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Carbon and nitrogen content and stable isotope compositions from particulate organic matter samples from lagoon, river, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, and analyzed for particulate organic carbon (POC) and particulate organic nitrogen (PON) content and stable isotopic composition.
Water Quality Data (Grab Samples) from the Taylor Slough, just outside Everglades National Park (FCE), for August 1998 to November 2006
Water quality samples are being collected using ISCO autosamplers at all wetland sites (that is, all sites except TS/Ph-9, 10, and 11). The autosamplers contain 24 1L bottles. Water is sampled by programming the autosamplers to take composite samples once every 3 days. These samples are a composite of four 250mL subsamples drawn every 18 hours (a sampling scheme that captures a dawn, noon, dusk, and midnight sample in every three day composite). The samples are collected every 3-4 weeks and analyzed for total phosphorus (TP), total nitrogen (TN), and salinity. When sites are visited to collect these samples, we also collect a grab sample that is immediately put on ice. A portion of these grab samples is filtered through a Whatman GF/F filter immediately upon return to the lab, and the filtered samples are analyzed for inorganic nutrients such as NO2-, NO3-, NH4+, SRP, and DOC. The unfiltered fraction of these grab samples is analyzed for TP, TN, and TOC. We use these montly grab samples to generate relationships between TP and SRP, and between TN and NO2- + NO3- + NH4+. Dissolved nutrients are measured using standard rapid flow analyzer (RFA) techniques. TP is analyzed with a modified Solorzano and Sharp (1980) technique. TN is measured with an Antec TN analyzer, TOC and DOC are quantified on a Shimadzu TOC Analyzer, and salinity is measured with a YSI conductivity meter. In addition to the regular water quality monitoring, we use the rain level actuators at all freshwater sites to trigger water sampling after rain events exceed a given threshold of duration and/or intensity. As currently programmed, when the threshold of = 2.5 cm of rain per hour is passed, the autosampler at that site collects a 1L sample every 15 minutes after the threshold has been reached and remains (previous to 2003- the autosampler was programmed to collect 500mL of water every 30 minutes while the threshold was being met). Rain event samples are collected, retrieved, and analyzed like out c
Sediment elevation measurements from the GCE LTER Seawater Addition Long-Term Experiment (SALTEx) sampling sites from July 2013 to August 2022
SALTEx (Seawater Addition Long-Term Experiment) is a field experiment designed to simulate saltwater intrusion in a tidal freshwater wetland to predict how chronic (Press) and acute (Pulse) salinization will affect this and other tidal freshwater ecosystems. The SALTEx experiment was initiated in 2012 and consists of 31 field plots, each 2.5 m on a side. There are three treatments (Press, Pulse, and Fresh) and two types of controls (with and without sides), each consisting of six replicates. The Press treatment plots receive regular (4 times each week) additions of a mixture of seawater and fresh river water. Pulse plots receive the same mixture of seawater and river water during September and October, which is historically a time of low flow in the river when natural saltwater intrusion occurs. The Fresh treatment plots receive regular additions of fresh river water. Treatment water is added during low tide to facilitate its infiltration into the soil, and all plots are inundated by astronomical tides at high tide. We are measuring soil surface elevation tables (SETs) in the plots as one of the response variables for the SALTEx project.
Abundance of the planthopper Prokelisia at GCE LTER sampling sites in October, 2003-2019
The abundance of the planthopper Prokelisia spp was estimated each year during the fall monitoring at each zone of each GCE site that was dominated by Spartina alterniflora. N Observations were made in the vicinity of the permanent vegetation plots and Prokelisia abundance was scored on a 5 point scale.
Annual monitoring of mid-marsh grazing scar at eight GCE LTER sampling sites from 2017 - 2025
Damage by chewing herbivores (grazing scars) was measured at eight sampling sites within the Georgia Coastal Ecosystems (GCE) LTER study area annually in July. Visual surveys were conducted along 8 2m by 10m transects randomly allocated within the mid-marsh zone at each site. Herbivore damage was measured by visually estimating the percent area of leaves missing (grazing scars) to haphazardly chosen leaves along the transect. This survey was conducted as part of the GCE invertebrate monitoring program, and will be performed annually to assess long-term changes in relative species abundances across the GCE study area. In 2024 only 3 sites were sampled due to time and weather constraints.
