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38 results for “Indoor environment”
A Danish high-resolution dataset for six office rooms with occupancy, indoor environment , heating, ventilation, lighting and room control monitoring
<p>A dataset containing measurement data for six office rooms in Aalborg Denmark.<br>All the measurements have been resampled to 5 minute resolution<br>The measurements consists of:</p> <ul> <li>BMS data for the rooms</li> <li>Occupancy for the rooms (from cameras)</li> <li>BMS data for the AHU supplying the rooms</li> <li>BMS data for the Heating system supplying the rooms</li> </ul> <p>Changes from v2<br>It was found that the pressure difference measurements across the exhaust fan was faulty and the following variables have therefore been removed:</p> <ul> <li>Ventilation:Fan__air_flow__exhaust</li> <li>Ventilation:Fan__pressure_difference__exhaust</li> </ul> <p>More data has been added, now increasing the dataset to span the rest of 2023. To better handle the changes between standard time and daylight-saving time the column named "timestamp" has been adjusted so the datetime format now follows the ISO 8601 format YYYY-MM-DDThh:mm:ss+hhmm. the +hhmm changes between 0100 (Danish standard time) and 0200 (Danish daylight-saving time).</p> <p> </p>
S35 | INDOORCT16 | Indoor Environment Substances from 2016 Collaborative Trial
<p>This is the collection associated with list S35 INDOORCT16 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S35 INDOORCT16 <strong>Indoor Environment Substances from 2016 Collaborative Trial</strong></p> <p>Lists of GC-MS and LC-MS compounds and DSFP output, plus merged files from the Indoor Dust Collaborative Trial, 2016 provided by Peter Haglund (UMU) and Pawel Rostkowski (NILU). Details in Rostkowski <em>et al</em>. 2019 DOI: <a href="https://link.springer.com/article/10.1007/s00216-019-01615-6">10.1007/s00216-019-01615-6</a></p> <p>Update 6 Feb 2020: two NA SMILES removed in CSV for PubChem upload. 17/7/2022: NA and N/A SMILES removed from CSV and XLSX, most replaced with structures; some are representative structures for classes.</p>
Supplementary Materials for "Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms"
<p>This dataset was created as suplementary material for research article: <strong>Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms</strong></p> <p>This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p> <p>The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office.</p> <p>The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files:</p> <p><strong>Data</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table:</p> <table> <caption>Data Point Coordinates</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"><strong>...</strong></th> </tr> </thead> <tbody> <tr> <th scope="row">A1</th> <td>0.85</td> <td>0.1</td> <td><strong>B1</strong></td> <td>1.85</td> <td>0.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A2</th> <td>0.85</td> <td>1.1</td> <td><strong>B2</strong></td> <td>1.85</td> <td>1.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A3</th> <td>0.85</td> <td>2.1</td> <td><strong>B3</strong></td> <td>1.85</td> <td>2.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">...</th> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A11</th> <td>0.85</td> <td>10.1</td> <td><strong>B11</strong></td> <td>1.85</td> <td>10.1</td> <td><strong>...</strong></td> </tr> </tbody> </table> <p><strong>Data_Eval</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected.</p> <p><strong>Processed_Data</strong></p> <p>Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values.</p> <p>The CSV files are in a format:</p> <blockquote> <p><code>X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</code></p> </blockquote> <p><strong>Data_Scenarios</strong></p> <p>This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table:</p> <table> <caption>Scenario Descriptions</caption> <thead> <tr> <th scope="col"> <p>Data Name</p> </th> <th scope="col"> <p>Scenario Description</p> </th> </tr> </thead> <tbody> <tr> <td>GPR00</td> <td>Only measured data, 50 samples per reference position</td> </tr> <tr> <td>GPR01</td> <td>Measured data with empty spots filled using Linear interpolation, 50 samples per reference position</td> </tr> <tr> <td>GPR02</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR03</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR04</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR05</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR06</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR07</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR08</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR09</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR10</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR11</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR12</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR13</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> </tbody> </table> <p>The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table:</p> <table> <caption>File Suffix Descriptions</caption> <tbody> <tr> <td> <p><strong>Suffix</strong></p> </td> <td> <p><strong>Suffix Description</strong></p> </td> </tr> <tr> <td>_trncrd</td> <td>Training Labels</td> </tr> <tr> <td>_trnrss</td> <td>Training RSSI Values</td> </tr> <tr> <td>_tstcrd</td> <td>Evaluation Labels</td> </tr> <tr> <td>_tstrss</td> <td>Evaluation RSSI Values</td> </tr> </tbody> </table> <p>These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc.</p> <p>The RSSI data are in format:</p> <blockquote> <p>RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</p> </blockquote> <p>The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0)</p> <blockquote> <p>X, Y, 0, 0, 0</p> </blockquote>
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>
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>
Build-in-Wood Regulation Analysis – Energy and Indoor Environment
