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79 results for “pedestrians”
Mobile Service Robots Crash Testing with Pedestrians: Safety Assessment with Child and Adult Dummies
<p>Data published with the manuscript: “<em>Estimating risks posed by personal mobility devices and service robots to pedestrians: comparative crash testing of adult versus child dummies</em>”. 2021 (Paez-Granados & Billard, 2021)<br> <strong>Summary:</strong></p> <p>This dataset contains injury measures during collisions between a mobile service robot - Qolo - (Paez-Granados, et al, 2018) and pedestrian dummies: male adult Hybrid-III (H3) and child model 3-years-old (Q3). We present multiple collision scenarios for the assessment of pedestrian safety, considering possible impacts at the legs for adult pedestrians, and legs, chest and head for children. In these tests, we followed known methods of safety analysis used in car crash testing and used a standing wheelchair robot "Qolo" as a representative system of mobile service robots, such as delivery bots (robot without occupant), person carrier robots, autonomous wheelchairs, standing mobility vehicles, and other transport robots expected to operate in pedestrian and public areas.</p> <p>The robot was equipped with an experimental front structure allowing different bumper heights and measurement of reaction forces. On the other hand, the human dummies were equipped with standard instrumentation calibrated in accordance with SAE J211-1 for impact tests, thus, the child dummy, Q3 provided head accelerations, neck forces and moments, chest deflections, and accelerations; and pelvis accelerations. The dummy H3 provided forces and moments at the tibia and femur, and accelerations at the pelvis, chest, and head. You will find scripts to read and plot the data, as well as, analysis of the injury risk based on standard crash testing metrics: Head Injury Criteria (HIC-15), head acceleration (a_3ms), Neck Injury (Nij), Chest deflection (CD), and tibia injury (TI).</p> <p><strong>Instructions: </strong></p> <p><em>This dataset contains the following main files:</em></p> <ol> <li><strong><em>Data Description.pdf</em>: </strong>Highly recommended to read through this file for understanding the setup of the collected dataset, as well as, the submitted manuscript.</li> <li><em><strong>collision_test_rawdata.zip</strong>: </em>This file contains all the raw data for each sensor as mentioned in table 3, organized in independent subfolders as described in table 2.<em> ‘test_name’/01_values/’testName’_CFC1000.xlsx</em></li> <li><em><strong>collision_test_analysis.zip</strong>: </em>This file contains all the processed data for each sensor in order to apply known injury metrics (Nij, HIC15, acc_3ms, TI, CC, VCI), organized in independent subfolders as described in table 2.<em>‘test_name’/01_values/’testName’_Analysis_v2.xlsx --> </em>Dataset with filtered sensor data accordingly to SAEJ21.</li> <li><em><strong>collision_data_matlab_structure.zip</strong>:</em><em> Matlab containers with all data - also available as .mat files for easy reading from Code Ocean capsule.</em></li> <li><em><em><strong>scripts-crash-test-service-robots.zip</strong>:</em> processing of the dataset is provided in this file with structure of data in Matlab containers and scripts for visualizing the data (see section III), further analysis scripts in the linked GitHub: <a href="https://github.com/epfl-lasa/crash-tests-service-robots">https://github.com/epfl-lasa/crash-tests-service-robots</a></em></li> </ol>
PROTECT project second RAW inertial data for pedestrian inertial localisation (ORDP initiative)
<p><strong>Contact person(s)</strong>: Enrico de Marinis</p> <p><strong>Data collector(s)</strong>: Enrico de Marinis. Fabrizio Pucci, Michele Uliana</p> <p><strong>Data curator(s)</strong>: Guido Rosi</p> <p><strong>Work package leader(s)</strong>: Fabrizio Pucci; Fabio Andreucci</p> <p><strong>Content</strong></p> <p>Inertial Measurement Unit raw data in TXT open and readable format, to be used for processing and testing the pedestrian dead reckoning algorithms by the inertial and indoor tracking scientific community.</p> <p>The raw inertial data have been collected and made publicly available in the frame of the SME Phase 2 project PROTECT (820867), co-funded by the European Commission</p> <p><strong>Experimental data</strong></p> <p>The publicly shared archive contains the following, distinct datasets:</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_143538_000002_000003_008.decod.grz; collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_150213_000024_000024_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_153840_000007_000024_005.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_155152_000024_000003_010.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_113457_000024_000004_006.