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Data Archiving and Networked ServicesData Archiving and Networked Services
Knowledge – dynamics –
landscape - navigation – what
have interfaces to digital
libraries to do with physics?
Andrea Scharnhorst
June 11, 2013
IMT Lucca, Italy
Andrea Scharnhorst – CV
•Head of eResearch at DANS and scientific coordinator of the Computational Humanities
programme at the eHumanities group of the Royal Netherlands Academy of Arts and
Sciences (KNAW) – DANS=Data Archiving and Networked Services Institute (DANS)
Analyzing the dynamics of information and knowledge landscapes
Ilya Prigogine
Eugene Garfield
Werner Ebeling
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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
Economics
DataScience
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
Bibliometrics
Scientometrics
Informetrics
Libraries
Science policy
General
information
sciences
Derek de Solla Price
Exponential growth of science
Price, D. de Solla 1963, Little Science, Big Science, Columbia Univ. Press, New York.
For an introduction into Scientometrics see: W. Glänzel, Scientometrics as a research
field, http://www.norslis.net/2004/Bib_Module_KUL.pdf
and F. Havemann (2009) Einführung in die Bibliometrie
(http://www.wissenschaftsforschung.de/Havemann2009Bibliometrie.pdf)
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Alfred James Lotka
Scientific productivity
A.J. Lotka, The frequency distribution of scientific productivity, J. Wash. Acad. Sci., 16, 317 (1926).
Figure taken from
Piotr Fronczak, Agata Fronczak, Janusz A. Holyst (2006) Publish or perish: analysis of scientific productivity using maximum entropy principle and
fluctuation-dissipation theorem. arXiv:physics/0606190v1
Samuel C. Bradford
Law of scattering
BRADFORD, S. C. (1934) Sources of information on specific subjects. Engineering, 137, 85-86. BRADFORD, S. C. (1948) Documentation,
London, Lockwood.
Figure from Eugen Garfield, Essays of an Information Scientist, Vol:4, p.476-483, 1979-80/ Current Contents, #19, p.5-12, May 12, 1980:
Bradford’s Law and Related Statistical Patterns.
http://www.garfield.library.upenn.edu/essays/v4p476y1979-80.pdf
http://en.wikipedia.org/wiki/Bradford%27s_law
“if scientific journals are arranged in order of
decreasing productivity on a given
subject, they may be divided into a nucleus of
journals more particularly devoted to the
subject and several groups or zones
containing the same number of articles as the
nucleus when the numbers of periodicals in
the nucleus and the succeeding zones will
be as 1: n : n² …”
2284 articles in 374 journals
Core C = 40 journals (1024 articles)
Bradford’s paradox -> Delineation of fields
Communication
Text Actors
words journals references authors institutions countries…
Co-word maps
Semantic maps
(Callon, Rip,
White)
Citation environments
of journals
(Leydesdorff)
Maps of science
(Boyack, Börner, Klavans;
Leydesdorff, Rafols)
Bibliographic coupling
Citation networks
Co-citation networks
(Marshokova, Small/Griffith)
Productivity
(Lotka)
Coauthorship
(…..)
Disciplinary profiles
Performance
Impact
(…..)
International
collaboration
(…..)
What is a topic?
What is a paradigm?
What are fields and
disciplines?
What are the hot areas and
research fronts?
What are the knowledge flows?
Core and periphery
of knowledge exchange in
a globalized economy
Biographies, key player,
Individual vs group dynamics
Key players, evaluation
Meaning of a citation, deeper understanding of knwoledge flows
Sentiment of citations Small, Thelwall, Boyack…
www.research-acumen.eu
The Matthew effect for countries
Physics and scientometrics
2010
Multipartite networks
• Researcher
– Has co-authors
– Has an affiliation
– Has publications
– Has citations
– Has Age
• Researcher/Institutions
Agent-based-modelling
• Epistemic landscape –
Weisbuch/Payette – DATA
• Research practices
– Peers and journals
– Time allocation
– Careers (building up while
travelling around)
FUTURE RESEARCH
Landscape idea
Knowledge production as search in an unknown landscape
A. Scharnhorst, W. Ebeling (2005): Evolutionary Search Agents in Complex Landscapes. A New Model for the Role of Competence and
Meta-competence (EVOLINO and other simulation tools). arxiv.org/abs/physics/0511232
W. Ebeling, L. Schimansky-Geier, A. Neiman, A. Scharnhorst (2005) Stochastic dynamics of Active Agents in External Fields. Fluctuation
and Noise Letters 5(2), L185-L192
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Occupation, observation
Fitness, value
evaluation
See: 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
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18
),();(),(),( tqMxxqwtqxtqxt

Characteristics
Occupation density
function
Valuation or Evaluation-
landscape
Mutation operator
Comparison Diffusion
Mathematical apparatus
Feistel, R. Ebeling, W. (1990) Evolution of complex systems.
