Opening Access, Virtual, Distributed Lab Edition

Welcome to Open Access Week 2021! This year's theme is building structural equity. In the Orthogonal Research and Education Lab and the DevoWorm group, this has been an ongoing priority: from the recruitment of scholars to the production and engagement with research. This week we will highlight some of the ways we open up the research process, and how this is the only way the principles of open access (Figure 1) can be fully realized.

Figure 1. From the short film "What is Open Access" (PhD Comics, 2012).

One thing that enables Open Access is an open collaboration structure. Both Orthogonal Lab and DevoWorm are based on a virtual, distributed framework. People can join in and collaborate as long as they have an internet connection and the initiative to work on a related problem. The communication structure is likewise flexible: you can join in our weekly meetings, participate in our Slack channels or Github teams, or join in on a collaborative doc. We also sponsor or participate in various open educational initiatives. Two of these are Google Summer of Code and Neuromatch Academy.

Figure 2. The global reach (physical and virtual) of the Orthogonal Lab.

This brings together participants from multiple continents and research specialties, while also enabling students, professional academics, and lifelong learners to collaborate in ways large and small. We participate in the academic community through virtual and hybrid conferences, peer-reviewed publication venues, book chapters, and preprints. Self-publication platforms (blogging platforms) and social media are also good for advancing fledgling ideas and chronicling progress. Along with an emphasis on open code and data, these venues are utilized to maximize access and reusability.

More recently, we have been focusing on the role of professional development in enabling the virtual, distributed research process. As many of our contributors aspire to further their research careers through participation, we have become more active in cultivating an individual's research agenda. Between active recruitment of participants and enabling them to take ownership of a research topic, we can contribute to greater equity and diversity in the research enterprise.

            

Finally, our Open Access agenda includes an interdisciplinary focus, as both Orthogonal Lab and DevoWorm engage individuals from a variety of different backgrounds. There is an intentionality towards enabling interdisciplinary skillsets, as well as a focus on providing individuals space to pursue these connections between traditional disciplines. For more information on how these components work to form a virtual, distributed lab, see our preprint "Building a Distributed Virtual Laboratory Adjacent to Academia". 

While there are still many administrative and functional barriers to pursuing this as a full-fledged research organization on par with a large corporation or University, this is a unique and emergent way of opening access. If you would like to participate, please contact us. Additionally, be sure to check out the #OAWeek hashtag for this blog (Synthetic Daisies), as we have content going back to 2016 on a variety of topics. 

ASAPBio Session on the "Past, Present, and Future of Preprints"

For Open Access Week 2020, Synthetic Daisies will feature an exciting panel discussion on preprints. On Monday (19th), I was part of a panel called "Past, Present, and Future of Preprints", hosted by ASAPBio. I live tweeted the event from the Orthogonal Research and Education Lab Twitter account. If you were not able to attend in person, the recording is on YouTube! The session started with a short introduction from each of our participants: Antonis Rokas, Soumya Swaminathan, Richard Sever, Ross Mounce, and Anjana Badrinarayan.

Yamini Ravichandran and Marco Fumasoni started us off with a short introductory presentation, followed by an introduction by each of our panelists. This part of the session culminated with Marco posing an initial question to the panel.

It turns out that there are many contributing factors to preprint adoption. Some of them involve legacy patterns from manuscript submissions and publications. But preprints also democratizes access to both the production and consumption of scientific literature. It turns out that cultural traditions (within fields and countries), researcher agency, and community incentives are also quite important.

The theme of research culture came up time and time again. But research culture is not only a motivating factor; pro-preprint behaviors can lead to other virtuous practices. For example, Ross Mounce suggests that preprints can encourage a culture of versioning, where different versions of a paper are viewed as important steps in the research process rather than simply being erratum.

There was also a discussion of the role traditional journals play in the research dissemination process. One future direction of preprint culture is to decouple papers from journals. Towards the end of our session, we heard a choice quote from Antonis Rokas and the Rokas Lab.

This combines nicely with observations earlier in the session regarding citation metrics: with the movement towards iteratively-developed preprints with multiple supporting components (open data sets, supplemental figures and notes), there will be a need to distinguish article quality from journal quality. Altmetrics are one path forward, but a more robust system is needed. 

Thanks to everyone for participating! Thanks also go to Sarah Stryeck, Jessica Polka, and of course Iratxe Puebla for being a great community manager! Happy Open Access Week! 

