November 18, 2017

New Badges (Microcredentials) for Fall 2017

I have some new badges to advertise, one set from the OpenWorm Badge System, and one set from the Orthogonal Lab Badge System. As discussed previously on this blog, badges are microcredentials we are using to encourage participation in our research ecosystems at an introductory level.

An education-centric sketch of the OpenWorm and Orthogoanl Laboratory research ecosystems.

The first new badge series is an introduction to what is going on in the DevoWorm group, but also gives biologists and computationalists unfamiliar with Caenorhabditis elegans developmental biology a chance to get their feet wet by taking a multidisciplinary approach to the topic.

Worm Development I focuses on embryonic development and associated pattern formation. Worm Development I is a prerequisite to II, so be sure to try this one first.

Worm Development II focuses on larval development, including the postembryonic lineage tree and what characterizes each life-history stage.

The second badge series is hosted on the Orthogonal Lab Badge System, and provides an overview of Peer Review issues and techniques. This series is meant to give young scholars a working familiarity with the process of peer review. It is notable that Publons Academy now offers a course on Peer Review, to which this badge might serve as an abbreviated complement.

Peer Review I covers the history of peer review and the basics of pre-publication peer review. Be aware that Peer Review I is a prerequisite for Peer Review II (but not Peer Review for Data).

Peer Review II delves into how to decompose an article for purposes of peer review. An evaluation strategy for post-publication peer review is also covered.

Peer Review for Data contains a brief how-to for conducting peer review for open datasets.

November 15, 2017

Deep Reading Brings New Things to Life (Science)

Here is an interesting Twitter thread from Jacquelyn Gill on 'deep reading':

The basic idea is that exploring older literature can lead to new insights, which in turn lead to new research directions. The new research of our era tends to focus on the most relevant and cutting-edge literature [1]. This recency bias excludes many similarly relevant articles, including articles that perhaps inspired the more recent citations to begin with [2]. 

I have my own list of deep reads that have influenced some of my research in a similar fashion. These references can be either foundational or so-called "sleeping beauties" [3]. Regardless, I am doing my part to maintain connectivity [4] amongst academic citation networks:

1) Woodger, J.H. The Axiomatic Method in Biology. 1937.

An argument for biological rules, an influence on cladistics (developed in the 1960s), and a natural bridge to geometric approaches to data analysis and modeling. While there is a strong argument to be made against the axiomatic approach [5], this directly inspired much of my thinking in the biological modeling area. 

2) Davis R.L., Weintraub H., and Lassar A.B. Expression of a single transfected cDNA converts fibroblasts to myoblasts. Cell 51, 987–1000. 1987.

This was the first proof-of-concept for direct cellular reprogramming, and predates the late 2000's Nobel-winning work in stem cells by decades. In this case, a single transcription factor (MyoD) was used to convert a cell from one phenotype to another without a strict regard for function. More generally, this paper helped inspired my thinking in the area of cellular reprogramming to go beyond a biological optimization or algorithmic approach [6].

3) Ashby, W.R. Design for a Brain. 1960.

"Design for a Brain" serves as a stand-in for the entirely of Ashby's bibliography, but this is the best example of how Ashby successfully merged explanations of adaptive behavior [7] with systems models (cybernetics). In fact, Ashby originally coined the phrase "Intelligence Augmentation" [8]. I first discovered Ashby's work while working in the area of Augmented Cognition, and has been more generally useful as inspiration for complex systems thinking.

Not so much a couple of sleeping beauty as easy reading technical reference guides for all things complexity theory.

5) Bourdieu, P. Outline of a Theory of Practice. Cambridge University Press. 1977 AND Alexander, C., Ishikawa, S., and Silverstein, M. A Pattern Language: towns, buildings, construction. Oxford
University Press. 1977.

This is a bonus, not because the references are particularly obscure or even from the same academic field, but because they partially influenced my own view of cultural evolution. This is yet another piece of advice to young researchers: take things that appear to be disparate on their surface and incorporate them into your mental model. If nothing else, you will gain valuable skills in intellectual synthesis.

UPDATE (11/17):
Here is another example of old (classic, not outdated) work influencing new scholarship.

[1] Evans, J.A. (2008). Electronic Publication and the Narrowing of Science and Scholarship. Science, 321(5887), 395-399 AND Scheffer, M. (2014). The forgotten half of scientific thinking. PNAS, 111(17), 6119.

[2] related topics discussed on this blog include distributions of citation ages and most-cited papers.

[3] van Raan, A.F.J. (2004). Sleeping Beauties in Science. Scientometrics, 59(3), 467–472.

[4] Editors (2010). On citing well. Nature Chemical Biology, 6, 79.

[5] For the semantic approach (which had been influential to my more recent work), please see: Lloyd, E.A. (1994). The Structure and Confirmation of Evolutionary Theory. Princeton University Press, Princeton, NJ.

[6] Ronquist, S. (2017). Algorithm for cellular reprogramming. PNAS, 114(45), 11832–11837.

[7] Sterling, P. and Eyer, J. (1988). Allostasis: A new paradigm to explain arousal pathology. In "Handbook of life stress, cognition, and health". Fisher, S. and Reason, J.T. eds. Wiley, New York. 

[8] Ashby, W.R. (1956). An Introduction to Cybernetics. Springer, Berlin.