CHRISTIAN GHIAUS is full professor at INSA Lyon, France. He has been working over 30 years on intelligent control of energy and air quality in the build environment with contribution to fuzzy, neural netwoks, and model predictive control. He developed a method for dynamic models for building energy management based on modelling heat transfer with thermal networks and their transformation in state-space representation. He also pioneered computational psychrometric techniques by defining the sizing of HVAC systems as a control problem. These methods are taught at master level in universities in France and Switzerland. In teaching, he uses extensively personalized tests (for which he developed a method in Python for generating cloze questions for MOODLE) and group projects. References: The imperative for reproducibility in building performance simulation research. Journal of Building Performance Simulation, 1-7 (2025); pELECTRE Tri: A computational framework and Python module for probabilistic ELECTRE Tri multiple-criteria decision-making. Software Impacts, 100781 (2025); Dynamic Models for Building Energy Management, https://cghiaus.github.io/dm4bem_book/intro.html (2025).

Summary:  Computational science uses advanced numerical and data science methods. Reproducibility, the ability to obtain the same results by using the same data and methods, is essential in computational science research to ensure the reliability and validity of the results. The benefits of reproducible research include enhanced scientific integrity, faster scientific advancements, and valuable educational resources. Despite its importance, reproducibility is often overlooked due to technical complexities, insufficient documentation, and cultural barriers such as the lack of incentives for sharing code and data. This presentation 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 are presented for the use of MATLAB and Python.

JOAQUÍN SILVESTRE-ALBERO is full professor of Inorganic Chemistry at the University of Alicante. He leads the Nanopore Group by LMA devoted to the design of nanomaterials (activated carbons, metal-organic frameworks – MOFs, zeolites) with tailored porous structure and surface chemistry to be applied in a wide range of processes such as gas adsorption, gas separation, drug delivery, catalysis, among others. He is co-author of 205 papers in internationally recognized journals (h index 56) and several book chapters. Furthermore, he leads many national, international and industrial projects. He is the coordinator of the Horizon Europe project CLEANWATER “Multifunctional sustainable adsorbents for water treatment assisted with plasma technologies and for health protection from xenobiotics”, and partner of Horizon projects WAVEE “Waste as Valuable Elements for Europe”, and MOST-H2 “Novel metal-organic framework adsorbents for efficient storage of hydrogen”. He is also COI of the spin-off company OMIX CIRCULAR TECHNOLOGIES.

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