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71 results for “railway”
Railways (Comune di Napoli)
<p>Urban Atlas based data subset, where every element with CODE 12230 was extracted as railways elements with the next information:</p> <p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint</p> <p>This data is an input for local effects calculation.</p>
Database for RailRad calculation method for simulating sound radiated by railway track vibrations
<p>This dataset contains precalculated acoustic transfer functions for efficiently calculating the sound radiated by railway track vibrations.</p> <p>The transfer functions contained in each file describe the complex sound pressure produced at a number of receiver locations given a unit velocity at a source element on the railway track surface, per frequency and at a fixed wavenumber along the track.</p> <p>Four different acoustic geometries are included: (1) a standard UIC60 rail in free space, (2) the rail in an acoustic half space, (3) the rail located above a slab track surface, and (4) identical geometry to (3) but including an acoustically hard hull of a passenger train geometry above the track.</p> <p>More information about the exact location of source and receiver coordinates can be found in the .hdf5 files, in the subgroup 'info'. The transfer functions themselves are located in the dataset 'tfs', which are matrices of size (Number of frequency lines x number of sources x number of receivers).</p> <p>More information can be found here https://github.com/janniktheyssen/railrad</p> <p>This collection of databases is part of ongoing work at CHARMEC / Chalmers University of Technology, Gothenburg, Sweden (https://www.charmec.chalmers.se/). Parts of the study have been funded from the European Union's Horizon 2020 research and innovation programme in the In2Track3 project under grant agreements No 101012456. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973.</p>
Experimenting with Formal Verification and Model-based Development in Railways: the case of UMC and Sparx Enterprise Architect - Complementary Data
<p>This repository contains the UMC and SPARX EA data used in the paper:</p> <p>Experimenting with Formal Verification and Model-based Development in Railways: the case of UMC and Sparx Enterprise Architect</p> <p>by Davide Basile, Franco Mazzanti and Alessio Ferrari.</p>
3D scans of two types of railway ballast including shape analysis information
<p>This data set contains 3D scanner data of two types of railway ballast “Calcite” (stems from Croatia) and “Kieselkalk”, also known as Helvetic Siliceous Limestone, (stems from Switzerland).<br> From each type of ballast 25 stones are scanned. The files are provided in .ply format.<br> For the scanned meshes several shape descriptors are provided: elongation, flatness, sphericity, convexity index.<br> Additional to the 3D scans, both simplified and rounded versions of the meshes are included.<br> For these meshes information on three different angularity indices are available.<br> The scanned ballast types are the same, as previously investigated in uniaxial compression tests and direct shear tests:<br> Suhr, Bettina, & Six, Klaus. (2018).<br> "Compression tests and direct shear test of two types of railway ballast [Data set]"<br> Zenodo. http://doi.org/10.5281/zenodo.1423742</p> <p> </p> <p>A detailed shape analysis of the results is conducted in:<br> Bettina Suhr, William A. Skipper, Roger Lewis, and Klaus Six<br> "Shape analysis of railway ballast stones: curvature-based calculation of particle angularity"<br> <em>Scientific Reports, </em><strong>2020</strong><em>, 10</em>, 6045<br> DOI: https://doi.org/10.1038/s41598-020-62827-w</p> <p>A summary of several shape descriptors can be found in:<br> B. Suhr and K. Six:<br> "Simple particle shapes for DEM simulations of railway ballast -- influence of shape descriptors on packing behaviour"<br> Granular Matter, <strong>2020</strong><em>, 22</em><br> DOI: https://doi.org/10.1007/s10035-020-1009-0</p> <p><br> This data set is organised as follows:<br> 1_ScanMeshesCleaned<br> scanned meshes:<br> K_1.ply - K_25.ply Calcite (German: Kalzit)<br> KK_1.ply - KK_25.ply Kieselkalk<br> 2_CSE1 <br> simplifications of the scanned meshes, little simplifications, used in the detailed shape analysis<br> CSE1_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 3_CSE2 <br> simplifications of the scanned meshes, more simplified, used in the detailed shape analysis<br> CSE2_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 4_CSE3 <br> simplifications of the scanned meshes, even more simplified, used in the detailed shape analysis<br> CSE3_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 5_CSE4 <br> simplifications of the scanned meshes, most simplified, used in the detailed shape analysis<br> CSE4_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 6_RoundedMeshes<br> artificially rounded versions of the scanned ballast meshes, used in the detailed shape analysis<br> RoundedMeshes_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 7_TestBodies <br> meshes of artificial test bodies, constructed for testing different angularity indices in the detailed shape analysis<br> TestBodies_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> scanMeshesInfo.csv: summary of several shape descriptors of the scanned meshes<br> README.txt </p> <p><br> Check the README.txt file for more information on the technical aspects of scanning.</p> <p> </p>
