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Filtered chlorophyll a time series for Beaverdam Reservoir, Carvins Cove Reservoir, Claytor Lake, Falling Creek Reservoir, Gatewood Reservoir, Smith Mountain Lake, Spring Hollow Reservoir in southwestern Virginia, and Lake Sunapee in Sunapee, New Hampshire, USA during 2014-2025
Water column chlorophyll a was analyzed from 2014 to 2025 in seven freshwater reservoirs in southwestern Virginia (VA), USA, and one freshwater lake in central New Hampshire (NH), USA. These waterbodies are: Beaverdam Reservoir (Vinton, VA), Carvins Cove Reservoir (Roanoke, VA), Claytor Lake (Pulaski, VA), Falling Creek Reservoir (Vinton, VA), Gatewood Reservoir (Pulaski, VA), Smith Mountain Lake (Bedford, VA), Spring Hollow Reservoir (Salem, VA), and Lake Sunapee (Sunapee, NH). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia; Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia; and Smith Mountain Lake is jointly treated by the Bedford Regional Water Authority and the Western Virginia Water Authority as a drinking water source for Franklin County, Virginia. Claytor Lake is managed for hydroelectric power generation by the Appalachian Power Company. Lake Sunapee is a glacially-formed lake known for its oligotrophic water quality. The dataset consists of depth profiles of chlorophyll a samples generally measured at the deepest site of each reservoir adjacent to the dam or at the buoy site of Lake Sunapee. The water column samples were collected approximately fortnightly from March-April and weekly from May-October from 2014 - present at Falling Creek Reservoir and Beaverdam Reservoir, approximately fortnightly from May-August in most years at Carvins Cove Reservoir, approximately fortnightly from May-August in Gatewood and Spring Hollow Reservoirs from 2014-2016, approximately fortnightly from May-August of 2014 in Smith Mountain Lake, sporadically from May-August of 2014 in Claytor Lake, and sporadically from June-August of 2021-2022 and 2024-2025 in Lake Sunapee. From 2018-2025, samples were collected primarily at a single depth in each reservoir, with sample collection at two depths in F
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths, and water level in Beaverdam Reservoir, Virginia, USA in 2009-2025
We monitored water level and water quality in Beaverdam Reservoir (Vinton, Virginia, USA; 37.31288, -79.8159) with visual observations and high-frequency (10- to 15-minute resolution) sensors in 2009-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of three datasets: 1) bvre-waterlevel_2009_2025.csv, 2) bvre-sensorstring_2016_2020.csv, and 3) bvre-waterquality_2020_2025.csv. 1) bvre-waterlevel_2009_2025.csv contains water level observations of the staff gauge at a platform near the reservoir's dam by both the Western Virginia Water Authority and the Virginia Tech Reservoir Group LTREB field crew. This dataset spans 2009 to 2025, with data collection still ongoing. 2) bvre-sensorstring_2016_2020.csv consists of a water temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by intermittent data collected by a dissolved oxygen logger deployed at 5 m or 10 m. A sonde measuring water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was additionally deployed at ~1.5 m depth. This dataset spans 2016 to 2020, with no additional data collection beyond the last observation. The third dataset is bvre-waterquality_2020_2025.csv, with data collection still ongoing and an accompanying maintenance log. This dataset contains: a) a temperature string with 13 temperature sensors deployed ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; b) two dissolved oxygen sensors, one in the middle of the string and one sensor above the sediments; and c) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 dataset is also included in this 2020-2025 d
Time series of optical measurements (absorbance, fluorescence) for Beaverdam Reservoir, Carvins Cove Reservoir, and Falling Creek Reservoir in southwestern Virginia, USA 2019-2025
Depth profiles and surface dissolved samples analyzed for optical analyses (absorbance, fluorescence) were sampled from 2019 to 2025 in three drinking water reservoirs located in southwestern Virginia, USA including Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), and Falling Creek Reservoir (Vinton, Virginia). The reservoirs are owned and operated by the Western Virginia Water Authority as either primary or secondary drinking water sources for Roanoke, Virginia. The dataset consists of depth profiles at the deepest site of each reservoir, surface water samples from reservoir tributaries, and additional samples at within-reservoir sites. In Beaverdam and Falling Creek Reservoir, we collected depth profiles, gauged weir, and wetland samples approximately fortnightly throughout the summer stratified period (June 2019 - November 2019) and surface samples approximately monthly from May 2019 to October 2019 and in March 2020. Beaverdam Reservoir depth samples were additionally collected in summer 2022. We collected depth profiles and tributary samples monthly to seasonally in Carvins Cove Reservoir from late 2021 - 2023. From May 2024 - April 2025, we sampled depth profiles at multiple transects across the reservoir and surface water samples in one tributary approximately monthly at Carvins Cove. Absorbance was measured as colored dissolved organic matter (CDOM) using a spectrophotometer. Fluorescence was measured as fluorescent dissolved organic matter (fDOM) using a spectrofluorometer as excitation emission matrices (EEMs). Absorbance and fluorescence results are reported along with PARAFAC model results applied to the collected EEMs samples. Data visualization and quality assurance/quality control (QA/QC) scripts accompany the data package.
