Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,111
datasets available to search
ShareScore release 0.7.1
Dataset results
9,111 results for “Sampling”
Particulate organic carbon and particulate organic nitrogen concentrations and stable isotope composition of seawater sampled during the Antarctic Circumnavigation Expedition (ACE) during the Austral Summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains particulate organic carbon and particulate organic nitrogen concentrations and stable isotope composition (delta 13C and delta 15N) sampled during the Antarctic Circumnavigation Expedition (ACE) Leg 1-3. Water samples were collected from the underway seawater supply every 3 hours, filtered onto pre-combusted glass fibre filters, acidified to remove inorganic compounds and analysed for both elements on the same filter using an elemental analyser. These samples provide an estimate of the organic carbon and organic nitrogen concentration and carbon and nitrogen stable isotope composition of living and detrital particles > 0.7 micrometres in size.</p> <p><strong>Dataset contents</strong></p> <ul> <li>README.txt, metatdata, text</li> <li>data_file_header.txt, metadata, text</li> <li>ace_uw_poc_pon_blanks_20200512CURRSGCMR.csv, data file, comma-separated values</li> <li>ace_uw_poc_pon_20200512CURRSGCMR.csv, data file, comma-separated values</li> </ul>
Seawater salinity sample measurements from the Antarctic Circumnavigation Expedition (ACE)
<p><strong>Dataset abstract</strong></p> <p>This data set contains salinity measurements from discrete seawater samples that were collected in the Southern Ocean (south of 30deg S) during the Antarctic Circumnavigation Expedition (ACE). 657 samples were collected during the period December 24th, 2016 and March 18th, 2017 in the Southern Ocean from the surface ocean using the ship's underway line (UW; 328 samples) and in vertical profiles using Niskin bottles mounted on the CTD rosette (273 samples). A few additional samples (56) were collected from a parallel cast with a trace-metal rosette, with a bucket, and as duplicates to ensure data quality. All samples were analyzed for their salinity and results are reported on the Practical Salinity Scale 1978 (PSS-78; Sea-Bird Electronics, Inc., 1989). Measurements were performed on a Guildline Autosal Laboratory Salinometer 8400(B) at CSIRO (Hobart, Australia) for samples collected during leg 1, and on a OPTIMARE Precision Salinometer (OPS) at the Alfred Wegener Institute (Bremerhaven, Germany) for samples collected during legs 2 and 3. This circumpolar data set provides insights into the hydrological cycle of the Southern Ocean and the processes (precipitation, evaporation, sea-ice melting and freezing, ice-berg and land-ice melting) that determine the salinity of a certain water mass. It is being used to calibrate the CTD sensor vertical profiles (Henry et al., 2020) and thermosalinograph sensor underway measurements (Haumann et al., 2020) from the ACE cruise.</p> <p><strong>Dataset contents</strong></p> <p>Processed Data</p> <ul> <li>ace_18_data_salinity_ctd_20200812.csv, text format; contains salinity measurements of seawater samples collected from the Niskin bottles mounted on the CTD rosette</li> <li>ace_18_data_salinity_uw_20200812.csv, text format; contains salinity measurements of seawater samples collected from the underway line</li> <li>ace_18_data_salinity_other_20200812.csv, text format; contains salinity measurements of miscellaneous samples: Duplicate seawater samples; seawater bucket sample from Cumberland Bay, South Georgia; seawater samples from Niskin bottles mounted on the trace-metal rosette.</li> </ul> <p>Metadata</p> <ul> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>figure*.pdf, metadata, portable document format</li> </ul> <p><strong>Dataset license</strong></p> <p>This physical and biogeochemical oceanography dataset is made available under the Creative Commons Attribution 4.0 License (CC BY 4.0) whose full text can be found at <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Gobabeb Site in Namibia
<p>The HYPERNETS project (www.hypernets.eu; Ruddick et al. 2024) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical satellite products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu; Kuusk et al. 2024) dedicated to land and water surface reflectance validation with instrument pointing capabilities. This instrument has been deployed over various sites covering a range of water and land types and a range of climatic and logistic conditions. Here, we provide the first fully quality-checked data for the Gobabeb HYPERNETS site in Namibia (GHNA). The HYPERNETS data products were processed using the HYPERNETS_processor (De Vis et al. 2024b).