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1,572 results for “Sedimentation”
Normalized water permeability of hydrate-bearing sediments
<p>This dataset include some normalized water permeability, hydrate saturation, porosity and the water saturation of hydrate-bearing sediments </p>
Code and datasets for "Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes"
<p>Code and datasets for:</p> <p>Delaney I., M. A. Werder, D. Felix, I. Albayrak, R. M. Boes, D. Farinotti, 2024, Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes. Water Resources Research. </p> <p>For more information, contact Ian Delaney (ianarburua.delaney@unil.ch).</p>
Table S1 The grain size, TOC and elemental composition of sediment samples in the northern South China Sea
<p><span>Grain size measurements of the sediments were carried out on a Mastersizer 3000 laser particle size analyzer</span><span>. Prior to analysis organic matter was removed by leaching the sediment with 15% H<sub>2</sub>O<sub>2</sub>. Total organic carbon (TOC) of the sediments was measured in </span><span>Thermo EA-IsoLink elemental analyzer</span><span>. Prior to analysis carbon bound to carbonate minerals was removed by leaching the sediment with 10% HCl. Total element concentrations (Al, Ca, Ti, Fe, Mn) of the bulk sediments were measured after acid digestion (HF, HNO<sub>3</sub> and HCl) by </span><span>Thermo iCAP 7400 ICP-OES</span><span>. Solid phase iron speciation data were measured following sequential Fe extraction (Fe<sub>carb</sub>, Fe<sub>ox</sub>, Fe<sub>mag</sub>, Fe<sub>py</sub>) by T</span><span>hermo iCAP 7400 ICP-OES</span><span>.</span></p>
Supporting Information: Sedimentary events at the Jurassic–Cretaceous transition in the Slovenian Basin (Alpine Atlantic): further evidences for palaeoenvironmental record in pelagic sediments
<p>Dataset containing raw elemental geochemistry data (ICP-MS, pXRF and GRS measurements), stable carbon and oxygen isotopes, as well as rock magnetic data from the Tithonian-Berriasian of the Petrovo Brdo section (Slovenia). Supplementary information for the research entitled "The Jurassic-Cretaceous transition in the Slovenian Basin (Alpine Atlantic): further evidence for palaeoenvironmental record in pelagic sediments" published in <em>Geochemistry, Geophysics, Geosystems. </em></p>
FTIR spectral library of the major components of archaeological sediments
<p>FTIR spectra of the major components of archaeological sediments. Spectra were collected using a Thermo Scientific Nicolet iS5 spectrometer equipped with an iD1 transmission compartment in the 4000-400 cm-1 range at 4 cm-1 resolution, and in 32 scans. Spectra were collected with the same instrument and settings in ATR mode using an iD7 ATR diamond crystal compartment. Spectra are available in .spa and .csv formats.</p>
Sediment flow Connectivity Index (SfCI) data in Apulia region (Italy)
<p><span>This dataset includes Sediment flow Connectivity Index (SfCI) data for Apulia region (Italy). The data are separated into folders: input data, graphs, index maps and product maps. The approach applied to build the geodatabase is based on the step-by-step processing of SfCI (Zingaro et al., 2019). The product maps of the geodatabase represent sediment mobility and connectivity maps of Apulia region, implemented also in a WebGIS platform.</span></p>
Validation of the UK'37 paleotemperature proxy in the South Brazilian Bight from core-top sediments
<p>Alkenone unsaturation index, water depth temperature, and nutrient concentration from the South Brazilian Bight (SBB). Temperature and nutrient content were retrieved from the World Ocean Atlas 2018. Residual analyses were performed using six paleoequations against observed temperature from WOA18. The PCA analysis was performed using alkenone unsaturation index, temperature, and nutrient data from each data point.</p>
Data included in "Sediments near industrial ports can be hotspots of fossil carbon accumulation"
<p>Dual carbon isotope ratios of sedimentary organic carbon (SOC) from the three ports (GG, PO, and PN) in South Korea.</p>
