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110 results for “river sediment”
Major and trace element composition of IODP Site U1429 and Yellow River sediments and simulated rainfall changes in East-Southeast Asia during past 300 ka
<p>This dataset includes major and trace element composition of IODP Site U1429 and Yellow River surface sediments. They are all analyzed based on the clay-sized fractions. It also includes the data of simulated rainfall changes in the northern and southern China, as well as in Southeast Asia during past 300 ka.</p>
Sr-Nd isotopic fingerprints of Red River sediments and its implication for provenance discrimination in the South China Sea
<p>Supplementary Data of the Manuscript titled 'Revisit Sr-Nd isotopic fingerprints of Red River sediments and its implication for provenance discrimination in the South China Sea'</p> <p>Table A1. elemental compositions of Red River estuarine sediment samples</p> <p>Table A2. Literature data on sediment Sr-Nd isotopic ratios, Rb/Sr and K<sub>2</sub>O/(Na<sub>2</sub>O+CaO) in the Red River catchment</p> <p>Table A3. Compiled data on Sr-Nd isotopic compositions of rocks in the Thao River, Song Da and Song Lo basin</p> <p>Table A4. Compiled data of sediment Sr-Nd isotopic composition in the Pearl River, Red River and Mekong River</p>
Moving bedforms control CO2 production and distribution in sandy river sediments
<p>This dataset includes raw and processed data, as well as modeling codes that were used to produce the results presented in Schulz et al. (submitted) "Moving bedforms control CO<sub>2</sub> production and distribution in sandy river sediments" submitted to Journal of Geophysical Research: Biogeosciences.</p> <p> </p> <p>The dataset contains:</p> <p>1) Data for sediment characterization: Grain size distribution, hydraulic conductivity, loss on ignition, and porosity data.</p> <p>2) Data on hydrology and morphodynamics: Bedform celerity, height, and length, Damkoehler number, shear velocities, and Python codes for modeling the hyporheic exchange flux.</p> <p>3) Data compilation: Data of online sensors that continuously measured various parameters of the streamwater, atmospheric pressure, and CO<sub>2 </sub>air concentrations.</p> <p>4) Digital raw images of dye tracer experiments.</p> <p>5-10) Image data: Raw and processed planar optode and IR images, spreadsheets containing data analysis results.</p>
Data for 'Rethinking variability in bedrock rivers: sensitivity of hillslope sediment supply to precipitation events modulates bedrock incision during floods'
<p>This is the repository for model code, sample data, and plotting scripts for the OTTER model for Python.</p> <p>The file OTTERPy.py contains the actual model code</p> <p>The file OTTERPlottingFuncs.py contains a few scripts for plotting model results.</p> <p>The four sample data files in the repository are results from two long (1.5million model year) runs at different rock uplift rates which took several days to run on the IU Bloomington Quartz supercomputing cluster. This data should be viewed and plotted using the OTTERPlottingFuncs.py script. See that code for information on how to select which model to plot.</p> <p> </p> <pre><code>Glossary of variables - this is pretty close to exhaustive, there are a probably a couple of throwaway variables not listed here. a: Sternberg's law multiplier Ah: drainage area from Hack's Law (m^2) beta: fraction of sediment transported as bedload from 0 to 1 D: grain size at each node (m) dA: change in drainage area from node to node (m^2) depth_thresh: sediment depth at which bedrock erosion can occur Do: initial grain size Do: grain size at headwaters (m) dQs: change in sediment supply from node to node dt: time step (years) dx: space step (m) dz_b: bedrock erosion at each node dz_s: change in sed depth dz_b_t: bedrock erosion saved through time dz_b_store: bedrock erosion stored dz_s_store: sed depth change stored eQ: exponent for scaling Qw from Ah eroded_sedsup: sediment created by bedrock erosion eW: exponent for scaling W from Qw F: fractional bedrock exposure g: gravitational acceleration (M/s^2) H: water depth Hc: Hack's Law constant He: Hack's law Exponent initial_sedsup: Sediment supply in first 10k yr (equal to uplift) kf: bedrock erodibility kQ: constant for scaling Qw from Ah kv: parameter controlling discharge variability - Crave & Davy, 2001. Lower kv is more variable kw: lateral bedrock erosion constant kWid: constant for scaling W from Qw last_z: elevation at channel mouth lmbda: sediment porosity n: manning's roughness coefficient onlyonce: changes from 0 to 1 to make sure the uplift pulse only happens one time onoff: turns sediment on and off entirely onoffsedvar: turns stochastic sediment on and off Qs: sediment supply along the length of the river Qs_down: downstream sediment flux at each node Qt: sediment transport capacity Qw: water discharge Qwo: initial water discharge QwScale: array of scalar multipliers for Qw, from the chosen distribution R: effective density of submerged sediment rho_w: density of water (kg/m^3) rho_s: density of sediment (kg/m^3) savesteps: at what time should we save outputs? sed_depth: depth of seidment at each node (m) SedStore: storing snapshots of sediment thickness through time slope: river gradient (negative usually) SlopeStore: storing snapshots of channel slope through time tarray: array of model time tau_b: basal shear stress tau_c: critical shields criterion for initiation of sediment motion time: model duration (years) topo: channel elevation (bedrock + sediment) upinc: size up uplift pulse, as a multiplier of initial uplift rate upinctime: when should the pulse happen? (years) uplift: uplift field across the entire domain uprate: initial uplift rate, if using constant uplift across domain W: channel width WidthStore: storing snapshots of channel width through time Wo: initial channel width yr2sec: number of seconds in a year z: bedrock channel elevation ZStore: storing snapshots of bedrock elevation through time</code></pre>
Contaminated sediment in the Detroit River selects for evolved CYP1A and p53 responses in wild brown bullhead (Ameiurus nebulosus) populations
<div> <div> <p><span>In a previous study, adaptive responses to a single polycyclic aromatic hydrocarbon (PAH), benzo[a]pyrene (BaP), were identified in brown bullhead (</span><span><em>Ameiurus nebulosu</em>s</span><span>) captured from contaminated sites across the Great Lakes. The tumor suppressor p53 and phase I toxin metabolizing CYP1A genes showed a protective and refractory response, respectively, up to the F1 generation (Williams and Hubberstey, 2014). As an extension to the first study, bullhead were exposed to sediment collected from sites along the Detroit River to see if these adaptive responses are attainable when fish from a contaminated site are exposed to a mixture of contaminants, instead of a single compound. p53 and CYP1A proteins were measured in both studies with the addition of phase II glutathione-s-transferase (GST) activity in the second. Three treatment groups were measured: acute (treated immediately), cleared (depurated for three months and subsequent treatment), and farm raised F1 offspring. All three treatment groups were exposed to clean and contaminated sediment for 24 and 96 hours. </span><span>Acute fish from contaminated sites exposed to contaminated sediment revealed an initial elevated p53 response that was not reached in cleared fish exposed to contaminated sediment. Instead, cleared and F1 bullhead from clean and contaminated sites had overlapping p53 expression patterns in response to contaminated sediment by 96 hours. Acute fish from contaminated sites exposed to contaminated sediment revealed refractory CYP1A expression, which disappeared in cleared fish and whose F1 refractory response overlapped with clean site F1 offspring. Decreasing GST activity was evident in both clean and contaminated fish over time, with clean site fish responding to contaminated sediment more deliberately. By 96 hours, the response patterns of F1 offspring from clean and contaminated sites to clean and contaminated sediment exposures were similar. </span><span> </span></p> </div> <div> <p><span>Because p53, CYP1A and GST activity responses to contaminated sediment dosing overlapped in clean and contaminated farm-raised F1 offspring, these results suggest that contaminated fish have acclimated to the contaminants present in their environments by reaching a tolerance threshold and no evidence of adaptation was detected in these biomarkers. </span></p> </div> </div>
