The Ronin Institute’s Research Scholars are drawn from many different career stages, levels of experience and backgrounds, and given that we don’t advocate a single model of a career in scholarship (in contrast to the traditional academic pipeline), it isn’t surprising that Research Scholars explore many different means to support their scholarship (we are still analyzing the results of the independent scholarship survey we did last year, but this much is clear). For many Research Scholars who are also freelancers, especially those in the sciences, one common means of support is being hired for short or long-term projects by academic institutions, private companies or non-profit organizations. This may be in in full-time or part-time capacity as an independent contractor or consultant. Ideally these projects utilise the scholars’ unique research background and skills and the experience and skills gained during consulting activities will…
Listen to, or read a transcript of, a podcast interviewwith Biosystems Analytics’ and Python for the Life Sciences co-author, Alex Lancaster. The interview was recorded for our digital publisher Leanpub’s author podcast series, by Leanpub co-founder Len Epp. In a wide-ranging discussing Len discussed Alex’s career, funding in science, evolutionary biology, the state of the book publishing industry and many other things. The podcast was recorded back in November 2016.
I’m very proud to announce that, together with my Amber Biology colleague Gordon Webster, that our book Python For The Life Sciences, is now available for purchase via Leanpub:
The book has ended up somewhat larger than originally planned, clocking in at over 300 pages, and covers a wide range of life science research topics from biochemistry and gene sequencing, to molecular mechanics and agent-based models of complex systems. We hope that there’s something in it for anybody who’s a life scientist with little or no computer programming experience, but who would love to learn to code.
You can download the complete first chapter for free at Leanpub and everybody who buys this first edition will have complete access to book updates to this particular edition. Help us improve the book by emailing us feedback or if you spot any errors to: firstname.lastname@example.org
Big Data has become an increasingly large presence in the life science R&D world, but as I have blogged about previously, increasingly larger datasets and better machine algorithms alone, will not leverage that data into bankable knowledge and can lead to erroneous inferences. My Amber Biology colleague, Gordon Webster has a great post over on LinkedIn leavening the hype around Big Data, pointing out that analytics and visualizations alone are insufficient for making progress in extracting knowledge from biological datasets:
Applying the standard pantheon of data analytics and data visualization techniques to large biological datasets, and expecting to draw some meaningful biological insight from this approach, is like expecting to learn about the life of an Egyptian pharaoh by excavating his tomb with a bulldozer
“-omics” such as those produced by transcriptomic and proteomic analyses are ultimately generated by dynamic processes consisting of individual genes, proteins and other molecules…
Python at the bench:
In which we introduce some Python fundamentals and show you how to ditch those calculators and spreadsheets and let Python relieve the drudgery of basic lab calculations (freeing up more valuable time to drink coffee and play Minecraft)
Building biological sequences:
In which we introduce basic Python string and character handling and demonstrate Python’s innate awesomeness for handling nucleic acid and protein sequences.
Of biomarkers and Bayes: In which we discuss Bayes’ Theorem and implement it in Python, illustrating in the…
My Amber Biology colleague, Gordon Webster, and I are working on an accessible introduction for biologists interested in getting into programming. Python for the Life Scientists will cover an array of topics to introduce Python and also serve as inspiration for your own research projects.
But we’d also like to hear from you.
What are the life science research problems that you would tackle computationally, if you were able to use code?
You can contact us here in the comments, on email@example.com or on the more detailed post:
The promise of the Internet as a means to “level the playing field’ has seriously gone off the rails. A two-day conference at The New School that just wound up this last weekend, explored the emergence of platform cooperativism. Platform cooperativism aims to return the democratic promise of the Internet away from the rapacious, heavily-leveraged extractive models of the so-called “sharing economy” such as Uber and AirBnB, and towards models of true user ownership and governance. As pointed out in a set of 5 summary essays that appeared in The Nation, these are not (mainly) technical challenges but legal and political ones. An example is FairCoop:
FairCoop is one among a whole slew of new projects attempting to create a more democratic Internet, one that serves as a global commons. These projects include user-owned cooperatives, “open value” companies structured like a wiki, and forms of community-based financing. Part of what distinguishes them from mainstream tech culture is the determination to put real control and ownership in the hands of the users. When you do that, the platform becomes what it always should have been: a tool for those who use it, not a means of exploiting them.
Many of these efforts will face an uphill battle, and as pointed out by Astra Taylor at the conference (she follows Douglas Rushkoff’s presentation in the video link), will probably be fiercely resisted by the newly entrenched platforms of Google, Facebook and the like. But we can also say the same thing about those platforms many of which were just small upstarts back in the 1990s. The real challenge is one that is familiar to evolutionary biologists in game theory: building systems that reduce the chance of “invaders” or “cheaters” (in this case, rapacious VC firms and super-capitalism in general) from swamping a population of mutually beneficial co-operators (or turning those cooperators into cheaters). It doesn’t have to be, and could never be, perfect: you’ll never reduce the population of cheaters to zero, but at least keep them from taking over your population completely.
Read more about platform cooperativism at The Nation…
Innovation. It’s as American as apple pie. From the US President on down, everybody is talking about innovation. From university presidents and corporate leaders to Silicon Valley tycoons, all agree that we need more of it. Airport bookstores have walls of books on innovation: a quick search on Amazon resulted in 70,140 titles containing the word “innovation”, 711 of which were published in the last 90 days alone. Many of them are little more than generic business advice books with the word “innovation” shoehorned into the title, including gems such as Creating Innovation Leaders (earning bonus points for including buzzwords “leadership” and “creativity”). So it was with some trepidation that I recently picked up Scott Berkun’s The Myths of Innovation – first published in 2007 – and found it had a refreshing and unpretentious take on the subject. Since it has become such an overused buzzword, Berkun argues that…
We are in the era of Big Data in human genomics: a vast treasure-trove of information on human genetic variation either is or will soon be available. This includes older projects such as the HapMap, and 1000 Genomes to the in-progress 100,000 Genomes UK. Two technologies have made this possible: the advent of massively parallel “next generation” sequencing where each individuals’ DNA is fragmented and amplified into billions of pieces; and powerful computational algorithms that use these fragments (or “reads”) to identify all the “variants” – any changes that are different to the “reference genome” – in each individual.
With existing tools this has become a relatively straightforward task. Identification of single nucleotide polymorphisms or variants (SNVs) – single base differences between the individual and the reference genome – especially medically relevant ones – is beginning to become routine. A project I worked on with…