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The Jefferson Project 2019 water quality data from three vertical profiler stations in Lake George, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project deployed three vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2019. These vertical profiler stations are named VP_AnthonysNose, VP_CalvesPen, and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data are transferred in near real-time to an off-site database for monitoring and review. The data provided here have undergone data correction by Jefferson Project researchers.
The Jefferson Project 2020 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2020, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2020 weather data from seven surface weather stations on Lake George, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2020, The Jefferson Project had seven weather monitoring stations around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidiity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, and WX_Glenburnie. The stations have a sensor payload that include some combination of the following sensors: Rotronic HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, Li-Cor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, and N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which has undergone data correction and downsampling to an hourly frequency.
Time series of in situ Uv-Vis absorbance spectra and high-frequency predictions of total and soluble Fe and Mn concentrations measured at multiple depths in Falling Creek Reservoir (Vinton, VA, USA) in 2020 and 2021
High-frequency measurements of light absorbance were collected at multiple depths in Falling Creek Reservoir (FCR; Vinton, VA, USA) using a s::can Spectrolyser UV-Visible spectrophotometer coupled with a multiplexor pumping system. The system pumps water samples from individual depths into a flow-through cuvette where the UV-vis absorbance spectra of the sample are measured by the spectrophotometer. The system used in our study collected measurements of light absorbance every 2.5 nm wavelengths from 200 nm to 732.5 nm (optical path length of 10 mm) approximately at an hourly time step for seven monitoring depths in the reservoir. Data was collected during two periods; the first deployment (16 October to 9 November 2020) was to observe changes in Fe and Mn concentrations before, during, and after reservoir fall turnover and the second deployment (26 May to 21 June 2021) was to observe the effects of engineered hypolimnetic oxygenation on Fe and Mn concentrations. Partial least squares regression models were developed to generate predictions of total and soluble Fe and Mn concentrations based on the correlation between absorbance spectra and sampling data.
Concentration of nutrients in water samples collected from the Upper Clark Fork River (Montana, USA) during water year 2019 (1 Oct 2018 - 30 Sept 2019)
The umbrella LTREB monitoring project generating these data is conducted separately and complementarily to the $200 million-dollar (USD) superfund project for ecological restoration of the Upper Clark Fork River (UCFR), associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the Upper Clark Fork River includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river's floodplain closest to contaminant sources. The UCFR Long Term Research in Environmental Biology (LTREB) project includes bi-weekly water quality monitoring across a 200-km gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The UCFR LTREB monitoring project is conducted within the first 200 km of the Upper Clark Fork River and associated tributaries located in western Montana. The current monitoring program began in 2017 and will be completed in the year 2023, with likely funding extension to 2028. Surface water samples represented in this data product are collected from fourteen sites along the mainstem of the UCFR, and one site representing a major tributary to the UCFR. Water samples are collected at each monitoring site in triplicate and filtered with a 0.7-µm glass fiber filter. Nutrient samples are analyzed using a spectrophotometric flow injection analyzer (AP2) for nitrate (NO3-N), soluble reactive phosphorus (SRP, as representative of PO4-P), and ammonium (NH4-N) concentrations reported in mg/L. The analysis-ready data of this dataset therefore represent Quality Assurance and Quality Control (QAQC) processed NH4-N, SRP, and NO3-N concentrations from fourteen sites along the mainstem of the UCFR and one tributary, collected in water year 2019 (1 Oct 2018 - 30 Sept 2019).
Densities and cover data for intertidal organisms from an LTREB project in the Gulf of Maine, USA, from 1996 to 2023.
