Collection MoMA Analysis - An Art Collector's Tool
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
In an era when female artists are recieving their (much overdue) recognition in both museum collections and the art market, it's time to consider some pieces for your growing 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.
- API Usage
- Data Visualization
Current Market Status:
Artworks by female artists, on average experienced a 72% increase in price from 2012 to 2018. This presents a strong argument for investing in works by female artists.
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 was collected by female artists until there was a jump in the '60s. It did not begin in earnest until the late '90s and 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 but I believe that the numbers would be shockingly low.
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 5 recommended artists that are similar to them