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The imperative for reproducibility in building performance simulation research.

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Title: The imperative for reproducibility in building performance simulation research.
Authors: Ghiaus, Christian1 (AUTHOR) christian.ghiaus@insa-lyon.fr
Source: Journal of Building Performance Simulation. Jun2025, Vol. 18 Issue 4, p523-529. 7p.
Subjects: Digital Object Identifiers, Building performance, Data science, Information sharing, Reproducible research
Abstract: Building Performance Simulation (BPS) uses advanced computational and data science methods. Reproducibility, the ability to obtain the same results by using the same data and methods, is essential in BPS research to ensure the reliability and validity of scientific results. The benefits of reproducible research include enhanced scientific integrity, faster scientific advancements, and valuable educational resources. Despite its importance, reproducibility in BPS is often overlooked due to technical complexities, insufficient documentation, and cultural barriers such as the lack of incentives for sharing code and data. This paper encourages the reproducibility of articles on computational science and proposes to recognize reproductible code and data, with persistent Digital Object Identifier (DOI), as peer-reviewed archival publications. Practical workflows for achieving reproducibility in BPS are presented for the use of MATLAB and Python. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Building Performance Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 185487622
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: The imperative for reproducibility in building performance simulation research.
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  Data: <searchLink fieldCode="AR" term="%22Ghiaus%2C+Christian%22">Ghiaus, Christian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> christian.ghiaus@insa-lyon.fr</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Building+Performance+Simulation%22">Journal of Building Performance Simulation</searchLink>. Jun2025, Vol. 18 Issue 4, p523-529. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Digital+Object+Identifiers%22">Digital Object Identifiers</searchLink><br /><searchLink fieldCode="DE" term="%22Building+performance%22">Building performance</searchLink><br /><searchLink fieldCode="DE" term="%22Data+science%22">Data science</searchLink><br /><searchLink fieldCode="DE" term="%22Information+sharing%22">Information sharing</searchLink><br /><searchLink fieldCode="DE" term="%22Reproducible+research%22">Reproducible research</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Building Performance Simulation (BPS) uses advanced computational and data science methods. Reproducibility, the ability to obtain the same results by using the same data and methods, is essential in BPS research to ensure the reliability and validity of scientific results. The benefits of reproducible research include enhanced scientific integrity, faster scientific advancements, and valuable educational resources. Despite its importance, reproducibility in BPS is often overlooked due to technical complexities, insufficient documentation, and cultural barriers such as the lack of incentives for sharing code and data. This paper encourages the reproducibility of articles on computational science and proposes to recognize reproductible code and data, with persistent Digital Object Identifier (DOI), as peer-reviewed archival publications. Practical workflows for achieving reproducibility in BPS are presented for the use of MATLAB and Python. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Building Performance Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/19401493.2024.2441385
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      – Code: eng
        Text: English
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        PageCount: 7
        StartPage: 523
    Subjects:
      – SubjectFull: Digital Object Identifiers
        Type: general
      – SubjectFull: Building performance
        Type: general
      – SubjectFull: Data science
        Type: general
      – SubjectFull: Information sharing
        Type: general
      – SubjectFull: Reproducible research
        Type: general
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      – TitleFull: The imperative for reproducibility in building performance simulation research.
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            – D: 01
              M: 06
              Text: Jun2025
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              Y: 2025
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