The kind of empirical and interdisciplinary music research carried out by our group depends on the existence of high quality, annotated collections of recordings and datasets: this is a particular problem in cross-cultural research. The Music & Science Lab is therefore committed to the development and sharing of such collections and datasets. These include recordings we have made ourselves in countries including India, Brazil and Ethiopia, as well as the UK. We also work with recordings made by other researchers, in collaboration with them, helping to annotate and prepare the recordings for wider sharing within the research community: this is a major part of the IEMP project, for instance. We also value Open Data and aim to publish the data accompanying our empirical studies involving lab experiments, surveys, or computational modelling.
Our largest original collection comprises recordings of public and private performances, teaching sessions and interviews relating to North Indian (Hindustani) classical music, featuring some of this music’s finest performers. The collection comprises over 200 sessions since 2003, mostly in India and the UK, many including multiple video and multi-track audio recordings. A subset of these recordings is being used for the IEMP project, while other uses include the preparation of educational materials. The following video playlist comprises clips prepared for the latter purpose.
Playlist of Indian Music Documentation Video Examples
We have also compiled and shared other types of data collections related to music and science. These comes from our empirical research projects, where we tend to release the full data in either Harvard Dataverse, UK Data ReShare, or Open Science Framework, depending on the particular requirements of the funder and the type of data.
Here is the list of shared data from our group
- Tuomas Eerola’s Open Science Framework projects
- Kelly Jakubowski’s Open Science Framework projects
- Martin Clayton’s Open Science Framework projects
- Bannister, S., & Eerola, T. (2017). Suppressing the Chills: Self-reports, physiological and psychoacoustic correlates. http://dx.doi.org/10.7910/DVN/IUCN1Q, Harvard Dataverse.
- Cespedes Guevara, J., & Eerola, T. (2017). Music communicates affects, not basic emotions – A constructionist account of attribution of emotional meanings to music. http://dx.doi.org/10.7910/DVN/VLVLX9, Harvard Dataverse.
- Clayton, M., Eerola, T., Jakubowski, K., Tarsitani, S. (2017). Interactions in duo improvisations. Data catalogue. UK Data Service. SN: 852847, http://dx.doi.org/10.5255/UKDA-SN-852847.
- Collins, N., Manning, P. & Tarsitani, S., (2018). A New Curated Corpus of Historical Electronic Music: Collation, Data and Research Findings. Transactions of the International Society for Music Information Retrieval. 1(1), pp.34–43. Accompanying data: http://composerprogrammer.com/emcorpus.html.
- Eerola, T., & Lahdelma, I. (2016). Data related to “Mild dissonance preferred over consonance in single chord perception”, http://dx.doi.org/10.7910/DVN/GE5PPL, Harvard Dataverse.
- Eerola, T., & Saari, P. (2015). Moods and activities in music. [Data Collection]. Colchester, Essex: UK Data Archive. http://dx.doi.org/10.5255/UKDA-SN-852024.
- Eerola, T. (2016), Music and emotion dataset (Primary Musical Cues). http://dx.doi.org/10.7910/DVN/IFOBRN, Harvard Dataverse.
- Maksimainen, J., Wikgren, J., Eerola, T., Saarikallio, S. (2017), The effect of memory in inducing pleasant emotions of musical and pictorial stimuli. http://dx.doi.org/10.7910/DVN/ZZR7WX, Harvard Dataverse.
- Saari, P., Eerola, T. (2013). Semantic computing of moods based on tags in social media of music. hdl:1902.1/21618, Harvard Dataverse.