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_141622_000007_000007_003.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_153554_000007_000007_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> </ul> <p><strong>Images of the experimental data</strong></p> <p>For each of the above data files, the image of the corresponding PDR (Pedestrian Dead Reckoning) processed track has been added as a geo-referenced JPG capture overlaid on the location satellite image. The image file name is the same as the corresponding data file.</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.jpg</li> <li>RawData_20200729_143538_000002_000003_008.decod.jpg</li> <li>RawData_20200729_150213_000024_000024_004.decod.jpg</li> <li>RawData_20200729_153840_000007_000024_005.decod.jpg:</li> <li>RawData_20200729_155152_000024_000003_010.decod.jpg</li> <li>RawData_20200730_113457_000024_000004_006.decod.jpg</li> <li>RawData_20200730_141622_000007_000007_003.decod.jpg</li> <li>RawData_20200730_153554_000007_000007_004.decod.jpg</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.jpg</li> </ul> <p><strong>Open and Accessible Data format</strong></p> <p>The data format is the following</p> <p>gyro(x) gyro(y) gyro(z) acc(x) acc(y) acc(z) mag(x) mag(y) mag(z) temperature altitude</p> <p>x, y, z indicate the axes of the Inertial Measurement Unit</p> <p>gyro stands for the angular velocity and is in rad/s</p> <p>acc stands for the acceleration and is in m/s^2</p> <p>mag is the magnetic field and is in milligauss</p> <p>temperature is in °C</p> <p>altitude is the output of the altimeter and is expressed in meters</p> <p>All the samples, in all datasets have been recorded with a 200 Hz sampling frequency.</p>
Destination Choice Model including panel datausing WiFi localization in a pedestrian facility
<p>A minimal example of a destination choice model including panel data on EPFL campus. It is based on the output of Danalet<em> et al. </em>(2014)<em>.</em> </p> <p>It runs on Pythonbiogeme. Some variables are removed from the dataset due to privacy issues. Thus, some parameters may not be significant.</p>
A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures: Data (Transp. Res. Part C, 2014)
<p>This dataset contains and describes the data used in</p> <p>Danalet, A., Farooq, B., & Bierlaire, M. (2014). A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures. <em>Transportation Research Part C: Emerging Technologies</em>, <strong>44</strong>, 146-170. doi:10.1016/j.trc.2014.03.015</p> <p>Specifically it contains WiFi traces, pedestrian Semantically-Enriched Routing Graph (SERG), and Potential Attractivity measure (PAM).</p>
Data-driven physics-based modeling of pedestrian dynamics - dataset: Pedestrian trajectories at Eindhoven train station
<p>Pedestrian trajectories measured at train station Eindhoven Centraal (the Netherlands) on platform 2 with acces to tracks 3 and 4.</p> <p>The dataset is partitioned in files containing 10 consecutive days each, recording 4 data fields:</p> <ul> <li><strong>time_ms:</strong> Passed time since start of the measurements. Unit: milliseconds.</li> <li><strong>object_identifier:</strong> unique id identifying an object.</li> <li><strong>x_position_mm: </strong>coordinates of the object along the x-axis at the given time. Unit: millimeters.</li> <li><strong>y_position_mm:</strong> coordinates of the object along the y-axis at the given time. Unit: millimeters.</li> </ul> <p>Each object resembles a pedestrian on the train platform recorded with 10 frames per second. We deliberately removed exact date and time information for privacy reasons (see additional note). The data set consists of 60 consecutive days starting at an unkown time between 00:00 AM and 01:00 AM of a random date between April 1st and May 1st 2022. An overhead image of the platform is included showing train track 3 in the bottom and train track 4 in the top of the image.</p> <p>The data set is supplemented to the paper <a title="Data-driven physics-based modeling of pedestrian dynamics" href="https://doi.org/10.48550/arXiv.2407.20794" target="_blank" rel="noopener">Data-driven physics-based modeling of pedestrian dynamics</a> and can be processed by the associated <a title="Software: Data-driven physics-based modeling of pedestrian dynamics" href="https://github.com/c-pouw/physics-based-pedestrian-modeling" target="_blank" rel="noopener">Python implementation</a> to create pedestrian models. </p>
Dataset for Detecting Motorcyclists in Pedestrian Areas
