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300 results for “tracers”
Assessing sedimentary detrital Pb isotopes as a dust tracer in the Pacific Ocean
<p>Dataset in Support of Manuscript "Assessing sedimentary detrital Pb isotopes as a dust tracer in the Pacific Ocean", in review at Paleoceanography and Paleoclimatology. Data includes Pb isotopes of detrital fractions from ocean sediments and source regions.</p>
PM10 and PM2.5 Concentrations of chemical tracers for natural sources
<p>The contribution of natural sources in particulate matter (PM) concentrations has been assessed for 5 Southern European cities: Porto (Portugal), Barcelona (Spain), Milan and Florence (Italy) and Athens (Greece). A database on the impact of natural source has been compiled, including concentrations of PM and chemical tracers used for the identification and quantification of African dust and sea salt contributions, as well as the calculated African net dust, and sea salt concentrations for each city. In addition, wildfires’ contribution is provided for Porto. Both PM<sub>10</sub> and PM<sub>2.5</sub> concentrations are reported for a total of six sites:</p> <ul> <li>Porto urban traffic site, POR-TR</li> <li>Barcelona urban background site, BCN-UB</li> <li>Milano urban background site, MLN-UB</li> <li>Florence urban background site, FI-UB</li> <li>Athens suburban site, ATH-SUB</li> <li>Athens urban traffic site, ATH-TR.</li> </ul> <p> </p>
Dataset for "Mesoscale Eddy-Induced Sharpening of Oceanic Tracer Front"
<p>Data of the tracer experiments and the associated diagnostics in the shallow water model for the ocean front study.</p><ul><li><strong>eforc.tar.gz</strong>: diagnosed eddy forcing fields for different tracers;</li><li><strong>exps_trs.tar.gz</strong>: solutions in offline tracer experiments on the coarse grid;</li><li><strong>forc_uvh.tar.gz</strong>: mass fluxes and layer thicknesses used to advect tracers;</li><li><strong>params.tar.gz</strong>: parameters used for tracer experiments</li></ul><p>Please contact Yueyang Lu via <strong>yueyang.lu@miami.edu</strong> if there are any questions.</p>
Datasets for the article "Evaporative controls on Antarctic precipitation: an ECHAM6 model study using innovative water tracer diagnostics"
Open the record for dataset details and reuse information.
Supplementary material for "Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers"
<p><span>Dataset presented and discussed in the manuscript of the research article “</span><span>Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers</span><span><span>” by Zocher et al. The manuscript will be submitted to <em>Environmental Pollution</em> and was prepared by the following authors: </span></span></p> <p> </p> <p><span><span>Anna-Lena Zocher (1), Tomasz Maciej Ciesielski (2,3), Stefania Piarulli (4), Julia Farkas (4) and Michael Bau (1). </span></span></p> <p><span> </span></p> <p><span><span>(1) School of Science, Constructor University, Bremen, Germany</span></span></p> <p><span><span>(2) Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway</span></span></p> <p><span><span>(3) </span></span><span><span>Department of Arctic Technology, The University Centre in Svalbard (UNIS), Longyearbyen, Norway</span></span></p> <p><span><span>(4) SINTEF Ocean, Trondheim, Norway</span></span></p> <p> </p> <p><span>This work was conducted within the ELEMENTARY project, and we appreciate funding from the Norwegian Research Council (grant No. 301236).</span></p>
Enhancing understanding of the hydrological cycle via pairing of process‐oriented and isotope ratio tracers
<p>This dataset contains monthly average output files from the iCAM6 simulations used in the manuscript "Enhancing understanding of the hydrological cycle via pairing of process-oriented and isotope ratio tracers," in review at the Journal of Advances in Modeling Earth Systems. A file corresponding to each of the tagged and isotopic variables used in this manuscript is included. Files are at 0.9° latitude x 1.25° longitude, and are in NetCDF format. Data from two simulations are included: 1) a simulation where the atmospheric model was "nudged" to ERA5 wind and surface pressure fields, by adding an additional tendency (see section 3.1 of associated manuscript), and 2) a simulation where the atmospheric state was allowed to freely evolve, using only boundary conditions imposed at the surface and top of atmosphere.</p> <p>Specific information about each of the variables provided is located in the "usage notes" section below.</p> <p>Associated article abstract:</p> <p>The hydrologic cycle couples the Earth's energy and carbon budgets through evaporation, moisture transport, and precipitation. Despite a wealth of observations and models, fundamental limitations remain in our capacity to deduce even the most basic properties of the hydrological cycle, including the spatial pattern of the residence time (RT) of water in the atmosphere and the mean distance traveled from evaporation sources to precipitation sinks. Meanwhile, geochemical tracers such as stable water isotope ratios provide a tool to probe hydrological processes, yet their interpretation remains equivocal despite several decades of use. As a result, there is a need for new mechanistic tools that link variations in water isotope ratios to underlying hydrological processes. Here we present a new suite of "process-oriented tags," which we use to explicitly trace hydrological processes within