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2,271 results for “transporte”
Daily bus transportation demand - São Paulo-Brazil - Jan-2017 to Jun-2022
<p>The raw demand data was obtained from SPTRANS, the public company responsible for bus transportation in São Paulo.</p> <p>São Paulo's greatest public transportation network is the bus network, which is mostly managed by public company SPTRANS. SPTRANS provides bus transportation services during business hours and night shifts, along with specific services directed towards impaired and disabled citizens who need support in traveling to hospitals and health centers. SPTRANS makes available online daily mobility data for all bus lines in São Paulo, providing an enormously valuable resource for data mining, urban geography studies, and transportation planning. By using this data, it is possible to effectively study the impacts of pandemics on São Paulo's bus transportation network and gain insights into the regional differences and if different kinds of lines were affected differently. </p> <p>The data is distributed in xlxs format and contains information about line name, demand by kind of users, such as users who pay with money, users who use travel cards, elderly users (who do not need to pay for travel), and total demand. The line name is a string containing a code made of letters and numbers. </p> <p>Demand data between January/2017 and June/2022 were automatically downloaded and accessed by a script in python, using pandas library for creating dataframes. There were significant challenges in using SPTRANS data, such as the irregular formatting of row and column names through the years, inconsistent abbreviations of keywords, such as terminal stations and metro stations (i.e "term.","terminal","metr","m", etc). These issues were solved by capturing the code and crossing the data with General Transit Feed Specification (GTFS) data, which is provided by SPTRANS as well on an almost weekly basis and contains information about route and schedule of lines for a given time period. This step enriched the data with standardized names and route points.</p> <p>Another difficulty in obtaining the data through automatic scripts is that Links do not have regular naming patterns. A significant number of downloaded XLSX files came with a bad configuration so that empty rows are read by pandas as filled with content, triggering an error related to a prohibitively large number of rows. This bug was solved by simply individually opening it and re-saving it. The details about the origin of this error are not understood by the authors. </p> <p>All the bus lines were enriched with geospatial information, containing all points belonging to each route, extracted from GTFS. After all these steps, the data was organized in a matrix where each row represents a day and each column represents a line. The geospatial information was stored separately. And since bus lines are created or deactivated with some regularity in São Paulo, The choice was to keep in the sample only the lines that remained active throughout the observation period. </p>
FESOM model data and particle tracking data used in publication 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean'
<p>FESOM model data and particle tracking data used in the paper 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean" by Andreas Rogge at al.</p> <p>1) FESOM velocity fields averaged over the top 200 m water depth, averaged over the time period 2015-2018.</p> <p>2) FESOM transect at 95°E in the Arctic Ocean (temperature, salinity and velocity), averaged over the time period 2015-2018.</p> <p>3a) Particle back-tracking data based on daily FESOM velocity fields in netcdf format. Particles were released at 95°E every 14 days during the year 2018 and tracked until they reached the surface. Three different constant sinking velocities were used, representative for small and large non-ballasted particles and small ballasted particles.</p> <p>3b) Distribution of particles at the surface for the experiments with three different sinking velocities as mat files. </p>
Competitive control of multiscale aquifer heterogeneity on solute transport in an alluvial aquifer
