Displaying: Gender Patterns at MoMA

Assignment 3

Introduction
This assignment examines gender representation in MoMA’s collection using two visual approaches. The dataset contains approx. 160,000 records with gender, acquisition year and display status. I cleaned the data and consolidated several non-binary gender labels into one “gender expansive” category, reflecting the idea that data classifications are constructed rather than neutral (D’Ignazio & Klein, 2020).
The first analysis uses a visual-analytics (VA) approach to explore long-term acquisition patterns. The second uses a feminist/critical perspective to examine who becomes visible in the museum’s public displays.

Analysis 1
To explore acquisition trends, I created a line chart showing yearly acquisitions by gender (Fig. 1). This provided the “overview first” stage emphasised in VA (Cui, 2019), allowing broad patterns to become visible before interpreting them.
The plot shows that works by male artists dominate across the entire timeline. Female acquisitions increase in recent decades, while gender-expansive entries appear only recently. Earlier decades contain many “unknown” records; keeping this as a separate line reflects course discussions about how missing data often signals structural issues rather than individual gaps. Spikes in the 1960s–70s and early 2000s suggest bulk accessions, illustrating how overview visualisations support hypothesis-forming (Cui, 2019).
This method helps surface long-term imbalances, but it cannot show which works are displayed, nor distinguish routine acquisitions from major donations. The gender categories also depend on MoMA’s metadata, not self-described identities, which limits interpretation.

Figure 1. Line chart showing yearly acquisition counts by gender.

Analysis 2
The second analysis shifts from collecting to visibility by focusing only on artworks currently “on view.” I created a pie chart showing displayed works by gender (Fig. 2). Unlike the descriptive VA plot, this visualisation highlights absence and uneven visibility.
Male artists constitute most works on display; women appear in smaller numbers. Although gender-expansive artists exist in the dataset, none of their works are exhibited, producing a missing slice. This absence illustrates how representation in metadata does not necessarily translate into public visibility. Unknown-gender works form a small segment, again reflecting limits of earlier cataloguing.
This design follows feminist visualisation principles that treat graphics as rhetorical rather than neutral objects (D’Ignazio & Klein, 2020). Highlighting the missing segment uses absence as an analytical finding and centres the standpoint of those not represented on the museum walls.
Limitations include simplified gender categories and the fact that display data reflects only a single moment in time.


Figure 2. Pie chart of works currently exhibited at MoMA.


Discussion & Conclusion
Using two different approaches to the same dataset demonstrates how visualisations can lead to distinct interpretations of institutional practices. The VA plot provided a descriptive overview of MoMA’s acquisition patterns. By using a simple line chart, I could identify long-term tendencies such as the dominance of male artists, occasional bulk-accession spikes and more recent increases in acquisitions of works by women and gender-expansive artists. This reflects the aim of visual analytics to support analytical reasoning through clear encodings and overview-first displays (Cui, 2019). The feminist visualisation, in contrast, redirected attention visibility. By focusing on works currently on display, the pie chart made uneven representation more immediately apparent. The missing slice for gender-expansive artists shows how a group can exist in the dataset yet remain absent in the museum space. This demonstrates that even though acquisitions of work by women and gender-expansive artists have increased in recent years, these additions do not automatically translate into what the public encounters in the galleries (D’Ignazio & Klein, 2020).
This comparison demonstrates that visualisations are never fully neutral and that design choices highlight different institutional dynamics. Taken together, the analyses show that MoMA’s acquisition history does not translate directly into exhibition practices and that combining analytical and feminist approaches offers a fuller understanding of how gender is recorded, collected and made visible.

References
Cui, W. (2019). Visual Analytics: A Comprehensive Overview. IEEE Access, 7, 81555–81573. https://doi.org/10.1109/ACCESS.2019.2923736
D’Ignazio, C., & Klein, L. (2020). 3. On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints. Data Feminism. https://data-feminism.mitpress.mit.edu/pub/5evfe9yd/release/5