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574 results for “NY”
Tree Ring Data from Goose Egg State Forest NY 1681-2014
Is it possible to reconstruct aboveground carbon/biomass from tree rings? If so, how far back in time can researchers go when using tree-ring data in the reconstruction of past biomass? Answers to these questions will have a significant impact on our understanding of dynamics in the terrestrial carbon sink. Long tree-ring records of biomass can reveal intra-annual to annual to multidecadal variations that cannot be resolved by forest census data that is not conducted at annual time steps. Additionally, while these dynamics might be resolved using remote sensing, most remotely-sensed products are only two decades or less in length. By having long records of carbon biomass, we can then identify not only the dominant drivers of biomass, but how the importance of these drivers might change during different eras as environmental factors change (e.g., climate, air pollution, disturbance). To test these and other questions, we collected tree-ring records from three 30m radius plots set in Goose Egg State Forest in New York State. We chose this location because it has old oak dominated forests that can be compared to the long-term forests being studied for carbon dynamics at the Harvard Forest. We can convert these data to biomass using allometric equations and compare tree-ring inferred aboveground biomass to the census data going back in time to understand forest recovery and carbon dynamics in a heavily disturbance forest. Recruitment dates for some of the trees from these plots have been published in Pederson et al. (2017). Pederson, N., Young, A. B., Stan, A. B., Ariya, U., Martin-Benito, D. 2017. Low-Hanging DendroDynamic Fruits Regarding Disturbance in Temperate, Mesic Forests. In: Amoroso, M. M., Daniels, L. D., Baker, P. J., Camarero, J. J., Dendroecology: Tree-Ring Analyses Applied to Ecological Studies, Springer, Cham., Switzerland.
Tree Ring Data from Rooster Hill in Adirondack State Park NY 1828-2015
Is it possible to reconstruct aboveground carbon/biomass from tree rings? If so, how far back in time can researchers go when using tree-ring data in the reconstruction of past biomass? Answers to these questions will have a significant impact on our understanding of dynamics in the terrestrial carbon sink. Long tree-ring records of biomass can reveal intra-annual to annual to multidecadal variations that cannot be resolved by forest census data that is not conducted at annual time steps. Additionally, while these dynamics might be resolved using remote sensing, most remotely-sensed products are only two decades or less in length. By having long records of carbon biomass, we can then identify not only the dominant drivers of biomass, but how the importance of these drivers might change during different eras as environmental factors change (e.g., climate, air pollution, disturbance). To test these and other questions, we collected tree-ring records from two 30m radius plots set Rooster Hill in New York State. We chose this location because it has oak dominated mixed forests that can be compared to the long-term forests being studied for carbon dynamics at the Harvard Forest. We can convert these data to biomass using allometric equations and compare tree-ring inferred aboveground biomass to the census data going back in time to understand forest recovery and carbon dynamics in a heavily disturbance forest.
Lake Sediment Pollen from Wildwood Lake in Long Island NY from 4500 BP to Present
Analyses of pollen, charcoal, and organic content in a lake-sediment core from Wildwood Lake, Long Island, New York, provide insights into the ecological and environmental history of this region. The early-Holocene interval of the record (~9800-8800 cal. a BP) indicates the presence of Pinus rigida-Quercus ilicifolia woodlands with high fire activity. A layer of sandy sediment dating to 9200 cal. a BP may reflect a brief period of reduced water depth, consistent with widespread evidence for cold, dry conditions at that time. Two other sandy layers, bracketed by 14C dates, represent a sedimentary hiatus from ~8800 to 4500 cal. a BP. This discontinuity may reflect the removal of some sediment during brief periods of reduced water depth at 5300 and 4600 cal. a BP. In the upper portion of the record (before 4500 cal. a BP), subtle changes at ~3000 cal. a BP indicate declining prevalence of Quercus-Fagus-Carya forests and increasing abundance of Pinus rigida, perhaps due to reduced summer precipitation. Elevated percentages of herbaceous taxa in the uppermost sediments represent European agricultural activities. However, unlike charcoal records from southern New England, fire activity does not increase dramatically with European settlement. These findings indicate that present-day Pinus rigida-Quercus ilicifolia woodlands on eastern Long Island are not a legacy of recent, anthropogenic disturbances.
