Studiegids

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Digital Data Acquisition and Analysis

Vak
2018-2019

Admission requirements

  • BA or BSc degree in Archaeology or a closely related discipline;

  • Admission to the MSc Archaeology programme Archaeological Science;

  • BA2 courses Data Analysis and Geographic Information Systems (GIS) (or undergraduate courses in Digital Archaeology deemed to be equivalent by the instructor) obtained.

Description

This course will provide you with an overview of Digital Archaeology as a sub-discipline of archaeology by reviewing current trends in digital data acquisition and analysis. The course is targeted towards students who already have a good idea of the What, Why and How of archaeological enquiry, as well as basic knowledge of common digital tools such as databases and GIS.

In this course you will be able to acquire in-depth knowledge about digital data in archaeology: how they are generated, collected and processed, what they represent (and what not), and how they can be managed, visualised, analysed, interpreted and presented using state-of-the-art tools. The following sub-fields of Digital Archaeology will be covered:

  • Field recording and geophysics;

  • Remote sensing and image analysis;

  • 3D modelling and virtual reconstruction;

  • Spatial analysis;

  • Data management.
    These sub-fields also define the thematic range from which Digital Archaeology students can choose their MSc thesis topic.

Throughout the course, we will furthermore discuss the general role of digital data and computational tools within archaeology. You will be required to write an essay on this topic.

Due to the overview nature of this course, the mode of instruction will be a series of lectures, combined with extensive autonomous study of current literature on techniques and methods, as well as case studies.
Within the MSc focus area Digital Archaeology, complementary laboratory courses and internships offer the opportunity to acquire corresponding practical skills according to your choices.

Course objectives

To provide students with a thorough knowledge of the acquisition and analysis of digital data used in archaeological research and an overview of Digital Archaeology as a discipline. At the end of the course, students will be able to:

  • understand how digital data are acquired and analysed in archaeology;

  • give an overview of Digital Archaeology as a discipline;

  • identify relevant data and tools for specific sub-fields of Digital Archaeology;

  • choose a suitable topic for further study in the framework of internships, laboratory work, and the Master’s thesis.

Timetable

Course schedule details can be found in the MA and MSc time schedule.

Mode of instruction

  • Lectures;

  • Self-study (reading assignments).

Course load

The course load will be distributed as follows:

  • 12 hours of lectures (1 ec);

  • 420 pages of literature (3 ec);

  • Essay of 1500-1800 words (1 ec).

Assessment method

  • Essay (50%);

  • Final written exam with open questions (50%).

The final grade will be the average of the partial grades of the essay and the written exam. Only the written exam can be retaken.

All assessment deadlines (exams, retakes, paper deadlines etc.) can be found in the MA and MSc examination schedule.

Reading list

  • T.L. Evans & P. Daly (eds), Digital Archaeology: Bridging Method and Theory. London: Routledge (2005);

  • G. Lock, Using Computers in Archaeology: Towards Virtual Pasts. London: Routledge (2003).

Registration

Registration via uSis is mandatory.

  • The Administration Office will register all BA1 students for their tutorials (not lectures; register via uSis!).

  • BA2, BA3, MA/MSc and RMA/RMSc students are required to register for all lectures and tutorials well in time.

  • The Administration Office registers all students for their exams, students are not required to do this in uSis.

Contact

For more information about this course, please contact dr. K. (Karsten) Lambers.

Remarks

Attendance is not compulsory but strongly recommended, as the written exam will be partly based on discussions during the classes.