Systematic Evaluation and Usability Analysis of Formal Tools for Railway System Design - Technical Annexes
<p>This package includes additional data for the paper ``Systematic Evaluation and Usability Analysis of Formal Methods Tools for Railway Signalig System Design'', by Alessio Ferrari, Franco Mazzanti, Davide Basile, and Maurice ter Beek, CNR-ISTI, Italy, accepted for publication in the IEEE Transactions on Software Engineering, DOI: 10.1109/TSE.2021.3124677</p> <p>The paper concerns the systematic evaluation and usability analysis of 14 formal tools for system design, namely CADP (2020-g), FDR4(4.2.7), NuSMV(1.1.1), ProB(1.9.3), Atelier B (4.5.1), Simulink (R2020a), SPIN (6.4.9), UMC (4.8), UPPAAL (4.1.4), mCLR2 (202006.0), SAL (3.3), TLA+ (2) and CPN Tools (4.0). The current package includes the following content:</p> <ol> <li>Tool Evaluation Template and .pdf: a document including the reference evaluation template, and the evaluation sheet of each tool. </li> <li>Tool Evaluation Table.xlsx: a table summarizing the results of the evaluation.</li> <li>System Usability Test - SUS Results.xlsx: an excel file with multiple sheets with all the raw results of the usability test for the tools.</li> </ol>
Railway services operated with diesel multiple units in Spain and Portugal
<p>Identifier: DOI</p> <p>Creator: German Aerospace Center, Institute of Vehicle Concepts</p> <p>nameType: Organizantional</p> <p>Title: Railway services operated with diesel multiple units in Spain and Portugal.</p> <p>Publisher: Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Fahrzeugkonzepte.</p> <p>Publication Year: 2022</p> <p>ResourceType: Simulated Trajectories</p> <p>Subject: This data set comprises railway services operated with diesel multiple units in Spain and Portugal with a set of infrastructural and operational attributes.</p> <p>Date: 2022-02-10</p> <p>Description:<br> This data set comprises railway services operated with diesel multiple units in Spain and Portugal with a set of infrastructural and operational attributes. Methodology is described in relatedItem.</p> <p>FundingReference: FCH2Rail; Fuel Cell Hybrid Power Pack for Rail Applications; Grant Agreement Number: 101006633</p> <p>RelatedItem: "D1.1 - Report on line and use case based requirements" of the FCH2Rail project.</p> <p>Related Item can be found on the project website (https://www.fch2rail.eu/en/projects/fch2rail) and/or in Cordis (https://cordis.europa.eu/project/id/101006633/results)</p> <p><br> This dataset comprises following attributes:</p> <p>service:<br> First and last station of the railway service.</p> <p>length:<br> Length of the railway service in km.</p> <p>electrified_length:<br> Length of electrified sections in km.</p> <p>not_electrified_length:<br> Length of not electrified sections in km.</p> <p>electrification_degree:<br> Electrfiicatioin degree in %.</p> <p>longest_autonomy:<br> Longest not electrified section in km.</p> <p>first_station_electrified:<br> Binary of first station is electrified with catenary.</p> <p>last_station_electrified:<br> Binary of last statin is electrified with catenary.</p> <p>elevation_first_station:<br> Elevation of first station in meter above sea level [m.a.s.l.]. Reference global sea level of Jaxa Alos 0.1*0.1.</p> <p>elevation_last_station:<br> Elevation of last station in meter above sea level [m.a.s.l.]. Reference global sea level of Jaxa Alos 0.1*0.1.</p> <p>daily_trips:<br> Count of daily trips on the service.</p> <p>vehicle:<br> Vehicle type used on this service.</p> <p>stop_number:<br> Numver of stops at stations throughout a trip.</p> <p>trip_time:<br> Trip duration in hours.</p> <p>gauge:<br> Railway gauge in mm.</p> <p>type:<br> Railway vehicle type. Mainline Loc = Mainline Locomotive, MU Iber. gauge = Multiple unit on iberian gauge, MU Feve Gauge = multiple unit on feve gauge.</p> <p>avg_stop_distance:<br> Average stop distance in km.</p> <p>avg_speed:<br> Average velocity in km/h.</p> <p>daily_autonomy:<br> Cumulated distance under not electrified sections in km.</p> <p>annual_train_km:<br> Train kilometers per year.</p> <p>annual_train_km_wo_catenary:<br> Train kilometers per year not under catenary. </p> <p> </p>
Compression tests and direct shear test of two types of railway ballast
