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7,856 results for “lakes”
North Temperate Lakes LTER: Phytoplankton - Madison Lakes Area 1995 - current
Phytoplankton samples for the 4 southern Wisconsin LTER lakes (Mendota, Monona, Wingra, Fish) have been collected for analysis by LTER since 1995 (1996 Wingra, Fish) when the southern Wisconsin lakes were added to the North Temperate Lakes LTER project. Samples are collected as a composite whole-water sample and are preserved in gluteraldehyde. Composite sample depths are 0-8 meters for Lake Mendota (to conform to samples collected and analyzed since 1990 for a UW/DNR food web research study), and 0-2 meters for the other three lakes. A tube sampler is used for the 0-8 m Lake Mendota samples; samples for the other lakes are obtained by collecting water at 1-meter intervals using a Kemmerer water sampler and compositing the samples in a bucket. Samples are taken in the deep hole region of each lake at the same time and location as other limnological sampling. Phytoplankton samples are analyzed by PhycoTech, Inc., a private lab specializing in phytoplankton analyses (see data protocol for procedures). Samples for Wingra and Fish lakes are archived but not routinely counted. Permanent slide mounts (3 per sample) are prepared for all analyzed Mendota and Monona samples as well as 6 samples per year for Wingra and Fish; the slide mounts are archived at the University of Wisconsin - Madison Zoology Museum. Phytoplankton are identified to species using an inverted microscope (Utermohl technique) and are reported as natural unit (i.e., colonies, filaments, or single cells) densities per mL, cell densities per mL, and algal biovolume densities per mL. Multiple entries for the same species on the same date may be due to different variants or vegetative states - (e.g., colonial or attached vs. free cell.) Biovolumes for individual cells of each species are determined during the counting procedure by obtaining cell measurements needed to calculate volumes for geometric solids (e.g., cylinders, spheres, truncated cones) corresponding to actual cell shapes. Biovolume concentratio
North Temperate Lakes LTER: Groundwater Levels 1984 - current
Water levels in monitoring wells are measured several times throughout the year. The number of monitored wells has ranged over the study period from 19 to 44 wells. Currently, 37 wells are being monitored 4 - 5 times per year. The wells are scattered throughout the Trout Lake hydrological basin and the data are used to calibrate and test regional groundwater flow models. In addition (see related data set - Groundwater Chemistry), water chemistry is measured annually in a subset of 11 of these wells to characterize regional groundwater chemistry in the Trout Lake area. Sampling Frequency: varies - generally from 4 - 9 times a year Number of sites: 44
Zooplankton community composition and trait data for Green Lakes Valley, 2009 - ongoing.
Starting in 2012 zooplankton sampling at Green Lake 4 was included in the long term monitoring data set at Niwot Ridge. Immediately after the ice has completely melted from the lakes, zooplankton samples are taken once a week for six consecutive weeks at the deepest portion of the lake from an inflatable raft. Zooplankton were sampled at the deepest location of each lake by pulling a conical net (Wisconsin net) vertically through the water column (i.e., vertical tow sample). For each zooplankton sample obtained, adult organisms were identified to species, or lowest taxonomic level (Chydoridae sp. and Bosminidae sp.). Larvae of cladocerans were counted together as neonates; calanoid and cyclopoid copepodites were counted together as nauplii. Individual body lengths of the first 50 -100 (when possible) individuals of each taxon were recorded using a calibrated eyepiece micrometer and means reported.
Temperature data for Green Lake 4 inlet and outlet, 2019 - ongoing
This dataset contains temperature data from two Onset HOBO temperature pendant loggers installed in Green Lake 4’s inlet and outlet from summer 2019. High-resolution water quality data are fundamental to observing rapid ecological responses to meteorology, climate, and other disturbance events. The inlet and outlet temperature data collected here, together with Niwot Ridge’s buoy deployed in Green Lake 4, allow us to understand lake hydrology, water budget, and stratification and mixing dynamics that drive seasonal in-lake processes to understand effects of warming.
Time lapse camera photos for Green Lakes Valley, 2011 - ongoing.
