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300 results for “indoor”
Keyahta/grabay: Indoor localization dataset
<p>HUSTData is an indoor localization dataset. It is collected from a typical lecture building D, with a total area of 482m2 in Huazhong University of Science and Technology. Source codes of our previous work using this dataset with detailed explanations are in progress. Click the GitHub button on the right or use https://github.com/Keyahta/grabay to go to the dataset. </p>
Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus - data set of indoor temperature and relative humidity
<p>This data supplements the journal article: </p> <p>Buechler E, Pallin S, Boudreaux P, Stockdale M. Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus. <em>Journal of Building Physics</em>. 2017;41(3):225-246. doi:<a href="https://doi.org/10.1177/1744259117701893">10.1177/1744259117701893</a></p> <p>Abstract:</p> <p>The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, heating, ventilation, and air-conditioning system performance, and occupant comfort. Therefore, indoor climate data are generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies are influenced by weather, occupant behavior, and internal loads and are generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The purpose of this study was to probabilistically model homes with the simulation engine EnergyPlus to generate indoor climate data that are widely applicable to residential buildings. Monte Carlo methods were used to perform 840,000 simulations on the Oak Ridge National Laboratory supercomputer (Titan) that accounted for stochastic variation in internal loads, air tightness, home size, and thermostat set points. The Effective Moisture Penetration Depth model was used to consider the effects of moisture buffering. The effects of location and building type on indoor climate were analyzed by evaluating six building types and 14 locations across the United States. The average monthly net indoor moisture supply values were calculated for each climate zone, and the distributions of indoor air temperature and relative humidity conditions were compared with ASHRAE 160 and EN 15026 design conditions. The indoor climate data will be incorporated into an online database tool to aid the building community in designing effective heating, ventilation, and air-conditioning systems and moisture durable building envelopes.</p> <p>This supplemental data set includes the hourly temperature and relative humidity for the 10th, 50th, and 90th percentile simulations for each building type in each climate zone. The column headings are of the following format buildingtype_climatezone_output_percentile.</p> <p>There are six building types, B1 (unfinished basement 1-story), B2 (unfinished basement 2-story), C1 (unvented crawlspace 1-story), C2 (unvented crawlspace 2-story), S1 (slab 1-story), and S2 (slab 2-story).</p>
A Dataset of IQ samples in Indoor Jamming Scenarios
<p>This dataset includes physical-layer radio information (IQ samples) acquired from indoor communications affected by different types of jamming techniques. Specifically, it includes data acquired from 7 different Software Defined Radios (SDRs), i.e., the USRP Ettus Research X310, operating in an office environment while the transmitter and receiver communicates without the Line of Sight (nLoS). Each experiment is characterized by a transmitter, a receiver, and a jammer. While the hardware of the transmitter and the receiver are kept the same for all the experiments, the hardware of the jammer is changed adopting 5 different radios of the same model and brand. The dataset includes different jamming types, e.g., no jamming (silent), tone (sinusoidal), and Gaussian noise. Moreover, the dataset includes different transmission distances and jamming power levels. In each experiment, a pre-determined sequence of bits ([0, 255]) has been modulated using the BPSK scheme, and then stored, at the receiver, as a 2-columns matrix of raw I/Q samples.</p>
The dataset for DATA 2022 paper "Dataset: An Indoor Smart Traffic Dataset and Data Collection System"
<p>The dataset for DATA 2022 paper "Dataset: An Indoor Smart Traffic Dataset and Data Collection System"</p> <p>This archive contains a traffic light dataset that can be used for traffic light detection/classification. The dataset is collected from an indoor smart traffic testbed. In this testbed, we use fences to simulate the road's boundaries and use movable toy traffic signs and traffic lights to simulate those in real-world traffic scenes. An F1TENTH vehicle drives along the fence autonomously. Two cameras are mounted on both sides of the vehicle, which capture images of traffic lights and traffic signs on both sides of the track.</p> <p>This dataset contains 3507 images captured by the F1TENTH vehicle. Each image comes with ground truth bounding boxes that enclose the traffic lights and a label indicating the current state of the traffic light, 0 for a green light and 1 for a red light.</p> <p>Please cite our paper if you are using this dataset.</p>
