I took a break from grading tonight to watch this. It’s great – thanks for the post DrugMonkey.
I took a break from grading tonight to watch this. It’s great – thanks for the post DrugMonkey.
Monty the Motivation Whale has completed his duties. He has also become three-dimensional. Happy Renn Fayre!
This is the last week of classes. Reed seniors are finalizing their theses — a culmination of their year-long projects — before sending them off to faculty readers. As we near the end, my computational biology lab has a new round of students working night and day. Don’t worry, though – Monty the Motivation Whale is there for you.
Monty’s appearance might be due to the fact that one of the Reed seniors is a lead scientist at the Orca Behavior Institute, a non-profit he started in 2015.
Tomorrow, I’ll sit on a panel about Open Data and Open Science as part of Reed’s Digital Scholarship Week. I am somewhat familiar with these topics in computer science, but I decided to read up on the progress with Open Access in Biology.
As a junior professor trying to get a foothold in a research program, I’ll admit that I haven’t spent a lot of time thinking about Open Science. In fact, the first thing I did was look up what it meant:
Open science is the movement to make scientific research, data and dissemination accessible to all levels of an inquiring society. – Foster Project Website
Ok, this seems obvious, especially since so much research is funded by taxpayer dollars. Surprisingly, Open Science is not yet a reality. In this post, I’ll focus on the speed of dissemination – the idea that once you have a scientific finding, you want to communicate it to the community in a timely manner.
Biology findings are often shared in the form of peer-reviewed journal publications, where experts in the field comment on drafts before they are deemed acceptable for publication. Peer-review may be controversial and even compromised (just read a few RetractionWatch posts), but in theory it’s a good idea for others to rigorously “check” your work. However, the peer-review process can be slow. Painfully slow. Findings are often published months to even years after the fact.
In computer science, my “home” research discipline, it’s a different story. Computer science research is communicated largely through conferences, which often includes paper deadlines, quick peer-review turnaround times, and a chance to explain your research to colleagues. Manuscripts that haven’t undergone peer-review yet may be posted to arXiv.org, a server dedicated to over one million papers in physics, mathematics, and other quantitative fields. Manuscripts submitted to arXiv are freely available to anyone with an internet connection, targeting “all levels of an inquiring society.”
A biology version of the site, BioRxiv.org, was created in 2013 — more than 20 years after arXiv was established. It only contains about three thousand manuscripts. What is the discrepancy here? Why is the field reluctant to change?
Last February, a meeting was held at the Howard Hughes Medical Institute (HHMI) Headquarters to discuss the state of publishing in the biological sciences. The meeting, Accelerating Science and Publication in Biology (appropriately shortened to ASAPbio), considered how “pre-prints” may accelerate and improve research. Pre-prints are manuscript drafts that have not yet been peer-reviewed but are freely available to the scientific community. ASAPBio posted a great video overview about pre-prints, for those unfamiliar with the idea. While the general consensus was that publishing needs to change, there are still some major factors that make biologists reluctant to post pre-prints (see the infographic below).
This is an excellent time to talk open science in Biology. It has become a hot topic in the last few months (though some in the field have been pushing for open science for years). The New York Times recently wrote about the Nobel Laureates who are posting pre-prints, and The Economist picked up a story about Zika virus experiment results that were released in real time in an effort to help stop the Zika epidemic.
Open Science has the potential to lead to more scientific impact than any journal or conference publication. The obstacles are now determining what pre-prints mean to an academic’s career – in publishing the manuscripts, determining priority of discovery (meaning “I found this first”), and obtaining grants. I rely on freely-available data and findings in my own research, yet I’ve never published a pre-print. After writing this post, I think I may start doing so.
Handful of Biologists Went Rogue and Published Directly to Internet, New York Times, 3/15/2016.
Taking the online medicine, The Economist, 3/19/2016.
As a new computational biologist at Reed College, I was excited about the prospect of continuing to do research while teaching innovative courses. I’ve written about the research opportunities at Reed, and faculty across campus have received over two million dollars of grant funding in 2014/2015.