Time series of water column pH from lagoon sites along the Alaska Beaufort Sea coast, 2018-ongoing
Beginning in August 2018, the Beaufort Lagoon Ecosystems Long Term Ecological Research (BLE LTER) program will record water column pH time series (hourly) from a benthic mooring containing a Seabird SeaFET V2 buoyed 10 cm from the lagoon seafloor. pH values are logged from the instrument’s internal sensor and reported on the total hydrogen ion scale. Site bottom water is collected and analyzed in the laboratory to employ a single point calibration to the data during post-processing. Another discrete water sample is collected months after instrument deployment to determine uncertainty (2018-2019 season: 0.002).
Phoenix, Arizona (USA) residential yard management and vegetation change over time: 2008-2019
This project sampled residential front yards in four Phoenix, AZ neighborhoods to address questions about how residential yard vegetation varies among neighborhoods and changes over time and in response to resident attitudes. Neighborhoods were located on an approximate north-to-south transect in the city of Phoenix and represented different dominant landscape types (xeric or mesic) and different socioeconomic conditions. The project originated in summer 2008, when approximately 100 parcels were selected in each of the four neighborhoods for front yard vegetation sampling. All yards which could be relocated and accessed in the summer of 2018 were resampled, and current residents were surveyed to understand their yard management motivations, attitudes, and changes made. Yards of 100 survey respondents were resampled a third time in 2019. Yard sampling primarily focused on yard woody vegetation identification, but also included ground cover, yard type of neighboring yards, and features such as fences, lighting, and social infrastructure. Social and yard survey data can be linked with unique identifiers provided in the datasets.
Nitrogen mineralization potential in soils collected from the Jornada Basin LTER-I transect and extracted at field collection time, 1989
This data package contains nitrogen mineralization data from soils collected along the Jornada Basin LTER (LTER-I) transects in southern New Mexico, USA. These transects are located in a livestock exclosure established in 1982 in the Chihuahuan Desert Rangeland Research Center (CDRRC) and run from the middle of the College Playa up to the foot of Mt. Summerford (2.7 km in length). Prior to the exclosure, the study site was moderately to heavily grazed for the past 100 years. The Treatment transect was treated annually with ammonium nitrate fertilizer (NH4NO3 at 10g N/m2/yr) until 1987. Along each transect, 91 stations, each with a plant intercept line, are spaced at 30 meter intervals. For this dataset, 60 soil samples (total) were collected along the control and fertilized treatment transects and mixed with potassium chloride solution (KCl) on Nov 27, 1989, then filter extracted the following day. The dataset contains a soil moisture correction factor, sample weights, total inorganic nitrogen (NO3+NO2-N), and nitrogen in ammonium (NH4-N) for Week F (field) of nitrogen mineralization potentials. The soil mineralization data complements the biomass harvest measurements that occurred in September 1989 (dataset knb-lter-jrn.210015001). This study is complete.
Nitrogen mineralization potential in soils collected from the Jornada Basin LTER-I transect and extracted at incubation time 0, 1989
This data package contains nitrogen mineralization data from soils collected along the Jornada Basin LTER (LTER-I) transects in southern New Mexico, USA. These transects are located in a livestock exclosure established in 1982 in the Chihuahuan Desert Rangeland Research Center (CDRRC) and run from the middle of the College Playa up to the foot of Mt. Summerford (2.7 km in length). Prior to the exclosure, the study site was moderately to heavily grazed for the past 100 years. The Treatment transect was treated annually with ammonium nitrate fertilizer (NH4NO3 at 10g N/m2/yr) until 1987. Along each transect, 91 stations, each with a plant intercept line, are spaced at 30 meter intervals. For this dataset, 60 soil samples (total) were collected along the control and fertilized treatment transects and mixed with potassium chloride solution (KCl) on Nov 27, 1989, then filter extracted four days later to give a time = 0 incubation value. The dataset contains a soil moisture correction factor, sample weights, total inorganic nitrogen (NO3+NO2-N), and nitrogen in ammonium (NH4-N) for Week 0 of nitrogen mineralization potentials. The soil mineralization data complements the biomass harvest measurements that occurred in September 1989 (dataset knb-lter-jrn.210015001). This study is complete.
