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54 results for “wifi”
WiFi RSS & RTT dataset with different LOS conditions for indoor positioning
<p>This is the second batch of WiFi RSS RTT datasets with LOS conditions we published. Please see <code><a href="https://doi.org/10.5281/zenodo.11558192" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11558192</a></code> for the first release.</p> <p> </p> <p>We provide three real-world datasets for indoor positioning model selection purpose. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures and is well separated so that training points and testing points will not overlap. Please find the datasets in the 'data' folder. The datasets contain both <strong>WiFi RSS and RTT signal measures</strong> with <strong>groud truth coordinates</strong> label and <strong>LOS condition label</strong>.</p> <ol> <li> <p>Lecture theatre: This is a entirely LOS scenario with 5 APs. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP). </p> </li> <li> <p>Corridor: This is a entirely NLOS scenario with 4 APs. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP). </p> </li> <li> <p>Office: This is a mixed LOS-NLOS scenario with 5 APs. At least one AP was NLOS for each RP. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP). </p> </li> </ol> <div> <h2><strong>Collection methodology</strong></h2> <p>The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.</p> <p>The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers. </p> <p>Only RTT-enabled APs were included in the dataset.</p> <h2><strong>The features of the dataset</strong></h2> </div> <p>The features of the lecture theatre dataset are as follows:</p> <div> <blockquote> <pre>Testbed area: 15 × 14.5 m2 Grid size: 0.6 × 0.6 m2<br>Number of AP: 5 Number of reference points: 120 Samples per reference point: 60 Number of all data samples: 7,200 Number of training samples: 5,400 Number of testing samples: 1,800 Signal measure: WiFi RTT, WiFi RSS Note: Entirely LOS </pre> </blockquote> <div> </div> </div> <p>The features of the corricor dataset are as follows:</p> <div> <blockquote> <pre>Testbed area: 35 × 6 m2 Grid size: 0.6 × 0.6 m2<br>Number of AP: 4 Number of reference points: 114 Samples per reference point: 60 Number of all data samples: 6,840 Number of training samples: 5,130 Number of testing samples: 1,710 Signal measure: WiFi RTT, WiFi RSS Note: Miexed LOS-NLOS. At least one AP was NLOS for each RP. </pre> </blockquote> <div> </div> </div> <p>The features of the office dataset are as follows:</p> <div> <blockquote> <pre>Testbed area: 18 × 5.5 m2 Grid size: 0.6 × 0.6 m2<br>Number of AP: 5 Number of reference points: 108 Samples per reference point: 60 Number of all data samples: 6,480 Number of training samples: 4,860 Number of testing samples: 1,620 Signal measure: WiFi RTT, WiFi RSS Note: Entirely NLOS </pre> </blockquote> <div> </div> </div> <div> <h2><strong>Dataset explanation</strong></h2> </div> <p>The columns of the dataset are as follows:</p> <div> <pre>Column 'X': the X coordinates of the sample. Column 'Y': the Y coordinates of the sample. Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP5 RTT(mm)': the RTT measure from corresponding AP at a reference point. Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP5 RSS(dBm)': the RSS measure from corresponding AP at a reference point. Column 'LOS APs': indicating which AP has a LOS to this reference point. </pre> </div> <p>Please note:</p> <ul> <li> <p>The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.</p> </li> <li> <p>The RTT value 100,000 mm indicates that no signal is received from the specific AP.