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300 results for “indoor”
METRIC - Multi-Eye To Robot Indoor Calibration Dataset
<p>The METRIC dataset comprises more than 10,000 synthetic and real images of ChAruCo and checkerboard patterns. Each pattern is securely attached to the robot's end-effector, which is systematically moved in front of four cameras surrounding the manipulator. This movement allows for image acquisition from various viewpoints. The real images in the dataset encompass multiple sets of images captured by three distinct types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455, and Intel RealSense Lidar L515. The purpose of including these images is to evaluate the advantages and disadvantages of each sensor network for calibration purposes. Additionally, to accurately assess the impact of the distance between the camera and robot on calibration, we obtained a comprehensive synthetic dataset. This dataset contains associated ground truth data and is divided into three different camera network setups, corresponding to three levels of calibration difficulty based on the cell size.</p>
HeatResilientCity II - work package 2.2: Influence of regional and urban climate on indoor overheating - Results of building performance simulation
<p>This repository contains the <strong>results of the building performance simulations</strong> carried out in the working package 2.2 Influence of regional and urban climate on indoor overheating of the project <a href="http://heatresilientcity.de/">HeatResilientCity II</a>. The buildings under consideration are a multi-residential so-called ‘Gründerzeithaus’ (GZH) and a large-panel construction (LPC) building. The results were extracted for two rooms on the top floor/attic of each building and follow a consistent name convention. Each file contains hourly resolved values for the outdoor air temperature, the indoor air temperature, the indoor operative temperature and the relative humidity indoors and outdoors. Further information can be found in the README of this repository. The simulations were performed for five <strong>different</strong> <strong>regions</strong> in Germany (Dresden, Hamburg, Köln, Stuttgart and Potsdam) for <strong>average present</strong> and <strong>future</strong> <strong>summers</strong> based on meteorological measurement data and under consideration of <strong>urban</strong> <strong>climate</strong>.</p> <p>In addition to the ‘plain’ simulation results, some <strong>heat-indicator variables</strong> were calculated and listed in the files <em>Calculated_Variables.txt</em>. The calculated quantities include temperature-weighted exceedance hours (TWEH) for the limits of 25, 26 and 27 °C (defined in DIN 4108-2:2013 as ‘Übertemperaturgradstunden’) and the maximum operative temperature calculated for the period from April to September.</p> <p>The used <strong>input data</strong> and <strong>building models</strong> can be found in the related repository.</p>
Dataset for "Indoor transmission of respiratory droplets under different ventilation systems using Eulerian approach"
<p>Dataset for figures and tables of the article "Indoor transmission of respiratory droplets under different ventilation systems using Eulerian approach".</p>
Data from: Do the health benefits of boiling drinking water outweigh the negative impacts of increased indoor air pollution exposure?
<p><strong>Background: </strong>Billions of the world's poorest households are faced with the lack of access to both safe drinking water and clean cooking. One solution to microbiologically contaminated water is boiling, often promoted without acknowledging the additional risks incurred from indoor air degradation from using solid fuels.</p> <p><strong>Objectives: </strong>This modeling study explores the tradeoff of increased air pollution from boiling drinking water under multiple contamination and fuel use scenarios typical of low-income settings.