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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
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>
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).
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.
Bacterial Production Data for lake and stream samples collected in summer 2012 through 2021, Arctic LTER, Toolik Lake Field Station, Alaska
File containing data on bacterial productivity in lakes and streams. Samples were collected at various sites near Toolik Lake Field Station (68 38'N, 149 36'W). Sample site descriptors include an assigned number (sortchem), site, date, time and depth, and bacterial production.
Little Rock Lake Experiment at North Temperate Lakes LTER: Secchi Disk Depth; Other Auxiliary Sample Data 1983 - 2000
The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. Secchi Disk data were collected from the treatment and reference basins of Little Rock Lake at one station in the deepest part of each basin. Auxiliary data associated with each sampling event include time of day, air temperature, wind direction and speed, wave height, and cloud cover at the time of sampling. Sampling Frequency: varies - Number of sites: 2
Antarctic Circumnavigation Expedition event log: recording data and sample collection in the Southern Ocean during the austral summer of 2016/17.
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE) spent 90 days circumnavigating Antarctica on the R/V Akademik Tryoshnikov during the austral summer of 2016/17. This dataset provides a record of the instrument deployments as well as dataset and sample collection events that took place during the expedition.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_events.csv, data file, comma-separated values</li> <li>sampling_method_descriptions.csv, metadata, comma-separated values</li> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This event log is made available under a Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p> <p> </p>
Raw Data on Extracellular Particles in 613 Human and 163 Canine Diluted Plasma and Blood Samples Assessed by Interferometric Light Microscopy
<p><span>Extracellular nanoparticles (EPs) are cellular fragments. After being released in cell exterior, they become mediators of the cell-cell interaction. Their characterization in bodily fluids may reflect the clinical status of the organism. Here we present data on the number density <em>n</em> and hydrodynamic diameter <em>D</em><sub>h </sub>of EPs assessed directly in diluted plasma and blood by using a recently developed technique, Interferometric Light Microscopy (Romolo et al., 2022). The data are presented in the attached Table. </span></p> <p><span>We collected 613 blood and plasma samples from human patients with Inflammatory Bowel Disease (IBD) taken into tubes with trisodium citrate and ethylenediaminetetraacetic acid (EDTA) anticoagulants and 163 blood and plasma samples from canine patients with Brachycephalic Obstructive Airway Syndrome (BOAS). </span><span>The human study was conducted in accordance with the Declaration of Helsinki, and approved by the National Medical Ethics Committee of the Republic of Slovenia (0120-271/2022/4; KME 27 July 2022). All procedures in the animal study complied with the relevant Slovenian government regulations (Animal Protection Act, Official Gazette of the Republic of Slovenia, No. 43/2007). The animal study was approved by the Animals in Experiments Welfare Commission of the Veterinary Faculty, University of Ljubljana, approval number 18-3/2022-1. </span><span>Information regarding sample preparation is documented in the MIBlood-EV reports.</span></p> <div> <div> <div><span><a name="_msocom_1"></a></span></div> </div> </div>
Sample data for "A weakly supervised framework for high resolution crop yield forecasts"
<p>This dataset includes sample data for the United States to run the weakly supervised framework as described in the paper titled <em>A weakly supervised framework for high resolution crop yield forecasts</em>, accessible at </p> <table summary="Additional metadata"> <tbody> <tr> <td><a href="https://doi.org/10.48550/arXiv.2205.09016">https://doi.org/10.48550/arXiv.2205.09016</a></td> </tr> </tbody> </table> <p> </p> <p>The updated paper (including results from the US) is published in Environmental Research Letters:</p> <p><a href="https://doi.org/10.1088/1748-9326/acf50e">https://doi.org/10.1088/1748-9326/acf50e</a></p> <p> </p> <p>The software implementation of the machine learning baseline is available at: https://github.com/BigDataWUR/MLforCropYieldForecasting/tree/weaksup.