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32 results for “thermal comfort”
Thermal Comfort and Perception of Different Materials for Tabletops: Datasets, Processing Code, and Supplementary Tables.
<p>In this data repository, data related to the study <em>Thermal Comfort and Perception of Different Materials for Tabletops </em> is deposited, and includes datasets, processing code, and supplementary tables.</p> <p> </p>
Seasonal analysis comparison of three air-cooling systems in terms of thermal comfort, air quality and energy consumption for school buildings in Mediterranean climates
<p>Efficient air-cooling systems for hot climatic conditions, such as Southern Europe, are required in the context of nearly Zero Energy Buildings, nZEB. Innovative air-cooling systems such as regenerative indirect evaporative coolers, RIEC and desiccant regenerative indirect evaporative coolers, DRIEC, can be considered an interesting alternative to direct expansion air-cooling systems, DX. The main aim of the present work was to evaluate the seasonal performance of three air-cooling systems in terms of air quality, thermal comfort and energy consumption in a standard classroom. Several annual energy simulations were carried out to evaluate these indexes for four different climate zones in the Mediterranean area. The simulations were carried out with empirically validated models. The results showed that DRIEC and DX improved by 29.8% and 14.6% over RIEC regarding thermal comfort, for the warmest climatic conditions, Lampedusa and Seville. However, DX showed an energy consumption three and four times higher than DRIEC for these climatic conditions, respectively. RIEC provided the highest percentage of hours with favorable indoor air quality for all climate zones, between 46.3% and 67.5%. Therefore, the air-cooling systems DRIEC and RIEC have a significant potential to reduce energy consumption, achieving the user’s thermal comfort and improving indoor air quality.</p>
OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information
<p><strong>OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information</strong></p> <p>This repository contains the official data from a USDOE-funded project at Carnegie Mellon University and Bosch Research Pittsburgh. </p> <p>The primary goal of the project was to investigate the relationship between indoor commercial building occupant thermal comfort and various biometric and environmental predictors. We performed 77 individual comfort experiments, approved by our Institutional Review Board (IRB) and in satisfaction of participant consent guidelines. Our goal was to generate a dataset than enables comprehensive study of human thermal comfort preferences, in a commercial building environment, across a wide range of indoor environmental conditions. The data is comprised of the following feature groups: depth camera frames, biometrics sensor data, body shape information, subjective comfort data from the mobile device application, environmental sensor data from the commercial building HVAC system, and outdoor weather station data.</p> <p>This is the official dataset release for the following conference paper:</p> <blockquote> <p>Jonathan Francis*, Matias Quintana*, Nadine von Frankenberg, Sirajum Munir, and Mario Bergés. 2019. OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information. In BuildSys '19: ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, November 13–14, 2019, New York, NY. ACM, New York, NY, USA, 10 pages.</p> </blockquote> <p>To use this dataset, first download <strong>all</strong> the files.</p> <p>Next, issue the following commands on, e.g., Linux terminal:</p> <pre><code class="language-bash">>$ cd /path/to/dataset/files >$ cat occutherm_dataset_v0-0-0.tar.gza* > archive.tar.gz >$ tar -xvzf archive.tar.gz</code></pre> <p>Modeling and mobile application code are available in our project repository: <a href="https://github.com/jonfranc/occutherm">https://github.com/jonfranc/occutherm</a></p> <p>If you find the repository or the dataset useful, please cite our paper:</p> <pre><code>@inproceedings{francis_buildsys2019, author = {Francis, Jonathan and Quintana, Matias and von Frankenberg, Nadine and Munir, Sirajum and Berges, Mario}, title = {OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information}, booktitle = {Proceedings of the 6th International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation}, series = {BuildSys '19}, year = {2019}, isbn = {978-1-4503-7005-9/19/11}, location = {New York, NY}, numpages = {10}, acmid = {3360858}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Thermal Comfort, Human Studies, Machine Learning}, }</code></pre> <p> </p>
ASHRAE global database of thermal comfort field measurements
Open the record for dataset details and reuse information.