Data and code for the publication "Microplastics in water and sediments at the confluence of the Elbe and Mulde rivers in Germany"
<p><strong>Background</strong></p> <p>The data set contains data on visual and chemical analysis of sediment and water samples taken at the confluence of the Elbe and the Mulde rivers in Germany. It was analysed in the paper by H. Laermanns, G. Reifferscheid, J. Kruse, C. Földi, G. Dierkes, D. Schaefer, C. Scherer, C. Bogner and F. Stock, 2021, “Microplastics in water and sediments at the confluence of the Elbe and Mulde rivers in Germany” (<a href="https://doi.org/10.3389/fenvs.2021.794895">https://doi.org/10.3389/fenvs.2021.794895</a>) published in Frontiers in Environmental Science, Research Topic: “Plastics in Aquatic Systems: from Transport and Fate to Impacts and Management Perspectives”.</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>The data is available in the folder <strong>data_and_results.zip</strong>, and there in the folder <strong>data</strong>. The folder <strong>pyrolysis</strong> contains the file</p> <ul> <li><strong>pyr-gc-ms_raw.csv</strong>. This is the pyrolysis analysis (see description in the paper for technical details). The calibration data are part of the supplemental material of the paper and can be downloaded there.</li> </ul> <p>The folder <strong>visual_analysis</strong> contains the files</p> <ul> <li><strong>elbe_filter_photos_complete_v2.csv</strong>: IDs of samples, number of the photographs of filters, shapes of particles located on the filter</li> <li><strong>elbe_samples_v2.csv</strong>: IDs of samples, medium (water or sediment sample), location and side (left or right riverside) of sampling, sampling tool, sampled volume and mesh size.</li> </ul> <p>The folder <strong>results</strong> contains the figures produced by the R code (see <strong>Description of the code</strong>).</p> <p> </p> <p><strong>Description of the code</strong></p> <p>The file <strong>MP_visual_and_chemical_analysis.Rmd</strong> is the R notebook we wrote for the analysis. For information on the software version, see <strong>Information on software version </strong>below<strong>.</strong></p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data and code are provided as is without any warranty.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the staff of the BfG laboratory for their support during the pyr-GC-MS analysis. Julia Horn helped with geochemical and sedimentological measurements in the laboratory of the Institute of Geography at the University of Cologne.</p> <p> </p> <p><strong>Funding</strong></p> <p>This study is embedded in the Project “Micro- and macroplastic in federal water ways of Germany” (Project 4.1.12, Project No. M39620304028) that intends to analyse the occurrence, transport mechanism, ecological risks and management options. This project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project Number 391977956, SFB 1357.</p> <p> </p> <p><strong>References</strong></p> <p>Hannes Laermanns, Georg Reifferscheid, Jonas Kruse, Corinna Földi, Georg Dierkes, Dirk Schaefer, Christian Scherer, Christina Bogner and Friederike Stock, 2021, Microplastics in water and sediments at the confluence of Elbe and Mulde rivers in Germany, <a href="https://doi.org/10.3389/fenvs.2021.794895">https://doi.org/10.3389/fenvs.2021.794895</a></p> <p>R Core Team, 2021, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, https://www.R-project.org/</p> <p> </p> <p><strong>Information on software version</strong></p> <p>sessionInfo()</p> <p>R version 4.1.0 (2021-05-18)<br> Platform: x86_64-pc-linux-gnu (64-bit)<br> Running under: Ubuntu 20.10</p> <p>Matrix products: default<br> BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3<br> LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3</p> <p>locale:<br> [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C <br> [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8 <br> [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8 <br> [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C <br> [9] LC_ADDRESS=C LC_TELEPHONE=C <br> [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C </p> <p>attached base packages:<br> [1] stats graphics grDevices utils datasets<br> [6] methods base </p> <p>other attached packages:<br> [1] lubridate_1.8.0 patchwork_1.1.1 ggforce_0.3.3 <br> [4] gridExtra_2.3 RColorBrewer_1.1-2 forcats_0.5.1 <br> [7] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 <br> [10] readr_2.0.2 tidyr_1.1.4 tibble_3.1.6 <br> [13] ggplot2_3.3.5 tidyverse_1.3.1 </p> <p>loaded via a namespace (and not attached):<br> [1] httr_1.4.2 sass_0.4.0 bit64_4.0.5 <br> [4] vroom_1.5.5 jsonlite_1.7.2 modelr_0.1.8 <br> [7] bslib_0.3.1 assertthat_0.2.1 highr_0.9 <br> [10] cellranger_1.1.0 yaml_2.2.1 sessioninfo_1.1.1<br> [13] pillar_1.6.4 backports_1.2.1 glue_1.5.0 <br> [16] digest_0.6.28 polyclip_1.10-0 rvest_1.0.0 <br> [19] colorspace_2.0-2 htmltools_0.5.2 pkgconfig_2.0.3 <br> [22] broom_0.7.6 haven_2.4.1 scales_1.1.1 <br> [25] tweenr_1.0.2 tzdb_0.2.0 generics_0.1.1 <br> [28] farver_2.1.0 ellipsis_0.3.2 withr_2.4.2 <br> [31] cli_3.1.0 magrittr_2.0.1 crayon_1.4.2 <br> [34] readxl_1.3.1 evaluate_0.14 fs_1.5.0 <br> [37] fansi_0.5.0 MASS_7.3-54 xml2_1.3.2 <br> [40] tools_4.1.0 hms_1.1.1 lifecycle_1.0.1 <br> [43] munsell_0.5.0 reprex_2.0.0 compiler_4.1.0 <br> [46] jquerylib_0.1.4 rlang_0.99.0.9000 grid_4.1.0 <br> [49] rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.11 <br> [52] gtable_0.3.0 DBI_1.1.1 R6_2.5.1 <br> [55] knitr_1.36 fastmap_1.1.0 bit_4.0.4 <br> [58] utf8_1.2.2 rprojroot_2.0.2 desc_1.3.0 <br> [61] stringi_1.7.5 parallel_4.1.0 Rcpp_1.0.7 <br> [64] vctrs_0.3.8 dbplyr_2.1.1 tidyselect_1.1.1<br> [67] xfun_0.28</p>
Combined data file for Jilbert et al. "Anthropogenic Inputs of Terrestrial Organic Matter Influence Carbon Loading and Methanogenesis in Coastal Baltic Sea Sediments", Frontiers in Earth Science 9, 2021
<p>The datafile contains all the new raw data presented in the figures in the publication.</p>
Data from: Hitting reset on sediment toxicity: Sediment homogenization alters the toxicity of metal‐amended sediments
<p>Laboratory testing of sediments frequently involves manipulation by amendment with contaminants and homogenization, which changes the physicochemical structure of sediments. These changes can influence the bioavailability of divalent metals, and field and mesocosm experiments have shown that laboratory-derived thresholds are often overly conservative. We assessed the mechanisms that lead to divergence between laboratory- and field-derived thresholds; specifically, we assessed the importance of slow equilibration to solid-phase ligands and vertical stratification. To mimic natural physicochemical conditions, we uniquely aged sediment with a flow-through exposure system. These sediments were then homogenized and compared, toxicologically, with freshly metal-amended sediments in a 28-d chronic toxicity bioassay with the amphipod <em>Hyalella azteca</em>. We assessed concentration–response relationships for 3 metals (copper, nickel, and zinc) and 5 geochemically distinct sediments. We observed minimal differences in growth and survival of <em>H. azteca</em> between aged and freshly spiked sediments across all sediments and metals. These trends suggest that a loss of toxicity observed during long-term sediment aging is reversed after sediment homogenization. By comparison with mesocosm experiments, we demonstrate that homogenizing sediment immediately before toxicity assays may produce artificially high toxicity thresholds. We suggest that toxicity assays with sediments that maintain vertical redox gradients are needed to generate field-relevant sediment metal toxicity thresholds.</p>
Dataset to "Fine-sediment erosion and ridge morphodynamics in coarse-grained immobile beds"
