Standardisation of digital research data is increasingly recognised as an issue in biological anthropology. It is related to transparency of research, formalised provision of datasets and data pooling, as demanded by funding organisations and research institutions. Digitisation opens up new possibilities but, at the same time, imposes challenges.
For scientific results to be credible, it is essential to document the methods employed in their production and the data structures by which they are presented. Data standardisation helps researchers with these tasks by providing precise specifications of both these aspects which can be readily cited in reports on research where the respective methods were employed. Furthermore, standardisation renders data sets from independent sources comparable and supports their digital processing. To these ends, a number of data standards have been developed in biological anthropology.
Digital research data can be enriched with annotations on their provenance, circumstances of their production and their intended use (data management policies) and with informal notes. These metadata help making research processes transparent, replicable or at least comprehensible. They also provide a basis for the publication of data sets in order to render them open for inspection and reuse by the scientific community. The publication of primary data is increasingly encouraged by funding agencies, scientific associations and infrastructure facilities. It is expected to become a major quality indicator in research management in the near future.
In order to be effective, data standards need to grow with the scientific demand. New and alternative methods and data types need to be incorporated on a regular basis and related to existing concepts. Backward compatibility and consistency with previously compiled data are essential in this process. Accordingly, data standards need to be integrated with each other to maximise benefits for all users of standardised research data.
Data standards are as good as their practical applicability in data acquisition, processing and analysis. Consequently, their implementation in software for data coding (e.g. Osteoware, AnthroBook), database management and statistics (e.g. R packages) is a crucial topic. Here, standards define conceptual data models according to which these tools operate. But their development should also take in account research designs, data queries and analytical strategies which they need to support in scientific investigations. Therefore, data standardisation and modelling are deeply interrelated.
Data standardisation opens up possibilities to create large data bases holding information of a high quality. These are open to continuous curation and improvement. The quality of research data can be monitored, even with highly descriptive information as commonly occurs in anthropology. Challenges are the effective integration of standards into research processes, the continuous maintenance of standards and the raising of additional funding.
Within the Society of Anthropology (Gesellschaft für Anthropologie, GfA), several members are directly involved in the formulation, maintenance and practical application of data standards and in modelling of the resulting data. But the topic is relevant for all anthropologists, for reasons given above. Digitisation of research data and the creation of complementary infrastructures in academic institutions are currently under way. The GfA working group ‘Data Standardisation and Modelling’ is dedicated to monitoring this process and to channel contributions from within the society and biological anthropology at large. It also acts as a platform for coordinating projects and provides contact support for external requests.