How to use science maps to navigate large information spaces? What is the link between science maps and predictive models of science?

1. Wie können Wissenschaftskarten zur Suche in grossen Informationsräumen eingesetzt werden? How to use science maps to navigate large information spaces? What is the link between science maps and predictive models of science? Invited lecture, Fraunhofer-Institut für Naturwissenschaftlich-Technische Trendanalysen, Euskirchen, Germany December 7, 2016 Andrea Scharnhorst DANS – Coordinator Research&Innovation Group Royal Netherlands Academy of Arts and Sciences

2. Story line • Where do I come from? • Global science maps as scientific revolution • KnoweScape and knowledge maps as new area • Insights • From maps to models • Science of science and science observatories • Forecast of complex dynamics – what is possible? • Models as heuristic devices

3. WHERE DO I COME FROM

4. NARCIS - http://www.narcis.nl/

5. EASY: https://easy.dans.knaw.nl/ui/home

6. Models, metrics, policies PhD on math models of science dynamics – measurement – scientometrics (e.g., # researcher in a field; # PhD students in a field) Use of metrics in science policy – EastEurope in the mirror of bibliometrics – Matthew effect of countries (Bonitz) New practices, new metrics Web indicators for scientific, technological and innovation research – WISER 2002-5 Academic Careers Understood through Measurement and Norms - ACUMEN 2011-14 Impact-EV - Evaluation of SSH 2013-17 Visualisation of structure and evolution of science Visualising NARCIS Mapping Digital Humanities Digital Observatory for DH (Pilot) Semantic web technologies - Open Data CEDAR Dutch Historic Census New practices Research Data - FAIR

7. Andrea Scharnhorst – “science located”

8. GLOBAL SCIENCE MAPS AND MACROSCOPES AS SCIENTIFIC REVOLUTION

9. MESUR Project Clickstream map of science www.mesur.org

10. FOSTERING KNOWLEDGE MAPS AS NEW INTERDISCIPLINARY AREA

11. Informa on Professionals/ Informa on Scien sts Social Scien sts Computer Scien sts Physics/Mathema cs Digital Humani es Information professionals • Collections, Information retrieval • WG 1 Phenomenology of knowledge spaces • WG 4 Data curation & navigation Social scientists • Simulating user behavior • WG 2 Theory of knowledge spaces • WG 4 Data curation & navigation Computer scientists • Semantic web, data models • WG 1 Phenomenology of Knowledge Spaces • WG 4 Data curation &navigation Physicists, mathematicians Digital humanities scholars • Collections, interactive design • WG 3 Visual analytics – knowledge maps • WG 4 Data curation & navigation Participating communities • Structure & evolution of complex knowledge spaces, big data mining • WG 2 Theory of knowledge spaces • WG 3 Visual analytics – knowledge maps www.knowescape.org

12. Designing interfaces to collections – visual enhanced browsing All datasets in the digital archive of DANS at one glance. www.drasticdata.nl Application areas

13. TD1210: Better interfaces to large collections – visual analytics and semantic browsing OCLC, Rob Koopman, Shenghui Wang, et al. “a workflow which allows the user to browse live entities associated with 65 million articles ….by clicking through, a user traverses a large space of articles along dimensions of authors, journals, Dewey classes and words simultaneously. “ Koopman, R., Wang, S., Scharnhorst, A., & Englebienne, G. (2015). Ariadne’s Thread. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA ’15 (pp. 1833–1838). Digital Libraries. doi:10.1145/2702613.2732781 Science dynamics and Information retrieval Application areas

14. Knowledge maps - insights TD1210 Visual analytics How clean are the data? Baseline statistics about the composition of data (time, geo, attributes) Visual enhanced browsing serendipity ranking contextualisation overview Purpose Feasibility Costs Ready-made tools versus Taylor made Part of a larger development: InfoViz DH LOD ….