SolarWalk Dataset: Occupant Identification using Indoor Photovoltaic Harvester Output Voltage
<p>We present the dataset containing time-series open circuit output voltage traces of indoor photovoltaic cell corresponding to occupant door crossing events to perform smart home occupant identification. We collect shadow patterns of five participants from two different doors in two rooms of a building. We collect a total of 900 door entry and exit events during different hours of the day. We sample the voltage at 50 hz and provide the raw timestamped data. We also pre-process the data to filter the event of interest and label the data with occupant id and type of door events. We provide two example scripts to demonstrate how to process raw data and apply machine learning models for occupant identification using solar cell voltage samples. </p>
Crowdsourced WiFi database and benchmark software for indoor positioning
<p>This dataset contains two Wi-Fi databases (one for training and one for test/estimation purposes in indoor positioning applications), collected in a crowdsourced mode (i.e., via 21 different devices and different users), together with a benchmarking utility software (in Matlab and Python) to illustrate various algorithms of indoor positioning based solely on WiFi information (MAC addresses and RSS values). </p> <p>The data was collected in a 4-floor university building in Tampere, Finland, during Jan-Aug 2017 and it comprises 687 training fingerprints and 3951 test or estimation fingerprints.</p> <p>13.10.2017: Version 2 uploaded; the revised version contains improved readme files and improved Python SW.</p> <p>The dataset and/or the associated software are to be cited as follows:</p> <p>E.S. Lohan, J. Torres-Sospedra, P. Richter, H. Leppäkoski, J. Huerta, A. Cramariuc, “Crowdsourced WiFi-fingerprinting database and benchmark software for indoor positioning”, Zenodo repository, DOI 10.5281/zenodo.889798</p>
Microdata on vector abundance and IRS quality assurance (Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study)
<p>This repository contains the microdata on vector abundance and quality assurance of indoor residual spraying (IRS) that was used to estimate the impact of IRS on sandfly abundance and incidence of visceral leishmaniasis (VL) in India, as described in the paper "Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study" by Coffeng et al (<a href="https://doi.org/10.1016/S1473-3099(24)00420-1">https://doi.org/10.1016/S1473-3099(24)00420-1</a>). These data were collected as part of a BMGF-funded project led by dr. Michael Coleman at the Liverpool School for Tropical Medicine, as described in an earlier paper by Deb et al (<a href="https://doi.org/10.1371/journal.pntd.0009101">https://doi.org/10.1371/journal.pntd.0009101</a>).</p> <p>This repository does not include microdata on VL cases as these are owned by India's National Center for Vector Borne Disease Control (NCVBDC, <a href="https://ncvbdc.mohfw.gov.in/" target="_blank" rel="nofollow noreferrer noopener">https://ncvbdc.mohfw.gov.in/</a>).</p>
Data set for study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"
<p>Input data for the study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"</p>
Fig. 4 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. 4 Parity rate of Anopheles gambiae (s.l.) collected in treated and control areas
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>
The University of California, Riverside Environmental Chamber Data Base for Evaluating Oxidant Mechanism. Indoor Chamber Experiments through 1993
<strong><span>THE UNIVERSITY OF CALIFORNIA, RIVERSIDE ENVIRONMENTAL CHAMBER DATA BASE FOR EVALUATING OXIDANT MECHANISMS</span></strong> <p>William P. L. Carter, Dongmin Luo, Irina L. Malkina and Dennis Fitz</p> <p>Project Report for<br>Cooperative Agreement 815779, United States Environmental Protection Agency</p> <p>March 20, 1995</p> <p>This two report describes the data base of University of California, Riverside, environmental chamber experiments for use when evaluating photochemical mechanisms for urban and regional airshed models. This includes data obtained using the Statewide Air Pollution Research Center (SAPRC) Evacuable Chamber (EC), Indoor Teflon Chamber #1 (ITC), Indoor Teflon Chamber #2 (ETC), Dividable Teflon Chamber (DTC), and Xenon arc Teflon Chamber (XTC) between September of 1975 through November of 1993. This document provides backing information and data for that data set as well. This document lists and summarizes the experiments, summarizes the facility and procedures employed, documents the analytical and monitoring methods and their calibration data and associated uncertainties, assigns and documents the input data needed to conduct model simulations of the experiments in the present data base, and describes the format of the data sets which are distributed with this document on computer diskettes. Files are included in the distribution to permit modeling of the experiments in the present data base using the SAPRC-90 and the Carbon Bond IV chemical mechanisms, though a full mechanism evaluation procedure is beyond the scope of this report. Recommendations are made concerning the steps that need to be taken before using these data to evaluate chemical mechanisms.