Modified "test_urban" dataset for thermal comfort in PALM
<p>The test configuration used in the manuscript "Calculating human thermal comfort and thermal stress in the<br> PALM model system 6.0" submitted to GMD along with the results and source code.</p>
Supplemental idf files used for paper "Refurbishmet Methodology to Attain Thermally Comfortable near-Zero Energy Buildings Using Customizable Solutions"
<p>These are the idf files used to study different technology alternatives offered by the Reco2st project on an idealized dormitory room located in London, UK. The files are presented "as is" and were done for EnergyPlus v8.9. Includes files that might have not made it to the final paper.</p> <p>Geometry: One zone room with one external window.<br> Materials: External construction is cavity wall, with bricks on both sides, gypsum plaster on internal side<br> Internal walls brick single layer with gypsum plaster.<br> Ceiling and floor reinforced concrete. Plaster finish on ceiling, nylon carpet on floor<br> Double glazed window. Five panes available: one large central fixed, two small clerestory opening, and two large side opening.<br> For simulation purposes a single equivalent area of all openings modelled.<br> Material data sources: gov.scot, puravent.co.uk, nature.com. SHGC from LBL.</p> <p>This is part of work done for ReCO2ST - Residential Retrofit assessment platform and demonstrations for near zero energy and CO2 emissions with optimum cost, health, comfort and environmental quality. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768576.</p>
Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings (Dataset v1.2.1)
<p><strong>THE PUBLICATION</strong></p> <p>The data set provided is complementary to the thermal comfort review by Mamulova et al., 2023, titled "<strong>Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings</strong>": <a href="https://doi.org/10.1016/j.buildenv.2023.110502">Link to full publication</a>. The scoping review examines 77 multi-domain thermal comfort studies and initiates a discussion on model scalability; a model parameter which facilitates the understanding and prediction of thermal comfort conditions in real-world practice.</p> <p><strong>THE DATA</strong></p> <p>This database contains 27 scalability parameters per study which are used to analyse current research practices. For the results, please consult the review publication, as this database only contains raw data. For clarity, a legend of the scalability parameters is provided below.</p> <p><strong>*** PLEASE NOTE ***</strong></p> <p><strong>This data set may be utilised, altered and/or expanded. However, you are kindly asked to cite this data set, the review publication (if applicable) and contact the corresponding author at eugenemamulova@gmail.com. </strong></p> <table> <tbody> <tr> <td><em>Citation</em></td> <td><em>Citation number used in Mamulova et al.,"Multi-Domain Thermal Comfort Models for Office Buildings: Are Current Practices Scalable?", (2023)</em></td> <td><em>E.g. 1</em></td> </tr> <tr> <td><em>First Author</em></td> <td><em>Surname of the main author, for reference purposes only.</em></td> <td><em>E.g. Al-Atrash</em></td> </tr> <tr> <td><em>Publication</em></td> <td><em>Publication year</em></td> <td><em>E.g. 2020</em></td> </tr> <tr> <td><em>Dependent A</em></td> <td><em>List of variables used to measure thermal perception</em></td> <td><em>E.g. Neutral temperature/ Thermal sensation</em></td> </tr> <tr> <td><em>Dependent B</em></td> <td><em>Scale used to measure each dependent variable</em></td> <td> </td> </tr> <tr> <td><em>Interaction A</em></td> <td><em>List of interaction effect(s) included in the explanatory/predictive model(s) </em></td> <td><em>E.g. Thermal and age/ Thermal and acoustical and personality</em></td> </tr> <tr> <td><em>Interaction B</em></td> <td><em>Is/are the effect(s) statistically significant?</em></td> <td><em>E.g. yes/ no/ (unknown)</em></td> </tr> <tr> <td><em>Crossed A</em></td> <td><em>List of crossed effect(s) included in the explanatory/predictive model(s) </em></td> <td><em>E.g. Acoustical/ Personality/ Age</em></td> </tr> <tr> <td><em>Crossed B</em></td> <td> </td> <td><em>*Note: Temperature is a main effect and is not included in the list</em></td> </tr> <tr> <td><em>Explanatory A</em></td> <td><em>Type of explanatory model</em></td> <td><em>E.g. Observation/ Statistical/ N/A</em></td> </tr> <tr> <td><em>Explanatory B</em></td> <td><em>Description of the explanatory model</em></td> <td><em>E.g. Asymptotic General Symmetry Test to check significance of difference in thermal perception between window conditions</em></td> </tr> <tr> <td><em>Predictive A</em></td> <td><em>Does the article include a predictive model?</em></td> <td><em>E.g. yes/ no</em></td> </tr> <tr> <td><em>Predictive B</em></td> <td><em>Type of predictive algorithm</em></td> <td><em>E.g. Logistic regression/ N/A</em></td> </tr> <tr> <td><em>Predictive C</em></td> <td><em>Description or formulation of the predictive model</em></td> <td><em>E.g. Probability of feeling too hot and probability of feeling too cold in relation to sound pressure level</em></td> </tr> <tr> <td><em>Performance</em></td> <td><em>Reported predictive performance</em></td> <td><em>E.g. Accuracy = 80%/ F-score = 0.8/ N/A</em></td> </tr> <tr> <td><em>Location</em></td> <td><em>City in which the measurements take place</em></td> <td><em>E.g. Paris</em></td> </tr> <tr> <td><em>Period</em></td> <td><em>Period over which the measurements take place</em></td> <td><em>E.g. Jan-Feb 2020</em></td> </tr> <tr> <td><em>Start time</em></td> <td><em>Time of day at which the measurements begin</em></td> <td><em>*Note: Time of day is not reported for most field studies. For this reason, time of day is only recorded for laboratory experiements.