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Source: Places and Spaces – scimaps.org – Klavans/Boyack
Visualizing knowldge spaces and landscapes – landscapes
based on networks: Katy Börner, Chaomei Chen, & Kevin Boyack: Visualizing
Knowledge Domains. In Blaise Cronin (Ed.), Annual Review of Information Science &
Technology, Volume 37, Medford, NJ: Information Today, Inc./American Society for
Information Science and Technology, chapter 5, pp. 179-255, 2003.
Bibliometrics
Visualization
Semantic analysis
On-line: http://ella.slis.indiana.edu/%7Ekaty/paper/arist02.pdf
Paul Otlet, Mundaneum, http://www.mundaneum.be/
“Alle Kennis van de Wereld” http://www.archive.org/details/paulotlet
Courtesy of Charles van den Heuvel
Epistemic spaces and libraries
Knowledge Space Lab
Almila Akdag Sahal, Cheng Gao, Krzysztof Suchecki,
Andrea Scharnhorst
Different knowledge representations
Dynamic Phenomena
time
time
How do changes in
classification influence
relational data ?
How do changes in
relations change
the classification?
Courtesy K. Suchecki
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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
Evolution of the Wikipedia
category level –
top topical level
http://arxiv.org/abs/1203.0788
Advances in Complex Systems
! !
Complexity insights as pointers where to look !
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Lambiotte, Ausloos, 2005!, PhysRefE, Physics World
ANALYZING THE DYNAMICS OF INFORMATION
AND KNOWLEDGE LANDSCAPES
Possible use-cases
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?
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
NARCIS
www.knowescape.org TD1201
Chair: A. Scharnhorst
ViceChair: S. Fortunato
WG 1: Phenomenology
A.Slavic/S. Thurner
WG 2: Theory
P. Ahrweiler/P. Richmond
WG 3: Visual analytics
A. Akdag/R. Lambiotte
WG 4: Data curation
C. Gueret/P.Mutschke
Pointers
• LinkedIn
• Academia.edu
• ResearchGate
• Mendeley -> KnowEscape
• Arxiv.org
• Slideshare
• www.knowescape.org
• www.easy.dans.nl -> wikipedia link networks
eResearch DANS
Thank you for your attention!
For more information please contact
Andrea.scharnhorst@dans.knaw.nl
Leen Breure
Enhanced publications,
eHistory
Dirk Roorda
Queries as annotations,
CLARIN, Circulation of
knowledge
Peter Doorn
eHistory, Clarin, Dariah,
Clariah
Director of DANS
Rene van Horik
Sustainability and
permanence, multi-media
sources, APARSEN, NEDIMAH
Frank van der Most
Scientific careers and cultures
of data sharing, ACUMEN
Albert Moroño Peñuela
Semantic web, CEDAR
Linda Reijnhoudt
NARCIS, Visualizations
Katy Börner
Indiana University
Visiting fellow DANS-KNAW
Christophe Guéret
Semantic web,
complex networks
CEDAR,
PI: WikiReg
Ashkan Askpour
History, information sciences,
IISH
CEDAR
Cristian Dinu
WikiReg
Marat Charlaganov
WikiReg

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Knowledge – dynamics – landscape - navigation – what have interfaces to digital libraries to do with physics?

  • 1. DANS is an institute of KNAW and NWO Data Archiving and Networked ServicesData Archiving and Networked Services Knowledge – dynamics – landscape - navigation – what have interfaces to digital libraries to do with physics? Andrea Scharnhorst June 11, 2013 IMT Lucca, Italy
  • 2. Andrea Scharnhorst – CV •Head of eResearch at DANS and scientific coordinator of the Computational Humanities programme at the eHumanities group of the Royal Netherlands Academy of Arts and Sciences (KNAW) – DANS=Data Archiving and Networked Services Institute (DANS) Analyzing the dynamics of information and knowledge landscapes
  • 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! 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 Economics DataScience 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
  • 5.