Lake Sediment Pollen and Charcoal from Sears Pond on Long Island NY from 10690 BP to Present
Aim We analyzed a dataset composed of multiple palaeoclimate and lake-sediment pollen and charcoal records from New England to explore how postglacial changes in forest composition and spatial patterns of vegetation and fire were controlled by regional-scale climate change, a subregional environmental gradient, and landscape-scale variations in soil characteristics. Location The 120,000-km2 study area includes parts of Vermont and New Hampshire in the north, where sites are 150-200 km from the Atlantic Ocean, and spans the coastline from southeastern New York to Cape Cod and the adjacent islands, including Block Island, the Elizabeth Islands, Nantucket, and Martha’s Vineyard. Results Boreal forest featuring Picea and Pinus banksiana was present across the region when conditions were cool and dry 14,000-12,000 calibrated 14C yrs before present (ybp). Pinus strobus became regionally dominant as temperatures increased between 12,000 and 10,000 ybp. The composition of forests in inland and coastal areas diverged in response to further warming after 10,000 ybp, when Quercus and Pinus rigida expanded across southern New England, while conditions remained cool enough in inland areas to maintain Pinus strobus. Fire severity was high during 10,000-8000 ybp. Increasing precipitation allowed Tsuga canadensis, Fagus grandifolia, and Betula to replace Pinus strobus in inland areas during 9000-8000 ybp, and also led to the expansion of Carya across the coastal part of the region beginning at 7000-6000 ybp. Abrupt cooling at 5500-5000 ybp caused sharp declines in Tsuga in inland areas and Quercus at some coastal sites, and the populations of those taxa remained low until they recovered around 3000 ybp in response to rising precipitation. Throughout most of the Holocene, sites underlain by sandy glacial deposits were occupied by Pinus rigida and Quercus. Main conclusions Postglacial changes in the composition and spatial pattern of New England forests were controlled by long-term t
Lake Sediment Pollen from Spruce Pond in Harriman State Park NY from 13778 BP to Present
Aim We analyzed a dataset composed of multiple palaeoclimate and lake-sediment pollen records from New England to explore how postglacial changes in the composition and spatial patterns of vegetation were controlled by regional-scale climate change, a subregional environmental gradient, and landscape-scale variations in soil characteristics. Location The 120,000-km2 study area includes parts of Vermont and New Hampshire in the north, where sites are 150-200 km from the Atlantic Ocean, and spans the coastline from southeastern New York to Cape Cod and the adjacent islands, including Block Island, the Elizabeth Islands, Nantucket, and Martha’s Vineyard. Results Boreal forest featuring Picea and Pinus banksiana was present across the region when conditions were cool and dry 14,000-12,000 calibrated 14C yrs before present (ybp). Pinus strobus became regionally dominant as temperatures increased between 12,000 and 10,000 ybp. The composition of forests in inland and coastal areas diverged in response to further warming after 10,000 ybp, when Quercus and Pinus rigida expanded across southern New England, while conditions remained cool enough in inland areas to maintain Pinus strobus. Increasing precipitation allowed Tsuga canadensis, Fagus grandifolia, and Betula to replace Pinus strobus in inland areas during 9000-8000 ybp, and also led to the expansion of Carya across the coastal part of the region beginning at 7000-6000 ybp. Abrupt cooling at 5500-5000 ybp caused sharp declines in Tsuga in inland areas and Quercus at some coastal sites, and the populations of those taxa remained low until they recovered around 3000 ybp in response to rising precipitation. Throughout most of the Holocene, sites underlain by sandy glacial deposits were occupied by Pinus rigida and Quercus. Main conclusions Postglacial changes in the composition and spatial pattern of New England forests were controlled by long-term trends and abrupt shifts in temperature and precipitation, as well as by
Lake Sediment Pollen from Sutherland Pond in Black Rock Forest NY from 13899 BP to Present
Aim We analyzed a dataset composed of multiple palaeoclimate and lake-sediment pollen records from New England to explore how postglacial changes in the composition and spatial patterns of vegetation were controlled by regional-scale climate change, a subregional environmental gradient, and landscape-scale variations in soil characteristics. Location The 120,000-km2 study area includes parts of Vermont and New Hampshire in the north, where sites are 150-200 km from the Atlantic Ocean, and spans the coastline from southeastern New York to Cape Cod and the adjacent islands, including Block Island, the Elizabeth Islands, Nantucket, and Martha’s Vineyard. Results Boreal forest featuring Picea and Pinus banksiana was present across the region when conditions were cool and dry 14,000-12,000 calibrated 14C yrs before present (ybp). Pinus strobus became regionally