Two months ago, Synthetic Daisies blog featured a post called “Playing the Long Game of Human Biological Variation”. In the post “One Evolutionary Trajectory, Many Processes”, I follow up on how dual process models (e.g. the DIT model) might be use to augment the population genetics of highly eusocial (and cultural) species. This has particular relevance to human evolution and the nature of population structure/phenotypic diversity.

Statistical conspiracy theory? Here is a link to John Williams’ Shadowstats site and (appropriately) three readings that critique the overall approach. For example, in one reading, it is suggested that the “shadow” in the Shadowstats name consists of an inappropriate modeling methodology.

Aziz   The Trouble with Shadowstats. Azizonomics, June 1 (2013).

Krugman, P.   Always Inflation Somewhere. Conscience of a Liberal blog, July 19 (2014).

Hiltzik, M.   A new right-wing claim: Obama must be lying about inflation. The Economy Hub, Los Angeles Times, July 23 (2014).

Here is a series of readings on the state of science funding and innovation for the future. These include:

Mayyasi, A.   Why scientists increasingly need to be salesman. Priceonomics, April 15 (2014).

* a recap of the current state of science funding, with a focus on the rise of private funding sources and the associated challenges of tapping into this resource.

Press, W.H.   What’s so special about science (and how much should we spend on it?). Science, 342, 817-822 (2013).

* discusses contributions of scientific research to the broader economy, particularly in terms of the Solow residual (a measure of productivity).

Benz, E., Goldberg, M., and Lo, A.   Can financial engineering save cancer research? Making Sense, February 27 (2014).

* “securitization” as a means to fund scientific projects (an interesting experiment in financial engineering).

Welcome to the long tail of science. Today, we have three readings: two on the sharing of “dark data”, and one on measuring “inequality” of citation rates. In [1, 2], the authors introduce us to the concept of dark data. When a paper is published, the finished product typically includes only a small proportion of data generated to create the publication (Supplemental Figures notwithstanding). Thus, dark data is the data that are not used, ranging from superfluous analyses to unreported experiments and even negative results. The authors of [2] contemplate the potential usefulness of sharing these data.

In the third paper [3], John Ioannidis and colleagues contemplate patterns in citation data that reveal a Pareto/Power Law structure. That is, about 1% of all authors in the Scopus database produce a large share of all published scientific papers. This might be related to the social hierarchies of scientific laboratories, as well as publishing consistency and career longetivity. But not to worry — if you occupy the long-tail, there could be many reasons for this, not all of which are harmful to one’s career.

[1] Wallis, J.C., Rolando, E., and Borgman, C.L.   If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology. PLoS One, 8(7), e67332 (2013).

[2] Heidorn, P.B.   Shedding Light on the Dark Data in the Long Tail of Science. Library Trends, 57(2), 280-299 (2008).

[3] Ioannidis, J.P.A., Boyack, K.W., and Klavans, R.   Estimates of the Continuously Publishing Core in the Scientific Workforce. PLoS One, 9(7), e101698 (2014).

Here are some readings on networking and open science from my reading queue. The first is a paper on the life-cycle of a preprint on the arXiv. The top image is Figure 2 in the paper. The other two readings advocate for the use of open access protocols and social media to disseminate research and counter cultural biases towards keeping research behind laboratory doors.

Shuai, X., Pepe, A., and Bollen, J.   How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations. PLoS One, 7(11), e47523 (2012).

Allen , E.   “All research should be OA”. We agree! ScienceOpen blog, July 14 (2014).

Konkiel, S.   How to become an academic networking pro on LinkedIn. ImpactStory blog, April 24 (2014).

Four readings from the reading queue on human culture, behavior, and evolution:

Benjamin, D.J.    The genetic architecture of economic and political preferences. PNAS, 10:1073/pnas.1120666109 (2014).

On the genetic architecture of economic and political preferences. Using a SNP analysis, the authors demonstrate that such traits have a polygenic architecture (e.g. many genes, small effect size for each). Studies that are underpowered (and no one knows what the appropriate sample sizes should be) can potentially generate many false positive associations between genes and behavior.

Mooney, C.   Scientists Are Beginning To Figure Out Why Conservatives Are …… Conservative. Mother Jones, July 15 (2014).

A story that equates (or perhaps confounds) the psychophysiology of political ideologies with the roots of more general ideological bias. Are we really looking at “natural” differences between liberals and conservatives? Or does this simply demonstrate that high-profile social issues with already polar liberal and conservative positions are undergirded by strong emotional responses? The standard evolutionary psychology explanation is a bit contrived as well. But it goes well with the previous article.

Ren   Cross-Cultural Color-Odor Associations. PLoS One, 9(7), e101651 (2014).

Crossmodal and cross-cultural comparisons, unite! In this study, people from several different cultures were asked to make both “congruent” and “incongruent” associations between smells and colors. The authors come to the conclusion that cultural context through experience has both statistical (covariance) and semantic (linguistic) components.

Stix, G.   Neuroplasticity: new clues to just how much the adult brain can change. Scientific American blog, July 14 (2014).

A gateway article to several recent studies in the area of neuroplasticity. Learn about the “neural volume control knob” and much, much more.

Top picture courtesy:

Here is some Artificial Life for your summer reading. The Proceedings of Artificial Life XIV (complement to the conference at the end of this month in New York City) is a peer-reviewed venue for papers covering the following topics: evolutionary dynamics, soft robots, agent behavior, collective behaviors, social dynamics and evolution, and cellular automata/ self-organizing systems.

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