UPDATE (11/3): A recording of the session is now on YouTube!

Google Summer of Docs congratulations!

Congrats to Casper daCosta-Luis (and co-mentors Bradly Alicea and Chee-Wai Lee) for successfully completing the inaugural Google Season of Docs! Casper's project involved automating project documentation (using Continuous Integration) at the OpenWorm Foundation. His final project report can be found here.

Thanks to our sponsor INCF for supporting our application. Speaking of Google Seasons, applications for Google Summer of Code (GSoC) 2020 will be opening soon. Once again, I am hosting two projects: one through the DevoWorm group (OpenWorm Foundation), and the other through Orthogonal Research and Education Laboratory. More information to come.

OAWeek: share your own case study!

This post is part of a series published over the course of OAWeek 2019.

Do you use, share, or have an opinion about open data? The Data Reuse Initiative would like to hear from you! In honor of OAWeek 2019, we are looking for personal and research group testimonials on how you share or otherwise practice open data. Submit at your leisure (there is no deadline), but we would like to hear from you!

RULES:
* submit a testimonial (under 200 words) by submitting a pull request to our Github repository or submit to this Google Form.

* if you choose to submit an image (screenshot, diagram, or cartoon), please issue a pull request on Github.

* if you cannot access either of the links, or need help with your submission, please [contact us](mailto:balicea@openworm.org).

OA Week: Digital Badges on Open Data

This post is part of a series published over the course of OAWeek 2019. Today's post will preview a series of digital badges related to Data Reuse. These badges were designed in conjunction with the new Data Reuse Initiative.

Overview of Data Reusability I. Click to enlarge.

The first digital badge (Data Reusability I) provides the learner with some practical skills in data sharing. The practical examples are mostly biology-oriented, but is useful for learners from a wide range of fields. Activities include work with a selected article from the journal Genome Biology, posting a sample data set to Figshare, and working with data sets published on the Dryad repository. While these activities provide just a taste of the work involved in sharing data, it nonetheless imparts some key skills in interacting with and publishing data in an open fashion. 

Overview of Data Reusability II. Click to enlarge.

The second digital badge (Data Reusability II) provides a tutorial that reviews public data sharing competencies in more depth. For this set of exercises, we have used the Mozilla Data Sharing Planning Template as a model for best practices community standards. Earners of this badge will develop competencies in metadata creation, data cleaning/processing, documenting data set provenance, assigning credit for the published work, and enabling easy and reproducible reuse of the data set. Check them out!

OA Week: History of Open Access

This post is part of a series published over the course of OAWeek 2019.

Timeline of scientific output from [1]. Click to enlarge.

This post will walk us through the History of Open Access (with a focus on Open Science) infographic mentioned in our inaugural blog post for this series. Randall Munroe [1] has previously summarized the progression of open science as a function of the scope of scientific output. The events and milestones for the featured historical overview were confirmed by internet search and synthesized from a survey of various tools and publications common in the field. This post characterizes the historical eras according to a developmental biology theme: from the embryo to developmental plasticity to an adult stage of life-history.

History of Open Access (1942-present), color-coded by historical era. Yellow: early, blue: transitional, green: contemporary. Click to enlarge. For a citable version and an alternate display type, please see [2].

1942-1999: Embryonic Ideas and Tools (early). Click to enlarge.

In the early period, there was an emergence of tools, ideas, and attempts to synthesize independent efforts. Early efforts such as the World Data System, MedLine, and Project Gutenberg served as inspiration for later efforts (particularly the development of MedLine into PubMed). Tools such as digital preprints (arXiv) and the internet (HTML, XML) served to provide the infrastructure of open science. Even tools such as Cyc (extraction of scientific rules from data) served to enable greater openness in the practice of science. The end of this era is marked by "Exploring the Development of the Independent, Electronic Scholarly Journal", a survey of open access journals in what coincides with the early internet era.

2000-2008: Institutional Plasticity (transitional). Click to enlarge.

The transitional period (or institutional plasticity) was a time for creating many of the institutions and established norms of the open science community. Many foundational ideas were either established (Creative Commons, digital object identifiers) or came to fruition (Human Genome Project) during this period. It is also of note that at least four declarations of practice were published during this period.

2009-present: A Juvenile No More! (contemporary). Click to enlarge.

