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2,271 results for “transporte”
Dataset: AI Transportation Acquisition Corp (AITRR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: AI Transportation Acquisition Corp (AITRR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: AI Transportation Acquisition Corp (AITRU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: AI Transportation Acquisition Corp (AITR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: P.A.M. Transportation Services, Inc. (PTSI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Marten Transport, Ltd. (MRTN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: J.B. Hunt Transport Services, Inc. (JBHT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Lattice Boltzmann simulation of liquid water transport in gas diffusion layers of proton exchange membrane fuel cells: Impact of gas diffusion layer and microporous layer degradation on effective transport properties
<p><span>Underlying data to publication Sarkezi-Selsky et al., <em>J. Pow. Sour.</em> 556 (2023) 232415,<span> https://doi.org/10.1016/j.jpowsour.2022.232415</span> <br><br>Polymer Electrolyte Membrane Fuel Cells (PEMFCs) represent a promising technology for clean drivetrain solutions, in particular for heavy-duty applications. However, lifetime requirements demand high durability of each cell component.<br></span><span>In this work, transport of liquid water through pristine and degraded gas diffusion layers (GDL) was simulated with a 3D Color-Gradient Lattice Boltzmann model. The GDL microstructure was reconstructed </span><span>from high-resolution X-ray micro-computed tomography (</span><span>μ</span><span>-CT) of an impregnated Freudenberg H14. The </span><span>effect of a microporous layer (MPL) was considered by reconstruction of an impregnated and MPL-coated H14. Aged microstructures were generated artificially, assuming loss of polytetrafluoroethylene (PTFE) within the GDL and increase of MPL macroporosity as main degradation mechanisms. Liquid water transport within aged microstructures was simulated by imposing a liquid phase flow rate until breakthrough was reached. Subsequently, the GDL microstructures were analyzed for their breakthrough characteristics by means of saturation and effective gas transport properties. When the MPL was pristine, no distinct GDL degradation effect was observable, this was attributed to the MPL dominating capillary transport. MPL aging, however, led to increased saturations and thus to a deterioration of the effective gas transport. With a partially degraded MPL, aging of the GDL then appeared to affect the breakthrough characteristics.</span></p>
Enhancement of bubble transport in porous electrodes and catalysts
<p>data for the publication "Enhancement of bubble transport in porous electrodes and catalysts" in <em>J. Chem. Phys.</em> 160, 194706 (2024)</p>
Research data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach"
<div><strong>Research Data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em></strong></div> <div> </div> <div>Dear reader,</div> <div> </div> <div>reasearch data are provided for the research article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em> (https://doi.org/10.1016/j.advwatres.2024.104763). The authors hope that the research data allows for a better understanding of the modeling workflow. The research data covers the following files:</div> <div> <ul> <li>Python scripts to create the models <ul> <li>Model scripts using FloPy (Bakker et al., 2016) are stored as .py files in './model_data/flopy_scripts/', named 'model_variant_vXYZ.py', where 'XYZ' is a wildcard for the model number. </li> <li>--> Note that model numbers correspond to the different model variants as referred to in the article, see overview below.</li> <li>The model scripts require postfix files, stored in './model_data/flopy_scripts/postfix/', a PHREEQC database file, stored in './model_data/flopy_scripts/template_database/', as well as spreadsheets that contain the initial concentrations as well as reaction rate parameters needed by PHT3D, stored as .xlsx files in './model_data/flopy_scripts/', to create the models.</li> <li>Note that the .xlsx files are used by PHT3D-FSP in the model scripts to generate relevant PHT3D input files (compare https://doi.org/10.5281/zenodo.7559750 for more details).</li> </ul> </li> <li>SEAWAT/PHT3D input files <ul> <li>Original SEAWAT and PHT3D input files, which were created with the corresponding model scripts previously (see step before).</li> <li>Input files are stored in './model_data/model_files/vXYZ/model_files/' for each model variant, where 'XYZ' is a wildcard for the model number.</li> <li>SEAWAT/PHT3D executables can directly run the model files files. Thus, the files don't need to be re-created via the previous step.</li> </ul> </li> <li>Model outputs <ul> <li>Model output data is stored as NumPy arrays in './model_data/model_files/vXYZ/npy_arrays/', where 'XYZ' is a wildcard for the model number.</li> <li>The script './model_data/flopy_scripts/template_output/pht3d_output_hpc_v006.py' was used to generate the output files.</li> <li>2-D species concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/species/', where 'XYZ' is a wildcard for the model number.</li> <li>Species min./max. concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/min_max/', where 'XYZ' is a wildcard for the model number.</li> <li>2-D water budget arrays (CH & WEL boundaries) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/budgets/', where 'XYZ' is a wildcard for the model number.</li> <li>Model discretization information (ncol, nrow, nlay etc.) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/discretization/', where 'XYZ' is a wildcard for the model number.</li> </ul> </li> <li>Figure files <ul> <li>Original figure files as well as the corresponding Python scripts to create the figures are stored in the subfolder'./figures'.</li> </ul> </li> </ul> <p>Numbering of the model variants is as follows:<br><br>v401 --> VAR-conservative<br>v402 --> VAR-OM<br>v403 --> VAR-C/I<br>v404 --> VAR-C/I/S<br>v405 --> VAR-C/I/P<br>v406 --> VAR-C/I/P/H<br>v407 --> VAR-C/I/P/V<br>v408 --> VAR-C/I/P-Co<br>v409 --> VAR-all<br>v410 --> VAR-all (no C)</p> </div> <div> </div> <div>Literature:</div> <div> </div> <div>Bakker, M., Post, V., Langevin, C.D., Hughes, J.D., White, J.T., Starn, J.J. and Fienen, M.N., 2016. Scripting MODFLOW model development using Python and FloPy. Groundwater, 54(5), pp.733-739. https://doi.org/10.1111/gwat.12413</div> <div> </div> <div>Seibert, S.L., Massmann, G., Meyer, R., Post, V.E.A., Greskowiak, J., 2024. Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2024.104763</div> <div> </div> <div><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Vincent E.A. Post (vincent@edinsi.nl), Rena Meyer (rena.meyer@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de)</div>
Fig. 3 in Efficacy of eugenol and the methanolic extract of Condalia buxifolia during the transport of the silver catfish Rhamdia quelen
Fig. 3. The net ion (Na+, Cl- and K+) fluxes measured for the transport of Rhamdia quelen in plastic bags with eugenol and with the methanolic extract of Condalia buxifolia added to the water. The values are the means ± SEM. The different letters indicate significant differences between the treatments for the same ion (P<0.05).