Data for: Pre-vegetation, single-thread rivers sustained by cohesive, fine-grained bank sediments: Mesoproterozoic Stoer Group, NW Scotland
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Biogeochemical and sediment characteristics of streambanks with and without preferential groundwater discharge in the Farmington River Watershed, CT, USA
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Contaminated sediment in the Detroit River provokes acclimated responses in wild brown bullhead (Ameiurus nebulosus) populations
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A genome catalogue of mercury-methylating bacteria and archaea from sediments of a boreal river facing human disturbances
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Data from: The influence of a semi-arid sub-catchment on suspended sediments in the Mara River, Kenya
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Repeated erosion of sediments with biofilms from Rowley River mud flats, year 2012, Rowley, MA
This study aims to explore the interplay between biofilms and erodability of cohesive sediments. Erosion experiments were run in four laboratory annular flumes with natural sediments. Mud from the Rowley River mudflats was taken at low tide and then placed in flumes. Two of the flumes were bleach to prevent biota, while the other two were allowed to grow; nutrients were also added to the flumes. On different intervals, the flumes were eroded and the amount of erosion and velocity of the flow were measured. Additionally, each day biofilm growth was measured using Pulse-Amplitude Modulation (PAM) Underwater Fluorometry. Reported here are 10 minute averages of shear stress (derived from velocity) and suspended sediment (uncalibrated, in NTUs) along with PAM measurements. The complete dataset is available upon request via .mat files, but the dataset is quite large (kendallv@bu.edu or pie_im@mbl.edu).
Sediment chlorophyll-a, pheophytin, biogenic silica, and carbohydrate content (EPS) from the Rowley River mudflat in September 2012, Rowley, MA
Tidal flats are critical components of coastal estuarine ecosystems characterized by high rates of benthic primary productivity and biogeochemical cycling. In order to investigate the impact of anthropogenic nutrient loading on tidal flat biogeochemistry we carried out a two-week fertilization experiment. Throughout the course of the study we conducted two light-dark, whole-core incubations and took measurements of three indicators of microphytobenthos activity in addition to quantifying the resident eastern mud snail (Ilyanassa obsoleta) population.
Cinca River Field Survey: sediment sampling and tree measurement
<p>A field survey was performed on 26-28 November 2018 by Melissa Latella, Matteo B. Bertagni, Paolo Vezza and Carlo Camporeale (Envirofluidgroup, Department of Environmental, Land and Infrastructure Engineering, Polytechnic of Turin, Italy). The survey was carried out over the riparian area of the Cinca River, in a river segment located between the towns of Ballobar and Zaidín (Spain).</p> <p>Sediment samples were collected through the Wolman Pebble Count method. Pictures of the samples constitute the input for the MATLAB-based image-processing tool BaseGrain. BaseGrain allows to extrapolate the intermediate diameter of a set of samples. The output diameters can be processed in order to obtain the grain size distribution, the d50 and d90, and the Manning coefficient.</p> <p>Also vegetation features were measured during the survey. Tree diameter and height were collected in order to regress allometric curves, whereas the number of trees in areas of known extension allows to determine the spatial tree density.</p>
