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7,355 results for “soils”
Forest Transition Experiment - Soil Pore Water Salinity in a Coastal Virginia Forest
A pore water sipper was be used to collect a pore water sample from the top 15 cm of the soil, and was read using a portable refractometer in the field.
Barrier Island Plant and Soil Properties on Hog and Metompkin Islands, Virginia, 2021-2022
Dune building has the potential to impact the entire barrier island ecosystem, and these grasses therefore serve as ecosystem engineers. Protection offered by dune ridges directly impacts the adjacent swale habitat, modifying both biotic and abiotic factors. In order to better understand how dune building impacts the island ecosystem as a whole, we quantified sediment accretion, plant percent cover, stem numbers, and soil characteristics (chlorides, bulk density, %OM, %C, %N). These characteristics were assessed on two islands with varied disturbance intensities. Hog island is infrequently disturbed, and resists change driven by storms and overwash. Metompkin island is frequently disturbed and undergoes high rates of overwash and island migration.
Dataset to Manuscript: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Marcus Schiedung et al. (Biogeosciences)
<p>Dataset to manuscript: Schiedung, M., Bellè, S.-L., Sigmund, G., Kalbitz, K., and Abiven, S.: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Biogeosciences, https://doi.org/10.5194/bg-17-6457-2020, 2020.</p> <p>All parameters and variables are described in "Var_names" files.</p>
Dataset to Schiedung et al. (2024): Millennial-aged pyrogenic carbon in high-latitude mineral soils
<p>Dataset to Schiedung et al. (2024, Communications Earth & Environment): Pyrogenic Carbon is Aged at Millennial Scale in High-Latitude Mineral Soils</p> <p>DOI: <a href="https://doi.org/10.1038/s43247-024-01343-5">10.1038/s43247-024-01343-5</a></p> <p>This repository includes the following files: </p> <p><strong><em>dd_all.csv</em> </strong>- Includes all data for the individual samples that are presented in the manuscript.</p> <p><strong><em>Var_names_dd_all.csv</em> </strong>- Describes all variables in <em>dd_all</em> with corresponding unit </p> <p><strong><em>dd_site_average.csv</em></strong> - Includes all data that has been determined on composite samples for each site or the average of all samples per site </p> <p><strong><em>Var_names_dd_site_average.csv</em></strong> - Describes all variables in <em>dd_site_average.csv</em> with corresponding unit</p> <p>All .csv use "," as separator. </p> <p>This data set is also connected to Schiedung et al. (2022, Catena <a href="https://doi.org/10.1016/j.catena.2022.106194"> https://doi.org/10.1016/j.catena.2022.106194</a> ) and the corresponding repository: <a href="../records/10609291">https://zenodo.org/records/10609291</a></p>
GHG Dataset for the frontiers publication "Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region"
<p>GHG Dataset used in the Frontiers Publication "Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region". Additionally including CO2 besides N2O and CH4. Includes 3 cropping seasons.</p> <p>The data is also available online on the GHG flux visualisation and calculation tool "gasflxvis": https://sae-interactive-data.ethz.ch/gasflxvis/</p> <p>Further details on the calulation are provided both on gasflxvis and the Frontiers publication. Calculation procedure according the following PLOS ONE publication: http://dx.doi.org/10.1371/journal.pone.0200876</p>
Assessing the role of soil microbes in the dynamics of P release from poorly soluble P forms
<p>Dataset and script used for the publication <em>Assessing the role of soil microbes in the dynamics of P release from poorly soluble P forms, </em>doi (to be determined).</p>
SERENA EJPSOIL SK SOIL Erosion ErosionControl
<div> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <div> <p><span><span>The present data was prepared according to the </span><span>methodology</span><span> of SERENA soil erosion control cookbook</span><span> for the territory of </span><span>Slovakia</span><span>. </span><span>The map of soil loss by water erosion (soil threat</span><span>) </span><span>was based on the </span><span>RUSLE model.</span> <span>or the soil erosion control, the difference between the erosion map without vegetation (C-factor = 1) and the erosion map with vegetation was calculated.</span></span><span> </span></p> </div> </div> <div> <p>The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. </p> </div> <div> <p>To create the soil loss map we used theese data: </p> </div> <div> <p> R factor - we used data from 100 automatic rain stations on minute rainfall for about 10-year period (national dataset) </p> </div> <div> <p>K factor – we used the source proposed in the cookbook from ESDAC dataset: Soil Erodibility (K- Factor) High Resolution dataset for Europe </p> </div> <div> <p>LS factor – we used the source proposed in the cookbook from ESDAC dataset: LS-factor (Slope Length and Steepness factor) for Slovakia </p> </div> <div> <p>C factor – we used LPIS database-this has information about crops on agricultural soil. We have values of C factor for all crops. </p> </div> <div> <p>P factor – we used the source proposed in the cookbook from ESDAC dataset: P factor for Slovakia. This map has values about 0.99 for Slovakia, so P-factor does not have much effect on the resulting erosion. </p> </div> <div> <p>The delivered map was prepared in GeoTIFF format in the resolution of 500 * 500 m. </p> </div>
