Up-Scaling to the Next Level: Transforming Archaeology into a Shared Data-Driven Discipline

Dear Colleagues,

Please find below a call for contributions to a Special Issue on the requirements for transforming archaeological methodology into a data-driven methodology based on the Open Science initiative and aligned with the FAIR data principles. The text below further details the challenge.

Archaeology is primarily a multi-disciplinary research domain, relaying on social sciences and cultural anthropology, art and architectural history, history as well as natural and exact sciences. As such, a fundamental requirement for conducting a reliable and far-reaching synthetic research is the ability to engage with data in a collaborative and integrated way. However, there is currently a lack of data-driven approaches in the archaeological research methodology, while there is an increasing need for a synthetic, integrated approach to address research on grand challenges in archaeology. Both relate to the handling of a huge amount of fragmented information, resulting from the convergence of investigations based on many diverse sources and methods, such as field campaigns, stylistic analyses, scientific measurements, historical sources and anthropological approaches. There is the risk that such data—increasingly available in digital format but, quoting Huggett [1, 2], “messy, complicated by their partial, fragmentary, interpretative nature”—are not incorporated in the archaeological interpretation, just because they are disregarded in the flood of available information. Consequently, natural science data, broadly grouped under terms such as archaeological sciences, bioarchaeology, etc., have yet to define agreed-upon protocols and canonical workflows for the production of data along its pipeline of acquisition, archiving, analysis and interpretation. There are no agreed-upon vocabularies and thesauri for describing such data, and the general tendency is to make available results of such research and not the primary data used to achieve these results. Therefore, typical data science techniques such as deep learning and AI, based on statistics, may not work, as “the impact of poor-quality data can increase rather than reduce as dataset size increases” [3, 4]. Quoting Kintigh [5, 6]: “Enormous quantities of archaeological information and knowledge are embedded in often-lengthy reports and journal articles…A number of factors conspire to frustrate synthetic research. They include the problems of discovery and access to archaeological data, the difficulty of integrating data from diverse sources, and the problem of extracting usable data, information, and knowledge from text”, demonstrating how misleading a naive approach based on simple word search can be. A substantial improvement in the data science approach for archaeology is absolutely necessary. Archaeology requires the definition of a complex semantic organization able to capture and organize the inner meaning of statements, arguments and interpretation. Therefore, data science must adapt to the specific needs of the discipline and focus on refined semantics along with large-scale processing.

We are looking for contributions detailing conceptual, technological or methodological solutions addressing the above.

References

[1] Huggett J. (2019) “Delving into Data Reuse”, https://introspectivedigitalarchaeology.com/2019/10/10/delving-into-data-reuse/

[2] Huggett J. (2015) “A manifesto for an introspective digital archaeology” Open Archaeology 1(1).

[3] Succi, S. and Coveney P. (2019) “Big data: the end of the scientific method?” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 377.

[4] Woodall P., Borek A., Gao J., Oberhofer M. and A. Koronios A. (2014) “An Investigation of How Data Quality is Affected by Dataset Size in the Context of Big Data Analytics” Proceedings of the 19^th International Conference on Information Quality (ICIQ-2014) 24-33.

[5] Kintigh K.W., Altschul J.A., Beaudry M.C., Drennan R.D., Kinzig A.P., Kohler T.A., Limp W.F., Maschner H.D.G., Michener W.K., Pauketat T.R., Peregrine P., Sabloff J.A., Wilkinson T.J., Wrigh, H.T. and Zede M.A. (2014). “Grand Challenges for Archaeology” American Antiquity 79(1), 5–24.

[6] Kintigh K.W. (2015) “Extracting Information from Archaeological Texts” Open Archaeology 1, 96-101.

Dr. Sorin Hermon
Dr. Achille Felicetti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Quaternary is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Archaeology as an Open Science discipline
• Semantic structures and domain ontologies
• Data Science methods for archeology
• Archaeological Digital Twins