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1,599 results for “US”
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - SARI SET Surface Water level data from Salt River Bay National Historical Park and Ecological Preserve, St. Croix, US Virgin Islands.
Surface water level data (m) was collected in Salt River Bay National Historic Park and Ecological Preserve (SARI) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - Mary's Point SET Surface Water level data from Virgin Islands National Park, St. John, US Virgin Islands
Surface water level data (m) was collected in Virgin Islands National Park, Mary's Point (MARY) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - Water Creek SET Surface Water level data from Virgin Islands National Park, St. John, US Virgin Islands
Surface water level data (m) was collected in Virgin Islands National Park, Water Creek (WACR) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
In Situ Carbon Dioxide and Methane Flux Measurements Using Opaque Chambers in a Sedge Fen Wetland (US-Los Lost Creek AmeriFlux Site, Wisconsin, Summer 2015)
This dataset contains in situ measurements of carbon dioxide (CO₂) and methane (CH₄) fluxes, collected using opaque closed chambers at the US-Los Lost Creek AmeriFlux fen shrub wetland site in northern Wisconsin during summer 2015. These data were collected to characterize variability of day-time mid-summer methane soil fluxes across the sampling area of the eddy covariance flux tower.
Interagency Ecological Program and US Fish and Wildlife Service: Juvenile/Larval Fish and Zooplankton collections at Liberty Island, California 2002-2005 & 2013-2019
The U.S. Fish and Wildlife Service (USFWS) Lodi Fish and Wildlife Office (LFWO) Delta Juvenile Fish Monitoring Program (DJFMP) has intermittently sampled Liberty Island with various equipment since 2002. Liberty Island was a reclaimed agricultural island until it flooded in 1997-1998 and we subsequently left to passively restore as a tidally influenced wetland. Larval trawls and beach were the only sampling methods used during both early (2002-2005) and late (2009-2019) sampling periods. The main purpose of the sampling was to gather information about fish presence and abundance in Liberty Island during the passive restoration, with an emphasis on reproductive and early life-stages of native species. Larval trawls, or tow nets, were used to catch larval fish in 2004-2005 and again from 2013-2019. Zooplankton nets were used 2013-2019. Water quality measurements were collected alongside each tow.
Meta-analysis of ecosystem services associated with oyster restoration on the Eastern and Gulf coasts of the US
We conducted a meta-analysis to systematically quantify the success and uncertainty of oyster reef restoration for a suite of biological, biogeochemical, and physical ecosystem services relative to both degraded and natural reference habitats. We focused on the eastern oyster, Crassostrea virginica. To evaluate whether restored eastern oyster reefs enhance ecosystem services relative to unaltered, degraded habitats and whether restored reefs provide ecosystem services equivalent to reference reefs, we synthesized data and calculated log response ratios for 245 restored-degraded reef pairs and 136 restored-reference reef pairs from 106 publications collected along 3500 km of U.S. Gulf of Mexico and Atlantic coastlines.
ReFAB 10,000 Year Statistical Estimate of Aboveground Woody Biomass, Midwest US, Level 2
How terrestrial biomass changed before the advent of industrial society is a major gap in our understanding of the Earth's carbon cycle. Here, we archive data used to reconstruct 10,000 years of aboveground woody biomass across the US Upper Midwest using statistical models based on historical forest surveys and fossil pollen assemblages. From our analyses we document a 5,000 year long carbon sink into vegetation, primarily caused by the range expansion of two late-successional species into the region during the late Holocene. The importance of such large slow-growing tree species in storing carbon during the pre-industrial past argues for protecting similar species in wild forests today. This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.
