Reinventing Discovery_ The New Era of Networked Science - Michael Nielsen [117]
p 7: A firsthand account of the Bermuda meeting, including a statement of the Bermuda Agreement, may be found in [211]. The Clinton-Blair statement on sharing of genetic data doesn’t explicitly name the Bermuda Agreement, but the principles espoused are essentially the principles agreed on in Bermuda. The statement may be found at [102].
p 7: I’ve used the Bermuda Agreement as an example of a collective agreement that drives data sharing. In fact, the amount of genetic data deposited in GenBank has doubled roughly once every 18 months since GenBank was founded, and this trend was not noticeably hastened by the Bermuda Agreement. You might wonder if the Bermuda Agreement was truly all that important to increased data sharing. Of course, part of the increase in data sharing is due to better sequencing technology. But the increase is also due in part to a broad drive by the biological community to share data more freely. The Bermuda Agreement is merely part of that broad drive, albeit perhaps the most visi manifestation.
p 7: On extensions of the Bermuda Agreement, see especially the Fort Lauderdale Agreement [237].
p 7: On the sharing of influenza data, see for example [20] and [60] on the avian flu outbreak of 2006, and [32] on the swine flu pandemic of 2009–10.
p 10 We are living in the time of transition to the second era of science: A related claim has been made by the database researcher Jim Gray [83] (see also the volume in which Gray’s essay appears [94]). Gray has claimed that we are today entering what he calls a “fourth paradigm” of scientific discovery, one based around highly data-intensive science in which computers help us find meaning in data. In Gray’s account this fourth paradigm is an extension of what he calls the first paradigm (empirical observation), second paradigm (the formation of models to explain observation), and third paradigm (the use of simulation to understand complex phenomena) of science. It’s true that data-intensive science is important, and we’ll discuss it in chapter 6. But Gray’s conception of the current change in science is too narrow. Science is about much more than just finding meaning in data. It’s also about the ways in which scientists work together to construct knowledge, and how the scientific community relates to society as a whole. Those aspects of science are also being transformed by online tools. Furthermore, each of these shifts impacts on and reinforces the others. So, for example, to really understand the impact of data-intensive science we must understand changes in the ways scientists work together. Gray’s fourth paradigm is just part of the changes being wrought by networked science.
Chapter 2. Online Tools Make Us Smarter
p 15: My account of Kasparov versus the World is based primarily on Kasparov’s book (with Daniel King) [107], and Irina Krush’s account of the game (with Kenneth Regan) [115].
p 15 “the greatest game in the history of chess”: from a Reuters interview with Kasparov conducted during the game [186], at move number 37. It is part of an interesting longer comment by Kasparov: “ ‘It is the greatest game in the history of chess. The sheer number of ideas, the complexity, and the contribution it has made to chess make it the most important game ever played.”
p 19: James Surowiecki, The Wisdom of Crowds, [214].
p 20: Nicholas Carr’s book The Shallows [35] is an expanded version of an earlier article, “Is Google Making Us Stupid?” [34]. Related arguments have also been made by Jaron Lanier [117].
Chapter 3. Restructuring Expert Attention
p 22: On ASSET India, InnoCentive, and Zacay Brown: [29, 222]. The text on InnoCentive is a much expanded and adapted version of text from my article [153].
p 23 Many of the successful solvers report, as Zacary Brown did, that the Challenges they solve closely match their skills and interests: see [116] for more on the characteristics of successful solvers. Note that this study also found that people often solve Challenges that are nominally outside their domain of expertise. A chemist might, for example,