Winter School: R for Archaeologists
The Winter School “R 4 aRcheologists” (R4R) will be held both online and in person (can be joined online or in person, equivalently) from January 12 to January 23, 2026, organized by the Department of Civilisations and Forms of Knowledge of the University of Pisa, Italy.
R4R will enable participants to conduct statistical analysis and visualization of Archaeological data. It is built around a new paradigm, which takes into consideration archaeologists as both producers and users of digital archaeological data. The over 90 attendees of the R4R editions learnt the concepts and methods of univariate and multivariate analysis, spatial analysis and data visualization through an integrated use of R ecosystem software packages, statistical and practical principles. One of the school activities will be dedicated to effective prompting strategies, encouraging a critical and informed use of Large Language Models, such as ChatGPT, Gemini or Copilot. R4R lasts 60 hours.
In Humanities, the exponential increase in digital documentation requires us to question its management, its use, its availability to the scientific community and its sustainability. In archaeology, these issues are even more crucial because they relate to non-reproducible primary data. In order to effectively retrieve, store, manage, prepare for analysis, and communicate the information and the scientific range of such amount of data, modern archaeologists should be able to deal with concepts and tools related to new technologies. Such digital competencies are not present in a standard archaeology background, though they are fundamental in order to effectively interact with ICT experts. The “R for Archaeologists” winter school aims for a fruitful combination of archaeology and statistics through the teaching of Data analysis, Data mining, and Data visualization techniques. It is conducted through R, programming language and free software environment for statistical computing and graphics. The large amount of data that are produced through archaeological work show a wide degree of heterogeneity, complexity, and interconnection, making the use of algorithmic methods unavoidable. R is one of the main programming languages of Data Science, and includes a wide variety of statistical and graphical techniques, including linear and nonlinear modelling, statistical tests, spatial statistics, time-series analysis, classification, clustering, and others.
Target Attendees
Students, graduates, PhD candidates, and post-docs in archaeology or related to Cultural Heritage. The course is open to EU (including University of Pisa students) and non-EU applicants. For an effective learning environment, the number of participants will be limited to 40.
ECTS: 6
Fees: 500 Euros (accommodation and food not included)
Further Info: please email (both)
Professor Gattiglia: gabriele.gattiglia@unipi.it (Scientific Manager)
Dr. Dubbini: nevio.dubbini@unipi.it (Operations Manager)
Deadline for application: December 1, 2025
Training Modules
Why R
- What is R
- R in data science
- R and Python
- R ecosystem
- R APIs
Technical introduction to R
- R and R Studio intallation
- R functions
- R packages
- Our datasets
- R variable types
- Simple commands: summary, table, class, is.na
- Data import and export (next year)
- Effective prompting strategies for LLMs (ChatGPT, Gemini, Copilot)
Statistical background
- Distributions
- Confidence intervals
- Statistical testing
Data visualization and publishing
- Good principles of visualizations
- ggplot2 package
- Aesthetics
- Scatter plots, histograms, boxplots and complex visualization
- Authoring, writing and publishing with Rmarkdown
Application to archaeological data
- Multiple regressions and GLM
- Principal Component Analysis
- Correspondence analysis
- Multidimensional scaling
- Cluster analysis
Spatial analysis
- Point Pattern Analysis
- Spatial Association and Spatial Autocorrelation
- Introduction to Geostatistics
Timetable:
- From Monday to Friday, from 9 am to 1 pm and from 2 pm to 4 pm (CET)
Nevio Dubbini
AI expert, Ph.D. in Applied Mathematics, having always worked in strongly interdisciplinary contexts, both in business and in academia. He has gained a long-standing experience in mathematical and statistical modelling, machine learning and data analysis software, applied to a variety of sectors, spanning from humanities to social and life sciences, and going through healthcare, IT and educational. He specializes in AI applied to Archaeology and Cultural Heritage. He has authored 30+ papers appeared in peer reviewed journals and conference proceedings, edited a volume, and has delivered 30+ talks in international conferences. More about Dr. Dubbini on his linkedin profile.
Gabriele Gattiglia
He obtained his Specialisation in Archaeology in 2003 and his PhD in 2010. He is a Researcher in Archaeological Method and Theory at the University of Pisa. He teaches Sources, tools, and methods for Archeology and Digital Archaeology. He leads the MAPPA Lab, which manages the MOD, the Italian repository for Open Archaeological Data. He was the coordinator of ArchAIDE (3-years European H2020 research project), aimed at creating a new system for the automatic recognition of archaeological pottery. He is one of the leading Italian expert in open archaeological data, and GIS, RDBMS and predictive models specialist. Dr. Gattiglia has been the coordinator of MAPPA project (funded by Regione Toscana, Italy), having created a predictive model of the archaeological potential of an urban area. He conducted as director 12 archaeological excavations and 4 archaeological surveys, participated in 100+ archaeological excavations. He published 11 books and 70 papers on national and international peer review journals or conference proceedings. More about Dr. Gattiglia on his X and on his academia.edu profiles.
Thomas Huet
Prehistorian and database manager at the University of Oxford, project EAMENA (Endangered Archaeology in the Middle East and North Africa). His main interests are data integration, data extraction, and data modeling, for Prehistorical times in the Mediterranean area, with a special focus on ancient iconography. He uses programming languages, GIS/databases, and web technologies, including web3D, linked data, and interactive apps. Thomas has published on very different topics, from the spread of the farming economy to the dynamics of Medieval urbs. For further information visit his GitHub account, professional page, or ResearchGate profile.
Salvatore Basile
How to apply
Follow this link, with all the instructions from the University of Pisa web page: how to apply
Other useful links here:
University of Pisa web page of the school
The Winter School R for Archaeologists will be organized at the University of Pisa learning centre “ex guidotti”, Via Trieste, 38, Pisa: https://goo.gl/maps/dRaTYC8a3ES2 (google maps link)
How to reach us
By Plane
The “Galileo Galilei” International Airport, in Pisa, is well connected with many Italian and European cities. It is served by both international airlines and low cost carriers. Byt the way, Pisa airport is one of the closest to the city centre in the world: central railway station is about 15 minutes walking!
By Car
Pisa is reachable following the A12/E80 “Genova – Livorno” Highway, or the A11/E6 “Firenze – Mare” Highway. There are two main parking areas where you can leave your car and get a bus to the city centre. If you arrive from north, leave your car in Pietrasantina Parking. If you arrive from East, leave your car in Brennero Parking.
By Train
The main train station of Pisa, “Pisa Centrale”, connects the town to Italian and European destinations through the nodes of Florence, Turin-Genova and Rome.
Useful links
Pisa Buses https://www.at-bus.it
International Airport Galileo Galilei www.pisa-airport.com/
Railway Station www.trenitalia.com/, www.italotreno.com/
Bibliography
– David R. Carlson, Quantitative methods in archaeology using R, Cambridge University Press (2017)
– Nakoinz O. & Knitter D., Modelling Human Behaviour in Landscapes, Springer (2016)