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North Temperate Lakes LTER: Physical and Chemical Limnology of Lake Kegonsa and Lake Waubesa 1994 - current
Physical and chemicals parameters of two Madison-area lakes in the Yahara chain not included as core NTL-LTER study lakes. Parameters include intermittently sampled water temperature, dissolved oxygen, ph, total alkalinity, chloride and sulfate. Nutrient data has been collected since 2015. Number of sites: 2.
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/344/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Cascade Project at North Temperate Lakes LTER Temperature Chain Data 2009 - 2019
Summer temperature chain data were collected from three lakes (Peter, Paul, and Tuesday) using NexSens temperature thermistors. Data are available for five years: 2013–2015 and 2018–2019. Peter and Paul Lakes have complete records for all five years, while Tuesday Lake has data for three years. During 2013–2015, thermistors recorded temperatures at depths of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, and 5.0 meters. Beginning in 2018, thermistors were added at 4.5 and 6.0 meters.
North Temperate Lakes LTER: Multiparameter Water Quality Data -- CFL Pier, Lake Mendota.
This is data from a SUNA V2 nitrate sensor and a YSI EXO 2 sonde instrumented with water temperature, dissolved oxygen, pH, chlorophyll, phycocyanin, conductivity, turbidity, and fDOM sensors. The sensors are located at the lake end of the pier serving the Center for Limnology on the UW-Madison campus. The YSI sonde is fixed on the pier with sensors nominally at 0.5 meters depth, while the SUNA is suspended below the pier at one meter depth in the open water season. In the winter season, the SUNA is placed in a cage on the lake bottom close to shore with the sensor 18cm off the bottom. The depth of the sensors will vary with lake level over the season. The water depth at the lake end of the pier is normally about 3 meters. YSI sonde data are sampled once per minute. Hourly and daily averages are provided as separate CSV files. The SUNA sample rate varies. In the winter (under the ice) it relies on single battery charge, so the wiper is deactivated and the sensor samples every 1-2 hours. Daily averages of SUNA are also provided as a separate CSV. The YSI sonde is deployed only during the ice-free season coinciding with the placement of the pier. Sensors are cleaned and maintained roughly every two weeks. Number of sites: 1. Location lat/long: 43.07758, -89.40297
North Temperate Lakes LTER: High Frequency Water Temperature Data - Sparkling Lake Raft 1989 - current
The instrumented raft on Sparkling Lake is equipped with a thermistor chain that measures water temperature from depths ranging from the surface to 18m at an interval of 0.5m near the surface to a one-meter interval throughout the rest of the water column. The surface temperature sensor is attached to a float so it's as close to the surface as feasible. Sampling frequency is currently one minute with hourly and daily averages provided. Number of sites: 1
North Temperate Lakes LTER: Fish Lengths and Weights 1981 - current
Data are collected annually to enable us to track the fish assemblages of eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, bog lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Mendota, Monona, Wingra and Fish). Sampling on Lakes Monona, Wingra, and Fish started in 1995; sampling on other lakes started in 1981. Sampling is done at six littoral zone sites per lake with seine, minnow or crayfish traps, and fyke nets; a boat-mounted electrofishing system samples four littoral transects. Vertically hung gill nets are used to obtain two pelagic samples per lake from the deepest point. A trammel net samples across the thermocline at two sites per lake. In the bog lakes only fyke nets and minnow traps are deployed. Parameters measured include species-level identification and lengths for all fish caught, and weight and scale samples from a subset. Dominant species vary from lake to lake. Perch, rockbass, and bluegill are common, with walleye, large and smallmouth bass, northern pike and muskellunge as major piscivores. Cisco have been present in the pelagic waters of four lakes, and an exotic species, rainbow smelt, is present in two. The bog lakes contain mudminnows. Beach seining was discontinued after the 2019 season. The only sampling done in 2020 were a single gill-netting replicate in Sparkling, Crystal, and Trout lakes. Sampling in Fish Lake was missed in 2021 due to significant lake level changes. Data from the two bogs is missing in 2022. Sampling Frequency: annually Number of sites: 11.
