Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
174
datasets available to search
ShareScore release 0.9.0
Dataset results
174 results for “Rail”
Dataset: FreightCar America, Inc. (RAIL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Amarāvatī, Andhra Pradesh. Drawing of railing cross bar with stūpa, tree and inscription.
<p>Amarāvatī, Andhra Pradesh. Drawing of railing cross bar with <em>stūpa</em>, tree and inscription.</p>
Pauni (पौनि Maharashtra). Railing pillar (OBNAG0008) from a stūpa, detail of tree and railing
<p>Pauni (पौनि Maharashtra). Railing pillar (OBNAG0008) from a stūpa, detail of tree and railing.</p>
Pauni (पौनि Maharashtra). Railing pillar from a stūpa, detail of inscription.
<p>Pauni (पौनि Maharashtra). Railing pillar from a <em>stūpa</em>, detail of inscription.</p>
Spherical Indentation tests on rail R260 steel
<p>This data set contains photos and measurement data from spherical indentation tests on R260 rail steel. <br>This data set belongs to the following journal article:</p> <p>Bettina Suhr, William A. Skipper, Roger Lewis, Klaus Six:<br>DEM modelling of surface indentations caused by granular materials: application to wheel–rail sanding,<br>Computational Particle Mechanics, 2024,<br>https://doi.org/10.1007/s40571-024-00816-w</p> <p>A flat specimen made of the R260 rail steel was indented by a stainless steel (AISI 440C) ball bearing of 8.73 mm diameter. <br>The following normal loads were applied for indentation tests: <br>100 N, 500 N, 1000 N, 2000 N, 3000 N, 4000 N, 5000 N, 6000 N, 7500 N. <br>For each load level, two tests were conducted to check the repeatability of the measurement. <br>After tests, the indent was analysed using the Alicona InfiniteFocusSL 3D optical profilometer. <br>The Alicona captured a 3D scan covering a 3.66 mm x 3.66 mm area (vertical resolution of 500 nm). </p> <p>Available data and file naming conventions</p> <p>For the test at 100 N load, the available files are named as follows:</p> <p>11-32__100N.jpg: image coloured by the indentation depth<br>11-32__100N_Diameter.jpg: image of the indent with visually measured diameter of the indent<br>11-32__100N.txt: point cloud of the analyzed surface</p> <p>The repetition measurements’ files at 100N load are named: 11-32__100N_a.jpg, 11-32__100N_Diameter_a.jpg, 11-32__100N_a.txt</p> <p>The files belonging to test with higher applied load are named accordingly.</p> <p><br>This research was funded in whole, or in part, by the Austrian Science Fund (FWF) project<br> P 34273: DEM modelling of adhesion in sanded wheel-rail contacts.</p> <p>This work was conducted at Virtual Vehicle Research GmbH in Graz, Austria. <br>The authors would like to acknowledge the financial support within the COMET K2 Competence Centers <br>for Excellent Technologies from the Austrian Federal Ministry for Climate Action (BMK), the <br>Austrian Federal Ministry for Labour and Economy (BMAW), the Province of Styria (Dept. <br>12) and the Styrian Business Promotion Agency (SFG). The Austrian Research Promotion <br>Agency (FFG) has been authorised for the programme management. </p> <p> </p>
Railing fragment with standing figure from Andhra, profile drawing of British Museum 1955,1017.1
<p>Railing fragment with standing figure from Andhra, profile drawing of British Museum 1955,1017.1</p>
Vision-Based Obstacle Detection on Rail Tracks
<p><strong>Acknowledgement and Disclaimers</strong></p> <p>These data are a product of a research activity conducted in the context of the <a href="http://rails-project.eu">RAILS (Roadmaps for AI integration in the raiL Sector) project</a>. RAILS has received funding from the Shift2Rail Joint Undertaking (JU) under the European Union’s Horizon 2020 research and innovation programme under grant agreement n. 881782 Rails. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Shift2Rail JU members other than the Union.</p> <p>The information and views set out in this description are those of the author(s) and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy of the data included in this dataset. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the use which may be made of the information contained therein.</p> <p>This "dataset" has been created for scientific purposes only to study the potentials of Deep Learning (DL) approaches when used to analyse Video Data in order to detect possible obstacles on rail tracks and thus avoid collisions. The authors DO NOT ASSUME any responsibility for the use that other researchers or users will make of these data.</p> <p> </p> <p><strong>Objectives of the Study</strong></p> <p>RAILS defined some pilot case studies to develop Proofs-of-Concept (PoCs), which are conceived as benchmarks, with the aim of providing insight towards the definition of technology roadmaps that could support future research and/or the deployment of AI applications in the rail sector. In this context, the main objectives of the specific PoC "Vision-Based Obstacle Detection on Rail Tracks" were to investigate: i) solutions for the generation of synthetic data, suitable for the training of DL models; and ii) the potential of DL applications when it comes to detecting any kind of obstacles on rail tracks while exploiting video data from a single RGB camera.