Scientist Whose Male Boss Won Nobel For Her Work Is Giving New $3 Million Prize Away→

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In 1974, Jocelyn Bell Burnell’s male PhD supervisor at the University of Cambridge won a Nobel Prize for a discovery that she was the first to notice. On Thursday, the 75-year-old acclaimed astrophysicist won a coveted science prize of her own ― the Special Breakthrough Prize in Fundamental Physics.

 

But instead of keeping the hefty $3 million award that comes with this distinction, Bell Burnell says she will be using it to help women, refugees, and other minority students follow in her footsteps and become physics researchers themselves. 

She will be donating her prize money to the Institute of Physics to create scholarships for people from underrepresented groups, the Institute said in a statement.

 

Some things I've found help reduce my stress around science→

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Good advice all around!

Redefine success. I’ve found that if I recalibrate what success means to include accomplishing tasks like peer reviewing papers, getting letters of recommendation sent at the right times, providing support to people I mentor, and the submission rather than the success of papers/grants then I’m much less stressed out.

Quick tips for data management and reproducible research→

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Good stuff here.

Another scientist should be able to reproduce your entire research pipeline, from data collection to final figures, without having to email you with questions. It sounds intimidating, but it doesn't have to be, and in practice it's usually not that much extra work. You already know all the information they would need, it's largely just a matter of being mindful of how you do things and keeping a record. More selfishly, working in a reproducible way will make your own life easier, especially when you have to come back to a project months or years later.

Women need to be seen and heard at conferences→

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Conference organizers should not feel that they have done their duty if they invite a top woman scientist who declines. The most successful women in science get inundated with invitations, but there will always be other successful women to choose from, and identifying them has been made easy. Anne’s List (created by computational neuroscientist Anne Churchland at Cold Spring Harbor Laboratory in New York) groups female neuroscientists easily into topic and seniority level. In Europe, AcademiaNet identifies women across scientific disciplines.

 

In Negotiations, The Pen Can be Mightier than the Mouth

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Good advice here.

  1. Negotiate with your pen (keyboard), not your mouth. Unfortunately, when women negotiate with their mouths, people generally respond based on how they look, the tone of their voice and how things went with the last woman who negotiated. When you make your request politely but firmly in writing, you are just stating your needs. The person reading the request can “hear” it in their own inner reading voice. They can respond to the content and take the time to consider what should be a yes and what must be a no.

Great memo on family-friendly scheduling from Brown University's Provost→

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Well-said.

Family-friendly scheduling does not mean that all events after 5:30 should be prohibited. Rather, it means that those engaged in programming should be conscious of the exclusions created by after-hours events and should take proactive steps to accommodate faculty unable to stay on campus into the evening. It requires chairs and directors to recognize the baseline pressures created by the scheduling grid and the fact that many faculty with children must teach courses that extend beyond the time of the university's daycare provision. It forces an acknowledgement that there is no perfect time for a lecture on campus; a 5:30 lecture excludes some faculty just as a lecture at 12:00, 2:00, or any other time typically associated with classroom teaching excludes others. Too often we hear that "5:30 is the only time that everyone can make," but this is patently not true.

A listener with a cochlear implant sets off on a quest to hear Boléro again→

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Fascinating first-person insight.

About a year after I received the implant, I asked one implant engineer how much of the device’s hardware capacity was being used. "Five percent, maybe." He shrugged. "Ten, tops."

 

I was determined to use that other 90 percent. I set out on a crusade to explore the edges of auditory science. For two years tugging on the sleeves of scientists and engineers around the country, offering myself as a guinea pig for their experiments. I wanted to hear Boléro again.

 

Six things your research mentor wants you to know (but probably won't think to tell you) →

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Most of these are pretty true for research mentors at all levels.

1. If I don’t hang out and chat at the lab, it doesn’t mean that I don’t like you.
It probably means that I’m overextended or don’t have much spare time each day. I might be in the lab more hours per day than you are in an entire week, and I still might not have enough time to accomplish my goals. Alternatively, your lab schedule might overlap with my busiest time of the day, or I might need to leave lab at a specific time each day, leaving me no extra time to socialize. Therefore, I might focus on conversations that teach you how to interpret results or gain a new research skill, because I want our limited time together to make the greatest impact on your research experience. That might mean sticking to conversations about research and science.

The principle of assumed error→

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Nice post from Russ Poldrack.

The principle is that whenever one finds something using a computational analysis that fits with one’s predictions or seems like a “cool” finding, they should assume that it’s due to an error in the code rather than reflecting reality.  Having made this assumption, one should then do everything they can to find out what kind of error could have resulted in the effect.  This is really no different from the strategy that experimental scientists use (in theory), in which upon finding an effect they test every conceivable confound in order to rule them out as a cause of the effect.  However, I find that this kind of thinking is much less common in computational analyses. Instead, when something “works” (i.e. gives us an answer we like)  we run with it, whereas when the code doesn’t give us a good answer then we dig around for different ways to do the analysis that give a more satisfying answer.  Because we will be more likely to accept errors that fit our hypotheses than those that do not due to confirmation bias, this procedure is guaranteed to increase the overall error rate of our research.  If this sounds a lot like p-hacking, that’s because it is

 

Using evaluations to change the culture of the conference you just attended→

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Inspired by my recent stint on a program organizing committee:

Many conferences and workshops provide forms to ask participants for feedback. It is important to participate in this process, and a great opportunity to provide input relating to issues such as speaker balance, childcare, and student/postdoc involvement.

Topics include childcare, speaker gender balance, and student/postdoc involvement. Make your opinions known!