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61 results for “travel time”
P-S waves 3D velocity model of Los Humeros area from earthquake based travel-time tomography using CAT3D software (OGS)
<p>The dataset contains the 3D velocity model (VP (m/s), VS (m/s) and VP/VS) obtained from the tomographic inversion of seismological data in the area of Los Humeros (Mexico). The model was performed in the frame of the GEMex project (Mexico‐Europe Cooperation for research of enhanced geothermal systems and super-hot geothermal systems, WP5 ‘Detection of deep structures’, Jousset et al., D5.3, 2019).</p> <p>The inversion used 2661 P arrivals and 2272 S arrivals associated to 395 earthquakes recorded by 37 stations. The picking data was provided by Toledo et al., 2019.</p> <p>The inversion was performed by CAT3D software, a tomographic tool developed by OGS, which uses the SIRT method (Simultaneous Iterative Reconstruction Technique, Stewart, 1993) as inversion algorithm and the ray tracing procedure based on minimum time principle (Böhm et al., 1999). The velocities used as initial model for tomography were provided by the interpolated values obtained from the velocity analysis of four 2D seismic lines acquired inside the same investigated area by the tomographic inversion (See GEMex deliverable D5.3).</p> <p>The 3D velocity model is defined by a 3D grid of 61 nodes in X, 69 nodes in Y and 29 nodes in Z, equally spaced by 250 m in all directions. The total dimensions of the model is 15x17x7 km and the borders positions are (m) (WGS 84/UTM ZONE 14N):</p> <p>Xmin = 655000, Xmax = 670000</p> <p>Ymin = 2168000, Ymax = 2185000</p> <p>Zmin = -3000, Zmax = 4000</p>
Helsinki Region Travel Time Matrix 2018-2023
<p><strong>Introduction</strong></p> <p>This travel time matrix records <strong>travel times</strong> and travel distances for routes between all centroids (N = 13132) of a 250 × 250 m grid over the populated areas in the Helsinki metropolitan area by <strong>walking</strong>, <strong>cycling</strong>, <strong>public transportation</strong>, and <strong>private car</strong>. If applicable, the routes have been calculated for different times of the day (rush hour, midday, off-peak), and assuming different physical abilities (such as walking and cycling speeds), see details below.</p> <p>The grid follows the geometric properties and enumeration of the versatile <a href="https://ckan.ymparisto.fi/dataset/ykr-ruutuaineisto"><em>Yhdyskuntarakenteen seurantajärjestelmä</em> (YKR) grid</a> used in applications across many domains in Finland, and covers the municipalities of Helsinki, Espoo, Kauniainen, and Vantaa in the Finnish capital region.</p> <p> </p> <p><strong>Data formats</strong></p> <p>The data is available in multiple different formats that cater to different requirements, such as different software environments. All data formats share a common set of columns (see below), and can be used interchangeably.</p> <ul> <li><em>Helsinki_Travel_Time_Matrix_2023.csv.zst</em>: comma-separated values (CSV) of all data columns, without geometries. This data set contains all routes in one file, and can be filtered by origin or destination according to the analysis at hand. The data records can also be joined to the geometries as available below. The file is compressed using the <a href="https://facebook.github.io/zstd/">Zstandard algorithm</a>, that many data science libraries, for instance, pandas, <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">support transparently, directly, and automatically</a>.</li> <li><em>Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip</em>: an <a href="https://www.ogc.org/standard/geopackage/">OGC GeoPackage standard</a> file containing all data columns <em>and</em> the geometries that relate to the destination grid cell. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., <a href="https://gdal.org/drivers/vector/gpkg.html">GDAL/OGR</a>, <a href="https://docs.qgis.org/">QGIS</a>, or <a href="https://geopandas.org/en/stable/docs/reference/api/geopandas.read_file.html">geopandas</a>, support natively.</li> <li><em>Helsinki_Travel_Matrix_2023_travel_times.csv.zip</em>: a set of 13132 comma-separated value files containing the routes to one destination grid cell each. The files contain all data columns, no geometry, and can be joined to the geometries as available below. Filenames of the individual files within the ZIP archive follow the pattern <em>Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv</em> where <em>5787545</em> is replaced by the <em>to_id</em> by which the rows in the file are grouped. Use the <em>from_id</em> column to join with the geometries from one of the files below.