OAWeek 2018: Barriers to Practice

In our final OAWeek post, I will present the current barriers to "open" practice. While there are many potential barriers to living up to the principles of complete openness, there are four major reasons why people or institutions make the decision to be open and their reasons for doing so. These include (but are not limited to): technological, financial, formal conventions, and learning curve.

Technological. The past few years have seen a boom in innovations and digital tools that enable open access, open science, and open source. Based on the above figure, we can see that the all areas of the conventional scientific process have been touched by this revolution. Distribution, publishing, notetaking, bibliographies, and engaging the broader community have all been impacted by new tools and (more importantly) their adoption by a critical mass of scientists. The development of formal pipelines for organizing this proliferation of tools into actionable steps [1] has also been a technological advance. Despite this convergence, this is not a single "killer app" that will solve the open problem. Nor should there be, as killer apps are often concentrated in the hands of single entities that are vulnerable to profiteering. Importantly, open-enabling technologies must be available to smaller research groups, particularly generators of smaller datasets [2], to get the most out of the scientific community's efforts.

101 Innovations in Scholarly Communication. ORIGINAL SOURCE: https://innoscholcomm.silk.co/  License: CC-BY.

Financial. While many tools are relatively cheap to use, other aspects of open science can be quite costly to individual scientists or even laboratories. In Wednesday's post on the three "opens", the various models of open access were discussed. Depending on which route to open access and/or open science is chosen, there are costs associated with manuscript, data archiving, curation, and annotation. A successful "open" strategy should include a consideration of these costs to ensure sustainability over the long term. There are also issues with the cost and public funding of large-scale community resources such as open access journals, preprint servers, data repositories that must be solved without making their use unaffordable or (by extension) unavailable. One open question is the incentive structure for sharing resources and making them accessible. This is particularly true for datasets, which require incentives related to research efficiency, social prestige, and intellectual growth [3]. Such incentives can also help to reinforce higher reproducibility standards and overall levels of scientific integrity [4]. 

An example of a set of formal conventions chosen from a large number of potential tools. COURTESY: Nate Angell, Joint Roadmap for Open Science Tools. License: CC-0.

Formal Conventions. Another barrier to "open" is cultural practice. In moving from concept to finished product, we do so by following a set of internalized practices. While science requires much formal training, many scientific practices are taught implicitly during the course of laboratory and scholarly research. Several recent studies characterize openness as a matter of evolving norms [5, 6] which define openness in terms of collegiality, and does not punish non-open endeavors. One critical aspect to encouraging open practices is education. However, there does seem to be a generational shift in attitudes and educational opportunities surrounding open practices. This has occurred at the same time information and computational technologies have emerged that encourage sharing and transparency. Whether this will change standards and expectations in a decade is unclear -- although governments and funding agencies are now embracing open access and open science in ways they previously have not.

Learning curve as compared to the diffusion of innovations [7]. COURTESY: Wikimedia.

Learning Curve. With all of the potential tools and steps in making research open, there is a learning curve for both individual scientists and small organizations (e.g. laboratory). While the learning curve for some practices (e.g. preprint posting) are trivial, other "open" practices (e.g. transparent protocol and methods) require more commitment and formal training. The learning curve is one major factor in the difference between merely "making things open" and making things accessible. In the domain of open datasets, accessibility can be hampered due to the fragmentation of resources across many obscure locations rather than a highly-discoverable set of repositories with fixed identifiers [8]. There are two additional barriers to accessibility and/or practice adoption: difficulty of learning and cultural learning. Difficulty in learning a specific tool or programming language does make a difference in how open practices are, and the harder or more time consuming a certain task is, the less likely the associated practice will be adopted. Cultural learning involves being exposed to a specific practice and then adopting that practice. This generally has little relation to difficulty, and depends more on personal and institutional preference. It is important to keep both of these in mind, both for adopting an "open" strategy and expectations of members of the broader community.


NOTES:
[1] Toelch, U. and Ostwald, D. (2018). Digital open science: Teaching digital tools for reproducible and transparent research. PLoS Biology, 16(7), e2006022. doi:10.1371/journal.pbio.2006022.

