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374 results for “traffic”
Synthetic dataset: Traffic Accidents & Vehicle Registry
<ul> <li><strong>Dataset A: Traffic Accidents</strong> -- Comprising 15,000 records with attributes such as Accident ID, Date, Time, Location, Vehicle ID, Severity, and Description.</li> <li><strong>Dataset B: Vehicle Registry</strong> -- Comprising 20,000 records with attributes including Vehicle ID, Owner Age, Owner Gender, Vehicle Model, and Registration Date.</li> </ul>
A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia
<p>This is the dataset for the NCST project <em>"A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia"</em> by the Georgia Tech research team.</p> <p> </p> <p>Here is the abstract of the research: </p> <p>In this study, a modeling framework for population exposure to traffic-related PM2.5 with high spatiotemporal resolution is proposed and applied to the I-575/I-75 Northwest Corridor (NWC) in Atlanta, GA, for environmental equity analysis. The analyses retrieved trip data from the Atlanta Regional Commission’s (ARC) Activity-Based Model 2020 (ABM2020), after implementing path retention algorithms (Zhao, et al., 2019) to generate individual travel paths for more than 20 million predicted vehicle trips. Emission rates for each link were retrieved from MOVES-Matrix given the ABM link speed and facility type, the ARC’s county-level fleet composition data, and regional fuel properties and I&M program parameters. High-resolution downwind concentration profiles were predicted using EPA’s AERMOD microscale dispersion model with AERMET meteorology profiles for a huge array of receptors. Trip-end locations were derived from the ABM trip data, and the on-road trajectories for each person-trip (vehicle trace data) were derived from the travel paths through network. ABM synthetic household and person data were used in demographic assessment, and linked to representative household latitude and longitude locations in the Epsilon 2019 household demographic dataset. Individual exposure to traffic-related PM2.5 in time and space (average hourly concentration) was assessed by overlaying the second-by-second person location profiles (for 24 hours) against the hourly predicted PM2.5 concentration profiles. The analyses summarize the results across 16 demographic groups and the aggregate population exposure are compared to assess potential impact differences across demographics. High-income households in the corridor were exposed to less traffic-related air pollution as they tended to live further from the freeways. The analyses did not reveal large disproportionate negative impacts on low income groups along this specific corridor, but lager disproportionate negative impacts are expected elsewhere in the metro area due to the spatial clustering of income groups along other corridors. Overall, the research demonstrates the applicability of the modeling framework and describes how the various elements (e.g., link screening, dispersion modeling, path tracing, etc.) are optimized on the supercomputing cluster.</p>
DDoS and host background traffic - Pcap traces
<p>This dataset includes Pcap files with DDoS and background traffic related to a host machine. Data can be used to test host-based DDoS detection solutions. DDoS traffic has been generated using the following tool: https://github.com/ricardojoserf/ddos simulation/tree/master</p> <p>Each archive include a different number of malicious IP addresses, specified in the .zip file name.</p> <p><br>If you use this dataset, please credit us by citing our paper:</p> <p><code>M. Zang, F. De Iaco, J. Wu, M. Savi, <em>In-Kernel Traffic Sketching for Volumetric DDoS Detection</em>, in IEEE International Conference on Communications (ICC), Jun. 2025</code></p> <p>If using LaTeX, you can use the following BibTeX:<br><br><code>@inproceedings{zang2025ebpfsketching,</code><br><code> title={In-Kernel Traffic Sketching for Volumetric DDoS Detection},</code><br><code> author={Zang, Mingyuan and De-Iaco, Federico and Wu, Jie and Savi, Marco},</code><br><code> booktitle={IEEE International Conference on Communications (ICC)},</code><br><code> year={2025},</code><br><code>}</code></p>
SUMMER-MUSTARD (Summer season Multi-cross Urban Signalized Traffic Aggregated Region Dataset)