SERENA EJPSOIL PL EROSION CONTROL SOIL MASS NOT ERODED
<p>General description of SERENA</p> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). The RUSLE method was used to prepare the attached files. </p>
SERENA EJPSOIL PL EROSION SOIL LOSS
<p>General description of SERENA</p> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). The RUSLE method was used to prepare the attached files. All attached GeoTIFFs were described below:</p> <p>SERENA_EJPSOIL_PL_EROSION__K_factor_2018.tif</p> <p>The result of modelling K factor - soil-erodibility factor </p> <p>SERENA_EJPSOIL_PL_EROSION_C_factor_2018.tif</p> <p>The result of modelling C factor - land cover and management factor</p> <p>SERENA_EJPSOIL_PL_EROSION_LS_factor_2018.tif</p> <p>The result of modelling LS factor - slope length and steepness factor (Source: https://esdac.jrc.ec.europa.eu/themes/slope-length-and-steepness-factor-ls-factor)</p> <p>SERENA_EJPSOIL_PL_EROSION_P_factor_2018.tif</p> <p>The result of modelling P factor - support practices factor (Source: https://esdac.jrc.ec.europa.eu/themes/support-practices-factor)</p> <p>SERENA_EJPSOIL_PL_EROSION_R_factor_2018.tif</p> <p>The result of modelling R factor -erosivity factor (rainfall event's ability to cause soil water erosion)</p> <p>SERENA_EJPSOIL_PL_EROSION_SOIL_LOSS_2018.tif</p> <p>Total soil erosion loss by water modelled for agricultural soils in Poland for 2018 (Map unit: <span>Mg ha<sup>−1</sup> yr<sup>−1</sup></span>)</p> <p>SERENA_EJPSOIL_PL_EROSION_SOIL_LOSS_MAX_EROSION_2018.tif</p> <p>Total maximum soil erosion loss by water modelled for agricultural soils in Poland for 2018 (Excluding C factor) (Map unit: <span>Mg ha<sup>−1</sup> yr<sup>−1</sup></span>)</p>
Global distribution of predicted soil types at 1 km resolution based on the WRB 2022 classification
<p>Global maps at 1 km spatial resolution of the predicted soil types (0–100% probabilities) at 1 km resolution based on the <a href="https://www.fao.org/soils-portal/data-hub/soil-classification/world-reference-base/en/">WRB 2022</a> (<strong>World Reference Base</strong> the international standard for soil classification) classification system. The training data comes from the following 3 main sources:</p> <ol> <li>WOSIS points available via: <a href="https://www.isric.org/explore/wosis">https://www.isric.org/explore/wosis</a>;</li> <li>HWSD v2 (random draw of cca 20,000 points): <a href="https://iiasa.ac.at/models-tools-data/hwsd">https://iiasa.ac.at/models-tools-data/hwsd</a>;</li> <li>Other national datasets / data from publications and projects.</li> </ol> <p>Predictions are based on using Rando Forest algorithm as implemented in the <a href="https://www.randomforestsrc.org/">randomForestSRC package</a> with cca 190 covariate layers representing soil forming factors (CHELSA Climate, Global Lithological DB GLiM, MODIS EVI and LST long-term derivatives, Digital Terrain model parameters and similar).</p> <p>All TIF files are provided as <a href="https://www.cogeo.org/">COGs</a>, which means that you can open them directly in QGIS or similar. Publication explaining all modeling steps is pending.</p> <p>Update of the predictions takes about 4–5 hrs and will be regularly run provided that new training points are available. Disclaimer: These are initial results with limited accuracy and possible issues with quality of training points, location errors and harmonization issues. Use at own risk.</p> <p>Note: original list of soil types have been subset to classes that appear at least 10 times and at least in 2 countries. If you notice an error or artifact <strong>please report via <a href="https://github.com/OpenGeoHub/SoilTypeMapping">the Github repository</a></strong>. Help us improve this dataset by contributing training points.</p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Bare soil at Marquardt, Germany
<p>The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the ATB HYPERNETS site in Marquardt, Germany [52°27'59.40"N, 12°57'35.16"E] (ATGE). It is a subset of the complete data record, consisting of the measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 dgrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-XR sensor was installed on 11 Oct 2022 at the top of a 5m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 52.466778°N, 12.959778°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angle.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full ATGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </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>
Salt River Wetlands denitrification rate, dissimilatory nitrate reduction to ammonium rate, dissolved organic carbon concentration in June 2016 as well as soil porosity and bulk density
Raw and derived data used to calculate denitrification and dissimilatory nitrate to ammonium (DNRA) from push-pull experiments with added isotopically labelled nitrate. Experiments were conducted in 2016 in the Salt River Accidental Wetlands in three different patch types: Unvegetated, dominated by Ludwigia peploides, and dominated by Typha species (T. domingensis and T. latifolia). Data include start and end of incubation concentration of nitrate, ammonium, atom percent 15N in ammonium, dissolved organic carbon, excess mass 29-N2, and excess mass 30-N2. Soil data was collected from the same patch types including soil moisture, porosity, and bulk density.