Microbial Observatory at North Temperate Lakes LTER Time series of bacterial community dynamics in Lake Mendota 2000 - 2009
With an unprecedented decade-long time series from a temperate eutrophic lake, we analyzed bacterial and environmental co-occurrence networks to gain insight into seasonal dynamics at the community level. We found that (1) bacterial co-occurrence networks were non-random, (2) season explained the network complexity and (3) co-occurrence network complexity was negatively correlated with the underlying community diversity across different seasons. Network complexity was not related to the variance of associated environmental factors. Temperature and productivity may drive changes in diversity across seasons in temperate aquatic systems, much as they control diversity across latitude. While the implications of bacterioplankton network structure on ecosystem function are still largely unknown, network analysis, in conjunction with traditional multivariate techniques, continues to increase our understanding of bacterioplankton temporal dynamics.
LAGOS-NE v.1.054.1 - Lake water quality time series and geophysical data from a 17-state region of the United States
Time series of mean summer total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP) and chlorophyll values from 2913 unique lakes in the Midwest and Northeast United States. Epilimnetic nutrient and chlorophyll observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NELIMNO version 1.054.1, and come from 54 disparate data sources. These data were used to assess long-term monotonic changes in water quality from 1990-2013, and the potential drivers of those trends (Oliver et al., submitted). Summer was used to approximate the stratified period, which was defined as June 15 to September 15. The median number of observations per summer for a given lake was 2, but ranged from 1 to 83. The rules for inclusion in the database were that, for a given water quality parameter, a lake must have an observation in each period of 1990-2000 and 2001-2011. Additionally, observations must span at least 5 years. Each unique lake with nutrient or chlorophyll data also has supporting geophysical data, including climate, atmospheric deposition, land use, hydrology, and topography derived at the lake watershed (variable prefix “iws”) and HUC 4 (variable prefix “hu4”) scale. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NEGEO version 1.03. For more specific information on how LAGOS-NE was created, see Soranno et al. 2015. Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., Gries C., Henry E.N., Skaff N.K., Stanley E.H., Stow C.A., Tan P.-N., Wagner T., and Webster K.E. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 4: 28. doi: 10.1186/s13742-015-0067-4.
Time-lapse camera (phenocam) imagery of sensor network plots, 2017 - ongoing.
Images from time-lapse cameras were analyzed to track the greenness curves of 16 plots in the Sensor Network at Niwot Ridge. Images were taken every 30 minutes during daylight hours throughout the growing season. Cameras were angled to view 1m^2 vegetation plots located at each sensor node. Pixels in the portion of the image capturing the vegetation plot were used to calculate the green chromatic coordinate (GCC). The change in GCC over the growing season represents the growth and phenology of the plant communities captured.
Time lapse camera photos for Green Lakes Valley, 2011 - ongoing.
Time lapse photography is a powerful tool to detect seasonal and interannual change in remote locations. In 2008, a time lapse camera was installed at Niwot Ridge, below D1, with a view overlooking Green Lake 4. The resulting photos give a view into the seasonal evolution of ice and snow cover over the Green Lakes Valley.
SBC LTER: Ocean: HFR-derived surface flow metrics, surface water retention times, and related factors in the Santa Barbara Channel (2012-2019)
This data package include three files: 1. daily maps of High-Frequency Radar (HFR) measured surface currents, indices of mesoscale eddy locations, and local retention times on a 2km grid; 2. monthly time series of wind stress, alongshore pressure gradient, surface current EOF principal components, vorticity, eddy area, eddy presence, and spatially averaged retention times from January 2012 to December 2019; 3. A MATLAB script for plotting the maps and timeseries. These data were processed in order to investigate the drivers of surface water retention in the Santa Barbara Channel, CA, details of which are available in the study: Brokaw, R.J., D.A. Siegel, and L. Washburn. Physical Drivers of Surface Water Retention in the Santa Barbara Channel. [In preparation for Journal of Geophysical Research: Oceans.]
SBC LTER: Reef: Annual time series of biomass for kelp forest species, ongoing since 2000
These data are annual estimates of biomass of approximately 225 taxa of reef algae, invertebrates and fish in permanent transects at 11 kelp forest sites in the Santa Barbara Channel (2-8 transects per site). Abundance is measured annually (as percent cover or density, by size) and converted to biomass (i.e., wet mass, dry mass, decalcified dry mass, ash free dry mass) using published taxon-specific algorithms. Data collection began in summer 2000 and continues annually in summer to provide information on community structure, population dynamics and species change. The time period of data collection varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. See Methods for more information.
