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 “rails”
Amarāvatī, Andhra Pradesh. Detail of a railing pillar from Amarāvatī.
<p><a href="https://discover.libraryhub.jisc.ac.uk/search?q=author:%20Tripe,%20Linnaeus">Tripe, Linnaeus, </a>Amarāvatī, Andhra Pradesh. Sculptures from Amarāvatī in Madras; the sculpture is now in the British Museum registered under the number 1880,0709.1.</p>
Udayagiri, Madhya Pradesh. Fragment of railing pillar upright with half-lotus design.
<p>Udayagiri, Madhya Pradesh. Fragment of railing pillar upright with half-lotus design, found to the immediate east of the ridge and central passage; probably early centuries BCE.</p>
High Speed Rail Seismic Observation in Baoding, Hebei Province of China
<p>H5 files includes all train events collected in the observation. Raw data in sac format is too big (600GB) to upload.</p> <p>To access all continuous data, please contact shiyxg@mail.iggcas.ac.cn/wenjc@pku.edu.cn/njy@pku.edu.cn</p> <p> </p> <table> <tbody> <tr> <td>File names</td> <td>Format</td> <td>date</td> </tr> <tr> <td>BSPK095*</td> <td>-100s-100s, dt=0.01</td> <td>0424-0504</td> </tr> <tr> <td>BSPKU87*</td> <td>-100s~100s, dt=0.01</td> <td>0510-0518</td> </tr> <tr> <td> <p>hsr_coor_stacked_201804*</p> </td> <td>stacked traces in different frequency bands</td> <td>0424-0504</td> </tr> <tr> <td> <p>hsr_coor_stacked_201805*</p> </td> <td>stacked traces in different frequency bands</td> <td>0510-0518</td> </tr> <tr> <td> <div>coor_all_201804_YNPK_CZ_158.npy</div> </td> <td> <p>ambient noise results at night, between 158 stations</p> <p>(S158_1804.txt)</p> </td> <td>0424-0504</td> </tr> <tr> <td> <div>coor_all_201805_YNPK_CZ_143.npy</div> </td> <td> <p>ambient noise results at night, between 143 stations</p> <p>(S143_18045txt)</p> </td> <td>0510-0518</td> </tr> <tr> <td> <div>1804_YNPK_CZ_f0.2_20_all_night.h5</div> </td> <td> <p>Continuous data at 13 nights of 4 stations for stability comparsion</p> </td> <td>0424-0504</td> </tr> <tr> <td> <div>H1.h5</div> </td> <td> <p>Correlation results of array in Baoding, 2023 March.</p> </td> <td>2023/0311-0328</td> </tr> </tbody> </table>
The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh
<p><strong>Note: Downloading the large data file could have a timeout issue. If you cannot directly download it here, please use the following link as a complementary method for getting the data. </strong></p> <p><a href="https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing">https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing</a></p> <p> </p> <p>This dataset contains the dynamic responses (acceleration records) of two passenger trains with corresponding GPS positions, environmental conditions and track maintenance schedules for a light rail network in the city of Pittsburgh, Pennsylvania in the United States of America.</p> <p>In particular, two light rail vehicles were instrumented (identified as LRV4306 and LRV4313): <br> LRV 4306 has 5 acceleration channels, corresponding to the two uni-axial accelerometers inside the train and the three channels of the tri-axial accelerometer on the wheel truck.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5<br> - Corresponding sensor channels: tri-axial x, tri-axial y, tri-axial z, front cabinet uni-axial, back cabinet uni-axial</em></p> <p><br> LRV 4313 has 8 acceleration channels, corresponding to the two uni-axial accelerometer and the two tri-axial accelerometers inside the train.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5, 6, 7, 8<br> - Corresponding sensor channels: front cabinet uni-axial, back cabinet uni-axial, front tri-axial x, front tri-axial y, front tri-axial z, back tri-axial x, back tri-axial y, back tri-axial z.<br> - x longitudinal (vehicle moving direction); y-axis, transverse; z-axis, vertical.</em></p> <p>The dataset contained in this repository is a condensed version of the original raw data. While the accelerometers on the train were sampled continuously, this dataset contains only those measurements for when the train was actually moving along the track (i.e. not idling at a terminal).</p> <p>The data is stored in binary MAT-files (a MATLAB/Octave data format). These files contain MATLAB objects of the class "pass", which is defined in the file pass.m that can be found in the "code" folder. Specifically, two MAT-files named "obj_dic.mat", and found in the "LRV4306" and "LRV4313" folders, contain the "pass" objects of the two trains, respectively.