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)

Name: 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)
Creator: Zenodo
Published: 2022-12-07T15:21:31+08:00

Zenodo2023-06-20 更新2026-05-25 收录

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https://zenodo.org/record/7378278 数据链接链接失效反馈

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THE PUBLICATION The data set provided is complementary to the thermal comfort review by Mamulova et al., 2023, titled "Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings": Link to full publication. 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. THE DATA 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. *** PLEASE NOTE *** 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. Citation Citation number used in Mamulova et al.,"Multi-Domain Thermal Comfort Models for Office Buildings: Are Current Practices Scalable?", (2023) E.g. 1 First Author Surname of the main author, for reference purposes only. E.g. Al-Atrash Publication Publication year E.g. 2020 Dependent A List of variables used to measure thermal perception E.g. Neutral temperature/ Thermal sensation Dependent B Scale used to measure each dependent variable Interaction A List of interaction effect(s) included in the explanatory/predictive model(s) E.g. Thermal and age/ Thermal and acoustical and personality Interaction B Is/are the effect(s) statistically significant? E.g. yes/ no/ (unknown) Crossed A List of crossed effect(s) included in the explanatory/predictive model(s) E.g. Acoustical/ Personality/ Age Crossed B *Note: Temperature is a main effect and is not included in the list Explanatory A Type of explanatory model E.g. Observation/ Statistical/ N/A Explanatory B Description of the explanatory model E.g. Asymptotic General Symmetry Test to check significance of difference in thermal perception between window conditions Predictive A Does the article include a predictive model? E.g. yes/ no Predictive B Type of predictive algorithm E.g. Logistic regression/ N/A Predictive C Description or formulation of the predictive model E.g. Probability of feeling too hot and probability of feeling too cold in relation to sound pressure level Performance Reported predictive performance E.g. Accuracy = 80%/ F-score = 0.8/ N/A Location City in which the measurements take place E.g. Paris Period Period over which the measurements take place E.g. Jan-Feb 2020 Start time Time of day at which the measurements begin *Note: Time of day is not reported for most field studies. For this reason, time of day is only recorded for laboratory experiements. Study type Type of building and whether the experimental conditions are controlled by the experiment leader E.g. Field (controlled)/ Field (uncontrolled)/ Lab (controlled)/ Lab (uncontrolled) Building layout Building layout E.g. Laboratory office (LO)/ Laboratory neutral (LN)/ Field office (FO) Exposure Exposure of the participant, in minutes, to the experimental conditions, excluding preparation time *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. Number of buildings/chambers Number of different locations used for conducting measurements E.g. 1 Number of participants Number of individuals who take part in each experiment *Note: Outliers who are subsequently excluded from the modelling phase are not included. Survey type Description of the type of survey used for subjective measurements E.g. Longitudinal questionnaire/ Transverse questionnaire/ N/A Survey content Are the contents of the survey provided in the article? E.g. Available/ unavailable Survey source Is/are the source(s) of the survey items mentioned in the article? E.g. Available/ unavailable Survey reliability Is the reliability of the survey items reported in the article? E.g. Available/ unavailable Survey duration Is the survey duration reported in the article? E.g. Available/ unavailable Context A Overview of the contextual information provided by the authors E.g. Room layout/ Room dimennsions Context B Qualitative/quantitative contextual information E.g. Figure containing room layout/ 3m x 3m x 5m Contextual variables A List of contextual variable(s) measured by the researchers (see Fig. A.) *Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models. Contextual variables B Range of values included in the experiment and their respective units. E.g. figure Social variables A List of social variable(s) measured by the researchers (see Fig. A.) *Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models. Social variables B Range of values included in the experiment and their respective units. E.g. [1,2,3,4,5] Personal variables A List of contextual variable(s) measured by the researchers (see Fig. A.) *Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models. Personal variables B Range of values included in the experiment and their respective units. E.g. [red, blue] Physical variables A List of physical variable(s) measured by the researchers (see Fig. A.) *Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models. Physical variables B Range of values included in the experiment and their respective units E.g. dB(A) Full-factorial Is/are the experiment(s) full-factorial? *Note: Uncontrolled field experiments are automatically labelled as fractional factorial. (Participant) Control Do participants have control over one or more experimental conditions? E.g. Yes/ No With/between subjects Are the experimental conditions shared between or within the participants? E.g. w/ b Fixed variables A List of variables reported as constant during the measurements E.g. Relative humidity/ Metabolic rate Fixed variables A (Range of) values and their respective units. E.g. 30-40%/ 1.2 met Summary Description of the research outome (outcome of the explanatory and/or predictive modelling) E.g. Lack of perceived control has a significant negative effect on neutral temperatures. Evaluation Are the participants invited to evaluate their experience once the experiment has been completed? E.g. Yes/ no Note: The data in v1.1.0 has not yet been optimised for analytics.

