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103 results for “lake water quality”
Water quality data for Green Lakes Valley, 2000 - ongoing.
This dataset contains water quality measurements made on Green Lakes 1, 2, 3, 4, 5, and Lake Albion. Green Lake 4 was initially sampled in 2000 and is ongoing. Ongoing sampling of Green Lakes 1 was started in 2014 and ongoing sampling of Lake Albion was started in 2016. Water samples were collected for analysis of chlorophyll a and nutrient analysis (which is available in glvwatsolu.dm.data) and field measurements for pH, temperature, specific conductivity, dissolved oxygen (DO), % saturation, secchi depth, PAR. Secchi depth is recorded at the 0m row however it is a measurement of depth and so the units are meters. Most samples were collected between 0800 and 1200 MST. The first sampling date each summer occurs shortly after the ice had melted. Data are collected from an inflatable raft at the point of deepest depth or from the lake inlet and outlet when surface flow is present. The majority of chlorophyll-a the measurements were taken at the surface (0m), the metalimnion (3m), and the hypolimnion nine (usually 8-11m). However, additional measurements were taken for side projects of the long-term dataset during several of the years and are included in this dataset. Water samples from the metalimnion or hypolimnion were collected using a Van Dorne sampler, and surface samples were collected as grab samples from the water column surface, the inlet and outlet. Field measurements were conducted using a YSI either DO or multiple probe meter (2014-2017, YSI MPS 556)(2018-ongoing, YSI ProPlus) and a Li-Cor meter with a quantum sensor. Chlorophyll-a was extracted from filtered samples and absorbance was measured before and after acidification to quantify chlorophyll a concentration.
Lake Sunapee Instrumented Buoy: High Frequency Water Quality Data - 2007-2022
The Lake Sunapee (Global Lake Ecological Observatory Network—GLEON) instrumented buoy, operated by the Lake Sunapee Protective Association (LSPA www.lakesunapee.org), is equipped with a thermistor chain, one optical dissolved oxygen probe suspended at approximately 10 meters depth (installed in 2013), and a multi-parameter sonde (installed in 2021) at 1 meter depth. An optical dissolved oxygen (DO) probe was located at 1.5 meter prior to the 2021 deployment of the multi-parameter probe from the inception of the buoy and the deep DO sensor was at 10.5 meters depth prior to 2021 since deployment. The number of thermistors and below-surface depth of the thermistors has fluctuated throughout the years and the additional water quality sensors have changed over time. The Lake Sunapee buoy is located near Loon Island Lighthouse (43.391°N, 72.058°W) during the summer months and in the Lake Sunapee Harbor (43.386°N, 72.081°W) (2010-current). During the first few years of data collection (2007-2010), the buoy was located near Loon Island year-round. In two instances, HOBO units were deployed at the buoy's location when sensors failed. This occurred in 2015 (in place of the thermistors) and in 2018 (in place of the shallow DO sensor). All data in this data package have been QAQC'd to remove obviously errant readings, highly suspicious readings, and artifacts of buoy maintenance. Additionally, flags have been added per sensor, to indicate calibration and non-calibration of the optical DO probe, location of the buoy, and to document other potential confounding observations. Dissolved oxygen data and data from the multi-parameter probe are “raw”: they are have not been corrected for calibration issues, drift, or fouling. Additional documentation and data are provided including manual DO measurements, visual comparisons of buoy-recorded DO and manual DO measurements, and an overview of how and where DO offsets applied in the data logger program were removed.
High-frequency, long-term measurements of water quality and chlorophyll in the Tempe Town Lake, Tempe, Arizona, USA, ongoing since 2018
Constructed in 1997, the Tempe Town Lake is a small reservoir that transforms a section of the typically-dry Salt River bed into a 224-acre lake in the heart of Tempe, Arizona. To accommodate the river when it flows, the lake features hydraulically-operated steel gates that allow water to pass through the system unimpeded. The lake has been a remarkable success as a community amenity and as a driver of economic growth in the area around the lake. The lake provides an ideal model system for the many artificial lakes constructed in arid-land cities owing to management decisions, such as draining, that affect their operation and ecology. At the same time, dramatic shifts in hydrology and chemistry when the lake is transformed to a flowing river and back into a Lake during and after floods, provide opportunities to study the system's dynamic evolution to new limnological steady states. The CAP LTER began measuring water quality, including temperature, pH, conductivity, dissolved oxygen, dissolved organic carbon (DOC), and total dissolved nitrogen (TDN) in the lake starting 2005. Initially, these data were collected through a mix of hand-held meters and analyses of water samples collected during regular visits to the lake (e.g., see [knb-lter-cap.630](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=630)). In summer 2018, the CAP LTER installed a *in situ* datasonde to measure water quality, including optical dissolved organic carbon characteristics, at high temporal resolution (~ 30 minutes). After a brief period during which data were collected both with the datasonde, and by hand-held meters and analyses of water samples, the project transitioned to collecting data exclusively with the datasonde. The data presented here reflect readings captured by the datasone.