SBC LTER: Ocean: Time-series: nearshore calibrated pH and temperature outside of reefs, ongoing since 2011
Calibrated pH (Total scale, SeaFET sensor) data was collected from 10 reefs in the Santa Barbara Channel along with in situ temperature. Most pH sensors are deployed together with SBC long-term mooring instruments. Data collection intervals and SeaFET sensor depths vary based on the site location.
SBC LTER: Time series of quarterly NetCDF files of kelp biomass in the canopy from Landsat 5, 7 and 8, since 1984 (ongoing)
This data file represents a time series of canopy area of giant kelp, Macrocystis pyrifera, and bull kelp, Nereocystis luetkeana, and canopy biomass of giant kelp derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), and Landsat 9 Operational Land Imager 2 satellite imagery, along with relevant metadata. The kelp canopy is composed of the portions of fronds and stipes floating on the surface of the water. Canopy area (m) data are given for individual 30 x 30 meter pixels for all coastal areas of Baja California, Mexico, California, Oregon, and the outer coast of Washington (including offshore islands). Biomass data (wet weight, kg) are given for individual 30 x 30 meter pixels in the coastal areas extending from near Ano Nuevo, CA through the southern range limit in Baja California (including offshore islands), representing the range where giant kelp is the dominant canopy forming species. Data were derived from the three Landsat sensors listed above. Observations are made on a 16 day repeat cycle, for each instrument, but the temporal coverage is irregular because of cloud cover, instrument failure, and the mission length of each sensor (TM: 1984 – 2011, ETM+: 1999 – present, OLI: 2013 – present). Estimates of canopy area are derived from the fractional cover of kelp canopy determined from satellite surface reflectance. Estimates of kelp canopy biomass are derived from the relationship between giant kelp fractional cover determined from satellite surface reflectance and empirical measurements of giant kelp canopy biomass in long-term SBC LTER study plots obtained using SCUBA. The different Landsat sensors were calibrated to each other using simulated Landsat data derived from hyperspectral imagery. Missing data due to the ETM+ scan line corrector error were filled using a synchrony-based gap filling method. Data are organized into a single NetCDF file and contain the quarterly area and
ASRT (alternating serial reaction time)
Open the record for dataset details and reuse information.
EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks
Open the record for dataset details and reuse information.
P-S waves 3D velocity model of Los Humeros area from earthquake based travel-time tomography using CAT3D software (OGS)
<p>The dataset contains the 3D velocity model (VP (m/s), VS (m/s) and VP/VS) obtained from the tomographic inversion of seismological data in the area of Los Humeros (Mexico). The model was performed in the frame of the GEMex project (Mexico‐Europe Cooperation for research of enhanced geothermal systems and super-hot geothermal systems, WP5 ‘Detection of deep structures’, Jousset et al., D5.3, 2019).</p> <p>The inversion used 2661 P arrivals and 2272 S arrivals associated to 395 earthquakes recorded by 37 stations. The picking data was provided by Toledo et al., 2019.</p> <p>The inversion was performed by CAT3D software, a tomographic tool developed by OGS, which uses the SIRT method (Simultaneous Iterative Reconstruction Technique, Stewart, 1993) as inversion algorithm and the ray tracing procedure based on minimum time principle (Böhm et al., 1999). The velocities used as initial model for tomography were provided by the interpolated values obtained from the velocity analysis of four 2D seismic lines acquired inside the same investigated area by the tomographic inversion (See GEMex deliverable D5.3).</p> <p>The 3D velocity model is defined by a 3D grid of 61 nodes in X, 69 nodes in Y and 29 nodes in Z, equally spaced by 250 m in all directions. The total dimensions of the model is 15x17x7 km and the borders positions are (m) (WGS 84/UTM ZONE 14N):</p> <p>Xmin = 655000, Xmax = 670000</p> <p>Ymin = 2168000, Ymax = 2185000</p> <p>Zmin = -3000, Zmax = 4000</p>
Data archive for "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network"
<p>This datasets supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network" submitted to IEEE Transactions in Geoscience and Remote Sensing. A preprint of the paper can be found here: <a href="https://arxiv.org/abs/2005.10374">https://arxiv.org/abs/2005.10374</a>. The code that uses these data is available at <a href="https://github.com/jleinonen/downscaling-rnn-gan">https://github.com/jleinonen/downscaling-rnn-gan</a>.</p> <p>The file "goes-samples-2019-128x128.nc" contains the training dataset called "GOES-COT" in the paper, consisting of cloud optical depth measurements from the GOES-16 satellite. The files "gen_weights*.nc" contain the generator weights saved at different time steps during training for the two different datasets described in the paper.<br> </p>