[2] Ferguson, A.R., Nielson, J.L., Cragin, M.H., Bandrowski, A.E., and Martone, M.E. (2014). Big Data from Small Data: Data-sharing in the ‘long tail’ of neuroscience. Nature Neuroscience, 17(11), 1442-1448. doi:10.1038/nn.3838.

[3] Gardner, D. et.al (2003). Towards Effective and Rewarding Data Sharing. Neuroinformatics, 1(3), 289-285. AND Piwowar, H.A., Becich, M.J., Bilofsky, H., Crowley, R.S. (2008). Towards a Data Sharing Culture: Recommendations for Leadership from Academic Health Centers. PLoS Medicine, 5(9), e183. doi:10.1371/journal.pmed.0050183.

[4] Gall, T., Ioannidis, J.P.A., Maniadis, Z. (2017). The credibility crisis in research: Caneconomics tools help? PLoS Biology, 15(4), e2001846. doi:10.1371/journal.pbio.2001846.

[5] Pham-Kanter, G., Zinner, D.E., and Campbell, E.G. (2014). Codifying Collegiality: recent developments in data sharing policy in the life sciences. PLoS One, 9(9), e108451. doi:10.1371/ journal.pone.0108451.

[6] Fecher, B., Friesike, S., and Hebing, M. (2015). What Drives Academic Data Sharing? PLoS One, 10(2), e0118053. doi:10.1371/journal.pone.0118053.

[7] Rogers, E. (1962). Diffusion of Innovations. Free Press of Glencoe, New York.

[8] Culina, A., Woutersen-Windhouwer, S., Manghi, P., Baglioni, M., Crowther, T.W., Visser, M.E.  (2018). Navigating the unfolding open data landscape in ecology and evolution. Nature Ecology and Evolution, 2, 420–426. doi:10.1038/s41559-017-0458-2

OAWeek 2018: Open Access, Open Science, Open Source

For this OAWeek post, we will discuss the connections between open access, open science, and open source. As an organizing principle, I will introduce each concept with a working definition, and then discuss relationships with other "open" concepts.


Open Access: availability to the general public, research output can be distributed freely without restrictions.

A typology of different forms of Open Access publishing.

As a publishing phenomenon, open access can take a number of forms [1, 2]. Aside from a distinction between peer-reviewed and non peer-reviewed materials, Open Access publishing is color-coded as green (self-archiving) or golden (archival at the publisher's site for a fee) [3]. There is also a version of golden open access called diamond open access, the difference being that diamond open access does not require the author to pay a fee to the publisher [4]. Self-archival can be done through a personal server (website), a preprint site such as bioRxiv, or a site that allows for public hosting of documents (ResearchGate, Figshare). Golden open access usually requires an APC fee, the funds for which go to the publisher. While cheaper, self-archival requires adherence to a set practices that ensure ease of access.

In a narrow sense then, open access is a publishing issue seemingly unconnected to open science and particularly open source. Yet in fact, open access is both critical to and an enabling factor in open science and open source. Aside from making materials open (free or affordable), they mush also be made accessible. There are many other benefits to open access [5], but the most important of which is that they enable access to many different components of a set of scientific results.


Open Science: make research and data (scholarly outputs) publically accessible. This requires efforts to make scholarly outputs transparent and accessible, which should enable reproducibility.


Open Science is an extension of open access in that not only is the manuscript made public, but the research products are made public as well [6, 7]. An open pipeline (or system) might include any number of the following: version-controlled manuscript editing, preprints, preregistration of study design, open datasets, demonstrable analyses, open source code, social media engagement, post-publication review, and open manuscript review. While it is up to the scientist or scientific organization what components to utilize, each component has value to both the scientist [8] and the scientific audience.

One way to make the benefits of being open explicit without violating the rights of scientists to their original work is to adopt an open license. While there are a number of options for both open science and open source, one popular type of license is Creative Commons (CC) [9]. There are many types of CC license, but one commonly used in open science is CC-BY (or alternatively CC-BY-NC). The BY license allows others to distribute and/or recombine your work with acknowledgement of the original author (you). BY-NC licenses explicitly disallow commercial derivatives.

