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6 results for “Sediment source fingerprinting”
A unified template for sediment source fingerprinting databases
<p>Over the last few years, the sediment source fingerprinting community has been engaged in promoting best practices to improve the design and the implementation of sediment fingerprinting techniques (<a href="https://doi.org/10.1007/s11368-022-03203-1">Evrard et al., 2022</a>). Data sharing is a key part of open science making research more reliable and accessible to the community. To move forward and improve data sharing, we propose these templates for databases and metadata.</p> <p>These templates include: common metadata for samples (soil, river flood deposit, sediment core...) description (name, IGSN, location, sampling date...), list and description of common properties (elemental geochemistry, organic matter, radionuclides…) used in sediment source fingerprinting studies. These templates are intended to evolve thanks to the participation of the community, as part of a collaborative project.</p> <p>In addition, the <strong>collectionneur </strong>R package was designed to help researchers and data managers maintain an up-to-date and well-organized database. is avalaible on <a href="https://github.com/tchalauxclergue/collectionneur"><strong>GitHub</strong> (https://github.com/tchalauxclergue/collectionneur)</a> and <a href="https://doi.org/10.5281/zenodo.15146958"><strong>Zenodo</strong> (https://doi.org/10.5281/zenodo.15146958)</a>. It facilitates the comparison and integration of new data entries into an existing database while keeping a detailed report of all modifications. All database formats are allowed, although it was initially designed for sediment source fingerprinting databases.</p> <p>Published databases following these templates are listed in the References section below. </p>
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Data to reproduce the results presented in Lake et al. 2024. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2024.131930. ("High-frequency spatial sediment source fingerprinting using in situ absorbance data")
<p>This repository contains data on the used absorbance data, measured at the field site, as described in Lake et al., 2024 (<span>h</span><span>t</span><span>t</span><span>p</span><span>s</span><span>:</span><span>/</span><span>/</span><span>d</span><span>o</span><span>i</span><span>.</span><span>o</span><span>r</span><span>g</span><span>/</span><span>1</span><span>0</span><span>.</span><span>1</span><span>0</span><span>1</span><span>6</span><span>/</span><span>j</span><span>.</span><span>j</span><span>h</span><span>y</span><span>d</span><span>r</span><span>o</span><span>l</span><span>.</span><span>2</span><span>0</span><span>2</span><span>4</span><span>.</span><span>1</span><span>3</span><span>1</span><span>9</span><span>3</span><span>0).</span> Furthermore, data on the turbidity, used calibration curves and R code to prepare the input data for the MixSIAR model are included in the data repository.</p>
Radionuclide, organic matter, geochemical and colorimetric properties of potential source material and target sediment for conducing sediment fingerprinting approaches in the Dzoumogné reservoir, Mayotte Island, France
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Colorimetric properties analysed with a portable diffuse reflectance spectrophotometer (Konica Minolta CM-700d) and geochemical contents obtained with an energy dispersive X-ray fluorescence spectrometer (ED-XRF Epsilon 4), organic matter and stable isotopes were analysed by EA-IRMS and radionuclides using coaxial N- and P- type HPGe detectors (Canberra/Ortec). These properties were analysed in potential source material that may supply sediment to the Dzoumogné reservoir, Mayotte island, France. Three potential soil source materials (n = 57) were considered: cropland (n = 29), forest (n = 13) and subsurface material originating from channel bank collapse, landslides, badlands (n = 16). A sediment core was collected in the Dzoumogné reservoir (Target) on the 8th October 2021 and 20 layers were sampled.</p><p>The current dataset comprises two Excel files including the metadata description and the data itself.</p>
Data to reproduce the results presented in Lake et al. 2022. Hydrological processes, https://doi.org/10.1002/hyp.14726 ("Using particle size distributions to fingerprint suspended sediment sources – evaluation at laboratory and catchment scales")
<p>This repository contains data on particle size distribution data obtained from the laboratory and field experiments as described in Lake et al., 2022. </p> <p>The data contains the input files as needed for the modelling:</p> <p>- In the excel files the particle size distribution data for the target SS</p> <p>- In the text file the particle size distribution data from the sources.</p> <p> </p> <p>Furthermore, the data contains the resulting output files.</p> <p> </p>
Data to reproduce the results presented in Lake et al. 2023. Science of The Total Environment, https://doi.org/10.1016/j.scitotenv.2023.162332 ("Use of a submersible spectrophotometer probe to fingerprint spatial suspended sediment sources at catchment scale")
<p>This repository contains the absorbance data measured on the water samples collected in all sampling sites, for the three campaigns, as described in Lake et al., 2023. </p> <p>Data consists of:</p> <p>- Absorbance data compensated for measured concentration and compensated for absorbance measured on filtered water </p> <p>- Absorbance data compensated for measured concentration</p> <p>Shown files are the input files for the MixSIAR modelling exercise as described in Lake et al., 2023.</p>
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