Time lapse photography is a powerful tool to detect seasonal and interannual change in remote locations. In 2008, a time lapse camera was installed at Niwot Ridge, below D1, with a view overlooking Green Lake 4. The resulting photos give a view into the seasonal evolution of ice and snow cover over the Green Lakes Valley.
Snow water equivalent 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. This dataset contains derived values of SWE from snow profile measurements.
Snow grain data for Niwot Ridge and Green Lakes Valley, 1995 - 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.
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.
Indicative distribution map for Ecosystem Functional Group F2.10 Subglacial lakes
<p>This archive contains indicative distribution maps and profiles for <strong>F2.10 Subglacial lakes</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.1). Please refer to Keith <em>et al.</em> (2020) and Keith <em>et al.</em> (2022) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Dataset for: A continuous classification of the 480,000 lakes of the conterminous US based on geographic archetypes
<p>These datasets were used in a journal article with the goal of developing a new geographic classification approach for ~480,000 lakes ≥ 1 ha in the conterminous U.S. based on archetypes defined as endmembers with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. We identified seven lake archetypes; each study lake was then assigned weights for each of the archetypes. The data used to develop the archetypes, archetype weights, and variables used in associated analyses is provided in three data tables. The first includes the lake-specific transformed predictors used to generate the seven archetypes, the weights corresponding to each archetype, the archetype with the maximum weight and the weight of that maximum archetype. The second provides lake-specific raw values for each predictor and for the 19 response variables used to explore aspects of the archetype classification. The final metadata table provides a data dictionary for all columns in the previously mentioned data tables.</p>
Methane concentrations and oxidation rates in land-terminating glacial runoff: measurements from three glacial rivers and a paraglacial lake in Iceland and a literature review
<div> <p>This dataset contains methane measurements from Icelandic lakes and rivers during the summer of 2018 and 2019. This includes data from net methane oxidation assays with sediment and overlying water from one paraglacial lake and one glacial river, and surface methane concentration data from grab samples in 3 glacial streams and 15 Icelandic lakes (1 of which is paraglacial). The dataset also contains methane concentration data from a synthesis of relevant aquatic ecosystems, used to compare against the original measurements collected. </p> </div> <div> <p>Data and Literature Review Synthesis is supplement to Strock et al. 2024 <em>Oxidation is a potentially significant methane sink in land-terminating glacial runoff</em> published in Nature Scientific Reports. </p> <div> <p>This study was funded by: National Geographic Society Changing Polar Systems grant (CP4-162R-18); In-kind support from the U.S. Geological Survey; Dickinson College Research and Development; Churchill Exploration Fund at Dickinson College </p> </div> </div>
LTER-Italy site Lake Iseo figure
<p>Geographical representation of the LTER-Italy site Lake Iseo (LTER_EU_IT_102) - DEIMS-ID <a href="https://deims.org/0667dab1-f857-45a1-b01b-4261e6a499bd">https://deims.org/0667dab1-f857-45a1-b01b-4261e6a499bd</a></p>
LTER-Italy site Lake Trasimeno figure
<p>Geographical representation of the LTER-Italy site Lake Trasimeno (LTER_EU_IT_096) - DEIMS-ID <a href="https://deims.org/54cd5daf-f7d8-4af8-8e0c-974ff44fdf83">https://deims.org/54cd5daf-f7d8-4af8-8e0c-974ff44fdf83</a></p>
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
LAGOS-US DEPTH v1.0: Data module of observed maximum and mean lake depths for a subset of lakes in the conterminous U.S.
The LAGOS-US LAKE DEPTH v1.0 module (hereafter, called DEPTH) contains in situ measurements of lake depth for a subset of all lakes (n = 17,675) in the conterminous U.S. > 1 ha (3.7% of 479,950) that are in the LAGOS-US LOCUS v1.0 data module (Smith et al. 2021). All 17,675 lakes in DEPTH have a maximum depth value and 6,137 lakes have a mean depth. DEPTH includes approximately 65 data sources obtained from community, government, and university monitoring programs, as well as academic reports and commercial websites. DEPTH includes lake identifiers, lake location, lake area, lake depth (both maximum and mean depth when available), source information, and data flags. The unique lake identifier (lagoslakeid) for all lakes is the same one used in LAGOS-US LOCUS v1.0.
