[MEI-L] Music and Digital Humanities: Monday's lecture (04. May): Frans Wiering. Followed by: 11. May: Terhi Nurmikko-Fuller

David M. Weigl weigl at mdw.ac.at
Thu Apr 30 16:25:19 CEST 2026


Reminder: Distinguished Lecture Series in Music and Digital Humanities

https://iwk.mdw.ac.at/music-dh

Monday's lecture in the Distinguished Lecture Series on Music and 
Digital Humanities taking place at the mdw — University of Music and 
Performing Arts Vienna on 4th May will be given by Frans Wiering 
(Utrecht University):

"Musicology and computing: A very short history"

Abstract: Frans Wiering have spent most of his professional life working 
in the exciting interdisciplinary area between musicology and computing 
known (among others) as computing in musicology, computational 
musicology, digital musicology, or music information computing. Each of 
these terms suggests a different focus, but they also indicate that the 
field hasn't quite settled, despite its over 70 years of existence. 
Musicological computing (to use yet another expression) has much in 
common with other forms of humanities computing, except for one thing: 
the processing of musical information, in the form of music notation and 
musical audio. This will also be the main topic of this talk.

The first, promising experiments in music processing date from the 
1950s. The 1960s and 1970s witnessed a period of grand ambitions for 
computer-supported inventorying, publication, and analysis of musical 
heritage. In practice, however, the need for technological groundwork
was so strong that these ambitions could only be realised to a limited 
extent, if at all. Furthermore, both computer science and musicology 
experienced a paradigm shift in 1980s. Consequently, computational 
musicology, with its ‘scientific’ nature, became largely separated from 
mainstream musicology. Since around 2015, this landscape has changed 
again for a variety of reasons, including the ready availability of 
digital resources, access to (free or cheap) software, mobile internet, 
online communication, and finally the pandemic. Doing musicology in the 
digital environment has become commonplace.

Bio: Frans Wiering was an Associate Professor at the Music Information 
Computing group of the Department of Information and Computing Sciences 
of Utrecht University (Netherlands). He retired in 2024, but remains 
associated with Utrecht University. He received his PhD in musicology 
from the University of Amsterdam (Netherlands) in 1995 for his 
dissertation ‘The Language of the Modes on the richness and variety of 
modality in 16th- and 17th-century polyphonic music. His current 
research is in computational musicology, music information retrieval and 
interactive technologies, which he combines in his ongoing work on the 
use and acceptance of new technologies in music research in the project 
‘What Do Musicologists Do All Day.’


The lecture will start at 17:00 (Vienna/CET). As always, it will be 
streamed via Zoom, and both in-person and remote participation is free.

Zoom Link:

https://mdw-ac-at.zoom.us/j/67606221415?pwd=9VUR9zPcIe43mV2Gj5IIXyd3jgWZw1.1

Please refer to https://iwk.mdw.ac.at/music-dh for further information.

--

The following lecture will be held on May 11 2026, 17:00 (Vienna/CET).

** Note for local participants: This lecture will take place in Room 
K0101, mdw Campus. Further opportunities for interaction with Dr. 
Nurmikko-Fuller during her visit to Vienna will be circulated on local 
DH mailing lists. **

Terhi Nurmikko-Fuller (RMIT University)

"Bridging Datasets: Linked Data for Digital Musicologists"

Abstract: As an information publication paradigm, Linked Data has a 
great deal to offer researchers in the areas of Digital Musicology and 
the Digital Humanities more broadly. In this talk, I will introduce the 
basics of the Linked Data methodology, including its potential and 
limitations, as applied in the context of broader interdisciplinary 
spaces that bridge the Humanities and Computer Science. My case study 
example, JazzCats, illustrates how musicological data in different 
formats from different sources can be successfully bridged, and queried 
for answers to questions that go far beyond what can be asked of a 
single dataset. The project aggregates three different kinds of 
information, namely a discography, performance metadata, and 
prosopographical information about musicians. These datasets come in 
three different formats; tabular data, in the form of a spreadsheet; 
relational data, as exported from a MySQL Lite database; and, graph data 
as RDF (.ttl).  Although the value of this aggregation, and in 
particular the benefit it has for researchers, is undisputed, the 
project itself has fallen victim to challenges of institutional change 
and policy regarding legacy projects. This talk will highlight how these 
challenges in academia are particularly disruptive to projects in the 
Digital Humanities, and have far-reaching consequences for Linked Data 
projects across disciplines and jurisdictions.

Bio: Terhi Nurmikko-Fuller is an Associate Professor, Information 
Interaction at the School of Computing Technologies at RMIT University 
in Melbourne, Australia. Her interdisciplinary research examines 
different methods for data linking and integration, and how digital 
technologies support and diversify research. She is the author of Linked 
Data for Digital Humanities (2023, Routledge), and has publications that 
cover a range of topics from the use, development, and critical 
evaluation of Linked Data to gamification and informal online 
environments in education. She has also created 3D digital models for 
the British Museum (cuneiform tablets), the National Museum of Australia 
(carved boab nuts), and UNESCO (Fels Cave in Vanuatu). Terhi is an 
Honorary Associate Professor at POLIS, the Centre for Social Policy and 
Research at the Australian National University; a member of the 
Territory Records Advisory Council, Policy and Cabinet Division, of the 
Chief Minister Australian Capital Territory Government; and a co-chair 
of the Australian Government Linked Data Working Group.


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