</p> <p>Each category is described in detail. For more detail on the regions of the track, refer to the 'region.fig' file in this folder. The track was divided into distinct regions so that the data over specific sections of track could be compared. These regions were chosen for two reasons: <br> (1) within a region, the train always followed the same track and <br> (2) there are no tunnels in them so the GPS data is relatively consistent. </p> <p>To get started, using MATLAB or Octave try running "main_script.m" in the "code" folder.</p> <p>A data descriptor paper with details of the data collection process was published.</p> <p>Please cite as</p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Scientific Data, 6, 146. <a href="https://doi.org/10.1038/s41597-019-0148-9">https://doi.org/10.1038/s41597-019-0148-9</a>(2019)</strong></p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Zenodo, <a href="https://doi.org/10.5281/zenodo.1432702">https://doi.org/10.5281/zenodo.1432702</a>(2018).</strong></p> <p>For questions or suggestions please e-mail Jingxiao Liu <liujx@stanford.edu></p>
First genetic data for the Critically Endangered Cuban endemic Zapata Rail Cyanolimnas cerverai, and the taxonomic implications
<p>Data associated with the publication First genetic data for the Critically Endangered Cuban endemic Zapata Rail <em>Cyanolimnas cerverai</em>, and the taxonomic implications.</p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Granular Material Candidates
<p>A database of 14 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
Data associated with the publication "The origin of the world's smallest flightless bird, the Inaccessible Island Rail Atlantisia rogersi (Aves: Rallidae)"
<p><strong>DESCRIPTION OF FILES</strong><br> These are files including data and additional results, that support the paper "The origin of the world's smallest flightless bird, the Inaccessible Island Rail Atlantisia rogersi (Aves: Rallidae)", by Stervander et al. 2018, published in Molecular Phylogenetics and Evolution (doi: 10.1016/j.ympev.2018.10.007).</p> <p>The phylogenetic analyses focus on rails (Aves: Rallidae) and outgroups based on (1) a dataset, 'MtProt' comprising the coding sequences (cds) from full mitochondrial genome assemblyes, and (2) a mixed-marker dataset, '2Nc3Mt', comprising the mitochondrial markers cytochrome <em>b</em> (cyt<em>b</em>), cytochrome oxidase subunit I (COI), and 16S ribosomal RNA (16S), and the nuclear markers β-fibrinogen intron 7 (bFib7) and recombination activating gene 1 (RAG1). The latter dataset i largely based on data from Garcia-R et al. (2014), with additions of the Inaccessible Island Rail <em>Atlantisia rogersi</em> and some further sequences (see our paper).</p> <p>Trees mentioned in our paper as "results not shown" can be found below.</p> <p><br> <strong>This deposition contains five groups of data:</strong><br> 1. Beast input xml files for phylogenetic analyses<br> 2. Beast output: log files<br> 3. Beast output: raw tree files<br> 4. Beast output: Maximum Clade Credibility trees<br> 5. Tree figures (pdf format)</p> <p><strong>The above are available for the following analyses:</strong><br> A. Mixed-marker dataset ‘2Nc3Mt’, one tree <br> B. Mixed-marker dataset ‘2Nc3Mt’, one tree; Micropygia schomburgkii excluded<br> C. Mixed-marker dataset ‘2Nc3Mt’, separate mitochondrial (‘3Mt’) and nuclear marker trees (RAG1 and bFib7)<br> D. Protein coding dataset ‘MtProt’ from entire mitochondrial genomes</p> <p>The files are thus the following, sorted according to dataset:<br> A1 Beast_input_2Nc3Mt_1tree.xml<br> A2 Beast_output_2Nc3Mt_1tree.log<br> A3 Beast_output_2Nc3Mt_1tree.raw.trees<br> A4 Beast_output_2Nc3Mt_1tree.max_clade_cred_burnin10M.trees<br> A5 Tree_2Nc3Mt_1tree.max_clade_cred_burnin10M.pdf<br> B1 Beast_input_2Nc3Mt_exclMicropygia_1tree.xml<br> B2 Beast_output_2Nc3Mt_exclMicropygia_1tree.log<br> B3 Beast_output_2Nc3Mt_exclMicropygia_1tree.raw.trees<br> B4 Beast_output_2Nc3Mt_exclMicropygia_1tree.max_clade_cred_burnin10M.trees<br> B5 Tree_2Nc3Mt_exclMicropygia_1tree.max_clade_cred_burnin10M.pdf<br> C1 Beast_input_2Nc3Mt_separate_trees.xml<br> C2 Beast_output_2Nc3Mt_separate_trees.log<br> C3 