Fall 2022 grasshopper monitoring -- mid-marsh grasshopper abundance and species diversity at eight GCE LTER sampling sites
Grasshopper abundance and species diversity were investigated at eight sampling sites within the Georgia Coastal Ecosystems (GCE) LTER study area in July 2022. Visual surveys were conducted along 8 2m by 10m transects randomly allocated within the mid-marsh zone at each site. All grasshoppers observed within each transect were counted and identified to species, if possible. This survey was conducted as part of the GCE invertebrate monitoring program, and will be performed annually to assess long-term changes in relative species abundances across the GCE study area.
Fall 2023 grasshopper monitoring -- mid-marsh grasshopper abundance and species diversity at eight GCE LTER sampling sites
Grasshopper abundance and species diversity were investigated at eight sampling sites within the Georgia Coastal Ecosystems (GCE) LTER study area in August 2023. Visual surveys were conducted along 8 2m by 10m transects randomly allocated within the mid-marsh zone at each site. All grasshoppers observed within each transect were counted and identified to species, if possible. This survey was conducted as part of the GCE invertebrate monitoring program, and will be performed annually to assess long-term changes in relative species abundances across the GCE study area.
Fall 2024 grasshopper monitoring -- mid-marsh grasshopper abundance and species diversity at eight GCE LTER sampling sites
Grasshopper abundance and species diversity were investigated at eight sites within the Georgia Coastal Ecosystems (GCE) LTER study area in August 2024. Visual surveys were conducted along eight 2m by 10m transects randomly located within the mid-marsh zone at each site. All grasshoppers observed within each transect were counted and identified to species, if possible. This survey is conducted annually to assess spatial patterns of grasshopper abundance and long-term changes in relative species abundances across the GCE study area. In 2024, only 3 sites were sampled due to time and weather constraints.
WSC - Gridded sample points at Wibu field site including yield, soil texture, water table depth, and estimated soil water retention parameters
A variety of data from gridded sampling points at the Wibu field site. The gridded sampling scheme is described in the Point Locations dataset. This dataset includes 2012 and 2013 absolute and normalized yield, soil textural characteristics (organic content, porosity, bulk density, particle size metrics, % sand/silt/clay), a variety of water table depth metrics (mean, percentiles, sum exceedance values, moving averages), and soil water retention parameters estimated using the Rosetta pedotransfer function. It was collected as part of a study of the impacts of water table depth, soil texture, and growing season weather conditions on corn production at the Wibu field site, described in Zipper et al. (in review). 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.
Water samples collected for dissolved inorganic carbon and nutrient analysis during tidal creek lateral exchange measurements approximately every 15 minutes from beginning of flood tide to the following low tide, Rowley, MA, PIE LTER.
Measurement of the lateral exchange of nutrients, sediment, and carbon in tidal creek systems draining predominantly low-elevation marsh dominated by Spartina alterniflora and high-elevation marsh dominated by Spartina patens located in Rowley, MA.
Sample data for "Machine learning for large-scale forecasting"
<p>This dataset includes sample data for the Netherlands to run the machine learning baseline as described in the paper titled <em>Machine learning for large-scale crop yield forecasting</em>, accessible at <a href="https://doi.org/10.1016/j.agsy.2020.103016">https://doi.org/10.1016/j.agsy.2020.103016</a>. The software implementation of the machine learning baseline is available at: <a href="https://github.com/BigDataWUR/MLforCropYieldForecasting">https://github.com/BigDataWUR/MLforCropYieldForecasting</a>.</p> <p><strong>Notes:</strong></p> <p>The NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU and the UK (see Eurostat, 2016) for more details).</p> <p>Data</p> <p>The dataset consists of 11 CSV files. They are formatted to work as sample inputs to the machine learning baseline.</p> <ol> <li><strong>Crop Area Fractions </strong>(NUTS2, NUTS1): We aggregated the predictions of the machine learning baseline from NUTS2 to national (NUTS0) level by weighting them on the modeled crop area. Cerrani and López Lozano (2017) have described in detail the algorithm used to model crop areas for different NUTS levels. The data comes from the MARS Crop Yield Forecasting System (MCYFS) of European Commission's Joint Research Centre (JRC) (see Lecerf et al., 2019).</li> <li><strong>Centroids (NUTS2)</strong>: Data includes latitude, longitude and distance to coast of the centroids of NUTS2 regions.</li> <li><strong>Meteo Daily Data and Meteo Dekadal Data </strong>(NUTS2): The data comes from MCYFS (see EC-JRC, 2020). By default, the implementation uses daily data.</li> <li><strong>Remote Sensing Data</strong> (NUTS2, see Copernicus Global Land Service, 2020): Data includes fraction of absorbed photosynthetically active radiation (FAPAR) aggregated to NUTS2.</li> <li><strong>Soil Data</strong>: Data includes soil moisture information that can be used to calculate soil water holding capacity. The data comes from MCYFS (see Lecerf et al., 2019).