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194 results for “Tributaries”
Discharge time series for the primary inflow tributary entering Falling Creek Reservoir, Vinton, Virginia, USA 2013-2025
Discharge rates and water temperature of the primary inflow tributary into Falling Creek Reservoir (Vinton, Virginia, USA), also known as Tunnel Branch, were measured at a gauged weir on a 15-minute temporal resolution from May 2013 to December 2025. The gauged weir is located at 37.30858, -79.83494. Falling Creek Reservoir is a drinking water supply reservoir owned and managed by the Western Virginia Water Authority (WVWA). The dataset consists of water temperatures and discharge rates calculated from a pressure transducer deployed by the WVWA in a rectangular weir (15 May 2013 - 06 June 2019) and in a v-notched weir (07 June 2019 - 31 December 2025). From 07 June 2019 to 31 December 2025, water temperature and discharge data were also collected from a Virginia Tech-deployed (VT) pressure transducer installed in the same weir. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.
Manually-collected discharge data for multiple inflow and outflow tributaries at Falling Creek Reservoir, Beaverdam Reservoir, and Carvins Cove Reservoir, Virginia, USA from 2019-2025
Discharge rates at multiple inflow streams into Falling Creek Reservoir (Vinton, Virginia, USA), Beaverdam Reservoir (Vinton, Virginia, USA), and Carvins Cove Reservoir (Roanoke, Virginia, USA), and one outflow at Falling Creek Reservoir were measured manually using multiple methods from 2019-2025. Falling Creek Reservoir, Beaverdam Reservoir, and Carvins Cove Reservoir are owned and operated by the Western Virginia Water Authority as drinking water sources for Roanoke, Virginia. The dataset consists of discharge rates calculated using one of four methods: handheld flowmeter, salt injection, velocity float, or bucket method. Data were collected weekly to monthly from February through October 2019 at Falling Creek and Beaverdam Reservoir, and approximately weekly to seasonally at Falling Creek and Carvins Cove from 2020 to 2025. The dataset is accompanied by a maintenance log and quality assurance/quality control analysis scripts.
The Jefferson Project 2018 hydrologic, water quality, and soil quality data from 11 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 2018, The Jefferson Project had eleven 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_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 is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2019 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 2019, 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 is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
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.
2017 hydrologic, water quality, and soil quality data from The Jefferson Projects 8 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 and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project had eight 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_ShelvingRock and TS_West. The stations have a sensor payload that may include some or all of the following sensors: EXO2 Multi-parameter sonde, CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter with five 3.0 MHz transducers, Argonaut-SL Doppler current meter, 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 is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and down sampling to an hourly frequency.
Chloride Concentrations, Conductivity, and Water Temperature Data from Upper Yahara River Watershed Tributaries in Dane County, WI: December 2019 – April 2021
Conductivity and chloride were measured for 2 years in nine tributaries of Lake Mendota and Lake Monona in Dane County, WI. HOBO Conductivity loggers continuously measured absolute conductivity and water temperature every 30 minutes. Breaks in data collection were due to a calibration period or if the loggers were out of the water. Grab samples for chloride concentration occurred weekly or biweekly. Conductivity and water temperature were measured with a field meter at each sampling excursion. This data was needed for a master’s research thesis with the goal of characterizing the spatial distribution and loading of chloride in the Upper Yahara River Watershed.
City of Seattle, Seattle Public Utilities, Annual Bull Trout Redd Surveys in Tributaries to Chester Morse Lake 1996-current, Cedar River Municipal Watershed, King County, WA
These data were collected during weekly annual redd surveys conducted by Seattle Public Utilities (SPU) in the Cedar River Municipal Watershed (CRMW), 1996 - current. Annual weekly bull trout redd surveys funded through the CRMW Habitat Conservation Plan (HCP) began in 2000 and ended in 2011 spawning year. To reinstate a monitoring program for the population, redd surveys in the most heavily used habitats by bull trout (termed the Core Zone), were opportunistically conducted in 2018. Weekly annual surveys in most of the Core Zone were reinstated in 2019. Approximately 77% of all redds observed 2000 - 2011 would have been observed during those years using the 2019 - 2022 spatial survey extent (SPU data on file). In 2023, the spatial and temporal coverage of surveys were on par with historical coverage, i.e., approximately 100% of all redds observed 2000 - 2011 would have been observed using the 2023 spatial survey extent. Information on redd location is used primarily to enable derivation of redd elevations. Redd elevation is required to estimate potential impacts to the spawning population and incubating embryos caused by reservoir inundation of stream spawning habitat after the spawning period during fall through spring. Redd weekly timing information is critical to accurately represent whether embryos remain in the gravel and are vulnerable to impacts of reservoir inundation as the reservoir is refilled starting in early spring. It is also vitally important that SPU understand timing and abundance of redds beyond the inundation zone to enable understanding of the overall impact to the population.
