13 July 2018
on course website
The Summer School will enable participants to manage the life cycle of digital archaeological data. It is built around a new paradigm, which takes into consideration archaeologists as both producers and users of digital archaeological data. Attendees will learn the concepts and methods of data retrieval, management, analysis and communication through an integrated use of software, statistical and practical principles. This will be accomplished through field activities connected to pottery analysis within the ArchAIDE project (www.archaide.eu), and ICT-related activities carried out in laboratory and taught in the lectures. The Summer School will take place from June 25th to July 13th 2018, at the University of Pisa, Italy.
The training modules sequence follows that one of archaeological data’s life cycle
1. Data acquisition
Starting from in-the-field processes and in collaboration with ArchAIDE project, the attendees will acquire competences related to the digital acquisition of archaeological data.
2. Data recording and related tools
Analysis of standard methods of recording archaeological data; definition of the possibilities and critical aspects of the different types of data; different standards and processing of data.
3. Search techniques and the use of data from the internet
Searching data from the internet: use, cleaning, re-usable formats, compatibility between different data bases.
4. Data Management
The format and the structure of the storage of data have to be chosen considering the aims of the data collection and the end use of the results. In this module the different structures of data storage will be treated, including relational databases, SQL and no-SQL standards, and GIS applications. Here we will consider the importance of preservation to ensure the authenticity, reliability and logical integrity of data in perpetuity, the use of open standards and open formats, metadata, and ontologies for linking the data.
5. Data Analysis
Overview of possibilities in a mathematical approach in Humanities. In-depth analysis of statistical analysis techniques, spatial analysis, predictive modelling, spatio-temporal modelling, data mining.
6. Web content resources for longlife learning
In this module we will consider repositories of contents openly available throughout the internet. There is a vast list of such resources, including open online courses, e-learning platforms, tutorials, mailing-lists, blogs, wikis, repositories of papers, books, slides. For each one of these resources, three levels of understanding will be considered, in order to get the best tools for an optimal longlife learning: the knowledge of each tool and of the type of content it brings; how to search for contents within such resources; how to aggregate different contents from different resources to get the required information.
Students, graduates, PhD candidates, and post-docs in archaeology or related to Cultural Heritage. Specific computer science or technology skills are not required.
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 very important in order to effectively interact with ICT experts.
The Data-Driven Archaeology summer school aims for a fruitful combination of archaeology and statistics through the teaching of Data analysis, Data mining, and Data visualization techniques, extremely relevant but by no means common across Humanities. The large amounts of data that are produced through archaeological work show a wide degree of heterogeneity, complexity, and interconnection, making the use of algorithmic methods unavoidable.
EUR 750: course fees
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