Open Science in Three Acts: Foundations, Practice, and Implementation — Third Act
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Abstract
This editorial concludes our three-act series dedicated to the implementation of open science (OS) in the field of research in management and applied social sciences. This text serves as the logical conclusion of the journey, offering a methodological proposal for the challenges and principles discussed previously (Limongi & Rogers, 2025a, 2025b). Our goal is to bridge the gap between theoretical knowledge and practical application, empowering researchers to adopt a more transparent, rigorous, and reproducible workflow.
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Rogers, P., & Limongi, R. (2025). Open Science in Three Acts: Foundations, Practice, and Implementation — Third Act. Brazilian Administration Review, 22(3), e250162. https://doi.org/10.1590/1807-7692bar2025250162
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Editorial
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Peikert, A., Van Lissa, C. J., & Brandmaier, A. M. (2021). Reproducible research in R: A tutorial on how to do the same thing more than once. Psych, 3(4), 836–867. https://doi.org/10.3390/psych3040053
Rodrigues, B. (2023). Building reproducible analytical pipelines with R. https://raps-with-r.dev/
Sawchuk, S. L., & Khair, S. (2021). Computational reproducibility: A practical framework for data curators. Journal of eScience Librarianship, 10(3), 1206. https://doi.org/10.7191/jeslib.2021.1206
Van Lissa, C. J., Brandmaier, A. M., Brinkman, L., Lamprecht, A.-L., Peikert, A., Struiksma, M. E., & Vreede, B. M. I. (2021). WORCS: A workflow for open reproducible code in science. Data Science, 4(1), 29–49. https://doi.org/10.3233/DS-210031
Ziemann, M., Poulain, P., & Bora, A. (2023). The five pillars of computational reproducibility: Bioinformatics and beyond. Briefings in Bioinformatics, 24(6), bbad375. https://doi.org/10.1093/bib/bbad375
Alston, J. M., & Rick, J. A. (2021). A beginner’s guide to conducting reproducible research. The Bulletin of the Ecological Society of America, 102(2), e01801. https://doi.org/10.1002/bes2.1801
Blischak, J. D., Davenport, E. R., & Wilson, G. (2016). A quick introduction to version control with git and github. PLOS Computational Biology, 12(1), e1004668. https://doi.org/10.1371/journal.pcbi.1004668
Crüwell, S., Van Doorn, J., Etz, A., Makel, M. C., Moshontz, H., Niebaum, J. C., Orben, A., Parsons, S., & Schulte-Mecklenbeck, M. (2019). Seven easy steps to open science: An annotated reading list. Zeitschrift für Psychologie, 227(4), 237–248. https://doi.org/10.1027/2151-2604/a000387
Domingos, A., & Batista, I. R. (2021). A map for transparency and replicability in empirical social science: The TIER Protocol. Revista Política Hoje, 40–86. https://doi.org/10.51359/1808-8708.2021.245776
Gilroy, S. P., & Kaplan, B. A. (2019). Furthering Open Science in Behavior Analysis: An Introduction and Tutorial for Using GitHub in Research. Perspectives on Behavior Science, 42(3), 565–581. https://doi.org/10.1007/s40614-019-00202-5
Goodman, S. N., Fanelli, D., & Ioannidis, J. P. A. (2016). What does research reproducibility mean? Science Translational Medicine, 8(341), 341ps12-341ps12. https://doi.org/10.1126/scitranslmed.aaf5027
Kathawalla, U.-K., Silverstein, P., & Syed, M. (2021). Easing into open science: A guide for graduate students and their advisors. Collabra: Psychology, 7(1), 18684. https://doi.org/10.1525/collabra.18684
Klein, O., Hardwicke, T. E., Aust, F., Breuer, J., Danielsson, H., Mohr, A. H., I. J. zerman, H., Nilsonne, G., Vanpaemel, W., & Frank, M. C. (2018). A practical guide for transparency in psychological science. Collabra: Psychology, 4(1), 20. https://doi.org/10.1525/collabra.158
Limongi, R., & Rogers, P. (2025a). Open science in three acts: Foundations, practice, and implementation - First act. Brazilian Administration Review, 22(1), e250079. https://doi.org/10.1590/1807-7692bar2025250079
Limongi, R., & Rogers, P. (2025b). Open science in three acts: Foundations, practice, and implementation - Second act. Brazilian Administration Review, 22(2), e250116. https://doi.org/10.1590/1807-7692bar2025250116
Mang, J. M., Prokosch, H.-U., & Kapsner, L. A. (2023). Reproducibility in 2023 – An end-to-end template for analysis and manuscript writing. In M. Hägglund, M. Blusi, S. Bonacina, L. Nilsson, I. Cort Madsen, S. Pelayo, A. Moen, A. Benis, L. Lindsköld, & P. Gallos (eds.), Studies in Health Technology and Informatics. IOS Press. https://doi.org/10.3233/SHTI230064
Peikert, A., Van Lissa, C. J., & Brandmaier, A. M. (2021). Reproducible research in R: A tutorial on how to do the same thing more than once. Psych, 3(4), 836–867. https://doi.org/10.3390/psych3040053
Rodrigues, B. (2023). Building reproducible analytical pipelines with R. https://raps-with-r.dev/
Sawchuk, S. L., & Khair, S. (2021). Computational reproducibility: A practical framework for data curators. Journal of eScience Librarianship, 10(3), 1206. https://doi.org/10.7191/jeslib.2021.1206
Van Lissa, C. J., Brandmaier, A. M., Brinkman, L., Lamprecht, A.-L., Peikert, A., Struiksma, M. E., & Vreede, B. M. I. (2021). WORCS: A workflow for open reproducible code in science. Data Science, 4(1), 29–49. https://doi.org/10.3233/DS-210031
Ziemann, M., Poulain, P., & Bora, A. (2023). The five pillars of computational reproducibility: Bioinformatics and beyond. Briefings in Bioinformatics, 24(6), bbad375. https://doi.org/10.1093/bib/bbad375