Digital Arts Initiative

Digital Arts Initiative supports training and research in digital humanities at the Faculty of Arts. In 2018, more than 40 researchers are working with projects in the Digital Literacy course. 

Foto: GCDH.de

Frank Fischer to guest School of Communication and Culture in November

2018.09.18 | Education news

Frank Fischer from Centre of Digital Humanities at Higher School of Economics in Moscow will guest School of Communication and Culture at Aarhus University during the month of November.

Foto: Lars Kruse, AU Foto

Grant awarded to Network for Digital Literature Studies

2018.09.05 | Awards

Independent Research Fund Denmark has awarded a grant exceeding 1 million dkk to Network for Digital Literature Studies.

Foto: ufm.dk

More focus on digital technologies and methods in education

2018.09.05 | Education news

In a press release, the Danish Ministry of Education and Research emphasizes the importance of implementing digital technologies and methods to all higher educations.

Wed 24 Oct
13:00-16:00 | Jens Chr. Skous Vej 7, 1467-215, Aarhus University
Introduction to Geographical Information Systems by Peder Klit, AU Bioscience
Peder Klith Bøcher, Bioscience (AU), will introduce to Geographical Information Systems with examples of how it is being used at Aarhus University. The participants should bring a computer for a few hands-on exercises. The talk is free and open for all, but registration is mandatory. Please send an email to emi@cc.au.dk if you wish to attend. Peter is GIS Manager at department of Bioscience at Aarhus University. Besides this, Peder also works at Department of Computer Science.
Mon 05 Nov
10:00-15:00 | Finlandsgade 27, 5361-144, Aarhus University
Nvivo Course by Marie Østergaard, AAU
The course aims to improve the participants' methodological expertise in quantitative data analysis in general and specifically in the Nvivo-programme and its functions, including coding and classification of different types of data, non-quantitative ways of going through data and identification of patterns between cases across coding or time.