</p> </li> </ul> <p> </p> <h2><strong>Citation request</strong></h2> <p>When using this dataset, please cite the following three items:</p> <p><code>Feng, X., Nguyen, K. A., & Zhiyuan, L. (2024). WiFi RSS & RTT dataset with different LOS conditions for indoor positioning [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11558792</code></p> <p> </p> <pre><code>@article{feng2024wifi, title={A WiFi RSS-RTT indoor positioning system using dynamic model switching algorithm}, author={Feng, Xu and Nguyen, Khuong An and Luo, Zhiyuan}, journal={IEEE Journal of Indoor and Seamless Positioning and Navigation}, year={2024}, publisher={IEEE} }</code><br><br><code>@inproceedings{feng2023dynamic, title={A dynamic model switching algorithm for WiFi fingerprinting indoor positioning}, author={Feng, Xu and Nguyen, Khuong An and Luo, Zhiyuan}, booktitle={2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, pages={1--6}, year={2023}, organization={IEEE} }</code></pre> <p> </p>
Exemplary dataset measured by Bresser WIFI Weather Station from 21.August 2023
<p><span>The Bresser WIFI Colour Weather Station is a convenient weather station for citizen science projects. It is equipped with sensors to measure wind speed, wind direction, humidity, temperature, rainfall, UV levels and light intensity. The WIFI feature allows users to share their data on weather platforms such as AWEKAS, Weather Underground or Weather Cloud.</span></p>
Ending the Anomaly: Achieving Low Latency and Airtime Fairness in WiFi
<p>This is the dataset and companion website to the paper <a href="https://www.usenix.org/conference/atc17/program/presentation/hoilan-jorgesen">Ending the Anomaly: Achieving Low Latency and Airtime Fairness in WiFi</a> which was published at USENIX ATC 17.</p> <p>Also published at <a href="https://www.cs.kau.se/tohojo/airtime-fairness/">https://www.cs.kau.se/tohojo/airtime-fairness/</a></p>
WiFi RSS measurements in Tampere University multi-building campus, 2017
<p>This package contains two Wi-Fi RSSI Datasets, namely TIE1 and SAH1.</p> <ul> <li>TIE1 was collected in the Tietotalo building from Tampere University during the period of August-December 2017 and it is composed of four files: <ul> <li>TIE1_training_rss.csv - Radio map or reference dataset as a matrix with 10633 reference samples (rows) and 613 APs (columns) </li> <li>TIE1_training_coordinates.csv - Ground truth location of the 10633 reference samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> <li>TIE1_test_rss.csv - Operational fingerprints as a matrix with 50 evaluation samples (rows) and 613 APs (columns) </li> <li>TIE1_test_coordinates.csv - Ground truth location of the 50 evaluation samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> </ul> </li> <li>SAH1 was collected in the Sahkotalo building from Tampere University during the period of October-December 2017 and it is composed of four files: <ul> <li>SAH1_training_rss.csv - Radio map or reference dataset as a matrix with 9291 reference samples (rows) and 775 APs (columns) </li> <li>SAH1_training_coordinates.csv - Ground truth location of the 9291 reference samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> <li>SAH1_test_rss.csv - Operational fingerprints as a matrix with 156 evaluation samples (rows) and 775 APs (columns) </li> <li>SAH1_test_coordinates.csv - Ground truth location of the 156 evaluation samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> </ul> </li> </ul> <p>Additional considerations:</p> <ul> <li>All the RSSI values are stored are they were registered with a regular smartphone. In order to generate the fingerprint vectors, the non-detected APs where filled with the default bogus value +100dBm. i.e., the value +100dBm in the training and test rss files indicates that the AP was not detected. Generally, this default bogus value has been replaced with a low RSSI value in experiments, such as -100dBm, -110dBm, -150dBm or -200dBm.</li> <li>The coordinates X,Y,Z are expressed in meters in a local coordinate system. Additionally, the sequential floor identifier is also provided.</li> </ul> <p>Please cite the source at Zenodo when using any of these two datasets:</p> <blockquote> <p>Lohan, E.S; Torres-Sospedra, J.; Gonzalez, A.; "WiFi RSS measurements in Tampere University multi-building campus, 2017", Zenodo, 2021.<br> https://zenodo.org/record/5174851<br> https://www.doi.org/10.5281/zenodo.5174851</p> </blockquote>
