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1,103 results for “moisture”
Georgia Salt Marsh: Soil Organic Carbon, Nitrogen, Bulk Density, Moisture, and Texture
As part of project predicting soil carbon at depth from that found at the surface using remote sensing, 28 soil cores were taken from six salt marshes along the Georgia coastline. Cores were taken as deep as possible (25 – 165 cm) and sectioned into 5 cm depths. Soils were analyzed for organic carbon (SOC), total nitrogen (N), bulk density (BD), and particle size (by horizon). Stable carbon isotopes were obtained in three marshes on Sapelo Island; a subset was also analyzed for radiocarbon.
Soil moisture and snowdepth measurements in the Black Sand experiment for East Knoll, Audubon, Lefty, Soddie and Trough, 2018 - 2024.
The purpose of the Black Sand experiment is to measure treatment effects on date of snowmelt on Niwot Ridge. Five sites with paired plots (treatment and control) were implemented across Niwot as part of the extended summer research program in order to understand the effects of earlier snowmelt. This dataset contains measurements of snowdepth throughout the melt season, manual measurements of volumetric water content and temperature data as an indication of snowmelt timing.
NRCS-USFS Soil Moisture Measurements - Fernow Experimental Forest, WV, 2022-2025
This dataset consists of soil moisture (volumetric water content and water potential), temperature, and electrical conductivity measurements at multiple depths within 20 soil pedons distributed across Watersheds 4, 5, 6, and 7 at the Fernow Experimental Forest from September 2022 to June 2025. This work is a part of a larger partnership between the U.S. Forest Service (USFS) and the Natural Resources Conservation Service (NRCS) to install, monitor and generate long-term soil moisture datasets across multiple forested watersheds in the U.S. Associated data packages from both the Coweeta Hydrologic Laboratory and Hubbard Brook Experimental Forest can be found on the EDI Data Portal. Dataset contributors: Fernow site selection and project planning conducted by Ben Rau (USFS), Ann Tan (NRCS), and James Leonard (NRCS). Megan Thomas (NRCS) and Joel Gebhard (NRCS) assisted with site installation. Site visits, data downloading, and logger maintenance was by Tyler Sharretts (USFS) and Chris Cassidy (USFS). The dataset was curated by Emily Piche (USFS, ORISE) and Amanda Pennino (NRCS). Overall partnership initiation and project management was by Stephanie Connolly (USFS) and Skye Wills (NRCS).
Soil moisture and soil temperature from Benchmark Stations at the HJ Andrews Experimental Forest, 1987 to present
A three-level hydro-climatological network for data monitoring was established in 1994. The networks at each level are nested to form a coordinated program of data acquisition and measurement. A future vision of linking the benchmark meteorological stations with regional weather stations to expand the future scope of studies was also considered in designing this network. The first-level in this top-down approach consists of Benchmark Meteorological Stations (BMS) and Benchmark Stream Stations. The BMS are designed to represent the environment across the Andrews. These stations are intended to provide complete, long-term, high temporal resolution, meso-scale hydroclimatological data. The location of the BMS network is based on factors such as elevation, aspect, vegetation gradients, and accessibility. Collected meteorological parameters are generally standardized across the BMS as well as methods and instrumentation. Secondary Meteorological Stations also follow standardized methods and serve similar purposes but are somewhat limited in meteorological parameters collected. The Primary Meteorological Station (PRIMET), Central Meteorological Station (CENMET), Upper Lookout Meteorological Station (UPLMET), and Vanilla Leaf Meteorological Station (VANMET) are the four Benchmark Stations, Climatic Station at Watershed 2 (CS2MET) and the Hi-15 Meteorological Station (H15MET) are Secondary Stations. These soil parameters were previously part of database code MS001, but were separated out into their own database in 2024.
NRCS-USFS Soil Moisture Measurements - Hubbard Brook Experimental Forest, 2023-2025
This dataset consists of soil moisture (volumetric water content and water potential), temperature, and electrical conductivity measurements at multiple depths within 12 soil pedons distributed across Watersheds 3, 6, and 9 at Hubbard Brook Experimental Forest from July 2023 to June 2025. This work is a part of the Forest Soil Moisture Monitoring Network (FSMMN), which is an interagency partnership between the U.S. Forest Service and the Natural Resources Conservation Service (NRCS) to install, monitor and generate long-term soil moisture datasets across multiple forested watersheds in the U.S. Dataset contributors: Hubbard Brook site selection and project planning was conducted by Amanda Pennino (NRCS), Scott Bailey (Virginia Tech) and Mark Green (Case Western). Site visits, data downloading, and logger maintenance was by Lucy Zendzian (NRCS), Paul Gadecki (NRCS), and Jack Ferrara (NRCS). The dataset was curated by Emily Piche (USFS, ORISE) and Amanda Pennino (NRCS). Overall partnership initiation and project management was by Stephanie Connolly (USFS) and Skye Wills (NRCS).
