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2,271 results for “transporte”
Transport Starter Data Kit: Historical socio-transport data for Gabon
<p>This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.</p>
Transport Starter Data Kit: Historical socio-transport data for Libya
<p>This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.</p>
Water will find its way: transport through narrow tunnels in hydrolases (Epx protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (Epx protein)</strong></p> <p><br>The input files and results used for the paper “<em>Water will find its way: transport through narrow tunnels in hydrolases</em>” are separated in the different folders depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the minimum sphere event is present. Also the txt files with the results from the H-bond and contact analyses are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>Epx.dat</strong> Parsed database of transport events for Epx system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/Epx_tt.ini -o Epx.dat</em></li> </ol>
Water will find its way: transport through narrow tunnels in hydrolases (hEpx protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (hEpx protein)</strong></p> <p>The input files and results used for the paper "<em>Water will find its way: transport through narrow tunnels in hydrolases</em>" are separated in the different files depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the "<em>Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results for hEpx and E470G systems together, since we employed the comparative approach to analyze the results with TransportTools (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the *minimum sphere event* is present. Also the txt files with the results from the H-bond analysis are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>hEpx_E470G.dat</strong> Parsed database of transport events for hEpx_E470G system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/hEpx_E470G_tt.ini -o hEpx_E470G.dat</em></li> </ol> <p> </p>
Water will find its way: transport through narrow tunnels in hydrolases (Lip protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (Lip protein)</strong></p> <p><br>The input files and results used for the paper “<em>Water will find its way: transport through narrow tunnels in hydrolases</em>” are separated in the different folders depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the minimum sphere event is present. Also the txt files with the results from the H-bond analysis are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>Lip.dat</strong> Parsed database of transport events for Lip system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/Lip_tt.ini -o Lip.dat</em></li> </ol>
Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream
<p>Structure from Motion orthoimagery, lidar differencing products, and grain size data to be published with the submission of "Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream" to <em>Journal of Geophysical Research: Earth Surface.</em> </p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Upper Big Creek:</p> <p>J_2016.tif, J_2021.tif, J_2022.tif, K_2016.tif, K_2021.tif, K_2022.tif, L_2016.tif, L_2021.tif, L_2022.tif</p> <p>Files titled K_[year].tif encompass our upstream study reach; files titled J_[year].tif encompass our middle study reach; files titled L_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Devil's Creek:</p> <p>G_2016.tif, G_2021.tif, G_2022.tif, _2016.tif, H_2021.tif, H_2022.tif, I_2016.tif, I_2021.tif, I_2022.tif</p> <p>Files labeled G_[year].tif encompass our upstream study reach; files labeled H_[year].tif encompass our middle study reach; files labeled I_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Upper Big Creek with units in meters:</p> <p>BC_2016.csv, BC_2021.csv, BC_2022.csv</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Devil's Creek with units in meters:</p> <p>DC_2016.csv, DC_2021.csv, DC_2022.csv</p> <p> </p> <p>Differenced lidar digital terrain models for Big Creek and Devil's Creek with units in meters:</p> <p>DoD_11_22.tif (difference between 2011 and 2022 lidar DTMs), DoD_11_15.tif (difference between 2011 and 2022 lidar DTMs)</p> <p> </p> <p>This work was funded by the Geological Society of America, the National Center for Airborne Laser Mapping, the Washington Section of the American Water Resources Association, the Western Washington University Research and Sponsored Programs Office, and the Western Washington University Geology Department.</p>
Dataset for "Experimental and Modeling Insights into Mixing-Limited Reactive Transport in Heterogeneous Porous Media: Role of Stagnant Zones"
<p>This dataset contains the observed and simulated BTC of bimolecular transport experiment that was involved in "Yin et al., Experimental and Modeling Insights into Mixing-Limited Reactive Transport in Heterogeneous Porous Media: Role of Stagnant Zones".</p>
Transition of laser-induced terahertz spin currents from torque- to conduction-electron-mediated transport
<p>Data for the publication "Transition of laser-induced terahertz spin currents from torque- to conduction-electron-mediated transport" published in Physical Review B. The following datasets are provided: Conductivities, electro-optic THz signals vs time and corresponding Fourier amplitude spectra for thin and thick YIG, GIG, Maghemite, Magnetite and Fe, spin currents of the mentioned materials vs time and superposition of Fe and Maghemite spin currents and raw data.</p>