WACA WiFi 5-GHz dataset (Camp Nou only)
<p>This folder contains <strong>ONLY</strong> the Camp Nou campaign included in the dataset presented in "<em>Sergio Barrachina-Muñoz, Boris Bellalta, and Edward Knightly. 2020. Wi-Fi All-Channel Analyzer. WinTech (2020)</em>".</p> <p>Please, refer to the GitHub repository for more details:<br> <a href="https://github.com/sergiobarra/WACA_WiFiAnalyzer">https://github.com/sergiobarra/WACA_WiFiAnalyzer</a></p> <p>-------------------------------------------------------------<br> <strong>*** Dataset structure ***</strong><br> The dataset is composed of 11 scenarios, each with a corresponding folder (campaign duration within brackets):<br> - 1_RVA: Rice Village Apartments, Houston; Apartment; 15th February 2019; 9600 iterations (~1 day)<br> - 2_RNG: Rice Networks Group, Houston; Campus office; 19th February 2019; 8750 iterations (~1 day)<br> - 3_TFA: Technology for All, Houston; Community center; 20th February 2019; 8609 iterations (~1 day)<br> - 4_FLO: Flo Paris (at Rice Village), Houston; Cafe; 23rd February 2019; 413 iterations (~1 hour)<br> - 5_VIL: Rice Village parking lot, Houston; Shopping mall; 23rd February 2019; 125 iterations (~20 min)<br> - 6_FEL: La Sagrera neighborhood, Barcelona; Apartment; 25th March 2019; 59500 iterations (~1 week)<br> - 7_WNO: Wireless Networking, Barcelona; Campus office; 3rd April 2019; 9600 iterations (~1 day)<br> - 8_22A: 22@ neighborhood, Barcelona; Office area; 2nd July 2019; 9600 iterations (~1 day)<br> - 9_GAL: Hotel Gallery, Barcelona; Downtown hotel; 10th July 2019; 9706 iterations (~1 day)<br> - 10_SAG: Sagrada Familia, Barcelona; Apartment; 11th February 2019; 38192 iterations (~4 days)<br> <em><strong>- 11_FCB: Camp Nou stadium, Barcelona; Futbol (soccer) Stadium; 4th August; 2001 iterations (~ 5 hours)</strong></em></p> <p><strong>*** File format ***</strong><br> - RSSI measurements are stored in .mat files inside each scenario folder.<br> - .mat files are named "it<iteration number>_<mm-dd-yy>_<hh:mm:ss>.mat". For instance file "it0251_02-19-19_08-03-09.mat" refers to iteration number 251, initiated on 19th February 2019 at 08:03:09.<br> - Every .mat file contains 24 arrays 10000x1, one per basic channel, where the ith element represents the RSSI value at sample i (of duration 10 microseconds) inside the iteration (of duration 1 second).<br> - The RSSI value is given in the units of the MAX2829 transceiver (10-bit values). Please, refer to the transceiver datasheet or to our GitHub repository to see how to convert from 10-bit values to dBm.<br> - Finally, every scenario folder also contains the "experiment_general.mat". This file is auxiliary and contains different constants of interest related to the configuration of the campaigns.</p> <p><strong>*** How do I process the dataset? ***</strong><br> We provide the RSSI measurements for every channel in every scenario. You may generate a Matlab snippet to process the data. Nevertheless, the Matlab code we used in the paper "Sergio Barrachina-Muñoz, Boris Bellalta, and Edward Knightly. 2020. Wi-Fi All-Channel Analyzer. WinTech (2020)" can be found in the GitHub repository.</p>
Figs. 59–71 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 59–71. Habitus images, SEMs, habitat, and genitalia illustrations of Selenophorus species. 59–63) S. striatopunctatus dorsal and ventral aspects, SEM images of pronotum, elytra, and elytral striae showing setigerous puncture; 64–68) S. elytrostictus, new species, dorsal and ventral aspects, SEM images of pronotum, elytra, and elytral striae showing setigerous puncture; 69) Laguna Atascosa NWR, loma-freshwater margin; 70–71) S. elytrostictus, new species, male median lobe left lateral and dorsal views.