<p>The dataset contains a total of 11361 images in .jpg format for a total of 0.99 GB, the resolution of the images is 1280x720 (HD), and the weight per image is 56. 7 KB, the images were obtained from a closed circuit television CCTV free public access located in the city of Medellín Colombia consisting of 80 cameras located on the road, these images were captured during both night and day hours, The time range in which they were acquired is from 04/10/2022 to 28/10/2022 a total of 24 days, the format of the name of each image is "image (camera number)_(date format"AAAAMMDD")_( time format "hhmmss"), where (AAAA) is the year, (MM) is the month, (DD) is the day, (HH) is the 24-hour hour, (MM) is the minute and (SS) is the second.</p> <p>The file (annotations.pickle) corresponds to a file in .pickle format that contains the labels made to each image and corresponds to a single main variable of dictionary type where its main key is the name of the image (image2_20221004_054300.jpg), and the sub keys are (box), which contains a list of lists of bounding boxes, (clippings), presents a list of arrays containing clippings made to the image, (class), which contains a list of integer values representing the class to which corresponds the nth bounding box or clipping.<br> </p>
Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology - Dataset
<p>This repository contains the distance matrices and code for the paper "Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology"</p>
PROTECT project RAW inertial data for pedestrian inertial localisation (ORDP initiative)
<p><strong>Content</strong></p> <p>Inertial Measurement Unit raw data in TXT open and readable format, to be used for processing and testing the pedestrian dead reckoning algorithms by the inertial and indoor tracking scientific community.</p> <p>The raw inertial data have been collected and made publicly available in the frame of the SME Phase 2 project PROTECT (820867), co-funded by the European Commission</p> <p> </p> <p><strong>Experimental data</strong></p> <p>The publicly shared archive contains the following, distinct datasets:</p> <ul> <li>RawData_5_2_20191113_180029.decod: collected in Rome (Italy) with the DUNE foot-mounted sensor unit n. 5 on November 13, 2019, in the frame of the project WP3 activities (system scale-up design); aprox. Duration, 30 mins.</li> <li>RawData_6_1_20191112_170005.decod; collected in Roma (Italy)with the DUNE foot-mounted sensor unit n. 6 on November 12, 2019, in the frame of the WP3 activities (system scale-up design), aprox. Duration, 30 mins.</li> <li>RawData_008_01_20191130_162759.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on November 30, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_02_20191201_125122.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on December 1<sup>st</sup>, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_03_20191201_153053.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on December 1<sup>st</sup>, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_06_20191120_182949.decod: collected in Roma (Italy)with the DUNE foot-mounted sensor unit n. 8 on November 20, 2019, in the frame of the WP3 activities (system scale-up design), aprox. Duration, 30 mins. The experiment has a mix of walk and fast run.</li> </ul> <p> </p> <p><strong>Open and Accessible Data format</strong></p> <p>The data format is the following</p> <p>gyro(x) gyro(y) gyro(z) acc(x) acc(y) acc(z) mag(x) mag(y) mag(z) temperature altitude</p> <p> </p> <p>x, y, z indicate the axes of the Inertial Measurement Unit</p> <p>gyro stands for the angular velocity and is in rad/s</p> <p>acc stands for the acceleration and is in m/s^2</p> <p>mag is the magnetic field and is in milligauss</p> <p>temperature is in °C</p> <p>altitude is the output of the altimeter and is expressed in meters</p> <p>All the samples, in all dataset have been recorded with a 200 Hz sampling frequency.</p>
Livorno, Urban driving, Pedestrian detection
<p><strong>Scenario description</strong>:</p> <p>The pedestrian detection use case aims to demonstrate the possibility for a vehicle to be informed, using V2X communication, of the presence of a pedestrian crossing the road when the traffic light is green for the vehicle. In case of presence of a pedestrian, the RSU with detection capabilities signals the presence of the pedestrian to the vehicles using DENM messages.</p> <p><strong>Session description</strong>:</p> <p>Pedestrian detection</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Drining in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces: Video Results
<p>A video illustrating the results presented in the paper: <em>"Prédhumeau M., Mancheva L., Dugdale J., and Spalanzani A. 2021. An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces. In the Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). IFAAMAS, Online."</em></p> <p> </p>
RoadTrafficMARKS: Dataset of 10057 images (256x256 pixels) of Curved arrow, Straight arrow, Pedestrian crossing, Straight-right merge arrow, Merge arrow, Left arrow, Yield, Stop, Right arrow, Straight-left merge arrow, Speed limit and Straight-right-left merge arrow markings and their BoundingBox labels