Soil Chemistry and Moisture in Macrosystems Biodiversity Project at Harvard Forest 2012
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. Soil chemistry (TN, TC, NH4-N, NO3-N, and pH) and moisture measurements were taken from soil cores from an array of 21 1m2 subplots and processed by the University of Oklahoma Institute for Environmental Genomics as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
WSC - Soil moisture, temperature, and water potential at Wibu field site
Soil moisture, temperature, and water potential measurements for 3 locations within Wibu field site: (1) WIBU-6, which is characterized by deep (greater than6 m) groundwater and coarse soil; (2) WIBU-7, which is characterized by intermediate (2-4 m) groundwater and intermediate soil; (3) WIBU-8, which is characterized by shallow (0-3 m) groundwater and fine soil. For more information about the soil and groundwater levels, see other datasets from this field site. The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site.
Soil moisture, temperature, and electrical conductivity data from the black sand extended growing season length experiment, 2018 - 2024, hourly.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how changes in growing season length may affect biotic and abiotic components, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These blocks include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control (black sand was added to these plots after snow had naturally melted). This dataset includes measurements of soil temperature, moisture, and electrical conductivity.
Saddle soil temperature and moisture, 2024 - ongoing.
In rugged mountain terrain, microclimate variation may provide refugia that buffer the effects of climate change. We expect that complex terrain causes microsite variation in surface and subsurface temperature and soil moisture across hillslopes, thus mediating the extent to which organisms are exposed to warming conditions. Further, we expect that hillslope position will determine the microclimate that regulates ecological and biogeochemical responses.
Turf Transplant temperature, soil moisture and turf depths, 2024 - ongoing.
The Turf Transplant Experiment was set up in the summer of 2024. Paired experimental sites were established in two tundra community types - dry meadow and moist meadow - with one site of each community type pair in a lower elevation/warmer area and one site in a higher elevation/cooler area. Subplot turfs (25 cm^2) were transplanted (1) between sites of the same community type at different elevations/temperatures, (2) between plots within the same site or (3) left in place as non-transplant controls. This data package contains dates and depths of turfs as installation as well as plot-level moisture and temperature.
NRCS-USFS Soil Moisture Measurements - Coweeta Hydrologic Laboratory, NC, 2022-2025
This dataset consists of soil moisture (volumetric water content and water potential), temperature, and electrical conductivity measurements at multiple depths within 12 soil pedons distributed across Watersheds 32 and 7 at the Coweeta Hydrologic Laboratory from March 2022 to April 2025. This work is a part of a larger partnership between the U.S. Forest Service (USFS) and the Natural Resources Conservation Service (NRCS) to install, monitor and generate long-term soil moisture datasets across multiple forested watersheds in the U.S. Associated data packages from both the Fernow and Hubbard Brook Experimental Forests can be found on the EDI Data Portal. Dataset contributors: Project planning led by Carlos Quintero (USFS, ORISE), with help from Amos Stead (NRCS) and Tiffany Allen (NRCS) in site selection. Scientific and logistical support from Chris Oishi (USFS), Amanda Pennino (NRCS), and Erin Rooney (NRCS). Seth Strickland (USFS), Amos Stead (NRCS), Ann Tan (NRCS), and Tiffany Allen (NRCS) assisted with site installation. Site visits, data downloading, and logger maintenance was by Seth Strickland (USFS). The dataset was curated by Emily Piché (USFS, ORISE) and Amanda Pennino (NRCS). Overall partnership initiation and project management was by Stephanie Connolly (USFS) and Skye Wills (NRCS)
Marcell Experimental Forest seasonal soil moisture, 1966 - ongoing
This data publication contains available soil water measured three times a year (1966 - ongoing) at the Marcell Experimental Forest (MEF) in Balsam Township, Itasca County, Minnesota. The data came from six peatland / upland forest watersheds instrumented for long-term hydrological and biogeochemical research. The Marcell Experimental Forest in Itasca County, Minnesota is operated and maintained by the USDA Forest Service, Northern Research Station, and was formally established in 1962 to study the ecology and hydrology of peatlands.
Summary of soil temperature, moisture, and thaw depth for 14 chamber flux measurements sampled near Arctic LTER shrub sites at Toolik Field Station, Alaska, summer 2012.