</li> <li><strong>WOFOST data </strong>(NUTS2): The World Food Studies (WOFOST) crop model (van Diepen et al., 1989; Supit et al., 1994; de Wit et al. 2019) is a simulation model for the quantitative analysis of the growth and production of annual field crops. It is a mechanistic, dynamic model that explains daily crop growth on the basis of the underlying processes, such as photosynthesis, respiration and how these processes are influenced by environmental conditions. The crop simulation is fed by weather, soil and crop data. Observed meteorological data is interpolated on a regular 25 km grid using a method based on the distance, altitude and climatic region similarity between the center of grid cells and weather stations (see Van der Goot, 1998). WOFOST runs on the intersection between the 25 km meteorological grid and soil units based on the European soil map (http://esdac.jrc.ec.europa.eu/). In order to have the output data aggregated to administrative regions such as countries or provinces, simulation units are further intersected with the boundaries of these regions. The outputs at soil unit (STU) level are aggregated to grid level in an area weighted manner. Gridded simulations are aggregated to lowest NUTS level 3 considering the arable land area of each grid, derived from GLOBCOVER and CORINE Land Cover (Cerrani and Lopez Lozano, 2017). From NUTS3 to higher levels, crop area fractions for the current year, retrieved from Eurostat, are used to weight and aggregate the output (Cerrani and Lopez Lozano, 2017).</li> <li><strong>GAES data</strong>: GAES data includes agro-climatic features of regions, such as elevation and slope (from USGS-EROS, 2021), field size (from Lesiv et al., 2019), irrigated (crop) areas (from EC-JRC, 2020) and crop areas (from EC-JRC, 2020).</li> <li><strong>National yield statistics </strong>(NUTS0): These are the official Eurostat national yield statistics (Eurostat, 2020a). We used these yield statistics as reference to compare the machine learning predictions aggregated to NUTS0 and the actual MCYFS forecasts (see van der Velde and Nisini, 2019).</li> <li><strong>Regional yield statistics </strong>(NUTS2): We used NUTS2 yield statistics as labels to train and evaluate machine learning algorithms. We got NUTS2 yield statistics from The Central Bureau of Statistics (CBS) of the Netherlands (NL-CBS, 2020).</li> <li><strong>Past MCYFS Yield Forecasts </strong>(NUTS0): These are actual forecasts made by MCYFS in the past (see van der Velde and Nisini, 2019). We used the official Eurostat national yield statistics (see point 7 above) as the reference to compare the machine learning predictions aggregated to NUTS0 and MCYFS forecasts.</li> </ol> <p><strong>Crop ID and name mapping</strong></p> <p>2 : grain maize</p> <p>6 : sugar beets</p> <p>7 : potatoes</p> <p>90 : soft wheat</p> <p>93 : sunflower</p> <p>95 : spring barley</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We would like to thank S. Niemeyer from the European Commission’s Joint Research Centre (JRC) for the permission to provide open access to the Netherlands data. Similarly, we would like to thank M. van der Velde, L. Nisini and I. Cerrani from JRC for sharing with us past MCYFS forecasts and Eurostat national yield statistics.</p>
City of Seattle, Seattle Public Utilities, Riparian Permanent Sample Plots 2003-current, Cedar River Municipal Watershed, King County, WA
The City of Seattle’s Cedar River Municipal Watershed is managed to support and supply clean drinking water to the greater Seattle area. The watershed covers 91,000 acres, hosts a rich diversity of plants, animals, and habitats, and is owned by the City of Seattle. From 2003 to 2005, Seattle Public Utilities established 61 permanent sample plots (PSP) in riparian habitats of the Cedar River Municipal Watershed. 31 of these plots (plot #s 1-31) were established in 2003 upstream of Chester Morse Lake, the drinking water reservoir for the City of Seattle. 30 additional plots (plot #s 32-61) were established in 2005 downstream of Chester Morse Lake. In 2012, 6 of the 61 total plots (plot #s 4, 11, 12, 27, 28, 29) were resampled and the sampling methods were simplified. Each sampling year had different surveyors. The riparian permanent sample plots were limited to low gradient, moderately to unconfined reaches along the Cedar River and its tributaries because they typically show the most variability in riparian condition, stream-riparian interactions are greatest, and fish use is highest. Sites were randomly selected along these reaches, with two plots located at each site, one on each side of the stream. Sites were stratified into four stand types: deciduous dominated, mixed deciduous/conifer, conifer dominated in stem exclusion phase with little understory, and conifer dominated differentiated with well-developed understory, with at least five plots in each type. Each plot was measured for tree species and diameter, snag diameter and height, shrub species and cover, herb species and cover, sapling species and cover, percent canopy cover, and other species found in the plots but not previously recorded in the tree, shrub, and herb sampling. Selection of riparian plots for resampling in 2012 was designed to include plots in second and old growth forests and with medium and large stream sizes. Future resampling is expected to occur at an approximately ten-year interval. T
Concentration of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water years 2017 and 2018 (1 Oct 2016 - 30 Sep 2018)
These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are Aurora Total Organic Carbon combustion analyses of the concentration of organic carbon dissolved in filtered samples of well-mixed river thalweg water. Data are from the 2017 and 2018 water year (1 Oct 2016 to 30 Sep 2018). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA.