Working time, energy throughput and value added embodied in production, consumption and trade by subsectors for the US, the EU, China and rest of the world (2011)
<p>This repository contains the data needed to reproduce the results in:</p> <p>Pérez-Sánchez, L., Velasco-Fernández, R., Giampietro, M., The international division of labor and embodied working time in trade for the US, the EU and China, Ecological Economics. <a href="http://doi.org/10.1016/j.ecolecon.2020.106909">https://doi.org/10.1016/j.ecolecon.2020.1069097</a></p> <p>Sources of data are specified in the dataset (under tab "references")</p> <p> </p>
US Atlantic and Gulf Coast Annual Wetland Land Cover and Change Maps, 1985 to 2022
<h3>This dataset is associated with the following article published in Remote Sensing Applications: Society and Environment, which can be accessed here: https://doi.org/10.1016/j.rsase.2024.101392</h3> <p>Shortly after publishing version 2, errors in the map projections were identified and corrected. Please use version 3 instead of version 2.</p> <p>Updates to version 2 were as follows:</p> <ul> <li>Includes watersheds in Texas that were not included in Version 1.</li> <li>A color map (using ArcPro) was added for improved interpretation.</li> <li>A sub-pixel scale offset in the change type map, related to map projection errors, was corrected.</li> </ul> <h2><strong>Mapping Coastal Wetland Changes from 1985 to 2022 in the US Atlantic and Gulf Coasts using Landsat Time Series and National Wetland Inventories</strong></h2> <p>Courtney A. Di Vittorio<sup>1</sup>, Melita Wiles<sup>2</sup>, Yasin W. Rabby<sup>2</sup>, Saeed Movahedi<sup>2</sup>, Jacob Louie<sup>1</sup>, Lily Hezrony<sup>1</sup>, Esteban Coyoy Cifuentes<sup>1</sup>, Wes Hinchman<sup>1</sup>, Alex Schluter<sup>1</sup></p> <p><sup>1</sup>Department of Engineering, Wake Forest University, Winston-Salem, North Carolina, USA.</p> <p><sup>2</sup>Department of Statistics, Wake Forest University, Winston-Salem, North Carolina, USA.</p> <h3>Abstract</h3> <p>The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), the widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution, and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses Google Earth Engine and the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing imagery with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The average overall accuracy of the algorithm is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsat-derived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the magnitude of severe changes matches that of the existing inventory and the magnitude of the moderate changes matches that of the more comparable product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.</p>
PsPM-FER01: PSR, SCR, ECG and respiration measurements from a discriminant delay fear conditioning task with visual CS and electrical US.
<p>This dataset includes pupil size response (PSR), skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information and shock expectancy ratings at the end of the experiment for 30 healthy unmedicated participants (12 males and 18 females aged 23.9+/-4.4 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. Fear acquisition consisted of 10 CS- and 32 CS+ trials (16 CSa+/16 CSb+). Half of the CS+ trials were paired with an electric shock. CS were colored triangles (yellow/red/blue). US consisted of a 500 ms train of 250 square pulses with individual pulse width of 0.2 ms. SOA between the CS onset and US was 3.5 seconds. CS and US co-terminated. The ITI was randomly determined as discrete values between 7-11 seconds (mean 9 seconds).</p> <p> </p>
Downscaled 8km March Snow Water Equivalent Estimates for the Western US, 1901-2010
<p>Downscaled estimates of March mean snow water equivalent at approximately 8km resolution across the western United States for the years 1901-2010. Data downscaled from the CERA-20c reanalysis using UA-SWE daily observations. Downscaled data using both the CERA-20c ensemble mean as well as each individual ensemble member as predictors are included. Units are in millimeters of snow water equivalent.</p>
Annual and 33-year water body frequency maps of the contiguous US from 1984 to 2016
<p>There are 50 5-by-5 degree tiles covering the entire CONUS. In each zipped tile folder, there are 33 annual water body frequency images (e.g. freq_2016_-070_040.tif), one 33-year water body frequency image (e.g. 33YearFreq_1984_2016_-070_040.tif), and one 33-year good observation image (e.g. 33YearGoodObs_1984_2016_-070_040.tif). </p> <p>The annual and 33-year water body frequency are defined as the ratio of water observations to total good observations in a year and in 1984-2016, respectively. The frequency stored in these image is compressed in 8 bits (1-255). To get the frequency in floating point (0-1.0), use the equation: f = (F-1)/254.0, where F is in 8 bits while f is in floating point. </p> <p>For each Landsat image, the CFmask band was used as a quality control band to remove the cloud, cloud shadow, and snow pixels. The solar azimuth and zenith angles of each image were used along with the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) to simulate terrain shadows and remove them. The remaining pixels were considered as good observations that can be used for water body detection. The total good observation number during 1984-2016 is stored in the 33-year good observation images.