Cedar Bog Lake Water Quality Data
This dataset contains water quality data from multiple locations and dates collected from surface waters of Cedar Bog Lake (CBL) during summers of 2016 -2018. Sampling of biogeochemical and water quality parameters measured include dissolved inorganic and organic carbon, total dissolved nitrogen, total phosphorus, particulate particulate and total dissolved phosphorus, soluble reactive phosphorus, and algal biomass (as chlorophyll a). These data were collected to support research examining energy flow pathways in the lake using stable isotopes, and build long term water data records for the lake as a reference site, given that it is largely protected from direct human impacts. These water quality data enhance previous water quality data available for CBL. The food web analyses project was developed to compare energy flow in CBL food webs between the foundational original studies by Raymond Lindeman in the 1930s to those inferred from stable isotope analyses. The food web project also examined constraints on resource use by consumers imposed by chemical conditions, and explored the potential role of methane as an energy source for consumers. Stable isotope data for food web components, including fish and invertebrates, will be published separately.
LAGOS-NE v.1.054.1 - Lake water quality time series and geophysical data from a 17-state region of the United States
Time series of mean summer total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP) and chlorophyll values from 2913 unique lakes in the Midwest and Northeast United States. Epilimnetic nutrient and chlorophyll observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NELIMNO version 1.054.1, and come from 54 disparate data sources. These data were used to assess long-term monotonic changes in water quality from 1990-2013, and the potential drivers of those trends (Oliver et al., submitted). Summer was used to approximate the stratified period, which was defined as June 15 to September 15. The median number of observations per summer for a given lake was 2, but ranged from 1 to 83. The rules for inclusion in the database were that, for a given water quality parameter, a lake must have an observation in each period of 1990-2000 and 2001-2011. Additionally, observations must span at least 5 years. Each unique lake with nutrient or chlorophyll data also has supporting geophysical data, including climate, atmospheric deposition, land use, hydrology, and topography derived at the lake watershed (variable prefix “iws”) and HUC 4 (variable prefix “hu4”) scale. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NEGEO version 1.03. For more specific information on how LAGOS-NE was created, see Soranno et al. 2015. Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., Gries C., Henry E.N., Skaff N.K., Stanley E.H., Stow C.A., Tan P.-N., Wagner T., and Webster K.E. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 4: 28. doi: 10.1186/s13742-015-0067-4.
WSC - The Influence of Legacy P on Lake Water Quality
Using a suite of numerical models, we investigated the influence of legacy P on water quality in the Yahara Watershed of southern Wisconsin, USA. The suite included Agro-IBIS, a terrestrial ecosystem model; THMB, a hydrologic and nutrient routing model; and the Yahara Water Quality Model which estimates water quality indicators in the Yahara chain of lakes. Using five alternative scenarios of antecedent P storage (legacy P) in soils and channels under historical climate conditions, we simulated outcomes of P yield from the landscape, lake P loading, and three lake water quality indicators. Data and code may also be found in https://github.com/SRCarpen/Yahara2070LakeModel_Fits2data.git
LAGOS-NE-LIMNO v1.087.3: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 1925-2013
This data package, LAGOS-NE-LIMNO v1.087.3, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. With this release, only this data package is being updated and users are expected to use prior releases of the other types of data. Please see the attached additional documentation for a full description of the changes that have been made for this new release.The data packages that make up LAGOS-NE include the following information on lakes and reservoirs in 17 lake-rich states in the Northeastern and upper Midwestern U.S. (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes greater than one hectare. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes and for all spatial resolutions, also called ‘zones’ (i.e., ecoregions, states, counties). These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. (3) LAGOS-NE-LIMNO v1.087.3: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This module includes variables that are most commonly measured by state agencies and researchers for studying eutrophication. For each water quality data value, we also include metadata related to the sampling program, methods, qualifiers with data flags from the original program (qual, not standardized for LAGOS-NE), censor codes from our quality control procedures (censorcode, standardized for LAGOS-NE), and the date of each sample. (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-N
The Jefferson Project 2017 water quality data from two 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 and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project deployed two vertical profiler stations on the lake, collecting data on water quality and meteorology. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2017. These vertical profiler stations are named VP_AnthonysNose and VP_TeaIsland. The water quality data are collected by 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 is transferred in near real-time to an off-site database for monitoring and review. The data provided have undergone data correction by Jefferson Project researchers.
