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Predicting the longevity of resources shared in scientific publications

Author

Listed:
  • Daniel E. Acuna

    (University of Colorado at Boulder)

  • Jian Jian

    (NetApp)

  • Tong Zeng

    (Virginia Tech)

  • Lizhen Liang

    (Syracuse University)

  • Han Zhuang

    (Ningbo Institute of Digital Twin, Eastern Institute of Technology)

Abstract

Research has shown that most resources shared in articles (e.g., URLs to code or data) are not kept up to date and mostly disappear from the web after some years (Zeng et al., 2019). Little is known about the factors that differentiate and predict the longevity of these resources. This article explores a range of explanatory features related to the publication venue, authors, references, and where the resource is shared. We analyze an extensive repository of publications and, through web archival services, reconstruct how they looked at different time points. We discover that the most important factors are related to where and how the resource is shared, while surprisingly little consideration is given to the author’s reputation or prestige of the journal. By examining the places where long-lasting resources are shared, we suggest that it is critical to educate researchers on modern sharing technologies. Finally, we discuss implications for reproducibility and acknowledge scientific datasets as first-class citizens of science.

Suggested Citation

  • Daniel E. Acuna & Jian Jian & Tong Zeng & Lizhen Liang & Han Zhuang, 2025. "Predicting the longevity of resources shared in scientific publications," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-16, December.
    • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04716-z
    DOI: 10.1057/s41599-025-04716-z
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    References listed on IDEAS

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    1. Michael P. Milham & R. Cameron Craddock & Jake J. Son & Michael Fleischmann & Jon Clucas & Helen Xu & Bonhwang Koo & Anirudh Krishnakumar & Bharat B. Biswal & F. Xavier Castellanos & Stan Colcombe & A, 2018. "Assessment of the impact of shared brain imaging data on the scientific literature," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
    2. Martin Klein & Herbert Van de Sompel & Robert Sanderson & Harihar Shankar & Lyudmila Balakireva & Ke Zhou & Richard Tobin, 2014. "Scholarly Context Not Found: One in Five Articles Suffers from Reference Rot," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-39, December.
    3. Ali Gazni & Fereshteh Didegah, 2011. "Investigating different types of research collaboration and citation impact: a case study of Harvard University’s publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(2), pages 251-265, May.
    4. Benjamin Haibe-Kains & George Alexandru Adam & Ahmed Hosny & Farnoosh Khodakarami & Levi Waldron & Bo Wang & Chris McIntosh & Anna Goldenberg & Anshul Kundaje & Casey S. Greene & Tamara Broderick & Mi, 2020. "Transparency and reproducibility in artificial intelligence," Nature, Nature, vol. 586(7829), pages 14-16, October.
    5. Sílvia Bonàs-Guarch & Marta Guindo-Martínez & Irene Miguel-Escalada & Niels Grarup & David Sebastian & Elias Rodriguez-Fos & Friman Sánchez & Mercè Planas-Fèlix & Paula Cortes-Sánchez & Santi González, 2018. "Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    6. repec:plo:pone00:0020885 is not listed on IDEAS
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