15. FROM MAPS TO MODELS

16. Knowledge landscapes – emergence, change, occupation, navigation Paul Otlet, Mundaneum, http://www.mundaneum.be/ “Alle Kennis van de Wereld” http://www.archive.org/details/paulotlet Searching agents in a problem space

17. TD1210: Better understanding the dynamics of science – the rise and fall of scientific fields Paris, David Chavalarias “.. introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries …sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low. Chavalarias, D., & Cointet, J.-P. (2013). Phylomemetic patterns in science evolution--the rise and fall of scientific fields. PloS One, 8(2), e54847. doi:10.1371/journal.pone.0054847 Science/knowledge dynamics

18. TD1210: Better understanding the dynamics of science – diversification and merging of fields Martin Rosvall “.. With increasingly available data, networks and clustering tools have become important methods used to comprehend instances of these large-scale structures. But blind to the difference between noise and trends in the data, these tools alone must fail when used to study change. Only if we can assign significance to the partition of single networks can we distinguish structural changes from fluctuations and assess how much confidence we should have in the changes.” Rosvall, M., & Bergstrom, C. T. (2010). Mapping change in large networks. PLoS ONE, 5(1). doi:10.1371/journal.pone.0008694 Science/knowledge dynamics

19. TD1210: Better understanding of the flaws of current methods to measure the impact of science – rankings, individual careers, interdisciplinarity ETH Zurich, Ingo Scholtes, Frank Schweitzer “authors importance in the collaboration network is indicative for the citation success of the papers in the network “ Sarigöl, E., Pfitzner, R., Scholtes, I., Garas, A., & Schweitzer, F. (2014). Predicting Scientific Success Based on Coauthorship Networks. EPJ Data Science, 3 doi:10.1140/epjds/s13688-014-0009-x Science/knowledge dynamics

20. SCIENCE OF SCIENCE DESCRIPTIVE VERSUS PREDICTIVE MODELS SCIENCE OBSERVATORY

21. From maps to monitoring

22. Local, rich, not interoperable Global, sparse, partly representative, partly curated Its all about data

23. FORECAST OF COMPLEX SOCIAL DYNAMICS – FORECAST OF SCIENCE What would we do with such an observatory? Knowledge discovery Head hunting, accountancy and advocacy, …. Role of boundary conditions and inner dynamics for scientific success

24. Scientific development based on competition between scientific fields and fieldmobility of scientists System-Umwelt-Grenze Teilsystem 1 Teilsystem i Teilsystem j 0 Di 0 Di 1 Ai 0 Aij 0, Mij Aij 1 x1 xi xj Ai 1 CijBij Physics Biology Chemistry Education Scientific schools Retirement Fieldmobility Ebeling, W., Scharnhorst, A. (1986) Selforganization Models for Field Mobility of Physicists. Czechoslovak Journal of Physics B36 , pp. 43-46. Bruckner, E., Ebeling, W., Scharnhorst, A. (1990) The Application of Evolution Models in Scientometrics. Scientometrics 18 (1-2), pp. 21-41 Models as heuristic devices

25. Self-citation network Models as heuristic devices

26. The clustered self-citation network Plasma Self-organization Complexity, active Brownian particles Models as heuristic devices

27. Hellsten Iina, Renaud Lambiotte, Andrea Scharnhorst, Marcel Ausloos. 2007 "Self-citations, co- authorships and keywords: A new approach to scientists' field mobility?", Scientometrics 72(3): 469-486 Models as heuristic devices

28. Models as heuristic devices

29. Toy model simulation Models as heuristic devices

32. Encourage field mobility, it supports interdisciplinarity + job opportunities. This increases the connectivity between fields but be aware: schematic, undirected, field mobility, e.g. regular pattern of job hopping, may act as random diffusion – destroying differentiation Support the search for the BEST (most attractive) BUT be aware: too much imitation leads to fashion waves which finally can also destroy a system Encourage scientific school formation, this enhances the attractivity of a field BUT be aware: big schools can work like a “dominant” design and blocking further development Possible science policy recommendation

33. “The more ‘credible’ predictions are, the more likely they are to not happen” (Peter Allen) Best models are not “problem solvers” they are “trouble makers” Thank you very much for your attention!

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