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Published on: November 22, 2019
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.
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.
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.
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