Experimental data on "Sediment storage and fluvial sediment transport linkages across an experimental flood sequence"
<p>The repository contains data used in manuscript "Sediment storage and fluvial sediment transport linkages across an experimental flood sequence" by Hassan, Pierce, Chartrand. </p>
Water will find its way: transport through narrow tunnels in hydrolases (Hal protein with different MD simulation settings)
<h1>Hal_2fs.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (Hal_2fs protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.<br> 2. <em>Hal_2fs.dat</em>: Parsed database of transport events for Hal system. Command used: <br><em>python3 01_build_database.py -c 04_TransportTools/Hal_2fs.ini -o Hal_2fs.dat</em></p> <p> </p> <h1>Hal_300K.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (Hal_300K protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.<br> 2. <em>Hal_300K.dat:</em> Parsed database of transport events for Hal system. Command used:<br><em>python3 01_build_database.py -c 04_TransportTools/Hal_300K.ini -o Hal_300K.dat</em></p> <p> </p> <h1>Hal_TIP3P.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (TIP3P protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</p> <p> 2. <em>Hal_TIP3P.dat</em>: Parsed database of transport events for Hal system. Command used:<br><em>python3 01_build_database.py -c 04_TransportTools/Hal_TIP3P.ini -o Hal_TIP3P.dat</em></p>
Resilience of transportation infrastructure networks to road failures
<p>We provide the code used for the analysis that we used in the article <em>Resilience of transportation infrastructure networks to road failures</em>. As the computation for the RoadNetworks that we analysed in this manuscript is quite large, the computational load is quite high. We pre-computed the load values on the cluster and provide the results here. The code for the Figures in the notebooks will therefore not compute the loads, but just load them from files. We provide a <code>My-road-network.ipynb</code> were you can play around with a smaller RoadNetwork, computing everything locally.</p> <p>Published in<br><strong>J. Wassmer, B. Merz, N. Marwan</strong>: Resilience of transportation infrastructure networks to road failures, Chaos, <strong>34</strong>, 013124 (2024). <a href="https://dx.doi.org/10.1063/5.0165839" target="_blank" rel="noopener">DOI:10.1063/5.0165839</a></p>
Dataset of "Switching Response in Organic Electrochemical Transistors by Ionic Diffusion and Electronic Transport"
<p>This dataset underpins the following article published in the Advanced Science:</p> <p>"Switching Response in Organic Electrochemical Transistors by Ionic Diffusion and Electronic Transport"</p> <p> </p> <p>Raw and processed data for the article "Switching Response in Organic Electrochemical Transistors by Ionic Diffusion and Electronic Transport". For further details see the readme.txt file.</p>
Codes and Data for "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy"
<p>This file contains the analysis code and a condensed version of data to reproduce figures in the paper "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy". </p>
Replication package for Transport and urban growth in the First Industrial Revolution
<p><span>Replication package for Alvarez-Palau, E. J., Bogart, D., Satchell, A.E., Shaw-Taylor, L.</span><strong><span> </span></strong><span>‘Transport and urban growth in the First Industrial Revolution’ Economic Journal<span> </span></span></p>
Dataset of the publication: Probing the spin dimensionality in single-layer CrSBr van der Waals heterostructures by magneto-transport measurements
<p>Dataset of the publication: Probing the spin dimensionality in single-layer CrSBr van der Waals heterostructures by magneto-transport measurements</p> <p>DOI: 10.1002/adma.202204940</p> <p>C. Boix-Constant, S. Mañas-Valero, A. M. Ruiz, A. Rybakov, K. A. Konieczny, S. Pillet, J. J. Baldoví, E. Coronado</p> <p>Adv. Mater., 34, 2204940 (2022)</p>
Analysis of policy measures on designing a renewable fuel supply chain in the transport sector- Supplementary materials
<p><strong>This repository contains supporting data for: "Analysis of policy measures on designing a renewable fuel supply chain in the transport sector"</strong></p> <ol> <li> <p><strong>Supplementary Materials</strong>: This collection encompasses the model input data, alongside a detailed formulation of the objective functions.</p> </li> <li> <p><strong>Pareto_Table</strong>: This file contains the Pareto frontier results. </p> </li> <li><strong>DoE_Results_Table</strong>: This file presents the results of various solution scenarios, each characterized by differing levels of policy measures.</li> </ol>
OpenAIRE Dataset for SciLake transport research pilot
<p>This dataset is related to the subset of the OpenAIRE graph relevant to the transport research pilot. The dataset is built according to the <a href="https://graph.openaire.eu/docs/data-model/">data model</a> of the OpenAIRE Graph dataset.</p>
FIG. 1. — Transport d in Les suidés dans un monde pluriel: imaginaires, relations et substances
FIG. 1. — Transport d'un cochon vers la basse-cour où il sera « défait». Crédits photo: Séverine Lagneaux, Bucovine, Roumanie, 2015.
