| Title: |
Knowledge preservation in the era of big science and AI: strategies for sustainable scientific research. |
| Authors: |
Rainford, Penn F.; Occhipinti, Annalisa; Wang, Bo; Stepney, Susan; Angione, Claudio; Verma, Suraj; Marucci, Lucia; Sharma, Kieren; Harris, Sarah A.; Füchslin, Rudolf M.; Klipp, Edda; Grierson, Claire S.; Weyland, Mathias S.; Mogre, Aalap; Fellermann, Harold; Doan, Le Minh Thao; Gherman, Ioana M.; Pyne, Alice L. B.; Rudge, Timothy J.; Berrelleza, Victor M. V. |
| Source: |
Nature Communications; 5/5/2026, Vol. 17 Issue 1, p1-12, 12p |
| Subject Terms: |
DOCUMENTATION standards; DIGITAL preservation; ARTIFICIAL intelligence; OPEN scholarship; UNIVERSITY research; DIGITAL technology |
| Abstract: |
Science is losing knowledge it cannot afford to lose. Negative results go unpublished, hard-won expertise walks out the door with departing researchers, and preservation efforts remain fragmented. The consequences are wasted resources, duplicated effort, and missed discoveries. In this perspective, we argue that the research community can act now by embracing alternative dissemination channels, improving documentation best practices, and building sustainable digital infrastructure. We envision moderated platforms for sharing null results and practical know-how, community-driven standards, and AI-powered tools that lower barriers to implementation. With coordinated effort, science can become more open, efficient, and resilient for future generations. Science routinely discards valuable knowledge, from unpublished negative results to the tacit expertise lost when researchers move on. Here, the authors propose a roadmap for preserving this hidden knowledge through community-driven platforms, open standards, and AI-assisted documentation. [ABSTRACT FROM AUTHOR] |
| : |
Copyright of Nature Communications is the property of Springer Nature 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: |
Complementary Index |