</em></td> </tr> <tr> <td><em>Study type</em></td> <td><em>Type of building and whether the experimental conditions are controlled by the experiment leader</em></td> <td><em>E.g. Field (controlled)/ Field (uncontrolled)/ Lab (controlled)/ Lab (uncontrolled)</em></td> </tr> <tr> <td><em>Building layout</em></td> <td><em>Building layout</em></td> <td><em>E.g. Laboratory office (LO)/ Laboratory neutral (LN)/ Field office (FO)</em></td> </tr> <tr> <td><em>Exposure</em></td> <td><em>Exposure of the participant, in minutes, to the experimental conditions, excluding preparation time</em></td> <td><em>*Note: Exposure is not reported for most field studies. For this reason, exposure is only recorded for laboratory experiements and is assumed to be longer than 60 minutes.</em></td> </tr> <tr> <td><em>Number of buildings/chambers</em></td> <td><em>Number of different locations used for conducting measurements</em></td> <td><em>E.g. 1</em></td> </tr> <tr> <td><em>Number of participants</em></td> <td><em>Number of individuals who take part in each experiment</em></td> <td><em>*Note: Outliers who are subsequently excluded from the modelling phase are not included.</em></td> </tr> <tr> <td><em>Survey type</em></td> <td><em>Description of the type of survey used for subjective measurements</em></td> <td><em>E.g. Longitudinal questionnaire/ Transverse questionnaire/ N/A</em></td> </tr> <tr> <td><em>Survey content</em></td> <td><em>Are the contents of the survey provided in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey source</em></td> <td><em>Is/are the source(s) of the survey items mentioned in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey reliability</em></td> <td><em>Is the reliability of the survey items reported in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey duration</em></td> <td><em>Is the survey duration reported in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Context A</em></td> <td><em>Overview of the contextual information provided by the authors</em></td> <td><em>E.g. Room layout/ Room dimennsions</em></td> </tr> <tr> <td><em>Context B</em></td> <td><em>Qualitative/quantitative contextual information</em></td> <td><em>E.g. Figure containing room layout/ 3m x 3m x 5m</em></td> </tr> <tr> <td><em>Contextual variables A</em></td> <td><em>List of contextual variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Contextual variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. figure</em></td> </tr> <tr> <td><em>Social variables A</em></td> <td><em>List of social variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Social variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. [1,2,3,4,5]</em></td> </tr> <tr> <td><em>Personal variables A</em></td> <td><em>List of contextual variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Personal variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. [red, blue]</em></td> </tr> <tr> <td><em>Physical variables A</em></td> <td><em>List of physical variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Physical variables B</em></td> <td><em>Range of values included in the experiment and their respective units</em></td> <td><em>E.g. dB(A)</em></td> </tr> <tr> <td><em>Full-factorial</em></td> <td><em>Is/are the experiment(s) full-factorial?</em></td> <td><em>*Note: Uncontrolled field experiments are automatically labelled as fractional factorial.</em></td> </tr> <tr> <td><em>(Participant) Control</em></td> <td><em>Do participants have control over one or more experimental conditions?</em></td> <td><em>E.g. Yes/ No</em></td> </tr> <tr> <td><em>With/between subjects</em></td> <td><em>Are the experimental conditions shared between or within the participants?</em></td> <td><em>E.g. w/ b</em></td> </tr> <tr> <td><em>Fixed variables A</em></td> <td><em>List of variables reported as constant during the measurements</em></td> <td><em>E.g. Relative humidity/ Metabolic rate</em></td> </tr> <tr> <td><em>Fixed variables A</em></td> <td><em>(Range of) values and their respective units.</em></td> <td><em>E.g. 30-40%/ 1.2 met</em></td> </tr> <tr> <td><em>Summary</em></td> <td><em>Description of the research outome (outcome of the explanatory and/or predictive modelling)</em></td> <td><em>E.g. Lack of perceived control has a significant negative effect on neutral temperatures.</em></td> </tr> <tr> <td><em>Evaluation</em></td> <td><em>Are the participants invited to evaluate their experience once the experiment has been completed? </em></td> <td><em>E.g. Yes/ no</em></td> </tr> </tbody> </table> <p>Note: The data in v1.1.0 has not yet been optimised for analytics.</p>
Supplementary material 3 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
Mean values of microclimatic parameters between 9am and 9pm, based on measurements in the four courtyards (CY): CY 1: light green, CY 2: dark green, CY 3: orange, CY 4: red
Supplementary material 6 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
Results from tree mapping and allometric equations, indicating above-ground biomass and carbon stocks
Supplementary material 5 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
Results from habitat mapping and biodiversity scores. Domin values = 1: < 4% cover with few individuals; 2: < 4% with several individuals; 3: < 4% with many individuals; 4: 4–10%; 5: 11–25%; 6: 26–33%; 7: 34–50%; 8: 51–75%; 9: 76–90%; 10: 91–100% cover
Determination of Optimal Thermal Comfort Conditions in the Iron Smelting Industry Work Environment Using Firefly Algorithm
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Evaluation of Thermal Comfort Conditions in The Working Environments of Seasonal Agricultural Workers in Csa Koppen Climate Type
Open the record for dataset details and reuse information.
