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50 results for “Lake profiles”
Snow cover profile data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
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 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.
Temperature and dissolved oxygen profiles for three Swiss lakes: 1972-2016
Understanding change through time in dissolved oxygen (DO) in lakes requires the use of long-term historical monitoring data. The data contained herein include historical temperature and dissolved oxygen profiles from three Swiss lakes collected between the years 1972 to 2017. These three lakes are a subset of a larger set of lakes that were used to examine the relationship between long-term changes in the timing of lake stratification and changes in the amount of oxygen-depleted water present in the water column.
GHG-depths: Greenhouse gas depth-profile data in 522 lakes worldwide
Lakes, ponds, and reservoirs (hereafter: “lakes”) are significant sources of the greenhouse gases carbon dioxide (CO2) and methane (CH4). Emissions of CO2 and CH4 from lakes are regulated in part by in-lake processes, including the production and storage of gases in the lower parts of the water column (bottom waters). However, while substantial efforts have been made to improve estimates of greenhouse gas emissions from lakes, limited data on gas concentrations along depth profiles have prevented the incorporation of bottom-water processes in global emission estimates. Here, we present GHG-depths: the largest existing dataset of depth-profile CO2 and CH4 measurements worldwide, including 522 lakes across 38 countries and all seven continents. These data include contributions from 45 research teams and 56 published studies, totaling 2558 discrete sampling events. As global change continues to alter biogeochemical cycling in lakes, these data can help improve mechanistic models to better predict greenhouse gas production and emission from lakes worldwide.
Cascade Project at North Temperate Lakes LTER: Weekly dissolved methane profiles (2018, 2019, and 2024)
Weekly profiles of dissolved methane concentrations, dissolved oxygen, temperature, and light were measured during three summers (2018, 2019, and 2024) in two north temperate lakes. One of the lakes was experimentally enriched with nitrogen and phosphorus during two summers (2019 and 2024), and darkened using a blue dye in one summer (2024). The other lake was an unmanipulated reference. Using this series of whole-lake enrichment and shading experiments across three years with varying ice phenology, we assessed how eutrophication and ice cover affect within-year methane storage.
Landscape Position Project at North Temperate Lakes LTER: Vertical Lake Profiles 1998 - 1999
Parameters characterizing the chemical limnology and spatial attributes of 45 lakes were surveyed as part of the Landscape Position Project. Parameters are measured at or close to the deepest part of the lake. A vertical profile of temperature, dissolved oxygen, and conductivity are collected at 1 meter increments Sampling Frequency: generally monthly for one summer; for some lakes, one or two samples in one summer Number of sites: 45
Lake Tahoe CTD Profile data
CTD profiles taken at Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Lake Tahoe UV Radiation Profile data
UV and PAR profiles taken at Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Lake Tahoe Particle size distribution (PSD) Profile data
Profiles of particle size distribution (PSD) taken at Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Hourly time series of Ives Lake (Huron Mountains, Marquette County, MI) Water Temperature-Depth Profiles, 2013-2022 (continuing study)
Long-term measurements of lake temperatures are essential to providing insights into local and regional changes in climate since large, still water bodies effectively act as a high-frequency filter. Ives Lake is a 30.7 m deep water body in northern Marquette County, in Michigan's Upper Peninsula. Beginning in 2013, temperature readings have been collected hourly from a string of twelve sensors located near the deepest point of the lake (approximately 46.84874 N lat, 87.84895 W long; identified by sonar survey in 2010 by first author). Measurements are continuing. The purpose of the project is to collect a data record of sufficient duration to determine if water temperatures are warming, and if the dates of autumn lake turnover are shifting toward later in the year.
The Jefferson Project 2021 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 2021, 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 2021. These vertical profiler stations are named VP_AnthonysNose, VP_HarrisBay, and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter or less 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 2022 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 2022, 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 2022. These vertical profiler stations are named VP_HarrisBay and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter or less 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.
Thermal profiles in ponds and shallow lakes during summer to winter shoulder season
Autumn is an important transition time for freshwater ecosystems where many lakes turnover, going from thermally stratified to mixed in a short time. Ponds are more globally abundant than lakes, yet, the seasonal transition of ponds is poorly understood. To evaluate the mixing regimes of ponds, we examined summer into autumn thermal dynamics in 37 ponds and shallow lakes across temperate North America and Europe. This dataset provides a time series dataset of water temperatures across the water column along with characteristics of each study waterbody, including some physical, chemical, and biological parameters. Data from four waterbodies (Eddy, Tumbledown, Cranberry, Horns) have more extensive datasets published in Gavin et al. (2025). Gavin, A.L., J.E. Saros, R. Hovel, S. Birkel, S. Nelson, W.H. McDowell, and J. Daly. 2025. Sub-Alpine Lake (>600 m) High-Frequency Water Temperature, DOC (2007-2021), and Weather Station (Fall 2023) Dataset, Maine, USA. ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/6c6286abeccc90448af0f73251843407 (Accessed 2025-09-16).