<p><strong>The dataset consists of 10057 PNG images (256x256 pixels) of </strong><strong>high resolution aerial orthoimages </strong><strong>taggged with </strong><strong>twelve classes of traffic signals</strong><strong>: (1) Curved arrow, (2) Straight arrow, (3) Pedestrian crossing, (4) Straight-right merge arrow, (5) Merge arrow, (6) Left arrow, (7) Yield, (8) Stop, (9) Right arrow, (10) Straight-left merge arrow, (11) Speed limit and (12) Straight-right-left merge arrow markings, together with their corresponding Bounding Boxes. The dataset has been created in the framework of the SROADEX project to train an identification process based on artificial neural networks.</strong></p> <p><strong>The dataset was created by manually tagging the twelve class of marks on orthoimage tiles of 256x256 pixels </strong><strong><strong>with the LabelMe tool</strong>. After the semantic labeling (manual digitalization of the contour of the signals), a transformation and random splitting process has been carried to prepare the data for the neural networks</strong><strong><strong> training</strong>. It resulted in 80% of the images for training (8031), 10% for validation (1000) and 10% for testing (1021).</strong></p> <p><strong>The next table presents the number of images of each class on the </strong><strong><strong>"train", "valid" and "test" </strong>sets.</strong></p> <table> <tbody> <tr> <td> </td> <td>Train</td> <td>Valid</td> <td>Test</td> <td>Total</td> </tr> <tr> <td>CURVED ARROW</td> <td>319</td> <td>43</td> <td>48</td> <td>410</td> </tr> <tr> <td>STRAIGHT ARROW</td> <td>6730</td> <td>806</td> <td>843</td> <td>8379</td> </tr> <tr> <td>PEDESTRIAN CROSSING</td> <td>3881</td> <td>465</td> <td>485</td> <td>4831</td> </tr> <tr> <td>STRIGNT-RIGHT MERGE ARROW</td> <td>1373</td> <td>187</td> <td>177</td> <td>1737</td> </tr> <tr> <td>MERGE ARROW</td> <td>550</td> <td>91</td> <td>59</td> <td>700</td> </tr> <tr> <td>LEFT ARROW </td> <td>238</td> <td>33</td> <td>31</td> <td>302</td> </tr> <tr> <td>YIELD</td> <td>356</td> <td>47</td> <td>40</td> <td>443</td> </tr> <tr> <td>STOP</td> <td>141</td> <td>19</td> <td>15</td> <td>175</td> </tr> <tr> <td>RIGHT ARROW</td> <td>836</td> <td>93</td> <td>111</td> <td>1040</td> </tr> <tr> <td>STRIGNT-LEFT MERGE ARROW</td> <td>152</td> <td>19</td> <td>21</td> <td>192</td> </tr> <tr> <td>SPEED LIMIT</td> <td>392</td> <td>50</td> <td>45</td> <td>487</td> </tr> <tr> <td>STRIGNT-RIGHT-LEFT MERGE ARROW</td> <td>82</td> <td>9</td> <td>9</td> <td>100</td> </tr> </tbody> </table>
Data from: Prediction of Pedestrian Speed with Artificial Neural Networks
<p>Corridor data are trajectories of pedestrians in a closed corridor of lenght 30m and width 1.8m. The trajectories are measured on a section of length 6m. Experiments are carried out with N=15, 30, 60, 85, 95, 110, 140 and 230 participants.</p> <p>Bottleneck data are trajectories of pedestrian in a bottleneck of lenght 8m and width 1.8m. Experiments are carried out with 150 participants for bottleneck widths w=0.7, 0.95 1.2 and 1.8m.</p> <p>See http://ped.fz-juelich.de/experiments/2009.05.12_Duesseldorf_Messe_Hermes/docu/VersuchsdokumentationHERMES.pdf page 20 and 24 for details (in German). The data are part of the online database http://ped.fz-juelich.de/database.</p> <p>Column names of the file are: ID FRAME X Y Z.</p> <ul> <li>ID is the pedestrian ID.</li> <li>FRAME is the frame number (frame rate is 1/16s).</li> <li>X Y and Z are pedestrian position in 3D.</li> </ul>
Comparing the route-choice behavior of pedestrians around obstacles in a virtual experiment and a field study
<p>There is a great controversy about the application of virtual experiment in pedestrian routing behavior research. We conducted field observations and virtual experiment to study the route choice behavior of pedestrians. The route choice behavior around obstacles are compared qualitatively. The results shows that distance to the exit routes as well as the density around the exits show great influence on the pedestrians' route choice behavior while the speed of frontal pedestrians shows no obvious impact on the route choice. Pedestrians prefer to choose local closer exit or the exit with less occupants. The results of logistic regression show that the similar results can be obtained in virtual experiment and field observation qualitatively. This work can verify the validity of the virtual experiment for studying route choice behavior of pedestrians, which is of great importance for the application of virtual experiment.</p> <p>The files are trajectories of the recorded videos in the field observation.</p>
Data-driven physics-based modeling of pedestrian dynamics