the isotopically enabled Community Atmosphere Model, version 6 (iCAM6). Using these tags, we test the hypotheses that precipitation isotope ratios respond to parcel rainout, variations in atmospheric RT, and preserve information regarding meteorological conditions during evaporation. We present results for a historical simulation from 1980 to 2004, forced with winds from the ERA5 reanalysis. We find strong evidence that precipitation isotope ratios record information about atmospheric rainout and meteorological conditions during evaporation, but little evidence that precipitation isotope ratios vary with water vapor RT. These new tracer methods will enable more robust linkages between observations of isotope ratios in the modern hydrologic cycle or proxies of past terrestrial environments and the environmental processes underlying these observations.<br> </p>
Seagrass deformation affects fluid instability and tracer exchange in canopy flow
<p>Data and code used for the preparation of the manuscript "Seagrass deformation affects fluid instability and tracer exchange in canopy flow" (Vieira, Allshouse & Mahadevan 2022).</p> <p><em>Data and Code Repository Organization</em></p> <ul> <li><strong>data/ </strong>: contains the data presented in the manuscript (in .cdf and .mat format);</li> <li><strong>code/ </strong>: contains the code used for the numerical simulations (PSOM) and in processing the data and generating figures (MATLAB)</li> </ul> <p><em>Manuscript Abstract:</em></p> <p>Monami is the synchronous waving of a submerged seagrass bed in response to unidirectional fluid flow. Here we develop a multiphase model for the dynamical instabilities and flow-driven collective motions of buoyant, deformable seagrass. We show that the impedance to flow due to the seagrass results in an unstable velocity shear layer at the canopy interface, leading to a periodic array of vortices that propagate downstream. Each passing vortex locally weakens the along-stream velocity at the canopy top, reducing the drag and allowing the deformed grass to straighten up just beneath it. This causes the grass to oscillate periodically. Crucially, the maximal grass deflection is out of phase with the vortices. A phase diagram for the onset of instability shows its dependence on the fluid Reynolds number and an effective buoyancy parameter. Less buoyant grass is more easily deformed by the flow and forms a weaker shear layer, with smaller vortices and less material exchange across the canopy top. While higher Reynolds number leads to stronger vortices and larger waving amplitudes of the seagrass, waving is maximized at intermediate grass buoyancy. All together, our theory and computations correct some misconceptions in interpretation of the mechanism and provide a robust explanation consistent with a number of experimental observations.</p>
Interactive VISION reports for "KP-Tracer Tumors from study "Lineage Recording Reveals the Phylodynamics, Plasticity and Paths of Tumor Evolution"
<p>This repository contains VISION reports for the KP-Tracer tumors described in the manuscript "Lineage Recording Reveals the Phylodynamics, Plasticity, and Paths of Tumor Evolution" (Yang*, Jones*, et al <em>bioRxiv</em> 2021).</p> <p>In this study, single-cell lineage tracing was performed in the <em>KP</em> autochthonous mouse model of non-small-cell lung cancer. Tumors were initiated with <em>Cre</em> recombinase (and optionally an additional gRNA targeting the other well-studied tumor suppressors) and allowed to grow for approximately 4-6 months at which point mice were sacrificed and tumors harvested. After purifying cancer cells by fluorescent markers, cells were profiled with the 10X chromium platform and four libraries were collected: a single-cell transcriptome library, a single-cell lineage tracing library, a single-cell multiplexing library, and a single-cell lenti-barcode library (used for confirming the clonality of tumors). Data was processed using the 10X Cellranger suite and custom pipelines as described in the manuscript above.</p> <p>For interactive work with these objects, users must first install VISION from Github (https://github.com/YosefLab/VISION) and then launch these reports either locally or on a server. For detailed instructions on how to launch reports, and create new reports for additional clones, please view our tutorials in our public reproducibility repository at https://github.com/mattjones315/KPTracer-release. </p>
Processed data for KP-Tracer Tumors from study "Lineage Recording Reveals the Phylodynamics, Plasticity and Paths of Tumor Evolution"
<p>This repository contains processed data associated with the manuscript "Lineage Recording Reveals the Phylodynamics, Plasticity, and Paths of Tumor Evolution" (Yang*, Jones*, et al <em>bioRxiv</em> 2021).</p> <p>In this study, single-cell lineage tracing was performed in the <em>KP</em> autochthonous mouse model of non-small-cell lung cancer. Tumors were initiated with <em>Cre</em> recombinase (and optionally an additional gRNA targeting the other well-studied tumor suppressors) and allowed to grow for approximately 4-6 months at which point mice were sacrificed and tumors harvested. After purifying cancer cells by fluorescent markers, cells were profiled with the 10X chromium platform and four libraries were collected: a single-cell transcriptome library, a single-cell lineage tracing library, a single-cell multiplexing library, and a single-cell lenti-barcode library (used for confirming the clonality of tumors). Data was processed using the 10X Cellranger suite and custom pipelines as described in the manuscript above. </p> <p>Data here represents the derived processed data from our analysis. The specific contents are described in a README contained within the repository.</p> <p>Briefly, however, we have provided here processed AnnData objects for the KP data (sgNT) and the integrated data across three genotypes (KP, KPA, and KPL). We also provide the gene lists associated with fitness, expansion annotations for each tree, as well as plasticity scores amongst other items. </p> <p>For code used in this study, we additionally have provided a reproducibility repository at https://github.com/mattjones315/KPTracer-release. </p>
