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1,572 results for “sediment”
High-marsh infauna densities within references and ice-rafted sediment deposits, Rowley, MA.
Following a historic bomb cyclone (Winter Storm Grayson) in January of 2018, a large volume of ice-rafted sediment was patchily deposited on the surface of salt marshes in the Great Marsh, MA. In May of 2018, twenty patches of ice-rafted sediments and paired reference sites (i.e., no sediment deposition) were delineated. In May 2018, August 2018, and August 2019, samples were collected to examine how ice-rafted sediments affected vegetation, infauna, and epifauna recovery over time. This specific dataset focuses on infauna species counts, with the primary species including: mites, Manayunkia aestuarina, and Cernosvitotviella immota. This dataset is complete and please see our publication (https://doi.org/10.1007/s12237-021-01023-z) for more information.
Length, width, perimeter, and sediment thickness of ice-rafted sediment deposits, Rowley, MA.
Following a historic bomb cyclone (Winter Storm Grayson) in January of 2018, a large volume of ice-rafted sediment was patchily deposited on the surface of salt marshes in the Great Marsh, MA. In May of 2018, twenty patches of ice-rafted sediments and paired reference sites (i.e., no sediment deposition) were delineated. In May 2018, August 2018, and August 2019, samples were collected to examine how ice-rafted sediments affected vegetation, infauna, and epifauna recovery over time. This specific dataset focuses on the length, width, perimeter, and sediment thickness measurements of each ice-rafted sediment deposit. This dataset is complete and please see our publication (https://doi.org/10.1007/s12237-021-01023-z) for more information.
SBC LTER: OCEAN: Particulate Organic Matter Content and Composition of Stream, Estuarine, and Marine Sediments
An unprecedented five-year drought in California, coupled with conditions of anomalously low ocean productivity and the prospect of one of the strongest El Niño periods on record with above average rainfall were the impetus for this RAPID award, which seeks to test specific hypotheses pertaining to the origin, distribution, processing, and bioavailability of terrestrial organic matter in coastal marine sediments and their potential for serving as a reservoir of nitrogen storage to fuel nearshore primary production during periods when nitrate concentrations are low. The goals of the research were to: (1) measure bulk properties and biomarker tracers of particulate organic matter (POM) in stream water and in coastal marine sediments at SBC LTER and other reef sites differing in exposure to terrestrial runoff prior to and following large storm events, (2) determine the bioavailability of dissolved organic matter (DOM) released from POM in marine sediments following large runoff events, and (3) measure changes in concentrations of dissolved inorganic and organic nitrogen in pore water of marine sediments near to and distant from stream mouths in the Santa Barbara Channel. Samples were analyzed for organic matter content using a loss-on-ignition combustion method, and samples were also analyzed for organic carbon and nitrogen content and isotopes using a stable isotope mass spectrometer interfaced with an elemental analyzer. Subsamples were shipped to the laboratory of Marc Lucotte at the University of Québec, Montréal for analysis of lignin content using the cupric oxidation method.
Sediment temperature at three depths below sediment surface in intertidal mudflats in Virginia, 2013-2014
We logged the sediment temperature at three intertidal mudflat sites between September, 2013 and September, 2014. At each site, three submersible temperature loggers (HOBO Pendant UA-002; Onset Computer Corporation) were buried at 3 cm, 10 cm and 20 cm below the sediment surface. Temperature was recorded in ten minute intervals. Each site was unvegetated with muddy sediment. For sites 1 and 2, data was recorded in two time series. Time series 1 from 09/02/2013 - 12/16/2013 and time series 2 from 12/19/2013 - 09/18/2014. At site 2, 20 cm depth, no data was recorded after 12/16/2013. At site 3, data was recorded at all depths continuously from 09/2/2013 to 08/28/2014. Date and time was recorded as GMT offset by -5 hours (Eastern Standard Time).
