Open Science Infrastructure for Convergent Research

Noah Long

Abstract

This review examines open-science infrastructure for convergent research. The organizing question is which provenance, metadata, software, governance, and incentive structures support reproducibility across disciplines. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to mandating openness without accounting for privacy, stewardship, and domain-specific standards. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in cross-disciplinary research programs.

Keywords

open scienceconvergence researchreproducibilityprovenanceresearch infrastructure

References

Aguinis, H., Banks, G. C., Rogelberg, S. G., & Cascio, W. F. (2020). Actionable recommendations for narrowing the science-practice gap in open science. Organizational Behavior and Human Decision Processes, 158, 27-35. https://doi.org/10.1016/j.obhdp.2020.02.007 DOI
Christensen, G., & Miguel, E. (2018). Transparency, Reproducibility, and the Credibility of Economics Research. Journal of Economic Literature, 56(3), 920-980. https://doi.org/10.1257/jel.20171350 DOI
Elliott, K. C., & Resnik, D. B. (2019). Making Open Science Work for Science and Society. Environmental Health Perspectives, 127(7), 75002. https://doi.org/10.1289/ehp4808 DOI
Fleischmann, M., Feliciotti, A., & Kerr, W. (2021). Evolution of Urban Patterns: Urban Morphology as an Open Reproducible Data Science. Geographical Analysis, 54(3), 536-558. https://doi.org/10.1111/gean.12302 DOI
Kapoor, S., & Narayanan, A. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns, 4(9), 100804. https://doi.org/10.1016/j.patter.2023.100804 DOI
Laird, A. R. (2021). Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use. NeuroImage, 244, 118579. https://doi.org/10.1016/j.neuroimage.2021.118579 DOI
Lyon, L. (2016). Transparency: The Emerging Third Dimension of Open Science and Open Data. LIBER Quarterly The Journal of the Association of European Research Libraries, 25(4), 153-171. https://doi.org/10.18352/lq.10113 DOI
Marwick, B. (2016). Computational Reproducibility in Archaeological Research: Basic Principles and a Case Study of Their Implementation. Journal of Archaeological Method and Theory, 24(2), 424-450. https://doi.org/10.1007/s10816-015-9272-9 DOI
Ng, J. Y., Wieland, L. S., Lee, M. S., Liu, J. P., Witt, C. M., Moher, D., & Cramer, H. (2024). Open science practices in traditional, complementary, and integrative medicine research: A path to enhanced transparency and collaboration. Integrative Medicine Research, 13(2), 101047. https://doi.org/10.1016/j.imr.2024.101047 DOI
Oellermann, M., Jolles, J. W., Ortiz, D., Seabra, R., Wenzel, T., Wilson, H., & Tanner, R. L. (2022). Open Hardware in Science: The Benefits of Open Electronics. Integrative and Comparative Biology, 62(4), 1061-1075. https://doi.org/10.1093/icb/icac043 DOI

Publication details

Journal: Frontiers in Integrative Science
Volume: 1 (2026)
Article number: fis20260004
License: CC BY 4.0

[1] Aguinis, H., Banks, G. C., Rogelberg, S. G., & Cascio, W. F. (2020). Actionable recommendations for narrowing the science-practice gap in open science. Organizational Behavior and Human Decision Processes, 158, 27-35. https://doi.org/10.1016/j.obhdp.2020.02.007 DOI

[2] Christensen, G., & Miguel, E. (2018). Transparency, Reproducibility, and the Credibility of Economics Research. Journal of Economic Literature, 56(3), 920-980. https://doi.org/10.1257/jel.20171350 DOI

[3] Elliott, K. C., & Resnik, D. B. (2019). Making Open Science Work for Science and Society. Environmental Health Perspectives, 127(7), 75002. https://doi.org/10.1289/ehp4808 DOI

[4] Fleischmann, M., Feliciotti, A., & Kerr, W. (2021). Evolution of Urban Patterns: Urban Morphology as an Open Reproducible Data Science. Geographical Analysis, 54(3), 536-558. https://doi.org/10.1111/gean.12302 DOI

[5] Kapoor, S., & Narayanan, A. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns, 4(9), 100804. https://doi.org/10.1016/j.patter.2023.100804 DOI

[6] Laird, A. R. (2021). Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use. NeuroImage, 244, 118579. https://doi.org/10.1016/j.neuroimage.2021.118579 DOI

[7] Lyon, L. (2016). Transparency: The Emerging Third Dimension of Open Science and Open Data. LIBER Quarterly The Journal of the Association of European Research Libraries, 25(4), 153-171. https://doi.org/10.18352/lq.10113 DOI

[8] Marwick, B. (2016). Computational Reproducibility in Archaeological Research: Basic Principles and a Case Study of Their Implementation. Journal of Archaeological Method and Theory, 24(2), 424-450. https://doi.org/10.1007/s10816-015-9272-9 DOI

[9] Ng, J. Y., Wieland, L. S., Lee, M. S., Liu, J. P., Witt, C. M., Moher, D., & Cramer, H. (2024). Open science practices in traditional, complementary, and integrative medicine research: A path to enhanced transparency and collaboration. Integrative Medicine Research, 13(2), 101047. https://doi.org/10.1016/j.imr.2024.101047 DOI

[10] Oellermann, M., Jolles, J. W., Ortiz, D., Seabra, R., Wenzel, T., Wilson, H., & Tanner, R. L. (2022). Open Hardware in Science: The Benefits of Open Electronics. Integrative and Comparative Biology, 62(4), 1061-1075. https://doi.org/10.1093/icb/icac043 DOI