Concentration of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water year 2020 (1 Oct 2019 - 30 Sep 2020)
These data were collected to support monitoring of the Upper Clark Fork River restoration, and data collection was funded by the US NSF Long Term Research in Environmental Biology (LTREB) program and the US NSF EPSCoR funded Montana Consortium for Research on Environmental Water Systems. The LTREB monitoring project consists of monthly or bi-weekly water quality monitoring across a 200-km restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and heavy metal contamination. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are Aurora Total Organic Carbon combustion analyses of the concentration of organic carbon dissolved in filtered samples of well-mixed river thalweg water. Data are from the 2020 water year (1 Oct 2019 to 30 Sep 2020). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA.
Multispectral absorbance and fluorescence analysis of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water years 2017 and 2018 (1 Oct 2016 - 30 Sep 2018)
The Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) umbrella monitoring project generating these data is conducted separately and complementarily to the 200-million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the UCFR in western Montana includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river’s floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across the first 200 km of the river and its major tributaries along a gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The monitoring program began in 2017 with funding likely to be extended through 2028. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are multispectral absorbance and fluorescence analyses of organic carbon dissolved in samples of well-mixed river thalweg water. Data include excitation-emission matrices, absorbance spectroscopy, as well as absorbance and fluorometric summary indices calculated at specific wavelengths of excitation and emission. Data are from the 2017 and 2018 water years (1 Oct 2016 to 30 Sep 2018). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at 13 project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. These data are a correction of a previously published data product (doi:10.6073/pasta/6ba30f4ebb63175a4399c5d0aa6a8698). Inconsistencies between availability of EEMS data, absorbance data, and fluorometric summary metrics have been corrected. P
Concentration of nutrients in water samples collected from the upper Clark Fork River (Montana, USA) during water years 2017 and 2018 (1 Oct 2016 - 30 Sep 2018)
The LTREB monitoring project is a portion of the 200 million-dollar superfund project for ecological restoration of the Clark Fork River, associated tributaries, and head water streams including Silver Bow and Warm Springs Creek. Restoration along the Clark Fork River includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river's floodplain closest to contaminant sources. The LTREB monitoring project consists of bi-weekly water quality monitoring across a 200-km restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and heavy metal contamination. The LTREB monitoring project is conducted within the first 200km of the Clark Fork River and associated tributaries located in Western Montana. This LTREB monitoring program began in 2017 and will be completed in the year 2022 with potential for funding extension. Surface water samples represented in this data product are collected from thirteen sites along the mainstem of the upper Clark Fork River. Water samples are collected at each monitoring site in triplicate and filtered with a 0.7 µm glass fiber filter. Nutrient samples are analyzed using a spectrophotometric flow injection analyzer (AP2) for nitrate (N-NO3), soluble reactive phosphorus ((SRP) P-PO4), and ammonium (N-NH4) concentrations reported in mg/L. This data package excludes from the final data product all but three WY2017 NO3N data due to column inefficiency during most measurements. The valid, analysis-ready data of this dataset therefore primarily represent two sets of Quality Assurance and Quality Control (QAQC) processed data from thirteen sites along the mainstem of the upper Clark Fork River: NH4N and SRP concentrations collected in water year 2017 (1 Oct 2016 - 30 Sept 2017) and NH4N, SRP, and NO3N collected in water year 2018 (1 Oct 2017 - 30 Sept 2018).