</p> <p>The provided NetCDF files are the L2B hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in these products is the Hemispherical-conical Reflectance Factor (HCRF) defined as: HCRF = π L / E where L is the conical upwelling radiance (with field of view of 5 degrees) 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 GHNA site has minimal daily variation in surface cover and weather conditions and is an ideal location for sustained, homogeneous measurements. The site is well characterised as it is very close to an instrument already recognised as a radiometric calibration site (GONA) as part of the RadCalNet network (Bialek et al. 2016). The HYPERNETS site itself (23.60153 degrees S, 15.12589 degrees E) is 650 m from the RadCalNet site, and is located on a gravel plain near a dry riverbed which separates it from the neighbouring dune sea. The HYPSTAR®-XR sensor was installed May 2022 at the top of a 9m mast on an extended 1 m horizontal boom to minimise interruption of the field of view. Data are collected every 30 minutes between 9am and 6pm local time (UTC+02) between viewing zenith angles of 0 and 60 degrees. No measurements are taken at 2pm and 2:30pm local time to avoid the hottest part of the day.</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 (De Vis et al. 2024b) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the 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). For an example using these data for satellite vicarious calibration, see De Vis et al. 2024a).</p> <p>To obtain this dataset, we start from the full GHNA data record and omit data that does not pass the relevant quality checks (QC). Some QC are performed during the near-real time processing done by the hypernets_processor (see https://hypernets-processor.readthedocs.io/en/latest/content/atbd/processing/quality_checks.html) to produce the L2A files. Then, a number of site-specific QC are performed as post-processing to produce the L2B files. These site-specific QC cover things such as removing flags in the L2A data, avoiding periods with bad deployment conditions, removing unsuitable viewing and solar angles, as well as poorly performing wavelength ranges and individual sequences. Any potential misalignment of the sensor is also corrected, affecting L1D irradiances, and L2B reflectances. These corrected data are then used in a more stringent clear sky check, and in a check that verifies the reflectances are within realistic ranges for a given angle and time of year for the given site. </p> <p>There was a rain event in Gobabeb in March 2025, resulting in the growth of grass at the site. We expect the site will be back to its normal surface cover in the near future. Since the rain event, less data passed the site-specific QC. A dedicated QC will be developed for this period, as the data with grass surface cover will still be useful for satellite validation. These updated data will be made available in the future. </p>
nuts-STeauRY dataset: hydrochemical and catchment characteristics dataset for large sample studies of Carbon, Nitrogen, Phosphorus and Silicon in french watercourses
<p><strong>nuts-STeauRY dataset: hydrochemical and catchment characteristics dataset for large sample studies of Carbon, Nitrogen, Phosphorus and Silicon in French watercourses</strong></p> <p>Antoine Casquin, Marie Silvestre, Vincent Thieu</p> <p>10.5281/zenodo.10830852</p> <p>v0.1, 18<sup>th</sup> March 2024</p> <p><strong>Brief overview of data: </strong></p> <p>· Carbon and nutrients data for 5470 continental French catchments</p> <p>· Modelled discharge for 5128 of catchments out of 5470</p> <p>· Geopackages with catchment delineations and outlets</p> <p>· DEM conditioned to delimit additional catchments</p> <p>· Land-use and climatic data for 5470 continental French catchments</p> <p><strong>Citation of this work<br></strong></p> <p>A data paper is currently being submitted with details of methods and results. Once published, it will be the preferential source to cite. The data paper will be link to the new version of the dataset that will be updated on doi.org/10.5281/zenodo.10830852. If you use this dataset in your research or report, you must cite it.</p> <p><strong>Motivations</strong></p> <p>Data was collected and curated for the nuts-STeauRY project (<a href="http://nuts-steaury.cnrs.fr">http://nuts-steaury.cnrs.fr</a>), which deployed a national generic land to sea modelling chain.</p> <p>Data was primarily used (see related works):</p> <ol> <li>To calibrate concentrations of dissolved organic carbon and dissolve silica in headwaters</li> <li>To validate spatially and temporally the modelling chain (DOC, NO3-, NH4+, TP, SRP, DSi)</li> </ol> <p>Hydrochemical large sample datasets have numerous other uses: trends computations elucidate transfer mechanisms, machine learning, retrospective studies etc.