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Challenge 1 Domain-specific versus domain-independent. This issue is concerned
with how much domain knowledge will be required to carry out an analysis.
This challenge remains to be an issue. Research in automatic ontology construc-
tion has made considerable progress and we hope, by providing a representation of
the domain structure, future systems can provide better support to accommodate the
needs from both inside and outside of a domain.
Challenge 2 Quality versus Timeliness. The quality comes from the collective
views expressed by domain experts in their scholarly publications. The timeliness
issue rises from the reality that by the time an article appears in print, it is
more likely that science has moved on. Nevertheless, the history of scientific
debates can provide valuable insights. If the analysis can be done frequently, such
visualizations can provide useful milestones for scientists to project the trajectory of
a paradigm. This issue also relates to the source of input, ranging from traditional
scientific literatures, gray literatures such as technical reports and pre-prints, to
communications between scientists of an invisible college.
The timeliness issue is relaxed by the use of social media. Studies have found
that how often an article is tweeted on Twiter soon after its publication may be a
good indicator of its subsequent citations in scholarly literature. On the other hand,
social media's impact is more likely to be transient rather than persistent because
it takes much more than a single manuscript of detailed experiments to convince
skeptical readers, let alone a brief one-liner tweet to change people's opinions. The
real value of social media in this context is its ability to draw our attention to some
potentially interesting work quickly. And that is a very good starting point.
Challenge 3 Interdisciplinary nature: To understand and interpret what is going on
in science, we must consider the practice of closely related disciplines, particularly,
history of science, philosophy of science, sociology of science as well as the scien-
tific domain itself. This challenging issue requires an interdisciplinary approach to
ensure that we are aware of the latest development in these disciplines and integrate
theoretical and methodological components properly. Getting a meaningful and
coherent big picture is a relevant issue.
We have a better understanding of the nature of interdisciplinarity. The diver-
sity that comes with interdisciplinarity is essential to the advance of scientific
knowledge. In Turning Points (Chen 2011 ), we have demonstrated that a common
mechanism of scientific creativity is to connect seemingly contradictory ideas.
Interdisciplinarity is the norm of how science works rather than its exception. We
have introduced the structure variation model in Chap. 8 to demonstrate a new way
of studying scientific discoveries and identifying criteria of an environment in which
they may emerge. Even if we can capture a small amount of scientific discoveries
in this way, its theoretical and practical implications would be too strong to ignore.
Thus addressing this challenge is a promising direction.
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