A successful open science strategy is more than simply the production of science and the least publishable unit. Open science also includes access to educational materials, such as screencasts, lecture notes, and even course development [10]. As a suitable example, Open Science MOOC provides all of their course modules at the level of a consumable lesson and a Github repository of sharable lesson plans.


Open Source: make source code publically available and editable. Software architecture is licensed so that it can be modified in collaborative fashion.

In many ways, open source (OS) can be considered a crucial component of open science, as the ability to collaboratively and transparently solve problems is a key part of the ethos. Yet open source has its own set of concerns surrounding project-building and the management of contributors. The development of open source software is not simply the production of free software, as there are significant version control and human resource issues that go into OS [11]. Open source projects (such as Wikimedia Foundation or Linux Foundation) tend to operate at a much larger scale than open science collaborations. In the case of hybrid open science/open source organizations (such as the OpenWorm Foundation), there are a number of management concerns that also draw from making research methods and data transparent.

Open Source provides not only an avenue to transparency, but also as a tool for collaboration. An open source infrastructure that provides version-control [12] and source code annotation in the public domain can serve to enable public discussion and encourage future development outside of a specific project or set of experiments. The ability to open up code used in analysis and simulation aids in the peer review process. For published methods, open source provides a means for people to improve upon and use the code base. Open source efforts such as the open hardware movement allows labs to share standardized plans for DIY lab equipment, lowering the costs of science.


NOTES:
[1] Jeffrey, K.G. (2006). Open Access: an introduction. ERCIM News. https://www.ercim.eu/publication/Ercim_News/enw64/jeffery.html.

[2] Suber, P. (2012). Open Access. MIT Press, Cambridge, MA

[3] Kienc, W. (2015). Green OA vs. Gold OA. Which one to choose? Open Science blog, June 3.

[4] Kelly, J.M. (2013). Green, Gold, and Diamond?: A Short Primer on Open Access. Jason M. Kelly blog, January 27.

[5] PLoS. Why Open Access? https://www.plos.org/open-access.

[6] Guide to Open Science Publishing. F1000Research.

[7] McKiernan, E.C., Bourne, P.E., Brown, C.T., Buck, S., Kenall, A., Lin, J., McDougall, D., Nosek, B.A., Ram, K., Soderberg, C.K., Spies, J.R., Thaney, K., Updegrove, A., Woo, K.H., and Yarkoni, T. (2016). How open science helps researchers succeed. eLife. 2016; 5: e16800. doi:10.7554/eLife. 16800.001.

[8] Ali-Khan, S.E., Jean, A., MacDonald, E., Gold, E.R. (2018). Defining Success in Open Science. MNI Open Research, 2, 2. doi:10.12688/mniopenres.12780.

[9] Creative Commons. About the licenses. https://creativecommons.org/licenses/

[10] Jhangiani, R. and Biswas-Diener, R. (2017). Open: the philosophy and practices that are revolutionizing education and science. Ubiquity Press. doi:10.5334/bbc.

[11] Fogel, K. (2017). Producing Open Source Software: how to run a successful free software project. Version 2.3088 http://producingoss.com/

[12] Blischak, J.D., Davenport, E.R, and Wilson, G. (2016). A Quick Introduction to Version Control with Git and GitHub. PLoS Computational Biology, 12(1), e1004668. doi:10.1371/journal.pcbi. 1004668.

Welcome to Open Access Week 2018!

Welcome to Open Access Week! Orthogonal Research and Education Laboratory is contributing to the week's activities through three blogposts: in this post, we will briefly discuss Open Annotation, while Wednesday will feature "Open Access, Open Science, and Open Source" and Friday will feature "Barriers to Practice".

Synthetic Daisies blog celebrated Open Access Week in 2016 (Working with Secondary Datasets, How Am I Doing, Altmetrics?) and 2017 (Version-Controlled Papers, Open Project Management). All posts will be tagged with #OAweek for easy retrieval.