</p> <p>The objective here is to provide a large sample curated dataset of carbon and nutrients concentrations along with modelled discharges, catchment characteristics and delimitations for the continental France. Such large sample dataset aims at easing the large sample studies over France and/or Europe. Although part of the data gathered here is obtainable via public sources, the catchments delineations, their characteristics and modelled hydrology were note not publicly available yet. Moreover, a unification of units and detection and removal of outliers was performed on carbon and nutrients data.</p> <p><strong>Data sources & processing</strong></p> <p>Sampling points where snapped on the CCM database v2.1 (<a href="http://data.europa.eu/89h/fe1878e8-7541-4c66-8453-afdae7469221">http://data.europa.eu/89h/fe1878e8-7541-4c66-8453-afdae7469221</a>)(Vogt et al., 2007) and catchments were delineated using a 100m resolution Digital Elevation Model (DEM) conditioned by the hydrographic network and elementary catchments’ delineations of the CCM data v2.1. <strong>More than 6000 catchments were delineated and screened manually</strong> to check consistency: 5470 were retained<strong>.</strong></p> <p>Nutrient data was collected mainly through the Naiades portal (<a href="https://naiades.eaufrance.fr/">https://naiades.eaufrance.fr/</a>), a database collecting water quality data produced by different water related actors across France. Nutrient data was also collected directly with regional water agencies (<a href="https://www.eau-seine-normandie.fr/">https://www.eau-seine-normandie.fr/</a>, <a href="https://eau-grandsudouest.fr/">https://eau-grandsudouest.fr/</a>, <a href="https://www.eaurmc.fr/">https://www.eaurmc.fr/</a>, <a href="https://www.eau-artois-picardie.fr/">https://www.eau-artois-picardie.fr/</a>, <a href="https://www.eau-rhin-meuse.fr/">https://www.eau-rhin-meuse.fr/</a> and <a href="https://agence.eau-loire-bretagne.fr/home.html">https://agence.eau-loire-bretagne.fr/home.html</a>), and pre-processed using a database management system relying on PostgreSQL with PostGIS extension (Thieu & Silvestre, 2015). A three-pass strategy was used to curate raw carbon and nutrients data: 1. Removal of “obvious outliers”, 2. Detection of baseline change and correction if possible (or removal of data) 3. Removal of outliers using a quantile based approach by element and temporal series.</p> <p>Hydrological time series are interpolation trough hydrograph transfer (de Lavenne et al., 2023) of 1664 time series of discharge completed with GR4J model (Pelletier & Andréassian, 2020; Pelletier 2021).</p> <p>Land cover data was extracted from Corine Land Cover dataset for years 2000, 2006, 2012, and 2018 (EEA, 2020). Raw CLC typology contains 44 classes. Results of percent cover per year per class were computed for each catchment. An aggregated typology of 8 classes is also proposed.</p> <p>Climatological data was extracted from daily reconstruction at 5 arcmin for temperatures and 1 arcmin for precipitation over Europe (Thiemig et al., 2022). Mean by catchment for min&max daily temperature and precipitation were computed for each catchment for the 1990-2019 period.</p> <p><strong>Nuts-STeauRY dataset</strong></p> <p><strong>Carbon and nutrients time series</strong></p> <p>Time series of carbon and nutrients within the 1962-2019 period on 5470 stations: Dissolved Organic Carbon (DOC), Total Organic Carbon (TOC) Nitrates (NO3-), Nitrites (NO2-), Ammonia (NH4+), Soluble Reactive Phosphorus (SRP), Total Phosphorus (TP) and Dissolved Silica (DSi).</p> <p><code>|var | n_unique_station| n_total_meas| mean_duration_y| mean_frequency_y|</code></p> <p><code>|:---|----------------:|------------:|---------------:|----------------:|</code></p> <p><code>|DOC | 4 992| 658 147| 14.3| 9.0|</code></p> <p><code>|DSi | 3 299| 333 866| 12.9| 8.3|</code></p> <p><code>|NH4 | 5 318| 907 343| 19.3| 8.7|</code></p> <p><code>|NO2 | 5 264| 891 886| 19.2| 8.6|</code></p> <p><code>|NO3 | 5 465| 939 279| 19.0| 9.0|</code></p> <p><code>|SRP | 5 361| 910 107| 19.1| 8.7|</code></p> <p><code>|TOC | 935| 111 993| 13.6| 9.6|</code></p> <p><code>|TP | 5 199| 802 841| 17.1| 8.8|</code></p> <p>Note that some SRP and DSi measurements were declared as realized on raw water. A thorough analysis of time series show no evidence of difference on baselines. For more accuracy, it is advised to filter out those analyses using the “fraction” attribute of each measurement.</p> <p><strong>Discharge modelled daily time series</strong></p> <p>Modelled naturalized discharge through hydrograph transfer and interpolated measured discharges when available for the 1980-2019 period.</p> <p>A daily discharge was computed for 5128 catchments. For small catchments (< 1000 km<sup>2</sup>, n = 4530), hydrograph transfer was used, while for big catchments, a direct interpolation of measured/completed discharges was performed. The direct interpolation was only possible for 598 catchments > 1000 km<sup>2</sup>. The criteria retained for a direct interpolation is 0.8*area_discharge_station < area_quality < 1.2*area_discharge_station when discharge and quality stations were nested.