Sediment Properties Drive Spatial Variability of Potential Methane Production and Oxidation in Small Streams
<ul> <li>This dataset contains 20 data tables (Fig.2.csv, Fig.3.csv, data_PLS_stream-main-stem.csv, Fig.4_a.csv, data_PLS_subcatch.-stream-sect.csv, Fig.4_b.csv, Fig.5.csv, Fig.S1.csv, Fig.S2.csv, Fig.S3.csv, Fig.S4.csv, Fig.S5_a.csv, Fig.S5_b.csv, Fig.S6_a.csv, Fig.S6_b.csv, Fig.S6_c.csv, Fig.S6_d.csv, TableS1_data-adjustment.csv, TableS1_lit-data.csv, PMO_surface-water.csv), we separated our data tables in the respective figures/analyses presented in our paper</li> <li>We added the units to each column title of each respective data table</li> <li>Please see "Metadata.pdf" and our paper (same title as the dataset) for more information</li> </ul> <p> </p>
Palaeoecological records from BJM2 sediment core (Sebkha Boujmel, Southern Tunisia. 33°18'30.96" N, 11°5'0.68" E)
<p>Palaeoecological records from BJM2 sediment core (Sebkha Boujmel, Southern Tunisia. 33°18’30.96” N, 11°5’0.68” E (Latitude Y 33.3086, Longitude X 11.083522).</p> <p>1. Conventional AMS radiocarbon dates and reservoir-corrected and 2σ range calibrated ages from Sebkha Boujmel (BJM2 core).</p> <p>2. Output of the age-depth model for BJM2 core indicating depth and associated mean date for each cm (cal yr BP). The age model was obtained by third-degree polynomial regression with 10k model iteration using the package Clam 2.2.</p> <p>3. Pollen percentage for the three ecological groups (Mediterranean, steppe and desert taxa). The percentages are calculated with respect to a basic sum that only includes these three groups. Pollen taxa and types from the same genus or family and with the same ecology are grouped; including Boraginaceae (Moltkiopsis ciliata, Onosma and Echium), Ephedra sp. (Ephedra fragilis-t. and Ephedra distachia-t.) and Zygophyllaceae (Fagonia, Nitraria and Zygophyllum). Percentage of aquatics pollen are calculated based on the total sum of pollen grains identified in each pollen spectrum.</p> <p>4. Pollen and clay mineralogy data from Sebkha Boujmel. Percentages of (1) <strong>fresh water</strong> (Cyperaceae, Glyceria, Juncus, Lemna, Potamogeton, Rumex aquaticus-t., Typha/Sparganium-t.) and <strong>(2) Mediterranean tree and shrub</strong> (Buxus, Ceratonia, Cistus, Juniperus, Lamiaceae, Myrtus, Nerium, Olea, Papaveraceae, Pinus, Pistacia, Quercus ilex-t., Quercus deciduous-t., Rhus tripartita-t.) pollen taxa. (3) <strong>Wet / dry (W / D) pollen ratio</strong> (Poaceae + Cyperaceae/Asteraceae Cichorioideae + Asteraceae Asteroideae + Amaranthaceae Cornulaca/Traganum-t.). (4) <strong>Percentages of desert pollen taxa</strong> (Apiaceae, Asphodelus, Asteraceae Asteroideae, Asteraceae Cichorioideae, Calligonum, Capparis, Cistanche, Cleome, Cornulaca/Traganum-t., Crassulaceae, Cucurbitaceae, Echium, Ephedra distachia-t., Ephedra fragilis-t., Fagonia, Helianthemum, Malvaceae, Moltkiopsis ciliata, Neurada, Nitraria, Onosma, Reaumuria, Tamarix and Zygophyllum). (5) <strong>Illite</strong> <strong>[%] (Ill) / Kaolinite [%] (Kln) ratio</strong> and (6) <strong>Palygorskite percentages [%] (Plg)</strong>.</p> <p>5. Pollen percentage of Artemisia and selected anthropogenic pollen indicators (APIs) including cultivated (Cerealia-t., Corchorus, Ficus, Olea, Phoenix, Vitis), nitrophilous (Aizoaceae, Emex, Peganum, Polygonum) and introduced (Acacia cyanophylla-t., Casuarina, Eucalyptus) plant taxa. Percentage are calculated based on the total sum of pollen grains identified in each pollen spectrum.</p> <p>6. Pollen counts for BJM2 core (pollen grain count for each taxon by sample). + Lycopodium (added), Lycopodium (counted) and Sample weight [gr].</p> <p>7. Clay Mineralogy of BJM2 sediment core. </p> <p>Smectite [%] (Sme), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Illite [%] (Ill), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Palygorskite [%] (Plg), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Kaolinite [%] (Kln), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Chlorite [%] (Chl), METHOD/DEVICE: X-ray diffraction, clay fraction</p>