Water chemistry data from synoptic sampling of 235 Lake Michigan tributaries: 10-15 July, 2018
This dataset includes nutrient (total nitrogen and phosphorus, soluble reactive phosphorus, and dissolved inorganic nitrogen [nitrate+nitrite+ammonium]) and chloride concentrations for 235 tributaries of Lake Michigan collected during a synoptic sampling event from 10-15 July, 2018. The dataset also includes modeled discharge metrics for the 235 sampled watersheds, as well as spatial watershed characteristics.
Data from Sand aggradation alters biofilm standing crop and metabolism in a low-gradient Lake Superior tributary
We conducted a comparative study of biofilm standing crop and metabolism in the Salmon Trout River, a tributary of Lake Superior where watershed disturbances have led to 3-fold increases in streambed fine sediments, predominately sand, in the past decade. We compared biofilm standing crop and metabolism rates using light–dark chambers in reaches where substrate consisted of predominately exposed rock or sand substrates. This data archive includes rates of primary production and respiration, biomass measurements from chambers, and benthic standing crop and water chemistry data collected from the same river sites over the course of a summer. All data were published in Journal of Great Lakes research in 2015, https://doi.org/10.1016/j.jglr.2015.09.004
The Jefferson Project 2021 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 2021, 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 2022 hydrologic, water quality, and soil quality data from 11 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 2022, The Jefferson Project had eleven 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_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.
Water quality monitoring on the Altamaha River and major tributaries from September 2000 through November 2001
Water samples were collected from the Altamaha River (approximately weekly) and several tributaries (bimonthly) from September 2000 through September 2001. Samples were then collected at less frequent intervals from September 2001 through November 2001. The concentration of dissolved nutrients (ammonium, nitrate+nitrite, phosphate) and dissolved organics (DOC, DON, DOP) were measured using standard methods. The concentrations of 6 elements (Ca, K, Mg, Na, Si, and Sr) were also determined using elemental analysis by inductively coupled plasma mass spectrometry (ICP-MS).
Long-term water quality monitoring on the Altamaha River and major tributaries from September 2000 through April 2009
Water samples were collected from the Altamaha River (approximately weekly) and several tributaries (bimonthly) from September 2000 through September 2001. Samples were then collected at less frequent intervals from September 2001 through April 2009. The concentration of dissolved nutrients (ammonium, nitrate+nitrite, phosphate, silicate), dissolved organics (DOC, DON, DOP) and total suspended solids were measured using standard methods. The concentrations of 20 elements (Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, Pb, Si, Sr and Zn) were also determined using elemental analysis by inductively coupled plasma mass spectrometry (ICP-MS). Total dissolved inorganic carbon (DIC) was measured using a custom automated DIC analyzer. Total alkalinity (TA)was determined by Gran titration and pH of surface water was measured using a glass electrode.
Heidelberg Tributary Loading Program (HTLP) Dataset
<p><strong>This dataset is updated more frequently and can be visualized on NCWQR's <a href="https://ncwqr-data.org/HTLP/Portal">data portal</a>.</strong></p> <p>If you have any questions, please contact <a href="https://www.heidelberg.edu/directory/laura-johnson">Dr. Laura Johnson</a> or <a href="https://www.heidelberg.edu/directory/nathan-manning">Dr. Nathan Manning</a>.</p> <p> </p> <p>The National Center for Water Quality Research (NCWQR) is a research laboratory at Heidelberg University in Tiffin, Ohio, USA. Our primary research program is the Heidelberg Tributary Loading Program (HTLP), where we currently monitor water quality at 22 river locations throughout Ohio and Michigan, effectively covering ~half of the land area of Ohio. The goal of the program is to accurately measure the total amounts (loads) of pollutants exported from watersheds by rivers and streams. Thus these data are used to assess different sources (nonpoint vs point), forms, and timing of pollutant export from watersheds. The HTLP officially began with high-frequency monitoring for sediment and nutrients from the Sandusky and Maumee rivers in 1974, and has continually expanded since then. </p> <p>Each station where samples are collected for water quality is paired with a US Geological Survey gage for quantifying discharge (<a href="http://waterdata.usgs.gov/usa/nwis/rt">http://waterdata.usgs.gov/usa/nwis/rt</a>). Our stations cover a wide range of watershed areas upstream of the sampling point from 11.0 km2 for the unnamed tributary to Lost Creek to 19,215 km2 for the Muskingum River. These rivers also drain a variety of land uses, though a majority of the stations drain over 50% row-crop agriculture. </p> <p>At most sampling stations, submersible pumps located on the stream bottom continuously pump water into sampling wells inside heated buildings where automatic samplers collect discrete samples (4 unrefrigerated samples/d at 6-h intervals, 1974–1987; 3 refrigerated samples/d at 8-h intervals, 1988-current). At weekly intervals the samples are returned to the NCWQR laboratories for analysis. When samples either have high turbidity from suspended solids or are collected during high flow conditions, all samples for each day are analyzed. As stream flows and/or turbidity decreases, analysis frequency shifts to one sample per day. At the River Raisin and Muskingum River, a cooperator collects a grab sample from a bridge at or near the USGS station approximately daily and all samples are analyzed. Each sample bottle contains sufficient volume to support analyses of total phosphorus (TP), dissolved reactive phosphorus (DRP), suspended solids (SS), total Kjeldahl nitrogen (TKN), ammonium-N (NH4), nitrate-N and nitrite-N (NO2+3), chloride, fluoride, and sulfate. Nitrate and nitrite are commonly added together when presented; henceforth we refer to the sum as nitrate. </p> <p>Upon return to the laboratory, all water samples are analyzed within 72h for the nutrients listed below using standard EPA methods. For dissolved nutrients, samples are filtered through a 0.45 um membrane filter prior to analysis. We currently use a Seal AutoAnalyzer 3 for DRP, silica, NH4, TP, and TKN colorimetry, and a DIONEX Ion Chromatograph with AG18 and AS18 columns for anions. Prior to 2014, we used a Seal TRAACs for all colorimetry. </p> <p> </p> <p><strong>2017 Ohio EPA Project Study Plan and Quality Assurance Plan</strong></p> <p><strong><a href="https://ncwqr.files.wordpress.com/2019/04/ncwqr-study-plan-20170127-with-addendums.pdf">Project Study Plan</a></strong></p> <p><strong><a href="https://ncwqr.files.wordpress.com/2019/04/quality-assurance-plan_version-6-20170127.pdf">Quality Assurance Plan</a></strong></p> <p> </p> <p><strong>Data quality control and data screening</strong></p> <p>The data provided in the River Data files have all been screened by NCWQR staff. The purpose of the screening is to remove outliers that staff deem likely to reflect sampling or analytical errors rather than outliers that reflect the real variability in stream chemistry. Often, in the screening process, the causes of the outlier values can be determined and appropriate corrective actions taken. These may involve correction of sample concentrations or deletion of those data points.</p> <p>This micro-site contains data for approximately 126,000 water samples collected beginning in 1974. We cannot guarantee that each data point is free from sampling bias/error, analytical errors, or transcription errors. However, since its beginnings, the NCWQR has operated a substantial internal quality control program and has participated in numerous external quality control reviews and sample exchange programs. These programs have consistently demonstrated that data produced by the NCWQR is of high quality.</p> <p> </p> <p><strong>A note on detection limits and zero and negative concentrations</strong></p> <p>It is routine practice in analytical chemistry to determine method detection limits and/or limits of quantitation, below which analytical results are considered less reliable or unreliable. This is something that we also do as part of our standard procedures. Many laboratories, especially those associated with agencies such as the U.S. EPA, do not report individual values that are less than the detection limit, even if the analytical equipment returns such values. This is in part because as individual measurements they may not be considered valid under litigation.</p> <p>The measured concentration consists of the true but unknown concentration plus random instrument error, which is usually small compared to the range of expected environmental values. In a sample for which the true concentration is very small, perhaps even essentially zero, it is possible to obtain an analytical result of 0 or even a small negative concentration. Results of this sort are often “censored” and replaced with the statement “<DL” or “<2”, where DL is the detection limit, in this case 2. Some agencies now follow the unfortunate convention of writing “-2” rather than “<2”.</p> <p>Censoring these low values creates a number of problems for data analysis. How do you take an average? If you leave out these numbers, you get a biased result because you did not toss out any other (higher) values. Even if you replace negative concentrations with 0, a bias ensues, because you’ve chopped off some portion of the lower end of the distribution of random instrument error.</p> <p>For these reasons, we do not censor our data. Values of -9 and -1 are used as missing value codes, but all other negative and zero concentrations are actual, valid results. Negative concentrations make no physical sense, but they make analytical and statistical sense. Users should be aware of this, and if necessary make their own decisions about how to use these values. Particularly if log transformations are to be used, some decision on the part of the user will be required.</p> <p> </p> <p><strong>Analyte Detection Limits</strong></p> <p><a href="https://ncwqr.files.wordpress.com/2021/12/mdl-june-2019-epa-methods.jpg?w=1024">https://ncwqr.files.wordpress.com/2021/12/mdl-june-2019-epa-methods.jpg?w=1024</a></p> <p> </p> <p><strong>For more information, please visit <a href="https://ncwqr.org/">https://ncwqr.org/</a></strong></p>
Metabolism and decomposition rates from 5 Lake Superior Tributaries, 2018-2019
Ecosystem respiration (ER), and decomposition are fundamental processes driving carbon cycling in streams. Most studies examine rates of autotrophic respiration (AR) and heterotrophic respiration (HR) together as ecosystem respiration (ER), even though these two processes are carried out by different groups of organisms, and these processes, alongside decomposition, may respond differently to ongoing changes in environmental factors. We measured metabolism (gross primary production and ER) and decomposition at eight sites in four streams in the Upper Peninsula of Michigan across gradients of canopy cover and DOC concentrations. We estimated AR and HR using quantile regression and used predictive modeling to determine the environmental drivers that predicted variation in these processes across the study streams. This data archive includes (1) continuous dissolved oxygen and temperature data used to model metabolism, (2) modeled rates of gross primary production, ecosystem respiration, autotrophic respiration and heterotrophic respiration, (3) estimates of decomposition rates determined using cotton strip assays, and (4) environmental model used for predictive modeling
Data used in the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan
This dataset includes modeled data describing the potential benefits of the Voluntary Agreements (VAs) from the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan.