North Temperate Lakes LTER: High Frequency Meteorological and Metabolism Data - Trout Bog Buoy 2003 - present
The instrumented buoy on Trout Bog is equipped with a dissolved oxygen sensor, a thermistor chain, light sensor, and in the past, meteorological sensors that provide fundamental information on lake thermal structure and lake metabolism. A surface buoy was used from 2003 - 2014, which included a met station (air temp and winds). Since then, only a subsurface buoy as been used (no met station), and includes frequent over-winter deployments under the ice. The thermistor chain data is included in a separate dataset (knb-lter-ntl.70). Hourly and daily averages are provided for the met data, while hourly averages are provided for the dissolved oxygen and light data. To accommodate the under-ice deployments, sensor depths vary somewhat year-to-year. Light data is collected with HOBO pendant light and temperature sensors. The make of the dissolved oxygen sensor has changed over the years: Greenspan DO (2003-2005), D-Opto (2006-2014), and PME miniDOT (2015-present).
North Temperate Lakes LTER: Fish Abundance 1981 - current
This data set is a derived data set based on fish catch data. Data are collected annually to enable us to track the fish assemblages of eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, bog lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Mendota, Monona, Wingra and Fish). Sampling on Lakes Monona, Wingra, and Fish started in 1995; sampling on other lakes started in 1981. Sampling is done at six littoral zone sites per lake with seine, minnow or crayfish traps, and fyke nets; a boat-mounted electrofishing system samples three littoral transects. Vertically hung gill nets are used to obtain two pelagic samples per lake from the deepest point. A trammel net samples across the thermocline at two sites per lake. In the bog lakes only fyke nets and minnow traps are deployed. Parameters measured include species-level identification and lengths for all fish caught, and weight and scale samples from a subset. Derived data sets include species richness, catch per unit effort, and size distribution by species, lake, and year. Protocol used to generate data: Day seines were only used in 1981 and have been eliminated from this data set to make sampling effort across years comparable. Number caught for each species is summed over repetitions of a gear within a lake and over depth. For information on fish stocking by the Wisconsin Department of Natural Resources in LTER lakes in Dane and Vilas counties, see https://dnr.wi.gov/fisheriesmanagement/Public/Summary/Index. Beach seining was discontinued after 2019. The only sampling done in 2020 were a single gill-netting sample in Sparkling, Crystal, and Trout lakes. Sampling in Fish Lake was missed in 2021 due to significant lake level changes. Data from the two bogs is missing in 2022. Sampling Frequency: annually. Number of sites: 11
North Temperate Lakes LTER: High Frequency Water Temperature Data - Trout Bog Buoy 2003 - current
The instrumented buoy on Trout Bog is equipped with a thermistor chain that measures water temperature from depths ranging from the surface to 7m placed every 0.5-1m throughout the water column. From the initial deployment in 2003 through 2014, a surface buoy was used in the open water season. Since 2015, a subsurface buoy has been used, including under-ice deployments during the winter. Recorded depths can vary slightly depending on specifics of the year's deployment hardware. The sampling frequency:varies for instantaneous samples. Prior to 2011, the sample frequency was every 10 minutes, with some short periods of 2 minutes in 2003. Beginning in 2011, the sample frequency has been one per minute. Hourly and daily averages are also provided. Number of sites: 1.
North Temperate Lakes LTER: Fish Length Frequency 1981 - current
This data set is a derived data set based on fish catch and length data. Data are collected annually to enable us to track the fish assemblages of eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, bog lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Mendota, Monona, Wingra and Fish). Sampling on Lakes Monona, Wingra, and Fish started in 1995; sampling on other lakes started in 1981. Sampling is done at six littoral zone sites per lake with seine, minnow or crayfish traps, and fyke nets; a boat-mounted electrofishing system samples three littoral transects. Vertically hung gill nets are used to obtain two pelagic samples per lake from the deepest point. A trammel net samples across the thermocline at two sites per lake. In the bog lakes only fyke nets and minnow traps are deployed. Parameters measured include species-level identification and lengths for all fish caught, and scale samples and weight from a subset. Derived data sets include species richness, catch per unit effort, and size distribution by species, lake, and year. Dominant species vary from lake to lake. Perch, rockbass, and bluegill are common, with walleye, large and small mouth basses, northern pike and muskellunge as major piscivores. Cisco have been present in the pelagic waters of four lakes, and the exotic species, rainbow smelt, is present in two. The bog lakes contain mudminnows. Protocol used to generate data: The number of fish caught in each five mm length interval (0<length<5, 5<=length<10, etc.) have been summed over gear. In cases in which only a random subsample of fish were measured, the unmeasured fish have been assigned to the length categories based on the proportions in length categories for the measured fish of the subsample. Day seines were only used in 1981 and have been eliminated from this data set to make sampling effort across years comparable. Beach seining was discontinued after 2019. The only sampling done in 2020 were a single gill-netting repli
North Temperate Lakes LTER: Color - Trout Lake Area 1989 - current
Color is measured four times per year in the seven northern study lakes (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes, unnamed lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog]) on water samples that are filtered in the field through 0.45 micron capsule filters (0.45 um nuclepore membrane filters before 2015). A spectrophotometer is used to quantify color in the lab as absorbance units, at 1nm intervals between the wavelengths of 200 and 800 nm. All values are in absorbance units and are given as measurements at the path length of the employed cuvette and should be divided by the cuvette length for a comparable value at a pathlength of 1 cm. Sampling Frequency: 4 times annually. Number of sites: 7.