</p> <p>Hydrological time series uncertainties varies a lot depending on: quality of data source, distance from pseudo-gauged outlets, land cover of the catchments, natural spatial and temporal variability of discharge, size of the catchment (de Lavenne et al., 2016). We advise a cautious use of those modelled discharges as uncertainties could not be computed.</p> <p><strong>Catchments, outlets and conditioned DEM</strong></p> <p>5470 catchments and outlets are delivered as geopackages (EPSG: 3035).</p> <p>The DEM, conditioned by CCM 2.1 is also delivered as a GeoTIFF (EPSG: 3035) as way to delimit new catchment for the area that are consistent with the dataset.</p> <p><strong>Catchments characteristics and climate</strong></p> <p>Refer to Data sources & processing and File descriptions.</p> <p><strong> </strong></p> <p><strong>File and attributes descriptions: </strong></p> <p>The key “sta_code” is present across all files. For time varying records, “date” can be a secondary key. </p> <p><strong>Description of CNPSi.csv data attributes</strong></p> <p>Each line is a couple measurement/parameter/station</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· var: Abbreviation of parameter name</p> <p>· fraction: "water_filtrated" or "water_raw"</p> <p>· date: date of sampling</p> <p>· hour: hour of sampling</p> <p>· value: analytical result (concentration)</p> <p>· provider: provider of the data</p> <p>· producer: producer of the data</p> <p>· from_db: "Naiades2022" (https://naiades.eaufrance.fr/france-entiere#/ dump from 2022) or "DoNuts" (Thieu, V., Silvestre, M., 2015. DoNuts: un système d’information sur les observations environnementales. Présentation Séminaire UMR Métis)</p> <p>· n_meas: number of observations for a given parameter / station</p> <p>· unit: unit of concentration</p> <p>· element: "C" "N" "P" or "Si"</p> <p>· year: year of observation</p> <p>· month: month of observation</p> <p>· day: day of observation</p> <p>· julian_day: julian day observation (1-366)</p> <p>· decade: decade of observation (one of "1961-1970", "1971-1980", "1981-1990", "1991-2000", "2001-2010", "2011-2020")</p> <p> </p> <p> </p> <p><strong>Description of CNPSi_stats.csv data attributes</strong></p> <p>Each line is a couple parameter / station</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· var: Abbreviation of parameter name</p> <p>· n_meas: number of observations for a given parameter / station</p> <p>· start_year: year of first observation for a given parameter / station</p> <p>· end_year: year of last observation for a given parameter / station</p> <p>· duration_y_tot: total duration of observation in years for a given parameter / station</p> <p>· duration_y_tot: duration of observation in years for a given parameter / station for years with at least 1 meas</p> <p>· mean_nmeas_per_y_tot: mean number of observations per year considering total duration</p> <p>· mean_nmeas_per_y_meas: mean number of observations per year considering years with measurements</p> <p>· is_fully_continuous: TRUE if at least one measurement per year for a given parameter / station</p> <p>· start_cont_seq: year in which starts the longest continuous sequence for a given parameter / station</p> <p>· end_cont_seq: year in which ends the longest continuous sequence for a given parameter / station</p> <p>· duration_y_cont_seq: duration in years for the longest continuous sequence for a given parameter / station</p> <p>· nmeas_cont_seq: number of measurements for the longest continuous sequence for a given parameter / station</p> <p>· mean_nmeas_per_y_cont_seq: mean number of observations per year for the longest continuous sequence for a given parameter / station</p> <p>· mean: mean value (concentration) for a given parameter / station</p> <p>· median: median value (concentration) for a given parameter / station</p> <p>· sd: standard deviation (concentration) for a given parameter / station</p> <p>· cv: coeficient of variation (concentration) for a given parameter / station</p> <p>· c05,c25,c50,c75,c95: centiles 5, 25, 50, 75 & 95 for a given parameter / station</p> <p><strong>Description of catchments.gpkg and outlets.gpkg data attributes</strong></p> <p>Each line is a catchment or an outlet (sampling point)</p> <p>File is a .gpkg (EPSG = 3035)</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· watercourse: Name of the water course (from spatial join on IGN BD Topo)</p> <p>· mun_name: Name of the municipality of the outlet (from spatial join on IGN BD Admin Express)</p> <p>· ccm_wso_id: Seaoutlet id from CCM v2.1 database</p> <p>· ccm_wso1_id: Elementary catchment id from CCM v2.1 database</p> <p>· ccm_strahler: Strahler order of the catchment from CCM v2.1 database</p> <p>· area_km2: Computed area in km2 of the catchment</p> <p><strong>Description of daily discharges data attributes</strong></p> <p>Each line corresponds to a daily modelled discharge at a quality station from 1980 to 2019</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· date: Date in format yyyy-mm-dd</p> <p>· flow_mm: Discharge expressed in mm.d-1</p> <p>· flow_m3s: Discharge expressed in m3.s-1</p> <p><strong>Description of climate data attributes</strong></p> <p>Each line in the pr_tmin_tmax_1990-2019_lt_mean.csv corresponds to a mean value within a catchment for the 1990-2019 period.