<p>The dataset is connected to the paper "Fine-sediment erosion and ridge morphodynamics in coarse-grained immobile beds".</p> <p>The dataset is subdivided in "topographic" and "PIV" data. Each dataset refers to an experiment presented in the paper.</p> <p>The filenames of the topographic data specify the location where the data were collected ("upstream" or "downstream"). The topographic data contain the bed elevations measured around each sphere over a measurement area as wide as the channel and covering nine patterns of spheres (see paper for a definition of the pattern of spheres). Each topographic dataset contains four structures:</p> <p>- A time vector "T" identifying the sampling times at which the bed topography was measured (unit: h)</p> <p>- A space vector "x" identifying the streamwise position of the spheres with the origin of the coordinate system positioned at the beginning of the measurement areas (upstream or downstream) - unit cm</p> <p>- A space vector "y" identifying the transverse position of the spheres with the coordinate system centred in the middle of the channel and positively oriented towards the left side of the flume looking in streamwise direction - unit cm</p> <p>- A matrix "Z" identifying the protrusion levels of the spheres (as defined in the paper) as a function of space and time (x times y times T) - unit cm</p> <p> </p> <p>The PIV data were acquired during experiments 1b and 5b at the upstream location. The data are a collection of the velocity fields measured above the top of the spheres and over two pattern of spheres in the middle "m" and in the quarter plane "q" of the channel for three different time instants (t1, t2, t3) during the experiments. Each dataset contains 6 variables:</p> <p>- the "sampling time" of the bursts - unit s</p> <p>- the "acquisition time" of the PIV run during the experiments - unit h</p> <p>- a vector "x" identifying the streamwise position of the velocity vectors for a coordinae system centred in the centre of the sphere and pointing in the longitudinal direction.</p> <p>- a vector "z" identifying the vertical direction of the coordinate system with the origin at the top of the sphere and pointing upwards - unit cm</p> <p>- a matrix "vx" containing 1000 fields of the streamwise velocity component - unit cm/s</p> <p>- a matrix "vz" containing 1000 fields of the vertical velocity component - unit cm/s</p>
Sedimentary structure discrimination with hyperspectral imaging in sediment cores
<p>The LDB17_P11Ax (IGSN: TOAE0000000243); Datation Age 1040 +/- 30 to 2017 CE by core correlation, 14C, lamina counting) core from the Bourget Lake (France) was analyzed in 2018 by hyperspectral imaging. We studied the potential of hyperspectral sensor to image a sediment cores and created machine learning models. The hyperspectral images were acquired in order to develop quantitative (estimating particle size and loss on ignition) and qualitative (detection of instantaneous events or lamina) methods.<br> All these methods allow to reconstruct the past environment and climate at high resolution (pixel size: 50-250 microns) and without destroying the sample for archiving for future analysis.<br> These images have been valorized in publications for the detection of instantaneous events with hyperspectral and combined with XRF data, for the combination of the two images into a composite image.<br> image (.hdr, .dat, .jpg)</p>
Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan by a new analysis method for distributed acoustic sensing data using a seafloor cable and seismic interferometry"
<p>Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan, derived from distributed acoustic sensing data collected using a seafloor cable with seismic interferometry", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
Dereplicated Metagenome assembled genomes (MAGs) from Columbia River hyporheic sediments
<p>Fasta file containing 55 metagenome assembled genomes (MAGs) from publication to be submitted titled "<strong>Microbial genome-resolved metaproteomic analyses frame intertwined carbon and nitrogen cycles in river hyporheic sediments". </strong></p>
Doodleverse/Segmentation Zoo Res-UNet models for identifying coins in photos of sediment.
