how to use science maps to navigate large information spaces? what is the link between science maps...
TRANSCRIPT
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 ScharnhorstDANS – Coordinator Research&Innovation GroupRoyal Netherlands Academy of Arts and Sciences
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
WHERE DO I COME FROM
EASY: https://easy.dans.knaw.nl/ui/home
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 metricsWeb indicators for scientific, technological and innovation research – WISER 2002-5Academic Careers Understood through Measurement and Norms - ACUMEN 2011-14Impact-EV - Evaluation of SSH 2013-17
Visualisation of structure and evolution of scienceVisualising NARCISMapping Digital HumanitiesDigital Observatory for DH (Pilot)
Semantic web technologies - Open DataCEDAR Dutch Historic Census
New practicesResearch Data - FAIR
Andrea Scharnhorst – “science located”
GLOBAL SCIENCE MAPS AND MACROSCOPES AS SCIENTIFIC REVOLUTION
FOSTERING KNOWLEDGE MAPS AS NEW INTERDISCIPLINARY AREA
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 &
navigationComputer 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 mapswww.knowescape.org
Designing interfaces to collections – visual enhanced browsing
All datasets in the digital archive of DANS at one glance.
www.drasticdata.nl
Application areas
TD1210: Better interfaces to large collections – visual analytics and semantic browsingOCLC, 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
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
PurposeFeasibilityCosts
Ready-made tools versusTaylor made
Part of a larger development:InfoVizDHLOD….
FROM MAPS TO MODELS
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
TD1210: Better understanding the dynamics of science – the rise and fall of scientific fieldsParis, 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
TD1210: Better understanding the dynamics of science – diversification and merging of fieldsMartin 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
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
SCIENCE OF SCIENCEDESCRIPTIVE VERSUS PREDICTIVE MODELSSCIENCE OBSERVATORY
From maps to monitoring
Local, rich, not interoperable
Global, sparse, partly representative, partly curated Its all about data
FORECAST OF COMPLEX SOCIAL DYNAMICS – FORECAST OF SCIENCE
What would we do with such an observatory? Knowledge discoveryHead hunting, accountancy and advocacy, ….Role of boundary conditions and inner dynamics for scientific success
Scientific development based on competition between scientific fields and fieldmobility of scientists
System-Umwelt-Grenze
Teilsystem 1 Teilsystem i
Teilsystem j0
Di0
Di1
Ai0
Aij0, Mij
Aij1
x1 xi
xj
Ai1
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
Self-citation networkModels as heuristic devices
The clustered self-citation network
Plasma
Self-organization
Complexity, active Brownian particles
Models as heuristic devices
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
Models as heuristic devices
Toy model simulation Models as heuristic devices
Models as heuristic devices
Models as heuristic devices
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 fashionwaves 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
“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!