</p> <p>This report consists of two volumes. Volume 1 contains the main body of the text documenting the data base, and Volume 2 contains the three appendices. Appendix A contains printouts of spreadsheets containing summaries of the runs in the data base. Appendix B contains tabulations of the NOx and GC calibration data, which are too lengthy to include in the main body of the report. Appendix C describes how to install the distributed data files and software on a computer and how to conduct initial model simulations of the runs using the SAPRC modeling software and the SAPRC-90 and Carbon Bond IV mechanisms.</p>
Associated raw data to the PhD thesis: Design and evaluation of a camera-based indoor positioning system for forklift trucks
<p>This is a test data set for marker-based augmented reality algorithms used to locate ground conveyors in an industrial environment. It was recorded in the testing area of the chair fml at TUM to develop and evaluate algorithms for locating forklift trucks in my PhD thesis "Entwicklung und Evaluierung einer kamerabasierten Lokalisierungsmethode für Flurförderzeuge" (see https://mediatum.ub.tum.de/?id=1395267 available in German only).</p>
Associated raw data to the publication: An accurate and efficient camera-based indoor positioning approach for intralogistic environments (MHCL 2015)
<p>This is a test data set for marker-based augmented reality algorithms used to locate ground conveyors in an industrial environment. It was recorded in the testing area of the chair fml at TUM to develop and evaluate algorithms for locating forklift trucks in the publication "An accurate and efficient camera-based indoor positioning approach for intralogistic environments" at MHCL 2015 conference (see https://mediatum.ub.tum.de/1286589 and http://www.fml.mw.tum.de/fml/images/Publikationen/MHCL_2015_jung_submitted.pdf). Originally these files were recorded and used as uncompressed 8-bit grayscale bitmaps. The images were losslessly compressed to png files in order to reduce the test set file size (by approx. factor 3.5)</p>
Raw Experimental Data for work presented in 'Leveraging Chaos for Wave-Based Analog Computation: Demonstration with Indoor Wireless Communication Signals'
<p>This is the raw experimental data for the work presented in 'Leveraging Chaos for Wave-Based Analog Computation: Demonstration with Indoor Wireless Communication Signals', to be published in Physical Review X.</p> <p> </p> <p>https://journals.aps.org/prx/accepted/dc07aKdcFa91ea06d2949139dac733fa62ce1c02c</p> <p> </p> <p>See the README files and sample pieces of codes for an explanation of the data.</p>
Surface Type Classification for Autonomous Robot Indoor Navigation - Dataset
<p>Surface Type Recognition with Inertial Measurement Unit (IMU).</p> <p>The dataset contains time series samples with 10 features each, related to orientation, velocity and acceleration. Each time series (of lenght 128) includes its corresponding surface type annotation.</p> <p>The data has been also divided in groups for easier cross-validation (80 groups present)</p> <p>A total of 9 different surface types are present in the dataset.</p> <p>"X_data.npy" contains the time series samples of dimension 7626x10x128<br> "label.npy" contains the label information for each sample (dimension 7626x1)<br> "groups.npy" contains the group information for each sample (dimension 7626x1)<br> "details.csv" contains for each sample the group information and the corresponding label</p> <p> </p>
Indoor Object Detection Dataset
<p>We introduce a new fully labeled object detection dataset collected from indoor scenes. This indoor dataset consists of 2213 image frames containing seven classes. In contrast to existing indoor datasets, our dataset includes a variety of background, lighting conditions, occlusion and high inter-class differences.<br> For detail information, please refer to our paper: <a href="https://doi.org/10.1109/EUVIP.2018.8611732">10.1109/EUVIP.2018.8611732</a></p>
Dataset: Indoor Localization with Narrow-band, Ultra-Wideband, and Motion Capture Systems