</p> <p><strong>Methods: </strong>We calculated the total change in disability-adjusted life years (DALYs) from indoor air pollution (IAP) and diarrhea from fecal contamination of drinking water for scenarios of different source water quality, boiling effectiveness, and stove type. We used Uganda and Vietnam, two countries with a high prevalence of water boiling and solid fuel use, as case studies. </p> <p><strong>Results: </strong>Boiling drinking water reduced the diarrhea disease burden by a mean of 1110 DALYs and 368 DALYs per 10,000 people for adults and children <5 years in Uganda, respectively, for high-risk water quality and the most efficient (lab-level) boiling scenario, with smaller reductions for less contaminated water and ineffective boiling. Similar results were found in Vietnam, apart from fewer avoided DALYs in children due to different demographics. In both countries, for households with high baseline IAP from existing solid fuel use, adding water boiling to cooking on a given stove was associated with a limited increase in IAP DALYs due to the log-linear dose-response curves. Boiling, even at low effectiveness, was associated with <em>net </em>DALY reductions for medium- and high-risk water, even if using unclean stoves/fuels. Replacing traditional stoves with improved stoves coupled with effective boiling practices significantly reduced total DALYs. </p> <p><strong>Discussion: </strong>Boiling water generally resulted in a net decrease in DALYs. Future efforts should empirically measure health outcomes from IAP vs. diarrhea associated with boiling drinking water using field studies with different boiling methods and stove types.</p>
Evaluation of underfloor accelerometers through fingerprinting for indoor localization
<div><strong>Fingerprinting</strong></div> <div>Code developed to test the effectiveness of an indoor positioning system where multiple accelerometers are placed under the floor and set up to collect data. This material complements the work done for the paper "Evaluation of Underfloor Accelerometers for Enabling Location-based Services in Intelligent Environments" </div> <div>and helps readers to reproduce and validate the results presented in that paper. </div> <div> </div> <p><strong>What the code does</strong><br>The execution of the main code performs the following:<br>1. generation of sensor maps through their absolute coordinates;<br>2. noise reduction on the raw data according to the average of the stress the accelerometers are subjected at quiet;<br>3. generation of the fingerprint maps per each data set;<br>4. generation of the clean ground truth files (deleting coordinates set to zero);<br>5. computation of the n-dimensional distance between observations at a given time step and the euclidean error between the minimum distance value coordinates and the respective temporally closest ground truth ones;<br>6. same as in 5 but with intra-user fingerprint maps;<br>7. same as in 5 but with inter-user fingerprint maps;<br>8. same as in 5 but with enhanced inter-user fingerprint maps.</p> <p><strong>To run the code please read the file README.md</strong></p>
Validation of Spectral Light Simulation Tools: Dataset of Simulated and Measured Indoor Light Exposure
<p>Since the discovery of a new photoreceptor in our eye, and with the growing awareness about the related ipRGC-influenced light (IIL) responses, design applications related to these responses are flourishing. Optimizing our ocular light exposure in buildings can have beneficial effects on our health, well-being, and performance through the action of this photoreceptor. To compare different design options and optimize the lighting conditions for building occupants, lighting simulations are typically used. However, as our IIL responses depend on various aspects of the light exposure including its spectral characteristics, spectral simulations are required. The dataset shared here was originally collected to validate two spectral simulation tools, <em>ALFA</em> and <em>Lark</em>, for the study of building design in relation to occupants’ IIL responses. The validation was done by comparing the simulation outputs against actual measurements, and assessing how reliable these tools were in predicting spectral irradiance under different indoor light conditions. Data were collected in two different experimental setups, one under daylight conditions only and the other one under electric light conditions only. The experimental protocol and README files contain detailed information on how the data was collected and what data was collected.</p>
Indoor localization using Wi-Fi and IMU at National Taiwan University CSIE 5F
