Empirical Musicology Review: Special Issue on Open Science in Musicology

Empirical musicology relies crucially on the creation, analysis, publication, and distribution of datasets. Despite the progress made over the past decades in this vibrating field, numerous issues regarding the sharing of data, the reproducibility of research findings, and the general role of transparency remain challenging. In many disciplines, these issues are addressed under the umbrella of the Open Science movement and the adherence to FAIR principles for scientific data management (findable, accessible, interoperable, reusable; https://www.go-fair.org/fair-principles). To advance the state-of-the-art in data-based music research, Empirical Musicology Review is devoting a special issue to a wide discussion of questions related to Open Science and Open Data, and introduces a new section on data reports that will remain a permanent part of the journal in all subsequent issues.

CfP: Research Articles and Think Pieces
We invite papers that address general aspects of Open Science / Open Data, discuss challenges in the application of the FAIR principles to music research, or reflect upon methodological and meta questions. Papers may also describe the generation of particular datasets and explore their characteristics in the context of the overall topic of this special issue. We envisage to include contributions from a wide variety of domains, such as music theory, music psychology, music information retrieval, historical musicology etc. The data must be accessible in an open repository or database. Papers should be 3000–6000 words in length.

CfP: Data Reports
Starting with this special issue, EMR is introducing a new section on Data Reports. In order to promote Open Science and to facilitate reproducibility, empirical studies of music are increasingly relying on openly available corpora and datasets. Since the scientific value of creating, cleaning, curating, enabling access, and maintaining data is of the utmost importance, EMR invites researchers to share their datasets and to apply the FAIR principles. Data Reports may describe a variety of datasets such as musical metadata, annotations of musical corpora in symbolic or audio formats, automatically extracted musical features, data from psychological experiments etc. Data Reports should not exceed a word limit of 2000 words.

Please register on http://emusicology.org/ and submit your contribution by 31 March, 2020.

If you have any further questions, please get in touch with the guest editors:
Fabian C. Moss (fabian.moss -at- epfl.ch) and Markus Neuwirth (markus.neuwirth -at- epfl.ch)
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