Data set for the physical, chemical and biochemical modelling of the primary sedimentation tanks at the WWTP of Eindhoven
<p>These files contain data about measurement campaigns on the primary sedimentation tanks of the WWTP of Eindhoven (The Netherlands) in 2013 and 2014 and the routinely collected data for 2011 till 2013.</p> <p>The data was processed in the PhD of Youri Amerlinck, entitled "Model refinements in view of wastewater treatment plant optimization: improving the balance in sub-model detail."</p> <p>http://www.biomath.ugent.be/biomath/publications/download/amerlinckyouri_phd.pdf</p> <p><br> WWTP of Eindhoven PST Routine Measurements 2011_2013.csv<br> January 5, 2011 - June 14, 2013: <br> Routine analysis for BOD5, COD, TKN, TP, PO4, TSS</p> <p>WWTP of Eindhoven PST Reduced Capacity 2013.csv<br> June 24, 2013 - July 23, 2013 - September 9, 2013<br> Evaluation of reducing the capacity of the PST (including dosing of chemicals) for CODT, CODS, TP, PO4 ,TSS </p> <p>WWTP of Eindhoven PST measurement campaign full ASM 20140506.csv<br> May 6, 2014: <br> Full ASM fractionation BOD5, CODT, CODS, TSS, VSS TP, PO4 ,TN, NH4, NO3, pH</p> <p>WWTP of Eindhoven PST measurement campaign full ASM and Cations 20140902.csv<br> September 2, 2014:<br> Full ASM fractionation (repetition) and cation analysis (BOD10, CODT, CODS, TSS, VSS, TP, PO4 ,TN, NH4, NO3, pH - Ca, Mg, Na, K, Fe)</p>
Geochemical sediment fingerprinting dataset from Oroua river catchment, New Zealand
<p>Geochemical dataset collected to determine key source contributions to overbank sediment deposition for specific particle size fractions as described in "Vale, S., Smith, H., Matthews, A., & Boyte, S. (2020). Determining sediment source contributions to overbank deposits within stopbanks in the Oroua River, New Zealand, using sediment fingerprinting. <i>Journal of Hydrology (New Zealand)</i>, <i>59</i>(2), 147-172." </p>
A Large Scale Side-Scan Sonar Dataset of Seafloor Sediments for Self-Supervised Pretraining
<p>This dataset serves as an extension to the dataset part of "A convolutional vision transformer for semantic segmentation of side-scan sonar data" published in Ocean Engineering, Volume 86, part 2, 15 October 2023,<strong> </strong>DOI: <a href="https://www.sciencedirect.com/science/article/pii/S0029801823020310">10.1016/j.oceaneng.2023.115647</a> for self-supervised pretraining.</p><p>This dataset consists of patches of side-scan sonar waterfalls collected along the coast of Catalunya during an extensive survey. The waterfalls were partitioned in batches of 384 lines to generate images of size 384 × 384 with a 192 pixel-overlap along-track and across-track. This resulted in a total of 434,164 images capturing various seafloor types including rocky bottoms, sand ripples, detrital funds, posidonia, cymocea, mud, corals, artificial reefs etc.</p><p>Additional tools for using the data for self-supervised pretraining can be found under <a href="https://github.com/DeeperSense/deepersense-seafloorscan">https://github.com/DeeperSense/deepersense-seafloorscan</a></p><p> </p><p><strong>Acknowledgements</strong></p><p>The data in this repository were collected by Tecnoambiente SL as part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020. Project Number: 101016958.</p>
Qiime2 classifiers (rbcl, Mollusc 18s) for testing the validity of using eDNA for carbon origin analysis from sediment cores
<p>Qiime2 formatted classifiers that were created for a Natural England funded project by researchers at the James Hutton Institute. The pilot project aims to test the validity of using eDNA for carbon origin analysis from sediment cores. These classifiers for the rbcl and 18 Mollusc genes were made using RESCRIPt and Qiime2. </p> <p>The scripts used to created these classifiers are available at the James Hutton ICS GitHub <a href="https://github.com/HuttonICS/blue-carbon-db">blue-carbon-db</a> . The files are as follows:</p> <p><a href="../api/records/10046481/draft/files/mollusc-espineira-classifier.qza/content" target="_blank" rel="noopener noreferrer">mollusc-espineira-classifier.qza</a> is a classifer built from ncbi 18s Mollusc sequences, trained on the primer set from Espiñeira et al (2009).