<p>A dataset composed of Wi-Fi fingerprints and IMU sensing data which collect by smartphone.</p> <p>We collected this dataset at National Taiwan University CSIE building 5F.</p> <p>The txt files are raw data.</p> <p>Fingerprint.txt is the Wi-Fi fingerprint set for reference map.</p> <p>Track1.txt and Track2.txt are the Wi-Fi fingerprints and IMU sensing data collect by android smartphone.</p> <p> </p> <p>The .npy files are the preprocessed data.</p> <p> </p> <p> </p> <p> </p>
Data Matrix Landmarks in Cluttered Indoor Environments
<p>We used the <a href="https://labelbox.com/">LabelBox</a> online toolbox to create this data set.</p> <p>It consists of 6 different cluttered environments:</p> <ol> <li>a laboratory </li> <li>3 different industrial-like environments </li> <li>a corridor</li> <li>hall</li> </ol> <p>We proposed to split the data set into three sets - training, validation, and test sets - as follows: (1) the training set has 156 frames equally distributed by the laboratory and 1 workshop; (2) the validation set is also divided into two environments - the corridor (158 frames) and a different workshop (66 frames); (3) the test set consists of 145 frames collected on a neat hall with overshadowed and over-lightened landmarks in different planes; a classroom laboratory with various electronic equipment arranged in an orderly manner; and a very challenging scenario with multiple pieces of machinery spread out all over the place.</p> <p>One should filter out images with no markers.</p>
Passive RFID indoor location dataset
<p>We use four monostatic UHF antennas that operate in the frequency range from 902 to 928 MHz with a 6 dBi gain (isotropic antenna gain) for the implementation environment preparation. The equipment we used to perform the readings was the ThingMagic Mercury 6, a high-performance UHF RFID reader, supporting up to four monostatic antennas, digital inputs and outputs, and a Wi-Fi connection. Both devices are commercially available.</p> <p>We affixed 400 labels to objects placed side by side on the shelves in an auto parts store. The distance between the antennas and tags was 115 cm, and the distance between the antenna group was 250 cm. The reader interrogates the tags every 5 seconds and organizes the input information in a data collection formed by TagID, RSSI, Read Count (RC), True_x, and True_y. This interrogation time was defined by the minimum limit at which all tags were identified at least once by the antenna. In order to carry out data collection, the objects were placed in known positions. We performed 100 readings on each target tag, measuring the RSSI and the RC of each of the four antennas, totaling 40,000 readings. </p>
Data set: Quantifying the Accuracy of Collaborative IoT and Robot Sensing in Indoor Settings of Rigid Objects
<p>This is the data set accompanying the paper "Quantifying the Accuracy of Collaborative IoT and Robot Sensing in Indoor Settings of Rigid Objects" by Sune L. Sørensen and Mikkel Baun Kjærgaard. Please refer to the paper for a description of the hardware used to record the data and how it is recorded.</p> <p>It consists of the following files:</p> <p><em>IoT camera images</em>: RBG images, named img_aa_bbb_0.jpg, where aa is the setup ID, bb is the camera ID (101, 102, 103 or 104).</p> <p><em>Robot RGB images</em>: RBG images, named aa0.png where aa is the setup ID.</p> <p><em>Robot point clouds</em>: pcd-files, named aa0.pcd where aa is the setup ID.</p> <p>The transformation from the IoT coordinate system to the robot coordinate system is:</p> <p>robotTiot = np.array([[0.914428, 0.134934, -0.378832, 3.76475],</p> <p>[0.393661, -0.49845, 0.772371, 0.791051],</p> <p>[-0.0846896, -0.855336, -0.509056, 2.37154],</p> <p>[0.0, 0.0, 0.0, 1.0]])</p> <p>Example, tranforming a pose in IoT coordinates to robot coordinates: p_rob = robotTiot * p_iot</p>
C-RIDGE: Indoor CO2 Data Collection System for Large Venues Based on Prior Knowledge