Figs. 72–75 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 72–75. Habitus images of Selenophorus and related genera, dorsal aspect: 72) S. maritimus; 73) Athrostictus punctatulus; 74) Discoderus discoderoides. 75) Sabal Palm Sanctuary, old-growth "core".
Figs. 44–50 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 44–50. Habitus images of Selenophorus species, dorsal aspect. 44) S. blanchardi; 45) S. pedicularius; 46) S. planipennis; 47) S. aeneopiceus; 48) S. breviusculus; 49) S. fatuus; 50) S. parumpunctatus.
Figs. 37–43 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 37–43. Habitus images and genitalia illustrations of Selenophorus species. 37) S. aequinoctialis, dorsal aspect; 38) S. palliatus, dorsal aspect; 39) S. sinuaticollis, dorsal aspect; 40–43) S. rileyi, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.
Figs. 29–36 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 29–36. Habitus images and genitalia illustrations of Selenophorus species. 29) S. chaparralus, dorsal aspect; 30) S. opalinus, dorsal aspect; 31) S. fabricii, dorsal aspect; 32) S. trepidus, dorsal aspect; 33–36) S. undatus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.
Figs. 15–18 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 15–18. Habitus images of Selenophorus species, dorsal aspect. 15) S. gagatinus; 16) S. concinnus; 17) S. semirufus; 18) S. schaefferi.
Figs. 8–14 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 8–14. Habitus images and genitalia illustrations of Selenophorus species. 8–11) S. pumilus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views; 12) S. seriatoporus, dorsal aspect; 13) S. discopunctatus, dorsal aspect; 14) S. fossulatus, dorsal aspect.
Figs. 1–7 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 1–7. Habitus images and genitalia illustrations of Selenophorus species. 1) S. contractus, dorsal aspect; 2) S. ellipticus, dorsal aspect; 3) S. granarius, dorsal aspect; 4–7) S. nonellipticus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.
ESP32C3 WiFi FTM RSSI Indoor Localization
<div> <div> <div> <h2><strong>Wi-Fi FTM RSSI Localization dataset</strong></h2> <p>Wi-Fi Fine Time Measurement for positioning / Indoor Localization in <strong>3 different locations</strong> and using <strong>8 different APs</strong> <br> <br>Custom APs using <strong>ESP32C3</strong> and Raw FTM is measured in nanoseconds <br> <br>Data is only measured at the Router Side <br> <br>Data is not measured at client side <br> <br>Has 4 datasets inside the zip folder with over <strong>100,000 data points</strong> <br> <br>Contains processed Wi-Fi FTM packets from various routers in: <br>1. University of Victoria, Engineering Office Wing (EOW) 3rd Floor <br>2. University of Victoria, Engineering Office Wing (EOW) 5th Floor <br>3. University of Victoria, Engineering and Computer Science (ECS) 1st Floor <br> <br>Each folder contains a training dataset and a testing dataset that is independent in time and space <br> <br>Router Time is synchronized using chrony</p> </div> </div> </div> <div> <div>Instructions: </div> <div> <div> <h2><strong>Dataset is in CSV format</strong></h2> <p>Relative Time (seconds) | X Position (meters) | Y Position (meters) | Feature 1 | Feature 2 | Feature 3 ..... <br> <br>Time resets at every new position and position accuracy is a few centimeters using LIDAR and RGBD camera <br> <br>Map is in ROS2 PGM format that can read by ROS2 programs <br> <br>Data for the paper <br> <br>Wi-Fi and Bluetooth Contact Tracing Without User Intervention</p> <p><br><a href="https://ieeexplore.ieee.org/document/9866766" rel="nofollow">https://ieeexplore.ieee.org/document/9866766</a></p> <p>Please Cite As</p> <pre><code>@article{yuen2022wi, title={Wi-Fi and Bluetooth contact tracing without user intervention}, author={Yuen, Brosnan and Bie, Yifeng and Cairns, Duncan and Harper, Geoffrey and Xu, Jason and Chang, Charles and Dong, Xiaodai and Lu, Tao}, journal={IEEE Access}, volume={10}, pages={91027--91044}, year={2022}, publisher={IEEE} }</code></pre> </div> </div> </div>
