On 10 April, astrophysicists announced that they had captured the first ever image of a black hole. This was exhilarating news, but none of the giddy headlines mentioned that the image would have been impossible without open-source software. The image was created using Matplotlib, a Python library for graphing data, as well as other components of the open-source Python ecosystem. Just five days later, the US National Science Foundation (NSF) rejected a grant proposal to support that ecosystem, saying that the software lacked sufficient impact. It’s a familiar problem: open-source software is widely acknowledged as crucially important in science, yet it is funded non-sustainably.
Debilitating hand pain is always bad news, but Harold Pimentel’s was especially unwelcome. As a computational-biology PhD student, his work involved constant typing — and he was born with only one arm. “My adviser jokingly said, ‘Can’t you do this by voice?’” he recalls. Three years later, as a computational-genomics postdoc at Stanford University in California, he does just that.
The esoteric world of pure math doesn’t usually play much of a role in promoting fairness in the U.S. political system, but Tufts mathematicians Moon Duchin and Mira Bernstein believe that needs to change. It is math, they say, that could help overcome gerrymandering—the practice of drawing legislative districts that favor one party, class or race.
Large genomic databases are indispensable for scientists looking for genetic variations associated with diseases. But they come with privacy risks for people who contribute their DNA. To address those concerns, a system developed by Bonnie Berger and Sean Simmons, computer scientists at MIT, masks the donor’s identity by adding a small amount of noise, or random variation, to the results it returns on a user’s query.