<p>Here, we provide the dataset for the publication "<strong>Competitive control of multiscale aquifer heterogeneity on solute transport</strong> <strong>in an alluvial aquifer</strong>" submitted to Journal of Hydrology for peer-review.</p>
Quasi-Armored Mud Clasts as Indicators of Transport Processes of Subaqueous Sediment Gravity-Flow
<p>Fig. S1 Quasi-armored mud clasts in different case studying. (A) Quasi-armored mud clasts in the H1 division of Hybrid event bed, Eocene Liushagang Formation; (B) Quasi-armored mud clasts in massive sandstone, Early Cretaceous Lingshandao Formation; (C) Quasi-armored mud clasts in massive sandstone, Early Cretaceous, North Falkland Basin (Dodd et al., 2018); (D) Quasi-armored mud clasts in the upper part of massive sandstone, Middle Eocene, Ainsa System (Pickering et al., 2015); (E) and (F) Quasi-armored mud clasts in massive sandstone, Upper Jurassic, North Sea rift system (Jackson et al., 2011); (G) Quasi-armored mud clasts in the H3 division, Late Jurassic, in the northern North Sea (Haughton et al., 2003) ; (H) and (I) Quasi-armored mud clasts in massive sandstone, Late Jurassic, in the northern North Sea (Williams, 2015).</p> <p>Fig. S2 (A) Location of Beibuwan Basin in China. (B) Location of Weixi’nan depression in the Beibuwan Basin. (C) Sedimentary facies distribution of the upper member of the Liushagang Formation in the Weixi’nan depression. (D) Sedimentary sequence and stratigraphic framework of the Weixinan Depression, Beibuwan Basin (Huang et al., 2013).</p> <p>Fig. S3 The lateral distribution of gravity-flow deposits in the Upper of the Liushagang Formation in the research area from proximal to distal. For the location of the section, see Fig. S2 C.</p> <p>Fig. S4 Facies analysis of Well WZ11-7-1 (in relatively distal), dominated by hybrid event beds with common quasi-armored mud clasts in different units.</p> <p>Fig. S5 Facies analysis of Well WZ11-7-4 (in relatively proximal), dominated by high-density turbidities beds with occasional quasi-armored mud clasts. For the legend of facies analysis, see Fig. S4.</p>
Design principles an autothermal chemical reactor with enhanced momentum transport for hydrogen production
<p><strong>Design principles an autothermal chemical reactor with enhanced momentum transport for hydrogen production</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Chemical engineering is the development of processes and the design and operation of plants in which materials undergo changes in their physical or chemical state. Applied throughout the process industries, it is founded on the principles of chemistry, physics, and mathematics. The laws of physical chemistry and physics govern the practicability and efficiency of chemical engineering operations. Energy changes, deriving from thermodynamic considerations, are particularly important. Mathematics is a basic tool in optimization and modeling. Optimization means arranging materials, facilities, and energy to yield as productive and economical an operation as possible. Modeling is the construction of theoretical mathematical prototypes of complex process systems, commonly with the aid of computers. Study of the fundamental phenomena upon which chemical engineering is based has necessitated their description in mathematical form and has led to more sophisticated mathematical techniques. The advent of digital computers has allowed laborious design calculations to be performed rapidly, opening the way to accurate optimization of industrial processes. Variations due to different parameters, such as energy source used, plant layout, and environmental factors, can be predicted accurately and quickly so that the best combination can be chosen.</p> <p>Streamwise distance (millimeters), Reforming channel centerline temperature (degrees kelvin)</p> <p>0 373.004</p> <p>0.00025 373.049</p> <p>0.0005 373.224</p> <p>0.00075 373.683</p> <p>0.001 374.581</p> <p>0.00125 376.009</p> <p>0.0015 377.985</p> <p>0.00175 380.465</p> <p>0.002 383.367</p> <p>0.00225 386.596</p> <p>0.0025 390.052</p> <p>0.00275 393.649</p> <p>0.003 397.317</p> <p>0.00325 400.998</p> <p>0.0035 404.65</p> <p>0.00375 408.237</p> <p>0.004 411.737</p> <p>0.00425 415.132</p> <p>0.0045 418.411</p> <p>0.00475 421.567</p> <p>0.005 424.596</p> <p>0.00525 427.498</p> <p>0.0055 430.273</p> <p>0.00575 432.923</p> <p>0.006 