A model is a simplified, and often abstract, representation of a real-world system. We all work with models, whether it is a map, a multiple regression analysis, a hypothesis (conceptual model) or a simulation. Scientific process consists predominantly of developing, testing and comparing different models and the use of formal, computational methods to do so has transformed many disciplines. Archaeology could benefit immensely from a wider adoption of such methods but the technological know-how remains a hurdle. This summer school aims to break this pattern.
The summer school will focus on: data modelling, semantic modelling, network science and, in particular, agent-based modelling. Click on any of the tiles to learn more about these techniques.
Agent-based Modelling is the most popular simulation technique in archaeology. It is often considered a ‘bottom up’ approach to modelling because it uses individual software agents as opposed to predefined ‘population level’ equations. These agents follow simple behavioural rules making them interact with each other and with their environment. These local interaction lead to global… Continue reading Agent-based Modelling
Network science is a field of research which studies data represented as networks. This can be networks of people, places, web addresses or even food-webs. A network consists of nodes and edges (links), where the former usually represents the distinctive elements of the studied system (e.g., cities in a transport network) or, in case of… Continue reading Network Science
The web is a rich source of data. Semantic web is a framework which enables reuse, sharing and management of data published online. It uses ontologies to formalise meanings and relationships and to enable mapping and merging of datasets.
Research Software Sustainability refers to the a number of guidelines and best practices that makes research open, reusable and working long-term. Following a simple set of rules results in code that is better written, more accessible and better documented making science behind it robust and reproducible.