</li> </ul> <p>Geometry, only:</p> <ul> <li><em>Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip</em>: an <a href="https://www.ogc.org/standard/geopackage/">OGC GeoPackage standard</a> file containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the <em>from_id</em> and <em>to_id</em> columns of the data files. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., <a href="https://gdal.org/drivers/vector/gpkg.html">GDAL/OGR</a>, <a href="https://docs.qgis.org/">QGIS</a>, or <a href="https://geopandas.org/en/stable/docs/reference/api/geopandas.read_file.html">geopandas</a>, support natively.</li> <li><em>Helsinki_Travel_Time_Matrix_2023_grid.shp.zip</em>: an <a href="https://gdal.org/drivers/vector/shapefile.html">ESRI Shapefile</a> archive containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the <em>from_id</em> and <em>to_id</em> columns of the data files.</li> </ul> <p> </p> <p><strong>Table structure</strong></p> <p><em>from_id</em>: ID number of the origin grid cell <br><em>to_id</em>: ID number of the destination grid cell <br><em>walk_avg</em>: Travel time in minutes from origin to destination by walking at an average speed <br><em>walk_slo</em>: Travel time in minutes from origin to destination by walking slowly <br><em>bike_avg</em>: Travel time in minutes from origin to destination by cycling at an average speed<br><em>bike_fst</em>: Travel time in minutes from origin to destination by cycling fast<br><em>bike_slo</em>: Travel time in minutes from origin to destination by cycling slowly<br><em>pt_r_avg</em>: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at an average speed <br><em>pt_r_slo</em>: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at a slower speed <br><em>pt_m_avg</em>: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at an average speed <br><em>pt_m_slo</em>: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at a slower speed <br><em>pt_n_avg</em>: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at an average speed <br><em>pt_n_slo</em>: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at a lower speed <br><em>car_r</em>: Travel time in minutes from origin to destination by private car in rush hour traffic <br><em>car_m</em>: Travel time in minutes from origin to destination by private car in midday traffic <br><em>car_n</em>: Travel time in minutes from origin to destination by private car in nighttime traffic <br><em>walk_d</em>: Distance from origin to destination, in meters, on foot</p> <p> </p> <p><strong>Data for 2013, 2015, and 2018</strong></p> <p>At the Digital Geography Lab, we started computing travel time matrices in 2013. Our methodology has changed in between the iterations, and naturally, there are systematic differences between the iterations' results. Not all input data sets are available to recompute the historical matrices with new methods, however, we were able to repeat the 2018 calculation using the same methods as the 2023 data set, please find the results below, in the same format.</p> <p>For the travel time matrices for 2013 and 2015, as well as for 2018 using an older methodology, please refer to <a href="https://doi.org/10.5281/zenodo.3247563">DOI:10.5281/zenodo.3247563</a>.</p> <p> </p> <p><strong>Methodology</strong></p> <p>Computations were carried out for Wednesday, 15 February, 2023, and Monday, 29 January, 2018, respectively. 'Rush hour' refers to an 1-hour window between 8 and 9 am, 'midday' to 12 noon to 1 pm, and 'nighttime' to 2-3 am.</p> <p>All routes have been calculated using <a href="https://github.com/r5py/r5py">r5py</a>, a Python library making use of the <a href="https://github.com/conveyal/r5">R5</a> engine by <a href="https://conveyal.com">Conveyal</a>, with modifications to consider local characteristics of the Helsinki use case and to inform the computation models from local real-world data sets. In particular, we made the following modifications:</p> <p><em>Walking</em></p> <p>Walking speeds, and in turn walking times, are based on the findings of <a href="https://doi.org/10.1016/j.jtrangeo.2022.103521">Willberg et al., 2023,</a> in which we measured walking speeds of people of different age groups in varying road surface conditions in Helsinki. Specifically, we chose to use the average measured walking speed in summer conditions for <em>walk_avg</em> (as well as the respective <em>pt_*_walk_avg</em>), and the slowest quintile of all measured walker across all conditions for <em>walk_slo</em> (and the respective <em>pt_*_walk_slo</em>).