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The following results are related to Rural Digital Europe. Are you interested to view more results? Visit OpenAIRE - Explore.

  • Rural Digital Europe
  • Open Access
  • Research data
  • National Science Foundation

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dakota S. Dale; Lu Liang; Liheng Zhong; Michele L. Reba; +1 Authors

    This dataset contains the two datasets detailed in "Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery" by D.S. Dale Et al. (2023). The file "LonokeComplete.zip" file contains 16 .lif files that were used in the training and testing phase of the study. The "55tilesComplete.zip" file contains 110 .tif files (55 image and 55 label). These images were used to assess the models spatial transferability. Both file configurations are processed by the code linked in the paper. Supported in Part by NASA Water Resources Award 80NSSC22K0923 and U.S. Geological Survey under Cooperative Agreement G20AC00448 and G21AC10729. {"references": ["DS Dale Et al., 2023"]}

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    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: ZENODO
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    Authors: John Gardner; Tamlin Pavelsky; Xiao Yang; Simon Topp; +1 Authors

    The River Sediment Database (RivSed) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected within each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters). The paper associated with RivSed: Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. Environmental Research Letters. https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8 Files: 1) Metadata (riverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. 2) RiverSed (RiverSed_USA_v1.1.txt). Table of SSC and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons_v1.0.shp). 5) The look up table for reach IDs of original (COMID) and modified (ID) NHDplusV2 centerlines. (COMID_ID.csv). Short reaches were joined together to optimize for remote sensing data collection and make more consistent reach lengths. 6) SSC-Landsat matchup database with extended metadata on locations and in-situ data derived from Aquasat (Ross et al., 2019) (Aquasat_TSS_v1.1.csv) 7) The final training data used to build the xgboost machine learning model (train_clean_xgb_v1.1.csv) 8) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (finalmodel_xgb_v1.1.rds and .RData). The model can only be loaded in R for now.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Sion, Brad; Samburova, Vera; Berli, Markus; Baish, Christopher; +2 Authors

    This dataset includes both raw and processed data associated with the publication entitled "Assessment of the effects of the 2021 Caldor megafire on soil physical properties, eastern Sierra Nevadas, USA", published in MDPI Fire (doi: 10.3390/fire6020066). Raw files include exported .xlsx files from Meter Group HYPROP analyses, .csv files from 10 replicate measurements of saturated hydraulic conductivity for each analyzed sample using the Meter Group KSAT device, and raw .dat files from measurement of bulk thermal properties. A single additional file also documents the laboratory results from particle size and loss on ignition analyses. Processed data includes curve fitting parameters associated with fitting the soil water retention curves (SWRC) and thermal conductivity functions (TCFs) for each sample, as described in Sion et al. (2023). Additional requests associated with data from Sion et al. (2023) should be directed to the lead author.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Christie, Frazer; Steig, Eric; Gourmelen, Noel; Tett, Simon; +1 Authors

    This study was supported by a Carnegie Trust for the Universities of Scotland Carnegie PhD Scholarship (to F.D.W.C.), hosted in the Edinburgh E3 U.K. Natural Environment Research Council (NERC) Doctoral Training Partnership (NE/L002558/1) and the Scottish Alliance for Geoscience, Environment and Society (SAGES) Graduate School. The study was also produced with the financial assistance of the Prince Albert II of Monaco Foundation (to F.D.W.C.), the NERC / U.S National Science Foundation (NSF) International Thwaites Glacier Collaboration grants NE/S006613 (ITGC-GHOST; to R.G.B.) and NE/S006796 (ITGC-PROPHET; to N.G.) (ITGC contribution no. ITGC-088), NERC grant NE/T001607/1 (QuORUM project to N.G. and S.F.B.T.), the ESA 4D Antarctica and Digital Twin Antarctica projects 4000128611/19/I‐DT (to N.G.), and NSF grant 2045075 (to E.J.S.). This dataset contains the grounding-line and ice-velocity change observations presented in Christie et al. (Nature Communications, 2023). Data are provided in ESRI .shp (grounding line location and change records) and .TIF (ice velocity and change records) formats, and detailed information about the data collection methods, sources and other technical information can be found within the accompanying README files inside the .ZIP folder.

