The scientific communication revolution: Why disrupt the scientific publishing industry?
In December of 2010, NASA published a paper in Science (Wolfe-Simon et al. 2010, A Bacterium That Can Grow by Using Arsenic Instead of Phosphorus.) in which they claimed to have isolated bacteria that substitutes arsenic for phosphorus on their macromolecules and metabolites. Of course, the implications of this finding would have been immense… had it been true. Soon after publishing, Dr. Rosie Redfield presented meticulous criticism of the original paper on her Wordpress blog (http://rrresearch.fieldofscience.com/2010/12/arsenic-associated-bacteria-nasas.html), and was essentially shunned by the NASA group, citing the non-official nature of her blog. It took one whole year for Dr. Redfield to submit her rebuttal paper to Science (http://rrresearch.fieldofscience.com/2012/01/arseniclife-manuscript-has-been.html), and five months more for her manuscript to be accepted for publication. Overall, it took one year and a half for traditional publishing to catch up to a Wordpress blog.
If there’s one thing in this world that needs to die and be reborn it’s the scientific publishing industry. One hundred years ago, the centralization of scientific publishing was necessary because content distribution was a hard problem. Now, as we all know, the problem of freely distributing content has largely been solved by the internet. Scientific publishing, as it stands today, is actually a hindrance to scientific progress. I’m not talking about the scientific method here, but purely the mechanisms by which we organize, compile, and distribute the vast amounts of scientific information.
Science is an old world institution where credit and reputation come mostly from journal article authorships. You can tell how well a scientist is performing by looking at their list of publications and by calculating something called an “impact factor”. An impact factor gauges the importance of a paper by calculating the number of times it is cited in the two year period after it is published. This is kind of like looking at the financial statements of a business. Both scientists and businesses require money to fuel their operations, and both must generate value from the fuel. In business, this value is typically represented by money. In science, value is knowledge in the form of publications. As a result, science has developed a strong dependence on the academic publishing industry. Over the past half-century, publishers have leveraged this dependence to build up the $20 billion scientific publishing industry that exists today.
Unfortunately for science, the traditional publishing model has become costly, painfully slow, and inefficient. It can cost anywhere between $1000-$5000 to have a paper published and even more (upwards of $8000) to publish an Open Access paper (free to read without a subscription). It can take up to a year for a paper to become accessible to subscribers (and/or the public) as it goes through the drawn-out publishing process. I’d venture to say that, because of this process, the vast majority of papers that come out today report on data that’s over a year old. Scientists are busy people, but they do most of the heavy lifting throughout the process. In academic publishing, unlike many other industries, the two most important inputs are provided virtually free of charge by researchers. Publishers argue that they add value by supporting peer review, typesetting, printing, and web publishing. The reality is that their main role is in representing their brand (the journal’s brand). All other processes can be fully automated over the internet (and they basically are already!).
As a way of mitigating the costs of publishing, some fields such as physics and math have collectively decided to bypass traditional journals altogether and use Open Access “e-print archives” such as arXiv.org. But, biologically-oriented fields are way more stubborn, most likely because of how afraid many researchers are of being scooped and the potential for valuable intellectual property. They’ll be sticking with traditional publishers until a reliable and trustworthy alternative comes along.
Between the rise of Open Access publishing houses and preprint servers such as PLoS and arXiv, and the more recent global boycotts of specific publishers (http://thecostofknowledge.com/), a lot of change is starting to happen. Many people have written about this issue (http://michaelnielsen.org/blog/is-scientific-publishing-about-to-be-disrupted/, http://www.nytimes.com/2012/02/14/science/researchers-boycott-elsevier-journal-publisher.html, http://scholarlykitchen.sspnet.org/2010/01/04/why-hasnt-scientific-publishing-been-disrupted-already/) and awareness is growing, but the future of scientific communication has yet to be discovered.
How many ways to tie a tie?
It turns out the answer is 85… or 10… or just 4, depending on the aesthetic inclinations of the knotter.
Thomas M. Fink & Yong Mao, two sartorial scientists, published a Nature paper in 1999 where they describe their method of counting all possibilities. Here’s what they did:
They mathematically defined the “tie knot” and precisely determined all possible knot sequences (called walks) that result in a proper knot. Of course, there are probably hundreds of ways to make a knot in a tie, so they limited their search to knots that take 9 moves or less. Using their mathematical model, they determined that the total number is 85.
Now, nobody wants to make it look like their tie knot has another tie knot growing out of it so the authors further narrowed down the count by considering only symmetric knots and knots that are tightly bound (and thus keep their shape). Using these constraints they came up with a final list of 10 knots, 4 of which are already known and 6 brand new ones.
Future directions include determining which knots are good for fat ties and which are good for skinny ties. Maybe some day we’ll even get to name them! Screw the Four-in-hand, I think I’m gonna tie a Hipster today!
Original paper attached for the mathematically inclined or just generally curious:
Molecular mechanics (MM) is the mathematical representation of atomic interactions. MM was developed and applied over 50 years ago for organic chemistry as a tool to estimate specific energetic properties of small molecules. Since then it has evolved into various functional forms (known as “force fields”) used to describe the potential energy of a system by computing atomic interactions. Force fields may vary in the way that they represent atomic particles to balance experimental accuracy with computational efficiency. Molecular dynamics (MD) is a MM-based method in which force fields are used to calculate the positions and velocities of atoms over time to produce a trajectory.