</p> <p><em>Cycling</em></p> <p>Cycling speeds are derived from two input data sets. First, we averaged cycling speeds per network segment from <a href="https://metro.strava.com/">Strava data</a>, and computed a ratio between the speed ridden in each segment and the overall average speed. We then use these ratios to compute fast, slow, and average cycling speeds for each segment, based on the mean overall Strava speed, the mean speeds cycled in the <a href="https://www.hsl.fi/kaupunkipyorat">Helsinki City Bike bike-share system</a>, and the mean between the two.</p> <p>Further, in line with the values observed by <a href="https://blogs.helsinki.fi/saavutettavuus/files/2013/01/Py%C3%B6r%C3%A4ily_osana_joukkoliikennett%C3%A4_kaupunkipy%C3%B6rien_vaikutus_alueiden_saavutettavuuteen_p%C3%A4%C3%A4kaupunkiseudulla.pdf">Jäppinen (2012)</a>, we add a flat 30 seconds each for unlocking and locking the bicycle at the origin and destination.</p> <p><em>Public Transport</em></p> <p>We used public transport schedules in General Transit Feed Specification (GTFS) format published by the <a href="https://www.hsl.fi/hsl/avoin-data">Helsinki Regional Transport Authority</a>, and adjusted the walking speeds (for connections between vehicles, as well as for access and egress to and from public transport stops) using the same methods as described above for <em>walking</em>.</p> <p><em>Private motorcar</em></p> <p>To represent road speeds actually driven in the Helsinki metropolitan region, we used floating car data of a representative sample of the roads in the region to derive the differences between the speed limit and the driven speed on different road classes, and by speed limit, see <a href="https://hdl.handle.net/10138/358181">Perola (2023)</a> for a detailed description of the methodology. Because these per-segment speeds factor in potential waiting times at road crossings, we eliminated turn penalties from R5.</p> <p> </p> <p>Our modifications were carried out in two ways: some changes can be controlled by preparing input data sets in a certain way, or by setting model parameters outside of R5 or r5py. Other modifications required more profound changes to the source code of the R5 engine.</p> <p>You can find a fully patched fork of the R5 engine in the Digital Geography Lab's GitHub repositories at <a href="https://github.com/DigitalGeographyLab/r5">github.com/DigitalGeographyLab/r5</a>. The code that handles input data mangling and model parameter estimations is kept together with the logic to read input parameters and to collate output data, in the repository at <a href="https://github.com/DigitalGeographyLab/Helsinki-Travel-Time-Matrices/">github.com/DigitalGeographyLab/Helsinki-Travel-Time-Matrices</a>.</p>
Catalog of synthetic seismic records from mineral physics and travel-time tables from Waszek et al., 2021, Nature Geoscience
<p>This release is associated with the accepted publication in Nature Geoscience:</p> <p>Waszek L., Tauzin B., Schmerr N., Ballmer M. and Afonso J.C. A poorly mixed mantle transition zone and its thermal state inferred from seismic waves. Nature Geoscience, 2021.</p> <p>This dataset must be used in conjunction with the NoLimit software package (https://zenodo.org/record/5512805).</p> <p>Both the software and dataset allow the prediction of synthetic seismic waveforms for SS and PP-precursors from mineral physics models, as well as their processing for reconstructing the surface of seismic boundaries associated with major mineralogical phase transitions in the Earth’s mantle (namely, the 410-km and 660-km depth discontinuities).</p> <p>For technical reasons (storage and quick access), the catalog is downsampled with respect to the one in Waszek et al. (2021), and it is provided with the HDF5 format. For more advanced applications such as changing mantle composition, or generating waveforms for deeper earthquakes, please contact Benoit Tauzin (benoit.tauzin@univ-lyon1.fr) and Lauren Waszek (lauren.waszek@jcu.edu.au).</p> <p>The dataset includes:</p> <p>* A fixed mantle composition, which is a mechanical mixture of basalt and harzburgite with a fraction of basalt f=0.2.<br> * A downsampled catalog of synthetic waveforms for event depths between 0 and 80 km by step of 10 km (enough for reproducing the processing of observed SS and PP precursors waveforms).<br> * Adiabatic temperature gradients with potential temperature Tpot between 1200 and 2100 K by step of 100 K.</p> <p>This catalog and associated travel-time tables will allow any user to generate synthetic waveforms for any moment tensor, and events within the pre-defined depth interval.<br> </p> <p><strong>How to cite this material?</strong></p> <p>Any use of the datasets or software must refer to:</p> <p>The reference paper: Waszek L., Tauzin B., Schmerr N., Ballmer M., Afonso J.C. A poorly mixed mantle transition zone and its thermal state inferred from seismic waves. Nature Geoscience. 2021.<br> <br> Software: Tauzin, Benoit, & Waszek, Lauren. (2021). NoLiMit MATLAB package v1.0. Non-Linear Bayesian partition Modeling of the Earth's Mantle Transition zone (Version 1). Zenodo. https://doi.org/10.5281/zenodo.5512805<br> <br> Datasets: Tauzin, Benoit, Waszek, Lauren, & Afonso, Juan Carlos. (2021). Catalog of synthetic seismic records from mineral physics and travel-time tables from Waszek et al., 2021, Nature Geoscience (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5512035</p>