The contemporary period has been defined by even more sophisticated tools (Altmetrics), quasi-historical summaries of past work for future development (Reinventing Discovery, The Future of OA), and the discussion of institutional standards at a greater level of specialization (FAIR Principles). This era is also marked by the use of open science to practice collaborative open science (Polymath Project), putting all of the pieces developed in previous eras into place.

NOTES:
[1] Munroe, R. (2013). The Rise of Open Access. Science, 342(6154), 58-59. doi:10.1126/science. 342.6154.58

[2] Alicea, B. (2019). History of Open Access Infographic. Figshare, doi:10.6084/m9.figshare. 9975713

Open Access Week 2019: Introduction

Welcome to OAWeek 2019! This year's features are being published in conjunction with the Orthogonal Research and Education Laboratory, the eLife Ambassadors program, and the associated Data Reuse Initiative.

The first feature for this year is an infographic called the History of Open Access [1]. Our history begins in 1943 with some Philosophy of Science [2], and proceeds through key innovations, publications, and institutions the span the late 20th and early 21st centuries. Below is a preview of the infographic, and will be discussed in more detail on Tuesday the 22nd.

History of Open Access infographic (Omega version).

The second feature is a series of digital badges (microcredentials) on Open Data practice [3]. The first badge in the series walks the learner through several lessons on how to identify, locate, and work with open datasets. The second badge walks the learner through preparing an open data set for publication. This lesson is based on the Mozilla Data Reuse Planning Template which help people adhere to best practices when making data public and shareable. These badges will be released on Thursday the 24th. Then, on Friday the 25th, we will give you the chance to make your own contributions (details to come). So join us for our week of celebrating Open Access!

NOTES:
[1] Figshare, doi:10.6084/m9.figshare.9975713

[2] Robert Merton, The Sociology of Science: theoretical and empirical investigations.

[3] Molloy, J.C. (2011). The Open Knowledge Foundation: Open Data Means Better Science. PLoS Biology, 9(12), e1001195.

January is DevoWorm month!

Blossoms or fireworks to ring in the New Year?

Welcome to 2019! And welcome to OpenWorm Foundation's project of the month for January, featuring DevoWorm. Here I will briefly go over progress in the DevoWorm group over the last year and a half. If you would like to know more, we have a group Slack channel (#devoworm) in the OpenWorm team, a group website, and a Github repository.

For the uninitiated, the DevoWorm group has a multifaceted set of interests. We are interested in simulating and analyzing data related to worm development, but have an interest in the development of other model organisms as well. In terms of results, we have focused mostly on publications and open datasets, but as you will see from the website, we have also been involved in the creation of unique demos and software development.

The DevoWorm group is also interested in education. Our educational efforts have largely spread out over four types of pedagogy: digital badges, tutorials via interactive notebooks, public lectures, and one-on-one mentorship through the Google Summer of Code (GSoC) program. The OpenWorm Foundation has hosted a DevoWorm GSoC student for the past two years (2017 and 2018), and will be offering a third opportunity this year (2019). 

This is the 15th anniversary for the GSoC program, and it is always an excellent experience. The application process begins on February 25th. If you are interested in a mixture of computational biology, image processing, and machine learning, please contact us for more information.

COURTESY: Image from "One, Two, Three,....GSoC!" by Vipal Gupta

While GSoC is well-compensated opportunity to participate in DevoWorm, there are also less formal ways through which one can collaborate. One of these ways is through a conventional research pathway such as analyzing data, building a simulation, or curating a dataset. Another way to collaborate is to help create new types of educational content. We are particularly interested in creating virtual reality-based offerings in the near future. If you enjoy creating educational content, or simply enjoy learning, please get in touch!

Another new initiative is called DevoZoo. The DevoZoo site aggregates open datasets, methods, and techniques relevant to computational developmental biology and data science biology. We currently host open datasets for the following model organisms: C. elegans, Drosophila, Zebrafish, Ascidians, and Mouse. DevoZoo also hosts raw microscopy data in the form of movies for many of these model organisms as well as Spiders. As if this were not enough, we also try to engage learners and open scientists with artificial life models. The DevoZoo presents three: Morphozoans, developmental Braitenberg Vehicles, and Multicell Systems. The artificial life models in particular could use some further development. Check out the DevoZoo webpage or ask us if you would like to learn more.