</p> <div>rbcl-vasselon-zimmerman-F3-R1-classifier.qza is a classifer built from ncbi rbcl sequences, trained on the F3 and R1 primer set fromVasselon et al (2017).</div> <p> </p> <p> </p> <p><strong>Important: </strong>If you use these classifiers please be aware of the process used to create them and be sure to review the methods. These databases were created by downloading data from the NCBI in October 2023, sequence data available at the NCBI changes over time. To create the most up to date database a fresh download and re-evaluations of the databases would be preferable. All method and scripts can be found at <a href="https://github.com/HuttonICS/blue-carbon-db">blue-carbon-db </a></p> <p>If you use these database please reference this repository along with RESCRIPt and Qiime2 </p> <p> </p> <p>Espiñeira, M., González-Lavín, N., Vieites, J. M. and Santaclara, F. J. 2009 Development of a method for the genetic identification of commercial bivalve species based on mitochondrial 18S rRNA sequences. J Agric Food Chem, 28, 495-502 https://doi.org/10.1021/jf802787d</p> <p> </p> <p>Vasselon, V., Rimet, F., Tapolczai, K. and Bouchez, A. 2017. Assessing ecological status with diatoms DNA metabarcoding: Scaling-up on a WFD monitoring network (Mayotte island, France). Ecological Indicators, 82, 1-12 <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ecolind.2017.06.024" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ecolind.2017.06.024</a></p>
Estimated field scale sediment loss on the North Wyke Farm Platform in typical and extreme wet winters
<p>Based on monitored runoff and turbidity at 15-minute intervals from the North Wyke Fam Platform - a UK National Bioscience Research Infrastructure (NBRI), sediment loss during both typical and more extreme wet winters (December - February, inclusive) over the past decade (2012-2013, 2013-2014, 2015-2016, 2019-2020 and 2023-2024) from 5 grassland field catchments and 5 recently converted arable field catchments was estimated, including uncertainty ranges. Daily rainfall totals for the corresponding winter periods are also included.</p>
Aqueous geochemical measurements and speciation calculations with concurrent copper resistance gene counts from sediment metagenomes over a seasonal cycle from 2015 to 2016 on Silver Bow Creek and Blacktail Creek near Butte, MT
<p>This dataset contains information from concurrently gathered geochemical and metagenomic samples collected from Silver Bow Creek and Blacktail Creek near Butte, MT (SBC/BC) during 2015 and 2016. SBC/BC is recovering from metal contamination related to extensive mining in the area. Full geochemical measurements, geochemical speciation calculations, and gene counts of sequences mapping to copper resistance genes using MG-RAST are included. </p>
Dataset for manuscript Tracing Quartz Provenance: A Multi-Disciplinary Investigation of Luminescence Sensitisation Mechanisms of Quartz from Granite Source Rocks and Derived Sediments
<p><span>Quartz optically stimulated luminescence (OSL) sensitivity as well as some electron spin resonance (ESR) and cathodoluminescence (CL) signals have been empirically proposed as provenance indicators. Sensitivity is defined as luminescence emitted in response to a given dose per unit mass. While it is largely believed to be acquired by earth surface processes, recent studies bring evidence that sensitisation processes depend on source geology.</span></p> <p><span>Here we combine OSL and thermoluminescence (TL), ESR and CL analyses to understand the mechanisms of quartz OSL sensitisation. We investigate granites and their derived sediments from catchments draining simple lithologies of known age that display contrasting OSL sensitisation behaviour both in nature and during irradiation and light exposure laboratory experiments. The sample displaying increased OSL sensitisation is characterised by TL emission at intermediate temperatures (150-250 °C), Ti-related signals in CL, and Ti and Ge lithium compensated signals in ESR. <span>The insensitive samples either lack or exhibit very weak such characteristics and contain several times less amount of trace titanium measured by </span></span><span>laser ablation inductively coupled plasma mass spectrometry (</span><span>LA-ICP-MS).