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    Apollo
    Dataset
    License: CC BY
    Data sources: Apollo
    Apollo
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      Apollo
      Dataset
      License: CC BY
      Data sources: Apollo
      Apollo
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Frost Mitchell; Aniqua Baset; Sneha Kumar Kasera; Aditya Bhaskara;

    Dataset Description This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters. The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus. Dataset Description Sample Count Receiver Count No-Tx Samples 46 10 to 25 1-Tx Samples 4822 10 to 25 2-Tx Samples 346 11 to 12 The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows: \(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) \) Measurement Parameters Description Frequency 462.7 MHz Radio Gain 35 dB Receiver Sample Rate 2 MHz Sample Length N=10,000 Band-pass Filter 6 kHz Transmitters 0 to 2 Transmission Power 1 W Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device. Usage Instructions Data is provided in .json format, both as one file and as split files. import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f) The json data is a dictionary with the sample timestamp as a key. Within each sample are the following keys: rx_data: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name. tx_coords: A list of coordinates for each transmitter. Each entry contains latitude and longitude. metadata: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in tx_coords File Separations and Train/Test Splits In the separated_data.zip folder there are several train/test separations of the data. all_data contains all the data in the main JSON file, separated by the number of transmitters. stationary consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers. train_test_splits contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with unused.json, is equivalent to the file all_data/single_tx.json. The random split is a random 80/20 split of the data. special_test_cases contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus. The grid split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the grid split. The seasonal split contains data separated by the month of collection, in April or July. The transportation split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The non-driving.json file contains the union of the walking and cycling data. campus.json contains the on-campus data, so is equivalent to the union of each split, not including unused.json. Digital Surface Model The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR survey. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models. To read the data in python: import rasterio as rio import numpy as np import utm dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T') Dataset Acknowledgement: This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product. DSM DOI: https://doi.org/10.5069/G9TH8JNQ

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Dell, Rebecca; Banwell, Alison; Willis, Ian; Arnold, Neil; +3 Authors

    Code in support of "Supervised classification of slush and ponded water on Antarctic ice shelves using Landsat 8 imagery" by R.L. Dell and others. The scripts provided facilitate the pre-processing of Landsat 8 images for the training, validation, and application of of a Random Forest Classifier. Scripts to train, validate, and apply a Random Forest Classifier are also provided. All scipts are written in Google Earth Engine. The methodological information relating to these scripts can be found in the companion paper: Dell RL, Banwell AF, Willis IC, Arnold NS, Halberstadt ARW, Chudley TR, Pritchard HD (2021). Supervised classification of slush and ponded water on Antarctic ice shelves using Landsat 8 imagery. Journal of Glaciology 1-14. https://doi.org/10.1017/jog.2021.114.

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    Apollo
    Dataset
    License: CC BY
    Data sources: Apollo
    Apollo
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      Apollo
      Dataset
      License: CC BY
      Data sources: Apollo
      Apollo
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Valente, André; Sathyendranath, Shubha; Brotas, Vanda; Groom, Steve; +73 Authors

    A global compilation of in situ data is vital to evaluate the quality of ocean-colour satellite data records. Here, we describe data compiled for the validation of ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO) and span the period from 1997 to 2021. Observations of the following variables were compiled: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient and total suspended matter. The data were obtained from multi-project archives acquired via open internet services, or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The result is a merged table available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were propagated throughout the work and made available in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. This paper also describes the changes that were made to the compilation in relation to the previous version.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Schramski, Sam; Barbosa de Lima, Ana Carolina;