Finally, you can participate by collaborating on a publication. The DevoWorm group has been featured in four publications in the past year. The OpenWorm article in the "Connectome to Behavior" special issue of Royal Society B provides a succinct description of the project and its current course. Some of our members served as editors and contributors to a special issue of BioSystems in honor of Dr. Lev Beloussov. This issue features 32 articles that provide a very broad and innovative look at the topic of morphogenesis. Our set of contributions (peer-reviewed papers) spanned from network models of the embryo to the developmental emergence of the connectome and quantitative approaches to organogenesis in the eye imaginal disc.

If you are interested in joining in on the discussion, we hold group meetings online every Monday at 9pm UTC. We are also starting to host hackathons on Fridays during the late morning/early afternoon North American time. Check out our scheduling page for more information. Hope to encounter you soon, and have a great month!

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.

4) Schroeder, M. Fractals, Chaos, and Power Laws: minutes from an infinite paradise. W.H. Freeman. 1991. AND Kauffman, S. At Home in the Universe: search for the laws of self-organization and complexity. Oxford University Press. 1995.

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.

NOTES:

[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. et.al (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.

Open Access Week 2017: Version-Controlled Papers

The subject of a recent workshop [1], the next-generation scientific paper will include digital tools that formalize things such as version control and data sharing/access. Orthogonal Laboratory is developing a method for version-controlled documents that integrates formatting, bibliographic aspects, and content management. While this is not a novel approach to writing and composition [2], this post will cover how to apply a version-controlled strategy to presenting a scientific workflow. Below are brief sketches of our system for generating next-generation papers.

The first element is the process through which a document is generated, styled, and published (assigned a unique digital identifier or doi):

The key element of our system is a version control repository. We are using Bitbucket, but Github or a more specialized platforms such as Authorea or Penflip might also be sufficient. The idea is to build documents using the the Markdown language [3], then incorporate stylistic elements using CSS and HTML. VScode is used to manage spellcheck and grammar in the Markdown documents (containing the authored content). Reference management is done via Zotero, but again, any open source alternative will do.

The diffs function [4] of version control can be used to operate on final versions of Markdown files for the purpose of alternating between document versions. The idea is to not only find a consensus between collaborators, but to use branches strategically to push alternative versions of content to the doi as desired. This combinatorial editing framework could be desirable in appealing to different audiences or stressing specific aspects of the work at different points in time. Note that this is distinct from the editorial function of pulls and merges, which are meant to be more "under the hood".

Pandoc serves as a conversion tool, and can style documents according to particular specifications. This includes conventions such as APA style, or document formats such as LaTeX or pdf [5]. Additional components include code and data repositories, supplemental materials, and post-publication peer review.

Orthogonal Lab generally uses a host such as Figshare to generate dois for such content, but there are other hosts that generate version-specific dois as well. It is worth noting that Github-hosted academic journals are beginning to appear. Two examples are ReScience and Journal of Open Science Software. What we are providing (for our community and yours) is a means to generate styled documents (technical papers, blogposts, formal publications) in a version-controlled format. This also means papers can be dynamic rather than static: content at a given doi can be updated as desired.


NOTES:
[1] Perkel, J. (2017). C. Titus Brown: Predicting the paper of the future. Nature TechBlog, June 1.

[2] Eve, M.P. (2013). Using git in my writing workflow. August 18. Also, much of this functionality is accessible in Overleaf using TeX and a GUI interface.

[3] Cifuentes-Goodbody, N. (2016). Academic Writing in Markdown. YouTube. AND Sparks, D. and Smith, E. Markdown Field Guide, MacSparky.

[4] Diffs are also useful in comparing different versions of a published document as events unfold. Newsdiffs performs this function quite nicely on documents containing unfolding news.

[5] A few references for further reading:

a) Building your own Document Processor Tools:
Building Publishing Workflows with Pandoc and Git. Simon Fraser University Publishing.

b) Git + Diffs = Word Diffs:
Diff (and collaborate on) Microsoft Word documents using GitHub. Ben Balter blog.

c) Using Microsoft Word with Git. Martin Fenner blog.

Live streaming of Orthogonal Lab content

Research live-streaming: an experiment in content [1].

The Orthogonal Research Laboratory, in conjunction with the OpenWorm Foundation, is starting to experiment with live video content. We are using YouTube Live, and live streams (composed in Xsplit Broadcaster) will be archived on the Orthogonal Lab YouTube channel. The intial forays into content will focus on research advances and collaborative meetings, but ideas for content are welcome. 