North Temperate Lakes LTER: Zooplankton - Madison Lakes Area 1997 - current
Zooplankton samples for the 4 southern Wisconsin LTER lakes (Mendota, Monona, Wingra, Fish) have been collected for analysis by LTER since 1995 (1996 Wingra, Fish) when the southern Wisconsin lakes were added to the North Temperate Lakes LTER project. Samples are collected as a vertical tow using an 80-micron mesh conical net with a 30-cm diameter opening (net mouth: net length ratio = 1:3) consistent with sampling conducted by the Wisconsin Dept. Natural Resources in prior years. Zooplankton tows are taken in the deep hole region of each lake at the same time and location as other limnological sampling; zooplankton samples are preserved in 70% ethanol for later processing. Samples are usually collected with standard tow depths on most dates (e.g., 20 meters for Lake Mendota) but not always, so tow depth is recorded as a variate in the database. Crustacean species are identified and counted for Mendota and Monona and body lengths are recorded for a portion of each species identified (see data protocol for counting procedure); samples for Wingra and Fish lakes are archived but not routinely counted. Numerical densities for Mendota and Monona zooplankton samples are reported in the database as number or organisms per square meter without correcting for net efficiency. [Net efficiency varies from a maximum of about 70% under clear water conditions; net efficiency declines when algal blooms are dense (Lathrop, R.C. 1998. Water clarity responses to phosphorus and Daphnia in Lake Mendota. Ph.D. Thesis, University of Wisconsin-Madison.)] Organism densities in number per cubic meter can be obtained by dividing the reported square-meter density by the tow depth, although adjustments for the oxygenated depth zone during the summer and early fall stratified season is required to obtain realistic zooplankton volumetric densities in the lake's surface waters. Biomass densities can be calculated using literature formulas for converting organism body lengths reported in the databa
SBC LTER: Land: Hydrology: Stream discharge and associated parameters at Rincon Creek, Hwy 101 Culvert, North (RN01)
Stream Discharge and water temperature were collected with a Solinst Model 3001 LT Levelogger at Rincon Creek, Hwy 101 Culvert, North in the Santa Barbara coastal area (site ID: RN01). Data are reported hourly. Stage values were converted to discharge using a rating curve developed with stream channel cross-sections, roughness estimates and the HEC-RAS model.
Rodent declines track regional climate variability in North American drylands
Regional long-term monitoring can enhance the detection of biodiversity declines associated with climate change, improving future projections by reducing reliance on space-for-time substitution and increasing scalability. Rodents are diverse and important consumers in drylands, which cover ~45% of Earth’s land surface and face increasingly drier and more variable climates. Here, we analyzed abundance data for 22 rodent species across grassland, shrubland, ecotone, and woodland habitats in the southwestern USA. We captured two time series: 1995-2006 and 2004-2013 that coincide with phases of the Pacific Decadal Oscillation (PDO), which influences drought in southwestern North America. Regionally, rodent species diversity declined 20-35%, with greater losses during the later time period. Abundance also declined regionally, but only during 2004-2013, with losses of ~5% of animals captured. During the first time series (PDO wet phase), plant productivity outranked climate variables as the best regional predictor of rodent abundance for 70% of taxa, whereas during the second period (dry phase), climate best explained rodent abundance for 60% of taxa. Temporal dynamics in rodent diversity and abundance differed spatially among habitats and sites, with the largest declines in woodlands and shrublands of central New Mexico and Colorado. Both habitat type and phase of the PDO modulated which species were winners or losers under increasing drought and amplified interannual variability in drought. Fewer taxa were significant winners (18%) than losers (30%) under drought, but the identities of winners and losers differed among habitats for 70% of taxa. Our results suggest that the sensitivities of rodent species to climate contributed to regional declines in diversity and abundance during 1995 - 2013. Whether these changes portend future declines in drought-sensitive consumers in the southwestern USA will depend on the climate during the next major phase of the PDO.
