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Visualizing Co-authorship Networks for Actionable Insights: Action Design Research Experiment


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Huhtamäki, J. (2016). Visualizing Co-authorship Networks for Actionable Insights: Action Design Research Experiment. In Proceedings of the 20th International Academic Mindtrek Conference (pp. 208–215). New York, NY, USA: ACM.

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Visualizing Co-authorship Networks for Actionable Insights: Action Design Research Experiment

  1. 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. 2. Context 21.10.20162 • 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. 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. 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. 5. Minimum experimentable product Information system includes • social, • information, & • technology artifacts (Lee, Thomas & Baskerville, 2015; Vartiainen and Tuunanen, 2016)
  6. 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. 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. 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. 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. 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. 11. Thank you! 21.10.2016 11 Jukka Huhtamäki @jnkka