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Dataset results
300 results for “indoor”
Social Media Indoor Tanning Study
ClinicalTrials.gov study NCT03834974. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Health Effects of Indoor Air Filtration in Healthy Chinese Adults
ClinicalTrials.gov study NCT02712333. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Quantifying the Epidemiological Impact of Targeted Indoor Residual Spraying on Aedes-borne Diseases
ClinicalTrials.gov study NCT04343521. IPD Sharing: YES. Countries: 1. Publications: 6.
Data from: Efficacy of Aedes aegypti control by indoor Ultra Low Volume (ULV) insecticide spraying in Iquitos, Peru
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Data from: Mechanical ventilation and indoor air quality in recently constructed homes in cool and humid climates of the U.S.
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Analysing indoor mycobiomes through a large‐scale citizen science study in Norway
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Data from: Indoor resting behavior of Aedes aegypti (Diptera: Culicidae) in Acapulco, Mexico
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Data from: Cooking, heating, insulating products and services (CHIPS) for Mongolian ger: Reducing energy, cost, and indoor air pollution
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Data from: Should we use ceiling fans indoors to reduce the risk of transmission of infectious aerosols?
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Data from: Mechanical ventilation and indoor air quality in recently constructed homes in the humid climate of the southeast U.S.
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Data for: The indoor mycobiomes of daycare centers are affected by occupancy and climate
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Controlled environment agriculture (CEA) breeding of watercress in an indoor vertical farm
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Impacts of life satisfaction, job satisfaction and Big Five personality traits on satisfaction with the indoor environment in Singapore
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Indoor air quality in new and renovated low‐income apartments with mechanical ventilation and natural gas cooking in California
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Data from: Indoor air quality in California homes with code-required mechanical ventilation
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Wi-Fi RSSI fingerprint dataset from two malls with validation routes in a shop-level for indoor positioning
<p><strong>Th</strong>e dataset is composed of the RSSI fingerprinting calibration of two malls employing four different smartphones with the random walking survey method. Moreover, the dataset includes validation routes performed with ten smartphones from various brands and labeled with the code of the shops where they were registered. For evaluation purposes, together with the samples, information related to the shopping center like the Euclidean distance between shops is provided. The files are described as follows:</p> <ul> <li> <p>building.csv’: two rows with the id of mall 1 and mall 2.</p> </li> <li> <p>‘floor.csv’: there is a row for each of the floors of the two malls. Three floors for mall 1 and two floors for mall 2 </p> </li> <li> <p>‘zones.csv’: for each zone is indicated the id of the mall and the id floor. There are 69 zones from mall 1 and 124 for mall 2 </p> </li> <li> <p>‘training.csv’: in this file are registered the ids and the description of the five different calibrations performed (one per smartphone) in each of the malls. </p> </li> <li> <p>‘training_sample.csv’: each row of the file has the RSSI in dBm recorded during the off-line calibration phase together with the timestamp, channel, and MAC of the AP.</p> </li> <li> <p>‘route.csv’: the id’s and the description of each of the validation routes are recorded in this file. </p> </li> <li> <p>‘route_sample.csv’: with a similar structure than the training_sample.csv, the RSSI, timestamp, channel, and MAC acquired during the validation routes are stored in this file.</p> </li> <li> <p>‘euclidea_distance.csv”: to evaluate the error of the different algorithms, this file recorded the euclidean distance from the center of a shop to each other in meters.</p> </li> </ul> <p>The dataset is to be cited as follows:<br> J. A. López-Pastor, A.J. Ruiz-Ruiz, A.J. García-Sánchez, J.L. Gómez-Tornero. Wi-Fi RSSI fingerprint dataset from two malls with validation routes in a shop-level for indoor positioning. Zenodo repository. DOI: 10.5281/zenodo.3698238</p>
Indoor low-cost sensor system data
