The Web in Science and Research: A tour through four topics


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Slides to my talk at the KMi Podium on July 24, 2012. The video can be found here:

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The Web in Science and Research: A tour through four topics

  1. 1. www.know-center.atKMi Podium – July 24, 2012A tour through four topics related toThe Web in Science andResearchPeter Kraker gefördert durch das Kompetenzzentrenprogramm
  2. 2. Collaborators Nicholas Balacheff Barbara Kump Günter Beham Derick Leony Erik Duval Stefanie Lindstaedt Ronald Fellmann Sandra Murg Angela Fessl Gonzalo Parra Denis Gillet David Pocivalnik Nina Grabowski Wolfgang Reinhardt Michael Granitzer Peter Scott Eelco Herder Thomas Ullmann Patrick Höfler Bram Vandeputte Kris Jack Claudia Wagner Fleur Jeanquartier Fridolin Wild Christian Körner Jerome Zeiliger 2
  3. 3. Scientific Activity on the WebOnline literature searchCollaborative writing andreference managementDissemination via preprintsand open archivesKnowledge transferin social networks 3Crowdsourcing approaches
  4. 4. Research 2.0Research E-Science Science onlineContext … Studying the use of the web in the scientific process Web Science The interdisciplinary science of the web Social Privacy Networks Online Learning 4
  5. 5. Overview Practices The Web in Analysis Science Tools and Research Infra- structure 5
  6. 6. The change inOverview scientific practices and the open science movement Practices The Web in Analysis Science Tools and Research Infra- structure 6
  7. 7. The STELLAR Network of ExcellenceThe STELLAR Network of Excellence in TechnologyEnhanced Learning ( unifying the diverse community of TechnologyEnhanced Learning (TEL)Key activity: supporting researchers with web tools andinfrastructure 7
  8. 8. Study on PracticesTwo exploratory focus groups with researchers fromTechnology Enhanced Learning 14 participants from all major disciplines involved in TEL Qualitative analysisGoals  Determine the research process in TEL  Collect web-based practices within the research process 8
  9. 9. Results 9Kraker, P., & Lindstaedt, S. (2011). Research Practices on the Web in the Field of TechnologyEnhanced Learning. Proceedings of the ACM WebSci’11. Koblenz, Germany.
  10. 10. Results 10
  11. 11. ResultsIdentified practices are mostly within the design and thepublication processExisting practices on the web do not necessarily work inresearchTools and technologies must be backed by existingpractice, or solve an obvious shortcoming in the existing practice 11
  12. 12. Problems in Technology Enhanced LearningDisjoint scientific communities (Gillet et al. 2009)Low-cross citation rate, low cross-authorship rate (Kirbyet al. 2005, Maurer and Khan 2010)Multi-disciplinarity instead of inter-disciplinarity Can an Open Science help? 12
  13. 13. Open Science “Open Science means opening up the research process by making all of its outcomes, and the way in which these outcomes were achieved, publicly available on the World Wide Web” Open Data Open Source Open Science Open Open Access MethodologyKraker, P., Leony, D., Reinhardt, W., & Beham, G. (2011). The Case for an Open Science in 13Technology Enhanced Learning. International Journal of Technology Enhanced Learning, 6(3),643-654.
  14. 14. Potential Benefits of an Open ScienceConnect research communities – exchange anddiscussionEnables reproducibility of research – increase validity,efficiency and comparabilityBenefits stakeholders – results are earlier available,fosters open innovation 14
  15. 15. Overview Practices The Web in Analysis Science Tools and Research The provision of Infra- web tools for structure opening up the research process 15
  16. 16. Open ArchiveE-print archivePublication metadataaggregation and disseminationsiteTEL Thesaurus andTEL Dictionary 16
  17. 17. TEL EuropeSocial network  Profiles  Groups  BlogsPodcastsProject resultsPersonalisable dashboard 17
  18. 18. Widgets on TEL Europe Stream: Mobile Learning (#mlearning) 18
  19. 19. Overview Practices The Web in Analysis Science Tools and ResearchThe development of Infra-an online structure 19infrastructure toconnect the tools
  20. 20. Publication Feed SystemPublication metadata has to be entered in differentlocations all the time  Institutional repository  Project reporting  Social reference management systemGoals  Entering the details only once  Web standards compliant  Can be used with existing infrastructure 20
  21. 21. Publication Feed SystemKraker, P., Fessl, A., Hoefler, P., & Lindstaedt, S. (2010). Feeding TEL: Building an Ecosystem 21Around BuRST to Convey Publication Metadata. Proceedings of the 2nd InternationalWorkshop on Research 2.0.
  22. 22. OverviewThe analysis ofdata generated Practicesby researcherson the web The Web in Analysis Science Tools and Research Infra- structure 22
  23. 23. Analysis of ScienceInformation overload is NOT a contemporaryproblem in scienceScience has been growing exponentially forthe last 400 years (Price 1961)  Number of papers (Larsen/von Ins 2010)  Number of researchers (NSF 2010)Problems  Missing overview of research fields  Missing awareness of current developments 23 Price 1961 Extended by Leydesdorff (2008)
  24. 24. Awareness of Current DevelopmentsKraker, P., Wagner, C., Jeanquartier, F., & Lindstaedt, S. (2011). On the Way to a Science 24Intelligence: Visualizing TEL Tweets for Trend Detection. Proceedings of the 6th EuropeanConference on Technology Enhanced Learning (pp. 220-232).
  25. 25. Tweet visualisations: StreamgraphHashtag: #www2012 25
  26. 26. Tweet visualisations: Weighted GraphHashtag:#arv11 26
  27. 27. Missing overview 27
  28. 28. Missing overview 28
  29. 29. Visualisation example 29
  30. 30. The usual way of doing visualisationsBasis: Citations  Co-citations as a measure of subject similarity (Small 1973) Paper 2 Never cited together Cited together 10 times Paper 1 Paper 7 Cited together 2 timesProblem: Citations take very long to appear in meaningfulquantities (~3-5 years) Visualisations actually a look into the past! 30
  31. 31. A new approachVisualisations based on the readership of publications  Assumptions: Publications that are often read together, are of a similar subject (Rowlands & Nicholas 2007, Bollen & van de Sompel 2008) Paper 2 Never read together Read together 10 times Paper 1 Paper 7 Read together 2 timesWith collaborative reference management systems suchas Mendeley, we can measure readershipReadership statistics are much earlier available thancitations 31
  32. 32. ResultsKraker, P., Körner, C., Jack, K., & Granitzer, M. (2012). Harnessing User Library Statistics for 32Research Evaluation and Knowledge Domain Visualization. Proceedings of the 21st InternationalConference Companion on World Wide Web (pp. 1017-1024). Lyon: ACM.
  33. 33. The change in Summary scientific practices and the open science movement The analysis of data generated Practices by researchers on the web The Web in Analysis Science Tools and Research The provision ofThe development of Infra- web tools foran online structure opening up theinfrastructure to research process 33connect the tools
  34. 34. www.know-center.atThank you for yourattention! gefördert durch das Kompetenzzentrenprogramm
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