Beast_output_2Nc3Mt_RAG1.raw.trees<br> C3 Beast_output_2Nc3Mt_bFib7.raw.trees<br> C3 Beast_output_2Nc3Mt_mt.raw.trees<br> C4 Beast_output_2Nc3Mt_RAG1.max_clade_cred_burnin10M.trees<br> C4 Beast_output_2Nc3Mt_bFib7.max_clade_cred_burnin10M.trees<br> C4 Beast_output_2Nc3Mt_mt.max_clade_cred_burnin10M.trees<br> C5 Tree_2Nc3Mt_RAG1.max_clade_cred_burnin10M.trees.pdf<br> C5 Tree_2Nc3Mt_bFib7.max_clade_cred_burnin10M.trees.pdf<br> C5 Tree_2Nc3Mt_mt.max_clade_cred_burnin10M.trees.pdf<br> D1 Beast_input_MtProt_1tree.xml<br> D2 Beast_output_MtProt_1tree.log<br> D3 Beast_output_MtProt_1tree.raw.trees<br> D4 Beast_output_MtProt_1tree.max_clade_cred_burnin1M.trees<br> D5 Tree_MtProt_1tree.max_clade_cred_burnin1M.pdf</p> <p>Or, sorted according to file type:<br> 1A Beast_input_2Nc3Mt_1tree.xml<br> 1B Beast_input_2Nc3Mt_exclMicropygia_1tree.xml<br> 1C Beast_input_2Nc3Mt_separate_trees.xml<br> 1D Beast_input_MtProt_1tree.xml<br> 2A Beast_output_2Nc3Mt_1tree.log<br> 2B Beast_output_2Nc3Mt_exclMicropygia_1tree.log<br> 2C Beast_output_2Nc3Mt_separate_trees.log<br> 2D Beast_output_MtProt_1tree.log<br> 3A Beast_output_2Nc3Mt_1tree.raw.trees<br> 3B Beast_output_2Nc3Mt_exclMicropygia_1tree.raw.trees<br> 3C Beast_output_2Nc3Mt_RAG1.raw.trees<br> 3C Beast_output_2Nc3Mt_bFib7.raw.trees<br> 3C Beast_output_2Nc3Mt_mt.raw.trees<br> 3D Beast_output_MtProt_1tree.raw.trees<br> 4A Beast_output_2Nc3Mt_1tree.max_clade_cred_burnin10M.trees<br> 4B Beast_output_2Nc3Mt_exclMicropygia_1tree.max_clade_cred_burnin10M.trees<br> 4C Beast_output_2Nc3Mt_RAG1.max_clade_cred_burnin10M.trees<br> 4C Beast_output_2Nc3Mt_bFib7.max_clade_cred_burnin10M.trees<br> 4C Beast_output_2Nc3Mt_mt.max_clade_cred_burnin10M.trees<br> 4D Beast_output_MtProt_1tree.max_clade_cred_burnin1M.trees<br> 5A Tree_2Nc3Mt_1tree.max_clade_cred_burnin10M.pdf<br> 5B Tree_2Nc3Mt_exclMicropygia_1tree.max_clade_cred_burnin10M.pdf<br> 5C Tree_2Nc3Mt_RAG1.max_clade_cred_burnin10M.trees.pdf<br> 5C Tree_2Nc3Mt_bFib7.max_clade_cred_burnin10M.trees.pdf<br> 5C Tree_2Nc3Mt_mt.max_clade_cred_burnin10M.trees.pdf<br> 5D Tree_MtProt_1tree.max_clade_cred_burnin1M.pdf</p> <p><strong>Note about the tree figures (pdf format): </strong>Nodes marked with a black circle are supported by a posterior probability (PP) of 1.0, for lower PP the number is given at the node. Blue bars represent the 95% highest posterior density intervals of the node age. MYA = Million years ago.</p> <p>/Martin Stervander (martin@stervander.com)</p>
Pauni (पौनि Maharashtra). Railing pillar (OBNAG0008) from a stūpa, donative inscription of Nāga
<p>Pauni (पौनि Maharashtra). Railing pillar (OBNAG0008) from a stūpa, donative inscription of Nāga</p>
Simulation data for eddy current rail testing - simulation accuracy and evaluation uncertainty quantification
<p>This dataset serve to quantify the simulation error and the evaluation uncertainties in the context of eddy current rail testing. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection) with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. For an analysis see the article below.</p>
Data from: First records of complete annual cycles in water rails Rallus aquaticus show evidence of itinerant breeding and a complex migration system
<p>In water rails <em>Rallus aquaticus</em>, northern and eastern populations are migratory while southern and western populations are sedentary. Few details are known about the annual cycle of this elusive species. We studied movements and breeding in water rails from southernmost Norway where the species occurs year-round. Colour-ringed wintering birds occurred only occasionally at the study site in summer, and vice versa. Geolocator tracks revealed that wintering birds (n = 10) migrated eastwards in spring to breed on both sides of the Baltic Sea, whereas a single breeding bird from the study site wintered in north Italy. Ambient light records of geolocator birds further indicated that all but one incubated 2–4 clutches per season. By combining information on incubation and movement, we found evidence for itinerant breeding in three individual birds: After a first breeding attempt (one did not incubate), all moved 129–721 km to breed again. This behaviour is rarely recorded in birds and was unexpected because the water rail is described as monogamous with both parents caring for eggs and chicks. The study greatly improves our knowledge about the annual cycle and reproduction in water rails. However, more studies are warranted to evaluate the generality of our findings and causes of breeding itinerancy.</p>