<p><strong>Localization Dataset README</strong></p> <p>In this, data for BLE and UWB calibration is included through the use of UWB and OptiTrack Motion capture system respectively. There are two sets of data each covering two scenarios; these being walking and trolley.</p> <p>The two groups containing the data are laid out identically as indicated below.</p> <p><strong>Bluetooth Low Energy / Ultra-Wideband</strong></p> <p>| Session ID | 8 x AoA | 8 x RSSI | BLE x | BLE y | UWB x | UWB y |</p> <p>Where :</p> <p>- AoA is Angle of Arrival, with two values given for each anchor node.<br> - RSSI is the Received Signal Strength Indicator, also with two values given for each anchor node.<br> - BLE x and y location estimates [1].<br> - UWB x and y location estimates.</p> <p><strong>Number of Samples (BLE/UWB)</strong></p> <p>The datasets contains the following number of samples:</p> <p>- Walk - 4896 samples.<br> - Trolley - 4856 samples.</p> <p><strong>Ultra-Wideband / OptiTrack Motion Capture</strong></p> <p>| Session ID | 4 x CIR | 4 x PSA | Distance | UWB x | UWB y | OPT x | OPT y |</p> <p>Where:</p> <p>- CIR is Channel Impulse Response for each received signal from the Anchors to the target tag.<br> - PSA is the Preamble Symbol Accumulation, from each of the 4 Anchors to the target tag.<br> - Distances of the tag to each of the 4 anchors.<br> - UWB x and y location estimates.<br> - OptiTrack location estimates.</p> <p><strong>Number of Samples (UWB/OPT)</strong></p> <p>- Walk - 2797 samples.<br> - Trolley - 3202 samples.</p> <p><strong>Total Number of Samples : 15751</strong></p> <p>[1]: A Khan, T Farnham, R Kou, U Raza, T Premalal, A Stanoev, W Thompson, "Standing on the Shoulders of Giants: AI-driven Calibration of Localisation Technologies", IEEE Global Communications Conference (GLOBECOM) 2019</p>
Raw environmental indoor sensor data
<p>Dataset used in Publication:</p> <p>C. Arendt, S. Böcker and C. Wietfeld, "Data-Driven Model-Predictive Communication for Resource-Efficient IoT Networks," <em>2020 IEEE 6th World Forum on Internet of Things (WF-IoT)</em>, New Orleans, LA, USA, 2020, pp. 1-6, doi: 10.1109/WF-IoT48130.2020.9221019. <a href="https://ieeexplore.ieee.org/document/9221019" target="_blank" rel="noopener">[link]</a> <a href="https://cni.etit.tu-dortmund.de/storages/cni-etit/r/Research/Publications/2020/Arendt_WF-IoT/Arendt_WF-IoT_04_2020.pdf" target="_blank" rel="noopener">[authors version]</a></p> <p>When utilizing this dataset, proper attribution to the original publication is required. Please ensure to reference the aforementioned publication in any derived works or research outputs.</p>
Table 7 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
<p><b>Table 7</b> Indoor density of <i>An. gambiae</i> (<i>s.l</i>.) in IRS and control areas</p><table><tbody><tr><th>Period</th><th>Area</th><th>No. of rooms</th><th>No. collected</th><th>Density/room</th><th><i>P</i> -value</th></tr></tbody><tbody><tr><th>Before IRS (June–October 2016)</th><td>Baseline</td><td>610</td><td>1001</td><td>1.64</td><td><0.0001</td></tr><tr><th>After 1st round of IRS (June–October 2017)</th><td>After IRS areas</td><td>673</td><td>273</td><td>0.41</td><td></td></tr><tr><th>After 1st round of IRS (June–September 2017)</th><td>After IRS areas</td><td>551</td><td>208</td><td>0.38</td><td><0.0001</td></tr><tr><th></th><td>Control areas</td><td>160</td><td>244</td><td>1.53</td><td></td></tr><tr><th>After 2nd round of IRS (June–August 2018)</th><td>After IRS areas</td><td>242</td><td>117</td><td>0.48</td><td><0.0001</td></tr><tr><th></th><td>Control areas</td><td>88</td><td>155</td><td>1.76</td><td></td></tr></tbody></table>
Table 4 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
<p><b>Table 4</b> Biting location of <i>An. gambiae</i> (<i>s.l</i>.) in IRS and control areas</p><table><tbody><tr><th>Period</th><th>Area</th><th>Location</th><th>No. collected</th><th>Person/night</th><th>HBR (b/p/n)</th><th><i>P</i> -value</th></tr></tbody><tbody><tr><th>Before IRS (June–October 2016)</th><td>Future IRS</td><td>Indoor</td><td>1156</td><td>128</td><td>9.03</td><td>0.00098</td></tr><tr><th></th><td></td><td>Outdoor</td><td>1002</td><td>128</td><td>7.83</td><td></td></tr><tr><th>After 1st IRS round (June–September 2017)</th><td>IRS</td><td>Indoor</td><td>544</td><td>112</td><td>4.86</td><td><0.0001</td></tr><tr><th></th><td></td><td>Outdoor</td><td>1150</td><td>112</td><td>10.27</td><td></td></tr><tr><th></th><td>Control</td><td>Indoor</td><td>678</td><td>52</td><td>13.04</td><td><0.0001</td></tr><tr><th></th><td></td><td>Outdoor</td><td>446</td><td>52</td><td>8.58</td><td></td></tr><tr><th>After 2nd IRS round (June–August 2018)</th><td>IRS</td><td>Indoor</td><td>599</td><td>80</td><td>7.49</td><td>0.233</td></tr><tr><th></th><td></td><td>Outdoor</td><td>642</td><td>80</td><td>8.03</td><td></td></tr><tr><th></th><td>Control</td><td>Indoor</td><td>568</td><td>40</td><td>14.2</td><td><0.0001</td></tr><tr><th></th><td></td><td>Outdoor</td><td>313</td><td>40</td><td>7.83</td><td></td></tr></tbody></table><p><i>AbbreviatioNS</i>: HBR,human biting rate;b/p/n,bite/person/night</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.