Neodymium isotopes as a paleo-water mass tracer: A model-data reassessment: Model Output Data
<p>This dataset contains model output for the simulations presented in <em>"Neodymium isotopes as a paleo-water mass tracer: A model-data reassessment, Quaternary Science Reviews 279 (2022), 107404".</em></p> <p>The NetCDF4 files contain the following variables:</p> <p>3D fields:</p> <ul> <li>Potential Temperature</li> <li>Salinity</li> <li>North Atlantic dye tracer</li> <li>Epsilon Nd</li> <li>Nd concentration</li> </ul> <p>2D field:</p> <ul> <li>AMOC stream function</li> </ul>
TS and tracer data for Yap−Mariana Junction
<p>A multi-tracer study at the Yap-Mariana Junction in the western Pacific was conducted using 85Kr, 39Ar and 14C. This dataset includes the T-S data and the tracer data. This dataset is the supporting material of a paper submitted to JGR Ocean entitled "Estimation of the ventilation transit time distribution at the Yap−Mariana Junction using 39Ar, 85Kr and 14C tracers".</p>
Upscaling tracer-aided ecohydrological EcH2O-iso model in larger catchments: model setup and model simulations in the Selke catchment, central Germany
<p>This data repository is associated with the scientific article "Upscaling Tracer-aided Ecohydrological Modeling to Larger Catchments: Implications for Process Representation and Heterogeneity in Landscape Organization" by Yang et al. (submitted to Water Resources Research).</p> <p>This dataset includes the model setup information of the EcH2O-iso model in the Selke catchment, central Germany (./model_setup_Selke), and all model simulations and data analyses that are necessary to rebuilt the work (./model_sim_data).</p> <p>Please also refer to https://github.com/XYang-EcoHydroWQ/EcH2O-iso_largescale for corresponding model source code of the EcH2O-iso model, including modifications for larger-scale modeling.</p>
Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on Shelf-Ocean Exchanges under Upwelling-Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network''
<p>This dataset contains the modified MAMEBUS source code, configuration files for the MITgcm and MAMEBUS simulations, model diagnostics used in the paper, and scripts to train the Artificial Neural Networks.</p>
Figure 8. Comparison between the exact function v p in Reconstruction of a passive tracer boundary source in an open water area
Figure 8. Comparison between the exact function v p and the reconstructed vα h.
Figure 6 in Reconstruction of a passive tracer boundary source in an open water area
Figure 6. Solution of the optimal control problem.
Figure 7 in Reconstruction of a passive tracer boundary source in an open water area
Figure 7. Difference between φ p and φα h.
Figure 2. Velocity field U, t in Reconstruction of a passive tracer boundary source in an open water area
Figure 2. Velocity field U, t = 0.
Figure 4 in Reconstruction of a passive tracer boundary source in an open water area
Figure 4. Convergence on the boundary.
Determining the EC50 of Tracer-6908 with ACVR1/ALK2-NanoLuc
<p>Promega had provided us with Tracer-6908 that was developed collaboratively with SGC. Before the tracer can be used in nanoBRET assay for compound IC50, it is necessary to determine the optimal tracer concentration. The tracer concentration needs to be sufficient to produce adequate BRET signal while at the same time low enough to not obscure the competition by test compounds.</p> <p>The blog post for this Zenodo page is as following:</p> <p>https://opennotebook.thesgc.org/?p=1282&preview=true</p>
Determining the IC50 of M4K1062 with ACVR1/ALK2-NanoLuc and different concentrations of Tracer-6908
<p>The EC50 of Tracer-6908 with ACVR1-c-nanoLuc and ideal conditions for the target engagement assay have been determined in the previous post. However, it is still necessary to verify that the IC50 values determined in the assay are closed enough approximation to the actual IC50 values of the compounds. If the IC50 values are strongly influenced by the concentration of Tracer-6908 used, multiple assays with progressively lower Tracer-6908 will need to be performed. The actual IC50 of the compounds can then be estimated by plotting the Tracer-6908 concentrations against the corresponding IC50 values and regressing to no Tracer-6908. To address this, multiple nanoBRET target engagement assays were setup with increasing concentrations of Tracer-6908 with the same serial dilution of M4K1962 (starting from the EC50 of 65nM).</p>
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International Brain Laboratory public data
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OpenNeuro
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