Data collected from the project "Experimental Investigation of Sediment Stability at Reservoirs on the Rhône River"
<p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------<br> Data collected from the project "Experimental Investigation of Sediment Stability at Reservoirs on the Rhône River"</p> <p>Details on the project including information on the materials and methods applied are provided in the available technical report<br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>A_Bulk_Density<br> - contains the vertical bulk density measurements of all withdrawn sediment cores from the Motz and Logis-Neuf reservoir<br> Motz: MZ08, MZ09, MZ10, MZ11, MZ12, MZ13, MZ14, MZ15, MZ16<br> Logis-Neuf: LN01, LN02, LN03, LN04, LN05, LN06, LN07, LN08, LN09, LN10, LN11, LN12, LN14<br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------<br> --> selected cores for detailed experimental investigations: MZ09, MZ14 and LN10, LN14<br> <br> B_Sediment_Composition_and_ParticleSizes<br> - contains the results of the depth-dependent particle size analyses (distribution curves)<br> - contains the derived sediment composition over core depth<br> <br> C_TOC_and_CEC<br> - contains the results of the Total Organic Carbon (TOC) measurements over core depth<br> - contains the results of the Cation Exchange Capacity (CEC) measurements over core depth</p> <p>D_Sediment_Stability<br> - contains the results of the measured sediment stability over core depth<br> <br> E_Sediment_Erosionrates<br> - contains the results of the measured erosion rates over core depth<br> --> two methods are considered: "last measured erosion rate" and "mean erosion rate"<br> <br> F_Sediment_Erosion_Volumes<br> - contains the raw data of the erosion volume measurements conducted with SETEG/PHOTOSED<br> --> the data is provided separately for each investigated sediment layer over core depth (denoted by "D**")<br> --> the data is evaluated for five considered time intervals dt =15s, dt =30s, dt =60s, dt =100s, dt =120s<br> <br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
Nd isotopes reveal the human-induced change of sediment routing processes in the Huanghe (Yellow River) catchment
<p>Table A1. Sampling locations, Sr-Nd isotopes, geochemical compositions and grain size parameters of Huanghe and loess sediments investigated.</p> <p>Table A2. Annual sediment load (Mt/yr) at major gauging stations along the Huanghe mainstream.</p> <p>Table A3. Nd isotopic compositions of major tectonic terranes and sources in the Huanghe basin (literature data).</p> <p>Table A4. Nd isotopes and geochemical compositions of rocks in the North China Craton.</p> <p>Table A5. Nd model ages of rocks from North China Craton (NCC), only rocks with Th/Sc, Th/Cr, Th/Co and Sm/Nd ratios within the range of references for UCC in the NCC have been selected.</p> <p>Table A6. Nd isotopes mixing of sediments from the lower Huanghe.</p> <p> </p>
Sediment routing and anthropogenic impact in the Huanghe River catchment, China: an investigation using Nd isotopes of river sediments
<p>Table A1. Sampling locations, Sr-Nd isotopes, geochemical compositions and grain size parameters of Huanghe and loess sediments investigated.</p> <p>Table A2. Annual sediment load (Mt/yr) at major gauging stations along the Huanghe mainstream.</p> <p>Table A3. Nd isotopic compositions of major tectonic terranes and sources in the Huanghe basin (literature data).</p> <p>Table A4. Nd isotopes and geochemical compositions of rocks in the North China Craton.</p> <p>Table A5. Nd model ages of rocks from North China Craton (NCC), only rocks with Th/Sc, Th/Cr, Th/Co and Sm/Nd ratios within the range of references for NCC-UC have been selected.</p> <p>Table A6. Nd isotopes mixing of sediments from the lower Huanghe.</p>
SELFE sediment model benchmark in the Columbia River estuary.