435.451</p> <p>0.00625 437.861</p> <p>0.0065 440.157</p> <p>0.00675 442.343</p> <p>0.007 444.425</p> <p>0.00725 446.406</p> <p>0.0075 448.291</p> <p>0.00775 450.084</p> <p>0.008 451.79</p> <p>0.00825 453.413</p> <p>0.0085 454.958</p> <p>0.00875 456.427</p> <p>0.009 457.825</p> <p>0.00925 459.155</p> <p>0.0095 460.421</p> <p>0.00975 461.627</p> <p>0.01 462.774</p> <p>0.01025 463.866</p> <p>0.0105 464.906</p> <p>0.01075 465.897</p> <p>0.011 466.84</p> <p>0.01125 467.74</p> <p>0.0115 468.598</p> <p>0.01175 469.417</p> <p>0.012 470.198</p> <p>0.01225 470.943</p> <p>0.0125 471.655</p> <p>0.01275 472.335</p> <p>0.013 472.984</p> <p>0.01325 473.605</p> <p>0.0135 474.199</p> <p>0.01375 474.767</p> <p>0.014 475.311</p> <p>0.01425 475.831</p> <p>0.0145 476.329</p> <p>0.01475 476.807</p> <p>0.015 477.265</p> <p>0.01525 477.704</p> <p>0.0155 478.125</p> <p>0.01575 478.53</p> <p>0.016 478.918</p> <p>0.01625 479.292</p> <p>0.0165 479.651</p> <p>0.01675 479.996</p> <p>0.017 480.329</p> <p>0.01725 480.649</p> <p>0.0175 480.958</p> <p>0.01775 481.255</p> <p>0.018 481.543</p> <p>0.01825 481.82</p> <p>0.0185 482.089</p> <p>0.01875 482.348</p> <p>0.019 482.598</p> <p>0.01925 482.841</p> <p>0.0195 483.076</p> <p>0.01975 483.304</p> <p>0.02 483.525</p> <p>0.02025 483.739</p> <p>0.0205 483.948</p> <p>0.02075 484.15</p> <p>0.021 484.347</p> <p>0.02125 484.538</p> <p>0.0215 484.724</p> <p>0.02175 484.906</p> <p>0.022 485.083</p> <p>0.02225 485.256</p> <p>0.0225 485.424</p> <p>0.02275 485.589</p> <p>0.023 485.75</p> <p>0.02325 485.907</p> <p>0.0235 486.061</p> <p>0.02375 486.212</p> <p>0.024 486.359</p> <p>0.02425 486.504</p> <p>0.0245 486.646</p> <p>0.02475 486.785</p> <p>0.025 486.921</p> <p>0.02525 487.056</p> <p>0.0255 487.188</p> <p>0.02575 487.317</p> <p>0.026 487.444</p> <p>0.02625 487.57</p> <p>0.0265 487.693</p> <p>0.02675 487.814</p> <p>0.027 487.933</p> <p>0.02725 488.051</p> <p>0.0275 488.166</p> <p>0.02775 488.28</p> <p>0.028 488.392</p> <p>0.02825 488.503</p> <p>0.0285 488.611</p> <p>0.02875 488.718</p> <p>0.029 488.823</p> <p>0.02925 488.928</p> <p>0.0295 489.023</p> <p>0.02975 489.143</p> <p>0.03 489.22</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Process intensification in a methanol steam reforming chemical reactor with enhanced momentum transport
<p><strong>Process intensification in a methanol steam reforming chemical reactor with enhanced momentum transport</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Consideration must be given to the rate at which each step in the planned sequence occurs. In many instances, a desired reaction is possible in principle but in practice takes place so slowly as to be ineffective. It is then necessary to investigate whether the rate can be increased to a practicable level by altering the conditions of the reaction, for example, by raising the temperature or by adding an extra species, called a catalyst, that increases the rate without altering the course of the reaction.</p> <p>Streamwise distance (millimeters), Oxidation channel centerline temperature (degrees kelvin)</p> <p>0 376.499</p> <p>0.00025 391.55</p> <p>0.0005 411.347</p> <p>0.00075 432.857</p> <p>0.001 451.349</p> <p>0.00125 465.437</p> <p>0.0015 475.536</p> <p>0.00175 482.591</p> <p>0.002 487.466</p> <p>0.00225 490.805</p> <p>0.0025 493.063</p> <p>0.00275 494.556</p> <p>0.003 495.508</p> <p>0.00325 496.074</p> <p>0.0035 496.366</p> <p>0.00375 496.469</p> <p>0.004 496.424</p> <p>0.00425 496.278</p> <p>0.0045 496.077</p> <p>0.00475 495.832</p> <p>0.005 495.555</p> <p>0.00525 495.26</p> <p>0.0055 494.955</p> <p>0.00575 494.646</p> <p>0.006 494.337</p> <p>0.00625 494.033</p> <p>0.0065 493.735</p> <p>0.00675 493.445</p> <p>0.007 493.164</p> <p>0.00725 492.894</p> <p>0.0075 492.635</p> <p>0.00775 492.386</p> <p>0.008 492.15</p> <p>0.00825 491.925</p> <p>0.0085 491.712</p> <p>0.00875 491.511</p> <p>0.009 491.323</p> <p>0.00925 491.146</p> <p>0.0095 490.98</p> <p>0.00975 490.825</p> <p>0.01 490.682</p> <p>0.01025 490.549</p> <p>0.0105 490.427</p> <p>0.01075 490.314</p> <p>0.011 490.212</p> <p>0.01125 490.119</p> <p>0.0115 490.036</p> <p>0.01175 489.96</p> <p>0.012 489.893</p> <p>0.01225 489.833</p> <p>0.0125 489.781</p> <p>0.01275 489.737</p> <p>0.013 489.699</p> <p>0.01325 489.668</p> <p>0.0135 489.643</p> <p>0.01375 489.624</p> <p>0.014 