Welcome!

Welcome to the Summer School in Digital Archaeology!

The aim of the Summer School is to provide comprehensive training in computational modelling in archaeology. In particular, we will focus on: agent-based modelling, network science, semantic technology, and research software development.

The Summer School will take place in Barcelona between 10-14 September 2018 immediately after the Annual Meeting of the European Association of Archaeologists (EAA2018).

If you are

a masters, or PhD student in archaeology, anthropology, history or a similar subject, a post-doc or an early career researcher, a lecturer, a commercial archaeologists or a heritage specialist,

and if

  • you are interested in computational modelling, or
  • you want to learn how to code your hypotheses rather than write them up, or
  • you work on a complex problem which resists standard treatments, or
  • your supervisor told you to ‘go learn how to do simulations/networks/semantic web’, or
  • your students seem to be doing some magic with computers and you want to help them but don’t know the tools,

then this workshop is for you!

What will you learn?

  • the modelling process, from finding the right research questions to publishing your groundbreaking results;
  • the theory and practice of agent-based modelling, network science and semantic modelling;
  • how to model archaeological data and how to create an archaeological simulation;
  • basic and intermediate programming skills in NetLogo and Pandas (Python data analysis library;
  • how to make your code better, clearer and faster;

Coding experience is NOT required.
All you need to bring is your own laptop.

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Attempts at quantification and simulation modelling appear essential. It is striking that such methods play a central role in other disciplines dealing with long term change (…) but have been neglected in palaeoanthropology.

– Mithen & Reed 2002

Innovative computational methods provide (…) new ways of conceptualizing scale and pattern, exploring social change, and utilizing large data sets.

– Rogers and Cegielski 2017