Pre-MAP: building diversity in STEM

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Today I'm putting up a guest-posting on behalf of a program being run in the UW Astronomy department. While this isn't the fun-with-data content most people look for on this site, it fits neatly under my other interests (astronomy and academia).

In brief, the department has focused on recruiting underrepresented students in their first year at UW. Of course being scientists they're also interested in how effective this program has been, and have written a short paper outlining some summary data-driven lessons.

Since its creation by astronomy graduate students in 2005, the Pre-Major in Astronomy Program (Pre-MAP) at the University of Washington Department of Astronomy has made a concentrated effort to recruit and retain underrepresented and low-income undergraduates interested in fields pertaining to science, technology, engineering and mathematics (STEM). About 90 students have participated; many have gone on to major in physics, astronomy, or other STEM fields.

The program begins in the fall (nominally in the students' first quarter at UW) with a keystone seminar where they learn astronomy research techniques (computer programing, paper reading, etc) and then apply their skills to research projects conducted in small groups. During this time, students work closely with research mentors (professors, post-docs, graduate students) as they learn what it really means to be a scientist. At the end of the quarter, each group presents their work to the astronomy department. Many students continue working on their research projects after the seminar ends.

Beyond the seminar, Pre-MAP provides many other resources for our students such as a collaborative “cohort” atmosphere, one-on-one academic mentoring, guided tours of research labs across campus, and a yearly field trip to an astronomical observatory. The idea is that by giving students early exposure to research within a collaborative and supportive community we will not only give them the skills necessary for success in STEM fields, but also allow them to gain confidence and enthusiasm for science.

But does the program actually accomplish these lofty goals? To look at this, we use data from the last 8 years of Pre-MAP students to evaluate the program and compare our students to the general UW population. We succeed in attracting students with a range of ethnicities and math backgrounds. Our students perform similarly to the overall UW population both overall and in the sciences. We find that STEM retention depends strongly on math placement and performance. However, even when controlling for these variables our students are significantly more likely to pursue STEM degrees than their peers. The entire paper can be found on the ArXiv.

Want to know more about Pre-MAP, including obtaining resources to help start a similar program at your institution?
Visit our website
Or send us an email!  mjt29 [at], sterrs [at], schmidt [at]

The Dimensions of Art

Some good soul on reddit posted a link to a very neat dataset: Metadata from the Tate CollectionThe files contain lots of interesting bits of information, but one particularly stood out to me: the dimensions of every piece of art that the Tate owns.

A major caveat: a lot of the art is 3D and has a 3rd dimension I'm not considering (e.g. sculpture). For your thoughtful viewing pleasure, here is the distribution of the aspect ratios for 65k pieces of artwork held by the Tate as a function of their width

Art dimensions, a technical view

Pixel color (light to dark) indicates density of pieces. There are some interesting clumps in this space, here are some thoughts:

1. On the whole, people prefer to make 4x3 artwork. 

This may largely be driven by stock canvas sizes available from art suppliers.

2. There are more tall pieces than wide pieces.

I find this fascinating, and speculate it may be due to portraits and paintings.

3. People are using the Golden Ratio.

Despite any obvious basis for its use, there are clumps for both wide and tall pieces at the so-called "Golden Ratio", approximately 1:1.681 (as a tribute, that's the ratio I rendered the above figure at)

Art becomes data becomes art

What I learned very quickly after producing the first figure is that nobody understands it. Even though it's very information rich and accurate, I'm violating a basic rule of data visualization: make it understandable! People gave me lots of feedback saying they couldn't wrap their heads around the figure, and I did almost nothing to break it down...

Because this is art, I felt compelled to re-visualize this into something more... visceral. Here is the same data (for art up to 3m x 3m), with each piece represented as a thin wire box.

Play along at home

If you'd like to play with this data and make your own version of these figures, I have replicated (nearly) the figures from this blog post in an IPython notebook, which is up on GitHub! (link to notebook).

Talk - Beauty in Data

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A few months ago I gave this talk at the Seattle Nerd Nite. It was a great event, and the small crowd of ~75 people who came out to the bar to see me and the other speaker were friendly and chatted me up with questions for probably another 30min after!


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