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Transparent exopolymer particle (TEP) and Coomassie stainable particle (CSP) data collected from the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>TEP are operationally defined as gel particles that are retained on 0.4 µm polycarbonate filters and stained with the cationic copper phthalocyanine dye Alcian Blue 8GX at pH 2.5. CSP are gel particles retained on 0.4 µm polycarbonate filters that are stained with a solution of Coomassie Brilliant Blue G (CCB) at pH 7.4. Seawater surface samples (5 m) were collected every 6 hours from the ship’s underway pump. In addition, vertical profiles (6 depths, generally from 5 to 100-150 m) were sampled from 19 CTD casts using a SBE 911 Plus attached to a rosette of 24 12-L PVC Niskin bottles. This dataset presents TEP and CSP from seawater samples collected from the ship’s underway pump and CTDs. Samples were collected around the Southern Ocean on the R/V Akademik Tryoshnikov in the austral summer of 2016/2017, as part of the Antarctic Circumnavigation Expedition (ACE).</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_seawater_csp.csv, data file, comma-separated values</li> <li>ace_seawater_tep.csv, data file, comma-separated values</li> <li>data_file_header_csp.txt, metadata, text</li> <li>data_file_header_tep.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This TEP and CSP dataset from ACE is made available under the 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>
Quality-checked horizontal particle flux data collected using a snow particle counter on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Flux of particles (snow, rain and other particles including sea spray) were recorded passing through a photo-electric snow particle counter installed on board the R/V Akademik Tryoshnikov as part of the Antarctic Circumnavigation Expedition (ACE). Data were recorded from January to March 2017 in the Southern Ocean. Here we present the finalised, quality-checked, horizontal particle flux data where counts have been averaged over a one-minute period.</p> <p><strong>Dataset contents</strong></p> <ul> <li>SPC_HPF_windtrue_1min.csv, data file, comma-separated values</li> <li>SPC_HPF_windtrue_1min.png, metadata, portable network graphics</li> <li>SPC_HPF_windtrue_saveplot.py, script, Python code</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This quality-checked horizontal particle flux dataset from ACE is made available under the 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>
Intermediate processing stage of horizontal particle flux data collected using a snow particle counter on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Flux of particles (snow, rain and other particles including sea spray) were recorded passing through a photo-electric snow particle counter installed on board the R/V Akademik Tryoshnikov as part of the Antarctic Circumnavigation Expedition (ACE). Data were recorded from January to March 2017 in the Southern Ocean. Here we present an intermediate step in data processing, with relative horizontal particle flux of particles with a size between 36 – 2000 μm averaged over one-minute periods. Data are presented in daily files.</p> <p><strong>Dataset contents</strong></p> <ul> <li>SPC_HPF_1min_YYYY_MM_DD.csv, data files, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged horizontal particle flux dataset from ACE is made available under the 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>
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>
Dataset related to the manuscript: "An open-source integrated framework for the automation of citation collection and screening in systematic reviews"
<p>Dataset related to the manuscript: “An open-source integrated framework for the automation of citation collection and screening in systematic reviews”, to be used together with the code stored at https://github.com/AD-Papers-Material/BART_SystReviewClassifier to reproduce the results.</p> <p>There are three datasets:<br> - The Record data collected from the online scientific databases;<br> - The session journal which describes the search session, i.e., how many records were collected and from which source, for each query/session pairs.<br> - The session data which is the outcome of the classification and review tasks;</p>
City features collection
<div> <div># City features collection</div> <br> <div>A collection of features for ~700 European cities, for the reference year 2018.</div> <br> <div>## Features</div> <br> <div>The features are divided in three main thematic areas: land, climate and socioeconomic characteristics. Find more information about the features in the codebook `cities_features_collection_codebook.csv`.</div> <div>Codelists for categorical features are in the same folder `codelist_<feature>.csv`.</div> <br> <div>## Cities</div> <br> <div>City selection (and outline polygon) is taken from the Eurostat Urban Atlas. More information [here](https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit). The original list of cities with geometries can be downloaded at these links:</div> <br> <div>- EPSG:4326 (WGS84) <https://gisco-services.ec.europa.eu/distribution/v2/urau/geojson/URAU_RG_01M_2018_4326_CITIES.geojson></div> <div>- EPSG:3035 <https://gisco-services.ec.europa.eu/distribution/v2/urau/geojson/URAU_RG_01M_2018_3035_CITIES.geojson></div> <br> <div>Note: the dataset `city_features_collection.geojson` only contains the city outline in CRS EPSG:4326.</div> <br><br> <div>## Example usage</div> <br> <div>Clustering analysis of European cities: check out this interactive demo notebook: `notebooks\demo\cities_clustering_interactive_demo.ipynb`.</div> </div> <p> </p>