Concentration of nutrients in water samples collected from the Upper Clark Fork River (Montana, USA) during water year 2020 (1 Oct 2019 - 30 Sept 2020)
The umbrella Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) monitoring project generating these data is conducted separately and complementarily to the $200 million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the Upper Clark Fork River includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river's floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across a 200-km gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The UCFR LTREB monitoring project is conducted within the first 200 km of the Upper Clark Fork River and associated tributaries located in western Montana. The current monitoring program began in 2017 and will be completed in the year 2023, with likely funding extension to 2028. Surface water samples represented in this data product are collected from fourteen sites along the mainstem of the UCFR, and three sites representing major tributaries to the UCFR. Water samples are collected at each monitoring site in triplicate and filtered with a 0.7-µm glass fiber filter. Nutrient samples are analyzed using a spectrophotometric flow injection analyzer (AP2) for nitrate (NO3-N), soluble reactive phosphorus (SRP, as representative of PO4-P), and ammonium (NH4-N) concentrations reported in mg/L. The analysis-ready data of this dataset therefore represent Quality Assurance and Quality Control (QAQC) processed NH4-N, SRP, and NO3-N concentrations from fourteen sites along the mainstem of the UCFR and three tributaries, collected in water year 2020 (1 Oct 2019 - 30 Sept 2020).
Concentration of nutrients in water samples collected from the Upper Clark Fork River (Montana, USA) during water year 2021 (1 Oct 2020 - 30 Sept 2021)
The umbrella Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) monitoring project generating these data is conducted separately and complementarily to the $200 million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the Upper Clark Fork River includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river's floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across a 200-km gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The UCFR LTREB monitoring project is conducted within the first 200 km of the Upper Clark Fork River and associated tributaries located in western Montana. The current monitoring program began in 2017 and will be completed in the year 2023, with likely funding extension to 2028. Surface water samples represented in this data product are collected from thirteen sites along the mainstem of the UCFR, and three sites representing major tributaries to the UCFR. Water samples are collected at each monitoring site in triplicate and filtered with a 0.7-µm glass fiber filter. Nutrient samples are analyzed using a spectrophotometric flow injection analyzer (AP2) for nitrate (NO3-N), soluble reactive phosphorus (SRP, as representative of PO4-P), and ammonium (NH4-N) concentrations reported in mg/L. The analysis-ready data of this dataset therefore represent Quality Assurance and Quality Control (QAQC) processed NH4-N, SRP, and NO3-N concentrations from thirteen sites along the mainstem of the UCFR and three tributaries, collected in water year 2021 (1 Oct 2020 - 30 Sept 2021).
Multispectral absorbance and fluorescence analysis of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water year 2019 (1 Oct 2018 - 30 Sep 2019)
The Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) umbrella monitoring project generating these data is conducted separately and complementarily to the 200-million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the UCFR in western Montana includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river’s floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across the first 200 km of the river and its major tributaries along a gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The monitoring program began in 2017 with funding likely to be extended through 2028. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are multispectral absorbance and fluorescence analyses of organic carbon dissolved in samples of well-mixed river thalweg water. Data include excitation-emission matrices, absorbance spectroscopy, as well as absorbance and fluorometric summary indices calculated at specific wavelengths of excitation and emission. Data are from the 2019 water year (1 Oct 2018 to 30 Sep 2019). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at 13 project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA.
Continuous stream CO2 and temperature data and sensor calibration grab samples from five NEON sites (CARI, COMO, KING, MART, WALK), August 2021-April 2024.
This package contains: 1) sensor-based measurements of dissolved CO2 concentration and temperature, and 2) dissolved CO2 concentration from grab samples that were used to calibrate the sensor data, collected at five stream sites in the NEON network (CARI- Caribou Creek, AK; COMO- Como Creek, CO; KING- Kings Creek, KS; MART- Martha Creek, WA; and WALK- Walker Branch, TN) between August 2021 - April 2024. The grab sample dataset contains a combination of samples collected by NEON (DP1.20097.001) and additional samples collected by project personnel. All samples were collected using the headspace equilibration method, and dissolved CO2 concentrations were calculated using the 'neonDissGas' R package (https://github.com/NEONScience/NEON-dissolved-gas). The sensor dataset contains CO2 concentrations measured with an eosGP CO2 gas probe, averaged to 15-minute intervals and corrected to align with grab sample concentrations using a site-specific grab versus sensor regression. Due to inaccuracies in the eosGP temperature data, we instead include the temperature data from NEON that was used to convert CO2 between units of ppmv and umol/L (DP1.20053.001 for CARI, KING, MART, and WALK, and data from the multiparameter sonde for COMO). All NEON data used in this data package references the RELEASE-2025 version of each data product (downloaded February 2025).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.