To kick off the discussion, we will now quickly discuss Open Annotation and the role it can play in enabling literature searches, peer-review, and collaboration. Two of the most well-known open annotation tools are Hypothes.is and Fermat's Library. A few posts from the Hypothes.is blog serve to establish the benefits and potential of open annotation and how it is currently being implemented on the web.

According to [1], open annotation can serve as a framework for new practices such as collective document review. This is a common function of collaborative document systems such as Overleaf and Authorea. However, the Hypothes.is vision for seems to be building a so-called "ecosystem" for commenting that can be used for peer review, reader notes, or links to relevant additional readings [1, 2]. In such a system, comments can be transferred across versions of a document, from draft to preprint to published manuscript [1].

Under the hood, open annotation relies upon standards such as the W3C Open Annotation data model. Once implemented, this allows for a separation of the discussion (annotations) from the main page [2]. This provides opportunities for meta-browsing [3] and distributed discussion threads that can be centralized in a common repository. There are also many opportunities for novel uses of open annotation, ranging from collaborative note-taking to adding references and data to an existing paper.

NOTES:
[1] Staines, H. (2017). Making Peer Review Transparent with Open Annotation. Hypothes.is blog, http://web.hypothes.is/blog/transparent-peer-review.

[2] Gerben (2014). Supporting Open Annotation. Hypothe.is blog, https://web.hypothes.is/blog/ supporting-open-annotation/.

[3] Wiesman, F., van den Herik, H.J., and Hasman, A. (2004). Information retrieval by metabrowsing. Journal of the American Society for Information Science and Technology, 55(7), 565-578.

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.

Open Access Week 2017!

Welcome to Open Access Week 2017! Synthetic Daisies participated in Open Access Week 2016 with two instructional posts on Altmetrics and Secondary Datasets.  On Twitter, several hashtags (#oaweek, #OpenAccess, #OpenScience, and #OpenData) will be full of related content over the next few days. And we will have longer posts on Tuesday and Thursday on the topics of Open Project Management and Version-Controlled Papers that will be worth reading.

Over the last year, the OpenWorm Foundation and Orthogonal Laboratory made a commitment to open access instruction in a series of microcredentials (digital badges). The OpenWorm badge system offers a series of badges on Literature Mining specifically and Open Science more generally. The Orthogonal Lab badge system offers a series of badges on Peer Review. Have a productive week!

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".

Claude Shannon, posthumously 1100100

How do you model a centennial birthday, Dr. Shannon? COURTESY: Hackaday blog.

Claude Shannon, the so-called father of information theory, was born 100 years ago today [1]. This is a Google Doodle-worthy event, even though he died in 2001. Hence, internet rule #34' [2]: "if there exists a milestone, there's a Google Doodle for it".

April 30, 2016 Google Doodle.

Claude was also a juggler and an inventor of mechanical toys, hence the zeros and ones being juggled in the Doodle. A few years ago I wrote a post detailing this "mechanical zoo". Not a real zoo, mind you, but a collection of mechanical wonders far removed from his information theory work [3].

NOTES:

[1] Horgan, J. (2016). Claude Shannon: tinkerer, prankster, and father of information theory. IEEE

Spectrum, April 27.

[2] I made up Rule #34' as a less-provocative variant of existing Rule #34.

[3] his Master's thesis and Bell Systems Technical Journal paper (pdf) were milestones in the then- emerging academic field.

Klinotactic Thoughts and Holonomic Fun

What a week for models of movement! The first item is the most recent OpenWorm Journal Club (hosted on Google Hangouts and YouTube) called "Closing the Loop from Brain Cells to Behavior". This session explored the implications of two papers by Eduardo Izquirdo and Randall Beer [1] on C. elegans  Neuromechanics.

This work focuses on the existence of klinotaxis in C. elegans movement generation. Klinotaxis occur as small but important neural circuit generates movement signals in response to the environment. Specifically, sinusoidal movement of the head occurs as a function of central pattern generation in the brain and behavioral response to the environment.