Evaluating Nitrate Removal and Travel Times in a Bare Deciduous Forest Soil Using a Tracer-Based Soil Column Experiment
<p> </p> <p>This dataset summarizes the experimental data from a study investigating the relationship between hydrodynamic properties, transport age characteristics (traced using the conservative tracer bromide), and nitrate consumption by native microbial communities in a deciduous forest soil near Lausanne, Switzerland.</p> <h4><strong>Experimental Setup</strong></h4> <ul> <li>Native soil was <strong>collected, sieved, and packed</strong> into homogeneous soil columns (lysimeters) with a porosity of <strong>0.4</strong>.</li> <li>To stimulate microbial activity, the soil underwent <strong>carbon amendment</strong> for several weeks before the experiment.</li> <li>On <strong>June 10, 2021</strong>, a <strong>pulse of bromide and nitrate</strong> (referred to as <strong>SPIKE</strong>) was applied to the bare soil that had undergone carbon amendment.</li> <li>The dataset covers experimental conditions recorded from <strong>May 25, 2021, to August 12, 2021</strong>.</li> </ul> <h4><strong>Included Data & Models</strong></h4> <ol> <li><strong><strong>SPIKE experiment (<strong>pulse of bromide and nitrate</strong>)</strong></strong> <ul> <li> <ul> <li><strong><strong>all other files (see table_of_contents.html and overview.pdf)</strong></strong></li> </ul> </li> </ul> </li> <li> <p><strong>HYDRUS-1D Simulations</strong></p> <ul> <li>The dataset includes HYDRUS-1D simulation files to reproduce <strong>nitrate and bromide breakthrough curves</strong>: <ul> <li><strong>0_Nitrate-double-compartment.zip</strong></li> <li><strong>0_Bromide-double-compartment.zip</strong></li> </ul> </li> </ul> </li> <li> <p><strong>Soil Characterization using Multi-Step Outflow (MSOM) Method</strong></p> </li> </ol> <ul> <li> <ul> <li>Before the main experiment, the <strong>multi-step outflow method</strong> was performed to characterize the soil. The original experimental data was collected at a <strong>15-minute resolution</strong> but has been <strong>upscaled to hourly resolution</strong> to reduce noise and facilitate data handling.<br> <ul> <li><strong>multi-step_outflow_method.csv</strong></li> </ul> </li> </ul> </li> </ul> <ol> <li> <ul> <li>The processed data (at <strong>hourly resolution</strong>) and <strong>HYDRUS-1D inverse modeling simulations</strong> for soil parameter estimation are provided under two configurations: <ul> <li><strong>config_1_MSOM.zip</strong></li> <li><strong>config_2_MSOM.zip</strong></li> </ul> </li> </ul> </li> </ol> <p> </p>
Supporting Datasets produced in Allen et al. (2018) Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data"
<p><strong>Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, <em>Geophysical Research Letters</em>, <a href="https://doi.org/10.1002/2018GL077914">https://doi.org/10.1002/2018GL077914</a></strong></p> <p>The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: <a href="https://doi.org/10.5281/zenodo.1219784">https://doi.org/10.5281/zenodo.1219784</a></p> <p><strong>Abstract</strong></p> <p>Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.</p> <p> </p> <p><strong>Description of repository datasets:</strong></p> <p>1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:</p> <ul> <li>"ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758</li> <li>"UP_CELLS" : number of upstream cells (pixels)</li> <li>"AREA" : upstream drainage area (km<sup>2</sup>)</li> <li>"DISCHARGE" : discharge (m<sup>3</sup>/s)</li> <li>"WIDTH" : mean bankfull river width (m)</li> <li>"WIDTH5" : 5th percentile confidence interval bankfull river width (m)</li> <li>"WIDTH95" : 95th percentile confidence interval bankfull river width (m)</li> <li>"DEPTH" : mean bankfull river depth (m)</li> <li>"DEPTH5" : 5th percentile bankfull river depth (m)</li> <li>"DEPTH95" : 95th percentile confidence bankfull river depth (m)</li> <li>"LENGTH_KM" : segment length (km)</li> <li>"ORIG_FID" : original