DANS is an institute of KNAW and NWO

Data Archiving and Networked ServicesData Archiving and Networked Services

If only I had a map!

Andrea Scharnhorst

October 27, 2014

iSchool, University of Washington

Context

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)

20012000 2010

@scharnhorstaResearchGate; Mendeley; Academics.edu; LinkedIn

www.knowescape.org

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scimaps.org – Katy Borner – Map maker: Klavans/Boyack

Research as search in aN unknown landscape

Only a metaphor?

Weisberg M, Muldoon R (2009) Epistemic landscapes and the division of cognitive labor. PhilosSci 76(2):225–252 (DOI:10.1086/644786, stable JSTOR URL: http://www.jstor.org/stable/10.1086/644786)See also Payette N (2012) Agent-based models of science. In: Scharnhorst A, Börner K, van den BesselaarP (eds) Models of science dynamics. Springer, Berlin, Ch 4, pp 127–158

Epistemic landscape – Weisberg/Muldoch

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Thinking and seeing

From theory to measurement

Cartography of science

Relations and positions

First Atlas of Science

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 http://arxiv.org/abs/1304.5753

Knowledge organization

Overview and high level features

Doorway to the Schola Moralis Philosophiae (School of Moral Philosophy) at the Bodleian Library Wikipedia

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Almila Akdag Sahal, Cheng Gao, Krzysztof Suckecki, Andrea Scharnhorst; Places and Spaces, 7th Iteration, see http://www.scimaps.org/flat/exhibit_info/#7

Structure and evolution

Understanding the emergence of knowledge organization and its meaning

1930-44

1945-60 1961-83

1915-29# types

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

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

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Knowledge maps

From sophisticated to simple – baseline statistics and overview

Book space of models of science

Gwinda, Borner,AS, 2014

Exploring an archive - EASY

www.drasticdata.nl

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.

ANALYZING THE DYNAMICS OF INFORMATION ANALYZING THE DYNAMICS OF INFORMATION AND KNOWLEDGE LANDSCAPES AND KNOWLEDGE LANDSCAPES

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)

Given the explosion of information how to navigate to find what is needed?

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 communitiesParticipating 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