<p>SELFE sediment model benchmark in the Columbia River estuary.</p> <p>This is an archive of the set of SELFE simulations used to benchmark a sediment module against idealized test cases and a realistic scenario in the Columbia River estuary.<br /> Each test is contained in a directory and includes model input files, the binary used, and scripts used for post-processing.<br /> The simulations, data, and analysis were used for the manuscript "Benchmarking an unstructured-grid sediment model in an energetic estuary" by JE Lopez and AM Baptista.</p> <p>The archive consists of data collected from CMOP research cruises and we would like to acknowledge the contributions of Jim Carlson, John Dunlap, Avery Snyder, Keaton Snyder, and Nate Lauffenburger (University of Washington) and Daryl Swensen and Byron Crump (Oregon State University) in the collection of this data.</p>
Organic carbon in Amargosa River bank sediment
<p>This dataset consists of measurements of organic carbon in the Amargosa River bank sediment and related parameters as well as a global compilation of organic carbon in river bank sediment.</p>
Dataset: Geochemical - mineralogical constraints on the provenance of sediment supplied from South African river catchments draining into the southwestern Indian Ocean
<p>Supplementary Information Table 1 from manuscript in AGU <span>Geochemistry, Geophysics, Geosystems, titled</span>: Geochemical - mineralogical constraints on the provenance of sediment supplied from South African river catchments draining into the southwestern Indian Ocean.</p> <p>Pryor, E.J<span>1,†*</span>; Hall, I.R<span>1</span>; Simon, M.H<span>2,3</span>; Andersen, M<span>1</span>; Babin, D<span>4</span>; Starr, A<span>5</span>; Lipp, A<span>6</span>; van der Lubbe, H.J.L<span>7</span></p> <p><span>1</span>Cardiff University, School of Earth and Environmental Sciences, Main Building, United Kingdom</p> <p><span>2</span>NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway</p> <p><span>3</span> SFF Centre for Early Sapiens Behaviour (SapienCE), Bergen, Norway</p> <p><span>4</span><span>Lamont-Doherty </span>Earth Observatory of Columbia University, 61 Rt 9W, Palisades, New York 10964-8000, USA</p> <p><span>5</span>Department of Geography, University of Cambridge, United Kingdom</p> <p><span>6</span>Department of Earth Sciences, University College London, United Kingdom</p> <p><span>7</span>Department of Earth Sciences, Cluster Geochemistry & Geology, Vrije Universiteit Amsterdam</p> <p>(VU).</p> <p>†Now at Department of Earth Sciences, University of Bergen, Norway; SFF Centre for Early Sapiens Behaviour (SapienCE), Bergen, Norway</p> <p>*Corresponding author: Ellie Pryor (ellie.pryor@uib.no)</p> <p>This table provides the bedrock geology data for each river catchment between Durban and Cape Town, South Africa which was required for the endmember mixing model discussed in the submitted manuscript. This data can be used for endmember mixing calculations or used for GIS mapping. </p> <p>We also provide the grain size data measured on a Sympatec HELOS KR laser diffraction particle sizer. This grain size was inputted into the grain size endmember mixing model Analysize package within MATLAB 2022b (from Paterson and Heslop, 2015).</p>
Riparian cottonwood trees and adjacent river sediments have different microbial communities and produce methane with contrasting carbon isotope compositions
<p class="Abstract">Rivers and their adjacent riparian forests are intimately linked by the exchange of water, nutrients, and organic matter. Both riparian cottonwood trees and adjacent river sediments host microbial communities including archaeal methanogens, supporting methane production and emission to the atmosphere. Here we combine microbial community and stable isotope analyses to characterize the drivers of methane cycling in distinct anoxic habitats (river sediments versus riparian cottonwood stems) in the Oldman River, southern Alberta (Canada). We demonstrate that, differences in the chemical characteristics of organic matter support divergent microbial communities that generate methane from distinct metabolic pathways. Organic matter in river sediments had C/N ratios approximately 50-fold lower than in tree stems and had more diverse dissolved organic components. Contrasting substrate availability between river sediment and tree stems was likely the primary mechanism for the observed differences in bacterial and methanogen community compositions, and greater microbial diversity in river sediments than in tree stems. The methane carbon isotope composition (δ<sup>13</sup>C values) differed for the tree stem (-103.6 to -70.6‰) and river sediment (-55.1 to -48.4‰) environments, suggesting that methane was primarily produced via CO<sub>2</sub>-reduction in tree stems by Methanobacteriales, while river sediments produced more methane through acetate fermentation primarily by Methanosarcinales. This study demonstrates the importance of organic matter quality and microbial community composition in driving metabolic processes contributing to methane production and emission in rivers and adjacent riparian forests.</p>
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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.