ID of segment</li> <li>"ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg </li> <li>"POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"SLOPE" : average slope of segment (m/m)</li> <li>"CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/ </li> <li>"CITY_POP_M" : population of joined city (max N inhabitants) </li> <li>"DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html </li> <li>"DAM_AREA_S" : surface area of joined dam (m<sup>2</sup>)</li> <li>"DAM_CAP_MC" : volumetric capacity of joined dam (m<sup>3</sup>)</li> <li>"CELER_MPS" : modeled river flow wave celerity (m/s)</li> <li>"PROPTIME_D" : travel time of flow wave along segment (days)</li> <li>"hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins</li> <li>"GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0 </li> <li>"FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution</li> <li>"SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.</li> <li>"UPSTR_DIST" : upstream distance to the basin outlet (km) </li> <li>"UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)</li> <li>"CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)</li> <li>"DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)</li> <li>"MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)</li> <li>"MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)</li> <li>"MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)</li> <li>"MC_LENGTH" : mean of Monte Carlo simulated river length (m)</li> <li>"MC_SLOPE" : mean of Monte Carlo simulated river slope (-)</li> <li>"MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)</li> <li>"MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))</li> <li>"CONTINENT" : integer indicating the HydroSHEDS region of shapefile</li> </ul> <p>2. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:</p> <ul> <li>Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles</li> <li>Col2: Downstream UID</li> <li>Col3: Number of upstream UIDs</li> <li>Col4 – Col12: Upstream UIDs</li> </ul> <p>3. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:</p> <ul> <li>FID : unique identifier of each polygon</li> <li>CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)</li> <li>CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)</li> <li>COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)</li> </ul> <p>4. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table. </p> <p>5. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:</p> <ul> <li>GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv</li> <li>GAUGE_SITE: USGS gauge site number of joined gauge</li> <li>GAUGE_HUC8: which hydrological unit code the gauge is located in</li> <li>OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R. </li> <li>OBS_CEL_MPS: gauge-based celerity estimate (m/s). </li> </ul> <p>6. tab1_latencies.csv contains data shown in Table 1 of the manuscript.</p> <p>7. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.</p> <p>8. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:</p> <ul> <li>tabdTT_b.csv : basin outlet travel times for all rivers</li> <li>tabdTT_b_swot.csv : basin outlet travel times for SWOT</li> <li>tabdTT_c.csv : next downstream city travel times for all rivers</li> <li>tabdTT_c_swot.csv : next downstream city travel times for SWOT</li> <li>tabdTT_d.csv : next downstream dam travel times for all rivers</li> <li>tabdTT_d_swot.csv : next downstream dam travel times for SWOT</li> </ul>
Delimiting the Neoproterozoic São Francisco Paleocontinental Block with P-wave travel-time tomography
<p>Tomographic data set for different depths (CSV-files with Longitude, Latitude anda Velocity Perturbation in percentage), Interpreted limit of the São Francisco Paleocontinent and the Abstract for the paper "Delimiting the Neoproterozoic São Francisco paleocontinental block with P-wave travel-time tomography" accepted by Geophysical Journal International.</p>
Travel time by rail as a determinant of the system of cross-border linkages of Polish metropolises – dynamic approach 1970–2020