</p> <p> </p> <p><strong>A Brief Overview of the Approach</strong></p> <p>A multi-modular approach has been proposed to achieve the objectives mentioned above. The resulting architecture includes the following modules:</p> <ul> <li>The Rails Detection Module (RDM) detects rail tracks. The output of the RDM is used by the ODM and ADM.</li> <li>The Object Detection Module (ODM) detects obstacles whose type is known in advance.</li> <li>The Anomaly Detection Module (ADM) identifies any possible anomaly on rail tracks. These include obstacles whose type is not known in advance.</li> <li>The Obstacle Detection Module merges the outputs from the ODM and the ADM.</li> <li>The Distance Estimation Module estimates the distance of objects and anomalies from the train.</li> </ul> <p>The research was specifically oriented at implementing the RDM-ADM pipeline. Indeed, the object detection approaches that would be used to implement the ODM have been widely investigated by the research community, instead, to the best of our knowledge, limited work has been done in the rails field in the context of anomaly detection. The RDM has been realised by adopting a <em>Semantic Segmentation</em> approach based on <em>U-Net</em>; while, to develop the ADM, a <em>Vector-Quantized Variational Autoencoder</em> trained in <em>Unsupervised </em>mode was leveraged. Further details can be found in the RAILS <a href="http://rails-project.eu/downloads/deliverables/">"Deliverable D2.3: WP2 Report on experimentation, analysis, and discussion of results"</a>.</p> <p> </p> <p><strong>Steps to implement the RDM-ADM pipeline and description of shared Data</strong></p> <p>The following list reports all the steps that have been performed to implement the RDM-ADM pipeline; the words in bold-italic refer to the files that are shared within this dataset:</p> <ol> <li>A Railway Scenario was generated in MathWorks' <a href="https://www.mathworks.com/products/roadrunner.html">RoadRunner</a>.</li> <li>A video (<strong><em>FreeTrackVideo</em></strong>) was recorded by simulating an RGB camera mounted in front of the train; no obstacles on rail tracks were considered in this phase.</li> <li>2000 frames (<strong><em>FreeTrack2KFrames</em></strong>) were extracted from the aforementioned video. The video contains 4143 frames, however, only 2000 (each other frame starting from the first one) were taken into account due to training time and GPU RAM constraints.</li> <li>Only 10% of the 2000 frames were manually labelled (i.e., 200 frames, a frame every 10 frames) by exploiting LabelMe; these frames were then subdivided into training and validation sets (<strong><em>InitialLabelledSet</em></strong>).</li> <li>Hence, a Semi-Automatic labelling algorithm was developed by leveraging self-training and transfer learning. This algorithm made it possible to label all the FreeTrack2KFrames starting from the InitialLabelledSet. The resulting labels can be found in <strong><em>FreeTrack2KLabels</em></strong>.</li> <li>Data Augmentation was then performed in order to introduce some aleatory in the dataset. Because of the same time and RAM constraints mentioned above, the FreeTrack2KFrames set of data was reduced further: 1600 frames were selected among the aforementioned 2000 and then 5 transformations (Bright, Dark, Rain, Shadow, and Sun Flare) were applied to obtain the dataset (<em><strong>FreeTrack16TrainSet</strong>, <strong>FreeTrack16ValSet</strong>, <strong>FreeTrack16TestSet</strong></em>) that was used to train, validate, and test the RDM.</li> <li>Once the RDM was trained, the FreeTrackVideo was processed to obtain the masked frames that were then used to build the dataset(s) to train, validate, and test the ADM. The ADM was studied by considering two different datasets: the <em>Non-Anomaly Dataset</em> (<strong><em>NAD</em></strong>), which basically contains all the frames of the FreeTrackVideo once processed by the RDM; and the <em>Augmented Non-Anomaly Dataset (<strong>A-NAD</strong>)</em>, which contains 9000 frames, 1500 of which were extracted from the NAD, while the remaining 7500 were obtained by applying the same transformations mentioned above.</li> <li>Lastly, when both the RDM and the ADM were trained, the performances of the whole RDM-ADM pipeline were tested on the <strong><em>WithCarVideo</em></strong> which depicts the same scenario as the FreeTrackVideo but it also depicts a car laying on the rail tracks (i.e., an obstacle).</li> </ol>
Material factors for buildings, roads and rail-based infrastructure in CONUS