<p>Dataset release for ECML-PKDD 2021 paper</p> <p><em>OBELISC: Oscillator-Based Modelling and Control using Efficient Neural Learning for Road Traffic Signal Offset Calculation</em></p> <p><em>Cristian Axenie, Daniele Foroni, Alexander Wieder, Mohamad Al Hajj Hassan, Paolo Sottovia, Margherita Grossi, Rongye Shi, Stefano Bortoli, Götz Brasche</em></p> <p>SUMMER-MUSTARD (Summer season Multi-cross Urban Signalized Traffic Aggregated Region Dataset) real-world dataset, contains 59 days of real urban road traffic data from 8 crosses in a city in China.<br> </p>
Data from: Stressful city sounds: glucocorticoid responses to experimental traffic noise are environmentally dependent
A major challenge in urban ecology is to identify the environmental factors responsible for phenotypic differences between urban and rural individuals. However, the intercorrelation between the factors that characterise urban environments, combined with a lack of experimental manipulations of these factors in both urban and rural areas, hinder efforts to identify which aspects of urban environments are responsible for phenotypic differences. Among the factors modified by urbanisation, anthropogenic sound, particularly traffic noise, is especially detrimental to animals. The mechanisms by which anthropogenic sound affects animals are unclear, but one potential mechanism is through changes in glucocorticoid hormone levels. We exposed adult house wrens, Troglodytes aedon, to either traffic noise or pink noise. We found that urban wrens had higher initial (pre-restraint) corticosterone than rural wrens before treatment, and that traffic noise elevated initial corticosterone of rural, but not urban, wrens. By contrast, restraint stress-induced corticosterone was not affected by noise treatment. Our results indicate that traffic noise specifically contributes to determining the glucocorticoid phenotype, and suggest that glucocorticoids are a mechanism by which anthropogenic sound causes phenotypic differences between urban and rural animals.
Madrid datasets (air quality, meteorological and traffic data) of 2019 and 2022
<p>The datasets consist of air quality, meteorological, and traffic data from January to June 2019 and from January to June 2022. The following are the features of the datasets: Nitrogen dioxide, Wind speed, Wind direction (in degrees), Temperature, Humidity, Pressure, Solar irradiance, Intensity, Occupancy time, Load, Average traffic speed, Wind direction (after converting to categorical data (north, northeast, east, southeast, south, southwest, west, northwest) and passing through One Hot Encoder). The following are the names of the above features (the names of the columns in the files): 'NO2', 'windSpeed', 'windDir', 'Temp', 'Humidity', 'Pressure', 'SolarRad', 'intensidad', 'ocupacion', 'carga', 'vmed', 'windDir_Categ_east', 'windDir_Categ_north', 'windDir_Categ_northeast', 'windDir_Categ_northwest', 'windDir_Categ_south', 'windDir_Categ_southeast', 'windDir_Categ_southwest', 'windDir_Categ_west'.</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2019_winddir.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2022_winddir.csv</a> include the above features for the defined grid in the city of Madrid for January-June 2019 and January-June 2022, respectively, with the following dimension: 4344×340×19: (January-June 2019); 4343 × 340 × 19 (January-June 2022).</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2019.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2022.csv</a> include the above features with the following dimension: 4344 × 24 × 19: (January-June 2019); 4343 × 24 × 19 (January-June 2022), extracted from the defined grid for cells only where air quality monitoring stations exist.</p>
Dataset literature review on cyberlaw and traffic
<p>Merupakan data dari litereature review tentang hukum cyber dan traffic </p>
A traffic graph dataset for autonomous driving research: Commonroad-Nuplan-Dataset
<p>We provide a standardized graph dataset for traffic based on the large-scale <a href="https://www.nuscenes.org/nuplan">NuPlan </a>v1.1 dataset, converted to a <a href="https://pytorch-geometric.readthedocs.io/en/latest/">PyTorch-Geometric </a>dataset using our <a href="https://commonroad.in.tum.de/tools/commonroad-geometric">CommonRoad-Geometric </a>tool. The dataset is collected from 3 megacities: Singapore, Boston and Pittsburgh.</p>