    Abstract Background The Amazon region of Brazil is known both for its significant biological and cultural diversity. It is also a region, like many parts of the country, marked by food insecurity, even amongst its rural agricultural populations. In a novel approach, this paper addresses the networks of exchanges of local food and their relationship to the agrobiodiversity of traditional riverine peoples��� (ribeirinho) households in the Central Amazon. Methodologically, it involves mapping the social networks and affinities between households, inventories of known species, and, finally, statistical tests of the relationships between network and subsequent agrobiodiversity. Results The diversity per area of each land type where food cultivation or management takes place shows how home gardens, fields and orchards are areas of higher diversity and intense cultivation compared to fallow areas. Our findings, however, indicate that a household���s income does appear to be strongly associated with the total agrobiodiversity across cultivation areas. In addition, a household���s agrobiodiversity is significantly associated with the frequency and intensity of food exchanges between households. Conclusions Agrobiodiversity cannot be considered separate from the breadth of activities focused on sustenance and yields from the cash economy, which riverine people engage in daily. It seems to be connected to quotidian social interactions and exchanges in both predictable and occasionally subtler ways. Those brokers who serve as prominent actors in rural communities may not always be the most productive or in possession of the largest landholdings, although in some cases they are. Their proclivity for cultivating and harvesting a wide diversity of produce may be equally important if not more so.

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    figshare
    Collection . 2022
    License: CC BY
    Data sources: Datacite
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    figshare
    Collection . 2022
    License: CC BY
    Data sources: Datacite
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      figshare
      Collection . 2022
      License: CC BY
      Data sources: Datacite
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      Collection . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Runge, Alexandra; Nitze, Ingmar; Grosse, Guido;

    Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Abrupt permafrost disturbances, such as retrogressive thaw slumps (RTS), expand by several meters each year and lead to an increased soil organic carbon release. We applied the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to Northeast Siberia (8.1 × 10^6km^2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.61). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very high-resolution RapidEye and PlanetScope imagery. The data set presents the results of the first automated detection and assessment of RTS and their temporal dynamics at large-scale for 2001–2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across Northeast Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331% compared to 2000 (2000: 20,158 ha, 2001-2019: 66,699 ha). Contrary to this, focus sites show spatio-temporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The detected increase in RTS dynamics suggests advancing permafrost thaw and underlines the importance of assessing abrupt permafrost disturbances with high spatial and temporal resolution at large-scales. This consistenly obtained disturbance product will help to parametrise regional and global climate change models.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Hendricks, Stefan; Itkin, Polona; Ricker, Robert; Webster, Melinda; +5 Authors

    The total snow and ice thickness (distance from the snow surface to the ice-ocean interface) was measured by the electromagnetic induction (EM) method. On MOSAiC transects, we used a broad-band EM instrument sensor (GEM-2 by Geophex Ltd) towed on a small sled (Hunkeler et al, 2015; Hunkeler et al, 2016). The instrument includes a real-time data processing unit including a GPS receiver which communicates with a pocket PC that is operates the sensor and records the EM and GPS data streams. The GEM-2 is a broadband sensor that can transmit multiple configurable frequencies in the kHz range simultaneously. The sensor setup during MOSAiC used 5 frequencies with an approximately logarithmic spacing throughout the frequency range of the sensor (1.525 kHz, 5.325 kHz, 18.325 kHz, 63.025 kHz, and 93.075 kHz). The transect measurements are based on an empirical approach based on a sensor calibration, where the GEM-2 was placed at known heights above the sea ice surface using a wooden ladder on top of level ice with a known thickness determined by 5 drill holes. An exponential function was then fitted to the frequency components as function of distance of the sensor to the ice/ocean interface and then applied to the transect data. The closest-in-time calibration result was used when a GEM-2 survey could not be accompanied with a calibration. The total thickness retrieval with the GEM-2 calibration and survey data was done on-board shortly after each profile. The dataset is therefore labeled as GEM-2 quickview data but has been subject to manual quality control. Using a direct relationship between total thickness and frequency component implies the assumption that the sea ice conductivity is negligible and the ice/water interface constant within the GEM-2 footprint. While this is a reasonable assumption for level ice, the peak thicknesses of ridges are known to be underestimated by as much as 50 % (Pfaffing et al, 2007) and will be subject of further processing. To estimate the snow depth and then subtract its thickness from the total thickness we rely on direct measurements of snow depth with Magnaprobe. The co-inciding snow depth measurements on MOSAiC transect can be found here: https://doi.pangaea.de/10.1594/PANGAEA.937781 Not every GEM-2 transect has complimentary snow depth measurements. An overview of all transect measurements at MOSAiC is given in the attached table. For more details we refer to the MOSAiC transect paper by Itkin et al, 2022: Sea ice and snow mass balance from transects in the MOSAiC Central Observatory, in review at Elementa – Science of Anthropocene.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dakota S. Dale; Lu Liang; Liheng Zhong; Michele L. Reba; +1 Authors

