2 sonde at a depth of 1 meter. The YSI EXO2 collects water temperature, dissolved oxygen, specific conductivity, pH, chlorophyll a fluorescence and phycocyanin fluorescence data. Additionally, the instrumented buoy is equipped with an In-Situ RDO sensor which collects water temperature and dissolved oxygen readings at a depth of 15 meters. Included in this data package are datasets containing 15-minute data as well as daily averages from both sensors from 2018 to present. Prior to 2018, the instrumented buoy was equipped with a YSI 6600 sonde, which collected data on the same variables (2011-2017). Data from the YSI 6600 sonde, as well as data from the In-Situ RDO sensor during the same period, are available in the EDI data package EDI559. Additionally, the instrumented buoy at Lake Lillinonah is equipped with a water temperature thermistor chain sensor. These data can be found in data packages EDI557 (2011-2015) and EDI558 (2018-current).
Water quality data collected by the Citizen-Led Environmental Observatory (CLEO) from multiple nearshore sites in Lake Lillinonah, Connecticut, USA, 2010-current
Included in this data package are water quality data from the Citizen-Led Environmental, a Observatory (CLEO) volunteer water quality monitoring program run by Friends of the Lake (FOTL) and Fairfield University at Lake Lillinonah, Connecticut, USA. The program has been operational since 2008 (data available 2010-current). Trained volunteer monitors collect data from dock locations across the lake on water temperature, Secchi disk depth, water color, presence of floating woody debris, recreation potential, trash, particle type and surface scum. Volunteers collect data between 3:00 and 7:00 PM three times per week from Memorial Day through Labor Day. In addition to the variables listed above, CLEO volunteers collect routine water samples on a biweekly basis, as well as any time there is a notable algal bloom. The routine samples are analyzed for nutrient (total nitrogen and total phosphorus) concentrations as well as concentrations of the cyanobacterial toxin microcystin. The blooms samples are analyzed for microcystin only. These data are available in EDI packages EDI568 (nutrients) and EDI569 (toxins).
Water Quality Data (Porewater) from the Taylor Slough, just outside Everglades National Park (FCE), from August 1998 to October 2006
Porewater samples are collected at SRS1b,SRS1c,SRS1d,SRS2,SRS3, TS/Ph-1-5 within the 1 m^ aboveground sawgrass nondestructive biomass plots. Airstones connected to Tygon tubing are inserted 10-15 cm below the soil surface. Biannually during the wet season, Monoject 140cc syringes are used to collect at least 80 mL of porewater sample and after collection they are immediately placed in an ice filled cooler. The lab processing of these samples include salinity measurements using a refractometer, and filtering the sample through a Whatman GF/F filter. Samples are analyzed for inorganic nutrients such as NO2-,NO3-,NH4+,SRP, and DOC. Dissolved nutrients are measured using standard rapid flow analyzer (RFA) techniques and DOC is quantified on a Shimadzu TOC Analyzer.
Water Quality Data (Porewater) from the Taylor Slough, Everglades National Park (FCE), South Florida from September 1999 to December 2006
Porewater samples are collected at SRS1b,SRS2,SRS3, TS/Ph-1-5, 6b within the 1 m^ aboveground sawgrass nondestructive biomass plots. Airstones connected to Tygon tubing are inserted 10-15 cm below the soil surface. Biannually during the wet season, Monoject 140cc syringes are used to collect at least 80 mL of porewater sample and after collection they are immediately placed in an ice filled cooler. The lab processing of these samples include salinity measurements using a refractometer, and filtering the sample through a Whatman GF/F filter. Samples are analyzed for inorganic nutrients such as NO2-,NO3-,NH4+,SRP, and DOC. Dissolved nutrients are measured using standard rapid flow analyzer (RFA) techniques and DOC is quantified on a Shimadzu TOC Analyzer.