FixMe: An Incremental Lightweight Method for Vulnerability Data Collection for Security Patch Prediction
<div> <div>This repository has the FixMe dataset and the source code for extracting the new dataset. is a lightweight approach for collecting code patches based on analyzing the commits of various version control systems. The practical framework is designed to generate patches across a wide array of programming languages. This open-source tool streamlines the process of gathering vulnerability records from the Common Vulnerabilities and Exposures (CVE) database through an incremental approach. By embracing an incremental methodology, we expedite the acquisition of data, ensuring the inclusion of newly identified vulnerabilities and their corresponding patch pairs. Our methodology involves extracting security issues, obtaining vulnerability-fixing commits, and retrieving relevant source code from various projects. The extracted dataset by the FixMe tool supports for the automated patch prediction, automated program repair, commit classification, vulnerability prediction and more.</div> </div>
Sub-micron aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The authors would highly appreciate to be contacted if the data is used for any purpose.</p> <p>We measured sub-micrometer aerosol particles with two scanning mobility particle spectrometers (SMPSs) between 11 and 400 nm (file name ACESPACE_submicron_aerosol_particle_size_distribution.csv) in 100 bins, and 11 and 181 nm in 77 bins - so no data entry in the remaining 23 bins - (ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv) at a time resolution of five minutes during the Antarctic Circumnavigation Expedition (ACE). Particles in this size range are important for cloud formation because a sub-set of them can act as cloud condensation nuclei (CCN).</p> <p>The time series of the size distribution shows that the particle population over the Southern Ocean can be quite variable featuring three dominant modes: a new particle formation mode (11 – 30 nm); an Aitken mode (20 – 70 nm); and an accumulation mode (> 70 nm). Often a concentration minimum between the Aitken and accumulation mode can be observed. It is known as Hoppel minimum (Hoppel and Frick, 1990; 10.1016/0960-1686(90)90020-N). Typically, particles larger than this minimum act as CCN. The variability of the particle size spectrum is a result of particle sources and atmospheric processes. Sea spray generation adds larger particles likely with a peak in the mode around 200 nm. Trace gas emissions from microbial communities in the ocean, such as dimethylsulfide (DMS) will be oxidized to either sulphuric acid or methanesulfonic acid in the atmosphere which condense onto pre-existing particles, hence growing those. Sulphuric acid can also form new particles (new particle formation mode). Rain and snow will remove particles larger than the Hoppel minimum.</p> <p>The data set can be used to explore the variability of the particle size distribution in three different oceans around Antarctica (Indian, Pacific, Atlantic Oceans) and from Cape Town to Europe in relation to weather patterns, air mass trajectories, microbial activity etc. It is best used in combination with CCN data to explore the importance of particles for cloud formation. This data set cannot be used to unambiguously determine sources of particles over the southern ocean or to trace anthropogenic impact in the region.</p> <p>The data have been cleaned from the influence of the exhaust of the research vessel.</p> <p>We give five-minute average data as dN/dlog(dp), where dN is the particle number concentration per measured size bin normalized over the logarithm of the bin width. The bin width is defined as the distance between two diameters. They are spaced equally in log-space with dlog(dp) = log(d_n+1/d_n) = 1/64. To derive the total particle number concentration between 11 and 400 nm one has to integrate over the diameter range taking into account the normalization by dlog(dp).</p> <p>Temporal coverage is from December 20, 2016 to April 10, 2017. The file “ACESPACE_submicron_aerosol_particle_size_distribution.csv” covers the entire time period except between 9 and 14 January 2017 due to instrument issues. The file “ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv” contains data for the period between 9 and 14 January 2017 and can be used to fill the above gap. The second data file stems from another SMPS with a smaller differential mobility analyser, hence the smaller diameter coverage.