The second item involves the BB8 droid from the upcoming Star Wars movie. As the first spherical rolling droid of the Star Wars metaverse, BB8 is also a very real mechanical prototype called the Sphero. And now you can build your own [2]! By capitalizing on a principle called holonomic motion, the body moves independently of the head, which balances on the rolling body. The following article (How does BB8 Work?) discusses the innovation and the details behind the patent registered by Disney Labs.

NOTES:
[1] Izquierdo, E.J. and Beer, R.D. (2015).  An integrated neuromechanical model of steering in C. elegans. Proceedings of ECAL, 199-206. MIT Press  AND  Izquierdo, E.J., Williams, P. and Beer, R.D. (2015).  Information flow through the C. elegans klinotaxis circuit. PLoS One, 10(10), e0140397.

[2] For more information, please see: Berkey, R.   Make your own Star Wars VII BB8 Droid. Nerdist blog, June 7 (2015)  AND  How does BB8 Work? http://www.howbb8works.com/

George Boole: 11001000

Today's Google Doodle is a real Boole Doodle, centuries of two. Said three times fast, of course. Here are blogposts by Stephen Wolfram and Mike James at I Programmer with more on its broader significance. The why of the blogpost title can be found here.

Maps, Models, and Concepts, August edition

Walcome back, Maps, Models, and Concepts series! In this edition, with content cross-posted to Tumbld Thoughts, we take a tour of Artificial Intelligence reconsidered (I) and the visualization of Economic History (II). Enjoy!

I. Can you haz intelligent behavior, internet bot?

Here are a few recent readings on the modeling and simulation of intelligence, broadly defined. The first two [1, 2] are part of a series by Beau Cronin on alternative ways to model intelligence. How do we produce "better" (e.g. more intuitive, or more human) artificial intelligence? Perhaps it is the model that counts, or perhaps it is the definition of intelligence itself. 

COURTESY: Figure 3 in [3].

The authors of [3] take the former view, and present a review on how various computational architectures can produce intelligent outputs. One example demonstrates how hierarchical Bayesian models (HBMs) can be used to acquire intuitive theories for various knowledge domains. But one can also use biologically-based architectural models to produce intelligent behavior. In [4], it is shown that fabrication and cell culture techniques can produce outputs similar to purely computational connectionist models.

COURTESY: Figure 2 in [4].

II. Did it begin with a bang, a boom, or a bust?

Aha! The moment of economic creation was not at 1650 after all! Conventional economic theory sometimes gives the impression that economists are creationists in spirit. Many historical graphs [5] only offer useful information back to the year 1650. Around 1650 or so, most economic indicators enter their exponential phase, which renders graphical information about previous eras incomparable.

But economist and modeler Max Roser [6] offers a historical view of global GDP going back 2,000 years. His "Our World in Data" website is an attempt to characterize global economics and other social phenomena as a series of visualizations. This includes maps (spatial distributions) and charts that make long-term comparisons more than a series of bad graphs. If John Maynard Keynes were to look at these data, he might say: in the long run, we are all wealthier [7].

NOTES:

[1] Cronin, B.   In search of a model for modeling intelligence. O'Reilly Radar blog, July 24 (2014).

[2] Cronin, B.   AI's dueling definitions. O'Reilly Radar blog, July 17 (2014).

[3] Tenenbaum, J.B., Kemp, C., Griffiths, T.L., and Goodman, N.D.   How to Grow a Mind: Statistics, Structure, and Abstraction. Science, 331, 1279-1285 (2014).

[4] Tang-Schomera, M.D., White, J.D., Tien, L.W., Schmitt, L.I., Valentin, T.M., Graziano, D.J., Hopkins, A.M., Omenetto, F.G., Haydon, P.G., and Kaplan, D.L.   Bioengineered functional brain-like cortical tissue. PNAS, 10:1073/pnas.1324214111 (2014).

[5] The bottom three pictures are courtesy of: Roser, M.   GDP Growth Over the Very Long Run. Our World in Data (2014).

[6] Matthews, D.   The world economy since 1 AD, in a single chart. Vox blog, August 15 (2014).

[7] Based on the quote "in the long run, we are all dead".