NOTES:

[1] obscure reference of the post: a shot of Felix the Cat, whose likeness was used to calibrate early experimental television broadcasts.

Wandering Towards an Essay of Laws

The winners of the FQXi "Wandering Towards a Goal" essay contest have been announced. I made an entry into the contest (my first FQXi contest entry) and did not win, but had a good time creating a number of interesting threads for future exploration. 

The essay itself, "Inverting the Phenomenology of Mathematical Lawfulness to Establish the Conditions of Intention-like Goal-oriented Behavior" [1], is the product of my work in an area I call Physical Intelligence in addition to intellectual discussions with colleagues (acknowledged in the essay). 

I did not have much time to polish and reflect upon the essay at the time it was submitted, but since then I have come up with a few additional points. So here are a few more focused observations extracted from the more exploratory essay form:

1) there is an underappreciated connection between biological physics, evolution, and psychophysics. There is an subtle but important research question here: why did some biological systems evolve in accordance with "law-like" behavior, while many others did not? 

2) the question of whether mathematical laws are discovered or invented (Mathematical Platonism) may be highly relevant to the application of mathematical models in the biological and social sciences [2]. While mathematicians have a commonly encountered answer (laws are discovered, notation was invented), an answer based on discovering laws from empirically-driven observations will likely provide a different answer.

3) how exactly do we define laws in the context of empirical science? While laws can be demonstrated in the biological sciences [3], biology itself is not thought of as particularly lawful. According to [4], "laws" fall somewhere in-between hypotheses and theories. In this sense, laws are both exercises in prediction and part of theory-building. Historically, biologists have tended to employ statistical models without reference to theory, while physicists and chemists often use statistical models to demonstrate theoretical principles [5]. In fields such as biology or the social sciences, the use of different or novel analytical or symbolic paradigms might facilitate the discovery of lawlike invariants.

4) the inclusion of cybernetic principles (Ashby's Law of Requisite Variety) may also bring together new insights on how laws occur in biological and social systems, and whether such laws are based on deep structural regularities in nature (as argued in the FQXi essay) or the mode of representating empirical observations (an idea to be explored in another post).

5) Aneural cognition is something that might guide information processing in a number of contexts. This has been explored further in another paper from the DevoWorm group [6] on the potential role of aneural cognition in embryos. It has also been explored in the form of the free-energy principle leading to information processing in plants [7]. Is cognition a unified theory of adaptive information processing? Now that's something to explore.

NOTES:

[1] A printable version can be downloaded from Figshare (doi:10.6084/m9.figshare.4725235).

[2] I experienced a nice discussion of this issue during an recent NSF-sponsored workshop. The bottom line is that while the variation typical of biology often makes the discovery of universal principles intractable, perhaps law discovery in biology simply requires a several hundred year investment in research (h/t Dr. Rob Phillips). For more, please see:

Phillips, R. (2015). Theory in Biology: Figure 1 or Figure 7? Trends in Cell Biology 25(12), 1-7.

[3] Trevors, J.T. and Saier, M.H. (2010). Three Laws of Biology. Water Air and Soil Pollution, 205(S1), S87-S89.

[4] el-Showk, S. (2014). Does Biology Have Laws? Accumulating Glitches blog, Nature Scitable. http://www.nature.com/scitable/blog/accumulating-glitches/does_biology_have-laws

[5] Ruse, M.E. (1970). Are there laws in biology? Australasian Journal of Philosophy, 48(2), 234-246. doi:10.1080/00048407012341201.

[6] Stone, R., Portegys, T.E., Mihkailovsky, G., and Alicea, B. (2017). Origins of the Embryo: self-organization through cybernetic regulation​. Figshare, doi:10.6084/m9.figshare.5089558.

[7] Calvo, P. and Friston, K. (2017). Predicting green: really radical (plant) predictive processing. Journal of the Royal Society Interface, 14, 20170096.

Innovation, Peer Review, and Bees

This post was inspired by a couple of Twitter conversations by people I follow, as well as my own experience with peer-review and innovation. The first is from Hiroki Sayama, who is contemplating a range of peer review opinions on a submitted proposal.