</p> <p><strong>Dataset contents</strong></p> <p>The data set contains two files with the size distribution of sub-micrometer aerosol particles. The rows are indexed by the time stamp, which is the end of the 5-minutes averaging interval. The columns are the normalized concentrations of particles in the respective size bin. See the data abstract for details.</p> <ul> <li>ACESPACE_submicron_aerosol_particle_size_distribution.csv, data file, comma-separated values</li> <li>ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv, data file, comma-separated values</li> <li>ACESPACE_particle_diameter_bins.csv, metadata, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> <p>NaN values in a complete row denote missing values because of e.g., calibration periods, ship exhaust contamination, instrument failure. NaN values which appear individually or only in small groups reflect that data were below detection limit. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period.</p> <p><strong>Dataset license</strong></p> <p>This sub-micron aerosol particle size distribution dataset collected during ACE is made available under the 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>
Coarse mode aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The authors would highly appreciate to be contacted if the data is used for any purpose.</p> <p>We measured coarse mode aerosol particle size distributions with an aerodynamic particle sizer (APS, model TSI 3321) at a time resolution of five minutes during the Antarctic Circumnavigation Expedition (ACE). The diameter range is 0.7 to 19 µm. Particles in this size range are indicative of primary sea spray aerosol, biological particles and potentially long-range transported mineral dust. These particles are also important for cloud formation as they act as cloud condensation nuclei or ice nucleating particles, the latter especially in the case of biological particles and mineral dust.</p> <p>Typically the instrument reports data starting from particles with a diameter greater than 500 nm, however, particle number concentrations in the channels below 723 nm were overestimated, which is a common artefact with this instrument.</p> <p>The data have been cleaned from the influence of the exhaust of the research vessel. Temporal coverage is from December 20, 2016 to April 10, 2017. We give five-minute averaged data as dN/dlog(dp), where dN is the particle number concentration per measured size bin normalized over the logarithm of the bin width. The bin width is defined as the distance between two diameters. They are spaced equally in log-space with dlog(dp) = log(d_n+1/d_n) = 1/32. To derive the total particle number concentration one has to integrate over the diameter range taking into account the normalization by dlog(dp).</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_coarse_mode_aerosol_particle_size_distribution.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> <p>NaN values in a complete row denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure. NaN values which appear individually or only in small groups reflect that data were below detection limit. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period.</p> <p><strong>Dataset license</strong></p> <p>This coarse mode aerosol particle size distribution dataset collected during ACE is made available under the 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>
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>
AVP-LAUT – Tree diameter data collected with Apple Vision Pro from Austrian forest Inventory plots
<p>This dataset consists of three zip archives containing valuable visual and measurement data related to tree assessments conducted using the Apple Vision Pro (AVP) technology. The first zip archive, <strong>images.zip</strong>, includes images taken in the forest, presented in .PNG and .JPG formats. These images capture various aspects of the study area and the measurement process.</p> <p>The second archive, <strong>videos_app_HR.zip</strong>, features videos recorded with the AVP using the "Handsruler" app, which focuses on measuring diameter at breast height (dbh) at 22 designated sample plots. Each video file is labeled with a numeric identifier that corresponds to the specific sample plot number, allowing for easy reference and organization.</p> <p>The third archive, <strong>videos_app_TM.zip</strong>, contains videos from the "Tape Measure" app, documenting dbh measurements taken at 17 sample plots. Similar to the previous videos, the file names indicate the respective sample plot numbers.</p> <p>In addition to the visual data, the dataset includes a comma-separated values (CSV) file named <strong>information_all_trees.csv</strong>, which consolidates all reference data regarding individual trees and sample plots. Each row in this file represents a single tree and includes several columns, each providing specific details about the measurements and observations.</p> <p>The column headers in <strong>information_all_trees.csv</strong> are as follows:</p> <ul> <li><strong>PLOT_ID</strong>: The numeric identifier for each sample plot.</li> <li><strong>tree_species_short</strong>: Abbreviation of the tree species.</li> <li><strong>caliper_dbh</strong>: The manually measured dbh of the tree in centimeters.