Influence of soil amendment and crop species on nutrient cycling in a St. Paul urban garden, 2017-2023
An experiment was conducted from 2017-2023 at the University of St. Thomas research garden (Saint Paul, MN) to determine rates of nutrient recycling and loss from compost applied to urban gardens. Thirty-two 4 m2 study plots received one of six different soil amendment treatments, with four different crops growing on each plot. Meteorological data includes hourly measurements of rainfall, solar radiation, temperature and relative humidity, and wind speed and direction, from June 2017-October 2023. Hourly soil moisture measurements were recorded at depths of 10 cm, 20 cm, and 30 cm, from June-December 2021, June-October 2022, and June-October 2023. Annual crop harvest totals from each subplot are reported for 2017-2023. Leachate was collected from lysimeters installed in each of the 132 subplots weekly from June-October of each year (2017-2023), recording total volume. Leachate subsamples were analyzed for NO3-N, NH4-N, and PO4-P. Soil samples were collected at the beginning and end of the growing season in 2017, and every two weeks during the growing season from 2018-2023, and analyzed for pH, organic matter, Bray-1 extractable P, available K, nitrate, and ammonium, at the University of Minnesota Analytical Research Laboratory.
International Soil Carbon Network version 3 Database (ISCN3)
The ISCN is an international scientific community devoted to the advancement of soil carbon research. The ISCN manages an open-access, community-driven soil carbon database. This is version 3-1 of the ISCN Database, released in December 2015. It gathers 38 separate data set contributions, totaling 67,112 sites with data from 71,198 soil profiles and 431,324 soil layers. For more information about the ISCN, its scientific community and resources, data policies and partner networks visit: http://iscn.fluxdata.org/. For information about processes used to construct the DB: https://iscn.fluxdata.org/data/data-information/.
Flume Erosion Testing Data of Root-Permeated and Organic Matter Amended Soil Samples Using Three Streambank Boundary Conditions.
The data published here is expected to accompany one publicly available dissertation (Chapter 6 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Artificial Roots and Soil Microorganisms Increase Soil Resistance to Fluvial Erosion
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.
Map of Soil Organic Carbon: Region of Murcia (Spain)
This data package contain four soil organic carbon (SOC) maps resulted from the best data-model agreement of the analysis carried out in the frame of the Ph.D. Thesis ‘MODELING ORGANIC CARBON FOR QUANTIFICATION OF RESERVOIRS IN TERRESTRIAL ECOSYSTEMS AT THE NATIONAL LEVEL’ (Pilar Durante). Theses maps correspond to the estimates of SOC concentration (SOCc, g/kg) and SOC stocks (SOCs, tC/ha), and their associated spatially explicit uncertainties maps, for the Region of Murcia at 0-30 cm and 100 m spatial resolution. To achieve this, we evaluated four different digital soil mapping (DSM) approaches to estimate SOCc and SOCs for the Region of Murcia (11,313 km2), a topographic and climatic complex area in southern Iberian Peninsula, at three spatial resolutions (100m, 250m, 1000m). Using a local SOC database (255 soil profiles), we founded that a Quantile Regression Forest (QRF) approach had the best data-model agreement at 100 m spatial resolution, with the best balance of accuracy, external validation, and interpretability. The QRF model showed a mean SOCc of 12.18 g/kg with an overall uncertainty of 10.54 g/kg and an accuracy percentage of 79%; meanwhile the mean SOCs was 27,572 GgC with an uncertainty of 0.016 GgC. The analysis showed that using local environmental covariates and local soil information to predict SOC within this region resulted in a relative improvement between ~40% (for SOCc) and ~65% (for SOCs) when compared with SOC products derived from national and global databases. Our results provided evidence that large discrepancy exists between national and global estimates for reporting SOC at a local scale. Consequently, local-to-regional efforts are needed to better describe SOC spatial variability to reduce uncertainty and improve the assessment of soil resources.
The Jefferson Project 2020 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2020, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
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.