dominant as temperatures increased between 12,000 and 10,000 ybp. The composition of forests in inland and coastal areas diverged in response to further warming after 10,000 ybp, when Quercus and Pinus rigida expanded across southern New England, while conditions remained cool enough in inland areas to maintain Pinus strobus. Increasing precipitation allowed Tsuga canadensis, Fagus grandifolia, and Betula to replace Pinus strobus in inland areas during 9000-8000 ybp, and also led to the expansion of Carya across the coastal part of the region beginning at 7000-6000 ybp. Abrupt cooling at 5500-5000 ybp caused sharp declines in Tsuga in inland areas and Quercus at some coastal sites, and the populations of those taxa remained low until they recovered around 3000 ybp in response to rising precipitation. Throughout most of the Holocene, sites underlain by sandy glacial deposits were occupied by Pinus rigida and Quercus. Main conclusions Postglacial changes in the composition and spatial pattern of New England forests were controlled by long-term trends and abrupt shifts in temperature and precipitation, as well as by
Inventory of Ants at the Black Rock Forest in Cornwall NY 2006-2015
Ants are key indicators of ecological change, but few studies have investigated how ant assemblages may respond to dramatic changes in vegetation structure in temperate forests. Pests and pathogens are causing widespread loss of dominant canopy tree species; ant species composition and abundance may be very sensitive to such losses. Prior to the experimental removal of red oak trees to simulate effects of sudden oak death and examine the long-term impact of oak loss at the Black Rock Forest (Cornwall, New York), we carried out a rapid assessment of the ant assemblage in the 10-hectare experimental area. We also determined the efficacy in a northern temperate forest of five different collecting methods - pitfall traps, litter samples, tuna-fish and cookie baits, and hand collection - routinely used to sample ants in tropical systems. A total of 33 species in 14 genera were collected and identified; the myrmecines Aphaenogaster rudis and Myrmica punctiventris, and the formicine Formica neogagates were the most common and abundant species encountered. Ninety-four percent (31 of 33) of the species were collected by litter sampling and structured hand sampling together, and we conclude that in combination, these two methods are sufficient to assess species richness and composition of ant assemblages in northern temperate forests. Using new, unbiased estimators, we project that 38-58 ant species are likely to occur at Black Rock Forest. Loss of oak from these forests may favor Camponotus species that nest in decomposing wood and open-habitat specialists in the genus Lasius.
LAGOS - Chlorophyll, TP, and water color summer epilimnetic concentrations and lake and catchment data for inland lakes in WI, MI, NY, and ME – a subset of lake data from LAGOSLimno v.1.040.1
This dataset includes lake total phosphorus (TP), true water color, and chlorophyll a (CHLa) concentrations from summer, epilimnetic water samples and is a subset of the larger LAGOS database (Lake multi-scaled geospatial and temporal database, described in Soranno et al. 2015). LAGOS compiles multiple, individual lake water chemistry datasets into an integrated database. We accessed LAGOSLIMNO version 1.040.0 for lake water chemistry data and LAGOSGEO version 1.02 for lake catchment geographic data. In the LAGOSLIMNO database, lake water chemistry data were collected from individual state agency sampling and volunteer programs designed to monitor lake water quality. Water chemistry analyses follow standard lab methods. In the LAGOSGEO database geographic data were collected from national scale geographic information systems (GIS) data layers. Lake catchments, defined as 'The area of land that drains directly into a lake, and into all upstream-connected, permanent streams to that lake exclusive of any upstream lake watersheds for lakes greater than or equal to 10 ha that are connected via permanent streams', were delineated for lakes greater than or equal to 4 ha. Lake-stream connectivity type was assigned to lakes greater than or equal to 4 ha using GIS tools that use the National Hydrology Dataset (See Soranno et al. 2015 for LAGOS geographic processing steps). A subset of lake and geographic data was created to examine spatial variation in TP and water color relationships with CHLa across broad geographic extents using spatially-varying coefficient models with a Bayesian framework. Lakes were selected that had complete records for summer epilimnetic total TP, true water color, and CHLa. In addition we selected lakes with surface area greater than or equal to 4 ha and less than 10,000 ha to exclude very small and very large lakes from the analyses. The resulting dataset includes 838 lakes in Wisconsin, Michigan, New York, and Maine with 7395 observations. The majo
Environmental, Taxonomic, and Stable Isotope Data from Aquatic Insects sampled from Beaver-Engineered Headwater Streams (Adirondack Park, NY; 2024).