<p><strong>Doodleverse/Segmentation Zoo models for identifying coins in photos of sediment.</strong></p> <p>These model data are based on images of sand and coins and associated labels. Models have been fitted to the following types of data</p> <p>1. RGB (3 band): red, green, blue</p> <p>Classes are: {0: other, 1: coin}.</p> <p>These files are used in conjunction with Segmentation Zoo*</p> <p>For each model, there are 3 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p> </p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images</p> <p> </p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p> </p> <p>References</p> <p>* https://github.com/Doodleverse/segmentation_zoo</p> <p>** https://github.com/Doodleverse/segmentation_gym</p>
Freshwater viral metagenome assembled genomes (vMAGs) used for vContact2 analysis in publication Genome-resolved metaproteomics decodes the microbial and viral contributions to coupled carbon and nitrogen cycling in river sediments
<p>This dataset contains all freshwater viruses that were mined from publicly available data in an effort to provide biogeographical context to viral communities identified from the Columbia River. These two files include data from:</p> <p>1) East River, CO (PRJNA579838)</p> <p>2) A previous study from the Columbia River, WA (PRJNA375338)</p> <p>3) Prairie Potholes, ND (PRJNA365086)</p> <p>4) Amazon River (PRJNA237344)</p> <p> </p> <p>Manuscript title Genome-resolved metaproteomics decodes the microbial and viral contributions to coupled carbon and nitrogen cycling in river sediments</p>
Individual glass and secondary standard analyses for tephra described in sediment cores from Skilak Lake, Alaska
<p>This dataset comprises individual glass shard and secondary standard analyses for tephra collected in sediment cores from Skilak Lake, Alaska. These geochemical analyses were used in the research article in Sedimentology titled “Unravelling a 2300 year long sedimentary record of megathrust and intraslab earthquakes in proglacial Skilak Lake, south-central Alaska” by Praet et al. (2022). DOI: <a href="https://doi.org/10.1111/sed.12986">10.1111/sed.12986</a></p>
Effect of Sediment Form and Form Distribution on Porosity
<p>Data to accompany the journal article "Effect of Sediment Form and Form Distribution on Porosity: A Simulation Study Based on the Discrete Element Method" by Rettinger et al. (2022).</p> <p>The data set contains simulation input and results obtained by the particle packing studies using the discrete element method, as described in the article.</p> <p>The folder `surface_meshes` features 63 processed scans of gravel sediments from the Rhine river in Germany. The original scans have been converted to a convex surface mesh, and then simplified to feature at most 300 surface triangles. Additionally, they were oriented along their principle axes of inertia and scaled such that their size, according to the applied definition, is 1.</p> <p>The folder `single_form` contains the simulation results of the single shape/form studies, where all particles featured a single shape, given by one of the meshes. Three different size distributions have been used: U1 is a unimodal one with a single size class, U7 is also unimodal but with seven size classes, B50 is a bimodal one with approximately half fine and half coarse particles. Each corresponding csv file contains the obtained porosity (`porosity`) of the actual packing and of the packing using equivalent ellipsoids (`porosityEquivEllip`). Furthermore, the considered form factors according to their respective definition, as well as their inverse (`invX`), are given. See the article for the definition and the meaning of the abbreviation.</p> <p>The folder `form_distribution` contains the simulation results of the form distribution study, where the particle form was prescribed by flatness and elongation which were sampled from truncated normal distributions. Again two different size distributions have been used: U1 and U7. Each corresponding csv file contains the obtained porosity (`porosity`) and statistical information about the form factors, given as number-based mean (`mean`) and standard deviation (`std`). In addition to the inverse of each form factor (`invX`), the inverse of each statistical quantity has been evaluated (`1/(X)`) and is contained as column.</p>
Grain size distribution of Amazon river sediment samples collected over the period 2005-2008; and ADCP water velocity profiles collected on the major tributaries of the Amazon in Bolivia and Peru, 2007-2008
<p>This dataset contains two items:</p> <p>- The grain size distribution of river sediment samples collected along the Amazon River and its tributaries during four sampling campaigns performed in June 2005 (lower Amazon, Brazil), March 2006 (lower Amazon, Brazil), May 2007 (Upper Madeira, Bolivia), and April 2008 (Upper Solimões-Amazonas, Peru). [spreadsheet "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_data.xlsx"].</p> <p>- River water velocity profiles derived from Acoustic Doppler Current Profiler (ADCP) measurements performed on the major tributaries of the Amazon in May 2007 (Upper Madeira, Bolivia) and April 2008 (Upper Solimões-Amazonas, Peru) [folder "ADCP_dataset_Amazon_2007-2008_Bouchez_data"].</p> <p>The dataset description and the relevant references are provided in the text files "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_description.docx" and "ADCP_dataset_Amazon_2007-2008_Bouchez_description.docx" .</p> <p>These data were acquired thanks to the support of the French National Service for Observation "HYBAM" ("Hydrogeochemistry of the Amazon Basin"), part of the CNRS National Infrastructure "OZCAR" ("Critical Zone Observatories: Applications and Research").</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.