The combined effect of climate change and building density on outdoor thermal comfort: a study in a southern middle sized Brazilian city)
<p>The files present all outputs (hourly data by sidewalk orientation) of physiological temperature equivalent from the simulations carried out in the research "The combined effect of climate change and building density on outdoor<br>thermal comfort: a study in a southern middle sized Brazilian city", classified according to the original ranges and Rosa's calibration.</p>
Thermal Sensation Classification Dataset based on ASHRAE Global Thermal Comfort Database II
<p><span>The dataset is based on ASHRAE Global Thermal Comfort Database II (version 2.01), which includes sets of objective indoor and outdoor climatic observations with accompanying “<em>right-here-right-now</em>” subjective evaluations by the building occupants. Data rows lacking information for any of the selected variables from the original dataset were excluded. Consequently, the dataset contains 10,618 data rows. Description of individual variables in the dataset are as follows:</span></p> <p><span><span>·<span> </span></span></span><span>Tair: Air temperature measured in the occupied zone (°C) ranging from 13.4 to 45.3 with 10,618 data points.</span></p> <p><span><span>·<span> </span></span></span><span>Vair: Air speed in the occupied zone (m/s) ranging from 0 to 4.71.</span></p> <p><span><span>·<span> </span></span></span><span>Relative Humidity (RH): Relative humidity in the occupied zone (%) ranging from 14.5 to 88.8.</span></p> <p><span><span>·<span> </span></span></span><span>SET: Standard Effective Temperature in Celsius (°C) ranging from 10.93 to 38.94.</span></p> <p><span><span>·<span> </span></span></span><span>CLO: Intrinsic clothing ensemble insulation of the occupant (clo) ranging from 0.23 to 2.87.</span></p> <p><span><span>·<span> </span></span></span><span>MET: Average metabolic rate of the occupant (met) ranging from 0.7 to 3.5.</span></p> <p><span><span>·<span> </span></span></span><span>Age: Age of the occupants ranging from 16 to 95 years.</span></p> <p><span><span>·<span> </span></span></span><span>Sex: Sex of the occupants, categorized as Male or Female.</span></p> <p><span><span>·<span> </span></span></span><span>Tout: Outdoor monthly average temperature during the field study (°C) ranging from 5.3 to 38.1.</span></p> <p><span><span>·<span> </span></span></span><span>Season: Season during which the study was conducted, categorized as Spring, Summer, Autumn, or Winter.</span></p> <p><span><span>·<span> </span></span></span><span>Building Operation Mode: Can be Air Conditioned (air, radiant, etc., with no operable windows), Naturally Ventilated (no mechanical cooling, with operable windows), or Mixed Mode (mechanical cooling and operable windows, with concurrent, changeover, or zoned control).</span></p> <p><span><span>·<span> </span></span></span><span>Building type: Type of building where the study was conducted, categorized as Classroom, Office, or Senior Center.</span></p> <p><span><span>·<span> </span></span></span><span>Thermal Sensation Vote (TSV): ASHRAE Thermal Sensation Vote of the occupant, ranging from -3 (cold) to +3 (hot).</span></p>
The Effects of Active Warming on Temperature on Core Body and Thermal Comfort
ClinicalTrials.gov study NCT04985617. IPD Sharing: YES. Countries: 1. Publications: 3.
The Effect of Active Warming During General Anaesthesia on Postoperative Body Temperature, Shivering and Thermal Comfort
ClinicalTrials.gov study NCT04907617. IPD Sharing: NO. Countries: 1. Publications: 1.
Thermal Comfort and Preference Data collected for the Brains4Buildings research at the Haagse Hogeschool, Hague, the Netherlands.
Open the record for dataset details and reuse information.
Supplementary material 4 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
Habitat types in the four courtyards
Supplementary material 2 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
Tree height in the four courtyards
Supplementary material 1 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
Questionnaire User Survey Potsdam Drewitz, August 2020
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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.