    This dataset contains the two datasets detailed in "Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery" by D.S. Dale Et al. (2023). The file "LonokeComplete.zip" file contains 16 .lif files that were used in the training and testing phase of the study. The "55tilesComplete.zip" file contains 110 .tif files (55 image and 55 label). These images were used to assess the models spatial transferability. Both file configurations are processed by the code linked in the paper. Supported in Part by NASA Water Resources Award 80NSSC22K0923 and U.S. Geological Survey under Cooperative Agreement G20AC00448 and G21AC10729. {"references": ["DS Dale Et al., 2023"]}

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    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: ZENODO
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    Authors: John Gardner; Tamlin Pavelsky; Xiao Yang; Simon Topp; +1 Authors

    The River Sediment Database (RivSed) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected within each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters). The paper associated with RivSed: Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. Environmental Research Letters. https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8 Files: 1) Metadata (riverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. 2) RiverSed (RiverSed_USA_v1.1.txt). Table of SSC and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons_v1.0.shp). 5) The look up table for reach IDs of original (COMID) and modified (ID) NHDplusV2 centerlines. (COMID_ID.csv). Short reaches were joined together to optimize for remote sensing data collection and make more consistent reach lengths. 6) SSC-Landsat matchup database with extended metadata on locations and in-situ data derived from Aquasat (Ross et al., 2019) (Aquasat_TSS_v1.1.csv) 7) The final training data used to build the xgboost machine learning model (train_clean_xgb_v1.1.csv) 8) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (finalmodel_xgb_v1.1.rds and .RData). The model can only be loaded in R for now.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Sion, Brad; Samburova, Vera; Berli, Markus; Baish, Christopher; +2 Authors

    This dataset includes both raw and processed data associated with the publication entitled "Assessment of the effects of the 2021 Caldor megafire on soil physical properties, eastern Sierra Nevadas, USA", published in MDPI Fire (doi: 10.3390/fire6020066). Raw files include exported .xlsx files from Meter Group HYPROP analyses, .csv files from 10 replicate measurements of saturated hydraulic conductivity for each analyzed sample using the Meter Group KSAT device, and raw .dat files from measurement of bulk thermal properties. A single additional file also documents the laboratory results from particle size and loss on ignition analyses. Processed data includes curve fitting parameters associated with fitting the soil water retention curves (SWRC) and thermal conductivity functions (TCFs) for each sample, as described in Sion et al. (2023). Additional requests associated with data from Sion et al. (2023) should be directed to the lead author.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Christie, Frazer; Steig, Eric; Gourmelen, Noel; Tett, Simon; +1 Authors

    This study was supported by a Carnegie Trust for the Universities of Scotland Carnegie PhD Scholarship (to F.D.W.C.), hosted in the Edinburgh E3 U.K. Natural Environment Research Council (NERC) Doctoral Training Partnership (NE/L002558/1) and the Scottish Alliance for Geoscience, Environment and Society (SAGES) Graduate School. The study was also produced with the financial assistance of the Prince Albert II of Monaco Foundation (to F.D.W.C.), the NERC / U.S National Science Foundation (NSF) International Thwaites Glacier Collaboration grants NE/S006613 (ITGC-GHOST; to R.G.B.) and NE/S006796 (ITGC-PROPHET; to N.G.) (ITGC contribution no. ITGC-088), NERC grant NE/T001607/1 (QuORUM project to N.G. and S.F.B.T.), the ESA 4D Antarctica and Digital Twin Antarctica projects 4000128611/19/I‐DT (to N.G.), and NSF grant 2045075 (to E.J.S.). This dataset contains the grounding-line and ice-velocity change observations presented in Christie et al. (Nature Communications, 2023). Data are provided in ESRI .shp (grounding line location and change records) and .TIF (ice velocity and change records) formats, and detailed information about the data collection methods, sources and other technical information can be found within the accompanying README files inside the .ZIP folder.