<p>This CO2 of C-RIDGE system dataset contains the high spatial and temporal resolution of the CO2<br> measures with the corresponding timestamp of static wireless sensors. <br> 45 sensors are densely deployed on the stand in the venue. The id and relative positions of sensors <br> are shown in device message. The sampling rate is adaptively adjusted according to the competition schedule. <br> The sampling interval is 5 minutes during the competition and 15 minutes otherwise. Please refer to <br> the description of the experimental setup in the data descriptor paper.</p> <p>In the process of data collection, the data cleaning process is performed to remove <br> and calibrate outliers and abnormal trend data. The script of data cleaning algorithm is <br> provided in this repository. For details about the data cleaning process, please refer to the script in <br> this repository and data descriptor paper.</p> <p>The dataset in this repository is processed version. The raw dataset is not included in this repository.</p> <p>Data is stored as CSV file. Each device is numbered in order of placement. There are 45 sensors in total,<br> 1 to 45 in the csv file are sensor numbers, timestamp as the China Standard Time (GMT+8), Each timestamp <br> corresponds to 45 CO2 concentration data from different sensors.</p> <p>To access the dataset, any programming language that can access the CSV file is appropriate. Users can <br> also directly open the CSV file. To successfully execute script files, Pycharm with Python 3.0 is required.</p>
Synthetic Indoor Climate and Occupancy Data from Office and Meeting Room Simulations
<p>This is the dataset used for the publication "Coddora: CO2-based Occupancy Detection model<br>trained via DOmain RAndomization". The goal is to provide training data for occupancy detection.<br><br>The dataset contains one million days of data including 10 occupied days for each of 100,000 randomized room models (50,000 rooms considering office activity and 50,000 meeting room activity). Data were generated in EnergyPlus simulations according to the methodology described in the paper.<br><br>When using the dataset, please cite:</p> <blockquote> <p><em>Manuel Weber, Farzan Banihashemi, Davor Stjelja, Peter Mandl, Ruben Mayer, and Hans-Arno Jacobsen. 2024. Coddora: CO2-Based Occupancy Detection Model Trained via Domain Randomization. In International Joint Conference on Neural Networks (IJCNN). June 30 - July 5, 2024, Yokohama, Japan.</em></p> </blockquote> <h2>Dataset Structure</h2> <p>The following files are provided:<br><br> 1. dataset_office_rooms.h5 (provided as zip file)<br> 2. dataset_meeting_rooms.h5 (provided as zip file)<br> 3. simulated_occupancy_office_rooms.csv<br> 4. simulated_occupancy_meeting_rooms.csv</p> <p>Please use an archiving tool such as 7zip to unzip the hdf5 files.<br>Both hdf5 files contain two datasets with the following keys:<br><br> 1. "<em>data</em>": contains the simulated indoor climate and occupancy data<br> 2. "metadata": contains the metadata that were used for each simulation</p> <p>The csv files contain the time series of occupancy that were used for the simulations.<br><br></p> <h2>Data</h2> <p><em>Data</em> includes the following fields:</p> <p><em>Datetime:</em> day of the year (may be relevant due to seasonal differences) and time of the day<br><em>Zone Air CO2 Concentration:</em> CO2 level in ppm<br><em>Zone Mean Air Temperature:</em> temperature in °C<br><em>Zone Air Relative Humidity: </em>relative humidity in %<br><em>Occupancy: </em>level of occupancy relative to the maximum capacity of the room (in the range [0-1])<br><em>Ventilation:</em> fraction of window opening in the range [0.01, 1]<br><em>SimID:</em> foreign key to reference the room properties the simulation was based on<br><em>BinaryOccupancy:</em> 0 or 1 denoting absence or presence (for binary classification)</p> <p> </p> <p>Example row:</p> <table> <tbody> <tr> <th><em>Datetime</em></th> <th><em>Zone Air CO2 Concentration</em></th> <th><em>Zone Mean Air Temperature</em></th> <th><em>Zone Air Relative Humidity</em></th> <th><em>Occupancy</em></th> <th><em>Ventilation</em></th> <th><em>simID</em></th> <th><em>BinaryOccupancy</em></th> </tr> <tr> <td> <p>10/09 11:21:00</p> </td> <td> <p>1084.5624647371608</p> </td> <td> <p>24.545635909907148</p> </td> <td> <p>41.18393114737054</p> </td> <td> <p>0.7</p> </td> <td> <p>0.0</p> </td> <td>99</td> <td>1</td> </tr> </tbody> </table> <pre> </pre> <h2>Metadata</h2> <p><em>Metadata</em> includes the following fields. <br>Underscores denote that the field was not selected during randomization but calculated from the other values.