Research data-as-practice

We used sonification to convey research data through sound. Our goal was to explore how sonification could enable open-ended interpretations of research findings, rather than just translating knowledge. By creating multiple sonifications from the same data set, we highlighted the inherent subjectivity, personal interpretation, and complexity of research data. Sonification effectively emphasizes that data is always already ‘processed’ or ‘cooked’.¹ Researchers can use sonification to corroborate findings, make inconsistencies audible, and uncover new insights. We approached our research data as an active practice rather than as a static object.

We were influenced by Nick Couldry’s ‘media as practice’ framework, which focuses on the regularities in our actions related to media and the context and resources that make certain media actions possible or not (p. 33)². This perspective came to us through Miriam Posner and Lauren Klein’s idea of understanding data as media³.They noted that critical media studies could help us explore “data’s underexplored textures” and question our assumptions about what data should be or do and we were interested in extending their work and giving thought to the ways critical media studies can enrich the field of data studies. We considered how our data practices, shaped by the disciplinary and methodological conventions we choose, impact how we analyze and interpret data ³ (p. 5). In turn, we considered how, as researchers, what we do with data and the disciplinary and methodological conventions and norms chosen to analyze and interpret the data would yield particular data practices. his led us to wonder if a data-as-practice approach could help us see research data as something that is always evolving rather than as something fixed and fully knowable.

In Sensing Sound: Singing and Listening as Vibrational Practice, musicologist Nina Sun Eidsheim reimagines the ways we think about sound and sound-related practices like music and listening. Eidsheim suggests that sound, listening and music should be seen not as knowable objects but as “an unfolding phenomenon that arises through complex material interactions”⁴ (p. 2). This idea was central to our approach. We came to sonification through critical data studies, and Eidsheim’s work provided a broader perspective on research data, data studies, and sound data. Her reimagining of sound inspired us to similarly rethink the ontological status of research data as a knowable object.

Our engagement with textual data in this study arose from a series of experimental applications emphasizing sound and somatic comprehension. These practices continue to yield fresh insights for digital humanities. Hayles observes that the so-called “second wave” of digital humanities emphasizes “attention to complexity, medium specificity, historical context, analytical depth, critique and interpretation”⁵ (p. 26). Her analysis of the recursive structures in literary texts, for example, reveals the musicality of prose and poetry, uncovering the rhythmic patterns and anomalies that contribute to a deeper understanding of an author’s style and thematic emphasis[^]5 (p. 245). Similarly, our methods aimed to demonstrate how sonification can extend beyond translation or representation. By inviting researchers to reconsider the fundamental nature and epistemological foundations of their data, as well as the discourses that inform us about data, we sought to highlight the implications of sonification as a critical and interpretive tool.

As a critical and experimental method, sonification enabled us to parse how our research data is subjective, multifaceted and even contradictory.⁴ After all, as Eidsheim reminds us, “[e]very listening practice and its attendant theory arises from and reinforces a particular set of values”⁴ (p. 5). Listening to research data can reveal and challenge our assumptions about the appropriate ways to engage with, represent, and disseminate research findings. We’ve shown our research data as being in flux, subjective, and inherently interpretive. Isn’t all research data to some extent?

Research data-as-practice encourages us to consider research data along with the networks of institutions and technologies that enable and limit its potential. This includes defining ‘best practices,’ determining appropriate software and tools, and understanding how expertise is recognized and granted. By focusing on research data in relation to the historical, cultural, and contextual factors that shape it, we can explore how we engage with and interpret it.³ This approach opens a critical space to question the institutional, disciplinary, and cultural norms that dictate evolving standards and models for what constitutes research data and how interdisciplinary and experimental research should be managed (or not).

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