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Data News: The Challenges of Big Data Analysis, and More

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Data News: The Challenges of Big Data Analysis, and More

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Some writing worth reading, as well as interesting graphs,

During the filming of Planet of the Apes in 1967, Charlton Heston noted “an instinctive segregation on the set. Not only would the apes eat together, but the chimpanzees ate with the chimpanzees, the gorillas ate with the gorillas, the orangutans ate with the orangutans, and the humans would eat off by themselves. It was quite spooky.” [to be continued...]

see also

and ”Life Expectancy at birth and health care spending per capita” via @aaronecarroll‘s http://theincidentaleconomist.com/… see

and http://static.davidchouinard.com/congress/  (via @BeaumontChris )

Let’s face it. Most researchers that I know who are lucky enough to be employed are doing the work of 4 or 5 people (see also this paper by Rosalind Gil). Even some of my friends, lucky enough to have tenure or tenure-track positions, seem miserable. Maybe it’s survivor guilt, but they are stressed, distracted, and harried. Time and attention are precious and spent judiciously, usually in a manner where rewards are clear and certain. Data management plans, data sharing or collaboration on GitHub? Who has time for all that?! They don’t count for much in the academic rat-race, and so the normative reward structures in the Academy create perverse incentives for neglecting or outright hoarding of data. Data sharing advocates talk about how data should get rewarded just like other forms of publication. Data should “count” with measurable impacts. As a data sharing advocate, much of this really does appeal to me. Making data sharing and collaboration part of the mainstream would be fantastic. If we convince universities to monitor data citation metrics, they can “incentivize” more data sharing. We can also monitor participation in social media (Twitter), version control (GitHub), etc. All of these statistics can be compiled and collated to provide an even more totalizing picture of a researcher’s contributions. But are more metrics (even Alt-metrics) really the solution to the perverse incentives embodied by our existing metrics? The much derided “Impact Factor” started out as a way for librarians to make more informed choices about journal subscriptions (at least according to this account). In that context, the Impact Factor was relatively benign (see this history), but it then became a tool for Taylorism and the (coercive) monitoring of research outputs by university bureaucracies. That metric helps shape who gets hired and fired. And while metrics can be useful tools, the Impact Factor case shows hows metrics can be used by bureaucracies to reward and punish.” [to be continued...]

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