Experimental clearings in macroalgal (Ascophyllum nodosum) stands were made in 1996 to determine if mussel beds and macroalgal stands on protected intertidal shores in New England represent alternative community states. Uncleared control plots and four sizes of circular clearings (1m, 2m, 4m and 8m in diameter), which mimicked ice scour events, were established in A. nodosum stands at 12 sites on Swan’s Island, Maine, USA. The purpose of these datasets is to provide access to data on densities and percentage cover in the 60 experimental plots from 1996 to 2023. Earlier versions of the data prior to 2007 can be found in Ecological Archives ( E087-047 and E089-032). The current EDI version includes corrections of errors in the versions in Ecological Archives. Data include densities of mussels (Mytilus edulis), an herbivorous limpet (Testudinalia testudinalis), herbivorous snails (Littorina littorea, Littorina obtusata), a predatory snail (Nucella lapillus), a barnacle (Semibalanus balanoides), and fucoid algae (Ascophyllum nodosum and Fucus vesiculosus), and percentage cover by mussels, barnacles, fucoids and other sessile organisms. Research was funding by NSF's LTREB program.
Long-term (1935-2019) tree population data from remeasurements of a large network of permanent study plots in old-growth forest, Dukes Research Natural Area, Marquette Co., MI, USA
The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 0.2-acre (~0.08 ha) permanent monitoring (CFI) plots. This package includes tree censuses for subsets of CFI plots conducted in 1935, 1948, and 1974-1980, and repeated censuses with mapped stems from 1989 to 2019. This 84-year record constitutes one of the longest repeated-measurement, permanent-plot data-sets for old-growth temperate forest.
Ice timing (formation or ice-on and clearance or ice-off) for Yellowstone Lake, Wyoming, USA (1927-2022)
Lakes are sentinels of environmental change. In cold climates, lake ice phenology–the timing and duration of ice cover during winter–is a key control on ecosystem function. Ice phenology appears to be driven by a complex interplay between physical characteristics and climatic conditions. Under climate change, lakes are generally freezing later, melting out earlier, and experiencing a shorter duration of ice cover; however, few long-term records exist for large, high-elevation lakes which may be particularly vulnerable to climate impacts. Here, we provide an ice phenology data over the last century (1927-2022) for North America’s largest high-elevation lake—Yellowstone Lake.
Water Monitoring of the Choptank River and Pocomoke River, Maryland, USA
Water monitoring at four locations on the Choptank River and four locations on the Pocomoke River in Maryland, U.S.A., was conducted from 2021 through 2023. Funding and scientific rationale were provided by the National Science Foundation grant 2049073 (“Resolving Sediment Connectivity between Rivers and Estuaries by Tracking Particles with their Microbial Genetic Signature”). The monitoring locations were chosen to measure estuary dynamics from the tidal freshwater zone through the mesohaline estuary. Parameters measured included water temperature, water level, water conductivity (reported as specific conductivity), water turbidity, and water velocity.
Root Biomass, Fine Root Production, Soil Mass, and Soil pH in Limed and Control Plots at the Woods Lake Watershed, Adirondack Park, NY, USA, 2021-2022
In 1989, 6.89 Mg/ha of pelletized lime (CaCO3) was applied by helicopter to two subcatchments at the Woods Lake Watershed in Adirondack Park, New York, USA to ameliorate ecosystem acidification. Two unlimed (control) subcatchments were paired with limed subcatchments. In the same year, 99 permanent plots (20 m x 20 m) were established. Between 2008 and 2010, tree inventory and soil physicochemical measurements were made in five plots in each of the four subcatchments (20 plots total). This dataset contains soil physicochemical properties (dry mass, depth, and pH); root biomass (<1 mm, 1-2 mm, and >2 mm diameter); and annual fine root production (<1 mm and 1-2 mm) measurements made between 2021 and 2022 in 19 of these same plots (5 plots per control subcatchment and 4 or 5 plots per limed subcatchment). Data include measurements for all properties for Oe, Oa, and 0-10 cm mineral soil samples collected from 5 locations within each plot.