</p> <p>Projection is WGS84, while spatial resolution is 0.000269494585236.</p> <p>For additional details, please go to our lab server (http://mangrove.rccc.ou.edu/eomfftp/conus_water_dataset/).<br> To use this data, please cite our articles: <br> Zou, Z., Xiao, X., Dong, J., Qin, Y., Doughty, R.B., Menarguez, M.A., Zhang, G., Wang, J. Divergent Trends of Open Surface Water Body Area in the Contiguous United States from 1984 to 2016, PNAS, doi: 10.1073/pnas.1719275115</p> <p>Zou, Z., J. Dong, M. A. Menarguez, X. Xiao, Y. Qin, R. B. Doughty, K. V. Hooker, and K. David Hambright (2017), Continued decrease of open surface water body area in Oklahoma during 1984-2015, Sci Total Environ, 595, 451-460, doi: 10.1016/j.scitotenv.2017.03.259.</p>
Parameters for PITRI Precipitation Temporal Disaggregation over continental US, Mexico, and southern Canada, 1981-2013
<p>This dataset contains parameter values for the Precipitation Isosceles Triangle (PITRI) precipitation disaggregation method (Bohn et al., 2019) over the CONUS+Mexico domain (southern Canada, the continental US, and Mexico; 14.65 - 53° N latitude, 65-125° W longitude), at 1/16° (6 km) spatial resolution. There are two parameters: "dur" (mean event duration [minutes]) and "t_pk" (mean time of peak precipitation intensity [minutes from beginning of day]). In each land grid cell, each parameter has 12 climatological mean monthly values for the period 1981-2013.</p> <p>This dataset contains 2 NetCDF-format files:</p> <ul> <li>domain.CONUS_MX.L2015.nc - this contains parameters over the entire CONUS+Mexico domain, using the land mask of the Livneh et al. (2015) daily meteorology dataset.</li> <li>domain.USMX.L2015.nc - this contains the same parameters, but clipped to exclude Canada (to be consistent with datasets that cover only that part of the domain).</li> </ul> <p>These files are structured as input "domain" files for 2 applications:</p> <ul> <li>MetSim meteorology simulator (https://github.com/UW-Hydro/MetSim/releases/tag/2.0.0_alpha; Bennett et al., 2018). The PITRI algorithm has been implemented as an option in MetSim. To use this algorithm within MetSim, set the "prec_type" option to "triangle" or "mix" in the configuration file. The "mix" option is a blend of the "uniform" (previous) method and the "triangle" method that fixes biases in snow accumulation rates yielded by the "triangle" method in some climates. "mix" uses the "uniform" method on days for which minimum daily temperature falls below 0 C, and "triangle" method on all other days.</li> <li>Variable Infiltration Capacity (VIC) model, release 5.0 and later (Liang et al., 1994; Hamman et al., 2018; https://github.com/UW-Hydro/VIC). VIC does not use the PITRI parameters, but does use the other variables such as mask, elevation, area, etc. For VIC to use the output of MetSim (disaggregated meteorological fields) as input, VIC needs to use the same domain file as was used in MetSim.</li> </ul> <p>Algorithm details can be found in the following paper, which should be cited if you use this dataset:</p> <p>Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019: A deterministic approach for approximating the diurnal cycle of precipitation for use in large-scale hydrological modeling, Journal of Hydrometeorology 20(2), 297-317, doi: 10.1175/JHM-D-18-0203.1.</p>
11 May 2024 Superstorm Ionospheric Observations in the Continental US
<p><strong>May11_150km.mat: </strong> MATLAB save file - Ionospheric total electron content data from worldwide GNSS receivers, processed by MIT Haystack Observatory through the Millstone Hill Geospace Facility. Data is line of sight and conversion assumes a non-standard 150 km ionospheric pierce point. </p> <p>Direct permanent link: Anthea Coster, MIT/Haystack Observatory. (2024) Data from the CEDAR Madrigal database. Available from https://w3id.org/cedar?experiment_list=experiments4/2024/gps/11may24&file_list=los_20240511.001.h5</p> <p>GPS TEC data products and access through the Madrigal distributed data system are provided to the community by the Massachusetts Institute of Technology under support from US National Science Foundation grant AGS-1952737. Data for the TEC processing is provided from the following organizations: UNAVCO, Scripps Orbit and Permanent Array Center, Institut Geographique National, France, International GNSS Service, The Crustal Dynamics Data Information System (CDDIS), National Geodetic Survey, Instituto Brasileiro de Geografia e Estatística, RAMSAC CORS of Instituto Geográfico Nacional de la República Argentina, Arecibo Observatory, Low-Latitude IonosphericSensor Network (LISN), Canadian High Arctic Ionospheric Network, Institute of Geology and Geophysics, Chinese Academy of Sciences, China