<p>This data set contains measurement data from uniaxial compression tests and direct shear tests<br> conducted on two types of railway ballast.<br> For a detailed description of the experiments see:</p> <p>B. Suhr, S. Marschnig and K. Six:<br> "Comparison of two different types of railway ballast in compression and direct shear tests:<br> experimental results and DEM model validation"<br> Granular Matter (2018)<br> Doi: 10.1007/s10035-018-0843-9</p> <p>For the uniaxial compression tests, measured normal forces and vertical paths are provided.<br> The direct shear tests are conducted directly afterwards, i.e. the information for<br> one compression and one shear test are contained in only one file.<br> For the shear tests shear paths, shear forces and vertical path are provided.</p> <p>At first the uniaxial compression test is carried out. At the end of this test, the normal load is equal to zero.<br> For the following direct shear test, the normal load is constant (according to the load specified in the file name)<br> and not recorded in the file.<br> The direct shear test starts, when the measured shear path is greater than zero.</p> <p>Check the README.txt file for more information. </p>
Towards the use of UHPFRC in railway bridges: the rehabilitation of Buna Bridge
<p>Dataset of experiment carried out on Buna bridge, before rehabilitation. Accelerations corresponding to a roving test, with the positions and orientations specified ont the pdf (pass1 vertical, pass 2 vertical, pass1 horizontal, pass2 horizontal. The structure was excited with a shaker in horizontal (confH) and vertical (confV) positions. </p>
Case studies related to the manuscript Tuning Trains Speed in Railway Scheduling
<p>This dataset is dedicated to the case studies related to the manuscript <strong>Tuning Trains Speed in Railway Scheduling</strong> by Étienne André, published in the proceedings of the 25th International Conference on Formal Engineering Methods (ICFEM 2024).</p> <p>See README.md for more information.</p>
Carbon emission and lifecycle costs supporting digital twins for managing railway maintenance and resilience
<p>The development of railway construction increases the system complexity, which results in difficulty in management with traditional methods. Building Information Modelling (BIM) as an interoperable concept is benefits via whole life-cycle assessment (LCA) of the project, and it has been widely adopted in architecture, construction, and engineering (ACE) fields. This dataset of lifecycle cost and carbon footprint supports the digital twins for managing railway maintenance and resilience.</p>
Formal Methods in Railways: a Systematic Mapping Study - List of Primary Studies and Data Extraction
<p>This Excel file includes the list of papers analyzed in the systematic mapping study titled "Formal Methods in Railways: a Systematic Mapping Study". The study has been submitted for publication, and its preprint is also included in this repository. </p>
Supplementary material for 'Station to Station: Linking and Enriching Historical British Railway Data'
<p>Supplementary material for the <a href="https://github.com/Living-with-machines/station-to-station">station-to-station</a> Github repository, containing the underlying code and materials for the paper 'Station to Station: Linking and Enriching Historical British Railway Data', accepted to CHR2021 (Computational Humanities Research).</p> <p>Mariona Coll Ardanuy, Kaspar Beelen, Jon Lawrence, Katherine McDonough, Federico Nanni, Joshua Rhodes, Giorgia Tolfo, and Daniel C.S. Wilson. "Station to Station: Linking and Enriching Historical British Railway Data." In Computational Humanities Research (CHR2021). 2021.</p>
Towards the Future Generation of Railway Localization Exploiting RTK and GNSS
<p>This repository contains the datasets acquired by ETH-PBL in conjunction with Unibo and SADEL during two days of testing in October 2022 near Modena, Italy.</p> <p>The data were acquired using two sensor nodes developed by ETH Zurich running a <a href="https://www.st.com/en/microcontrollers-microprocessors/stm32l452ce.html">STM32L452CEU6</a> MCU.<br> Each node collected data on the motion of the train using an <a href="https://www.st.com/en/mems-and-sensors/asm330lhh.html">ST ASM330LHH</a> automotive grade IMU as well as a <a href="https://www.u-blox.com/en/product/zed-f9p-module">u-blox ZED-F9P</a> GNSS module fed with live RTCM-data from a closeby RTK base station provided by SADEL. The base station utilized another ZED-F9P GNSS module connected to a Raspberry Pi which transmitted the generated RTCM correction packages over a raw TCP socket.<br> The data was then received using a <a href="https://www.u-blox.com/en/product/sara-r4-series">u-blox SARA-R4</a> cellular network module.</p> <p>The track was chosen as it exposes a variety of interesting GNSS environments. Encountered environments are ranging from urban over suburban to open field environments as well as one tunnel. Due to this composition, the availability of cellular connection and thus RTK correction data was patchy but mostly stable.