MCN 2024

Museum collections are many things, but primarily they function as repositories of information, where data-intensive work has been conducted for decades. Like other knowledge infrastructures, the information in these collections is built on installed bases and requires highly skilled technicians to maintain these long-lasting digital systems. However, in representations of this computing work, women's voices and perspectives are under-appreciated, even though they have significant labor contributions in this area. This presentation will share early results from my dissertation, where I ask 1) What are the experiences of women doing computing work in museum collections? and 2) To what extent have they enacted non-normative/ transformative data practices in their work?

I answer these questions through semi-structured interviews with women practitioners engaged in museum computing. This presentation will share early results of transformative data work currently happening in collections led by women practitioners. The transformative paradigm is a framework that “addresses power issues, social justice, and cultural complexity throughout the research process” (Mertens 2007). I will share the results of the ways that practitioners are transforming collection data in ways that similarly center social justice and challenge oppressive powers. Part of understanding women’s contributions to museum computing is to further unpack their resistance to the harmful legacies of museum collections and their data. It is apparent that many practitioners working in museums have goals for their data to be inclusive, and to repair past harms of institutions, yet these practices vary based on institutional history, number of staff, and available funding. This presentation will share empirical evidence of work currently happening, adding to a more robust definition of what ‘museum computing’ can and should entail.

This research aligns with many conversations in critical data studies, urging for a more intersectional approach to data creation, maintenance, use, and reuse that is less oppressive to all (Benjamin 2019; Cifor et al 2019; D’Ignazio and Klein 2020). A further goal of this presentation is to deepen conversations between museum practitioners and work in big data studies, and more importantly, begin a conversation about what other data-intensive environments can learn from museum computing.