The purpose of the Datasprint
- To facilitate the development of digital humanities competence among members of staff and students at Aarhus University and University of Copenhagen
- To spread the fact that a part of the Danish digital newspaper collection is out of copyright and available in Mediestream and LOAR
- To challenge and explore the possibility of applying text and data mining to this collection
After attending this Datasprint the participant:
- has gained an understanding of text and data-mining with either Python or R
- is acquainted with the digital collections of Det Kgl. Bibliotek | Royal Danish Library
- is acquainted with the digital humanities related activities in the University Libraries in both Aarhus and Copenhagen
- has gained insight into a important part of Danish history and culture
The learning approach
With a strong emphasis on the practical side of training we have primarily adopted a functional approach to learning. Secondly, since we will give brief introductions, we use a deductive approach. We emphasis dialogue during the introductions to ensure that the participants gains the highest possible learning from these presentations.
The Datasprint will be tightly facilitated. This means that the structure is outlined from the beginning, and the tasks, instructions, notebooks, and data sets are carefully selected.
The participants will be introduced to the determined topic (The Danish Borderland 1830-1870) and the determined tasks where they must use the notebooks and data sets to solve the tasks. The organizers are responsible for the brief introductions to make sure the participants are fully equipped to overcome the challenges they will meet throughout the Datasprint.
Once the sprint has begun, it is up to the participants to solve as much of their tasks they can. The organizers are present throughout to offer any guidance the participants may need. Once the time is up – and all participants have crossed the finish line – hopefully they have learned a whole lot more about the topic, Python, R, and digital methods.