1.
Visualizing Co-authorship
Networks for Actionable
Insights: Action Design
Research Experiment
Jukka Huhtamäki
Tampere University of Technology
Academic Mindtrek 2016
@jnkka #cobweb #weakties

2.
Context
21.10.20162http://bit.ly/cobwebtut
• Computational methods for intelligent matchmaking for knowledge work
• Increase serendipity and inter-disciplinary cross-pollination of ideas through
data-driven matchmaking
• Tampere University of Technology: IHTE, NOVI, and Mathematics
• Funded by the Academy of Finland

3.
Objective of the experiment
• How to use visual network analytics to create
additional value for bibliographical data?
• More specifically, what kinds of insights does
network analytics of bibliographic data
afford?
• How should the digital research infrastructure
(academic ecosystem) support data-driven
analytics?
21.10.2016 3

4.
• Current Research Information Systems
(CRIS) are used to collect, manage, and
search bibliographic data
• The CRIS used in the experiment must be
operated manually with a user interface
• One can export a set of research results
only if there are less than 1000 articles
found
• The exported data includes only a fraction
of the data maintained by the CRIS

5.
Minimum experimentable product
Information system
includes
• social,
• information, &
• technology
artifacts
(Lee, Thomas & Baskerville,
2015; Vartiainen and
Tuunanen, 2016)

6.
Social artifact
1. “Start with what you know, then grow” (Heer
and boyd, 2005)
2. What are the mechanisms behind the structure:
organizational, research groups, shared
interests?
3. Explore and benchmark ways of working: What
lead to productivity and success? International
collaboration?
4. Networks provide a way to measure research in
a more systemic manner
21.10.2016 6

7.
Information artifact
• Two key entities: individual articles and
collections or articles
1. No unique identifiers for authors in article
metadata
2. No organizational information on the authors
available
3. Cap of 1000 articles to be exported
4. No explicit license for the data (Elsevier Pure)
21.10.2016 7

8.
Technology artifact
• Data access and processing in Python
(Pandas and NetworkX)
• Network analysis and visualization in Gephi
• Interactive network provision in Gexf.js
• Developing a visual analytics system
operating in self-service mode could be
conducted as an open source project
21.10.2016 8

9.
Discussion – please join!
• Use Open data license (CC) for bibliographical data with a specific
open data license
• Provide a REST interface for accessing bibliographic data
• Provide a REST interface for the search
• Provide complete data on articles and other entities in JSON and
XML
• Provide unique identifiers for articles, authors, and organizational
and other entities
• Apply linked data practices to support traversing the metadata (URI
schema)
• Do not limit the number of articles that can be fetched from the
repository.
• To enable the launch of external, third-party visual analytics tools
(see the paper and Salonen and Huhtamäki (2010) for details)21.10.2016 9

10.
Concluding remarks
• We need a digital ecosystem for research
• Open computational access to bibliographic
data is an important first step
• In national level, VIRTA REST gives some
support to data access (delay, not tested)
• “we conclude with the strongest possible
recommendation for university policy makers
to step into the open science sphere”-open
bibliographic data is the imperative first step
21.10.2016 10

11.
Thank you!
21.10.2016 11
Jukka Huhtamäki
@jnkka
jukka.huhtamaki@tut.fi
fi.linkedin.com/in/jukkahuhtamaki/