</p> <p>width: room width in m<br>length: room length in m<br>height: hoom height in m<br>infiltration: infiltration per exterior area in m³/m²s<br>outdoor_co2: co2 concentration in the outdoor air in ppm (set to a random value between [300, 500])<br>orientation: angle between the room's facade orientation and the north direction in degrees<br>maxOccupants: room occupation limit, i.e. the maximum number of occupants<br>_floorArea: floor area in m² (calculated from room dimensions)<br>_volume: room volume in m³ (calculated from room dimensions)<br>_exteriorSurfaceArea: surface area of the facade wall (calculated from room dimensions)<br>_winToFloorRatio: ratio between total window area and floor area (calculated from room model)<br>firstDayUsedOfOccupancySequence: selected starting day in the sequence of occupancy data for rooms with the respective maxOccupants value<br>simID: unique identifier of the simulation to relate between simulation metadata and resulting simulated data</p> <p> </p> <p>Example row:</p> <table> <tbody> <tr> <th>width</th> <th>length</th> <th>height</th> <th>infiltration</th> <th>outdoor_co2</th> <th>orientation</th> <th>maxOccupants</th> <th>_floorArea</th> <th>_volume</th> <th>_exteriorSurfaceArea</th> <th>_winToFloorRatio</th> <th>firstDayOfUsedOccupancySequence</th> <th>simID</th> </tr> <tr> <td>5.481</td> <td>5.190</td> <td>3.264</td> <td>0.000214</td> <td>438.0</td> <td>316.0</td> <td>4.0</td> <td>28.446</td> <td>92.849</td> <td>16.940</td> <td>0.216</td> <td>192</td> <td>0</td> </tr> </tbody> </table> <p> </p> <h2>Occupancy Data</h2> <p>The occupancy data provided through the separate csv files contain the data from the upfront occupancy simulations that the climate simulation was based on. For each level of considered room occupancy limit (maxOccupants), the datasets provide minute values of occupancy throughout 1000 days.</p> <p><em>Datetime, </em><em>Date, </em><em>Timestamp: fictive time of simulated occupancy record (sequences are in 1-minute resolution)</em><br><em>Occupants: number of present occupants</em><br><em>Occupancy: binary occupancy state (0=unoccupied, 1=occupied)</em><br><em>WindowState: binary state of ventilation (0=windows closed, 1=room is ventilated)</em><br><em>maxOccupants: maximum number of occupants considered for the simulated sequence</em><br><em>WindowOpeningFraction: fractional extent to which windows are opened, within the interval [0.01, 1]<br><br></em></p> <p>Example row:</p> <table> <tbody> <tr> <th>Datetime</th> <th>Date</th> <th>Timestamp</th> <th>Occupants</th> <th>Occupancy</th> <th>WindowState</th> <th>maxOccupants</th> <th>WindowOpeningFraction</th> </tr> <tr> <td>2023-01-01 00:00:00</td> <td>2023-01-01</td> <td>1.672531e+09</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>0.0</td> </tr> </tbody> </table> <p> </p> <p> </p>
Fig. 3 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
Fig. 3 Seasonal variation of sibling species (An. coluzzii and An. gambiae) in the study area. Abbreviations: DS, dry season; RS, rainy season
Fig. 2 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
Fig. 2 Mortality rate of Anopheles gambiae Kisumu (laboratory susceptible strain) after 30 min exposure to cement and mud walls treated with pirimiphos-methyl in 2017 (a) and 2018 (b). The red line indicates the WHO efficacy threshold (mortality of 80%) of an insecticide
Microsoft Indoor Localization Competition 2018 Dataset
<p>Detailed ground truth measurements and error visualization for each team, as well as the 3D point cloud of the evaluation area related to the Microsoft Indoor Localization Competition 2018.</p> <p>Additional details can be found here:</p> <p>https://www.microsoft.com/en-us/research/event/microsoft-indoor-localization-competition-ipsn-2018/</p>
Dataset for Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning
<p>The dataset titled <strong>"Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning</strong><strong>"</strong> was collected to support the development of an indoor localization system that operates at the room level. The dataset includes measurements of Received Signal Strength Indication (RSSI) from Bluetooth Low Energy (BLE) beacons (specifically the iBKS105 model) recorded by an ESP32 device. These RSSI values were captured across various rooms, allowing for precise localization within an indoor environment. The dataset is particularly useful for research in indoor localization system including machine learning-based localization algorithms.</p>