Starstuff Squared, Rubik's Cubed

Welcome to the 250th Synthetic Daisies post! This post consists of three subthemes cross-posted from Tumbld Thoughts. The first is in honor of the Google Doodle for the 40th Anniversary of the Rubik's Cube, while the latter two are the supplemental readings for the tenth and eleventh episodes of the Cosmos reboot. 

I. Rubik's 3-D CSS Cubes

Today is the 40th anniversary of the Rubik's Cube. Aside from an invention that sold 350 million copies, Rubik's Cube is also an example of a permutation puzzle that contains an interesting problem related to group theory. The doodle itself is unique in that it utilizes a technology called CSS 3-D transforms [1]. Naturally, there is a Google Doodle.

[1] Edidin, R.   How Google Built Its 3-D Interactive Rubik’s Cube Doodle. May 19 (2014). Also check out the Chrome Cube Lab, which uses this technology to render interactive cube-based puzzles beyond Rubik's namesake.

II. All I Want For Christmas is an Electric Charge

Here are the supplemental readings for the tenth episode of the Cosmos reboot ("The Electric Boy"). Readings are organized by theme.

The Electric Boy and his Legacy:

Michael Faraday (1791-1867). BBC History.

History of the Christmas Lectures. The Royal Institution.

Cody, D.   Social Class. The Victorian Web. July 12 (2002).

Burgess, M.P.D.   Semiconductor History: Faraday to Shockley. Transistor History (2008).

Williams, A.T.   Faraday vs. Maxwell and Faraday and the Ether. Consciousness, Physics, and the Holographic Paradigm (2010).

Electromagnetic Spectrum. NASA Goddard Space Flight Center.

Frog Legs and Televisions:

Galvani's animal electricity experiments. Institute of Engineering and Technology.

Luigi Galvani (1737-1798). Center for Integrating Research + Learning, Magnet Lab, Florida State University.

Borgens, R.B., Vanable, J.W., and Jaffe, L.F.   Bioelectricity and regeneration. I. Initiation of frog limb regeneration by minute currents. Journal of Experimental Zoology, 200(3), 403–416 (1977).

Iconoscope. Wikipedia, March 20 (2014).

Philo Farnsworth (1906-1971), Electronic Television. Inventor of the Week Archive, Lemelson-MIT (1999).

Researching Faraday Cages and Electromagnetic Fields on Google is a sad statement on Internet culture:

Chandler, N.   How Faraday Cages Work. How Stuff Works.

Trottier, L.   A Growing Hysteria. Committee for Skeptical Inquiry, CFI. October (2009).

Hruska, J.   New research confirms that our electronics and radio waves disrupt migratory birds. Extremetech, May 8 (2014).

Magnetic Fields of the Earth. HyperPhysics.

Inventions/Discoveries of the Electric Boy:

Faraday's Inventions. Michael Faraday's World.

Homopolar Generator, Wikipedia. April 1 (2014).

Electrolysis, Wikipedia. May 7 (2014).

Faraday Cage, Wikipedia. March 19 (2014).

Electric Motor, Wikipedia. May 10 (2014).

Static Electricity, Wikipedia. May 5 (2014).

III. Leaving Nothing but Footprints, but Still Living On.

Here are the supplemental readings for the eleventh installment of the Cosmos reboot ("The Immortals"). As usual, readings are organized by theme.

Entropy Is Not Immortality, Time Can Be Written Down:

Matson, J.   What Keeps Time Moving Forward? Blame It on the Big Bang. Scientific American, January 7 (2010).

Mlodinow, L. and Brun, T.A.   Relation between the psychological and thermodynamic arrows of time. Physical Review E, 89, 052102 (2014).

Wall, M.   Video Tour of Alien Planets Shows How Time Flies on Strange New Worlds. Space.com, May 07 (2014).

Jones D.L.   Aging and the germ line: where mortality and immortality meet. Stem Cell Reviews, 3(3), 192-200 (2007).

Barksdale, M.   10 Methods of Measuring Time. Discovery TV: Relativity and Time.

Origins of Writing Systems. AncientScripts.com.

Fun With the Origins of DNA:

Akst, J.   RNA World 2.0. The Scientist, March 1 (2014).