I like the using the notion of entropy to describe a wide range of peer-review opinions based on the same piece of work. This reminds me of the "bifurcating opinion" phenomenon I sketched out a few years ago [1]. In that case, I conceptually demonstrated how a divergence of opinion can prevent consensus decision-making and lead to editorial deliberation. Whether this leads to subjective intervention by the editor is unclear and could be addressed with data.

Hiroki points out that "high-entropy" reviews (wider range of opinions) represent a high degree of innovation. This is an interesting interpretation, one which leads to another Twitter conversation-turned complementary blog posts from Michael Neilsen [2] and Julia Galef [3] on the relationship between creativity and innovation.

In my interpretation of the conversation, Michael point out that there is a tension between creativity and rational thinking. On one side (creativity) we have seemingly crazy and irrational ideas, while on the other side we have optimal ideas given the current body of knowledge. In particular, Michael argues that the practice of "fooling oneself" (or being overly confident of the novel interpretation) is critical for nurturing innovative ideas. An overconfidence in conventional knowledge and typical approaches both work to stifle innovation, even in cases where the innovation is clearly superior.

Feynman though that "fooling oneself" was generally to be avoided, but also serves as a hallmark of scientific rationality. However, the very act of thinking (cognitive processes such as focusing attention) might be based on fooling ourselves [4], and thus might define any well-argued position. 

Julia disagrees with this premise, and thinks there is no tension between rationality and innovative ideas. Rather, there is a difference between confidence that an idea can be turned into an artifact and confidence that it will be practical. Innovation is stifled by a combination of overconfidence in practical failure combined with a lack of thinking in terms of expected value. I take this to be similar to normative risk-aversion by the wider community. If individual innovators are confident in their own ideas, despite the sanctions imposed by negative social feedback, they are more likely to pursue them.

Nikola Tesla's approach was "irrational", it was also a sign of his purposeful self-delusion and perhaps even his social isolation from the scientific community [5]. Remember, in the context of this blogpost, these are all good things.

Putting this in the context of peer review, it could be said that confidence or overconfidence is related to the existence and temporary suspension of sociocultural mores in a given intellectual community. A standard definition of social mores are customs and practices enforced through social pressure. In the example given by Michael Neilsen, fooling oneself in order to advance a controversial position requires an individual to temporarily suspend social mores held by members of a specific intellectual community. In this case, mores are defined as commonly-held knowledge and expected outcomes, but can also include idiosyncratic practices and intuitions [6]. From a cognitive standpoint, this may be similar to the requisite temporary suspension of disbelief during enjoyable experiences.

While this suspension allows for innovation, violations of social mores can also lead to a generally negative response, including moral panics and the occasional face full of bees [7]. Therefore, I would amend Hiroki's observation by saying that innovation is marked not only by a wide range of peer-review opinion, but also by universal rejection. Separating the wheat from the chaff amongst the universally rejected works is work for another time.

The price of innovation equals a swarm of angry bees!

NOTES:

[1] Alicea, B. (2013). Fireside Science: The Consensus-Novelty Dampening. Synthetic Daisies blog, October 22.

[2] Nielsen, M. (2017). Is there is tension between creativity and accuracy? April 8.

[3] Galef, J. (2017). Does irrationality fuel innovation? Julia Galef blog, April 7.

[4] Scientific American (2010). How We Fool Ourselves Over and Over. 60-second Mind podcast, June 19.

[5] Bradnam, K. (2014). The Tesla index: a measure of social isolation for scientists. ACGT blog, July 31.

[6] Lucey, B. (2015). A dozen ways to get your academic paper rejected. Brian M. Lucey blog, September 9.

[7] "Face full of bees" is a term I just coined to describe the universal rejection of a particularly innovative piece of work. "Many bees on face" = "Stinging rebuke".

Embryology Special Issue

Me and my colleagues are pleased to announce an upcoming special issue of the journal Biology (Basel). The topic is "Computational, Theoretical, and Experimental Approaches to Embryogenesis" (see announcement). Our view of what constitutes embryogenesis research is rather broad, spanning experimental studies, cellular reprogramming, bioinformatics, and artficial life. Therefore, we seek submissions from a wide variety of researchers and article types.

As the lead editor, I will take any questions you might have about interesting ideas, types of articles, or if you are interested in peer-review. As noted on the poster, the deadline for submissions is August 31, 2017. Looking forward to an excellent issue.

UPDATED (5/17):
With the initial dealine fast approaching, we have decided to extend the submission deadline to December 31. 