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 2018 water quality data from two 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 2018, The Jefferson Project deployed two 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 2018. These vertical profiler stations are named VP_AnthonysNose and VP_TeaIsland. The water quality data are collected by 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 is transferred in near real-time to an off-site database for monitoring and review. The data provided have undergone data correction by Jefferson Project researchers.
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.
High-Frequency and Water Quality Monitoring Data of Long Pond at Grafton Lakes State Park, New York, United States, 2024
This collection of datasets contains high frequency data captured through Hobo, Minidot, and water level sensors, as well as data collected from manual sampling days. Long Pond is located in Grafton New York, USA named for its long shape and shallower depth (max depth is around 8 meters). Using a buoy, sensors were attached to a rope at the deepest point discoverable and deployed. Data covers all information recorded from 2024-04-30 to 2024-10-09. Times are recorded in Eastern Standard Time. Temperature Readings were taken every 10 minutes at 1 meter intervals by both Minidots and hobo sensors (1.52-7.52 m). Dissolved Oxygen was similarly collected every 10 minutes by the Minidots at depths 1.52, 6.52, and 7.52 meters. Manual measurements (YSI and Secchi disk) were recorded on sampling days, as well as water samples that were assessed for water quality parameters from the top and bottom of the lake. Water level data was also collected in 12 hour intervals.
Nearshore high-frequency temporal water quality observations and process-based modeling of aquatic ecosystem metabolism in Lake Tahoe completed by members of the Blaszczak Lab at the University of Nevada Reno, 2021-2023
The overarching goal of this project was to develop a process-based understanding of how watershed-to-lake connections drive nearshore productivity dynamics in a large oligotrophic mountain lake (Lake Tahoe). We addressed this goal through a combined approach of high-frequency sensor deployment and maintenance, ecosystem metabolism modeling, laboratory incubations, and routine monitoring of water chemistry and other parameters. The data we collected as part of this project and the ecosystem metabolism estimates we generated demonstrate how variable ecosystem productivity is in time and space in the nearshore of Lake Tahoe. Although maintenance of the sensor arrays during the exceptional winter of 2023 was challenging, we were able to capture the data necessary to estimate a complete time series of metabolic activity across two years with very different hydroclimatic conditions. Throughout this project we accomplished the following: 1. We generated over two years of daily estimates of ecosystem metabolism (gross primary productivity, ecosystem respiration, and net ecosystem productivity) from multiple locations on both the east and west shores of the lake and from areas in close proximity to and far away from stream water inflows. 2. We measured ammonium (NH4+) and nitrate (NO3-) concentrations in surface water samples from both Glenbrook and Blackwood creeks and the nearshore of Lake Tahoe for over two years. 3. We quantified rates of NH4+ and NO3- uptake in benthic samples of the dominant substrate type collected during peak streamflow, the receding limb, and baseflow conditions in 2023 from multiple locations in the nearshore using established laboratory incubation methods. 4. Finally, we used a combination of time series models and structural equation modeling to integrate our results and improve understanding of the direct and indirect effects of hydroclimatic variability on observed patterns in ecosystem metabolism in the nearshore. See this git code repository
Clear Lake water quality monitoring data from 2019 to 2023 by the University of California, Davis
A major barrier to effective water quality restoration at Clear Lake is the absence of quantitative data on the anticipated response to restoration projects. In-lake monitoring (in-situ measurements) is needed to understand better the processes contributing to poor water quality. This data package contains the in-situ measurements collected by the University of California, Davis at Clear Lake between 2019 and 2023, which include: continous stream properties at three locations (Middle, Scott, and Kelsey Creeks); continuous meteorological variables at seven locations around the perimeter of the lake; continuous lake temperature and dissolved oxygen at multiple depths and locations across the lake (six permanent water quality stations); continuous lake surface temperature in the shoreline; and discreate samples to measure nutrient concentrations and phytoplankton biovolumes and species identification throughout the water column and across all three lake basins every 6-8 weeks.