<p>Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. The data presented hereinafter constitute that basis for quantifying material stocks in a country that exhibits one of the highest material stocks in the world, the United States.</p><p><strong>Data</strong><br>This dataset includes the following material intensities:</p><ul><li>material intensity in mass per volume of above-ground building (kg/m³) per building type</li><li>material intensity in mass per area of road (kg/m²) per road type</li><li>material intensity in mass per area of railway track (kg/m²) per railway type</li><li>material intensity in mass per area (kg/m²) per other infrastructure type</li></ul><p>Material intensity factors are split into the following 15 material categories: metals (iron/steel, copper, aluminum, all other metals), non-metallic minerals (concrete, bricks, glass, aggregate except for concrete, all other minerals), biomass-based materials (timber, other biomass-based materials), petrochemical-based materials (bitumen, other petrochemical-based materials), insulation, and other materials. </p><p>Material intensity factors are available for each of the following 19 aggregated stock type categories: </p><ul><li><i>Buildings</i>: low-rise residential (RES-LR), mid-rise residential (RES-MR), low/mid-rise residential / commercial mixed use (RCMU), high-rise residential / commercial mixed use (RCMU-HR), residential / commercial mixed use skyscrapers (RCMU-SKY), commercial / industrial (C/I), and mobile homes and light-weight buildings (MLB)</li><li><i>Roads</i>: motorway, primary roads, secondary roads, tertiary roads, local roads, rural roads</li><li><i>Rail-based infrastructure</i>: railway, subway, tram</li><li><i>Other</i>: airport runways, parking lots, other remaining impervious</li></ul><p>Since construction standards for residential buildings and gravel roads vary between different climate zones across the conterminous United States, material intensities for low-rise residential buildings (RES-LR), local roads and tracks were further differentiated according to climate zones.</p><p>The dataset provides three sets of material intensity factors for sensitivity purposes: mean, low, and high. Low and high material intensity factors represent 25th and 75th percentiles of data points per material category and stock type. </p><p>In addition, the following building volume conversion factors required for deriving material intensity factors for buildings are included in the dataset:</p><ul><li>floor-to-floor height per building type</li><li>roof volume factors (m³/m² footprint) per building type</li><li>share of useable area (SUA) in gross floor area per building type</li></ul><p>Building volume conversion factors are based on Haberl et al. (2021) and were used in the calculation of the above-ground volume for those case studies where either the floor-to-floor height or information regarding the roof volume were unavailable, or where only the UA, but not the GFA necessary for the calculation of the above-ground volume were specified.</p><p><strong>Further information</strong><br>The dataset complements a scientific article which includes further information and an in-depth dataset description. For further information, please see the publication below or contact Dominik Wiedenhofer (dominik.wiedenhofer@boku.ac.at). <br><br>D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl, Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. <i>Nat Commun</i>. <strong>14</strong>, 8014 (2023), doi: <a href="https://doi.org/10.1038/s41467-023-43755-5">10.1038/s41467-023-43755-5</a><br><br>Check out this peer-reviewed article detailing the overall approach and novel method:</p><p>H. Haberl, D. Wiedenhofer, F. Schug, D. Frantz, D. Virág, C. Plutzar, K. Gruhler, J. Lederer, G. Schiller, T. Fishman, M. Lanau, A. Gattringer, T. Kemper, G. Liu, H. Tanikawa, S. van der Linden, P. Hostert, High-Resolution Maps of Material Stocks in Buildings and Infrastructures in Austria and Germany. <i>Environ Sci Technol</i>. <strong>55</strong>, 3368–3379 (2021), doi:<a href="https://doi.org/10.1021/acs.est.0c05642">10.1021/acs.est.0c05642</a></p><p>Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p><p><strong>Funding</strong><br>This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p>
MERICS Podcast: China's advances in rail, sea and air transportation, with Alexander Brown and Gregor Sebastian
<p>China has extended its High-Speed Rail network from about 9.000 to 42.000 km of tracks with a fleet of Chinese made trains running on it. The first fully domestically engineered passenger aircraft C919 has started its commercial service and on June 6, the first domestically built large cruise ship, has left Shanghai for sea trials. China is undoubtedly making rapid progress in air, sea and rail transportation.</p> <p>What are China’s aims and what challenges does it face on the way? And what does this mean for European industries? To answer these questions, <strong>Alexander Brown</strong> and <strong>Gregor Sebastian</strong>, analysts in the economics team at MERICS, join <strong>Johannes Heller-John</strong> for a conversation.</p> <p>This podcast episode is part of the “Dealing with a Resurgent China” (DWARC) project, which has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101061700.</p> <p>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p>
irish Historical Rail Stations
<p>Locations of irish rail stations to present day</p>
Data from: First records of complete annual cycles in water rails Rallus aquaticus show evidence of itinerant breeding and a complex migration system
Open the record for dataset details and reuse information.
