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If only I had a map!


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The title of this talk borrows from the title of a chapter in a recently published book by Richard Smiraglia, Cultural Synergy in Information Institutions (7.9: What if There Were a Map?). The use of visualizations in the exploration of bodies of knowledge and for the organization of knowledge has a long history. Think in terms of the tree(s) of knowledge and large-scale maps of science (see Atlas of Science by Katy Börner). This talk introduces the work of a European network of research collaboration (a so-called COST Action) KnoweScape. KnoweScape explores how knowledge maps (from simple to sophisticated) can be made and applied to better understand, navigate, and curate collections held by libraries and archives. In terms of general research methodology, this talk is also a plea for creating overview prior to in-debt analysis and to seek for relative stable reference frameworks against which rapid changes of our knowledge can be interrogated. Looking at results produced by this community of scholars so far, it will become clear why the making of knowledge maps requires the collaboration of physicists, computer scientists, sociologists of knowledge, digital humanities scholars, and information scientists and professionals.

Published in: Education

If only I had a map!

  1. 1. Data AArrcchhiivviinngg aanndd NNeettwwoorrkkeedd SSeerrvviicceess If only I had a map! Andrea Scharnhorst October 27, 2014 iSchool, University of Washington DANS is an institute of KNAW and NWO
  2. 2. Context
  3. 3. Andrea Scharnhorst – “science located” •Head of Research&Innovation at DANS: Data Archiving and Networked Services Institute •Scientific coordinator of the Computational Humanities programme at the eHumanities group of the Royal Netherlands Academy of Arts and Sciences (KNAW) 22000001 2010 @scharnhorsta ResearchGate; Mendeley;; LinkedIn
  4. 4. Only use this slide to present a screenshot of an application. As no style is applied, the screenshot can take up the whole slide. For all other information please use the slide with preset style! – Katy Borner – Map maker: Klavans/Boyack
  5. 5. Research as search in aN unknown landscape Only a metaphor?
  6. 6. Epistemic landscape – Weisberg/Muldoch Weisberg M, Muldoon R (2009) Epistemic landscapes and the division of cognitive labor. Philos Sci 76(2):225–252 (DOI:10.1086/644786, stable JSTOR URL: 1086/644786) See also Payette N (2012) Agent-based models of science. In: Scharnhorst A, Börner K, van den Besselaar P (eds) Models of science dynamics. Springer, Berlin, Ch 4, pp 127–158
  7. 7. Only use this slide to present a screenshot of an application. As no style is applied, the screenshot can take up the whole slide. For all other information please use the slide with preset style!
  8. 8. Thinking and seeing From theory to measurement
  9. 9. Cartography of science Relations and positions
  10. 10. First Atlas of Science
  11. 11. Internet Science - EINS Akdag Salah, A., Wyatt, S., Passi, S., & Scharnhorst, A. (2013). Mapping EINS - An exercise in mapping the Network of Excellence in Internet Science. In Conference Proceedings of the First International Conference on Internet Science, April 9-11, 2013 Brussels (pp. 75–78). Brussels: The FP7 European Network of Excellence in Internet Science. Retrieved from
  12. 12. Knowledge organization Overview and high level features
  13. 13. Doorway to the Schola Moralis Philosophiae (School of Moral Philosophy) at the Bodleian Library Wikipedia
  14. 14. Only use this slide to present a screenshot of an application. As no style is applied, the screenshot can take up the whole slide. For all other information please use the slide with preset style! Almila Akdag Sahal, Cheng Gao, Krzysztof Suckecki, Andrea Scharnhorst; Places and Spaces, 7th Iteration, see
  15. 15. Structure and evolution Understanding the emergence of knowledge organization and its meaning
  16. 16. 1930-44 # types 1915-29 1945-60 1961-83 Scharnhorst, A. (2001) Constructing Knowledge Landscapes within the Framework of Geometrically Oriented Evolutionary Theories. In: Integrative Systems Approaches to Natural and Social Sciences. Ed. by M. Matthies, H. Malchow, J. Kriz. Springer, Berlin, pp. 505-515
  17. 17. Suchecki, Krzysztof, Almila Akdag Salah, Cheng Gao, and Andrea Scharnhorst. 2012. “Evolution of Wikipedia’s Category Structure.” Advances in Complex Systems 15 (supp01): 1250068–1. doi:10.1142/S0219525912500683