Measured throughput of WiFi
<p>Measured throughput of WiFi</p>
PoliFi: Airtime Policy Enforcement for WiFi
<p>This is the companion website to the paper "PoliFi: Airtime Policy Enforcement<br> for WiFi" by Toke Høiland-Jørgensen, Per Hurtig and Anna Brunstrom, which will<br> be presented at the <a href="https://wcnc2019.ieee-wcnc.org/">2019 IEEE Wireless Communications and Networking<br> Conference</a></p> <p>Also published at <a href="https://www.cs.kau.se/tohojo/polifi/">https://www.cs.kau.se/tohojo/polifi/</a></p>
Dataset of WiFi-based Environment-independent In-baggage Object Identification System
<p><strong>Description:</strong></p> <p>The dataset of environment-independent in-baggage object identification system leveraging low-cost WiFi. The dataset contains the extracted CSI features from 14 representative in-baggage objects of 4 different materials. The experiments are conducted in 3 different office environments with different sizes. We hope this dataset will help researchers to reproduce the former work of in-baggage object identification through WiFi sensing. </p> <p> </p> <p><strong>Dataset Format: </strong></p> <p>.mat files</p> <p> </p> <p><strong>Section 1: Device Configuration: </strong></p> <ul> <li> <p><strong>Transmitter: </strong>Aaronia HyperLOG 7060 direction antenna with a Dell Inspiron 3910 desktop for control. </p> </li> <li> <p><strong>Receiver: </strong>Hawking HD9DP orthogonal antennas with a Dell Inspiron 3910 desktop for control</p> </li> <li> <p><strong>NIC:</strong> Atheros QCA9590. The configuration and installation guide of CSI tool can be found at <a href="https://wands.sg/research/wifi/AtherosCSI/">https://wands.sg/research/wifi/AtherosCSI/</a></p> </li> <li> <p><strong>WiFi Packet Rate: </strong>1000 pkts/s </p> </li> </ul> <p> </p> <p><strong>Section 2: Data Format</strong></p> <p>We provide the CSI features through .mat files. The details are shown in the following:</p> <ul> <li> <p>14 different objects made of 4 different materials are included in 3 different environments and 3 different days.</p> </li> <li> <p>Each object is tested for 60 seconds and repeated for 3 times. </p> </li> <li> <p>The dataset file name is presented as "Object_Number". The detailed information are:</p> <ul> <li> <p>Object: The object we involved in the experiment (e.g., book, laptop)</p> </li> <li> <p>Number: The number of repeats. </p> </li> </ul> </li> </ul> <p> </p> <p><strong>Section 3: Experimental Setups</strong></p> <p>There are 3 different office experiment setups for our data collection. The detailed setups are shown in the paper. For the objects, we involve 14 types of objects made of 4 different materials. </p> <ul> <li> <p><strong>Environments: </strong></p> <ul> <li> <p>3 different environments are involved, including 3 office environments with the size of 15 ft × 13 ft, 16 ft × 12 ft, 28 ft × 23 ft, respectively. </p> </li> <li> <p>For each room environment, data is collected on different days and with different furniture settings (i.e., 2 desks and 2 chairs are moved at least 3 ft. )</p> </li> </ul> </li> <li> <p><strong>Representative objects: </strong></p> <ul> <li> <p>Data is collected using 14 representative objects of 4 different materials including fiber: book, magazine, newspaper; metal: thermal cup, laptop; cotton/polyester: cotton T-shirts (×2), cotton T-shirts (×4), hoodie, polyester T-shirts, polyester pants; water: 1L bottle with 1L water, 1L bottle with 500ml water, 500ml bottle with 500ml