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300 results for “indoor”
Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots
<h2><strong>License</strong></h2> <p>The MDDRobots dataset is made available under the CC BY 4.0 license <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>.</p> <h2><strong>Summary</strong></h2> <p>The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor visual place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions. </p> <p>It is obligatory to cite the following paper in every work that uses the dataset: <br><strong>Wozniak, P., Krzeszowski, T. & Kwolek, B. Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots. <em>Sci Data</em> 12, 817 (2025). https://doi.org/10.1038/s41597-025-05124-3</strong></p> <h2><strong>Data description</strong></h2> <p>The data are divided into five sets (containing data for different cameras), which have further subsets. Each of the subsets: Training, Test 1, Test 2, and Test 3 consists of nine image sequences. A total of 89,550 three-channel RGB color images in PNG format are organized into 20 zip folders with a whole size of 34.3 GB. Each image in the sequence has a label that represents a room. The number of images for each subset differs due to the split into training and testing data. The difference also results from different methods of recording the image sequences. In order to have balanced data in the subsets, each room in the sequence has the same number of images. Different environmental changes were introduced in each subset. The data from Test 1 are closest to those from the training set. The differences between the sequences are mainly due to changes in the route, robot, and recording equipment. The rooms are well lighted, but not overexposed. The sequences from Test 3 present changed conditions, such as a different time of day, a changed lighting system, and intensive layout changes. The key change is the different paths of the human and the robot. This means a different perspective from previously recorded scenes. The Test 2 sequences pose the most difficult challenge because they contain various recorded activities performed by people moving around rooms. People can occlude important parts of the scene and pass in front of the camera. The images were anonymized by manually blurring the faces of observed people.</p> <h2><strong>Dataset structure<br></strong></h2> <ul> <li>RobotPiCamera_DataSet <ul> <li>DataSet_RobotPiCamera_RGB_train</li> <li>DataSet_RobotPiCamera_RGB_test1</li> <li>DataSet_RobotPiCamera_RGB_test2</li> <li>DataSet_RobotPiCamera_RGB_test3</li> </ul> </li> <li> Xtion_DataSet <ul> <li>DataSet_XTION_RGB_train</li> <li>DataSet_XTION_RGB_test1</li> <li>DataSet_XTION_RGB_test2</li> <li>DataSet_XTION_RGB_test3</li> </ul> </li> <li> GOPRO_DataSet <ul> <li>DataSet_GOPRO_RGB_train</li> <li>DataSet_GOPRO_RGB_test1</li> <li>DataSet_GOPRO_RGB_test2</li> <li>DataSet_GOPRO_RGB_test3</li> </ul> </li> <li>iPhone_DataSet <ul> <li>DataSet_IPHONE_RGB_train</li> <li>DataSet_IPHONE_RGB_test1</li> <li>DataSet_IPHONE_RGB_test2</li> <li>DataSet_IPHONE_RGB_test3</li> </ul> </li> <li>P40PRO_DataSet <ul> <li>DataSet_P40PRO_RGB_train</li> <li>DataSet_P40PRO_RGB_test1</li> <li>DataSet_P40PRO_RGB_test2</li> <li>DataSet_P40PRO_RGB_test3</li> </ul> </li> </ul> <p><em>Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png.</em></p> <p>Total Images (Images per Place)</p> <table> <tbody> <tr> <td>Subset</td> <td>Mounted</td> <td>Training</td> <td>Test 1</td> <td>Test 2</td> <td>Test 3</td> </tr> <tr> <td>Pi Camera</td> <td>Robot</td> <td>7200 (800)</td> <td>5400 (600)</td> <td>5400 (600)</td> <td>5400 (600)</td> </tr> <tr> <td>Xtion</td> <td>Robot</td> <td>7200 (800) </td> <td>1800 (200) </td> <td>1800 (200)</td> <td>1800 (200) </td> </tr> <tr> <td>GoPro</td> <td>Hand</td> <td>5400 (600)</td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500)</td> </tr> <tr> <td>iPhone</td> <td>Hand</td> <td>5400 (600) </td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500) </td> </tr> <tr> <td>P40Pro</td> <td>Hand</td> <td>5400 (600)</td> <td>4050 (450)</td> <td>3150 (350) </td> <td>3150 (350) </td> </tr> </tbody> </table> <h2><br>Further information</h2> <p>For any questions, comments or other issues please contact Piotr Woźniak <p.wozniak@prz.edu.pl>.</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.