Data from: Convergent morphological responses to loss of flight in rails (Aves: Rallidae)
<p>The physiological demands of flight exert strong selection pressure on avian morphology and so it is to be expected that the evolutionary loss of flight capacity would involve profound changes in traits. Here we investigate morphological consequences of flightlessness in a bird family where the condition has evolved repeatedly. The Rallidae include more than 130 recognised species of which over 30 are flightless. Morphological and molecular phylogenetic data were used here to compare species with and without the ability to fly in order to determine major phenotypic effects of the transition from flighted to flightless. We find statistical support for similar morphological response among unrelated flightless lineages, characterised by a shift in energy allocation from the forelimbs to the hindlimbs. Indeed flightless birds exhibit smaller sterna and wings than flighted taxa in the same family along with wider pelves and more robust femora. Phylogenetic signal tests demonstrate that those differences are independent of phylogeny and instead demonstrate convergent morphological adaptation associated with a walking ecology. We found too that morphological variation was greater among flightless rails than flighted ones, suggesting that relaxation of physiological demands during the transition to flightlessness frees morphological traits to evolve in response to more varied ecological opportunities.</p>
A fence-rail from Bharhut
This carving, from a stupa at the site of Bharhut in northwest India, provides an early document of Buddhist faith and art. Images of a man and a woman are repeated three times to represent their movement as they walk around the stupa and kneel before it in worship. Flying celestial beings above bear offerings of flower garlands with which to adorn the dome of the stupa. Hand prints on the stupa's base indicate worship by ritual touching. https://www.freersackler.si.edu/object/F1932.26/ Source: Objaverse 1.0 / Sketchfab
Brennan Rail - Cliffe
Derelict launch rail for Brennan torpedo at Cliffe fort on the Thames estuary, Kent, England. Model of Cliffe Fort showing the context here : https://skfb.ly/opnNV Source: Objaverse 1.0 / Sketchfab
Class 97 Network Rail 97302
Class 97 Netword Rail 97302 Modelled in Blender 3.1.2 **** Not Presented as Game Ready **** If you are into your trains, checkout this youtube channel. It's been so helpful to me with my models. https://www.youtube.com/channel/UCRqa3Y2GVMKLyoRmZ80Ep9w Source: Objaverse 1.0 / Sketchfab
Another rail crossing at Medemblik
Another of the few rail crossings at the train station at Medemblik. This one is a bit more interesting as you can see the bricks near to the actual tracks have a black deposit on them, along with being lower to the surrounding bricks. This is likely due to the water and steam coming from the steam locomotive that frequently uses this track to this day for tourist trips. Camera: Canon EOS M100 Photos: 103 Resolution: 6000x4000 Photogammetry processing was done on our custom Meshroom cluster. Hardware used was a Phenom II X6 1100T, R5 3600 (2x), R7 1700, R7 3700X, i3 4340, i5-8300H, GTX650, GTX660Ti BOOST, GTX1070, GTX1050Ti, GTX1650, RTX2070S, RTX2060. Source: Objaverse 1.0 / Sketchfab
Dataset for: Neutrons on Rails -- trans-regional monitoring of soil moisture and snow water equivalent
<p>Using the railway system for regular environmental monitoring could extend the measurement capability to trans-regional and nationwide scales. Cosmic-ray neutron detectors in trains respond to spatial patterns of water content in their environment. Three distinct real world experiments support a proof of concept for soil and snow water monitoring using trains on short and long-range tracks across Germany:</p> <ul> <li>Supplement S4: Data (raw and processed) for the train journey from Leipzig to Berlin.</li> <li>Supplement S5: Data (raw and processed) for the train journey from Dessau to Zerbst, the subsequent car-borne Rover measurements, and the TDR measurements.</li> <li>Supplement S6: Data (raw and processed) for the train journey from Garmisch-Partenkirchen to Munich to Leipzig.</li> </ul> <p>This is the dataset supplementing the corresponding GRL publication "Neutrons on Rails -- trans-regional monitoring of soil moisture and snow water equivalent", preprint available from: https://doi.org/10.1002/essoar.10507363.1</p>
King Rail and Common Moorhen egg pattern matching data
