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.
Koordynatorzy przedmiotu
Rodzaj przedmiotu
Tryb prowadzenia
Efekty uczenia się
Wiedza (zna i rozumie)
WG_01 w stopniu umożliwiającym rewizję istniejących paradygmatów – światowy
dorobek, obejmujący podstawy teoretyczne oraz zagadnienia ogólne i wybrane
zagadnienia szczegółowe – właściwe dla dyscyplin humanistycznych
WG_03 metodologię badań naukowych w obrębie nauk humanistycznych
WK_01 fundamentalne dylematy współczesnej cywilizacji z perspektywy nauk
humanistycznych
Umiejętności (potrafi)
K_U01 komunikować się na tematy specjalistyczne w stopniu umożliwiającym
aktywne uczestnictwo w międzynarodowym środowisku naukowym w obrębie nauk
humanistycznych
UW_01 wykorzystywać wiedzę z różnych dyscyplin nauk humanistycznych do
twórczego identyfikowania, formułowania i innowacyjnego rozwiązywania złożonych
problemów lub wykonywania zadań o charakterze badawczym, a w szczególności
definiować cel i przedmiot badań naukowych w dziedzinie nauk humanistycznych,
właściwie rozwijać metody, techniki i narzędzia badawcze oraz twórczo je stosować;
formułować hipotezę badawczą oraz wnioskować na podstawie wyników badań
naukowych
Kompetencje społeczne (jest gotów do)
KK_03 uznania priorytetu wiedzy w rozwiązaniu problemów badawczych,
poznawczych i praktycznych, w obrębie dyscyplin humanistycznych, z zachowaniem
szacunku dla standardów pracy i debaty naukowej
Kryteria oceniania
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.
Literatura
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.