</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· period: 1990-2019</p> <p>· source: EMO-1 (pr) & EMO-5 (tmin, tmax)</p> <p>· pr: mean yearly precipitation (mm)</p> <p>· tmin: mean daily minimal temperature (°C)</p> <p>· tmin: mean daily maximal temperature (°C)</p> <p><strong>Description of land cover data attributes</strong></p> <p>Each line in the clc_8class.csv and clc_44class.csv corresponds to Corine Land Cover (CLC) class for a year (1990, 2000, 2006, 2012, or 2018) and a catchment. Raw CLC typology describes 44 classes that were aggregated to 8 classes (see clc_44class_to_8class.csv).</p> <p>· clc_44class.csv</p> <p>o sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>o year: Year as stated in CLC product</p> <p>o clc_name: Description of land cover class in CLC product</p> <p>o clc_code: Code for land cover class in CLC product</p> <p>o percent_cover: Percent cover by CLC class in the catchment (0-100)</p> <p>· clc_8class.csv</p> <p>o sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>o year: Year as stated in CLC product</p> <p>o label_clc_8class: Description of land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p>o code_clc_8class: Code for land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p>o percent_cover: Percent cover by aggregated CLC class in the catchment (0-100)</p> <p>· clc_44class_to_8class.csv</p> <p>o code_clc: Code for land cover class in CLC product (44 classes)</p> <p>o code_clc_8class: Code for land cover class in aggregated CLC product (8classes)</p> <p>o label_clc_8class: Description of land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p> </p> <p> </p> <p><strong>Acknowledgement</strong></p> <p>This publication has been prepared using European Union's Copernicus Land Monitoring Service information; <a href="https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac">https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac</a></p> <p>The authors thank Vasken Andréassian for communicating the discharge data and discharge station data and Alban de Lavenne for its help in using the transfr package, both for INRAE UR HYCAR.</p> <p> </p> <p><strong>References</strong></p> <p>de Lavenne, A., Skøien, J. O., Cudennec, C., Curie, F., & Moatar, F. (2016). Transferring measured discharge time series: Large-scale comparison of Top-kriging to geomorphology-based inverse modeling: transferring measured discharge time series. Water Resources Research, 52(7), 5555–5576. https://doi.org/10.1002/2016WR018716</p> <p>de Lavenne, A., Loree, T., Squividant, H., & Cudennec, C. (2023). The transfR toolbox for transferring observed streamflow series to ungauged basins based on their hydrogeomorphology. Environmental Modelling & Software, 159, 105562. <a href="https://doi.org/10.1016/j.envsoft.2022.105562">https://doi.org/10.1016/j.envsoft.2022.105562</a></p> <p>EEA. (2020). Corine Land Cover édition 2018. CLC 2018. <a href="https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-corine">https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-corine</a></p> <p>Pelletier, A., & Andréassian, V. (2020). Hydrograph separation: An impartial parametrisation for an imperfect method. Hydrology and Earth System Sciences, 24(3), 1171–1187. <a href="https://doi.org/10.5194/hess-24-1171-2020">https://doi.org/10.5194/hess-24-1171-2020</a></p> <p>Pelletier, A. (2021). Complétion d'hydrogrammes avec le modèle GR4J - Note méthodologique. INRAE, UR HYCAR.</p> <p>Thiemig, V., Gomes, G. N., Skøien, J. O., Ziese, M., Rauthe-Schöch, A., Rustemeier, E., Rehfeldt, K., Walawender, J. P., Kolbe, C., Pichon, D., Schweim, C., and Salamon, P.: EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe, Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, 2022</p> <p>Thieu, V., Silvestre, M., 2015. DoNuts : un système d'information sur les observations environnementales. Présentation Séminaire UMR Métis</p> <p>Vogt, J., A. de Jager, E. Rimaviciute, W. Mehl, S. Foisneau, K. Bódis, J. Dusart, M.L. Paracchini, P. Haastrup, & C. Bamps. (2007). A pan-European river and catchment database. (European Commission. Joint Research Centre. Institute for Environment and Sustainability.). Publications Office. https://data.europa.eu/doi/10.2788/35907</p>
Summaries of temperature and water table depth prior to peat sampling in Stordalen Mire, 2011-2017