UTMInDualSymFi: A Dataset of Dual-band Wi-Fi RSSI Data in Symmetric Indoor Environments
<p>The UTMInDualSymFi database contains a comprehensive source of dual-band Wi-Fi RSSI data.</p> <p>In total, Wi-Fi RSSI data for fingerprinting positioning were collected from 4 residential buildings <br> within Universiti Teknologi Malaysia (UTM) campus:</p> <p> - building 1 = F04 (raw training data + test data + radio maps)<br> - building 2 = CX1 (raw training data + test data + radio maps)<br> - building 3 = F03 (test data only)<br> - building 4 = CY2 (test data only)</p> <p>Buildings 1,3 are similar in structure, symmetric in layout and locations of access points.<br> Buildings 2,4 are similar in structure, symmetric in layout and locations of access points.</p> <p>Each building is multi-floor, with multiple wings at each floor. Wings are labeled as A,B,C.</p> <p>The floor(s)-wing(s) of each building at which data were collected are listed:</p> <p> Building Floor-wing<br> -------- ----------<br> F04 3-A<br> F04 3-B<br> F04 3-C<br> F04 4-A<br> F04 4-B<br> F04 4-C<br> CX1 3-A<br> CX1 3-B<br> CX1 5-A<br> CX1 5-B<br> F03 4-A<br> F03 4-B<br> F03 4-C </p> <p>Data were collected on two laptop devices. Devices are distinguished by wireless network adapters:</p> <p> - Device 1 = Intel<br> - Device 2 = Qualcomm</p> <p>RSSI values range between -50 dBm and -100 dBm<br> RSSI of (+)100 dBm signifies non detection of a certain access point/source</p> <p>Further details are provided in 'README.txt' (included in the data package).</p> <p>Detailed, data elaboration and benchmark performance analysis are reported in the related data descriptor publication: </p> <p>Abdullah, A.; Haris, M.; Aziz, O.A.; Rashid, R.A.; Abdullah, A.S. UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments. <em>Data</em> <strong>2023</strong>, <em>8</em>, 14. https://doi.org/10.3390/data8010014</p> <p><br> </p>
Graphs for indoor building scenarios for IEEE 802.11 Networks
<p>Graph files and the corresponding EPS figures for the IEEE 802.11 indoor building scenarios used in the experiments for the following publications:</p> <p>* Tejedor-Romero, M., Gimenez-Guzman, J. M., Cruz-Piris, L., Herranz-Oliveros, D., & Marsa-Maestre, I. (2024). Optimal channel assignment on dense Wi-Fi networks using Thermodynamic Threshold Accepting. <em>Engineering Science and Technology, an International Journal</em>, <em>57</em>, 101797, https://doi.org/10.1016/j.jestch.2024.101797</p> <p>* J.M. Gimenez-Guzman, I. Marsa-Maestre, L. Cruz-Piris, D. Orden, M. Tejedor-Romero, "IEEE 802.11 graph models", Alexandria Engineering Journal, https://doi.org/10.1016/j.aej.2022.12.016</p>
Indoor overheating in different neighbourhoods in Colombo, Sri Lanka - dataset
<p>This dataset contains modelled indoor variables for a dwelling archetype in Colombo, Sri Lanka using EnergyPlus during a heatwave (23 to 28 Feb, 2020). The variables were modelled with different occupancy patterns, including elderly and family, and in two different neighborhoods, central and suburban.</p> <p> </p>
IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization
<p>IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization. </p> <p>This data is split up into two folders. </p> <p>"stationary_magnetometer_data" 0.1Hz samples of a RM3100 magnetometer that was kept in one position from July 2022 through February 2023. The data is partitioned into separate files for analytical convenience of our research. Each file here is a CSV with a timestamp (synchronized with chrony to a central computer) and the three components of the measured magnetic field. We did not calibrate this stationary magnetometer.</p> <p>"UAV_and_mocap_data" has many subfolders. Each subfolder is labeled by a date and a small description of the goals for that test segment. There is a single "EXPLANATION" file in each subfolder that gives more detail on the provided data. The data here includes the trajectory flown by the UAV, outdoor calibration data to adjust the raw magnetometer measurements, and IMU/motion capture data for each listed flight test. The EXPLANATION file should explain what trajectory was flown for each individual flight test. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.