<p>This dataset is a combination of egg images and processed output on pattern matching of eggshell surfaces from the images using NaturePatternMatch (NPM), additional data extracted from NPM, and field data. The data are separated into four folders based on species and analyses performed. There are two folders of scaled single egg images, one for King Rails, Rallus elegans, and one for Common Moorhens, Gallinula chloropus chloropus. Each photograph is identified by year, clutch and egg identity. Most of the Common Moorhen eggs are numbered in the order of their laying sequence, and they are further identified by laying hen. The NMDS folder includes NPM matching output and clutch identities needed to perform NMDS and PERMANOVA analyses. It includes consolidated and organized output from NPM for each species as well as files with clutch names needed to create merged datasets for graphing. These data were used to perform non-metric multidimensional scaling (NMDS) and permutational analysis of variance (PERMANOVA). The Linear Discriminant Analyses folder includes field data (egg length, width, and identity), pattern data extracted from NPM, as well as the estimated proportion of pigment measured within a scaled oval on binary images of eggs. This was used as a proxy for the relative amount of pigmentation on each egg. These data were used to conduct linear discriminant analyses for each species.</p>
Big data analytics in support of the under-rail maintenance management at Vitória – Minas Railway
<p>This video describes an ongoing study using data collected by an instrumented ore car on Vitória–Minas Railway, operated by Vale in Brazil. The research uses big data analysis methods over collected data by the instrumented car during its voyages. Railway geometry issues can cause undesirable movements on the wagons that can cause discomfort for passengers or instability for the cargo. In the worst scenario, derailments can occur. Each second, several sensors installed on the instrumented car collect data about velocity, acceleration, and movements on the wagon. The volume of collected data is impressive since the railway has about 2,000 km of extension. That volume compels us to use big data analytics methods. As the result of the research, the team aims to establish some levels of operational conditions, named as severity indexes, which can indicate to the maintenance teams the necessity of intervention on the railway.</p>
Safety of Vulnerable Road Users (VRU's) in Light-Rail Transit (LRT) Environment
<p>Light-rail transit (LRT), which includes modern streetcars, trolleys, and heritage trolleys, is one of the fastest growing modes of public transportation in the United States. To reduce the cost and complexity of construction, most LRT systems have their tracks placed on city streets, in medians, or in separate at-grade rights-of-way with at-grade crossings. Operating light-rail vehicles (LRVs) along these alignments introduces new conflicts and increases the risk of collisions with vulnerable road users (VRUs) including pedestrians, bicyclists, and electric scooter riders.<br> This study has two main objectives: (1) to review and evaluate the existing body of knowledge and the state of practice regarding safety of VRUs in LRT environments; and (2) to synthesize this information and package the results in a “Best Practices Resource Guide” and a companion “PowerPoint Presentation” for use in improving the safety of VRUs in existing LRT systems and advancing the professional capacity of transit workforce. Metropolitan Planning Organizations and State DOTs should also benefit from this resource information in the planning and design of new LRT systems.<br> This report presents a wide range of physical, educational, and enforcement treatments for improving the safety of VRUs in LRT environments. The selection of a particular treatment for use at an LRT grade crossing or station should be based on an engineering study whose scope and complexity depend on local conditions. Factors that should be considered during device selection include 1) pedestrian‐LRV collision experience, 2) pedestrian volumes and peak flow rates, 3) train speeds, frequency of trains, number of tracks, and railroad traffic patterns, 4) sight distances available to pedestrians and LRV operators approaching the crossing, and 5) skew angle, if any, of the crossing relative to the LRT tracks.</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.