This data package contains environmental and biological data from a field study examining aquatic insect assemblage composition and basal resource use in beaver-engineered headwater streams in Adirondack Park, New York. Data was collected from six streams across two watersheds; the Oswegatchie River Watershed and Upper Hudson River Watershed. Three streams were sampled within the Oswegatchie River Watershed; East Creek, Sucker Brook, and Chair Rock Creek located near the Cranberry Lake Biological Station in St. Lawrence County. Three streams were sampled from the Upper Hudson River Watershed; Big Sucker Brook, Little Sucker Brook, and Panther Brook located near SUNY ESF’s Newcomb Campus in Essex County. Site conditions were characterized using densiometer measurements of canopy cover, visual assessments of substrate composition, and river discharge measurements collected with an OTT MF Pro flow meter. Aquatic insect assemblages were sampled using multihabitat active sampling and Hester–Dendy and leaf-bag passive samplers, with specimens identified to genus and assigned to functional feeding groups. Carbon and nitrogen stable isotopes were analyzed for a subset of insect taxa and three basal resource pools; coarse particulate organic matter (CPOM), fine particulate organic matter (FPOM), and periphytic algae. The Bayesian mixing model MixSIAR was used to estimate the proportional contribution of these primary sources to aquatic insect biomass. All data was collected between June and August 2024.
Stage height, discharge, water temperature, pH, conductivity, and dissolved oxygen at Cascade Brook in Black Rock Forest, Cornwall, NY 1998 - 2015
Black Rock Forest established a stream monitoring station in the Cascade Brook watershed in 1998. The station is equipped with a 120-degree sharp-crested v-notch weir and was built to continuously monitor the flow, temperature, pH, conductivity, and dissolved oxygen content of the stream water in Cascade Brook in New York. The Cascade Brook watershed encompasses 135 hectares (334 acres), is generally a southern aspect, and has been little affected by human activity in the last century. The forest is deciduous and dominated by oak species. The station records data hourly, and while water chemistry sensors have been used intermittently through the decades, stage height and discharge has been measured consistently from1998 - 2015.
ICON-LEM Ny-Ålesund low-level clouds polar night and polar day 2021/2022
<h3>Low-level clouds during the polar night and polar day simulated in ICON-LEM for Ny-Ålesund </h3> <p>This data set was created using the ICON-LEM model with ca. 600m resolution and a diagnostic tool "microphysical wrapper". It contains the meteogram output of the Ny-Ålesund column (Svalbard) and the microphysical process rates. The data was created for the polar night (Nov 2021- Feb 2022) and polar day (May - Aug 2022). Clouds are classified as low-level if their cloud top height (CTH) is below 2.5 km and the distance between any cloud with CTH above 2.5 km is at least 500 m higher. The data set was first used and described in the <em>publication: </em></p> <p>T. Kiszler, D. Ori, V. Schemann<em>. </em>(preprint) Microphysical processes involving the vapour phase dominate in simulated low-level Arctic clouds. <em>Atmospheric Physics and Chemistry, </em>https://doi.org/10.5194/egusphere-2023-2986<em><br></em></p> <p>This data is related to the repository <a href="https://github.com/TracyMcBean/Kiszler_et_al_2023_microphysics">https://github.com/TracyMcBean/Kiszler_et_al_2023_microphysics</a></p> <p><em>File description:</em></p> <p>*_PN is polar night data</p> <p>*_PD is polar day data</p> <p>LLC_<em>meteo_<yyyymm>_ICONv1</em>_v6.nc : Contains the meteogram variables (thermodynamics, surface variables, hydrometeors)</p> <p>LLC_wrapper_mass_<yyyymm>_ICONv1_v6.nc : Contains hydrometeors masses after diagnostic run of a microphysical wrapper</p> <p>LLC_wrapper_tend_<yyyymm>_ICONv1_v6.nc : Contains the mircophysical process rates showing the mass change per timestep </p> <p>low_cloud_times_v6_*.csv : Contains the date and time when a low-level cloud was detected</p>
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.
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).
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 weather data from eight surface weather stations on Lake George, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at <https://jeffersonproject.rpi.edu/> In 2018, The Jefferson Project had six weather monitoring stations around the lake collecting data on precipitation, temperature, wind, and air quality. These stations are WX_CedarLane, WX_DFWI, WX_PilotKnob, 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: Rotronic HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, Li-Cor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, 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 downsampling to an hourly frequency.
The Jefferson Project 2019 weather data from ten surface weather stations on Lake George, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project had seven weather monitoring stations around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidiity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, and WX_Glenburnie. Weather data from three vertical profiler sites (VP_AnthonysNose, VP_CalvesPen, and VP_TeaIsland) are also included in this dataset. The stations have a sensor payload that include some combination of the following sensors: Rotronic HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, Li-Cor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, 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 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.
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.