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32 results for “Thermal Comfort”
Supplementary material 7 from: Schmidt K, Walz A (2021) Ecosystem-based adaptation to climate change through residential urban green structures: co-benefits to thermal comfort, biodiversity, carbon storage and social interaction. One Ecosystem 6: e65706. https://doi.org/10.3897/oneeco.6.e65706
Accessibility and amenities assessment
Dataset: Longitudinal personal thermal comfort preference datain the wild
<p>Thermal comfort affects the well-being, productivity, and overall satisfaction of building occupants. However, due to economical and practical limitations, the number of longitudinal studies that have been conducted is limited, and only a few of these studies have shared their data publicly. Longitudinal datasets collected indoors are a valuable resource to better understand how people perceive their environment. Moreover, they provide a more realistic scenario to those conducted in thermal chambers. Our objective was to share publicly a longitudinal dataset comprising data collected over a 4-week long experiment. A total of 17 participants completed thermal preferences surveys which accounted for a total of approximately 1400 unique responses across indoor and outdoor 17 spaces. For the whole duration of the study, we monitored environmental variables (e.g., temperature and relative humidity) throughout 3 buildings. Participants completed comfort surveys from the screen of their smartwatches using an open-source application named Cozie. Their indoor location was continuously monitored using a custom-designed smartphone application.<br> Location data were used to time and spatially align environmental measurements to thermal preference responses provided by the participants. Background information of participants, such as physical characteristics and personality traits (satisfaction with life scale, highly sensitive person scale, the Big Five personality traits), was collected using an on-boarding survey administered at the beginning of the experiment.</p> <p>You can find the features descriptions, raw data processing, and raw files in our project repository:</p> <p><a href="https://github.com/buds-lab/longitudinal-personal-thermal-comfort">https://github.com/buds-lab/longitudinal-personal-thermal-comfort</a></p>
Air temperature and thermal comfort data measured and modelled for 121 workplaces in the Upper Rhine Valley
<p>The uploaded files contain the measured and modelled indoor data at different workplaces in the Upper Rhine Valley between August 1, 2021, and July 31, 2022 presented in the article "Predicting Indoor Air Temperature and Thermal Comfort in Occupational Settings Using Weather Forecasts, Indoor Sensors, and Artificial Neural Networks" by Sulzer et al. (2023), <a href="http://doi.org/10.1016/j.buildenv.2023.110077">doi.org/10.1016/j.buildenv.2023.110077</a>. Information about the characteristics of each workplace can be found in the appendix of the article. For every workplace two files are uploaded, one for the data of the indoor air temperature (Ta) and one for indoor physiological equivalent temperature (PET). The workplace ID and variable are stated in the filename. The columns in the files contain the following data:</p> <ul> <li>"Datetime (UTC)": This column contains the timestamp in UTC of the starting point of the interval used for the one-hour mean values .</li> <li>"MoBiMet data": This column contains the one-hour mean values of Ta or PET of the data derived every five minutes by the low-cost Mobile Biometeorology System (MoBiMet) at the corresponding workplace in °C. The MoBiMet are presented in detail in <a href="http://doi.org/10.3390/s22051828">doi.org/10.3390/s22051828</a>.</li> <li>"Used for": This column contains the information if the data point was used for training of the models (t), evaluation of the models (e), or not used for model training or evaluation due to missing indoor observation data (n).</li> <li>"Product 0 (ICON_outdoor)": This column, in the files for the indoor air temperature, contains the ICON-D2 data of the air temperature 2m a.g.l. in °C of the grid cell in which the associated work station is located.</li> <li>"Product 0 (ICON_outdoor) air temperatur 2m (C) input for PET calculation using RayMan","Product 1 (ICON_outdoor) vapor pressure 2m (hPa) input for PET calculation using RayMan","Product 1 (ICON_outdoor) wind speed 10m (m/s) input for PET calculation using RayMan", and "Product 1 (ICON_outdoor) global radiation surface (W/m²) input for PET calculation using RayMan": This columns contain the data of the outdoor air temperature 2m a.g.l. in °C, the vapor pressure 2m a.g.l., derived from the ICON-D2 weather forecast data of the grid cell in which the associated work station is located, which were used in RayMan Pro to calculate the PET for outdoors.