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    Apollo
    Dataset
    License: CC BY
    Data sources: Apollo
    Apollo
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      Apollo
      Dataset
      License: CC BY
      Data sources: Apollo
      Apollo
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Frost Mitchell; Aniqua Baset; Sneha Kumar Kasera; Aditya Bhaskara;

    Dataset Description This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters. The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus. Dataset Description Sample Count Receiver Count No-Tx Samples 46 10 to 25 1-Tx Samples 4822 10 to 25 2-Tx Samples 346 11 to 12 The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows: \(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) \) Measurement Parameters Description Frequency 462.7 MHz Radio Gain 35 dB Receiver Sample Rate 2 MHz Sample Length N=10,000 Band-pass Filter 6 kHz Transmitters 0 to 2 Transmission Power 1 W Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device. Usage Instructions Data is provided in .json format, both as one file and as split files. import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f) The json data is a dictionary with the sample timestamp as a key. Within each sample are the following keys: rx_data: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name. tx_coords: A list of coordinates for each transmitter. Each entry contains latitude and longitude. metadata: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in tx_coords File Separations and Train/Test Splits In the separated_data.zip folder there are several train/test separations of the data. all_data contains all the data in the main JSON file, separated by the number of transmitters. stationary consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers. train_test_splits contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with unused.json, is equivalent to the file all_data/single_tx.json. The random split is a random 80/20 split of the data. special_test_cases contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus. The grid split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the grid split. The seasonal split contains data separated by the month of collection, in April or July. The transportation split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The non-driving.json file contains the union of the walking and cycling data. campus.json contains the on-campus data, so is equivalent to the union of each split, not including unused.json. Digital Surface Model The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR survey. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models. To read the data in python: import rasterio as rio import numpy as np import utm dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T') Dataset Acknowledgement: This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product. DSM DOI: https://doi.org/10.5069/G9TH8JNQ

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
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    Authors: Dell, Rebecca; Banwell, Alison; Willis, Ian; Arnold, Neil; +3 Authors

    Code in support of "Supervised classification of slush and ponded water on Antarctic ice shelves using Landsat 8 imagery" by R.L. Dell and others. The scripts provided facilitate the pre-processing of Landsat 8 images for the training, validation, and application of of a Random Forest Classifier. Scripts to train, validate, and apply a Random Forest Classifier are also provided. All scipts are written in Google Earth Engine. The methodological information relating to these scripts can be found in the companion paper: Dell RL, Banwell AF, Willis IC, Arnold NS, Halberstadt ARW, Chudley TR, Pritchard HD (2021). Supervised classification of slush and ponded water on Antarctic ice shelves using Landsat 8 imagery. Journal of Glaciology 1-14. https://doi.org/10.1017/jog.2021.114.

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    Apollo
    Dataset
    License: CC BY
    Data sources: Apollo
    Apollo
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      Apollo
      Dataset
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      Apollo
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Valente, André; Sathyendranath, Shubha; Brotas, Vanda; Groom, Steve; +73 Authors

    A global compilation of in situ data is vital to evaluate the quality of ocean-colour satellite data records. Here, we describe data compiled for the validation of ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO) and span the period from 1997 to 2021. Observations of the following variables were compiled: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient and total suspended matter. The data were obtained from multi-project archives acquired via open internet services, or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The result is a merged table available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were propagated throughout the work and made available in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. This paper also describes the changes that were made to the compilation in relation to the previous version.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Schramski, Sam; Barbosa de Lima, Ana Carolina;

    Abstract Background The Amazon region of Brazil is known both for its significant biological and cultural diversity. It is also a region, like many parts of the country, marked by food insecurity, even amongst its rural agricultural populations. In a novel approach, this paper addresses the networks of exchanges of local food and their relationship to the agrobiodiversity of traditional riverine peoples��� (ribeirinho) households in the Central Amazon. Methodologically, it involves mapping the social networks and affinities between households, inventories of known species, and, finally, statistical tests of the relationships between network and subsequent agrobiodiversity. Results The diversity per area of each land type where food cultivation or management takes place shows how home gardens, fields and orchards are areas of higher diversity and intense cultivation compared to fallow areas. Our findings, however, indicate that a household���s income does appear to be strongly associated with the total agrobiodiversity across cultivation areas. In addition, a household���s agrobiodiversity is significantly associated with the frequency and intensity of food exchanges between households. Conclusions Agrobiodiversity cannot be considered separate from the breadth of activities focused on sustenance and yields from the cash economy, which riverine people engage in daily. It seems to be connected to quotidian social interactions and exchanges in both predictable and occasionally subtler ways. Those brokers who serve as prominent actors in rural communities may not always be the most productive or in possession of the largest landholdings, although in some cases they are. Their proclivity for cultivating and harvesting a wide diversity of produce may be equally important if not more so.