Soil temperature at 5cm and 10cm depth, volumetric water content (VWC) and depth of thaw for 14 shrub canopy flux plots measured in vicinity of the Arctic LTER shrub site, Toolik Field Station, AK in 2012.
Leaf Litter Moisture Content at Harvard Forest HEM and LPH Towers 2006
Leaf litter was collected at the time of soil respiration measurements and its moisture content was measured in order to determine the contribution of leaf litter decomposition to measurements of total soil respiration including the litter layer. Leaf litter moisture has been shown to strongly affect CO2 release from organic soil layers (Borken et al. 2003).
Soil Respiration, Temperature and Moisture at Harvard Forest EMS Tower 1995-2014
We have been making long-term soil respiration measurements at our transect sites since the summer of 1995. The rainfall exclusion experiment began in May of 2001 and autochamber measurements began in 2003. A root exclusion experiment (trenching) was conducted from 2012-2014.
Groundwater, soil moisture, light and weather data in Brownsville forest, Nassawadox, VA, 2019-2023
This dataset includes groundwater, soil moisture, weather and light data collected in eight study sites in the Brownsville forested area (VA). In particular, sites H5 and H7 characterize the high forest where healthy Pinus taeda dominate, sites L1 and L6 characterize the low forest, where barren or dead Pinus taeda are present, sites M1 and M2 characterize the medium forest, representing transition between high and low forest, transition site where dead trees and saltmarsh vegetation live together and marsh site dominated by saltmarsh vegetation. Data collection, started in January 2019, is done for VCR-LTER and CCZN (Coastal Critical Zone Network) long term projects. CTD-Diver are used to measure groundwater pressure, specific conductance and temperature. They hang from a cable in six wells, one for each study site. Water pressure is compensated using barometric pressure data collected by the weather station nearby. Water levels are georeferenced to NAVD 88. Two soil moisture sensors for each site are placed 7-10 cm and 30 cm below the ground surface to detect water content, specific conductance and temperature of the first soil layer. Two weather stations are always installed in different sites. Their position is periodically changed to cover all sites. This material is based upon work supported by the National Science Foundation under Grant No. 2012322, Collaborative Research: Network Cluster: The Coastal Critical Zone: Processes that transform landscapes and fluxes between land and sea.
Fine Fuel Moisture Code - ERA-Interim
<p>The Fine Fuel Moisture Code (FFMC) is a numeric rating of the moisture content of litter and other cured fine fuels. This code is an indicator of the relative ease of ignition and the flammability of fine fuel.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
Duff Moisture Code - ERA-Interim
<p>The Duff Moisture Code (DMC) is a numeric rating of the average moisture content of loosely compacted organic layers of moderate depth. This code gives an indication of fuel consumption in moderate duff layers and medium-size woody material.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
Calculated moisture sources for the Yangtse River Valley for past, present and future climate using a Lagrangian moisture source diagnostic
<p>This dataset contains calculated moisture sources for the Yangtse River Valley (110–122°E and 27–33°N, eastern China) for past, present and future climate using a Lagrangian moisture source diagnostic. The dataset comprises gridded monthly moisture source data files and monthly time series files for a Last Glacial Maximum (LGM) simulation and a Pre-Industrial reference simulation (PRE) with CAM5.1 using prescribed sea surface temperatures, and a control simulation (CTL, 2001-2010) and a climate scenario run with representative concentration pathway 6 (RCP, 2061-2070) with the coupled NorESM-1M model. Each file covers a 10-year time period, computed with the Lagrangian moisture source diagnostic WaterSip (Sodemann et al., 2008).</p>
Soil moisture sensor network, design, location attributes and soil properties, Hainich, Germany, project AquaDiva
<p>This dataset contains information of the small scale highly resolved soil moisture measurement network that is part of the of the AquaDiva Critical Zone exploratory, Hainich National Park, Germany. The dataset contains information on soil measurement locations, as well as attributes to the location, the design type (random locations vs transects), as well as locations attributes like distance to the next tree and soil properties. Measurement design was first introduced by Metzger et al., (2017), and used in Fischer et al., 2023. See there for more information.</p> <p><strong>References</strong></p> <p>Fischer-Bedtke, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics – empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023.</p> <p>Metzger, J. C., Wutzler, T., Dalla Valle, N., Filipzik, J., Grauer, C., Lehmann, R., Roggenbuck, M., Schelhorn, D., Weckmüller, J., Küsel, K., Totsche, K. U., Trumbore, S., and Hildebrandt, A.: Vegetation impacts soil water content patterns by shaping canopy water fluxes and soil properties, Hydrological Processes, 31, 3783–3795, https://doi.org/10.1002/hyp.11274, 2017.</p>
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.