<p>Data used for a comparison of the Matterport Pro2 3D, Ricoh Theta V, and Leica BLK360 in two indoor settings using the Leica RTC360 as reference. The test sites are lecture hall 101 at the Aalto University Department of Machine Engineering in Espoo, Finland, and the Tetra Conference Hall at the Hanaholmen Swedish-Finnish Cultural Centre in Espoo, Finland.</p> <p>The data are divided into four sets - one room geometry with the furniture removed and one detailed segment for both test sites - with four registered point clouds and three registered meshes being provided for each, as well as the reference. The point clouds stem from data obtained with each of the three sensor systems and processed with Matterport's processing system, with the Leica BLK360 data also being processed with Leica's proprietary processing system for a fourth point cloud. For the meshes, the Matterport processing system has been used to produce one mesh from each sensor system.</p>
Supplementary evaluation files for the paper: Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones
<p>This package contains evaluation supplementary files for the paper: <em>Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones</em> by Miroslav Opiela and František Galčík.</p> <p><strong>Contents: </strong></p> <ul> <li>ground_truth - real positions of checkpoints for given input files</li> <li>input - sensor measurements recordings with initial positions (also after floor transitions) and checkpoint labels </li> <li>maps - processed map models containing positions of points and connections (e.g., walls) in custom coordinate system. Reference to GNSS and map rotation is inducted in maps-meta.xml</li> <li>output - data processed by the localization system. JSON containing the applied method, its configuration, and all estimated positions. Errors for every folder are summarized in the csv file</li> <li>visualization - trajectories visualized for selected output files</li> <li>readme.txt - describes data formats used for particular files in this dataset and summarizes output files</li> </ul> <p><strong>Venues</strong></p> <p>Data are recorded in three buildings:</p> <ul> <li>codename: SA1, SA1_rotated - recorded by the author in the faculty building (Park Angelinum 9, 04001, Košice, Slovakia) using Lenovo tablet</li> <li>codename: AtlantisR0, AtlantisR-1, AtlantisR+1, AtlantisR+2 - the shopping mall Atlantis Le Centre (Boulevard Salvador Allende, 44800 Saint-Herblain, France). Dataset is from IPIN 2018 competition and loc_20180922_160206 is recorded by the author using Xiaomi Mi 5.</li> <li>codename: CNR_0, CNR_1, CNR_2 - the research institute building CNR (Via Giuseppe Moruzzi, 56127 Pisa, Italy). Dataset is from IPIN 2019 competition. </li> </ul> <p><strong>Used datasets</strong></p> <p>A subset of input data is derivated from available logfiles provided by organizers of IPIN 2018 and IPIN 2019 competitions:</p> <ul> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823964">http://dx.doi.org/10.5281/zenodo.2823964</a></li> <li>Jiménez, A. R.; Perez-Navarro, A.; Crivello, A.; Mendoza-Silva, G.; Ortiz, M.; Perul, J.; Seco, F. and Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site), Zenodo 2019. <a href="http://dx.doi.org/10.5281/zenodo.3606765">http://dx.doi.org/10.5281/zenodo.3606765</a> </li> </ul> <p><strong>Funding</strong></p> <p>The work was partially supported by the Slovak Grant Agency of the Ministry of Education and Academy of Science of the Slovak Republic under grant no. 1/0056/18 and by the Slovak Research and Development Agency under the contract no. APVV-15-0091.</p> <p><strong>Contact</strong></p> <p>For any further questions, please contact:</p> <p>Miroslav Opiela, miroslav.opiela@upjs.sk Institute of Computer Science, Faculty of Science, P. J. Šafárik University (UPJS), Košice, Slovakia</p>
Data from: Thirdhand smoke uptake to aerosol particles in the indoor environment
Aerosol composition measurements made in an indoor classroom indicate the uptake of thirdhand smoke (THS) species to indoor particles, a novel exposure route for THS to humans indoors. Chemical speciation of the organic aerosol fraction using mass spectrometric data and factor analysis identified a reduced nitrogen component, predominantly found in the indoor environment, contributing 29% of the indoor submicron aerosol mass. We identify this factor as THS compounds partitioning from interior surfaces to gas phase and then aerosol phase. Partitioning of THS vapors to aerosols requires an aqueous phase for reactive uptake of the reduced nitrogen species (RdNS), leading to seasonal differences in THS concentration indoors. RdNS protonate under the acidic conditions expected for indoor aerosols of outdoor origin. Controlled laboratory measurements performed using cigarette smoke deposited into a Pyrex vessel showed a similar partitioning behavior to aerosol of outdoor origin and mass spectral features comparable to the measured indoor THS factor after 1 week of residence time in the closed vessel. This study reports a new, potentially large THS exposure route from partitioning of surface volatile organic compounds into the aerosol phase and subsequent dispersion in a mechanically ventilated building.
Indoor Positioning Database
<p>Database containing measurements from wireless networks.</p>
ScienceDex guides
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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.