The Future of Bioarchaeological Data: Why FAIR, CARE, and Machine Learning Are Essential for Sustainable Research

Bioarchaeological materials represent finite and irreplaceable resources, with many analytical techniques requiring consumptive sampling that permanently limits future research opportunities. This challenge is particularly acute for Modern era contexts (1492–1945 CE), where industrial and colonial period collections offer crucial evidence of health transitions and disease emergence yet remain under-utilised due to data accessibility challenges. Evidence from 145 bioarchaeology specialists across 23 countries demonstrates that whilst 97% recognise data reuse as critical, fewer than half consistently implement basic measures such as persistent identifiers. Only ancient DNA research consistently meets FAIR (Findable, Accessible, Interoperable, Reusable) standards. Meanwhile, data volumes expand exponentially through technological advances. The situation is unsustainable and ethically problematic. This perspective argues three integrated commitments are essential: universal adoption of FAIR principles with appropriate infrastructure, implementation of CARE (Collective benefit, Authority, Responsibility, Ethics) principles ensuring ethical treatment of human remains, and strategic development of artificial intelligence and machine learning tools for knowledge extraction. The finite nature of bioarchaeological materials makes transformation urgent. Every sample destroyed without proper data preservation represents irreversible loss of knowledge about human heritage.