Crustacean and rotifer density and biomass for Beaverdam Reservoir, Falling Creek Reservoir, Carvins Cove Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2014-2025
Crustacean and rotifer density and biomass were measured from 2014 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Falling Creek Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Falling Creek, Carvins Cove, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia. The dataset consists of integrated vertical tow samples from the whole water column, just the epilimnion, and just the hypolimnion (as the difference between the full water column and epilimnion tows), as well as discrete depth measurements collected with a Schindler trap. Most samples were collected at the deepest site of each reservoir adjacent to the dam. Sampling frequency and duration varied among reservoirs and years and included weekly to monthly routine monitoring as well as intensive 24-hour sampling campaigns. In 2014-2016, zooplankton samples were collected approximately fortnightly in the spring, summer, and autumn months at Beaverdam Reservoir, Carvins Cove Reservoir, and Gatewood Reservoirs. Falling Creek Reservoir samples were collected weekly to monthly in spring and summer 2014, and Spring Hollow Reservoir samples were collected approximately fortnightly in the spring, summer, and autumn months of 2015 and 2016. In 2019, zooplankton samples were collected approximately weekly to monthly from April to November at Beaverdam Reservoir and April to September at Falling Creek Reservoir. In 2020, zooplankton samples were collected approximately weekly to monthly from May to December at Beaverdam Reservoir and June to September at Falling Creek Reservoir. In 2021, zooplankton were collected monthly from M
High-frequency time series of inorganic carbon chemistry in the Upper Clark Fork River (Montana, USA) during the summer of 2021.
This dataset was collected in the Upper Clark Fork River, MT from July to August in 2021. The dataset contains high-frequency measurements of dissolved oxygen, alkalinity, partial pressure of carbon dioxide and other environmental variables.
Water quality in restored urban streams in Lexington, KY, USA
Stream-water grab samples were collected periodically from sampling locations upstream and downstream of restored stream reaches in Lexington, KY, USA, and analyzed for a suite of water quality characteristics including nitrate, cations, and pH. Study sites included streams receiving riparian reforestation or other conservation, as well as streams restored using a natural channel design approach. The goal of this sampling program was to evaluate to what extent stream restoration interventions can influence stream-water quality in an urban context.
Water chemistry time series for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2024
Depth profiles of dissolved organic and inorganic carbon and total and dissolved nitrogen and phosphorus were sampled from 2013-2024 in five drinking water reservoirs in southwestern Virginia, USA. The five drinking water reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). 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, and Gatewood Reservoir is a drinking water source for the town of Pulaski, Virginia. The dataset consists of depth profiles of water chemistry samples measured at the deepest site of each reservoir adjacent to the dam. Additional water chemistry samples were collected at a gauged weir on Falling Creek Reservoir's primary inflow tributary, as well as multiple upstream, inflow, and outflow sites at Falling Creek Reservoir 2014-2024 and Beaverdam Reservoir in 2019, 2020, and 2022. Inflow sites at Carvins Cove Reservoir were sampled from 2020-2024, and additional within-reservoir sites were sampled in 2021-2024. The water column samples at Falling Creek Reservoir and Beaverdam Reservoir were collected approximately fortnightly from March-April, weekly from May-October, and monthly from November-February. Water column samples at Carvins Cove Reservoir were collected approximately fortnightly from May-August in most years, and approximately fortnightly from 2014-2016 in Gatewood and Spring Hollow Reservoirs, though sampling frequency and duration varied among reservoirs and years. A few additional samples collected in 2025 from Falling Creek Reservoir and Carvins Cove Reservoir are included in this dataset as they were analyzed with 2024 samples.