Kluged Curiosities and Network Connectivity, February 2016

While this blog has matured past the stamp-collecting phase of inquiry, we will nevertheless review a series of curiosities from the last few months. This includes a few readings from the network science literature that have been percolating (pardon the pun) through my reading queue.

The first of these is a game that relies on your pattern recognition skills as well as a keen eye for outliers. The "Guess the Correlation" game trains you to see the signal through the noise, provided that signal is a linear correlation amongst less than 100 datapoints.

Visual approximation of an embedded signal. COURTESY: guessthecorrelation.com

This is also a nice example of domain expertise versus the precision of statistical techniques [1], and perhaps a lesson in naive feature creation.

Crowdfunding, or raising modest amounts of money from large numbers of individuals, is emerging as an alternative way of raising money for side projects and attaining short-term research goals. A new paper on crowdfunding in PLoS Biology [2] called "A Guide to Scientific Crowdfunding" gives excellent tips for starting your own crowdfunding campaign and a bibliography for further reading on scientific crowdfunding.

Last year was the 55th anniversary marking the discovery of the giant component (a key foundation in the area we now call network science) by Erdos and Renyi [3]. Disrupting this giant component by reducing the connectivity of a complex network has many practical applications [4]. Kovacs and Barabasi [5] introduce us to the concept of connective destruction, which refers to the selective removal of nodes that partitions a network into smaller, disconnected components (effectively isolating subnetworks)?

Morone and Makse [6] attempt to solve this NP-hard problem by developing the collective influence algorithm. Collective influence takes into account a node's extended degree, or the degree of a central nodes as well as its connections up to several links away. By taking into account both the strong and weak links of a given node of high-degree, the computational complexity of optimal network partitioning can be reduced to O(n log n).

Explosive percolation, now officially famous. COURTESY: Allen Beattie and Nature Physics.

A related (and perhaps in some ways inverse) problem is that of explosive percolation. Explosive percolation is the sudden emergence of large-scale connectivity in networks [7]. As connective destruction makes it easier to control, say, a disease outbreak, explosive percolation makes it harder. Fortunately, we are discovering ways to control this transition [8]. For example, approaches based on Achlioptas processes (a form of competitive graph evolution) can be successful at delaying or otherwise controlling the onset of explosive percolation [9]. 

Recently, I got into a series of conversations about the use of Lena Soderberg's image as a standard computer vision benchmark. Apparently, one reason it is used is because it is the visual equivalent of a pangram [10]. Regardless, here is the historical background on one of the most benchmarked photos in Computer Science history [11].

The oft-benchmarked photo (circa 1972).

NOTES:
[1] Driscoll, M.E. The Data Science Debate: domain expertise or machine learning? Data Utopian blog. Accessed on 2/26/2016.

[2] Vachelard, J., Thaise Gambarra-Soares, T., Augustini, G., Riul, P., Maracaja-Coutinho, V. 2016. A Guide to Scientific Crowdfunding. PLoS Biology, 14(2), e1002373.

[3] Spencer, J. 2010. The Giant Component: the golden anniversary. Notices of the AMS, June/July, 720-724.

* discusses interesting historical links between discovery of the giant component and Galton-Watson processes (the mathematics of branching processes in biology).

[4] Kovacs, I.A. and Barabasi, A-L. 2015. Destruction perfected. Nature News and Views, 524, 38-39.

[5] Keeling, M.J. and Eames, K.T.D. 2005. Networks and epidemic models. Journal of the Royal Society Interface, 2(4), 295–307.

[6] Morone, F. and Makse, H.A. 2015. Influence maximization in complex networks through optimal percolation. Nature, 524, 65-68.

[7] Ouellette, J. 2015. The New Laws of Explosive Networks. Quanta Magazine, July 14.

[8] Achlioptas, D., D'Souza, R.M., and Spencer, J. 2009. Explosive Percolation in Random Networks. Science, 323(5920), 1453-1455.

[9] D'Souza, R.M. and Nagler, J. 2015. D'Souza, R.M. and Nagler, J. 2015. Anomalous critical and supercritical phenomena in explosive percolation. Nature Physics, 11, 531-538.

[10] A phrase that uses all of the letters in the available alphabet. One example: "the quick brown fox jumped over the lazy dog".

[11] Hutchinson, J. 2001. Culture, Communication, and an Information-Age Madonna. IEEE Professional Communication Society Newsletter, 45(3).