</span></p> <p><span>We demonstrate that the OSL sensitisation results as an effect of the existence of certain defects and impurities in the quartz crystal in the parent rock, such as titanium and germanium. However, the degree of sensitisation reached in nature is significantly higher than in the laboratory. <span> </span>As such, the existence of this precursor represents the potential for sensitisation, which can later be amplified by environmental factors during sedimentary history.</span></p>
Floodplain Sediment Storage Times in a Simulated Meandering River
<p>This dataset contains four files which enable another researcher to bypass the most computationally intensive parts of the simulations (in MATLAB) associated with the publication of my dissertation thesis. These files result as the analysis of the Simulation of the Long-Term Evolution of a Meandering River (https://doi.org/10.5281/zenodo.5651840).</p> <p>The first of these files contain the storage time and age distributions of simulated sediments from the upstream reach of the simulated river, captured after analyzing the upstream 16 mini reaches of the floodplain.</p> <p>The second file contains the same distributions from two reaches after analyzing 36 mini reaches (the downstream reach values can be found by subtraction of the data in these two files)</p> <p>The third file contains other useful parameters tracked throughout the simulation</p> <p>The fourth file contains the X & Y coordinates which mark the boundaries of each mini reach</p> <p>See the accompanying dissertation document (to be referenced once the official reference is available) and the GitHub repository which includes the code required.</p>
Data to reproduce the results presented in Lake et al. 2021. Journal of Soils and Sediments, https://doi.org/10.1007/s11368-021-03107-6 ("High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting")
<p>This repository contains data on (1) the absorbance data and (2) the measured concentrations, to reproduce computational results as presented in:<br> "High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting".</p> <p> <br> 1. Absorbance data (200-730 nm wavelengths):</p> <p> * Average absorbance compensated for measured concentrations (average absorbance value per concentration)<br> * Average absorbance compensated for theoretical concentrations (average absorbance value per concentration)<br> * Average raw absorbance measured (average absorbance value per concentration)<br> * Raw absorbance measured (all absorbance values for all concentrations)</p> <p> Data in all 3 files is indicated per soil sample / mixture, with corresponding fraction(s) of soil sample(s) and corresponding (theoretical) input concentration.<br> <br> 2. Measured concentration data:</p> <p> * Measured concentration (average concentrations, tested for all experiments and for all theoretical input concentrations)</p> <p> </p>
Data and code for: A conceptual model-based sediment connectivity assessment for patchy agricultural catchments
<p>Authors: Pedro V G Batista, Peter Fiener, Simon Scheper, Christine Alewell</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Abstract</p> <p>The accelerated sediment supply from agricultural soils to riverine and lacustrine environments leads to negative off-site consequences. In particular, the sediment connectivity from agricultural land to surface waters is strongly affected by landscape patchiness and the linear structures that separate field parcels (e.g. roads, tracks, hedges, and grass buffer strips). Understanding the interactions between these structures and sediment transfer is therefore crucial for minimising off-site erosion impacts. Although soil erosion models can be used to understand lateral sediment transport patterns, model-based connectivity assessments are hindered by the uncertainty