Page 65 - Special Topic Session (STS) - Volume 3

STS515 Jim R. et al.
• Automation of literature searches, and the conduct of meta-analyses;
• Creating semantic nets of academic papers in terms of both content
and authorship in order to document the flow of discovery processes;
• Methodology classification systems, that support automated
classification;
• Analogy generators, to suggest developments in fields other that the
one in which a method or tool was developed;
• Methods for analysing large corpora of research in different fields to
examine the epistemological assumptions made (including
pragmatism).
Knowledge gaps
There are some glaring gaps in our knowledge that need to be remedied,
we need: more formal theories of data analysis; more work on the cognitive
psychology of data visualisation and interpretation; and more and better
modelling of emotion, social behaviour, and cognition; better understanding
of the processes of knowledge generation, distribution and use, and more
tools for working with very large data sets.

4. Competences for students of data wrangling
So what do students need to know in order to work in this brave new
world? Here, we offer some more lessons for young minds.
• Be aware of the politics of technology: technologies are never neutral
(e.g. cars cannot be driven by the very young or old, or the poor)
• Attend to unintended consequences (e.g. cyberbullying via social
media) via ‘what if’ games
• Engage with moral issues (e.g. the dangers of the Panopticon)
• Be aware of epistemological issues: the nature of knowledge as
conceived in different academic disciplines - how it is created, shared,
learned, and used (and by whom, and for what purposes)
• Understand modelling and the limits of modelling, and the principles
of model validation;
• Explore the reasons for the existence of data sets – adopt a
hermeneutical approach
• Create a conceptual web to link between seemingly different methods
• Understand the principles underpinning different techniques (e.g.
neural nets)
• Learn to represent the same problem in a variety of ways
• Become fluent in the use of major data repositories
• Share your code and workflows
• Invent and modify data visualisations (including dashboards)

54 | I S I W S C 2 0 1 9