Evolutionary history of the Galápagos Rail revealed by ancient mitogenomes and modern samples
<p>Beast v. 2.6.3 input (<em>.xml</em>) files and output (<em>.log</em> and <em>.trees</em>) files for phylogenetic analyses of rails, used to determined the evolutionary history of the Galápagos Rail <em>Laterallus spilonota</em>. There are two main datasets: coding sequences of the mitochondrial genome ('mtCDS'), partitioned per codon position, and a two mitochondrial/one nuclear marker dataset ('2mt1nc'). For each of the datasets, separate runs have been made in which the fossil calibration of Rallidae is applied to the stem of the present-day family ('calRallidaeStem') or the crown node ('calRallidaeCrown), and finally all runs have been replicated with three different starting seeds ('seed_NNNNNNNNN', with the different seeds 123456789, 456789123, and 789123456).</p> <p>We provide raw output (<em>.log</em> and <em>.raw.trees</em>) as well as maximum clade credibility ('mcc') trees (<em>.mcc.trees</em>), calculated after discarding 10% of the trees as burn-in, using median ('heights_median') or mean ('heights_mean') node heights as estimated node age.</p> <p>The runs used for Table 1 (and Figure 2) in the accompanying paper are:</p> <ul> <li>Dataset mtCDS, Rallidae calibration of stem: seed 123456789</li> <li>Dataset mtCDS, Rallidae calibration of crown: seed 456789123 </li> <li>Dataset 2mt1nc, Rallidae calibration of stem: seed 789123456</li> <li>Dataset 2mt1nc, Rallidae calibration of crown: seed 123456789</li> </ul> <p>This version of the data includes <em>Pellornis mikkelseni</em> among the fossils making up the calibration distribution for crown Gruiformes. In a previous version of this data deposit, that data point was represented by <em>Messelornis cristata </em>(see accompanying paper).</p>
Fig. 1 in Morphoecological Peculiarities Of Pelvis In Several Genera Of Rails With Some Notes On Systematic Position Of The Coot, Fulica Atra (Rallidae, Gruiformes)
Fig. 1. Pelves of studied birds (not to the scale): 1 — Crex crex; 2 — Phasianus colchicus; 3 — Rallus aquaticus (in lateral view); 4 — Gallinula chloropus (in lateral view); 5 — Fulica atra; 6 — Anas querquedula; 7 — Phalacrocorax carbo. N o t e. a — in dorsal view; b — in lateral view; is — ischium, il — ilium, p — pubis, ac — acetabulum, pr dl — processus dorsolateralis.
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>
Container flows on road, rail and waterways along Rhine-Alpine corridor (Rhine section) at NUTS-2 level with cost-time-emissions estimates and accessibility-frequency-availability of modes
<p>The present dataset is used to estimate the heterogeneous mode choice preferences of shippers, that are presented in the following article :<br> "A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data"<br> (Nicolet, A., Negenborn, R. R. & Atasoy, B., A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data. IEEE Open Journal of Intelligent Transportation Systems, Vol. 3, 2022, pp. 650-661.)</p>
IN00012 Sanchi Stone Rail Inscription of the Time of Candragupta II
<p>Bhandarkar, Devadatta Ramakrishna, Bahadur Chand Chhabra, and Govind Swamirao Gai, <em>Inscriptions of the Early Gupta Kings</em> (New Delhi: Archaeological Survey of India, 1981): 250-252.</p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Sander Position & Angle Aimed at Nip & Wheel
<p>A database of 5 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Sander Position & Angle Aimed at Rail
<p>A database of 4 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
Fig. 1 in A New Feather Mite Species of the Genus Metanalges (Acariformes: Analgidae) from the Okinawa Rail, Hypotaenidia okinawae (Gruiformes: Rallidae), in Okinawa Island, Japan
Fig. 1. Metanalges agachi sp. n., male, MPM Coll. No. 25251 (A) and female, MPM Coll. No. 25252 (B), ventral views. Scale bars: 50 µm.
Dataset: Rail Vision Ltd. (RVSN) 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.
Dataset: Rail Vision Ltd. (RVSNW) 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.
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.