Modal shift in North Atlantic seasonality during the last deglaciation
<p>Raw data for Brummer et al. (2020) published in Climate of the Past (https://doi.org/10.5194/cp-16-1-2020)</p>
A set of allsky camera images from latitude 55 degrees North
<p>A set of R G and B images from an allsky camera situated in Copenhagen, Denmark. The images have been darksubtracted and split into these 16-bit FITS format images. Each image is the sum of something like 9 PNG images which each, originally, were 14 bit images from a ZWO camera cmos detector.</p>
NAQPMS simulation results for analysis the effects of regional transport on haze in the North China Plain
<p>This directory contains data generated and used for paper "Effects of regional transport on haze in the North China Plain: transport of precursors or secondary inorganic aerosols".</p> <p>This dataset contains the observed and simulated aerosols components of Beijing and surrounding cities; regional sources of secondary inrganic aerosols in Beijing; sources of secondary inorganic aerosols under relative clean and polluted conditions; and average differences of regional contribution to secondary inorganic aerosols between clean and polluted conditions; and data for supporting figures.</p>
Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला). View of the ghāṭ from the north east, with resevoir, showing part of the modern dam.
<p>Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला). View of the ghāṭ from the north east, with resevoir, showing part of the modern dam.</p>
ESA-WOC North Atlantic Sea Surface Salinity maps from a multivariate combination of satellite and in situ surface measurements (2010-2018)
<p>We deliver here the daily sea surface salinity level 4 (SSS L4) product developed in the framework of the European Space Agency World Ocean Circulation project (ESA-WOC), covering the period 2010-2018. This product was obtained by adapting to a 1/10° North Atlantic grid the multidimensional optimal interpolation algorithm used within the Copernicus Marine Environment Monitoring Service to retrieve the global SSS multi-year dataset (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: MULTIOBS_GLO_PHY_REP_015_002, dataset_id: dataset-sss-ssd-rep-weekly). This algorithm interpolates SMOS observations and in situ SSS observations considering a space-time-thermal decorrelation function, estimated by including information from high-pass filtered daily SST data (Droghei et al., 2016; Buongiorno Nardelli, 2012). Here, we ingested the L3OS 2Q debiased daily valid ocean salinity values product from SMOS satellite, produced and disseminated by the Centre Aval de Traitement des Données SMOS (CATDS, 2017), OSTIA SST data (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) and CORA5.2 surface salinity values (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) as input data, and used CMEMS weekly SSS dataset to build our background field (linearly interpolating it in time between the two closest analysis dates, and upsizing to the 1/10° grid through a cubic spline). All other interpolation parameters were set as in Droghei et al. (2018). </p> <p> </p> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity, J. Atmos. Ocean. Technol., 29(6), 867–879, doi:10.1175/JTECH-D-11-00099.1, 2012.</p> <p>CATDS (2017). CATDS-PDC L3OS 2Q - Debiased daily valid ocean salinity values product from SMOS satellite. CATDS (CNES, IFREMER, LOCEAN, ACRI). http://dx.doi.org/10.12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface, J. Atmos. Ocean. Technol., 33, 1211–1223, doi:10.1175/JTECH-D-15-0194.1, 2016.</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084, 2018.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p>
Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: test datasets.
<p>We provide here the datasets used for the test and assessment of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>). </p> <p>The test dataset presented here includes different sets of co-located temperature and salinity vertical profiles: </p> <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units)</li> </ul> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, <em>submitted</em>.</p> <p>Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p> <p>Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.</p> <p> </p>
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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.