Vessel traffic observations and associated waterbird surveys, March 2020 – February 2021, Roberts Point and Ogden Point, Victoria, BC, Canada
<p>The coastal waters of southern British Columbia, Canada, encompass habitat of international conservation significance to coastal and marine birds, including sizeable areas designated in the early 1900s as Migratory Bird Sanctuaries (MBS) to protect overwintering waterfowl from hunting near urban centres. Two of these, Shoal Harbour (SHMBS) and Victoria Harbour (VHMBS), have seen significant marine infrastructure development in recent decades and experience considerable vessel traffic. We conducted a pilot study using shore-based observers to develop small-vessel baselines for the winter months when regional waterbird numbers are highest. During our surveys, we recorded considerable inter-site variability in vessel traffic characteristics, with one site (SHMBS) a source of nearly twice as many vessels in total as the other (VHMBS). For waterbirds, hourly 3-minute 'snapshot' surveys, combined with daily tallies of incidental observations, showed that community composition also varied by locality, with one site dominated by gulls (Laridae), cormorants (Phalacrocoracidae), and seaducks (Tribe Mergini), and the other by gulls, cormorants, and alcids (Alcidae). Our results demonstrate that fine-scale local variability must be taken into account when managing for vessel traffic disturbance of waterbirds, particularly at sites of high human population density and increasing coastal development. Results are in press in PLoS ONE (doi:10.1371/journal.pone.0283791).</p>
A Benchmark Dataset for Vision-based Bridge Traffic Load Monitoring in a Cable-stayed Bridge
<p>Traffic load monitoring based on deep learning and computer vision has garnered significant attention in bridge engineering worldwide. Unlike traditional traffic load monitoring systems, computer vision-based techniques can accurately extract the spatiotemporal load distribution across the entire bridge in an autonomous manner. However, many of the related studies in the literature used datasets that were collected from a few specific areas of different bridges, and there are very limited datasets that provide complete coverage of the entire bridge, making a detailed comparison of different methods difficult. This paper presents a benchmark dataset that provides a series of annotations and field measurements required for traffic load detection, tracking and continuous monitoring on the bridge. The dataset was collected by five cameras, and two weigh-in-motion systems installed on a cable-stayed bridge and is divided into three subsets. The first subset contains over 32,000 images and annotation files of eleven types of vehicle-related targets, which are necessary for the training of vehicle detection models. The second subset consists of photos of the calibration board and coordinates of reference points that are used for camera calibration. The last subset is designated for the field verification of various algorithms, providing synchronized vehicle weight data and monitoring videos covering the whole bridge. To the author’s knowledge, this dataset is the first open-source dataset for vision-based traffic load monitoring in a bridge, which will have tremendous value in promoting research in the area of innovative bridge health monitoring technologies.</p>
COVID-19 and Traffic Safety: Exploring Exposure, Crash Frequency and Severity, and Roadway and Network Design
<p>Early COVID-19 lockdowns in the first half of 2020 largely kept people at home, thereby reducing motor vehicle traffic levels. Theoretically, reduced traffic exposure should have resulted in reduced motor vehicle crashes. However, a variety of factors may have complicated this relationship. In order to better understand the impact of COVID-19 lockdowns on traffic safety outcomes, we explore fatalities, injuries, and total crashes before and during the lockdowns on both the national and state levels. We provide descriptive statistics and create negative binomial regressions exploring the role of vehicle, user, and built environment factors on traffic safety outcomes. Findings suggest that crash counts in Region 6 were 35%-50% lower in 2020 during the COVID-19 lockdowns. Crashes that