From question to publication: designing reproducible data science for Archaeology 1500-SDN-FQTP
This course is designed to equip PhD students with the skills to navigate the entire research pipeline, from a socio-environmental question to a robust, publishable output, using modern, transparent, and reproducible digital methodologies .
Theoretical Concepts
Understanding what are complex human-environmental systems.
Generating specific and testable research questions. Introduction to research design and hypothesis formulation. Defining variables: spatial (coordinates), temporal (dates), and attribute (pottery types, soil chemistry, etc.). Introduction to legacy data, "Big Data," and issues of bias and representativeness in existing datasets . The concept of data quality and uncertainty.
Principles of database design for archaeology: tables, fields, keys, and controlled vocabularies. Introduction to the FAIR principles . Overview of how AI is being used for data extraction.
Principles of effective data visualization for exploration (identifying patterns, outliers, and structure). Introduction to descriptive and exploratory statistics.
The role of space and time in archaeological interpretation. Key GIS concepts: vector vs. raster data, scale, projections, and spatial analysis. Chronological modelling.
Moving from description to inference. Why model? Introduction to probability and the logic of Bayesian statistics as a way to update beliefs with data. Applications in archaeology: chronological modelling of radiocarbon dates, and its potential for modelling past processes .
Practical work
Students present their PhD topic and work individually or in groups to refine a broad interest into a single, focused question suitable for a mini-project.
Sourcing and evaluating a dataset. Students will identify and acquire a small dataset relevant to their question. This could be from online repositories (like RADOGOST or Pandora), published articles, or their own pilot study data. They will perform a critical assessment of its strengths and weaknesses.
Building a simple, robust database (e.g., using CSV files). Students will compile and clean their own dataset. Data cleaning and parsing.
Hands-on workshops using R and Pandora & IsoMemo software. Students will create summary statistics and visualizations of their dataset to generate initial observations and refine their analytical strategy.
Students will import their spatial data, create a base map, and perform a relevant spatial analysis (e.g., mapping site distributions against environmental variables).
Introduction to basic Bayesian modelling using R packages and Pandora & IsoMemo software to be applied to students’ datasets.
Publishing data and results.
Course coordinators
Type of course
Mode
Learning outcomes
Upon successful completion of this course, students will be able to:
1) Formulate a precise, researchable historical or archaeological question that can be addressed with quantitative and spatiotemporal data.
2) Critically evaluate and define the specific data needed to address their research question, understanding issues of sampling, bias, and data representativeness .
3) Design and build a standardized, documented, and FAIR (Findable, Accessible, Interoperable, Reusable) database, leveraging AI-assisted tools for data extraction and structuring from sources like digitized texts, site reports, or legacy datasets .
4) Employ a core set of analytical tools, including R and Pandora & IsoMemo software tools for exploratory data analysis and visualization , and GIS for spatial analysis , to identify patterns relevant to their research.
5) Apply advanced modelling techniques, with a specific focus on Bayesian modelling, to formally test hypotheses, model uncertainty, and quantify spatiotemporal processes .
6) Execute a mini research project that integrates these skills, producing a reproducible report containing code, data, and visualizations, which can serve as a pilot study or the core of a future larger project, publication, or funding proposal.
Assessment criteria
The course should be assessed through a practical, portfolio-based approach. Students will be evaluated on a series of interconnected deliverables: a research proposal and database design document, the cleaned database itself, their analytical code and visualizations, and a final mini-project report. The core criteria for assessment are the precision of the research question, critical engagement with data biases, technical proficiency and reproducibility of the code, analytical rigor in applying methods, and the overall ability to synthesize the work into a coherent project that could form the basis for a future publication or larger project.
Bibliography
Daems, D., 2021. Social complexity and complex systems in archaeology. Routledge.
Barton, C.M., 2014. Complexity, social complexity, and modeling. Journal of Archaeological Method and Theory, 21(2), pp.306-324.
Gil, T.L., 2021. How to make a database in historical studies. Springer.
Carlson, D.L., 2017. Quantitative methods in archaeology using R. Cambridge University Press.
Graham, S., 2022. An enchantment of digital archaeology: raising the dead with agent-based models, archaeogaming and artificial intelligence. Berghahn Books.
Conolly, J. and Lake, M., 2006. Geographical information systems in archaeology. Cambridge University Press.
Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.W., da Silva Santos, L.B., Bourne, P.E. and Bouwman, J., 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1), pp.1-9.
Buck, C.E., Cavanagh, W.G., Litton, C.D. and Scott, M., 1996. Bayesian approach to interpreting archaeological data (pp. 226-233). Chichester: Wiley.
Buck, C.E. and Juarez, M.A., 2025. Bayesian radiocarbon modelling for beginners. Archaeometry, 67, pp.31-48.
Lindsay, D., 2020. Scientific writing= thinking in words. Csiro Publishing.