In this paper, we present how the COREX ERC project has developed the Big Interdisciplinary Archaeological Database (BIAD) for the exploration of complex relationships in response to multifaceted research questions. BIAD is an initiative aimed at integrating and harmonising large, heterogeneous archaeological datasets within the current scientific move towards Big Data.
At present, BIAD integrates detailed information of archaeological sites, including material culture, human data (burials, individual characteristics, palaeopathology, and links to ancient DNA), ¹⁴C, isotope and strontium data, metallurgy, faunal and botanical datasets. Critically, all datasets are internally linked and traceable to verified source citations.
We outline the core principles of the BIAD architecture, together with its conceptual differences from existing archaeological repositories and databases. Key project outcomes and methodological challenges related to the integration of non-standardised datasets from diverse European research traditions are addressed, with particular emphasis on strategies, data structures, and vocabularies developed to improve cross-dataset comparability and data quality.
Although BIAD entries now focus primarily on Europe, the database is designed to be scalable and flexible, allowing for the integration of data from any region. BIAD is conceived as a fully relational system built for long-term growth and potential globally. Beyond data aggregation and linkage, BIAD is conceived as an integrated analytical environment that tightly articulates complex data queries with statistical analysis. This has been made possible by the development of BIADConnect, a versatile R interface that enables direct interaction with the database and supports a range of analytical workflows. This interface also facilitates the development of complementary applications, such as BIAD TaxaExplorer, a web-based tool for visualising data distributions and summary statistics for archaeobotany and zooarchaeology. TaxaExplorer allows exploration within user-defined spatial polygons and time slices, enabling rapid visual assessment of sample distributions and dimensionality reduction using correspondence analysis.

First Author: Natalia Ryabogina

Co-Authors: Adrian Timpson, Simon Carrignon, Jelena Bulatović, Robert Staniuk, Jan Kolář, Samantha Reiter, Stephen Shennan, Mark Thomas, Kristian Kristiansen

Keywords:
relational database
interdisciplinary
aggregation
internally linkage
statistical analysis