Meteorology Administration, Centro di Ricerche Sismologiche, Système d'Observation du Niveau des Eaux Littorales (SONEL), RENAG: REseau NAtional GNSS permanent - https://doi.org/10.15778/resif.rg, GeoNet - the official source of geological hazard information for New Zealand, Finnish Meteorological Institute, SWEPOS - Sweden, Hartebeesthoek Radio Astronomy Observatory, TrigNet Web Application, South Africa, Australian Space Weather Services, RETE INTEGRATA NAZIONALE GPS, Estonian Land Board, TU Delft, Western Canada Deformation Array, EUREF Permanent GNSS Network, GeoDAF: Geodetic Data Archiving Facility, African Geodetic Reference Frame (AFREF), Kartverket - Norwegian Mapping Authority, Geoscience Australia, IGS Data Center of Wuhan University, Pacific Northwest Geodetic Array, Nevada Geodetic Laboratory, Earth Observatory of Singapore, National Time and Frequency Standard Laboratory - Taiwan, and Korea Astronomy and Space Science Institute.</p> <p><strong>teczero.mat:</strong> MATLAB save file - 5-minute averaged background median TEC map, derived from May11_150km.mat file.</p> <p><strong>TECmaps0.m:</strong> MATLAB script - plotting script, employing teczero.mat, and used for Figure 3 and Figure 4B of manuscript.</p> <p><strong>gps_map.mat:</strong> MATLAB save file - source data for Figure 4A of manuscript: median vertical TEC map over 2-minute interval 0207-0208 UTC on 2024-05-11. </p> <p><strong>Fig4a_map.m: </strong>MATLAB script - plotting script, employing gps_map.mat, and used for FIgure 4A of manuscript.</p> <p><strong>cntr.m</strong>: MATLAB script - coastline plotting function.</p> <p><strong>burst.m</strong>: MATLAB script - plots individual LOS TEC data showing TEC bursts. Saves to "bursts.mat". Inputs "May11_150km.mat".</p> <p><strong>bursts.mat</strong>: MATLAB save file - individual TEC burst results.</p> <p><strong>1 Missouri_Skies_-_All-Sky_Fisheye_Missouri_Skies_Fishey_2030UT_2127UT.mp4: </strong>MP4 image file. Fisheye lens image of aurora from Missouri during 2024-05-11 storm. Used in Figure 2 of manuscript.</p> <p><strong>{amt,att,bmt,bot,het,hmt,nmt,omt,pat,sum,vmt}240511.g.001.hdf5</strong> - MagStar magnetometer measurements in HDF5 self-documenting file format, used for magnetometer figures in manuscript. Downloaded from the Madrigal database system. Direct permanent link: Jenn Gannon, Paul E. Meade, Eframir Franco-Diaz, Computational Physics. (2024) Data from the CEDAR Madrigal database. Available from https://w3id.org/cedar?experiment_list=experiments/2024/het/11may24&file_list=het240511g.001.hdf5. (Replace "het" with appropriate 3-letter site name from above list).<br><br></p>
LAPSO PM2.5 over Europe and US
<p>Estimating Near-Surface Concentrations of Major Air Pollutants From Space: A Universal Estimation Framework LAPSO</p> <p>Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM2.5, PM10, O3, NO2, SO2, and CO), we propose a robust estimation framework called learning air pollutants from satellite observations (LAPSO). The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models (CTMs), lower hardware requirements, and a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites ( R2> 0.8 in polluted areas and uncertainty ≪5 μg/m3 for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOspheric monitoring instrument (TROPOMI) onboard the Sentinel-5P demonstrated marginally better performance for estimating PM2.5. Although the selection of satellite observations did not significantly affect the results of O3 estimation, the number and spatial sampling density of in situ sites imposed large impacts on O3 estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.</p>
LAGOS-US LANDSAT: Data module of remotely-sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020
This data package, LAGOS-US LANDSAT, is one of the extension data modules of the LAGOS-US platform that provides six water quality estimates (chlorophyll, Secchi depth, dissolved organic carbon, total suspended solids, turbidity, and true water color) from remote sensing for lakes ≥ 4 ha in the conterminous U.S. (48 states plus the District of Columbia) for the years 1984-2020. These estimates are generated through machine learning models on in-lake water quality matchups from LAGOS-US LIMNO with Landsat 5, 7, and 8 whole lake median reflectance values and pixel-wise band ratios that are subsequently used to make predictions across the U.S. The LANDSAT module contains remotely sensed reflectance values for 136,977 of the 137,465 lakes ≥ 4 ha from the LAGOS-US research platform. Within the module are a total of 45,867,023 sets of reflectance values, a matchup dataset with a window of up to 7 calendar days with in situ data, and associated water quality parameter predictions for each reflectance set. Additional quality control flags are provided for predictions indicating whether reflectance extractions included negative values, the percent of the maximum pixels ever retrieved for that lake that the predictions are based on, and whether there are shared calendar day predictions due to scene overlap.