The Biology Department just secured two more research grants from the M.J. Murdock Charitable Trust to investigate neurogenesis in zebrafish (Dr. Kara Cerveny) and discover candidate driver genes in cancer (me!).
Small schools also have an opportunity to play a large role in undergraduate education programs. Another NSF grant was recently awarded to Dr. Suzy Renn to organize a STEM workshop on undergraduate involvement in the NSF’s Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative.
All in all, 2016 seems like it will be another great research year.
Portland is known for being a rainy city, and this winter was no exception. Last week had the first truly sunny and warm day, hinting at spring’s arrival.
Today I hung out in my computer lab as dozens of high school students toured the Physics, Chemistry, and Biology Departments during the Junior Visit Day. As I readied the lab for the rounds of visitors, I noticed a new drawing on the whiteboard.
I could not agree more.
I am currently designing my upper-level undergraduate class I will teach next fall. The proposed course description* begins with:
Computational Systems Biology
A survey of network models used to gain a systems-level understanding of biological processes. Topics include computational models of gene regulation, signal transduction pathways, protein-protein interactions, and metabolic pathways…
As a result, I’ve been keeping my eye out for networks (or, mathematically-speaking, graphs) in biology. I found a fascinating network in this recently-published paper:
Males Under-Estimate Academic Performance of Their Female Peers in Undergraduate Biology Classrooms
Grunspan DZ, Eddy SL, Brownell SE, Wiggins BL, Crowe AJ, et al. (2016) Males Under-Estimate Academic Performance of Their Female Peers in Undergraduate Biology Classrooms. PLoS ONE 11(2): e0148405. doi: 10.1371/journal.pone.0148405
I often see reports on gender bias in computer science, but I somehow thought that biology would be the least gender biased of the STEM disciplines. I was surprised that this type of bias has been uncovered in biology, and in classes with more female students than male students. The paper has already been highlighted on sources such as Science Daily, The Atlantic, and the Huffington Post, among others. The wealth of information in the paper — from the experimental design to the study setting to the final results — warrants an important, broad discussion.
In this post, however, I’ll focus on the networks.
The authors conducted multiple surveys where students nominated the “best performers” in their introductory biology courses at a large American university. These surveys were given at different parts of the course, and they were conducted across three different iterations of the same undergraduate biology class. Figure 1 of the paper shows two networks displaying two surveys from the same class, six weeks apart.
These networks show the students (represented as nodes in the graph) in a particular class, and “votes” as directed edges from nominators to nominees. Male students are shown in green, and female students are shown in orange. The size of nodes indicates the number of nominations received by each student. The structure of these networks is striking. There are many students who do not nominate anyone and are not nominated by anyone, resulting in “singleton” nodes. In both networks, there is a general cohort of students that receive nominations; however the distribution of these nominations are much more skewed in the second survey.
The intuitive trend that we see in these graphs is that “the green nodes tend to get bigger” corresponding to a larger proportion of nominations go to male students. However we see female students also receive more nominations in the second survey compared to the first. The authors quantify these aspects using exponential-family random graph models (ERGMs) to assign coefficients on model statistics relating to gender, outspokenness, and grade. They found a specific gender bias, that male students tend to nominate other male students, after controlling for grade and outspokenness. Female students, on the other hand, do not exhibit a gender bias toward nominating males (or females for that matter), after controlling for these factors.
There are many, many other factors that may contribute to these observations, and some are noted in the paper. The courses were taught (and in some cases co-taught) by four male instructors and only one female instructor, the classes ranged in size from 196 to 760 students, one class employed “random call” lists rather than calling on raised hands. Besides outspokenness, interactions in lab sections and outside class would undoubtedly affect students’ perceptions. This paper opens a tremendously important conversation about implicit gender bias in the classroom, even in majors with more female students than male students. As the paper concludes,
This gender biased pattern in celebrity was experienced by over 1,500 students in our analyses. This number is striking, but less worrisome than the millions of students who attend college STEM classes that may perpetuate the same biases described here.
Grunspan et al., PLOS ONE 2016.
* Pending approval of various college committees – it may change