  18. 18. Only use this slide to present a screenshot of an application. As no style is applied, the screenshot can take up the whole slide. For all other information please use the slide with preset style!
  19. 19. Knowledge maps From sophisticated to simple – baseline statistics and overview
  20. 20. Book space of models of science Evolution And Struc... Positions And Roles Growing Artificial ... Social Sciences Models Of Science D... Game Theory Evolution Of Social... A Course In Game Th... The The New Production ... New Invisible C... Patterns Of Scienti... About Science Invisible Colleges:... The Professional Qu... Science -- Social A... Research Science Principles Of Econo... Bibliometrics Technological Innov... Economics Constructing Knowle... Dynamical Processes... Social Sciences -- ... Agent-based Modelli... An Inquiry Into The... Analysing Scientifi... Analysis Of Cross-d... Approximate Dynamic... Mathematics Artificial Science:... Behavioral Game The... Bibliometrics And C... Evolution Bewertungen In Der ... Re-thinking Science... Science -- Philosophy Scientific Cognitio... Computational Philo... Philosophy System Theory Creativity, Innovat... Innovation Cultural Boundaries... Die Geschichte Des ... Mathematical Models Diffusion And Ecolo... Diffusion Of Innova... Dynamical Modeling ... Einfuhrung In Die B... Einfuhrung In Die S... Elements Of Mathema... Internet Evolution And The T... Evolution Of Moral ... Social Networks Evolutionare Spielt... Evolutionary Dynami... Evolutionary Episte... Evolutionary Game T... Evolutionary Models... Exploratory Social ... Financial Networks:... Generative Social S... Handbook Of Stochas... How Nature Works: T... Internationalizatio... Sociology Knowledge Diffusion... Linked Data: Evolvi... Linked: The New Sci... Little Science, Big... Mathematical Biology Mathematical Models... Mathematical Models... Models For Innovati... Models For Longitud... Network Analysis In... Network Science On Evolution Opening Pandora's B... Patents, Citations ... Physik Der Evolutio... Population Games An... Power Laws In The I... Prometheus Bound. S... Quantity And Qualit... Real Science. What ... Scale-free Networks... Science And Selecti... Science As A Proces... Science In Society Science In Society.... Science Since Babylon Scientific Communic... Selbstorganisation ... Selforganization: P... Simulating Innovati... Simulation For The ... Social Network Anal... Social Network Anal... Social Stratificati... Social Structures: ... Social Systems Stability And Compl... The Advancement Of ... The Citation Proces... The Complexity Of C... The Fokker-planck E... The Intellectual An... The Knowledge-based... The Knowledge Creat... The Laws Of The Web The Logic Of Scient... The Scientific Comm... The Social Function... The Sociology Of Sc... The Statistical Eva... The Structure And D... The Structure Of Sc... The Theory Of Games... The Theory Of The I... The Web Of Knowledg... Theories Of Communi... Theory Of Self-repr... Toward A Metric Of ... Weaving The Web: Th... Znanstveno Raziskov... Gwinda, Borner, AS, 2014
  21. 21. Exploring an archive - EASY
  22. 22. Overview and exploration Hoptrees: Branching History Navigation for Hierarchies. Michael Brooks, Jevin D. West, Cecilia R. Aragon, Carl T. Bergstrom. INTERACT 2013. Cape Town, South Africa.
  23. 23. Given the explosion of information how to navigate to find what is needed? Browse a collection or a database Map size, structure, composition and evolution of the collection Locate your search on such an interactive knowledge map • Domain overview for students, interdisciplinary teams, lay experts and funding agencies • Tools for scholars of history and philosophy of science and bibliometrics • Overview of BigData collections (incl. social media) ANALYZING TTHHEE DDYYNNAAMMIICCSS OOFF IINNFFOORRMMAATTIIOONN AANNDD KKNNOOWWLLEEDDGGEE LLAANNDDSSCCAAPPEESS
  24. 24. Information professionals •Collections, Information retrieval •WG 1 Phenomenology of knowledge spaces • WG 4 Data curation & navigation Informa on Professionals/ Informa on Scien sts Social Scien sts Social scientists •Simulating Computer user behavior Scien sts •WG 2 Theory Physics/of Mathema knowledge cs spaces •WG 4 Data Digital curation Humani es & navigation Digital humanities scholars •Collections, interactive design •WG 3 Visual analytics – knowledge maps •WG 4 Data curation & navigation Computer scientists •Semantic web, data models •WG 1 Phenomenology of Knowledge Spaces •WG 4 Data curation &navigation Physicists, mathematicians • Structure & evolution of complex knowledge spaces, big data mining • WG 2 Theory of knowledge spaces • WG 3 Visual analytics – PPaarrttiicciippaattiinngg ccoommmmuunniittiieess knowledge maps