<p>The dataset is an outcome of the research project "<strong>Travel time by rail as a determinant of the system of cross-border linkages of Polish metropolises – dynamic approach 1970–2020</strong>" supported by the National Science Centre (Poland) under the "Miniatura 4" funding scheme (project no. 2020/04/X/HS4/00525).<br> <br> The exploratory aim of the project is to assess a dynamics of the system of cross-border linkages of Polish metropolises over the past five decades, by investigating the duration of international rail connections. Covering such a long period was possible thanks to utilising the archival train timetables, copies of which can be found, among others, in the holdings of the Polish National Library in Warsaw. Furthermore, the methodological aim of the project intends to test a universal measure of functional linkages – the relative travel deceleration index. The linkages between two sets of cities were studied: eleven Polish cities, considered in the literature as existing or potential metropolises (Warszawa, Katowice, Kraków, Gdańsk, Łódź, Poznań, Wrocław, Szczecin, Bydgoszcz, Lublin, Białystok), and twelve foreign cities – capitals of neighbouring countries (Berlin, Praha, Bratislava, Kyiv, Minsk, Vilnius) and the cities with more than 400,000 inhabitants, located within a buffer of 250 km from the Polish border (Leipzig, Dresden, Wien, Budapest, Lviv, Kaliningrad). Eleven time sections are taken into account, from 1970 to 2021.<br> <br> Spatial analyses using GIS software were the starting point. The characteristics of the ideal linkage arrangement were identified. The actual situation occurring in selected years, as described in the timetables, was then compared with it. The proposed deceleration index, measured in %, makes it possible to assess a degree of variation of the shortest travel time observed between a given pair of cities in relation to the time that would occur in a situation with a straight railway line, constant speed, no stops and barriers such as national borders. The factors contributing to this deceleration (the so-called components of deceleration) were also analysed: a layout of rail network, a course of train route, a condition of infrastructure, an organisation of services (waiting time for a change of train) and operations associated with crossing of border affecting its permeability (in the case of Polish eastern border additional time-consuming gauge change of carriages).<br> <br> The dataset consists of:</p> <ol> <li><strong>Methodological framework for the research </strong>explaining the way in which the components of travel deceleration are defined [.pdf file; name: 1_schemat_metodyki_badan; description in Polish].</li> <li><strong>Geospatial vector data in shapefile format</strong> [.zip file; name: 2_dane_GIS_shapefile] featuring: <ul> <li>buffer of 250 km around the state borders of Poland [name: bufor_250km; type: polygon];</li> <li>spatial distribution of the sets of 11 cities in Poland [name: miasta_polskie; type: point] and 12 cities abroad [name: miasta_zagranica; type: point];</li> <li>ideal arrangement of the 132 studied cross-border linkages – the shortest paths connecting pairs of the cities and representing a physical distance measured along the orthodrome [name: powiazania_miast_stan_idealny; type: line];</li> <li>optimal arrangement of the 132 studied cross-border linkages – the shortest paths connecting pairs of the cities and representing a distance measured along the railway network in two variants, before and after the opening of the CMK and LHS lines in Poland [names: najkrotsze_trasy_1970-1975_komponent_1.shp and najkrotsze_trasy_1980-2020_komponent_1.shp; type: polyline]; </li> </ul> </li> <li><strong>Records of the train timetable analyses</strong> including the relative travel deceleration index and the structure of the components of deceleration [.xlsx file; name: 3_struktura_komponentow_spowolnienia; description in Polish];</li> <li><strong>Supplementary table 1 </strong>– cross-border distances (in km) according to the train timetable [.xlsx file; name: 4_tebala_pomocnicza_odleglosci_transgraniczne; description in Polish];</li> <li><strong>Supplementary table 2 </strong>– domestic distances (in km) according to the train timetable [.xlsx file; name: 5_tebala_pomocnicza_odleglosci_w_polsce; description in Polish];</li> <li><strong>Supplementary table 3 </strong>– estimation of time losses in the total travel time associated with accelerating and braking of train for the theoretical variant without intermediate stops (simplified calculation based on kinematics principles) [.xlsx file; name: 6_tebala_pomocnicza_straty_czasu_rozruch_hamowanie; description in Polish].</li> </ol>
Fig. 6 in Activity budget, travel distance, sleeping time, height of activity and travel order of wild East Bornean Grey gibbons (Hylobates funereus) in Danum Valley Conservation Area
Fig. 6. Average daily travel distance and average sleeping time of the JACUZZI male for May–June (dry season) and December (wet season) from 2011 to 2013. Travel distance was counted for 13 days in August and for 13 days in December. Sleeping time was counted for 14 days in August and for 15 days in December. Solid line: travel distance. Dotted line: sleeping time.