in model structures and input data. In specific, the representation of linear landscape features in numerical soil redistribution models is often compromised by the spatial resolution of the input data and the quality of the process descriptions. Here we adapted the WaTEM/SEDEM model using high resolution spatial data (2 m x 2 m) to analyse the sediment connectivity in a very patchy mesoscale catchment (73 km<sup>2</sup>) of the Swiss Plateau. We used a global sensitivity analysis to explore model structural assumptions about how linear landscape features (dis)connect the sediment cascade, which allowed us to investigate the uncertainty in the model structure. Furthermore, we compared model simulations of hillslope sediment yields from five sub-catchments to tributary sediment loads, which were calculated with long-term water discharge and suspended sediment measurements. The sensitivity analysis revealed that the assumptions about how the road network (dis)connects the sediment transfer from field blocks to water courses had a much higher impact on modelled sediment yields than the uncertainty in model parameters. Moreover, model simulations showed a higher agreement with tributary sediment loads when the road network was assumed to directly connect sediments from hillslopes to water courses. Our results ultimately illustrate how a high-density road network combined with an effective drainage system increases sediment connectivity from hillslopes to surface waters in agricultural landscapes. This further highlights the importance of considering linear landscape features and model structural uncertainty in soil erosion and sediment connectivity research.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Metainformation</p> <p>This dataset includes:</p> <p>1 - The input data used for running the WaTEM/SEDEM model in the Baldegg catchment.</p> <p>2 - The discharge and sediment concentration data used for producing the sediment rating curves for the tributaries of the Lake Baldegg.</p> <p>3 - The model and sediment rating curve output data.</p> <p>4 - The R scripts for running the WaTEM/SEDEM model in the Baldegg catchment, the code for producing the sediment rating curves, and the code for summarising and analysing the model output data.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The sediment concentration and water discharge data were supplied by Robert Lovas, from the Department of Environment and Energy of the Canton of Lucerne.</p> <p>The model input data were adapted from freely available ©swisstopo geodata products:</p> <p>Swisstopo. SwissALTI3D. Das hoch aufgelöste Terrainmodell der Schweiz, 2014.</p> <p>Swisstopo. Swiss Map Vector 25 Beta, Das digitale Landschaftsmodell der Schweiz. 2018.</p> <p>Swisstopo. SwissTLM3D. Das grossmassstäbliche Topografische Landschaftsmodell der Schweiz, 2020.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further information we refer to our preprint: https://doi.org/10.5194/hess-2021-231</p> <p> </p> <p> </p>
IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments"
<p>IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments"</p> <p>Includes SEM QEMSCAN® and <sup>40</sup>Ar/<sup>39</sup>Ar data for International Ocean Discovery Program (IODP) Expedition 382 Site U1538. Also includes a movie of a 3D-volume realization of an iceberg-rafted sedimentary layer from this site based on non-destructive X-ray microtomography imaging.</p> <p> </p> <p><strong>Data Set Captions:</strong></p> <p> </p> <p><strong>Data Set S1. </strong>Modal mineralogy data based on QEMSCAN® analyses, which infer minerals from chemistry. The mineral name assignations for each chemistry-based category stated in this table are aided by visual (microscope-based) inspection of the raw sieved samples.