Water quality data for Narragansett Bay, RI (USA) from 2014 to 2018
<p>This dataset includes results of a monthly monitoring program conducted in Narragansett Bay, RI (USA) between 2014 and 2018. Eight stations were sampled at three depths (surface, middle, and bottom); at each station, water clarity was noted (Secchi depth). and dissolved oxygen, salinity, and pH were measured. Discrete water samples were analyzed in the laboratory for chlorophyll-a, dissolved inorganic nutrients (nitrate, nitrite, ammonium, and orthophosphate), dissolved organic carbon, stable isotopes of particulate carbon and nitrogen, and total suspended solids. These water quality data are being made available to use, in conjunction with pre-existing datasets collected by others, to better understand the ecological dynamics of the Narragansett Bay ecosystem.</p> <p>A description of the sample collection methods and data analyses are included as a companion file to the dataset.</p> <p> </p>
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
ROSSyndicate Cameron Peak Fire (CPF) reservoir water quality data: Latest Release: 2021- 11/2023 Dataset
<p><strong>Data Description:</strong> The majority of this dataset is water chemistry grab sample data collected post-Cameron Peak Fire in the Cache la Poudre Watershed between the years of 2021 and 2023. This dataset also includes historical data collected pre Cameron Peak Fire by the Rhoades lab at the US Forest Service Rocky Mountain Research Station. These data are focused on basic water quality parameters, as well as cations and anions. Data were collected at various reservoirs in the Cache la Poudre watershed as well as the mainstem of the Cache la Poudre River. This project is ongoing and additional data will be released as it is analyzed.</p> <p><strong>Background Information:</strong> The 2020 Cameron Peak wildfire (CPF) was the largest wildfire in Colorado history at over 200,000 acres. The CPF burned a large proportion of the Cache la Poudre watershed, in particular areas surrounding high elevation reservoirs. This work is funded to support ongoing source water protection programs by the City of Fort Collins, Greeley, Thornton and Northern Water. In collaboration with the Rocky Mountain Research Station (USFS, RMRS), we are sampling various reservoir, tributary, and mainstem sites of the Cache la Poudre watershed. This field campaign allows us to analyze trends in water quality focusing on nutrients and other key constituents mobilized post-fire. The goal of this project is to understand how these nutrients affect algal growth in reservoirs and how those changes are propagated downstream. The reservoirs studied are the following: Barnes Meadow Reservoir, Chambers Lake, Comanche Reservoir, Hourglass Reservoir, Joe Wright Reservoir, Long Draw Reservoir, and Peterson Lake. Historical data (prior to 2021) was collected by the Rhoades Lab at the USFS' Rocky Mountain Research Station.</p> <p><strong>The primary data file is data/cleaned/CPF_reservoir_chemistry_up_to_202301027.csv.</strong> Column definitions and units are defined in the file <em>metadata/Units_Cam_Peak.xlsx</em>. Methods used to collect these data are outline below or in <em>metadata/rmrs_procedures.png</em></p> <p>Location metadata file is <em>data/metadata/cpf_sites.csv</em>. A basic map showing all sampling locations is available at cpf_sites_map.html.</p> <p>Code is housed in the <em>scripts</em> folder and contains the following files:</p> <p>- <em>00_analysis_setup.R</em> provides loads packages and metadata files to be collated in <em>01_chem_prep.qmd</em>.</p> <p>- <em>01_chem_prep.qmd</em> adds metadata to most recent .csv of water chemistry data supplied by RMRS lab.</p> <p>- <em> distance_finder.R</em> uses NHDflowlines to calculate distances from furthest downstream site, PBD.</p> <p>- <em>cpf_sites_map.R</em> uses location metadata to create <em>cpf_sites_map.html</em></p> <p>- <em>demo.R</em> provides an example of how to download data from Zenodo directly in RStudio</p> <p><strong>Data are housed in the data folder and it contains the following:</strong></p> <p>- cleaned: This folder contains the most recently available dataset and has associated burn severity and location data added to the chemistry data. The addition of the metadata was accomplished using the `01_chem_prep.qmd` R script.</p> <p>- cleaned_archive: This folder contains an archive of previously cleaned data. <strong>Downstream users are encouraged to use the collated data file `CPF_reservoir_chemistry_up_to_20231027.csv`</strong> in the `cleaned` directory.