Limnological data from nearly 400 lakes across the Americas and New Zealand with a focus on vertical profiles of temperature, UV radiation, and optical properties
Two and a half decades of limnological data have been collected from nearly 400 lakes, encompassing a wide range of systems and a broad range of geography. This data set comprises one of the largest and most complete sets of measurements of underwater ultraviolet (UV) transparency available in the world. The data include a suite of 36 variables, with a focus on the optical characteristics. Lakes range from pristine natural lakes to manmade reservoirs. The systems represented in this data set are largely located in North America, from the northeastern United States to Alaska, and alpine and subalpine lakes in the Rocky Mountains of the United States and Canada. Lakes included range from iconic Lake Tahoe, and Castle Lake in northern California, to lakes in the South American Patagonian region, as well as New Zealand. Data were most often collected during the summer, and in some lakes span multiple years (with year-round data since 2006 in Lake Tahoe). The data here are contained in four files, including LakeData.csv, SiteInformation.csv, Methods.csv, and Variables.csv. The main data are in LakeData.csv. SiteInformation.csv, Methods.csv, and Variables.csv support the main data file with descriptions of the sampling sites, methods by which samples were processed, and descriptions of the variables that were measured, respectively. This data set complements the site-intensive limnological data that we published in EDI on 30+ years of data from 3 lakes in the Poconos Mountains region of Pennsylvania, USA. This complementary data set can be accessed at https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=186
Global data set of long-term summertime vertical temperature profiles in 153 lakes
Climate change and other anthropogenic stressors have led to long-term changes in the thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients, in many lakes around the world. Though many studies highlight warming of surface water temperatures in lakes worldwide, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with one time series beginning as early as 1894. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data to help understand changes and drivers of lake thermal structure in a time of rapid global and ecological change.
Conductivity, temperature, and depth (CTD) vertical profiles from Lake Joyce, McMurdo Dry Valleys, Antarctica, November 2014
Vertical profiles of conductivity, temperature, and depth (CTD) were collected from Lake Joyce, a perennially ice-covered lake in the McMurdo Dry Valleys of Antarctica. Water column observations were made in November 2014 using a YSI 6600 multiparameter probe in the deepest region of the lake, as identified by Mackey et al. (2018). The water column was accessed by drilling a 10-inch wide hole through the perennial ice cover using a Jiffy drill. This data package includes CTD profiles, as well as measurements of salinity, total dissolved solids (TDS), dissolved oxygen (DO), pH, and pressure.
Lake Fryxell under-ice conductivity, temperature, and depth (CTD) profiles, McMurdo Dry Valleys, Antarctica, January 2025
To characterize the upper ~2 m of the water column beneath the ~3 m ice cover in Lake Fryxell, located in the McMurdo Dry Valleys of Antarctica, 27 CTD (conductivity, temperature, depth) profiles were collected at ~5 m horizontal spacing along a transect spanning the dive hole to the shoreline. Measurements were made using a YSI Castaway CTD mounted on a 1 m mast atop a remotely operated vehicle (ROV), profiling upward from ~2 m below the ice-water interface. Using an ROV rather than drilling a hole through the ice was intended to minimize artifacts associated with ice openings. Sampling late in the austral summer targeted conditions during maximum seasonal ice melt.
Lake Fryxell dissolved oxygen profiles, McMurdo Dry Valleys, Antarctica
Dissolved oxygen (DO) profiles were collected in Lake Fryxell, located in the McMurdo Dry Valleys region of Antarctica, on 18 November 2012 and 6 January 2025 to document decadal changes in water column oxygen structure. In 2012, a 50-µm Unisense oxygen microelectrode (90% response time less than 2 s) coupled to a Richard Brancker Concerto CTD recorded oxygen at 1 Hz and CTD parameters at 6 Hz during ice-penetrating profiling. In 2025, the same microelectrode and picoammeter were paired with an In-Situ Rugged TROLL 100 logger and sampled at 1 Hz. Electrodes were calibrated at ambient temperature using air-saturated and deoxygenated (sodium dithionite) water prior to deployment.
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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.