occurred during the COVID-19 lockdowns were more likely to be more severe. Fatal pedestrian crashes across the U.S. decreased during COVID-19 (although not as much as overall fatal crashes) and fatal bicyclist crash counts increased. Drunk drivers were less prevalent in nationwide fatalities but more prevalent in overall Region 6 crashes. Overall, crashes were more likely single-vehicle fixed-object or rollover crashes involving unsafe speeds. In Texas, suburban areas saw the most crashes before and during COVID-19, although they also saw the greatest decrease. Rural Texas crashes were most likely to result in a fatality or serious injury, and that likelihood got worse during the COVID-19 lockdowns. While Texas freeways and arterials saw the largest decreases in crash counts, these functional classifications still had the most crashes. Urban interstates and rural local roads in Texas were notable because these two functional classifications actually saw increases in the number of fatal and serious injury crashes during COVID-19 lockdowns.</p>
Psychoacoustic indicators of pass-by road traffic noise
<p>This dataset contains files related to a measurement campaign of pass-by road traffic noise using the Statistical Pass-By (SPB) method according to ISO 11819-1 (2023) on road surfaces in hot mix asphalt. </p> <p>The compressed folder "Audio files, timestamps and calibration file" is divided into subfolders per measurement day, named according to the measurement date and location. Each subfolder contains continuous recordings as .wav audio files and Excel files with timestamps at 2 seconds before (t0) and 2 s after (tf) the moment a vehicle passage produced the peak noise level in that audio file, besides the air temperature (Tair) and vehicle category (Category). The pass-bys are classified into four categories (Passenger cars, Vans, Dual-axle heavy vehicle - HD and Multiple-axle heavy vehicle - HM).</p> <p>The file "Calibration file 1Hz 113.7 dB.wav" is a pure-tone 113.7 dB calibration file recorded from the sound level meter and microphone used in the measurements.</p> <p>The Excel file "Psychoacoustic indicators of pass-by road traffic noise.xlsx" presents nine acoustic and psychoacoustic indicators calculated from the 4s audio excerpts of 2199 pass-by vehicle noise obtained from this measurement campaign. Calculations were conducted using the algorithms implemented on PsySound3. The columns in this Excel file mean the following:</p> <ol> <li>L<sub>A,max</sub>: average maximum A-weighted sound level, in dB</li> <li>ΔL: the difference between the peak L<sub>A,max </sub>and the L<sub>A,max</sub> at 2 s before the peak was reached, in dB</li> <li>L<sub>A,eq</sub>: A-weighted equivalent continuous sound level, in dB</li> <li>L10: 10% percentile statistical noise level, in dB</li> <li>L90: 90% percentile statistical noise level, in dB</li> <li>N50: 50% percentile loudness, in sone</li> <li>S50: 50% percentile sharpness, in acum</li> <li>R50: 50% percentile roughness, in asper</li> <li>FS50: 50% percentile fluctuation strength, in vacil</li> </ol>
Data for internet traffic prediction using RNN
<p>Contains three .csv files which are from Wireshark captures. The device used for capturing was a MacBook Pro 2019:<br> <br> - Game streaming data captured using PS Remote Play.</p> <p>- Video streaming data captured from a movie in Prime Video.</p> <p>- File download data captured from downloading a 6GB file from Google Drive.</p>
LoRaWAN Traffic Analysis Dataset
<p>This dataset was created by a LoRaWAN sniffer and contains packets, which are thoroughly analyzed in the paper <a href="https://www.mdpi.com/1424-8220/23/17/7333"><em>Exploring LoRaWAN Traffic: In-Depth Analysis of IoT Network Communications</em></a>. Data from the LoRaWAN sniffer was collected in four cities: Liege (Belgium), Graz (Austria), Vienna (Austria), and Brno (Czechia).