<div> <p>This dataset provides summaries of temperature (T) and water table depth (WTD) conditions prior to the collection of peat samples from Stordalen Mire, Sweden, in July of 2011-2017. These summaries include the following files:</p> <h2><strong>t_wtd_summaries_July2011-2017samplings.csv</strong></h2> </div> <p>This file gives summary statistics over various time intervals for the following environmental measurements:</p> <ul> <li><strong>AirTemperature</strong>: Mean daily air temperature (°C), obtained from automatic sensors at the nearby Abisko Scientific Research Station (ANS) (station ID 188790; the source file [ANS_Daily_Wx_Jul84_Dec17.txt] is not included due to sharing restrictions).</li> <li><strong>WTD</strong>: Water table depths (cm), obtained from <a href="https://doi.org/10.5281/zenodo.10420396">Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)</a> (from Patrick Crill et al.).</li> </ul> <p>The time intervals for these summaries are defined relative to the peat sampling date at each site (see <a href="https://doi.org/10.5281/zenodo.12827096">EMERGE Sample Metadata Sheet for Samples with Microbiomes</a>), which varies by site and year. The specific intervals are defined as follows:</p> <ul> <li><strong>7d</strong>: 7 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>14d</strong>: 14 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>21d</strong>: 21 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>28d</strong>: 28 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>growing</strong>: Time from beginning of growing season (defined as June 1) until (and including) the sampling date.</li> <li><strong>all_growing</strong>: Entire growing season (June 1 – Sept. 30).</li> </ul> <p>For clarity, the start and end dates for each time interval (inclusive) are also given under the columns <strong>Start_Date</strong> and <strong>End_Date</strong>, where End_Date=<strong>Sampling_Date</strong> for all intervals except all_growing.</p> <p>Summary statistics for each interval include: measurement count (<strong>n</strong>), median (<strong>median</strong>), mean (<strong>mean</strong>), and standard deviation (<strong>sd</strong>), and are given under the column names beginning with these statistic labels.</p> <p><em>IMPORTANT NOTE: </em>For temperature, these statistics are calculated based on the average temperature measured on each day, meaning that<strong> </strong><em>the standard deviations do NOT account for within-day temperature variation.</em> To provide short-term (1 day) temperature variation context for each sampling date, the within-day mean, minimum, and maximum air temperatures for the sampling date only (taken directly from the corresponding row & columns in the source ANS data file) are provided in the columns <strong>samplingdate_mean_AirTemperature</strong>, <strong>samplingdate_min_AirTemperature</strong>, and <strong>samplingdate_max_AirTemperature</strong>.</p> <div> <div> <h2><strong>wtd_summaries_July2011-2017samples.csv</strong></h2> </div> <p>This file gives the percentage of time that each peat sample's depth midpoint (<strong>DepthAvg__</strong>) was at or below the water table depth (WTD), over each of the longer time intervals (≥21 days) defined above for the temperature & WTD summaries. (Intervals <21 days are not included due to the lower frequency of WTD measurements, which results in low <em>n</em> for shorter intervals.)</p> <p>The first few columns are taken directly from the <a href="https://doi.org/10.5281/zenodo.12827096">EMERGE Sample Metadata Sheet for Samples with Microbiomes</a>, for the samples collected in July of 2011-2017 from the MainAutochamber sites. The last set of columns include the following, with the time interval labels (defined as in the above temperature summaries) appended at the end of each column name:</p> <ul> <li><strong>n_WTD_*</strong>: Number of WTD measurements used in the calculation.</li> <li><strong>pct_time_below_WTD_*</strong>: Fraction (relative to 1) of measured WTDs over the given time interval that were at or above the DepthAvg__ for each sample, which equates to the fraction of measurement timepoints during which the given sample was at or below the WTD. This is the same method used for calculating "% Time below water table" in Figure 6 of <a href="https://doi.org/10.1038/s41396-018-0065-5">Singleton et al. (2018)</a>. For palsa sites, this value is automatically set to 0 based on the lack of a water table at all timepoints in the analysis.)</li> </ul> <p>As above, the WTD values used for these calculations were obtained from <a href="https://doi.org/10.5281/zenodo.10420396">Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)</a> (Patrick Crill et al.).</p> <h1>Funding acknowledgments</h1> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>This research was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.</p> <p>The temperature summary has been made possible by data provided by Abisko Scientific Research Station and the Swedish Infrastructure for Ecosystem Science (SITES).</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.</p> </div>