**关联文献** 本数据集为Mamulova等人2023年发表的题为《Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings》（我们来聊聊可扩展性：支撑办公建筑设计的多领域热舒适模型研究现状）的热舒适综述的补充数据，完整文献链接详见原文。该范围综述（scoping review）共梳理77项多领域热舒适相关研究，并围绕模型可扩展性展开讨论——模型可扩展性是指在实际场景中便于理解和预测热舒适状况的模型参数。 **数据集内容** 本数据库包含每项研究对应的27项可扩展性参数（scalability parameters），用于分析当前的研究实践。相关分析结果请查阅前述综述文献，本数据库仅包含原始数据。为便于理解，下文附可扩展性参数说明表。 *** 重要声明 *** 本数据集可被使用、修改或扩充，但请务必引用本数据集、相关综述文献（如适用），并联系通讯作者：eugenemamulova@gmail.com。 *引用说明* *Mamulova等人2023年发表的《Multi-Domain Thermal Comfort Models for Office Buildings: Are Current Practices Scalable?》（办公建筑多领域热舒适模型：当前研究实践是否具备可扩展性？）中使用的引用编号* *示例1* *第一作者* *主要作者姓氏，仅用于参考* *示例：Al-Atrash* *发表文献* *发表年份* *示例：2020* *因变量A* *用于测量热感知的变量列表* *示例：中性温度/热感觉* *因变量B* *用于测量各因变量的量表* *交互效应A* *解释/预测模型中包含的交互效应列表* *示例：热与年龄/热与声学与人格* *交互效应B* *该效应是否具有统计学显著性？* *示例：是/否/（未知）* *交叉效应A* *解释/预测模型中包含的交叉效应列表* *示例：声学/人格/年龄* *交叉效应B* *注：温度属于主效应，不纳入该列表* *解释模型A* *解释模型的类型* *示例：观测/统计/无* *解释模型B* *解释模型的描述* *示例：用于检验窗环境间热感知差异显著性的渐近广义对称性检验* *预测模型A* *该文献是否包含预测模型？* *示例：是/否* *预测模型B* *预测算法的类型* *示例：逻辑回归/无* *预测模型C* *预测模型的描述或公式* *示例：与声压级相关的感到过热和过冷的概率* *模型性能* *报告的预测性能指标* *示例：准确率=80%/F值=0.8/无* *研究地点* *测量开展所在的城市* *示例：巴黎* *测量周期* *测量开展的时间段* *示例：2020年1-2月* *开始时间* *每日测量的起始时刻* *注：多数实地研究未报告每日时刻，因此仅记录实验室实验的每日时刻* *研究类型* *建筑类型及实验条件是否由实验负责人控制* *示例：实地（可控）/实地（非可控）/实验室（可控）/实验室（非可控）* *建筑布局* *建筑布局类型* *示例：实验室办公室（LO）/实验室中性环境（LN）/实地办公室（FO）* *暴露时长* *参与者在实验条件下的暴露时长（分钟，不含准备时间）* *注：多数实地研究未报告暴露时长，因此仅记录实验室实验的暴露时长，且默认大于60分钟* *建筑/舱室数量* *用于开展测量的不同地点数量* *示例：1* *参与者数量* *每项实验的参与人数* *注：建模阶段后续剔除的异常值不计入统计* *调查类型* *用于主观测量的调查类型描述* *示例：纵向问卷/横向问卷/无* *调查内容* *文章中是否提供了调查内容？* *示例：可获取/不可获取* *调查来源* *文章中是否提及调查条目来源？* *示例：可获取/不可获取* *调查信度* *文章中是否报告了调查条目的信度？* *示例：可获取/不可获取* *调查时长* *文章中是否报告了调查时长？* *示例：可获取/不可获取* *情境信息A* *作者提供的情境信息概述* *示例：房间布局/房间尺寸* *情境信息B* *定性/定量情境信息* *示例：包含房间布局的图/3m×3m×5m* *情境变量A* *研究人员测量的情境变量列表（见图A）* *注：包含文章中提及的所有变量，无论是否纳入解释/预测模型* *情境变量B* *实验中包含的数值范围及其对应单位* *示例：图* *社会变量A* *研究人员测量的社会变量列表（见图A）* *注：包含文章中提及的所有变量，无论是否纳入解释/预测模型* *社会变量B* *实验中包含的数值范围及其对应单位* *示例：[1,2,3,4,5]* *个人变量A* *研究人员测量的个人变量列表（见图A）* *注：包含文章中提及的所有变量，无论是否纳入解释/预测模型* *个人变量B* *实验中包含的数值范围及其对应单位* *示例：[红,蓝]* *物理变量A* *研究人员测量的物理变量列表（见图A）* *注：包含文章中提及的所有变量，无论是否纳入解释/预测模型* *物理变量B* *实验中包含的数值范围及其对应单位* *示例：dB(A)* *全因子实验* *该实验是否为全因子实验？* *注：非可控实地实验自动归类为部分因子实验* *（参与者）控制权* *参与者是否对一项或多项实验条件拥有控制权？* *示例：是/否* *组间/组内设计* *实验条件是在参与者间共享还是在参与者内开展？* *示例：组间/组内* *固定变量A* *测量期间报告为恒定的变量列表* *示例：相对湿度/代谢率* *固定变量B* *（数值范围）及其对应单位* *示例：30-40%/1.2 met* *研究总结* *研究结果描述（解释和/或预测建模的结果）* *示例：感知控制缺失对中性温度具有显著负向影响* *事后评估* *实验结束后是否邀请参与者评估其体验？* *示例：是/否* 注：v1.1.0版本的数据尚未针对分析场景进行优化。

提供机构：

Zenodo

创建时间：

2022-12-07