<p>This dataset contains an analysis of selected EU Member State building regulations covering energy and indoor environment in multi-storey wood buildings. The data has been collected as part of the Build-in-Wood project (<a href="https://www.build-in-wood.eu/)">https://www.build-in-wood.eu/)</a> which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862820.</p> <p>Disclaimer: The presented data might be outdated, flawed, or otherwise incomplete. Users are responsible for checking the correctness of the presented data.</p>
Pre-Analysis Bioinformatics Files for The Microbiome and Volatile Organic Compounds Reflect the State of Decomposition in an Indoor Environment
<p>Data statistics before and after trimming, FastQC reports before and after trimming, MultiQC reports before and after trimming, commands for the Kraken2-Bracken analysis, and classification reports. Read the read.me file for file names and descriptions. </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>
Dataset for Vehicle Indoor Positioning in Industrial Environments with Wi-Fi, inertial, and odometry data
<p>Dataset collected in an indoor industrial environment using a mobile unit (manually pushed trolley) that resembles an industrial vehicle equipped with several sensors, namely, Wi-Fi, wheel encoder (displacement), and Inertial Measurement Unit (IMU).</p> <p>Sensors were connected to a Raspberry Pi (RPi 3B +), which collected the data from the sensors. Ground truth information was obtained with video camera pointed towards the floor, registering the times when the trolley passed by reference tags.</p> <p>List of sensors:</p> <ul> <li>4x <strong>Wi-Fi interfaces</strong>: Edimax EW7811-Un</li> <li>2x <strong>IMUs</strong>: Adafruit BNO055</li> <li>1x <strong>Absolute Encoder</strong>: US Digital A2 (attached to a wheel with a diameter of 125 mm)</li> </ul> <p>This dataset includes:</p> <ul> <li>1x <strong>Wi-Fi radio map</strong> that can be used for Wi-Fi fingerprinting.</li> <li>6x <strong>Trajectories</strong>: including sensor data + ground truth.</li> <li><strong>APs Information</strong>: list of APs in the building, including their position and transmission channel.</li> <li><strong>Floor plan:</strong> image of the building's floor plan with obstacles and non-navigable areas.</li> <li><strong>Python package</strong> provided for: <ul> <li>parsing the dataset into a data structure (Pandas dataframes).</li> <li>performing statistical analysis on the data (number of samples, time difference between consecutive samples, etc.).</li> <li>computing Dead Reckoning trajectory from a provided initial position.</li> <li>computing Wi-Fi fingerprinting position estimates.</li> <li>determining positioning error in Dead Reckoning and Wi-Fi fingerprinting.</li> <li>generating plots including the floor plan of the building, dead reckoning trajectories, and CDFs.</li> </ul> </li> </ul> <p> </p> <p>When using this dataset, please cite its data description paper:</p> <p>Silva , I.; Pendão, C.; Torres-Sospedra, J.; Moreira, A. Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data. <em>Data</em> <strong>2023</strong>, <em>8</em>, 157. <a href="https://doi.org/10.3390/data8100157" target="_blank" rel="noopener">https://doi.org/10.3390/data8100157</a> </p> <p> </p>
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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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.
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>
Supplementary material 1 from: Visagie CM, Yilmaz N, Renaud JB, Sumarah MW, Hubka V, Frisvad JC, Chen AJ, Meijer M, Seifert KA (2017) A survey of xerophilic Aspergillus from indoor environment, including descriptions of two new section Aspergillus species producing eurotium-like sexual states. MycoKeys 19: 1-30. https://doi.org/10.3897/mycokeys.19.11161
Species isolated from house dust using selective xerophilic media : Explanation note: Species isolated from house dust using selective xerophilic media, their occurrence and GenBank numbers for sequences generated for these strains.
Multi-Sensor Dataset in outdoor and indoor environment from Android Smart Devices and ULISS
<p>This dataset contains data acquired on various Android smart devices, i.e., smartphone and smartwatches (Mimir, TAU) and ULISS devices (AME-GEOLOC). The surveys have been performed in multiple environment (open-sky, urban canyon, light indoor, deep indoor), in different carrying mode (texting, swinging, pocket). The raw sensor data logged are : GNSS raw measurements and position fix, accelerometer, gyroscope, magnetometer, barometer, step counter/detector. The dataset is provided under the CC-BY 4.0 license. More information are provided inside the ''notes.txt' provided along the dataset, as well as in our related publication.</p>
Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts
<p>A detailed description of our dataset can be found here: <br> Nor Hisham, A.N.; Ng, Y.H.; Tan, C.K.; Chieng David, Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts. Data 2022, to appear.</p> <p>Please cite the paper when using the dataset.</p>
Clinical Effectiveness and Economical Impact of Medical Indoor Environment Counselors Visiting Homes of Asthma Patients
ClinicalTrials.gov study NCT02212483. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Polyphasic taxonomy of Aspergillus section Aspergillus (formerly Eurotium), and its occurrence in indoor environments and food
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Data from: Thirdhand smoke uptake to aerosol particles in the indoor environment
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Figure 6 from: Visagie CM, Yilmaz N, Renaud JB, Sumarah MW, Hubka V, Frisvad JC, Chen AJ, Meijer M, Seifert KA (2017) A survey of xerophilic Aspergillus from indoor environment, including descriptions of two new section Aspergillus species producing eurotium-like sexual states. MycoKeys 19: 1-30. https://doi.org/10.3897/mycokeys.19.11161
Figure 6 - Aspergillus megasporus (DAOMC 250799). a Colonies on MEA, MEA20S, MY10-12 (top row, from left to right), DG18, CY20S, MY50G (bottom row, from left to right) b Texture on DG18 c Asci d Ascospores e Cleistothecium f, g Conidiophores h Conidia. Scale bars: e = 50 µm, c, d, f–h = 10 µm.
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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.