<p>Python package to create physics-based pedestrian models from crowd measurements</p> <p>Github: <a href="https://github.com/c-pouw/physics-based-pedestrian-modeling">https://github.com/c-pouw/physics-based-pedestrian-modeling</a></p>
Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Study at the Fête des Lumières in Lyon
<p>We present one of the first comprehensive field datasets capturing dense pedestrian dynamics across multiple scales, ranging from macroscopic crowd flows over distances of several hundred meters to microscopic individual trajectories, including approximately 7,000 recorded trajectories.</p> <p>The dataset also includes a sample of GPS traces, statistics on contact and push interactions, as well as a catalog of non-standard crowd phenomena observed in video recordings. Data were collected during the 2022 Festival of Lights in Lyon, France, within the framework of the French-German <a href="We%20present%20one%20of%20the%20first%20comprehensive%20field%20datasets%20capturing%20dense%20pedestrian%20dynamics%20across%20multiple%20scales,%20ranging%20from%20macroscopic%20crowd%20flows%20over%20distances%20of%20several%20hundred%20meters%20to%20microscopic%20individual%20trajectories,%20including%20approximately%207,000%20recorded%20trajectories.%20%20The%20dataset%20also%20includes%20a%20sample%20of%20GPS%20traces,%20statistics%20on%20contact%20and%20push%20interactions,%20as%20well%20as%20a%20catalog%20of%20non-standard%20crowd%20phenomena%20observed%20in%20video%20recordings.%20Data%20were%20collected%20during%20the%202022%20Festival%20of%20Lights%20in%20Lyon,%20France,%20within%20the%20framework%20of%20the%20French-German%20MADRAS%20project,%20covering%20pedestrian%20densities%20up%20to%204%20individuals%20per%20square%20meter." target="_blank" rel="noopener">MADRAS project</a>, covering pedestrian densities up to 4 individuals per square meter.</p>
Dataset for Detecting Motorcyclists in Pedestrian Areas
<p>The file PedestrianDataSet.zip contains 2 directories and 1 file, the directory named images contains all the images captured; the directory named “etiquetas” contains all annotations done based on four classes ordered as [MMNAP, MMAP, MNAP, PCP], the name's correspondence with the label is [motorcycle with motorcyclist not in pedestrian area, motorcycle with motorcyclist in pedestrian area, motorcycle without motorcyclist in pedestrian area, pedestrian in crosswalk]; the file Distribution.png is an image that explain how the DataSet is distributed.</p> <p>The dataset contains a 6324 images in .jpg format for a total of 643.9MB, the resolution of each image is 1280x720 (HD), and the weight per image is under 120 KB, the images were obtained from a closed circuit television CCTV free public access located in the city of Medellín Colombia consisting of 80 cameras located on the road, these images were captured mainly during daylight hours. The format of the name of each image is "image (camera number)_(date format"AAAAMMDD")_( time format "hhmmss"), where (AAAA) is the year, (MM) is the month, (DD) is the day, (HH) is the 24-hour hour, (MM) is the minute and (SS) is the second.</p>
Data from: Breeding Sternula antillarum (Least Terns) disturbance distances and duration of escape behaviors: pedestrians necessitate larger conservation buffers than do passing vehicles
Open the record for dataset details and reuse information.
Simulation data used in "An attempt to distinguish physical and socio-psychological influences on pedestrian bottleneck"
<p>The dataset contains all simulated trajectory data used in the main text analysis of "Self-organisation phenomena in pedestrian bottleneck flow under varying corridor width". The data was simulated using JuPedSim. The source code used can be downloaded from: https://github.com/JonasRzez/jpsnewnoise.git</p>
A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures: Data (tech. report 2013)
<p>This dataset contains the data used in:</p> <p>Danalet, A., Farooq, B. and Bierlaire, M. (2013). A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures, Technical report, Transport and Mobility Laboratory, ENAC, Ecole Polytechnique Fédérale de Lausanne, Lausanne. URL: http://infoscience.epfl.ch/record/189759 (full text available)</p> <p>It contains data and a technical report describing</p> <ul> <li>WiFi traces</li> <li>Pedestrian Semantically-Enriched Routing Graph (SERG), and</li> <li>Potential Attractivity measure (PAM).</li> </ul>
Technical and user evaluation of mobile application for pedestrian safety
<p>This file contains the evaluation results of the tests carried out a Versailles during the Show project with external participants:</p> <ul> <li>Technical indicators have been collected in one sheet</li> <li>Feedbacks from the user questionnaires have been collected in one other sheet</li> </ul>
ScienceDex guides
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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.