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300 results for “indoor”
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.
Data for "What it Takes to Get There: Spatial Cognition and Autonomous Indoor Robot Navigation"
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Indoor Positioning - de Blasio et al - dataset paper IEEE Access (2018)
<p>This zip file contains the raw BLE data in .xlsx format obtained in the tests detailed in the following article (attached pdf):</p> <p>Gabriel de Blasio, Alexis Quesada-Arencibia, Carmelo R. García, José Carlos Rodríguez-Rodríguez, Roberto Moreno-Díaz jr. A Protocol-Channel-Based Indoor Positioning Performance Study for Bluetooth Low Energy, IEEE Access vol. 6, pp. 33440-33450 (2018)<br>DOI: 10.1109/ACCESS.2018.2837497</p>
Indoor Positioning - de Blasio et al - dataset paper Sensors (2019)
<p>This zip file contains the raw BLE data in .xlsx format obtained in the tests detailed in the following article (attached pdf):</p> <p>de Blasio, Gabriele Salvatore, Rodríguez-Rodríguez, José C., Garcia, Carmelo R., Quesada-Arencibia, Alexis. Beacon-related parameters of bluetooth low energy: development of a semi-automatic system to study their impact on indoor positioning systems, Sensors vol. 19(4), (2019) DOI: 10.3390/s19143087</p>
BLE RSSI Dataset for Indoor localization
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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>
Gas cooking impact on indoor NO2 pollution
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Data Set for Probabilistic Indoor Temperature Forecasting
<h1>1. Dataset Manifest</h1> <p>This text provides a description of the dataset used for model training and evaluation in our study "A Tutorial on Deep Learning for Probabilistic Indoor Temperature Forecasting". The dataset consists of various simulated thermal and environmental parameters for different room configurations. Below, you will find a table detailing each column in the dataset along with its description and unit of measurement. </p> <h2>1.1. Columns Description</h2> <table> <tbody> <tr> <th>Column Name</th> <th>Description</th> <th>Unit</th> </tr> <tr> <td><code>time</code></td> <td>Time stamp of the measurement</td> <td>-</td> </tr> <tr> <td><code>ZweiPersonenBuero.TAir</code></td> <td>Air temperature inside a two-person office</td> <td>°C</td> </tr> <tr> <td><code>heatStat.Heat.Q_flow</code></td> <td>Heating rate in the room</td> <td>W</td> </tr> <tr> <td><code>weaDat.AirPressure</code></td> <td>Atmospheric pressure</td> <td>Pa</td> </tr> <tr> <td><code>weaDat.AirTemp</code></td> <td>Outside air temperature</td> <td>°C</td> </tr> <tr> <td><code>weaDat.SkyRadiation</code></td> <td>Longwave sky radiation</td> <td>W/m²</td> </tr> <tr> <td><code>weaDat.TerrestrialRadiation</code></td> <td>Terrestrial radiation</td> <td>W/m²</td> </tr> <tr> <td><code>weaDat.WaterInAir</code></td> <td>Absolute humidity</td> <td>g/kg</td> </tr> <tr> <td><code>VAir</code></td> <td>Air volume in the room</td> <td>m³</td> </tr> <tr> <td><code>AExt0</code></td> <td>Exterior wall area facing the south</td> <td>m²</td> </tr> <tr> <td><code>AExt1</code></td> <td>Exterior wall area facing the north</td> <td>m²</td> </tr> <tr> <td><code>AInt</code></td> <td>Total interior wall area</td> <td>m²</td> </tr> <tr> <td><code>AFloor</code></td> <td>Floor area of the room</td> <td>m²</td> </tr> <tr> <td><code>AWin0</code></td> <td>Window area facing the south</td> <td>m²</td> </tr> <tr> <td><code>AWin1</code></td> <td>Window area facing the north</td> <td>m²</td> </tr> <tr> <td><code>azi0</code></td> <td>Azimuth (direction) of the first exterior wall</td> <td>rad</td> </tr> <tr> <td><code>azi1</code></td> <td>Azimuth (direction) of the second exterior wall</td> <td>rad</td> </tr> <tr> <td><code>id</code></td> <td>Unique identifier for the room configuration</td> <td>-</td> </tr> <tr> <td><code>is_holiday</code></td> <td>Indicator whether the day is a holiday (1 for yes, 0 for no)</td> <td>-</td> </tr> </tbody> </table> <h2>1.2. Note on Multi-Value Columns</h2> <div> <p>For rooms with multiple exterior walls (rooms 15-30):</p> <ul> <li><strong>AExt</strong>: {Exterior wall 1 area, Exterior wall 2 area}</li> <li><strong>AWin</strong>: {Window area on exterior wall 1, Window area on exterior wall 2}</li> <li><strong>azi</strong>: {Azimuth of exterior wall 1, Azimuth of exterior wall 2}</li> </ul> <p>Example:</p> <ul> <li><strong>AExt</strong> = {10, 15}</li> <li><strong>AWin</strong> = {2, 0}</li> <li><strong>azi</strong> = {0, 3.1415}</li> </ul> <p>This indicates two exterior walls with areas of 10 m² and 15 m² facing south (0 rad) and north (3.1415 rad), respectively. The south-facing wall has a window of 2 m², while the north-facing wall has no window.</p> <h2>1.3. Data Sources</h2> <div> <ul> <li><strong>Room Model</strong>: Simulated using the reduced-order package of the Modelica Buildings Library.</li> <li><strong>Weather Data</strong>: Provided by the German Meteorological Service (DWD) in Test Reference Year (TRY) format.</li> </ul> <p>This comprehensive dataset provides crucial parameters required to train and evaluate thermal models for different room configurations. The simulation data ensures a diverse range of environmental and occupancy conditions, enhancing the robustness of the models.</p> <h2>1.4. Data scaling</h2> <p>The data set contains the raw data as well as the scaled data used for training and testing the model. The scaling was carried out using the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html" target="_blank" rel="noopener">StandardScaler</a> package.</p> <h2>1.5. Weather data license</h2> <p>This data set contains weather data recorded by the DWD under license „Datenlizenz Deutschland – Namensnennung – Version 2.0" (<a href="https://www.govdata.de/dl-de/by-2-0" target="_blank" rel="noopener">URL</a>). The data is provided by "<a href="https://www.bbsr.bund.de" target="_blank" rel="noopener"><em>Bundesinstitut für Bau-, Stadt- und Raumforschung</em></a>". The data can be downloaded from <a href="https://www.bbsr.bund.de/BBSR/DE/forschung/programme/zb/Auftragsforschung/5EnergieKlimaBauen/2013/testreferenzjahre/01-start.html?pos=2" target="_blank" rel="noopener">here</a>. We use data from the year 2015 from Heilbronn. We have added the weather data to the data set unchanged.</p> </div> </div>
