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Archaeology of the future. Data mining, Data analysis, Data driven archaeology.

Promossa dal Progetto Mappa dell'Università di Pisa la Summer School "Archaeology of the future. Data mining, Data analysis, Data driven archaeology" permetterà ai partecipanti di gestire l'intero ciclo dei dati archeologici. È costruito attorno ad nuovo paradigma che prende in considerazione il ruolo dell'archeologo sia come produttore che come utente di dati archeologici digitali.

Si impareranno concetti e metodi per il recupero, la gestione, l'analisi e la comunicazione dei dati archeologici, attraverso un uso combinato di tecnologie e principi matematici.
La scuola è aperta a studenti, laureati, dottorandi e dottori di ricerca in archeologia o nell'ambito dei beni culturali. Non sono richieste particolari conoscenze informatiche o tecnologiche .

La Summer School si terrà dall'11 al 29 Luglio 2016, all'Università di Pisa. Il numero di partecipanti è limitato a 20.
La scadenza per candidarsi è il 26 Giugno 2016


English version
Archaeology of the future. Data mining, Data analysis, Data driven archaeology.

The Summer School aims at providing the contents and the methodologies necessary to manage the whole life cycle of archaeological data, through a new paradigm considering the archaeologist both as a producer and as an end-user of digital data. This school will supply attendants with the notions and the tools necessary for the retrieval, management, analysis and communication of archaeological data, through a combined use of technology and mathematical know-how. The Summer School will take place from the 11th to the 29th of July 2016, at the University of Pisa, Italy.

Students, graduates, PhD candidates, and post-docs in archaeology or related to Cultural Heritage. Specific computer science or technology skills are not required.

In all human sciences, 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 crucial because they relate to non-reproducible primary data. In order to effectively store, manage, prepare for analysis, and communicate the information and the scientific range of such an 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 IT experts.

For young archaeologists being trained, the target is to get full control over digital tools for recording, processing, interpreting and publishing archaeological data, and to be aware of open source software and open formats. In this respect, this summer school aims for a fruitful combination of archaeology and mathematics through the teaching of Data analysis, Data mining, and Data visualization techniques, together with a Big Data approach, 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.

The summer school will be organized in training modules whose sequence follows that one of the life cycle of archaeological data.

    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.
    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.
    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.
    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.
    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.
    Data visualisation and communication. From the raw data to story-telling addressed to different audiences. Publication methods and tools, dissemination and communication of the archaeological data, data visualisation tools and storytelling.


Per ulteriori informazioni/ Further information

Fonte: Progetto Mappa


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