</li> <li>"Product 2 (ANN_Generic)": This column contains the indoor data of PET or air temperature in °C modelled by an artificial neural network using generic data as input. The generic data contain hourly solar altitude and azimuth at each location, the weekday, and a sine and cosine function of the daily and yearly cycle.</li> <li>"Product 3 (ANN_AWS) without past data": This column contains the indoor data of PET or air temperature in °C modelled by an artificial neural network using generic data and the meteorological data of air temperature, vapor pressure, mean sea level pressure, global radiation, longwave downwelling radiation, and wind speed of an automated weather station in Freiburg (Station FRCHEM; 48°00’04’’ N; 7°50’55’’ E).</li> <li>"Product 3 (ANN_AWS) with past data": This column contains similar data than the column before but the artificial neural network models used "past data" of the automated weather station as additional input variables to model indoor air temperature and PET in °C. Additional to the hourly average of the meteorological data for each actual time (t), hourly averages for t-1 h, t-3 h, t-6 h, t-12 h, and t-24 h of air temperature, global radiation, and Longwave downwelling radiation are used as so called "past data".</li> <li>"Product 4 (ANN_ICON) without past data": This column contains the indoor data of PET or air temperature in °C modelled by an artificial neural network using generic data and the meteorological data of air temperature, vapor pressure, mean sea level pressure, global radiation, longwave downwelling radiation, and wind speed derived from the ICON-D2 weather forecast data of the grid cell in which the associated work station is located.</li> <li>"Product 4 (ANN_ICON) with past data": This column contains similar data than the column before but the artificial neural network models used "past data" of the ICON-D2 weather forecast data as additional input variables to model indoor air temperature and PET in °C. Additional to the hourly average of the meteorological data for each actual time (t), hourly averages for t-1 h, t-3 h, t-6 h, t-12 h, and t-24 h of air temperature, global radiation, and Longwave downwelling radiation are used as so called "past data".</li> <li>"Product 5 (ANN_Mixed) without past data": This column contains the indoor data of PET or air temperature in °C modelled by the same artificial neural network models as in Product 3 but applied for the same input data of Product 4.</li> <li>"Product 5 (ANN_Mixed) with past data": This column contains similar data than the column before but also takes into account the "past data".</li> </ul> <p>The data of Product 3 (ANN_AWS) and Product 5 (ANN_Mixed) are only available for locations in Freiburg, because the data of an automated weather station in Freiburg was used.<br> Data which is not available is stated as NA.</p>
The Effect of Warmed Socks Developed as Wearable Technology on Hypothermia, Thermal Comfort Perception and Shivering
ClinicalTrials.gov study NCT06283771. IPD Sharing: NO. Countries: 0. Publications: 5.
Effect of Maintenance of Intraoperative Normothermia in Cesarean Section on Thermal Comfort, Serum Cortisol and Apgar Score: A Randomized Controlled Trial
ClinicalTrials.gov study NCT06962241. IPD Sharing: NO. Countries: 0. Publications: 1.
Personal thermal comfort models with wearable sensors
Open the record for dataset details and reuse information.
Personal thermal comfort models with wearable sensors
Open the record for dataset details and reuse information.
Nanotechnology Applications in Interior Design of hospitals to enhance thermal comfort and decrease the infection spread for the occupants
<p>The interior design process, especially the selection of materials, plays a pivotal role in ensuring a hygienic and safe environment within healthcare facilities. Walls, floors, ceilings, and furniture materials are key elements that contribute to the overall design, impacting the well-being of patients. Recognizing the importance of this phase, a study was conducted to investigate the application of nano materials in healthcare facility interior design in Egypt.</p>
Management and Thermal Comfort of Premature Infants Under 32 Weeks
ClinicalTrials.gov study NCT03648606. IPD Sharing: NO. Countries: 0. Publications: 0.
The Effect of Heating on Thermal Comfort and Anxiety
ClinicalTrials.gov study NCT04244071. IPD Sharing: NO. Countries: 1. Publications: 0.
Visual and Acoustic Effects of Human Thermal Comfort and Perception in a Micro-Climatically Steady Environment
ClinicalTrials.gov study NCT06985940. IPD Sharing: YES. Countries: 1. Publications: 0.
Thermal Comfort of Different Desktop Materials
ClinicalTrials.gov study NCT03733366. IPD Sharing: NO. Countries: 1. Publications: 0.
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
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DANDI Archive for NWB datasets
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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.