Fig. 3 in Activity budget, travel distance, sleeping time, height of activity and travel order of wild East Bornean Grey gibbons (Hylobates funereus) in Danum Valley Conservation Area
Fig. 3. Activity budget of the SAPA male in both wet season (December) and dry season (August) from 2005 to 2008.
Fig. 2 in Activity budget, travel distance, sleeping time, height of activity and travel order of wild East Bornean Grey gibbons (Hylobates funereus) in Danum Valley Conservation Area
Fig. 2. Location of the BRL, the territory of the SAPA group and the territory of the JACUZZI group. The grey area represents the territory.
Fig. 1 in Activity budget, travel distance, sleeping time, height of activity and travel order of wild East Bornean Grey gibbons (Hylobates funereus) in Danum Valley Conservation Area
Fig. 1. Location of the Borneo Rainforest Lodge (BRL) in the Danum Valley Conservation Area (DVCA; arrow), Sabah, Malaysia.
Fig. 5 in Activity budget, travel distance, sleeping time, height of activity and travel order of wild East Bornean Grey gibbons (Hylobates funereus) in Danum Valley Conservation Area
Fig. 5. Average daily travel distance and average sleeping time of the SAPA male for August (dry season) and December (wet season) from 2003 to 2008. Travel distance was counted for 38 days in August and for 35 days in December. Sleeping time was counted for 39 days in August and for 37 days in December. Solid line: travel distance. Dotted line: sleeping time.
Fig. 4 in Activity budget, travel distance, sleeping time, height of activity and travel order of wild East Bornean Grey gibbons (Hylobates funereus) in Danum Valley Conservation Area
Fig. 4. Activity budget of the JACUZZI male and the JACUZZI female in both wet season (December) and dry season (May–June) from 2011 to 2013.
Fig. 8 in Activity budget, travel distance, sleeping time, height of activity and travel order of wild East Bornean Grey gibbons (Hylobates funereus) in Danum Valley Conservation Area
Fig. 8. Heights of diurnal activity of three gibbons (two males and one female) from 0530–1600 hours.
Data for: Analysis of travel time to HIV treatment in sub-Saharan Africa reveals inequities in access to antiretrovirals
Open the record for dataset details and reuse information.
Extract of Local Notice to Mariners, 2018-2019, for Development of AIS Model of Texas Gulf Intracoastal Waterway Travel Times
<p>These files summarize mentions of restrictions or cautions for navigation on the Texas Gulf Intracoastal Waterway contained in Coast Guard Local Notice to Mariners files.</p>
Weekly travel times by direction and sample count for each link in Development of AIS Model of Texas Gulf Intracoastal Waterway Travel Times
<p>Excel spreadsheet containing all of the travel times and sample counts for each link (by direction).</p>
Time Travelers Tavern Block out
here i have started the idea for my envifroment brief in Uni The idea is some sort of time travellers tavern type thing where all these time travellers can just stop and rewind so im gonna have references to a bunch of different time travel movies so i've got the phone box coming out of the worm hole from bill and ted and i've got a DeLorean and the funky device from men in black and i'm thinking of trying to add the hot tub time machine as well. Source: Objaverse 1.0 / Sketchfab
Travel Time Statistics for Links with Locks from L&D 16 to L&D 20
<p>This is an Excel spreadsheet that contains multiple travel time performance statistics for the Upper Mississippi River from L&D 16 to L&D 20. It includes a map depicting the links in the table.</p>
Travel Time and Speed Statistics for Links Containing Locks 16 through 20
<p>This is an Excel spreadsheet that records the travel times/speeds of each trip through a lock in the range of Lock & Dam 16 through Lock & Dam 20 in the Upper Mississippi River. Each lock is part of a three sublink set: a sublink upriver from the lock, the lock itself, and a sublink downriver . A table of links used by the study, a link map, and heat maps are also included.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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