Interagency Ecological Program and US Fish and Wildlife Service: Beach Seine Efficiency Study, collected by the Delta Juvenile Fish Monitoring Program, 2013-2018
Within the San Francisco Estuary, beach seining has been used since the 1970s by the U.S. Fish and Wildlife Service's Delta Juvenile Fish Monitoring Program (DJFMP) to monitor the relative abundance and distribution of juvenile Chinook salmon and other fishes of management concern occurring in near shore habitats. Over 2,000 seine samples are currently being collected throughout the San Francisco Estuary annually. Data from these surveys are used to make various water operation decisions and help inform managers on the status of fish populations within the San Francisco Estuary. By not accounting for the variability of species- and size-specific efficiency of beach seines across environmental gradients, the metrics developed by the DJFMP are likely underestimated and biased to an unknown extent. Consequently, the ability of the DJFMP to document the true occupancy of fishes for water operations and population vicissitude may be inhibited. Therefore, the first objective of this study is to estimate the species- and size-specific capture efficiency of DJFMP beach seines for fishes occurring in near shore habitats within the San Francisco Estuary. The second objective is to model the observed efficiency estimates using habitat variables to allow managers to account for incomplete detection when estimating fish abundance and occupancy from DJFMP beach seine data.
Plant survey data from LTER and other US grasslands
Understanding factors that influence ecological stability is a key question in ecology. Population ecology has highlighted that synchrony within a species across locations is an important indicator of species stability. Community ecology, in contrast, has highlighted that asynchrony between species within a location may enhance the stability of aggregate properties (such as total productivity). We compiled LTER and other data across 20 metacommunities in grassland and coastal marine biomes to integrate population and community approaches to synchrony to understand drivers of ecosystem stability at different scales. All datasets feature long-term (10-34 years) measurements of the primary producer community across multiple (6-49) discrete plots, with taxonomic identifications to species in most or all cases. This data package contains data from grasslands sites: Hayes, Kansas; Jasper Ridge Biological Preserve, Jornada LTER, Kellogg Biological Station LTER, Short Grass Steppe LTER, Konza Prairie LTER, Cedar Creek LTER and Sevilleta LTER.
LAGOS-US NETWORKS v1.0: Data module of surface water networks characterizing connections among lakes, streams, and rivers in the conterminous U.S
Knowing the degree of surface water connectivity among aquatic ecosystems can help scientists better understand and predict the movement of materials and biota across ecosystems. Methods to quantify surface water networks that include lake and stream connections at broad spatial scales are rare because it is difficult to balance accurate estimates of surface water connectivity and computational challenges. The LAGOS-US NETWORKS (NETS) module contains surface connectivity metrics for lake networks across the conterminous United States. We applied a graph theory approach to identify lake networks (i.e. a set of lakes connected by streams either upstream, downstream, or both) created from the medium resolution NHD lakes, streams, and rivers and subsequently derive surface water connectivity metrics for lakes and networks. Using this approach, we created a total of 898 networks that include 86,511 lakes. The NETS module includes a table with metrics for connections between lakes (both upstream and downstream), dams, network position, and whole networks. NETS also includes a flow table and bidirectional and unidirectional distance tables that provide the distances between every pair of connected lakes.
Interagency Ecological Program and US Fish and Wildlife Service: Enhanced Delta Smelt Monitoring Program Experimental Larval Survey Data 2018-2019
The United States Fish and Wildlife Service’s (USFWS) Experimental Larval Survey was designed and implemented by the Enhanced Delta Smelt Monitoring Program to assess alternative sampling methods for monitoring the larval life stages of the federally endangered Delta Smelt (Hypomesus transpacificus) within the San Francisco Estuary, California, USA. From 2018 to 2019, four different sampling methods (Beach Seine, Manta Trawl Net, 20-mm Surface Trawl Net, 20-mm Midwater Trawl Net) nested in time and space were used to estimate the occupancy rate, relative density, and size distribution of larval Delta Smelt across sampling methods and water depth strata within the San Francisco Estuary. For more information on the Lodi USFWS Office and the Enhanced Delta Smelt Monitoring Program: https://www.fws.gov/lodi/.
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.