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>
BiVib - Audio-Tactile Piano Sample Library
<p><strong>BiVib</strong> is an extensive piano sample library consisting of <strong>bi</strong>naural sounds and keyboard <strong>vib</strong>ration signals.<br>Samples were acquired with high-quality audio and vibration measurement equipment on two <a href="https://en.wikipedia.org/wiki/Disklavier">Yamaha Disklavier pianos</a> (one grand and one upright model) by means of computer-controlled playback of each key at ten different MIDI velocity values.<br>Project files (<em>instruments</em> and <em>multis</em>) are provided for use with the software sampler <a href="https://www.native-instruments.com/en/products/komplete/samplers/kontakt-6/">Native Instruments Kontakt</a> (version 5 and above, available for Windows and Mac OS).<br>The nominal specifications of the equipment used in the acquisition chain are reported in a companion document, allowing researchers to calculate physical quantities (e.g. acoustic pressure, vibration acceleration) from the recordings.<br>The library is especially suited for acoustic and vibration research on the piano, as well as for research on multimodal interaction with musical instruments.</p>
Seawater stable isotope sample measurements from the Antarctic Circumnavigation Expedition (ACE)
<p><strong>Dataset abstract</strong></p> <p>This data set contains oxygen and hydrogen isotope measurements from discrete seawater samples that were collected in the Southern Ocean (south of 30 deg S) during the Antarctic Circumnavigation Expedition (ACE). 637 samples were collected during the period December 24th, 2016 and March 18th, 2017 in the Southern Ocean from the surface ocean using the ship's underway line (UW; 338 samples) and in vertical profiles using Niskin bottles mounted on the CTD rosette (287 samples). A few additional samples were collected from a parallel cast with a trace-metal rosette, with a bucket, and from the surface of a tabular iceberg. All samples were analyzed for their oxygen isotopic composition (reported as permille deviation of the oxygen-18 to oxygen-16 ratio from VSMOW2: DEL18O) and a few samples (80) from the Pacific sector were analyzed for their hydrogen isotopic composition (reported as permille deviation of the hydrogen to deuterium ratio from VSMOW2: DELD) by mass spectrometry at the British Geological Survey. This circumpolar data set provides insights into the hydrological cycle of the Southern Ocean and the processes (precipitation, evaporation, sea-ice melting and freezing, iceberg and land-ice melting) that determine the salinity of a certain water mass.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_18_data_d18o_dd_ctd_20190219.csv, data file, comma-separated values</li> <li>ace_18_data_d18o_dd_other_20190219.csv, data file, comma-separated values</li> <li>ace_18_data_d18o_dd_uw_20190219.csv, data file, comma-separated values</li> <li>README.md, metadata, markdown</li> </ul>
Simulated metagenomes with quality and abundance distributions derived from real samples
<p>Species abundances and quality values were derived from the following list of samples:</p> <pre><code>SAMEA2466896 SAMEA2466916 SAMEA2466952 SAMEA2466953 SAMEA2466965 SAMEA2466996 SAMEA2467015 SAMEA2467039 SAMEA2621010 SAMEA2621033 SAMEA2621107 SAMEA2621155 SAMEA2621229 SAMEA2621247 SAMEA2621300 SAMEA2622357 </code></pre> <p>Reference abundances (.abund files) were generated using <a href="https://github.com/motu-tool/mOTUs_v2">mOTUs profiler</a>.<br> Metagenomes were simulated with <a href="https://sourceforge.net/projects/cmessi/">cMESSi</a> using <a href="http://progenomes.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> for species and the aforementioned abundances. In cases where a <em>ref_mOTU_v2</em> corresponded to more than one genome, the abundance of said <em>ref_mOTU</em> was distributed equally over all genomes.<br> GFF location files were produced using location information generated by cMESSi.<br> Two variants of truth values were obtained by intersecting coordinates of simulated reads with coordinates of <a href="http://eggnogdb.embl.de">eggNOG</a> orthologous groups (OG at NOG level) as predicted by <a href="https://github.com/jhcepas/eggnog-mapper">eggNOG-mapper</a>.</p> <ol> <li>.cog-simulated files contain the NOG distribution that was effectively simulated, <em>i.e.</em> a count of the number of reads overlapping with genes annotated with each NOG. A read overlapping multiple genes is considered for each gene. If a gene possesses multiple NOG annotations, each annotation gets assigned the total number of overlapping reads. Longer genes will (in expectation) generate more reads, all else being equal.</li> <li>.cog-distribution file contains the expected distribution for every NOG on all samples. The number of genes annotated with each NOG is multiplied by the abundance of the corresponding species. Length of the gene is not taken into account.</li> </ol> <p>If you use this dataset, please cite: <a href="https://www.biorxiv.org/node/111718.full">NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language</a></p>