</p> <p>The two sensor nodes were fixed to the Train Chassis, one centered in the train and the other positioned on the left side in driving orientation. Node 1 was placed on the floor in front of the driver's seat and positioned to be aligned with the center of the train in the lateral direction. A TOPGNSS TOP106 L1/L2 multi-band antenna was placed below the rear-facing windscreen also aligned with the same axis. Node 2 was mounted on a window on the left side of the train when facing in the direction of travel. This is approximately 1m above the floor and 1.4m left to the lateral center of the train. An ANN-MB00 L1/L2 antenna was attached to the outside frame of the train above the window.</p> <p>This dataset is linked with the GitHub repository at <a href="https://github.com/ETH-PBL/Railway-Precise-Localization">Railway-Precise-Localization</a> where the data format description and the pre-processing scripts are provided.</p>
3D Point Cloud of a railway slope - MOMIT (Multi-scale observation and monitoring of railway infrastructure threats) EU project - H2020-EU.3.4.8.3. - Grant agreement ID: 777630
<p>3D point cloud of a railway trench in Lavancia-Épercy (France). The 3D point cloud has been generated from pictures obtained by means of a UAV (DJI Matrice 600 Pro) and processed using Agisoft Metashape. The 3D point cloud is composed of 110,356,682<strong> </strong> million points containing XYZ and RGB information.</p> <p>The original file is in .bin format and is compressed in zip format.</p> <p> </p>
Railway tram curving noise datasets
<p>This dataset is part of the data descriptor. The data are collected by recording the noise generated by the trams at the center of the curve alignment when the trams traverse along the curve alignment. The comprehensive dataset and analysis contribute valuable insights into tram curve noise, aiding urban planning and noise mitigation efforts.</p> <p>Note:</p> <p>Datasets: datasets.csv and wav_files.zip</p> <p>Python code: curve_noise.py</p> <p>Algorithm: curve_noise.ipynb</p> <p> </p>
Supplementary Material: Comparing Formal Tools for System Design: a Case Study from the Railway Domain
<p>The package includes a set of models for a railway moving-block system:</p> <p>(a) a PDF document named Moving-block Model and Requirements.pdf, which includes a UML model of a moving-block system together with a set of requirements for the system;</p> <p>(b) a set of 10 folders, each one associated to a formal or semi-formal development tool. Each folder contains one or more model of the moving-block system from (a), developed by means of the tool.</p>
Data basis of "Investigation of Railway Network Capacity by Means of Dynamic Flows"
<p>Input data for the article <strong>Investigation of Railway Network Capacity by Means of Dynamic Flows (Nikolayzik, Maus and Nießen).</strong></p> <p>The dataset contains two files for each analysed scenario (complete network, upper subnetwork, lower subnetwork).</p> <p>The first file ("input_data_infrastructure_{scenario}.csv") contains information on the investigated infrastructure:<br>For each station the number of available tracks is listed and for the lines information on whether it is a single- or double-track line, the average minimum headway time, hourly capacity limits and travel times for the different train types are included. The information is thereby split into two parts, depending on whether the core network or the linking lines are described.</p> <p>The second file ("input_data_trains_{scenario}.csv") contains the trains that can generally be scheduled in the considered network, including information on the corresponding train type, departure frequencies, their routes and a minimally allowed dwell time.</p> <p> </p> <p>Further, the file "input_data_route_conflicts_nodes.py" contains the information on which routes inside a station exclude each other as is described in the article.</p> <p> </p> <p> </p>
Passage Railway Greenway Waterbird Survey, Winter 2020/21
<p>Waterbird surveys carried out for the Passage Railway Greenway project in winter 2020/21.</p>
Drones for Railway Infrastructure Inspection
<p>LMT in collaboration with Latvijas Gaisa Satiksme and Airborne RF performed an operation deployment – Inspection of Railway Infrastructure with Rail Baltica as a use case. With this trial, we enabled 3rd autonomy level of drone flight, the development of a new business case, BVLOS, and remote detection of security threats and C2 only through the cellular network. This is a significant step forwards to increased railway security!</p>
Virtual Coupling in Railways: A Comprehensive Review. Support data files
<p>Support files for VOSviewer maps and Scopus statistics</p>
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.