Female Artists: MoMA Analysis for Art Collectors
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Introduction
In an era when female artists are finally receiving recognition in both museum collections and the art market, it's time to consider how to go about building up your own MoMA collection.
My Shiny App is a exploratory data tool that analyses The Museum of Modern Art's (MoMA) online collection of female artists. These artist were then fed into Artsy.net's "similar artists" recommendations accessed via their API to generate suggestions for your next aqcuisition by a female artist.
Data used:
MoMa Collection on Kaggle
Artsy API
Skills Displayed
- API Usage
- Data Visualization
- Shiny
- Analysis
Current Market Status:
Artworks by female artists, on average experienced a 72% increase in price from 2012 to 2018. This dramatic appreciation in investment value presents a strong argument for investing in works by female artists.
Why MoMA?
MoMA is one of the world’s largest museums devoted to modern and contemporary art. Its preeminent collection and distinguished scholarship make it one of the most influential and important institutions of the art world.
In 2019 MoMA closed its doors for four months to address its lack of diversity and representation of artists of different races, genders, and nationalities within their works on view. However, they did not say they were committed to collecting more artists that fit into these distinctions.
The above chart is focused on the general collection of artworks based on the artistic medium and gender of the artist. Although MoMA has championed itself as showcasing women in exhibitions such as "Making Space: Women Artists and Postwar Abstraction" (2017), it's clear their collection is seriously lacking in equal representation.
We can see in the chart above that practically no art by female artists was collected until the spark of interest in 1960s. It took more than three decades for that interest to make a significant mark, as it did so only the era of the late 1990s to early 2000s.
Observe there is a clear drop in "American" pieces when "International" starts to appear. I speculate that they took away funding from acquisitions of American female artists to fund the other pieces rather than increasing the amount across the board. It is also important to note that there is no "race" or "nationality" tag within this dataset. I believe that the numbers for that would be shockingly low.
Similar Artists
Artsy has a "Similar Artists" tab that uses their internal data. This is where they analyze style, color, and themes to suggest similar artists to their buyers. My app uses their suggestions by feeding in MoMA's collection of female artists and returning a list of suggested artists.
Using the app
Let's say you enjoy artworks by the American Artist Jennifer Losch Bartlett (b.1941); you can type in the artist name and the app will return a photo sample of their work, along with five recommended artists that are similar to them
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