</p> <p> </p> <p>Data</p> <p>1. County data (county-data.zip) for county-level strongly supervised models:</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p> <p>* CSSF_COUNTY_US.csv: Crop productivity indicators including total above-ground production (kg ha<sup>-1</sup>), total weight of storage organs (kg ha<sup>-1</sup>), development stage (0-2). Source: de Wit et al. (2022).</p> <p>* METEO_COUNTY_US.csv: Meteo data including maximum, minimum, average daily air temperature (℃); sum of daily precipitation (PREC) (mm); sum of daily evapotranspiration of short vegetation (ET0) (Penman-Monteith, Allen et al., (1998)) (mm); climate water balance = (PREC - ET0) (mm). Source: Boogaard et al. (2022).</p> <p>* REMOTE_SENSING_COUNTY_US.csv: Fraction of Absorbed Photosynthetically Active Radiation (Smoothed) (FAPAR). Source: Copernicus GLS (2020).</p> <p>* SOIL_COUNTY_US.csv: Soil water holding capacity. Source: WISE Soil Property Database (Batjes, 2016).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p> </p> <p>2. 10-km grid data (grid-data.zip) for grid-level strongly supervised models:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level (similar to county data above).</p> <p>* METEO_GRIDs_US.csv: Meteo data at 10km grid level (similar to county data above).</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level (similar to county data above).</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level (similar to county data above).</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021), Lobell et al. (2020).</p> <p> </p> <p>3. County labels and 10-km grid inputs (dscale-US.zip) for weak supervision:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level.</p> <p>* METEO_GRIDs_US.csv: Meteo indicators at 10km grid level.</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level.</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level.</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p>
Single-pulsar search for eccentric SMBHBs using NANOGrav 12.5-year data of PSR J1909--3744: Posterior samples
<p>This repository contains posterior samples for a Bayesian single-pulsar search for nanohertz gravitational waves originating from eccentric supermassive binaries, done using the NANOGrav 12.5-year dataset for PSR J1909-3744. The analysis is presented in Susobhanan 2023 [https://arxiv.org/abs/2210.11454].</p>
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
Stream Storm Sample Data from White Clay Creek (WCC) Watershed in 2021
These data were collected to explore Nitrogen update dynamics in the water column of small streams during storm events. Data was collected using a combination of passive sampling and grab sampling of suspended sediments from the stream channel. Analysis of samples include: dissolved and total chemistry, suspended sediment load, biological and microbiological characteristics, and Nitrogen uptake rates.
Hydrochemical Data from a Tropical Andean Glacierized Catchment: δ18O, electrical conductivity, maximum fluorescence intensity, and dissolved organic carbon concentrations from short-term sampling campaigns, Ecuador (2022 and 2024)
Fluorescent dissolved organic matter (FDOM) quality, dissolved organic carbon (DOC) concentration, electrical conductivity (EC), and stable water isotopes (δ¹⁸O and δ2H) were determined in water, snow, and ice samples from a tropical glacierized catchment in the Ecuadorian Andes. The sampling locations were selected to capture the major hydrologic inputs to the main stream channel (glacial melt, tributaries, wetlands, and groundwater springs) and constrain the in-stream spatiotemporal variation in DOM quality and other hydrochemical characteristics. Two sets of high-resolution time series were collected on Oct 13, 2022 and Jun 14, 2024. Time series samples were collected at various upper catchment locations and the outlet simultaneously. DOM quality was characterized via fluorescence spectroscopy and processed using parallel factor analysis (PARAFAC). The DOM quality data are expressed as %FMax values obtained through a 4-component PARAFAC model, where %Fmax 1– 4 are interpreted as terrestrial humic-like, tyrosine-like, tryptophan-like, and microbial humic-like fluorescent components, respectively. DOC concentrations were quantified using high-temperature catalytic combustion, stable water isotopes were analyzed using laser-based spectroscopy, and EC was measured in situ with handheld multiparameter water quality probes.