Ljubljana Multi-Sensor Indoor and Outdoor PM Exposure, Environment, and Personal Health Dataset
<p>This dataset was compiled between February 16, 2019, and May 25, 2019, involving 82 participants residing in the municipality of Ljubljana. Data collected before March 12th represents the "heating season," while data after April 27th corresponds to the "non-heating season." This dataset is the cleaned/filtered version with outliers caused by software or hardware errors removed.</p><p>Derived from the larger ICARUS project in Ljubljana, this dataset integrates various data sources, including:</p><ul><li>Participant Questionnaires: Containing certain individual information, i.e., age, height, gender.</li><li>Time Activity Diaries: Providing hourly activity records for each participant.</li><li>Personal PM Monitors: Measuring indoor and outdoor PM concentrations.</li><li>Smart Activity Trackers: Recording heart rate and movement data.</li><li>Indoor Air Quality Station: Capturing indoor air quality parameters.</li></ul><p>The dataset includes the calculation of inhalation rate based on heart rate data, allowing for the determination of inhalation rate-adjusted exposure or intake dose.</p><p>This version of the dataset is in .csv format.</p>
RAV4D: A Radar-Audio-Visual Dataset for Indoor Multi-Person Tracking
Open the record for dataset details and reuse information.
Indoor climate projections at 90 workplaces in the Upper Rhine Valley modelled by artificial neural networks
<p>The uploaded files contain the modelled indoor temperature (Ti) and physiologically equivalent temperature (PETi) data at 90 different workplaces in the Upper Rhine Valley, presented in the article "Climate projections of human thermal comfort for indoor workplaces" by Sulzer and Christen (2024), <a href="https://doi.org/10.1007/s10584-024-03685-7">https://doi.org/10.1007/s10584-024-03685-7</a>. The different csv files contain metadata to the different workplaces, the training data recorded in 2021 and 2022, the modelled data for the historical time period 1970-1999 using ERA5-Land data as input data, and for the future time period 2070-2099 using 22 different climate projections as input data. </p> <p>In the file <a href="../api/records/8229253/draft/files/Workplaces_training_2021-2022.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_training_2021-2022.csv</a> you can find the measured data at the workplaces used for training of the models and in <a href="../api/records/8229253/draft/files/Workplaces_metadata.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_metadata.csv</a> you can find some metadata about each workplace.</p> <p> </p>
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>
Thermo-staat project citizen science indoor temperature and humidity measurements for researching heat stress in the Netherlands
<p>These datasets contains raw data collected within the Thermo-staat citizen science project per the full year.<br>The aim of the project was to get insight in heat stres and if this problem is subject to social inequality.<br>Measurements contain indoor temperature and humidity data. Sensors in the dataset are bound to different rooms in a home and have different periods of activity. Each sensor has metadata about the room/situation attached. The aim was to get most sensors active in the summer.<br>Measurements where not done at a constant frequency, depending on the connectivity sensors did send up to once every 20 seconds.</p> <p>More information on the project on the website of the <a href="https://thermo-staat.nl/">Thermo-staat project</a></p> <p>More infromation on the <a href="https://thermo-staat.nl/download">data</a> </p> <p>Live data collection <a href="https://thermo-staat.waag.org/api/status_all">status</a> </p>
The impact of indoor residual spraying on Plasmodium falciparum microsatellite variation in an area of high seasonal malaria transmission in Ghana, West Africa
<p>Here, we report the first population genetic study to examine the impact of indoor residual spraying (IRS) on Plasmodium falciparum in humans. This study was conducted in an area of high seasonal malaria transmission in Bongo District, Ghana. IRS was implemented during the dry season (November-May) in three consecutive years between 2013 and 2015 to reduce transmission and attempt to bottleneck the parasite population in humans towards lower diversity with greater linkage disequilibrium. The study was done against a background of widespread use of long-lasting insecticidal nets, typical for contemporary malaria control in West Africa. Microsatellite genotyping with 10 loci was used to construct 392 P. falciparum multilocus infection haplotypes collected from two age-stratified