AP-4-mediated axonal transport controls endocannabinoid production in neurons - Imaging data 3
<p>Imaging data associated with Fig. 6c,e,f, 7e-h, S5a,b, S6b-d and S7 from the article "AP-4-mediated axonal transport controls endocannabinoid production in neurons", published in Nature Communications by Davies et al.</p> <p>DAGLB_Axon_iPSC_derived_neurons: High-throughput confocal imaging was used to assay the density of DAGLB puncta in axons of iPSC neurons from a patient with AP-4 deficiency syndrome (patient 1) and their matched control, labelled with anti-DAGLB, the axonal marker antibody cocktail SMI312 and DAPI.</p> <p>DAGLB_Soma_iPSC_derived_neurons: High-throughput confocal imaging was used to assay the distribution of DAGLB in iPSC-derived neurons from two patients with AP-4 deficiency syndrome (LoF/LoF) and their matched unaffected controls (WT/LoF). Neurons in 96-well plates were labelled with antibodies against DAGLB, GOLGA1 (a TGN marker) and TUJ1, and DAPI. The ratio between the area of high intensity (HI; overlaps with TGN) and low intensity (LI) DAGLB labelling was quantified from three differentiations per cell line.</p> <p>iPSC_derived_neurons_neurite_outgrowth: Neurite outgrowth was assayed in iPSC-derived cortical neurons from two patients with AP4B1-associated AP-4 deficiency syndrome (SPG47) and their unaffected same sex heterozygous parents (control), using automated live cell imaging. Neurons were cultured in the presence of DMSO (vehicle control) or the MGLL inhibitor ABX-1431 at 10, 50, 100 or 500 nM (the highest two doses were administered only to the patient neurons). Neurons were monitored from 4 h post-plating, with images captured every 3 h until 25 h post-plating.</p> <p>See article for methods and further detail.</p>
AP-4-mediated axonal transport controls endocannabinoid production in neurons - Imaging data 2
<p>Imaging data from the article "AP-4-mediated axonal transport controls endocannabinoid production in neurons", published in Nature Communications by Davies et al., from Fig. 5 and S4.</p> <p>Super resolution structured illumination microscopy (SR-SIM) was used to image HeLa cells expressing endogenously-tagged SERINC1-Clover or SERINC3-Clover, treated with siRNA to knock down AP-4 or non-targeting control siRNA, and labelled with anti-GFP and either anti-ATG9A or anti-DAGLB.</p> <p>SR-SIM was performed on a Zeiss Elyra PS.1 microscope equipped with a 63x/1.46 oil objective (alpha Plan-Apochromat 63x/1.46 Oil Korr M27) and a PCO pco.edge 4.2 sCMOS Camera, and controlled with Zeiss ZEN 2 software with the SR-SIM module. Imaging was performed sequentially, with SERINC1 or SERINC3 (labelled with Alexa Fluor Plus 488) imaged first and DAGLB or ATG9A (labelled with Alexa Fluor 568) imaged second, using 3 rotations of the grid pattern and 5 phases for each rotation. Experiments were performed in biological duplicate, from separate dishes of cells, and with immunofluorescence and microscopy performed independently for each replicate. 20 cells were imaged per condition per replicate, and cell selection and manual focus were performed on the first acquired channel only, without viewing the second channel. For channel alignment, slides with multi-coloured fluorescent beads were imaged before and after each experiment using the same acquisition settings. The data were processed using the SIM module in Zen 2 software in manual mode, using default settings and a theoretical PSF model, except for the following settings: Noise filter -1; SR frequency weighting 2; Max.Isotrop on; Baseline shifted on; Raw scale on. Channel alignment fit parameters were calculated in Zen 2 software using a multi-coloured bead image as input, in affine mode. The resulting parameters were tested on further bead images and then applied to the experimental images. The channel-aligned images were used for colocalisation analysis (see methods of article for details).</p> <p>This dataset includes the raw unprocessed data and the processed and channel aligned input data used for the colocalisation analysis.</p> <p>See article for methods and further detail.</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.