</p> <p>Gateway ID: <code>b827ebafac000001</code></p> <ul> <li>Uplink reception (end-device => gateway)</li> <li>Only packets containing CRC, inverted IQ</li> <li>RX0: 867.1 MHz, 867.3 MHz, 867.5 MHz, 867.7 MHz, 867.9 MHz - BW 125 kHz and all SF</li> <li>RX1: 868.1 MHz, 868.3 MHz, 868.5 MHz - BW 125 kHz and all SF</li> </ul> <p>Gateway ID: <code>b827ebafac000002</code></p> <ul> <li>Downlink reception (gateway => end-device)</li> <li>Includes packets without CRC, non-inverted IQ</li> <li>RX0: 867.1 MHz, 867.3 MHz, 867.5 MHz, 867.7 MHz, 867.9 MHz - BW 125 kHz and all SF</li> <li>RX1: 868.1 MHz, 868.3 MHz, 868.5 MHz - BW 125 kHz and all SF</li> </ul> <p>Gateway ID: <code>b827ebafac000003</code></p> <ul> <li>Downlink reception (gateway => end-device) and Class-B beacon on 869.525 MHz</li> <li>Includes packets without CRC, non-inverted IQ</li> <li>RX0: 869.525 MHz - BW 125 kHz and all SF, BW 125 kHz and SF9 with implicit header, CR 4/5 and length 17 B</li> </ul> <p>To open the <code>pcap</code> files, you need Wireshark with current support for LoRaTap and LoRaWAN protocols. This support will be available in the official 4.1.0 release. A working version for Windows is accessible in the <a href="https://www.wireshark.org/download/automated/win64/">automated build system</a>.</p> <p>The source data is available in the <code>log.zip</code> file, which contains the complete dataset obtained by the sniffer. A set of conversion tools for log processing is <a href="https://github.com/alpov/lorawan-sniffer">available on Github</a>. The converted logs, available in Wireshark format, are stored in <code>pcap.zip</code>. For the LoRaWAN decoder, you can use the attached root and session keys. The processed outputs are stored in <code>csv.zip</code>, and graphical statistics are available in <code>png.zip</code>.</p> <p>This data represents a unique, geographically identifiable selection from the full log, cleaned of any errors. The records from Brno include communication between the gateway and a node with known keys.</p> <p>Test file :: <code>00_Test</code></p> <ul> <li>short test file for parser verification</li> <li>comparison of LoRaTap version 0 and version 1 formats</li> </ul> <p>Brno, Czech Republic :: <code>01_Brno</code></p> <ul> <li>49.22685N, 16.57536E, ASL 306m</li> <li>lines 150873 to 529796</li> <li>time 1.8.2022 15:04:28 to 17.8.2022 13:05:32</li> <li>preliminary experiment</li> <li>experimental device <ul> <li>Device EUI: <code>70b3d5cee0000042</code></li> <li>Application key: <code>d494d49a7b4053302bdcf96f1defa65a</code></li> <li>Device address: <code>00d85395</code></li> <li>Network session key: <code>c417540b8b2afad8930c82fcf7ea54bb</code></li> <li>Application session key: <code>421fea9bedd2cc497f63303edf5adf8e</code></li> </ul> </li> </ul> <p>Liege, Belgium :: <code>02_Liege</code> :: <strong>evaluated in the paper</strong></p> <ul> <li>50.66445N, 5.59276E, ASL 151m</li> <li>lines 636205 to 886868</li> <li>time 25.8.2022 10:12:24 to 12.9.2022 06:20:48</li> </ul> <p>Brno, Czech Republic :: <code>03_Brno_join</code></p> <ul> <li>49.22685N, 16.57536E, ASL 306m</li> <li>lines 947787 to 979382</li> <li>time 30.9.2022 15:21:27 to 4.10.2022 10:46:31</li> <li>record contains OTAA activation (Join Request / Join Accept)</li> <li>experimental device: <ul> <li>Device EUI: <code>70b3d5cee0000042</code></li> <li>Application key: <code>d494d49a7b4053302bdcf96f1defa65a</code></li> <li>Device address: <code>01e65ddc</code></li> <li>Network session key: <code>e2898779a03de59e2317b149abf00238</code></li> <li>Application session key: <code>59ca1ac91922887093bc7b236bd1b07f</code></li> </ul> </li> </ul> <p>Graz, Austria :: <code>04_Graz</code> :: <strong>evaluated in the paper</strong></p> <ul> <li>47.07049N, 15.44506E, ASL 364m</li> <li>lines 1015139 to 1178855</li> <li>time 26.10.2022 06:21:07 to 29.11.2022 10:03:00</li> </ul> <p>Vienna, Austria :: <code>05_Wien</code> :: <strong>evaluated in the paper</strong></p> <ul> <li>48.19666N, 16.37101E, ASL 204m</li> <li>lines 1179308 to 3657105</li> <li>time 1.12.2022 10:42:19 to 4.1.2023 14:00:05</li> <li>contains a total of 14 short restarts (under 90 seconds)</li> </ul> <p>Brno, Czech Republic :: <code>07_Brno</code> :: <strong>evaluated in the paper</strong></p> <ul> <li>49.22685N, 16.57536E, ASL 306m</li> <li>lines 4969648 to 6919392</li> <li>time 16.2.2023 8:53:43 to 30.3.2023 9:00:11</li> </ul>
Leveraging IoT Data Stream for Near-Real-Time Calibration of City-Scale Microscopic Traffic Simulation