Development of Indoor Vegetable Plant Rack Design Based on IOTs to Prevent Stunts
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Quantification of methane emissions from indoor-fed Fogera dairy cows using laser methane detector
<p>Using the laser methane detector (LMD) in a respiration chamber (Linze Grassland Agriculture Trial Station, Lanzhou University, Gansu Province, China).</p>
Quantification of methane emissions from indoor-fed Fogera dairy cows using laser methane detector
<p>Using the laser methane detector to measure the methane emissions from Fogera dairy cows (Andassa Livestock Research Center, Amhara Region Agricultural Research Institute, Ethiopia).</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>
Indoor Environmental Quality (IEQ) questionnaire
<p>Advanced methodology for continuous and non-intrusive feedback collection on Indoor Environmental Quality (IEQ) and comfort perception in indoor environments, personal and behavioural variables, developed within the PROMET&0 (PROactive Monitoring for indoor EnvironmenTal quality & cOmfort) project.</p>
Datasets for Indoor Positioning with Single-AP Wi-Fi Fingerprinting
<p>Datasets for Indoor Positioning with Single-AP Wi-Fi Fingerprinting.</p> <p> </p>
Fast Indoor Radio Propagation Prediction Using Deep-Learning Dataset
<p>We show a dataset composed by Radio Maps Estimation (RME) and Cells Maps Estimation (CME) for the 5GHz band WIFI in indoor scenarios: it has 60 indoor constructions plans and 1000 distributions initially for a training process and 20 indoor constructions plans and 50 distributions aditionals for a test process of access points to even construction. These distributions are random and several WLAN's structures: 1 to 5 access points.</p> <p>The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels.</p> <p>Every coverage map have like maximum power delivered is <em>Pr = Pt = 26</em> dBm (according to data from <a href="https://www.cisco.com/c/en/us/products/wireless/catalyst-9100ax-access-points/index.html">current commercial equipment</a>) and like minimum power a value noise established in <em>Pr = KTB</em>, where K is the Boltzmann's constant, T the enviroment temperatura equal to 290°K and B the band width equal to 80MHz.</p> <p>Dataset DeepFIRP is the result of a lot of simulations by a <a href="https://doi.org/10.5281/zenodo.7983595">own software developed in MATLAB</a> that work with the<a href="https://mentor.ieee.org/802.11/dcn/14/11-14-0882-04-00ax-tgax-channel-model-document.docx"> IEEE 802.11ax channel model</a>.</p> <p>The pictures have a depth of 8 bits and size of <em>256pixels X 256pixels</em> equivalents to indoor constructions of <em>20 X 20</em> m<sup>2</sup>. These ones make reference to offices's spaces at general or classroom. </p> <p>A application to this dataset and the codes used for generate it is found <a href="https://github.com/johanflorez98/Fast-Indoor-Radio-Propagation-Prediction-Using-Deep-Learning">here</a>, where we implement a U-Net model for theRME and CME in indoor enviroments. This investigation contribute in novels methods for estimate by fast way coverage and cells maps using deep-learning in comparation with the conventional phisics methods like dominath-path model or ray-tracing. Whats allows save a lot of amount time in the WLANs's designs.</p>
Indoor Surface Classification for Mobile Robots
<p>In this project, we generated a dataset that contains three different types of indoor floor surfaces: carpet, tile and wood. Then, we used this dataset to train eight CNN-based models, including our proposed model, <em><strong>MobileNetV2-modified</strong></em>.</p> <ul> <li>The dataset comprises a total of 2081 samples, consisting of images captured with cameras in various indoor environments and lighting conditions.</li> <li>These images were taken from different angles in accordance with the overall dimensions of the indoor robots.</li> <li>This dataset includes samples collected from more than 20 different indoor environments.</li> <li>The dataset consists of 870 carpet samples, 638 tile samples and 573 wood surface samples.</li> </ul> <p> </p>
Indoor Air Quality and Respiratory Morbidity in School-Aged Children With BPD
ClinicalTrials.gov study NCT04107701. IPD Sharing: NO. Countries: 1. Publications: 1.
Randomised Exposure Study of Pollution Indoors and Respiratory Effects
ClinicalTrials.gov study NCT01047696. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Indoor Woodsmoke PM and Asthma
ClinicalTrials.gov study NCT00807183. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Effects of a Blended Indoor and Outdoor Physical Exercise Program on Depressive Symptoms in Hong Kong Older Adults
ClinicalTrials.gov study NCT06190327. IPD Sharing: NO. Countries: 1. Publications: 9.
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.