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    figshare
    Collection . 2022
    License: CC BY
    Data sources: Datacite
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    Collection . 2022
    License: CC BY
    Data sources: Datacite
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      Collection . 2022
      License: CC BY
      Data sources: Datacite
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      Collection . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Runge, Alexandra; Nitze, Ingmar; Grosse, Guido;

    Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Abrupt permafrost disturbances, such as retrogressive thaw slumps (RTS), expand by several meters each year and lead to an increased soil organic carbon release. We applied the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to Northeast Siberia (8.1 × 10^6km^2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.61). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very high-resolution RapidEye and PlanetScope imagery. The data set presents the results of the first automated detection and assessment of RTS and their temporal dynamics at large-scale for 2001–2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across Northeast Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331% compared to 2000 (2000: 20,158 ha, 2001-2019: 66,699 ha). Contrary to this, focus sites show spatio-temporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The detected increase in RTS dynamics suggests advancing permafrost thaw and underlines the importance of assessing abrupt permafrost disturbances with high spatial and temporal resolution at large-scales. This consistenly obtained disturbance product will help to parametrise regional and global climate change models.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Hendricks, Stefan; Itkin, Polona; Ricker, Robert; Webster, Melinda; +5 Authors

    The total snow and ice thickness (distance from the snow surface to the ice-ocean interface) was measured by the electromagnetic induction (EM) method. On MOSAiC transects, we used a broad-band EM instrument sensor (GEM-2 by Geophex Ltd) towed on a small sled (Hunkeler et al, 2015; Hunkeler et al, 2016). The instrument includes a real-time data processing unit including a GPS receiver which communicates with a pocket PC that is operates the sensor and records the EM and GPS data streams. The GEM-2 is a broadband sensor that can transmit multiple configurable frequencies in the kHz range simultaneously. The sensor setup during MOSAiC used 5 frequencies with an approximately logarithmic spacing throughout the frequency range of the sensor (1.525 kHz, 5.325 kHz, 18.325 kHz, 63.025 kHz, and 93.075 kHz). The transect measurements are based on an empirical approach based on a sensor calibration, where the GEM-2 was placed at known heights above the sea ice surface using a wooden ladder on top of level ice with a known thickness determined by 5 drill holes. An exponential function was then fitted to the frequency components as function of distance of the sensor to the ice/ocean interface and then applied to the transect data. The closest-in-time calibration result was used when a GEM-2 survey could not be accompanied with a calibration. The total thickness retrieval with the GEM-2 calibration and survey data was done on-board shortly after each profile. The dataset is therefore labeled as GEM-2 quickview data but has been subject to manual quality control. Using a direct relationship between total thickness and frequency component implies the assumption that the sea ice conductivity is negligible and the ice/water interface constant within the GEM-2 footprint. While this is a reasonable assumption for level ice, the peak thicknesses of ridges are known to be underestimated by as much as 50 % (Pfaffing et al, 2007) and will be subject of further processing. To estimate the snow depth and then subtract its thickness from the total thickness we rely on direct measurements of snow depth with Magnaprobe. The co-inciding snow depth measurements on MOSAiC transect can be found here: https://doi.pangaea.de/10.1594/PANGAEA.937781 Not every GEM-2 transect has complimentary snow depth measurements. An overview of all transect measurements at MOSAiC is given in the attached table. For more details we refer to the MOSAiC transect paper by Itkin et al, 2022: Sea ice and snow mass balance from transects in the MOSAiC Central Observatory, in review at Elementa – Science of Anthropocene.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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