the student's and researcher's guide to discovery: exploring scientific fields with open...
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www.know-center.at
The Student's and Researcher's Guide to Discovery:
Exploring Scientific Fields with Open Data and Tools
Mozfest 2015
London, November 7
Peter Kraker
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First things first
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Introduction:
Please say your name and add three hashtags that
describe you:
#1: Your occupation (student, researcher, activist…)
#2: Your current field of interest (biology, open peer
review…)
#3: Of your own choosing
If you can, please also add this to:
http://is.gd/mozfest
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The Hitchhiker‘s Guide to the Galaxy
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How to get an overview of the universe
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How to get an overview of a research field
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Discussion:
How do you get an overview of an unknown field?
Discuss with your neighbour(s)
Report back to the plenum
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How to get an overview of a research field
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How to get an overview of a research field
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Exemplary visualization of „educational technology“
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http://openknowledgemaps.org
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Overview of the publications in a conference
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Development of a knowledge domain
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Try it yourself!
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Group exploration:
Get together in groups of three (similar field of interest
preferred)
Go to http://openknowledgemaps.org/mozfest
and visualize a PLOS search!
Discuss the results that you got
Report back to the plenum
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Static visualization of all of science
[Bollen et al. 2009]
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What‘s needed for a knowledge domain
visualization?
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Bibliographic data of the works in a domain
Bibliometric data/full text of items to find interesting/important
works
Relational data to compute the
similarity between items
Domain experts and classification specialists to evaluate and
adapt the visualizations
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Challenge 1: Availability of open data – Bibliographic
data
Danowski et al. (2013)
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Challenge 2: Availability of open data –
Bibliometric data & full text
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Challenge 3: Usability and usefulness
Most static visualizations are useful to understand the
structure of science, but not in researchers‘ daily work
A lot of the existing tools are made for experts and they
are based on closed data (Web of Science, PubMed)
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Challenge 4: Systematic bias and algorithmic errors
Characteristics of the underlying dataset influence the
visualizations (Bollen et al. 2008, Kraker et al. 2014)
Algorithmic errors cannot be avoided in automated
systems
Dedicated community of domain experts,
classification specialists, programmers … (think
Wikipedia)
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Vision: Collaborative visualizations of all of science
based on open data
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If you‘d like to know more
Blog:
http://blogs.lse.ac.uk/impactofsocialsciences/2015/02/16/crowd-
sourced-overview-visualizations-of-knowledge-domains/
Publikation: Kraker, P., Schlögl, C., Jack, K., & Lindstaedt, S.
(2015). Visualization of Co-Readership Patterns from an Online
Reference Management System. Journal of Informetrics, 9(1),
169–182. http://arxiv.org/abs/1409.0348
Source Code: https://github.com/pkraker/Headstart
