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The billion-dollar case for sustaining palaeontology's digital databases.

Elizabeth M Dowding1, Emma M Dunne2,3, Katie S Collins4

  • 1GeoZentrum Nordbayern, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. dowding.e.m@gmail.com.

Nature Ecology & Evolution
|February 10, 2026
PubMed
Summary

Community-curated paleontological databases, vital for studying Earth's history, face short lifespans, risking significant investment. Sustained funding and better infrastructure are crucial for their long-term survival and continued research.

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Area of Science:

  • Palaeontology and Earth Sciences
  • Digital Data Management
  • Macroevolutionary Studies

Background:

  • Openly accessible, community-driven databases have revolutionized palaeontology.
  • These databases underpin large-scale studies of life's history and macroevolutionary patterns.
  • Significant global scientific and financial investment is required to maintain these critical resources.

Purpose of the Study:

  • To survey the diversity dynamics and lifespans of palaeontological and Earth science databases.
  • To analyze trends in database creation effort and identify factors contributing to database loss.
  • To advocate for strategies that enhance the longevity of scientific databases.

Main Methods:

  • Conducted a survey of 118 palaeontological and allied Earth science databases.
  • Analyzed database diversity dynamics, including origination and extinction rates.
  • Examined trends in database creation effort over the past 30 years and correlated database loss with funding cycles.

Main Results:

  • Approximately 85% of community-curated databases have lifespans under 15 years.
  • Database creation effort has increased in the last three decades.
  • Peaks in database loss correlate with 5-year funding cycles, jeopardizing long-term data investment.

Conclusions:

  • The short lifespan of scientific databases poses a significant risk to decades of investment and research.
  • Strategies for enhancing database longevity are urgently needed.
  • Recommendations include sustained funding models, robust institutional support, and modular architectures for better data integration.