Baltimore Ecosystem Study: Stream chemistry for Gwynns Falls Upper Tributaries
In the Baltimore urban long-term ecological research (LTER) project, (Baltimore Ecosystem Study, BES) we use the watershed approach to evaluate integrated ecosystem function. The LTER research is centered on the Gwynns Falls watershed, a 17,150 ha catchment that traverses a gradient from the urban core of Baltimore, through older urban residential (1900 - 1950) and suburban (1950- 1980) zones, rapidly suburbanizing areas and a rural/suburban fringe. Our long-term sampling network includes four longitudinal sampling sites along the Gwynns Falls as well as several small (40 - 100 ha) watersheds located within or near to the Gwynns Falls. The longitudinal sites provide data on water and nutrient fluxes in the different land use zones of the watershed (rural/suburban, rapidly suburbanizing, old suburban, urban core) and the small watersheds provide more focused data on specific land use areas (forest, agriculture, rural/suburban, urban). Each of the gaging sites is continuously monitored for discharge and is sampled weekly for chemistry. Additional chemical sampling is carried out in a supplemental set of sites to provide a greater range of land use. Weekly analyses includes nitrate, phosphate, total nitrogen, total phosphorus, chloride and sulfate, total suspended solids, turbidity, fecal coliforms, temperature, dissolved oxygen and pH. Cations, dissolved organic carbon and nitrogen and metals are measured on selected samples. This dataset presents stream chemistry from the Upper Gwynns Falls tributaries. From April 1999 to August 2000 Johns Hopkins University graduate student Mark Colosimo sampled a group of sites in the Upper Gwynns Falls (Red Run, Horsehead Branch, Scotts Level Branch, Holly Branch). There were two sites in the Red Run drainage. This watershed drains approximately 19 km2 and has been rapidly suburbanizing since the early 1990s. Percent impervious surface was approximately 10% as of 2002. Sampling station Red Run 1 (RR1) was approximately 35 m upstre
Seasonal high-frequency measurements of discharge, water temperature, and specific conductivity from Harnish Creek Tributary (Relict Channel) at F11, McMurdo Dry Valleys, Antarctica (1996-2020, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This package contains data pertaining to continuous monitored water quality and quantity parameters measured with automatic recording devices on streams in this region. Specifically, this metadata record describes the hydrology data set for the McMurdo Dry Valleys' Harnish Creek Tributary (Relict Channel) at the F11 streamgage, located in the Fryxell Basin of Taylor Valley. Measurements commenced during the 1996-97 season and are ongoing. This dataset extends through the first half of the 2019-20 field season.
Daily summarized seasonal measurements of discharge, water temperature, and specific conductivity from Harnish Creek Tributary (Relict Channel) at F11, McMurdo Dry Valleys, Antarctica (1996-2020, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This package contains daily summaries derived from 15-minute measurements of water quality and quantity parameters measured with automatic recording devices on streams in this region. Specifically, this metadata record describes the daily hydrological summaries for the McMurdo Dry Valley's Harnish Creek Tributary (Relict Channel) at F11, located in the Fryxell Basin of Taylor Valley. Measurements commenced during the 1996-97 austral summer and are ongoing. This dataset extends through the first half of the 2019-20 field season.
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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)
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DANDI Archive for NWB datasets
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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.