489.61</p> <p>0.01425 489.603</p> <p>0.0145 489.6</p> <p>0.01475 489.601</p> <p>0.015 489.607</p> <p>0.01525 489.618</p> <p>0.0155 489.632</p> <p>0.01575 489.651</p> <p>0.016 489.673</p> <p>0.01625 489.699</p> <p>0.0165 489.728</p> <p>0.01675 489.76</p> <p>0.017 489.796</p> <p>0.01725 489.834</p> <p>0.0175 489.875</p> <p>0.01775 489.918</p> <p>0.018 489.964</p> <p>0.01825 490.012</p> <p>0.0185 490.063</p> <p>0.01875 490.115</p> <p>0.019 490.169</p> <p>0.01925 490.225</p> <p>0.0195 490.283</p> <p>0.01975 490.343</p> <p>0.02 490.404</p> <p>0.02025 490.467</p> <p>0.0205 490.531</p> <p>0.02075 490.597</p> <p>0.021 490.663</p> <p>0.02125 490.731</p> <p>0.0215 490.8</p> <p>0.02175 490.869</p> <p>0.022 490.94</p> <p>0.02225 491.011</p> <p>0.0225 491.083</p> <p>0.02275 491.155</p> <p>0.023 491.228</p> <p>0.02325 491.301</p> <p>0.0235 491.374</p> <p>0.02375 491.448</p> <p>0.024 491.521</p> <p>0.02425 491.595</p> <p>0.0245 491.668</p> <p>0.02475 491.741</p> <p>0.025 491.813</p> <p>0.02525 491.885</p> <p>0.0255 491.955</p> <p>0.02575 492.025</p> <p>0.026 492.094</p> <p>0.02625 492.161</p> <p>0.0265 492.228</p> <p>0.02675 492.292</p> <p>0.027 492.355</p> <p>0.02725 492.415</p> <p>0.0275 492.474</p> <p>0.02775 492.529</p> <p>0.028 492.583</p> <p>0.02825 492.633</p> <p>0.0285 492.68</p> <p>0.02875 492.724</p> <p>0.029 492.764</p> <p>0.02925 492.801</p> <p>0.0295 492.832</p> <p>0.02975 492.863</p> <p>0.03 492.881</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Transport Rail cart
this is the remaining chassie of the transportation rail cart used in the moving of excavated stone or debris. this cart was used in the minimg insdustry or the creation of tunnels in mountain scapes Photogrammetry by **[Imad Farhat](https://instagram.com/imad.farhat)** Source: Objaverse 1.0 / Sketchfab
Warrington Transporter Bridge
100m-long steel bridge in Warrington near Liverpool, UK. World's only rail Transporter Bridge. It's on Heritage at Risk Register. https://historicengland.org.uk/advice/heritage-at-risk/search-register/list-entry/46484 Helpful website when you visit: http://www.warringtontransporterbridge.co.uk/ . . 1003 photos with DJI Mini 2 in 2022 April. Created in RealityCapture and Blender.  Location: https://goo.gl/maps/taUXh4k75wjfeq6r6 Source: Objaverse 1.0 / Sketchfab
Transport Amphora
Location: Museo Archeologico di Aidone, Aidone, Sicily, Italy Catalogue Number: 57-3159 Description: Reconstructed transport amphora Data Capture Method: Laser Scanning Processing Software: Artec Studio 12 Citation: The data for this project were originally collected as a joint effort between the University of Catania and CVAST at the University of South Florida (USF), with the collaboration of the Fondazione Bruno Kessler (FBK). Dr. Mariarita Sgarlata and Dr. Herbert Maschner, Principal Investigators. We gratefully acknowledge the participation of the administrators of the Villa Romana del Casale and the Museo Archeologico di Aidone. The data have been transferred to Global Digital Heritage (GDH) for processing and analysis. Funding for this project, both at USF and at GDH, has been provided by the Hitz Foundation, Herbert Maschner, Principal Investigator. Source: Objaverse 1.0 / Sketchfab
Travel Times by Transportation Mode in Nairobi, Kenya
<p>This directory contains data files that describe travel times in<br> Nairobi, Kenya. Three different modes of transportation are included:</p> <p> - Walking, according to MapQuest and filled in with Google Maps where<br> MapQuest fails to find a route.</p> <p> - Driving, under normal traffic conditions.</p> <p> - Matatus, the informal public transportation network.</p> <p>The travel times are computed from a gridded origin location to a<br> collection of gridded destination locations. In the directories, each<br> file contains destination location times for a given origin location.<br> These are titled <mode>-###.csv. The CSV is organized from north to<br> south, along rows, and west to east, along columns. An additional<br> <mode>.csv file in each directory gives the organization of the<br> numbered files, again from north to south, along rows, and west to<br> east, along columns.</p> <p>There are also "extended" versions of each grid, with larger<br> destination grids for each origin point, formed by "daisy-chaining"<br> together the original destination grids.</p> <p>The directory also contains source files for working with the data:</p> <p> - calc_access.R shows how to calculate a simple mobility measure,<br> using the functions in tools_calc_access.R</p> <p> - tools_analyze.R are a more general set of functions for performing<br> arbitrary callback-based operations on the travel time data.</p> <p> - tools_calc_access.R contains underlying functions for setting up<br> access to the grids and calculating access.</p> <p> - tools_map.R contains the grid origin and destination definitions<br> and functions for plotting.</p>
Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study
<p>Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input–output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ± 40 % variation range for the UK emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub>, from which regression coefficients for each input-output combination and each model grid (>10,000 across the UK) were calculated. Surface concentrations of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO<sub>3</sub> and particulate SO<sub>4</sub><sup>2-</sup>, NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup> to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (± 4 %, ± 10 % and ± 20 % respectively). The uncertainties in the surface concentrations of NH<sub>3</sub> and NO<sub>x</sub> and the depositions of NH<sub>x</sub> and NO<sub>y</sub> were dominated by the uncertainties in emissions of NH<sub>3</sub>, and NO<sub>x</sub> respectively, whilst concentrations of SO<sub>2</sub> and deposition of SO<sub>y</sub> were affected by the uncertainties in both SO<sub>2</sub> and NH<sub>3</sub> emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and HNO<sub>3</sub>) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.</p> <p>The dataset contains model outputs used for the sensitivity and uncertainty analyses.</p>
Data from: Seamounts generate efficient active transport loops to nourish the twilight ecosystem
<p>Seamounts are ecological oases nurturing abundant fisheries resources and epibenthic megafauna in the vast oligotrophic ocean. Despite their significance, the formation mechanisms underlying these seamount ecological oases remain uncertain. To shed light on this phenomenon, this study conducted interdisciplinary <em>in-situ</em> observations focusing on a shallow seamount in the oligotrophic ocean. In this dataset, we report high-resolution hydrographic and biochemical observational data from both sectional observations and time-series stations observations.</p>
The Model Grid for The atmosphere of HD 149026b: Low metal-enrichment and weak energy transport
<p>This grid contains cloud-free 1-dimensional radiative-convective-thermochemical equilibrium atmosphere models created using the Python-based code <a href="https://natashabatalha.github.io/picaso/">PICASO</a>. The parameters varied for this grid are the atmospheric metallicity (<em>[M/H]</em>), Carbon-to-Oxygen ratio (<em>C/O</em>), heat redistribution factor (<em>rfacv</em>), and the intrinsic temperature of the planet (<em>Tint</em>). The ranges of these parameters have been outlined in the paper. </p> <p>The profile and spectra are provided for each model as a .dat file. Each profile contains the temperature and abundance for a variety of chemicals at each of the 91 pressure levels modeled for the atmosphere. The spectra file contains the wavelength in microns, transit depth, eclipse depth, and emission flux from the planet in ergs/s/cm^3. The isolated planetary thermal emission spectrum needs to be multiplied by 1e6 to be in ppm. There are four types of models, ones with VO, ones with TiO, ones with TiO and VO, and ones without TiO or VO. The files are labeled based on each of the 4 atmospheric parameters and whether they contain TiO and VO.</p> <p>Note on TiO: The inclusion of gaseous TiO in the atmosphere was found to cause strong inversions in the temperature-pressure profile and a worse fit of the thermal emission spectrum to the data. This finding has been described in the paper.</p>
VR Interaction Techniques for Constrained Transport Spaces
<p>This is the dataset for the study reported in "A Lack of Restraint: Comparing Virtual Reality Interaction Techniques for Constrained Transport Seating": https://ieeexplore.ieee.org/document/10058530</p>