cross-sectional surveys at the end of the wet seasons pre- and post-IRS. Three-rounds of IRS, under operational conditions, led to a >90% reduction in transmission intensity and a 35.7% reduction in the P. falciparum prevalence (p < .001). Despite these declines, population genetic analysis of the infection haplotypes revealed no dramatic changes with only a slight, but significant increase in genetic diversity (H<sub>e</sub> : pre-IRS = 0.79 vs. post-IRS = 0.81, p = .048). Reduced relatedness of the parasite population (p < .001) was observed post-IRS, probably due to decreased opportunities for outcrossing. Spatiotemporal genetic differentiation between the pre- and post-IRS surveys (D = 0.0329 [95% CI: 0.0209 - 0.0473], p = .034) was identified. These data provide a genetic explanation for the resilience of P. falciparum to short-term IRS programmes in high-transmission settings in sub-Saharan Africa.</p>
Spatiotemporal variation of the indoor mycobiome in daycare centers
Abstract Background Children spend considerable time in daycare centers in parts of the world and are exposed to the indoor micro- and mycobiomes of these facilities. The level of exposure to microorganisms varies within and between buildings, depending on occupancy, climate, and season. In order to evaluate indoor air quality, and the effect of usage and seasonality, we investigated the spatiotemporal variation in the indoor mycobiomes of two daycare centers. We collected dust samples from different rooms throughout a year and analyzed their mycobiomes using DNA metabarcoding. Results The fungal community composition in rooms with limited occupancy (auxiliary rooms) was similar to the outdoor samples, and clearly different from the rooms with higher occupancy (main rooms). The main rooms had higher abundance of Ascomycota, while the auxiliary rooms contained comparably more Basidiomycota. We observed a strong seasonal pattern in the mycobiome composition, mainly structured by the outdoor climate. Most markedly, basidiomycetes of the orders Agaricales and Polyporales, mainly reflecting typical outdoor fungi, were more abundant during summer and fall. In contrast, ascomycetes of the orders Saccharomycetales and Capnodiales were dominant during winter and spring. Conclusions Our findings provide clear evidences that the indoor mycobiomes in daycare centers are structured by occupancy as well as outdoor seasonality. We conclude that the temporal variability should be accounted for in indoor mycobiome studies and in the evaluation of indoor air quality of buildings.
Selected SSB-based RF-EMF Indoor Measurement Campaigns data
<p>This presents the selected Synchronisation Signal Block (SSB) based radio frequency electromagnetic field (RF-EMF) measurement data acquired under measurement campaign in indoor environment. This data links to the findings shown in Section 3.3 of D1 report at http://empir.npl.co.uk/5grfex/wp-content/uploads/sites/55/2022/01/updated-EMPIR-18SIP02-5GRFEX-Deliverable-Report-D1.pdf. </p> <p>This work was supported by the EU project 5GRFEX entitled – ‘Metrology for RF exposure from Massive MIMO 5G base station: Impact on 5G network deployment’ (this project has received funding from the support for impact (SIP) programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme), under European Association of National Metrology Institutes (EURAMET) Reference 18SIP02.</p>
Unity project for VR user study on adaptive mobile indoor route guidance
<p>This unity project was used to conduct a user study in VR that investigates the link between the cognitive load induced by route instruction types and building configuration during indoor route guidance. In the project a 3D model can be found of a fictive building, 10 scenes for 10 different routes in this building, 3 types of route instructions for these 10 routes, code to conduct the experiment and track eye movements and the location of participants during the experiment.</p>
Indoor Positioning Simulation For Examination And Correction Of Occupancy Limits In Architectural Design
<p>Dataset that contains the images of scenarios used for the analysis and the analysis itself.</p>
Raw data used in the paper User involvement before the development of an indoor RPAS for the creative industries
<p>This file includes the data used in the paper: de-Miguel-Molina, B., de-Miguel-Molina, M., Santamarina-Campos, V., & Segarra-Oña, M. (2021). User involvement before the development of an indoor RPAS for the creative industries. <em>International Journal of Micro Air Vehicles</em>, <em>13</em>, 1756829321992140.</p> <p>Please, cite the paper if you use the data.</p> <p> </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.