Increasing the equitability of data citation in paleontology: capacity building for the big data future

Author:

Smith Jansen A.ORCID,Raja Nussaïbah B.ORCID,Clements ThomasORCID,Dimitrijević DanijelaORCID,Dowding Elizabeth M.ORCID,Dunne Emma M.ORCID,Gee Bryan M.ORCID,Godoy Pedro L.ORCID,Lombardi Elizabeth M.ORCID,Mulvey Laura P. A.ORCID,Nätscher Paulina S.ORCID,Reddin Carl J.ORCID,Shirley BryanORCID,Warnock Rachel C. M.ORCID,Kocsis Ádám T.ORCID

Abstract

Abstract Data compilations expand the scope of research; however, data citation practice lags behind advances in data use. It remains uncommon for data users to credit data producers in professionally meaningful ways. In paleontology, databases like the Paleobiology Database (PBDB) enable assessment of patterns and processes spanning millions of years, up to global scale. The status quo for data citation creates an imbalance wherein publications drawing data from the PBDB receive significantly more citations (median: 4.3 ± 3.5 citations/year) than the publications producing the data (1.4 ± 1.3 citations/year). By accounting for data reuse where citations were neglected, the projected citation rate for data-provisioning publications approached parity (4.2 ± 2.2 citations/year) and the impact factor of paleontological journals (n = 55) increased by an average of 13.4% (maximum increase = 57.8%) in 2019. Without rebalancing the distribution of scientific credit, emerging “big data” research in paleontology—and science in general—is at risk of undercutting itself through a systematic devaluation of the work that is foundational to the discipline.

Funder

Deutsche Forschungsgemeinschaft

National Science Foundation

Volkswagen Foundation

Publisher

Cambridge University Press (CUP)

Subject

Paleontology,General Agricultural and Biological Sciences,Ecology,Ecology, Evolution, Behavior and Systematics

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Artificial intelligence in paleontology;Earth-Science Reviews;2024-05

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