Survival Data of Strengthened and Non-Strengthened Oysters on Two Restored Reefs in Georgia, USA
The eastern oyster, Crassostrea virginica, is known to respond to chemical cues from their predators by strengthening their shell in defense. The chemical cues homarine and trigonelline, found in the urine of blue crabs, Callinectes sapidus, are two metabolic waste products known to cause this inducible defense in juvenile oysters. We tested whether this shell strengthening defense is beneficial for juvenile oysters in a restored reef setting by inducing oyster spat with chemical cues, placing them onto a restored reef and measuring their survival for 50-100 days. The first reef location is a previously restored reef (approximately 10 years old) with limited physical exposure to wind waves and boat wake. Juvenile oysters were placed at this site in September of 2021 and survival was measured for 49 days. The second reef location is a newly restored oyster reef (less than 1 year old) that acts as a living shoreline, with significant exposure to boat wake and wind waves. Juvenile oysters were protected from predation using mesh wrapping with an opening of 1 square cm and placed at the reef site in April 2023, where survival was monitored for 103 days. Site locations: Reef 1 (dock site) - 31.988948°, -81.024001° Reef 2 (living shoreline reef) - 32.067957°,-80.985005°
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC1 SET Surface Water level data from in Biscayne National Park, Florida, USA (2016-2025)
Surface water level data (m) was collected in Biscayne National Park (BISC) 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 2016 to 2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 1, known as BISC-SET-1 or BISC1. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC2 SET Surface Water level data from in Biscayne National Park, Florida, USA (2017-2025)
Water level data (m) was collected in Biscayne National Park (BISC) by the National Park Service - 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 2017-2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 2, known as BISC-SET-2 or BISC2. This data-package is complete.
Time series of total metal and nutrient loads into Falling Creek Reservoir in southwestern Virginia, USA from 2019-2024
Metal and nutrient loads were calculated from 2019-2024 from the inflow stream to Falling Creek Reservoir (FCR), a drinking water reservoir located in Vinton, Virginia, USA. The reservoir is owned and operated by the Western Virginia Water Authority and is managed as a secondary drinking-water source for the city of Roanoke, VA. Only Fe, Mn, and nutrients (TN and TP) were analyzed and calculated in 2019. The full suite of metals (Li, Na, Mg, Al, K, Ca, Fe, Mn, Cu, Sr, Ba) and nutrients were analyzed from 2020-2024. The loads that were collected using an ISCO automated sampler located at the main inflow tributary to FCR. Sampling frequency was approximately fortnightly from spring to fall (March - November). Load calculations were performed using the calculated cumulative flow over the sampling period from the ISCO and the analyzed total metal and nutrient concentrations. Please note we are publishing this data package before the nutrient samples have been analyzed, but will be included in later versions.
Metabolism estimates from dissolved oxygen and inorganic carbon in the Judith River Watershed, MT, USA, 2019-2021.
This dataset provides necessary supporting data and models for the manuscript titled "Stream corridor structure drives patterns in lateral inorganic carbon inputs and CO2 emissions in agricultural headwaters". The entire dataset consists of sensor data, alkalinity and metabolism estimates from oxygen and carbon models, collected in three reaches located in the Judith River Watershed, MT. The sensor data include partial pressure of carbon dioxide in water, dissolved oxygen and temperature. At each reach, we established a two station approach, meaning two pairs of sensor suits were distributed upstream and downstream. In addition to data products, we also provide R packages for metabolism models.
Data for: Techno-economic analysis of a novel laccase production process utilizing perennial biomass and the aqueous phase of bio-oil, Iowa, USA 2023-2025
This dataset contains the experimental design, measurements, and derived variables used to parameterize a techno‑economic model of laccase production via two‑stage solid‑state fermentation of prairie biomass with bio‑oil aqueous phase induction. It includes nutrient screening data for Pleurotus ostreatus growth on prairie biomass with alternative nitrogen sources and a corn‑steep solids dose series; factorial/response‑surface experiments varying substrate bed depth, substrate‑to‑inoculum (S:I) ratio, and pre‑induction growth time; and time‑resolved induction measurements. For each run and replicate, the data record the full set of spectrophotometric absorbances at 0–210 s, fitted slopes and r-square values, dilution and volume factors, and laccase activities normalized per mL and per gram of biomass, alongside the exact culture timings and environmental conditions used in the ABTS assay at 420 nm. Results tables provide the fitted central‑composite design model terms (coefficients, F‑statistics, and p‑values) used directly as inputs to the minimum laccase selling price (MLSP) calculations, together with the underlying per‑condition raw results.
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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.