</li> <li><strong>AVP_App1_dbh</strong>: The dbh measurement obtained from the AVP app "Handsruler" in centimeters.</li> <li><strong>AVP_App2_dbh</strong>: The dbh measurement obtained from the AVP app "Tape Measure" in centimeters.</li> <li><strong>res_App1</strong>: The difference between the dbh measured by the "Handsruler" app (AVP_App1_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li><strong>res_App2</strong>: The difference between the dbh measured by the "Tape Measure" app (AVP_App2_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li><strong>tree_species</strong>: The Latin name of the tree species, with genus and species connected by an "_".</li> <li><strong>tree_class</strong>: Classification of the tree into a species-specific category.</li> <li><strong>date</strong>: The date of the recordings.</li> <li><strong>time_App_1_min</strong>: The duration of all dbh measurements at the entire sample plot using the "Handsruler" app, in minutes.</li> <li><strong>time_App_2_min</strong>: The duration of all dbh measurements at the entire sample plot using the "Tape Measure" app, in minutes.</li> <li><strong>time_manual_caliper_min</strong>: The duration of all dbh measurements at the entire sample plot conducted manually, in minutes.</li> <li><strong>measuring_person</strong>: The individual field worker for conducting all dbh measurements (manual and both AVP apps) at the sample plot.</li> <li><strong>mean_slope_degrees</strong>: The average slope of the terrain across the sample plot, expressed in degrees.</li> </ul> <p>This comprehensive dataset provides essential insights into the effectiveness of the AVP technology for measuring tree dimensions and contributes to ongoing research in forest management and ecological studies. The included videos and images serve as a visual reference for the measurement processes, while the CSV file encapsulates the quantitative data necessary for analysis. Each row in the CSV file represents a single tree, facilitating detailed examinations of individual measurements and comparisons across different sample plots.</p>
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
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>
Niedertiefenbach: neolithic collective burial
<p>Spatialite database (SQLite) with data of the Neolithic collective grave Niedertiefenbach in Hesse (Germany). Data collected from the published images and copies of the corresponding lithographs in the archive (Wurm et al., Fundberichte aus Hessen Bd. 3, 1963, 56-78). Data collection from CRC 1266: "Scales of Transformation - Human-Environmental Interaction in Prehistoric and Archaic Societies". Project: "Regional and Local Patterns of 3rd Millennium Transformations of Social and Economic Practices in the Central German Mountain Range (D2)". Funded by the DFG. DFG project number: 2901391021</p>
From the collective to the individual: transformation processes at the transition from the 4th to the 3rd millennium BC in the German low mountain zone
<p>Data collected by Clara Drummer, Kiel 2022.</p> <p>Clara Drummer, Vom Kollektiv zum Individuum: Transformationsprozesse am Übergang vom 4. zum 3. Jahrtausend v. Chr. in der Deutschen Mittelgebirgszone. Scales of transformation Bd. 13 (Leiden 2022).https://d-nb.info/1241580332</p> <p>CRC 1266: "Scales of Transformation - Human-Environmental Interaction in Prehistoric and Archaic Societies."<br> "Regional and Local Patterns of 3rd Millennium Transformations of Social and Economic Practic-es in the Central German Mountain Range (D2)" Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 128675135 https://gepris.dfg.de/gepris/projekt/316739879</p> <p>Data for the analyses of the decisive transformation in the Hessian-Westphalian area from the Wartberg society to the Corded Ware groups. The work discusses above all the social aspects of the change. This includes, on the one hand, a more detailed analysis of burial rituals and, on the other hand, the integration of, for example, available aDNA results into the overall analysis.</p>
Harmful algal bloom and aquatic weeds data from the Sacramento-San Joaquin Delta, collected to evaluate the impact of the 2021 Temporary Urgency Change Order and Emergency Drought Barrier
Condition 8 of the June 2021 Temporary Urgency Change Order for the Central Valley Project (CVP) and State Water Project (SWP) requires a special study of harmful algal blooms (HABs) in the Sacramento–San Joaquin Delta (Delta) and the spread of submersed aquatic vegetation (SAV), and floating aquatic vegetation (FAV), also referred to as “aquatic weeds”. A report on the study was submitted to the State Water Resources Control Board on June 1, 2022. This data package contains all publicly available data used in the report, including visual cyanobacteria reports, cyanotoxin data, water quality, nutrients, flow/hydrodynamics, chlorophyll-a concentrations, temperature, coverage of SAV and FAV, use of herbicides, and human populations. Many of these data were derived from other datasets, though some were collected specifically for this study
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).
Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)
This dataset contains estimates of gas exchange velocity, gas exchange rate, and hydraulic parameters for streams calculated from tracer-gas experiments and conservative tracer injections collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, the NEON Reaeration field and lab collection data product (DP1.20190.001) was used to calculate these estimates. Gas exchange was estimated in two ways: first, following an unpooled frequentist approach and second, following a partially pooled Bayesian approach. In addition, a salt-correction was applied to gas exchange estimates for sites where it was possible and necessary. All estimates of gas exchange are included in the file gasExchange_ds.csv. A recommended selection of these estimates is included in the dataset (best_k600_mPerDay and best_K600_mPerDay). The stanfit objects used for the partially pooled Bayesian approach are also included as site-specific model objects for gas exchange velocities and rates. In addition, water velocity was calculated from conservative tracer injections, and mean water depth was calculated from these water velocity estimates and measurements of wetted width and water discharge. All hydraulic parameters are included in the file hydraulics_ds.csv. All processing code is available in the reaRates R package. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.
Species-level estimated abundances and zero counts of nighttime collected female mosquitoes 2014 - 2022 (Derived from NEON Mosquitoes sampled from CO2 traps (DP1.10043.001, RELEASE-2024))
This Level 2 data package contains species level estimated abundances, including zero counts, and estimated mean number of female mosquitoes per trap derived from the NEON Mosquitoes sampled from CO2 traps (DP1.10043.001), RELEASE-2024 Level 0 data (https://doi.org/10.48443/3cyq-6v47). The data set includes mosquito records of traps collecting mosquito samples at night, for up to 24 trap hours, across a total of 20 terrestrial core and 27 terrestrial gradient sites from 2014 to 2022. To ensure high confidence in abundance estimates, records were only included when at least 90% of collected individuals were identified to sex, and 90% of female specimens were identified to species. Information across multiple QC/QA fields within the NEON mosquito data was evaluated to identify and exclude records where confidence in estimated abundances may have been compromised. Species level zero counts were added for all species collected at least once within the sampling year and trap location. Additionally, species level zero counts were included for trap events where only male mosquitoes had been collected or where QC/QA remarks indicated traps were inactive due to cold temperatures. The data set provides an analysis ready time series of estimated abundances across NEON sites and plots. An R Markdown file that contains descriptions of the QC/QA and data filtering steps along with annotated code, as well as data tables used to filter active and inactive trap events based on QC/QA fields, are published with the data package. Any questions about this data package should be directed to Amely Bauer listed under contacts.
Field Evidence of Carbon and Nitrogen Stabilization through Mineral Associated Organic Matter Formation in Coastal Wetland Soils from Apalachicola, Florida, collected in June, 2022.
This data set was used to observe the role of Mineral Associated Organic Matter Formation (MAOM) on biogeochemical soil properties in three coastal wetlands in Apalachicola, Florida. One wetland was restored using beneficial dredged sediment, increasing the soil's inorganic matter content. Soil samples were collected in June 2022 from this wetland and two nearby reference wetlands: one with high organic matter and the other with higher inorganic matter content. The samples were analyzed at the University of Central Florida for biogeochemical properties to determine which properties were most related to MAOM pools.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.