Fig. 1 in Efficacy of eugenol and the methanolic extract of Condalia buxifolia during the transport of the silver catfish Rhamdia quelen
Fig. 1. Time to reach the light sedation stage in Rhamdia quelen juveniles of two different weight classes exposed to the methanolic extract of Condalia buxifolia. The following equations were fitted to the data: For fish weighing 1.50 ± 0.02 g; y = 209.629 e0.015 x; r2 =0.996. For fish weighing 165.7 ± 22.5 g; y = 2039.020 e0.017 x; r2 =0.999. Where x = the concentration of the methanolic extract of C. buxifolia (µL L-1) and y = time for sedation(s).
Fig. 1 in Anesthesia and transport of fat snook Centropomus parallelus with the essential oil of Nectandra megapotamica (Spreng.) Mez
Fig. 1. Mortality after transport of fat snook Centropomus parallelus in plastic bags with essential oil from old leaves of Nectandra megapotamica (15 or 30 µL L-1) or ethanol (E) added to the water. W: control with only water. Data presented as means ± SEM (n = 3). a, freshwater - no significant difference between groups or times was observed and the treatments E and 15µL L-1 are superimposed on the first line; b, seawater - values with different superscripts are significantly different (P <0.05). # Significant difference from arrival (0 h).
Fig. 2 in Too hot to move: temperatures during transportation might reduce the survival of salvinia weevils (Coleoptera: Curculionidae)
Fig. 2. Bivariate analysis of air temperature and temperature experienced inside transportation totes for Cyrtobagous salviniae (a) with 1.3 cm circulation holes in modified fitted lids (n = 80), and (b) conventional lids without circulation holes (P ≤ 0.05; n = 80) in May 2016.
Fig. 3 in Too hot to move: temperatures during transportation might reduce the survival of salvinia weevils (Coleoptera: Curculionidae)
Fig. 3. Percent mortality of Cyrtobagous salviniae exposed to 4 temperatures in environmental growth chambers: (a) 35 °C, (b) 40 °C, (c) 45 °C, and (d) 50 °C.
Fig. 3 in Effect of beta 1,3 glucan in stress responses of the pencilfish (Nannostomus trifasciatus) during transport within the rio Negro basin
Fig. 3. Whole-body cortisol levels (mean ± SD) during transportation of pencilfish (Nannostomus trifasciatus) previously fed with different levels of beta 1,3 glucan. No significant differences between all sampling times using the Kruskal-Wallis ANOVA on ranks were observed (P>0.05).0: initial time of transport; 24: 24 hours of transport; 24 AT: 24 hours after transport (recovery time).
Fig. 2 in Effect of beta 1,3 glucan in stress responses of the pencilfish (Nannostomus trifasciatus) during transport within the rio Negro basin
Fig. 2. Net Na+ (A) and K+ (B) fluxes during transportation of pencilfish (Nannostomus trifasciatus) previously fed with different levels of beta 1,3glucan. Positive values indicate net influxes and negative values net effluxes. N = 4 boxes per treatment in each sample time. Different lower case letters indicate significant difference among times of transport within the same group and * indicate significant difference from control group within the same time of transport by Scheirer-Ray-Hare extension of the Kruskal-Wallis test followed by the Nemenyi test (P<0.05).
Fig. 4 in The essential oil from Lippia alba induces biochemical stress in the silver catfish (Rhamdia quelen) after transportation
Fig. 4. LPO/CAT+GPx ratio in the liver of silver catfish (Rhamdia quelen) transported in plastic bags containing water treated with the essential oil from Lippia alba. The values are expressed as the means ± SEM. Different letters indicate levels of significance between the treatments (P<0.05).
Fig. 3 in The essential oil from Lippia alba induces biochemical stress in the silver catfish (Rhamdia quelen) after transportation
Fig. 3. TBARS (A) and protein carbonilation (B) levels in the liver of silver catfish (Rhamdia quelen) transported in plastic bags containing water treated with the essential oil from Lippia alba. The values are expressed as the means ± SEM. Different letters indicate difference levels of significance between the treatments (P<0.05).
Fig. 2. Glutathione-S in The essential oil from Lippia alba induces biochemical stress in the silver catfish (Rhamdia quelen) after transportation
Fig. 2. Glutathione-S-transferase (GST) activity (A), glutathione peroxidase (GPx) activity (B), non-protein thiol group (NPSH) content (C) and ascorbic acid (D) content in the liver of silver catfish (Rhamdia quelen) transported in plastic bags containing water treated with the essential oil from Lippia alba. The values are expressed as the means ± SEM. Different letters indicate levels of significance between the treatments (P<0.05).
ScienceDex guides
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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.