water. </p> </li> </ul> </li> </ul> <p> </p> <p><strong>Section 4: Data Description</strong></p> <p>For our data organization, we separate the data files into different folders based on different days and different environments. Under these folders, data are further distributed in terms of different objects and repeat times. All the files are .mat files, which can be directly read for further applications. </p> <ul> <li> <p><strong>Features of CSI amplitude: </strong>We calculate 7 different types of statistical features, including mean, variance, median, skewness, kurtosis, interquartile range and range, and polarization feature from CSI amplitude. Particularly, we calculate the features for all 56 subcarriers with different operating frequencies and responses to the target object. </p> </li> <li> <p><strong>Features of CSI phase: </strong>For the features of CSI phase, the same features with CSI amplitude are extracted and stored in the dataset. </p> </li> </ul> <p> </p> <p><strong>Section 6: Citations</strong></p> <p>If your work is related to our work, please cite our papers as follows. </p> <p><a href="https://ieeexplore.ieee.org/document/9637801">https://ieeexplore.ieee.org/document/9637801</a></p> <p>Shi, Cong, Tianming Zhao, Yucheng Xie, Tianfang Zhang, Yan Wang, Xiaonan Guo, and Yingying Chen. "Environment-independent in-baggage object identification using wifi signals." In 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), pp. 71-79. IEEE, 2021.</p> <p> </p>
Assessing a WeChat-based Integrative Family Intervention (WIFI) for Schizophrenia
ClinicalTrials.gov study NCT04393896. IPD Sharing: YES. Countries: 1. Publications: 6.
Fig. 76. Normal Q-Q in Indoor Radio Map localization WiFi fingerprint datasets
Fig. 76. Normal Q-Q plot of standardized body length (SBL) for selected Texas Selenophorus species.
WiFi and Bluetooth RSSI SQI Indoor Localization
<h2><strong>Wi-Fi BLE RSSI SQI Localization dataset</strong></h2> <p>Wi-Fi BLE RSSI for positioning / Indoor Localization in <strong>4 different locations</strong> and using <strong>18 different APs</strong></p> <p>Data is only measured at the <strong>Router Side</strong></p> <p>Data is not measured at client side</p> <p>Has <strong>12 datasets</strong> inside the zip folder with over <strong>1,000,000 data points</strong></p> <p>Contains processed Wi-Fi and BLE packets from various routers in: <br>1. University of Victoria, Engineering Office Wing (EOW) <br>2. University of Victoria, Engineering Lab Wing (ELW) <br>3. University of Victoria, Engineering and Computer Science (ECS) <br> <br>Each folder contains a training dataset and a testing dataset that is independent in time and space</p> <p>Router Time is synchronized using chrony</p> <p> </p> <h2><strong>Dataset is in CSV format</strong></h2> <p>Relative Time (seconds) | X Position (meters) | Y Position (meters) | Feature 1 | Feature 2 | Feature 3 ..... <br> <br>Time resets at every new position and position accuracy is a few centimeters using LIDAR and RGBD camera <br> <br> <br>Map is in ROS2 PGM format that can read by ROS2 programs <br> <br> <br>Data for the paper <br> <br>Wi-Fi and Bluetooth Contact Tracing Without User Intervention</p> <p><br><a href="https://ieeexplore.ieee.org/document/9866766" rel="nofollow">https://ieeexplore.ieee.org/document/9866766</a></p> <p> </p> <p>Please Cite As</p> <blockquote> <pre>@article{yuen2022wi, title={Wi-Fi and Bluetooth contact tracing without user intervention}, author={Yuen, Brosnan and Bie, Yifeng and Cairns, Duncan and Harper, Geoffrey and Xu, Jason and Chang, Charles and Dong, Xiaodai and Lu, Tao}, journal={IEEE Access}, volume={10}, pages={91027--91044}, year={2022}, publisher={IEEE} }</pre> </blockquote>
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.