Molecular Dynamics (MD) Simulation Data for Dynamics Underlie the Drug Recognition Mechanism by the Efflux Transporter EmrE
<p>MD simulations on the proton bound (PDB 8UWU), deprotonated on E14A (PDB 8UWU), TPP Bound (PDB 8UWU) on our NMR derived structures.</p> <p> </p> <p>MD simulations on the proton bound (7MH6) and deprotonated on E14A (7MH6) on X-ray structures. </p> <p> </p> <p>Total raw simulation data would be too large for uploading to repositories. To reduce size of file, starting structure and tpr files are uploaded. Final structure at 2.5 μs are also uploaded. </p>
Structural insights into drug transport by an aquaglyceroporin
<p>Equilibrated system, parameter files for performing umbrella sampling, MD trajectories for the MM/GBSA.</p>
Geocoded variant of United Nations Code for Trade and Transport Locations
<div></div> <div> <div> <div> <div>The <a href="https://unece.org/trade/uncefact/unlocode">United Nations Code for Trade and Transport Locations</a> dataset provides information on the codes known as "UN/LOCODE". </div> <div>This version of the dataset, based on the csv files of version 232, has extra information based on pycountry (names of administrative regions, country names, flags) and on most important on location.</div> <div> </div> <div> </div> <div>The dataset has the following extra fields:<br> <table> <tbody> <tr> <td>Column name</td> <td>Example</td> <td>Description</td> <td>Source</td> </tr> <tr> <td> <pre>alpha_2_country</pre> </td> <td> <pre>AD</pre> </td> <td>2 letter based country code, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>alpha_3_country</pre> </td> <td> <pre>AND</pre> </td> <td>3 letter based country code, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>flag_country</pre> </td> <td> <pre>🇦🇩</pre> </td> <td>unicode flag</td> <td>pycountry</td> </tr> <tr> <td> <pre>name_country</pre> </td> <td> <pre>Andorra</pre> </td> <td>Country name, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>numeric_country</pre> </td> <td> <pre>020</pre> </td> <td>Country code</td> <td>pycountry</td> </tr> <tr> <td> <pre>official_name_country</pre> </td> <td> <pre>Principality of Andorra</pre> </td> <td>Country official name</td> <td>pycountry</td> </tr> <tr> <td> <pre>score</pre> </td> <td>95</td> <td>Confidence code of geocoding [0-100/nan]</td> <td>ArcGIS</td> </tr> <tr> <td> <pre>geocoded</pre> </td> <td>True</td> <td>Was the coordinate derived from geocoding [true/false]</td> <td>ArcGIS</td> </tr> <tr> <td> <pre>geometry</pre> </td> <td> <pre>POINT (1.516666666666667 42.5)</pre> </td> <td>Point coordinate</td> <td>ArcGIS if geocoded else original UNLO </td> </tr> </tbody> </table> <br>The figure below shows the locations (n=23663) that have been geocoded in this dataset in green and the existing locations in pink. </div> <div><br></div> <div> </div> <div>Ownership of the original data recides at UNLO and the participants. </div> </div> </div> </div>
Dimensiones de Carga de Transporte en Jalisco
<p>Este dataset contiene las dimensiones detalladas de una variedad de objetos frecuentemente transportados en camiones, recopilados por una empresa líder de logística en México. Incluye muebles grandes como sofás y escritorios, electrodomésticos pesados, cajas de cartón llenas de diversos artículos, colchones, equipos de gimnasio, instrumentos musicales voluminosos, artículos de jardinería, grandes obras de arte, equipos electrónicos y muebles de oficina.</p>
Data for manuscript "Estimating grain stress and distinguishing between mobility and transportability improves bedload transport estimates in coarse-bedded mountain rivers"
<p>This repository contains data collected that was used in the manuscript:</p> <p><span>Estimating grain stress and distinguishing between mobility and transportability improves bedload transport estimates in coarse-bedded mountain rivers</span></p> <p> </p>
Fig. 2 in Transportation of microplastic during high-flow and low-flow seasons in southeastern Black Sea: A modelling approach
Fig. 2 — Mountly mean wind, and current during high-flow (a, b) and low-flow (c, d) seasons
ScienceDex guides
Understand access before you commit
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.