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 2017 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 and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project had five weather monitoring stations around the lake collecting data on precipitation, temperature, wind, and air quality. These stations are 'WX-CedarLane', 'WX-DFWI', 'WX-GullRock', 'WX-MossyPoint' and 'WX-WhaleRock'. Weather data from two vertical profiler sites, 'VP-AnthonysNose' and 'VP-TeaIsland', are also included in this dataset. The stations have a sensor payload that include some combination of the following sensors: HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, LiCor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, N-Con wet deposition sampler. 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 has undergone data correction and down sampling to an hourly frequency.
Water properties of Arco Lake, Budd Lake, Deming Lake, and Josephine Lake in Itasca State Park from 2006-2009 and 2019-et seq.
Depth profiles of water column chemical and physical properties were assessed with seasonal-scale frequency from four lakes in the Itasca State Park from 2006-2009 and from 2019-et seq. The data was used to assess the mixing status and major geochemical constituents within the lakes. Several parameters were routinely measured with deployable probes at meter or sub-meter resolution at the deepest location in each lake. Water samples were also collected for laboratory analysis. Bathymetry data collected in 2022 is supplied as rasters.
LAGOS-NE Shallow Lakes: a dataset of lake variables and multi-scaled ecological context variables used to predict and compare trophic status and TP:CHLa relationships between shallow and non-shallow lakes in the Upper Midwest and Northeastern United States.
We conducted a macroscale study of 2,210 shallow lakes (mean depth ≤ 3m or a maximum depth ≤ 5m) in the Upper Midwestern and Northeastern U.S. We asked: What are the patterns and drivers of shallow lake total phosphorus (TP), chlorophyll a (CHLa), and TP–CHLa relationships at the macroscale, how do these differ from those for 4,360 non-shallow lakes, and do results differ by hydrologic connectivity class? To answer this question, we assembled the LAGOS-NE Shallow Lakes dataset described herein, a dataset derived from existing LAGOS-NE, LAGOS-DEPTH, and LAGOS-CLIMATE datasets. Response data variables were the median of available summer (e.g., 15 June to 15 September) values of total phosphorus (TP) and chlorophyll a (CHLa). Predictor variables were assembled at two spatial scales for incorporation into hierarchical models. At the local or lake-specific scale (including the individual lake, its inter-lake watershed [iws] or corresponding HU12 watershed), variables included those representing land use/cover, hydrology, climate, morphometry, and acid deposition. At the regional scale (e.g., HU4 watershed), variables included a smaller set of predictor variables for hydrology and land use/cover. The dataset also includes the unique identifier assigned by LAGOS-NE(lagoslakeid); the latitude and longitude of the study lakes; their maximum and mean depths along with a depth classification of Shallow or non-Shallow; connectivity class (i.e., whether a lake was classified as connected (with inlets and outlets) or unconnected (lacking inlets); and the zone id for the HU4 to which each lake belongs. Along with the database, we provide the R scripts for the hierarchical models predicting TP or CHLa (TPorCHL_predictive_model.R), and the TP—CHLa relationship (TP_CHL_CSI_Model.R) for depth and connectivity subsets of the study lakes.
Otsego Lake (NY, USA) high-frequency buoy data 2017-2021
Yokota Lab, SUNY Oneonta BIological Field Station Volunteer Dive Team and Otsego Lake Association has been operating an NSF-funded (award #1624527) automated lake data buoy since 2017. This data set contains Ice-free season data from 2017 to 2021. Additional support for this project has been provided by New York State Water Research Institute, SUNY Oneonta Faculty Research Grant, and NSF awards to the Global Ecological Lake Observatory Network (GLEON.org).Yokota Lab, SUNY Oneonta BIological Field Station Volunteer Dive Team and Otsego Lake Association has been operating an NSF-funded (award #1624527) automated lake data buoy since 2017. This data set contains Ice-free season data from 2017 to 2021. Additional support for this project has been provided by New York State Water Research Institute, SUNY Oneonta Faculty Research Grant, and NSF awards to the Global Ecological Lake Observatory Network (GLEON.org).
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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.