</p> <p><strong>Data Set S2. </strong>Mineral association data based on QEMSCAN® analyses. Please read data in columns, mineral against mineral (down then across left). These data define what touches what in the sample and is displayed as a percentage. Association refers to adjacency. Two minerals are “associated” if a pixel of one of the minerals occurs adjacent to a pixel of the other mineral. iExplorer software used scans the measured particles horizontally, from left to right, counting the associations that occur in the images (so the more pixels/closer the x-ray spacing the more accurate the data). Each column is independent. That is, it is split into a percentage of what touches what, so it is not expected that any two minerals’ data are reciprocal. The background category primarily reflects the free boundaries of ‘grains’ rather than liberated grains/particles. While it may provide an indicator of liberation, it does not represent liberation since it does not describe ‘particles’ which are made up of mineral grains. Inclusions and composite particles are therefore not described. Please consider the modal mineralogy (Tab. S1) when examining these mineral association data.</p> <p><strong>Data Set S3. </strong>Lithotyping data based on QEMSCAN® analyses. Particles have been digitally filtered using a set of lithotype rules (also displayed in this data set). These rules are based on the mineral grains in the particles themselves and use their area percent within each particle and their size in microns. The lithotype names stated here are largely assigned based on the dominant mineral grain in each category.</p> <p><strong>Data Set S4. </strong>40Ar/39Ar ages of individual sand-sized hornblende and mica. See main text for method used to generate these ages.</p> <p><strong>Data Set S5.</strong> Ties to place Hole U1538A NGR data on Dove Basin Stack (Reilly et al., 2021) depths.</p> <p><strong>Movie S1. </strong>3D-volume realization based on non-destructive X-ray microtomography imaging of a centimeter-scale iceberg-rafted debris-rich layer in Hole U1538A-36X-3W. 3D images were generated using a helical scanning trajectory that allows for long scan sequences and fast acquisition time. Based on the sample geometry, a voxel (pixel) resolution of ~14-μm was achieved. The 7000+ projection images were reconstructed to produce a 3D volume of image intensities (where higher values indicate greater x-ray attenuation). Avizo software was used for 3D segmentation and volume rendering to visualize gravel and sand to create this animation. The different colors assigned to each clast were chosen arbitrary.</p>
Electrical conductivity of the world ocean and marine sediments
<p>Copy of dataset (as was on 2022-01-12) of electrical conductivity and conductance grids for the ocean and marine sediments at 0.1 degree lateral resolution, from https://github.com/agrayver/seasigma. These models are presented in the work</p> <p>Grayver, A. V. (2021). Global 3-D electrical conductivity model of the world ocean and marine sediments. Geochemistry, Geophysics, Geosystems, 22, e2021GC009950. <a href="https://doi.org/10.1029/2021GC009950">doi: 10.1029/2021GC009950</a></p> <p>Please cite this publication if you use the provided models in your work.</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part IV: Post-processing)
<p>This is Part IV of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the post-processing of all model results.</p> <p>To be able to run the scripts as is, the folder structure should be as follows:</p> <p>Runs (includes all model run folders from Part II and Part III)<br>Post/Basic/Channels<br>Post/Basic/Cross_sections<br>Post/Basic/Integrals<br>Post/Basic/Median_neighborhood_analysis (includes all unzipped MNA_TIGER_XX.zip folders)<br>Post/Basic/Skeleton_clean<br>Post/Basic/Skeleton_final<br>Post/Basic/Skeleton_raw<br>Post/Basic/Unchanneled_path_length<br>Post/Basic/Watersheds<br>Post/Basic/Scenarios.txt<br>Post/Basic/TIGER_2km_5m.slf<br>Post/Paper_1/Erosion-deposition<br>Post/Paper_1/Fluxes<br>Post/Paper_1/Profiles<br>Post/Paper_1/Std</p>
Research data for: Preventing the coffee-ring effect and aggregate sedimentation by in situ gelation of monodisperse materials
<p>Raw data for the publication: Preventing the coffee-ring effect and aggregate sedimentation by in situ gelation of monodisperse materials</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.