Moran, L.A.   Changing Ideas About the Origin of Life. Sandwalk blog, August 7 (2012).

Joshi, S.S.   Origin of Life: the Panspermia Theory. December 2 (2008).

Klyce, B.   Cosmic Ancestry. 

Saenz, A.   Venter creates first synthetic self-replicating bacteria from scratch. SingularityHub, May 20 (2010).

Moving Life (via Dispersal):

Levin, S.A., Muller-Landau, H.C., and Nathan, R.   The Ecology and Evolution of Seed Dispersal: a theoretical perspective. Annual Review of Ecology, Evolution, and Systematics, 34, 575-604 (2003).

Gronstal, A.   Space Rocks Could Reseed Life on Earth. Astrobiology Magazine, May 15 (2008).

Interstellar Dust Could Have Building Blocks Of Life – On Science. RedOrbit, January 28 (2014).

Can our TV signals be picked up on other planets? BBC News, August 6 (2008).

Civilization is (not) Forever:

Chandler, G.   Desertification and Civilization. Saudi Aramco World, 58, 6 (2007).

Arbesman, S.   210 Reasons for the Fall of the Roman Empire. Social Dimension blog, June 26 (2013).

Kunzig, R.   Geoengineering: How to Cool Earth--At a Price. Scientific American, November (2008).

Duncan, R.C.   The Olduvai Theory: sliding towards a post-industrial Stone Age. Institute on Energy and Man, June 27 (1996).

Math Program Cracks Cause of Venus Hell Hole. Space Daily, March 21 (2001).

Passing the Baton (CoE) and Visualized Creativity

As the first of May has come and gone, it is once again time to pass on the Carnival of Evolution baton. Number 70 (the game of evolution) was a success [1], and the latest edition of Carnival of Evolution (#71: A Theory of Evolution or the Evolution of a Theory?) is now live on Chimeras blog. A nice set links to evolutionary biology-related blog posts for the the month of April. Thanks go to scientist, artist, and author E.E. (Elena) Giorgi for hosting. If you are interesting in hosting a future edition, please consult the official CoE style guide and contact Bjorn Ostman to confirm.

In the continued spirit of creativity, I would like to highlight an infographic (above) and its corresponding legend (below) showing the work of R.J. Andrews [2] on the daily schedules of famous people. Oddly, very few of the creative types featured worked late into the night, nor did many of them take naps. This may be somewhat inconsistent with what we know about these type of people [3]. Data for the poster is from Mason Currey's book "Daily Rituals" [4].

A few notes on sleeping patterns: 

Many of the featured daily routines are mundane. There are exceptions, such as Maya Angelou, but I would have expected much more quirkiness. Also to my surprise was that only a few of the featured people are reported to have taken regular naps [5]. This was a staple of Edison's daily routine, as well as Freud and myself. My initial intuition was that many of the creative people would arise relatively late, nap, and then work at night. But apparently, this is only true for a few examples shown here.

Although this infographic seems to be consistent with the old adage "early to bed, early to rise" (or at least some version of that), I have two issues with the daily routine presented here relative to a set of general principles. One is that many of these people lived in a world without lights, thus biasing their habits. Contrary to this, but perhaps just as anecdotal, is the observation that many creative and iconoclastic people tend to be night owls [6]. Thoughts?

NOTES:
[1] well-received by readers, with a very modest but almost academically-viral 300 or so reads over the course of the month. SOURCE: Blogger analytics.

[2] Andrews, R.J.   Creative Routines. InfoWeTrust blog, March 26 (2014).

[3] Maas, J.B.   Power Sleep: the revolutionary program that prepares your mind for peak performance. NY Times Books on the Web (1998) AND Popova, M.   Thomas Edison, Power-Napper: The Great Inventor on Sleep and Success. Brain Pickings, February 11 (2013).

[4] Currey, M.   Daily Rituals: how artists work. Knopf (2013).

[5] Meyer, N.   Guide to Optimized Napping. Priceonomics blog, January 25 (2014).

[6] Dobson, R.   If you want to get ahead, be a night owl. Independent, March 24 (2013).