<p>This repository includes input and output data of the methodology presented in the <a href="https://arxiv.org/abs/2210.17315">paper</a> for generating a calibrated dynamic microscopic traffic simulation.</p> <ul> <li>The input data includes the network, initial normalized origin-destination matrix, and hourly traffic counts from stationary city sensors.</li> <li>The output is a 24-hour calibrated microscopic traffic simulation for the city of Tartu, Estonia.</li> </ul> <p>All source codes are available at <a href="https://github.com/Khoshkhah/NRTCalib">https://github.com/Khoshkhah/NRTCalib</a>.<br> </p>
Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level
<p>Data and codes for the XGBoost model training in our manuscript titled "Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level"</p>
Understanding the impact of host networking elements on traffic bursts: Raw measurement data
<p>This record contains the raw trace files gathered by the Valinor network traffic burst measurement framework in Redis dump (rdb) format. Please refer to the artifact repository for instructions on how to parse and use the datasets:</p> <p><a href="https://github.com/hopnets/valinor-rawdata">hopnets/valinor-rawdata: Raw Redis datasets containing the measurement results of Valinor NSDI '23 paper (github.com)</a></p>
Aircraft Traffic Density Database: /Data
<p>This dataset is the associated input for the software, <a href="https://github.com/mit-ll/traffic-density-database">traffic-density-database</a>. It is intended to process observed aircraft traffic information into unmitigated collision rates given user specified geographic regions or aircraft tracks. The user may also specify the time of day, month of year, and day of week to be evaluated. Unmitigated collision rates are useful for aircraft safety assessments, and may be combined with the efficacy of mitigations—e.g., air traffic control, detect and avoid, see and avoid—in order to estimate the total operation safety.</p> <p>The input data included with this release has the following attributes. See the README in the Data directory for more detail on the contents of each data file.</p> <ul> <li>The data span 8727 observed hours, from 4 November 2018 to 3 November 2019. Note that there are periods where there are no radar data available, so the data span does not correspond directly to the full date range extent.</li> <li>There are over 31.65 million flight hours observed (includes both discrete-code and 1200-code aircraft).</li> <li>The vertical spatial (height) discretization corresponds to standard altitude feature thresholds in the national airspace: <ul> <li>100 feet Above Ground Level (AGL): Cutoff to exclude on-ground traffic</li> <li>500 feet AGL: nominal lower manned aircraft operating altitude</li> <li>1200 feet AGL: typical lower altitude for Class E airspace</li> <li>3000 feet AGL: approximate upper bound for Class D airspace</li> <li>5000 feet AGL: approximate upper bound for Class C airspace</li> <li>10,000 feet Mean Sea Level (MSL): typical Class B upper bound, and lower threshold for transponder requirements</li> <li>18,000 feet MSL: lower bound for Class A Airspace</li> <li>29,000 feet MSL: Reduced Vertical Separation Minimum (RVSM) lower altitude bound</li> <li>41,100 feet MLS: RVSM upper altitude bound</li> <li>60,100 feet MSL: upper bound for Class A Airspace</li> </ul> </li> <li>The temporal discretization is 3 hours.</li> <li>The latitude range is 22.83°N to 50.00°N.</li> <li>The longitude range is 127.00°W to 64.83°W. (Note: MATLAB indicates W longitude using negative numbers, e.g., 127.00°W is -127.00.)</li> <li>Two tables with different horizontal spatial discretization are provided: one for all altitudes, where the discretization is 1/6 of a degree in latitude and longitude (corresponding to approximately 10 NM); and one for altitudes below 5000 feet, where the discretization is 1/24 of a degree (approximately 2.5 NM). The table containing all altitudes is the default.</li> </ul>
'Guardians of Traffic' Pillar
This is one of the art deco styized pillars of the Hope Memorial Bridge in Cleveland, OH (Lorain Bridge). Sorta of becoming rather iconic with the city. Cleveland Pride! It was free hand sculpted based on photos. Source: Objaverse 1.0 / Sketchfab
A Feasibility Trial of Eye Movement Desensitization and Reprocessing Therapy- Integrative Treatment Group Protocol for Ongoing Traumatic Stress In Road Traffic Accident Survivors for Reduction of Post
ClinicalTrials.gov study NCT07027930. IPD Sharing: NO. Countries: 1. Publications: 1.
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.