Time-series of shoreline change along the Pacific Rim
<p>This repository contains 40 years of tidally-corrected shoreline change time-series for most sandy coastlines around the Pacific Rim derived from Landsat imagery. <br><br><strong>The time-series were last updated in May 2025. For the latest data always refer to <a href="http://coastsat.space/">http://coastsat.space/</a>.</strong></p> <p>The dataset was used to investigate the impact of ENSO on beach erosion and accretion in:<br>- Vos, K., Harley, M.D., Turner, I.L. <em>et al.</em> Pacific shoreline erosion and accretion patterns controlled by El Niño/Southern Oscillation. <em>Nat. Geosci.</em> <strong>16</strong>, 140–146 (2023). <a href="https://doi.org/10.1038/s41561-022-01117-8">https://doi.org/10.1038/s41561-022-01117-8</a><em> </em></p> <p><em>CoastSat </em>was used to map shoreline changes on Landsat 5, Landsat 7 and Landsat 8 imagery between 1984 and 2025. The <em>Coastsat </em>toolbox is publicly available at https://github.com/kvos/CoastSat and described in <em>Vos et al. 2019, </em><a href="https://doi.org/10.1016/j.envsoft.2019.104528">https://doi.org/10.1016/j.envsoft.2019.104528</a>. The time-series of shoreline change were tidally-corrected along cross-shore transects using tide levels from a global tide model (FES2022) and a satellite-derived estimate of the beach slope (as described in <em>Vos et al. 2020, "Beach slopes from satellite-derived shorelines", </em><a href="https://doi.org/10.1029/2020GL088365">https://doi.org/10.1029/2020GL088365</a><em>)</em>.</p> <p>This dataset covers wave-dominated sandy coasts in the Pacific basin where Landsat imagery was available, including a total of 3,000 beaches and more than 100,000 cross-shore transects (100-m alongshore spaced). This includes coastlines in Australia, New Zealand, Japan, Chile , Peru, Mexico and USA (California and Hawaii only).</p> <p>The data is structured as follows:</p> <ul> <li>There is a folder for each country (e.g. Australia)</li> <li> In the country folder, there is a folder for each site (e.g. aus0001, aus0002 etc)</li> <li>In the site folder, there are 4 CSV files: <ul> <li><em>time_series_tidally_corrected.csv</em>: this file contains the tidally-corrected time-series of shoreline change along each transect belonging to the site (e.g. aus0001-0001, aus0001-0002 etc). This is the final product used for coastal change analyses.</li> <li><em>time_series_raw.csv</em>: this file contains the raw time-series of shoreline change, which have not be tidally-corrected. Note that each image is taken at a different stage of the tide.</li> <li><em>tide_levels_fes2022</em>: this file contains the tide levels at the time of image acquisition extracted from FES2022 (global tide model publicly available on AVISO+).</li> <li><em>transect_coordinates_and_beach_slopes.csv</em>: this file contains the coordinates (in WGS84 lat/lon coordinates) as well as the estimated beach slope for each transect, including confidence intervals.</li> </ul> </li> </ul> <p> In addition, there are four geospatial layers (.GEOJSON) which contain important spatial information:</p> <ul> <li> <em>polygons.geojson</em>: this layer contains the polygons that were used to run CoastSat for each beach.</li> <li><em>shorelines.geojson</em>: this layer contains the sandy shorelines that were used to generate the cross-shore transects (also used as reference shorelines in CoastSat). Each beach has the following attributes: beach length, median orientation, median slope, and mean springs tidal range.</li> <li><em>transects.geojson</em>: this layer contains the cross-shore transects, which are spaced 100 m along each beach. Each transect has the following attributes: orientation, beach slope, linear trend (in m/year), alongshore distance relative to the northern end of the beach (absolute and normalised).</li> <li><em>transects_edit.geojson</em>: this layer is the same as transects.geojson but the transects that are not suitable for shoreline mapping were manually deleted (rocky shores, submerged reef, coastal lagoons and inlets, coastal defences etc...).</li> <li><em>transects_ENSO.geojson</em>: this layer (similar to transects.geojson) contains the transects that were used to analyse ENSO effects on shoreline changes in the Pacific (a total of 83,000).</li> </ul> <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.