Ultra-high frequency water quality sonde data from Lake George and Chautauqua Lake, NY, 2021
Forecasting rapid ecological change represents a major challenge in environmental science. In aquatic ecosystems, the shift from clearwater conditions to an algal bloom-dominated state represents an important transition resulting in harmful algal blooms (HABs). Methods for forecasting HABs using sensors are problematic, as lower frequency sampling can miss early warning indicators. Here, using sensor data from two lakes we show that antecedent information essential to forecasting HABs was best characterized using ultra-high frequencies (UHF; sampling ≤ 1 second). This publication is comprised of four individual datasets, each containing water quality measurements sampled at 4 Hz using an EXO2 sonde from two lakes in New York State, USA (Lake George and Chautauqua Lake). An accompanying R script is provided to read in each dataset, format it, and undertake a variety of analyses to demonstrate the utility of UHF data in identifying subtle environmental changes linked to HABs. The first dataset, "CHQ_StaticEXO.csv," includes UHF data from a stationary EXO2 sonde deployed in the South Basin of Chautauqua Lake, NY. This dataset spans a 24-hour period and is utilized to examine the behavior of different water quality sensor technologies during the rapid onset of a HAB. The second and third datasets, "LG_HarrisBayVP.csv" and "LG_HarrisBayVP_Cal.csv" were collected using a vertical profiler in Lake George, NY. These datasets provide UHF phycocyanin fluorescence data from a profiling EXO2 sonde over a two-month period, and are used to demonstrate how antecedent information critical for forecasting HABs is best characterized using UHF frequencies. Lastly, the dataset "GloeotrichiaExp.csv" was gathered as part of a laboratory experiment measuring cultured Gloeotrichia sp. colonies. As with the Lake George datasets, this dataset includes phycocyanin fluorescence data from an EXO2 sonde collected at 4 Hz, and is used to help explain some of the trends observed in the previous th
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.
Como and Phalen lake ChatBot water quality and recreation text survey, 2022 and 2023
We collected data from visitors to two urban lakes in Saint Paul, Minnesota, using a conversational chatbot to assess visitor perception of current lake water quality, trends in water quality over time, and other questions relevant to park managers. Data were collected at Como Lake in 2022 and 2023, and at Lake Phalen in 2023. Signs were installed at three locations around each lake with high pedestrian traffic. Each sign had a hook question (“How many watercraft are on the lake right now? Text the number to XXX-XXX-XXXX”). Visitors who responded to this question initiated a series of optional follow-up questions, using a conversational chatbot run by software that automates the sending and receiving of text messages. Survey questions included asking respondents about their primary purpose for visiting the lake today, how often they have visited the lake in the past 12 months, and whether they perceive that water quality in the lake is improving, getting worse, or remaining about the same. Respondents were asked to provide their ZIP code, used to estimate distance traveled to the lake. Respondents also had the opportunity to opt-in to future data collection via phone or text. An AI language model was then used to process the information and parse and synthesize responses. Unique anonymous identifiers were used to key survey response data.
CLA Yahara Lakes Citizen Offshore Water Quality Monitoring 2016 - 2017
In 2013, Clean Lakes Alliance (CLA) launched a Citizen Water Quality Monitoring pilot. Objectives included evaluating and tracking nearshore water quality conditions on all five Yahara lakes: Lakes Mendota, Monona, Waubesa, Kegonsa and Wingra. In 2016, in order to fully understand the interaction between the offshore and nearshore environment, CLA volunteers will begin sampling the deepest point (deep hole) of all Yahara lakes. The offshore monitoring program will focus on two components: water clarity sampling and dissolved oxygen and temperature measurement. Data from the offshore monitoring program will be compared to data from the nearshore program.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.