Concentration of nanoparticles per mL for water samples collected from Venice Lagoon
<p>The concentration of nanoparticles from surface seawater collected from the three sites of Venice Lagoon, Venice-Lido Port Inlet, Grand Canal under Rialto Bridge, and Saint Marc basin was analyzed via the Nanoparticle Tracking Analysis technique. Five replications were tested for each sample. Sampling locations: Venice-Lido Port Inlet, GPS coordinates: latitude: 45.431508, longi-tude: 12.406952; Grand Canal under Rialto Bridge, GPS coordinates: latitude: 45.438350, longitude: 12.336311; and Saint Marc basin, GPS coordinates: latitude: 45.431962, longitude: 12.340953.</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>
Dataset for paper "Target selection for Near-Earth Asteroids in-orbit sample collection missions"
<p>This dataset can be used to reproduce the results of the paper titled "Target selection for Near-Earth Asteroids in-orbit sample collection missions."</p> <p>The "results" folder contains the data to reproduce the maps and the rankings of the target asteroids.</p> <p>The "trajectories" folder contains the propagation of the sample trajectories used to obtain the grids.</p>
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>
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>
Concentration 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)
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 2019 water year (1 Oct 2018 to 30 Sep 2019). 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.
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
Long-term trends in pesticide residues and physical chemical parameters of superficial water samples with accompanying macro-benthic invertebrate community surveys from the Palo Verde National Park, Costa Rica: 1993-1994; 2001; 2004-2005; 2009-2011
During the years 1993-1994, 2001, 2003-2005 and 2009-2011, the Central American Institute for Studies on Toxic Substances (IRET-UNA) executed independent research projects which quantified the presence of pesticide residues on superficial water samples from the Palo Verde National Park (PVNP) and surrounding areas. The PVNP (5460 sq km) is a RAMSAR wetland of international importance, which has been subjected to pesticide pressure from agricultural fields (mainly rice and sugarcane) since the 1960s and 1970s. In 1993, the PVNP wetlands were placed on the RAMSAR Montreux Record, indicating that it was considered an “impaired ecosystem” due to ecotoxicology concerns. Water is the key component of all issues regarding the biodiversity, management, restoration, and economic development of this region. Therefore, water quality is a critical component of many social ecological discussions and research efforts. This data package contains uniform pesticide, biological and water quality data from all PVNP wetland projects (1993- 2011) in order to present long-term trends in the environmental water quality and accompanying biological patterns for this conservation area. Study sites were spatially determined to compare clean upstream waters with a gradient of pesticide-affected waters. Superficial water samples were collected at various sites for chemical (pesticide) analysis and water quality parameters were recorded in situ for environmental monitoring. Corresponding biological sampling was completed to survey benthic macroinvertebrate communities and compare with local eco-toxicological profiles. This data package contains information from four separate projects.
Concentration of nutrients in water samples collected from the Upper Clark Fork River (Montana, USA) during water year 2019 (1 Oct 2018 - 30 Sept 2019)
The umbrella LTREB monitoring project generating these data is conducted separately and complementarily to the $200 million-dollar (USD) superfund project for ecological restoration of the Upper Clark Fork River (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 Long Term Research in Environmental Biology (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 one site representing a major tributary 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 one tributary, collected in water year 2019 (1 Oct 2018 - 30 Sept 2019).
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.
ScienceDex guides
Understand access before you commit
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.