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Updated: Aug 25, 2025
06:18
Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains
Published on: November 30, 2021
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Promoting data quality and reuse in archaeology through collaborative identifier practices.
Eric C Kansa1, Sarah Whitcher Kansa1
1Alexandria Archive Institute, Open Context, San Francisco, CA 94127.
Summary
Globally unique and persistent identifiers are crucial for ensuring research data quality and enabling reuse in archaeology. Implementing these identifier practices enhances data context, reduces errors, and supports broader scientific discovery.
Area of Science:
- Archaeological data management
- Digital archaeology
- Research data infrastructure
Background:
- Investments in data management aim to foster research outcomes through data reuse.
- Understanding data creation and quality is essential for effective data reuse.
- Archaeological domain specialists are the primary creators and users of datasets.
Purpose of the Study:
- To explore how data quality and creation practices influence data reuse potential.
- To highlight the role of identifier practices in promoting data quality and reusability.
- To provide actionable insights for scientific domain specialists in archaeology.
Main Methods:
- Analysis of data creation and quality concerns in archaeological research.
- Examination of identifier practices and their impact on data reusability.
- Case studies using archaeological examples to illustrate the benefits of persistent identifiers.
Main Results:
- Identifier practices are critical, yet often overlooked, for data quality and reuse.
- Globally unique and persistent identifiers improve contextual communication and reduce errors.
- These identifiers facilitate data integration and enable more open-ended data reuse.
Conclusions:
- Adopting robust identifier practices is key to maximizing the value of research data investments.
- Data creators and reusers share responsibility for maintaining data's contextual integrity.
- Ethical and professional considerations are integral to effective data management and reuse.
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