Environmental Data for Soil, Leaf, and Root samples Boston Street Trees and Massachusetts Rural and Urban Forests in Summer 2021
This dataset provides detailed environmental and tree-level data and metadata for over 850 samples collected from 91 trees across an urban-to-rural gradient in Massachusetts. The dataset captures key variables characterizing urban environmental gradients, including soil moisture, pH, temperature, and nitrogen availability. Tree-level attributes include species identification, diameter at breast height (DBH), and growth rate based on previous tree census data. Geographic coordinates and site-specific context (urban forest, rural forest, street tree, forest edge, forest interior) are included to enable spatial analyses. The microbial sequence data associated with this environmental metadata can be found in the NCBI SRA under BioProject accession number PRJNA1297772.
Vegetation Data Collected with Point Frame for 83 Locations of 6-163 Years Old Black Spruce, Alaska Paper Birch, and Aspen Stands Across Interior Alaska. Sampled in 2008-2010 and 2013-2015.
This dataset contains point frame data for vegetation less than 1.3 m, including vascular plants, bryophytes, lichens, leaf litter and bare ground, as well as species codes used, as described in Jean et al. 2017 Canadian Journal of Forest Research. Samples of all encountered unknown species were collected for identification in the lab. Bryophyte nomenclature followed Anderson et al. (1990).
Bryophyte Cover Summary Data for 83 Locations of 6-163 Years Old Black Spruce, Alaska Paper Birch, and Aspen Stands Across Interior Alaska. Sampled in 2008-2010 and 2013-2015.
This dataset contains the summarized bryophyte (percent cover) data obtained from point frame measurements, as used in Jean et al. 2017 Canadian Journal of Forest Research. ?Samples of all encountered unknown species were collected for identification in the lab. Bryophyte nomenclature followed Anderson et al. (1990).
Water Quality Data (Grab Samples) from the Shark River Slough, Everglades National Park (FCE LTER), Florida, USA, May 2001 - ongoing
Water quality samples are being collected using ISCO autosamplers at all Florida Coastal Everglades Long Term Ecological Research (FCE LTER) Program 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 filter (Whatman GF/F from 2000 to 2017 and Whatman polydisc GW from 2017 to ongoing) 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 and TN. We use these monthly 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 ANTEK 9000N analyzer, TOC and DOC are quantified on a Shimadzu TOC Analyzer, and salinity is measured with a YSI conductivity meter or refractometer. In January 2007 a 6 days interval began for SRS1d, SRS2 & SRS3; the ISCOs did not change their sampling scheduled, they kept collecting water sample every 18 hours and switching bottles every three days; once in the lab, the 3 days was turned into 6 days by combining two bottles into 2 liters container. In addition to the regular water quality monitoring, we use the rain level actuators at all freshwater sites to trigger water sampling after rai
Water Quality Data (Grab Samples) from the Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, May 2001 - ongoing
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 500mL sample 30 minutes after the threshold has been reached. Rain event samples are collected, treated, and analyzed as all other water quality samples.
Water Quality Data (Grab Samples) from the Taylor Slough, Everglades National Park (FCE), Florida, USA, September 1999 - ongoing
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 500mL sample 30 minutes after the threshold has been reached. Rain event samples are collected, treated, and analyzed as all other water quality samples.
Periphyton and Associated Environmental Data Relative from Samples Collected from the Greater Everglades, Florida, USA from September 2005 to November 2014
This data package contains peripihyton and environmental data collected annually during the wet season between 2005 and 2014 from sites distributed throughout the greater Everglades ecosystem. This project is part of the Comprehensive Everglades Restoration Program's Monitoring and Assessment Plan intended to document baseline variability in periphyton attributes for assessing the effectiveness of restoration projects. A total of 200 primary sampling units (PSU) of 800 m x 800 m are nested in 32 landscape units and each year, random coordinates are 'drawn' within each PSU and one sampleable draw is visited in each. Sampled periphyton is processed for diatoms, slides are prepared, and 500 frustules are enumerated and identified to the lowest possible taxonomic resolution per slide. Taxon abundances are then relativized to the total count. These data accompany environmental, periphyton biomass, and soft algal abundance datasets.
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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.