</p> <p>- raw: These data were directly received by the ROSSyndicate from RMRS lab managers. Downstream users are encouraged to use the collated data file `CPF_reservoir_chemistry_up_to_20231027.csv` in the `cleaned` directory.</p> <p>- metadata: this contains location data, parameter/column name definitions, units, and methods used at the RMRS Lab. The `README` file in this folder explains burn severity classifications used in the files `sbs_watershed.csv`,`sbs_watershed.csv` and `cpf_sites.csv`</p> <p><strong>Sample Collection</strong></p> <p>Field measurements were taken using a Thermo Orion Star with RDO Optical and Conductivity probes. Time data, when present, are listed in MST. Samples were collected and processed using the Rocky Mountain Research Station's Biogeochemistry Lab, overseen by Timothy Fegel and Charles Rhoades, according to the methods described in rmrs_procedures.png</p> <p><strong>Version: v2023.12.13</strong></p>
Global hydrology and water quality data from 1980-2019, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<p>Global ~10km (5 arcmin) output data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual and monthly temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Discharge (m3 s-1)</li> <li>Channel storage (m3) </li> <li>Water temperature (K)</li> <li>Total dissolved solids (TDS) load (g s-1)</li> <li>Biological oxygen demand (BOD) load (g s-1)</li> <li>Fecal coliform (FC) load (million cfu s-1)</li> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Note. a minimum discharge threshold of 0.1 m3 s-1 was used when computing salinity (TDS), organic (BOD) and pathogen (FC) concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Thus, if the the average discharge for the month was below 0.1 m3 s-1, concentrations are not calculated (assigned as NA).</p> <p>In-stream water quality aggregated to 0.5 degree (i.e. 30 arcmin) spatial resolution (daily, monthly and annual) can be found at: <a href="https://zenodo.org/records/14675270">https://zenodo.org/records/14675270</a>. </p>
Global surface water quality data from 1980 - 2019, derived from the dynamical surface water quality model (DynQual) at 30 arcmin spatial resolution
<p>Global ~50km (30 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual, monthly and daily temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Simulations were originally made at 5-arcmin resolution and aggregated to 30 arcmin 0.5 degree by summing the in-stream (routed) loadings and channel storage over the aggregated area (at daily, monthly and annual timesteps), and subsequently calculating in-stream concentrations. Please note the aggregation technique is provisional and thus the data is subject to change.</p> <p>Note. A minimum discharge threshold of 0.1 m3 s-1 was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.</p> <p>Hydrology and water quality simulations made at DynQuals native spatial resolution (5 arcmin) can be found at: <a href="https://zenodo.org/records/14673871">https://zenodo.org/records/14673871</a>.</p>
Data and R code for long-term study of fire and climate effects on water quality in Clear Lake, California
<p>Long-term relationships between water quality, fire and climate for Clear Lake, California. Although the watershed has historically experienced frequent fire, the 2018 Mendocino Complex, which was the largest wildfire complex in state history, burned approximately 40% of the watershed, sparking concerns about drinking water quality and lake ecosystem health. This analysis spans approximately 1968-2021.</p>
Rinker's Treatment Wetland Water Quality monitoring data (2017 - 2019), South Daytona, Florida
<p><span>Water quality was monitored at a constructed treatment wetland in South Daytona, Florida through </span>a<span> </span><span>two-year</span><span> </span><span>project</span><span> </span><span>(Oct</span><span> </span><span>2017-</span><span> </span>Sept<span> </span><span>2019). </span></p> <p><strong><span>The 2017-2018 INDIAN<span> </span><span>RIVER</span><span> </span><span>LAGOON</span><span> </span><span>NATIONAL</span><span> </span><span>ESTUARY</span><span> </span><span>PROGRAM funded Project</span></span></strong></p> <p><strong><span>Submitted to the IRL Council</span></strong></p> <p><strong><span>Oct 8<sup>th</sup>, 2018</span></strong></p> <p><span> </span></p> <p><strong><span>Project<span> </span>Title</span></strong><span>: <span><span> </span></span>Reed<span> </span><span>Canal</span> <span><span> </span></span>Basin <span><span> </span></span>Stormwater <span><span> </span></span>Improvement <span><span> </span></span>through <span><span> </span></span>Treatment <span><span> </span></span>Wetland</span><span> </span><span>Construction<span> </span>in<span> </span>South<span> </span>Daytona,<span> </span>FL</span></p> <p><strong><span>Project<span> </span>Applicant:<span> </span></span></strong><span>Bethune-Cookman</span><span> </span><span>University</span><span> </span><span>(B-CU)</span></p> <p> </p> <p><strong><span>Amount</span></strong><strong><span> </span></strong><strong><span>of</span></strong><strong><span> <span>Request</span></span></strong><span>:</span><span> </span><span>$181,148</span></p> <h3><span>Other</span><span> </span><span>Funding</span> <span>Sources</span><span> </span><span>and</span> <span>Amount</span><span> </span><span>of</span> <span>Total</span><span> </span><span>Match</span><span>:</span><span> </span><span>$183,095</span></h3> <p><span><span>B-C</span></span>U <span><span> </span></span><span>($74,631);</span> <span><span> </span></span><span>EPA</span> <span><span> </span></span><span>319</span> <span><span> </span></span><span>($11,250);</span> <span><span> </span></span><span>Local</span> <span><span> </span></span><span>wetland/tree/shoreline</span> <span><span> </span></span><span>restoration</span> <span><span> </span></span><span>funds</span> <span><span> </span></span><span>(pending</span></p> <p><span>$60,000); AE-Group ($5,000); </span><span>Volunteers</span><span> </span><span>($2,214);</span><span> </span><span>Project </span>H2O<span> </span><span>Academy</span><span> </span><span>and</span><span> </span><span>partners</span><span> </span><span>($30,000)</span></p> <p><span>The project goals were </span><span>to</span><span> </span><span>design,</span><span> </span><span>construct,</span><span> </span><span>and</span><span> </span><span>assess</span><span> </span>a<span> </span><span>treatment</span><span> </span><span>wetland,</span><span> </span><span>retrofit</span><span> </span><span>Rinker’s</span><span> </span><span>pond</span><span> </span><span>within<span> </span><span>the</span><span> </span><span>city</span></span><span>-owned</span> <span>plat, monitor water quality,</span><span> and</span><span> </span><span>conduct plant</span><span> </span><span>surveys</span><span> </span><span>to</span> test<span> the</span> <span>effectiveness</span><span> </span><span>of </span><span>treatment wetlands</span><span> </span>in<span> stormwater</span> <span>management </span><span>and</span><span> habMayitat quality improvement.</span><span> </span><span>This</span><span> </span><span>project</span><span> </span><span>will</span><span> </span><span>engage</span><span> </span><span>the</span><span> </span><span>residents</span><span> </span><span>into</span><span> </span><span>public</span><span> </span><span>education</span><span> </span><span>programs</span><span> </span>in<span> </span><span>order</span><span> </span><span>to</span><span> </span><span>help</span><span> </span><span>them</span><span> </span><span>enhance</span><span> </span><span>their</span><span> </span><span>awareness</span><span> </span><span>of</span> <span>issues</span><span> </span>of <span>and</span><span> solutions</span><span> </span><span>to</span> <span>stormwater</span><span> </span><span>associated</span> <span>problems.</span></p>
Marcellus shale water and air quality data
<p>Data summary for water and air quality of the Marcellus shale</p>
EOMORES earth observation and in situ data of water quality in lakes and coastal areas - year 1
<p>EOMORES is a European innovation project aiming to develop commercial services for monitoring the quality of inland and coastal water bodies, using data from Earth Observation (EO) satellites and in situ sensors to measure, model and forecast water quality parameters.</p> <p>The current data set is a sample of the data generated within the first project year (2017), and consists of Earth Observation (EO) data and in situ data from lakes and coastal areas. For full data sets, please contact the respective contact point listed for each area.<br> Data sets of the second (2018) and third (2019) year of EOMORES will also be submitted.</p> <p>The following is included:<br> - Estonia lakes and coast: in situ data 2017<br> - Finland: links to repositories of EO data<br> - Italy Trasimeno: sample of EO data 2017<br> - Lithuania Curonian Lagoon: sample of EO data 2017<br> - Netherlands Lake Markermeer: sample of EO data 2017<br> - Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017<br> - UK Scotland: in situ data Loch Leven and Loch Lomond 2017<br> - UK WCO Sentinel2A match ups: Western Channel Observatory match ups with Sentinel-2 satellite 2016, 2017</p> <p>http://eomores-h2020.eu</p>
Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers
<p>Hydraulic and tracer data used in the Chillan case study presented in "Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers"</p>
Water quality data with nitrate
<p>Water quality data in a drinking water distribution network (Helsinki and Vantaa, in Finland). This data includes all the samples that have nitrate analysis. Monochloramine is used in the distribution network as secondary disinfection chemical. The water originates from Lake Päijänne. The titles of the Excel file are in Finnish.</p> <p>Included are an Excel file, an abstract and a poster, where this data was partly utilized. The reference of the poster and abstract: <a href="https://research.aalto.fi/portal/pirjo.rantanen.html">Rantanen, P</a> 2015, <a href="https://research.aalto.fi/en/publications/nitrification-in-drinking-water-distribution-network-in-helsinki-and-vantaa(c7db018f-3225-4e9f-9c05-237533d3669a).html">Nitrification in drinking water distribution network in Helsinki and Vantaa</a>. in Finnish Conference of Environmental Sciences 12th May 2015, Jyväskylä. ed. / Elijah Ngumba; Tuula Tuhkanen; Matti Leppanen; Jaakko Mannio; Sanna Pynnonen. Jyväskylä : FCES, 2015. p. 51.</p>
Multiparameter Water Quality Monitoring System for Continuous Monitoring of Fresh Waters Calibration and Measurement Data Set
<p>This data set contains calibration data for all sensors incorporated in the sensor node. It provides comparison measurements of TPL fluorescence taken by the node and reference spectrofluorimeter. Initial test measurements, as well as site measurements, are also provided. Finally, data from a heuristic method of TPL detection in the presence of algae and mud are also given.</p>
Dataset for the paper submitted for peer-review with the title "Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model"
<p>The proposed dataset is related to the following article submitted for peer review:</p> <p>Hasanyar, M., Flipo, N., Romary, T., Wang, S. (2023), Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model, UNDER PEER-REVIEW</p> <p>It consists of command files for the prose-pa0.74 software available here: https://gitlab.com/prose-pa/prose-pa </p> <p>To run the model :</p> <p>1. Compile prose-pa0.74</p> <p>2. Copy the executable in the current directory</p> <p>3. In a terminal launch</p> <p>> ./prose-pa0.74 simulation.COMM test.log</p> <p>The “simulation.COMM” holds the settings for the ProSe-PA simulation related to the paper mentioned in the front head of the current file. </p> <p>The information on different parameters of “simulation.COMM” are included in “bathymetrie”, “Cmds”, “Inflows”, “layers”, “meteo”, “o2_obs”, “param_bio”, “Reaches” and “Singularities” folders.</p> <p>The “bathymetrie” folder holds the geometric information of several cross-sections along the river. </p> <p>The Cmds folder holds the “simulation.COMM” file. </p> <p>The “Inflows” folder the information about the boundary condition inflows to the river such as discharge, concentration of organic carbon, etc.</p> <p>The layer folder holds data of the initial conditions of the model (Table 2 in the article).</p> <p>The “meteo” folder holds the meteorological information.</p> <p>The “o2_obs” folder holds the observed oxygen data needed to do data assimilation. </p> <p>The “param_bio” folder holds information on the physiology of bacteria, phytoplankton, and other model species.</p> <p>The “Reaches” folder holds information about river reaches and their manning coefficient. </p> <p>The “param_range” file holds the variation range of model parameters considered in data assimilation together with their perturbation percentage.</p> <p>The output files are written in $HOME/Outputs folder. It is possible to change it directly in simulation.COMM, last entry “Output_folder”.</p>
Assessment of the quality of water for human consumption in the Laguna Verde area of Valparaiso in Central Chile (DATA)
<p>Data set paper Assessment of the quality of water for human consumption in the Laguna Verde area of Valparaiso in Central Chile .</p>
Summer Water Quality Data from four ponds at Grafton Lakes State Park, New York, United States: 2022
Lake water quality data was collected on four ponds in Grafton Lakes State Park, NY, USA to establish long-term monitoring sites. Samples were collected once a month from May to July of 2022. Temperature and dissolved oxygen profiles were collected using a YSI Sonde. Secchi disk depths were recorded during the July sampling event.
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.