  • 7. Derek de Solla Price Exponential growth of science Price, D. de Solla 1963, Little Science, Big Science, Columbia Univ. Press, New York. For an introduction into Scientometrics see: W. Glänzel, Scientometrics as a research field, http://www.norslis.net/2004/Bib_Module_KUL.pdf and F. Havemann (2009) Einführung in die Bibliometrie (http://www.wissenschaftsforschung.de/Havemann2009Bibliometrie.pdf)
  • 8. 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!
  • 9. Alfred James Lotka Scientific productivity A.J. Lotka, The frequency distribution of scientific productivity, J. Wash. Acad. Sci., 16, 317 (1926). Figure taken from Piotr Fronczak, Agata Fronczak, Janusz A. Holyst (2006) Publish or perish: analysis of scientific productivity using maximum entropy principle and fluctuation-dissipation theorem. arXiv:physics/0606190v1
  • 10. Samuel C. Bradford Law of scattering BRADFORD, S. C. (1934) Sources of information on specific subjects. Engineering, 137, 85-86. BRADFORD, S. C. (1948) Documentation, London, Lockwood. Figure from Eugen Garfield, Essays of an Information Scientist, Vol:4, p.476-483, 1979-80/ Current Contents, #19, p.5-12, May 12, 1980: Bradford’s Law and Related Statistical Patterns. http://www.garfield.library.upenn.edu/essays/v4p476y1979-80.pdf http://en.wikipedia.org/wiki/Bradford%27s_law “if scientific journals are arranged in order of decreasing productivity on a given subject, they may be divided into a nucleus of journals more particularly devoted to the subject and several groups or zones containing the same number of articles as the nucleus when the numbers of periodicals in the nucleus and the succeeding zones will be as 1: n : n² …” 2284 articles in 374 journals Core C = 40 journals (1024 articles) Bradford’s paradox -> Delineation of fields
  • 11. Communication Text Actors words journals references authors institutions countries… Co-word maps Semantic maps (Callon, Rip, White) Citation environments of journals (Leydesdorff) Maps of science (Boyack, Börner, Klavans; Leydesdorff, Rafols) Bibliographic coupling Citation networks Co-citation networks (Marshokova, Small/Griffith) Productivity (Lotka) Coauthorship (…..) Disciplinary profiles Performance Impact (…..) International collaboration (…..) What is a topic? What is a paradigm? What are fields and disciplines? What are the hot areas and research fronts? What are the knowledge flows? Core and periphery of knowledge exchange in a globalized economy Biographies, key player, Individual vs group dynamics Key players, evaluation Meaning of a citation, deeper understanding of knwoledge flows Sentiment of citations Small, Thelwall, Boyack…
  • 13. The Matthew effect for countries
  • 15. Multipartite networks • Researcher – Has co-authors – Has an affiliation – Has publications – Has citations – Has Age • Researcher/Institutions Agent-based-modelling • Epistemic landscape – Weisbuch/Payette – DATA • Research practices – Peers and journals – Time allocation – Careers (building up while travelling around) FUTURE RESEARCH
  • 16. Landscape idea Knowledge production as search in an unknown landscape A. Scharnhorst, W. Ebeling (2005): Evolutionary Search Agents in Complex Landscapes. A New Model for the Role of Competence and Meta-competence (EVOLINO and other simulation tools). arxiv.org/abs/physics/0511232 W. Ebeling, L. Schimansky-Geier, A. Neiman, A. Scharnhorst (2005) Stochastic dynamics of Active Agents in External Fields. Fluctuation and Noise Letters 5(2), L185-L192
  • 17. 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! Occupation, observation Fitness, value evaluation See: 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
  • 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! 18 ),();(),(),( tqMxxqwtqxtqxt  Characteristics Occupation density function Valuation or Evaluation- landscape Mutation operator Comparison Diffusion Mathematical apparatus Feistel, R. Ebeling, W. (1990) Evolution of complex systems.
  • 19. 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! Source: Places and Spaces – scimaps.org – Klavans/Boyack
  • 20. Visualizing knowldge spaces and landscapes – landscapes based on networks: Katy Börner, Chaomei Chen, & Kevin Boyack: Visualizing Knowledge Domains. In Blaise Cronin (Ed.), Annual Review of Information Science & Technology, Volume 37, Medford, NJ: Information Today, Inc./American Society for Information Science and Technology, chapter 5, pp. 179-255, 2003.
  • 22. Paul Otlet, Mundaneum, http://www.mundaneum.be/ “Alle Kennis van de Wereld” http://www.archive.org/details/paulotlet Courtesy of Charles van den Heuvel Epistemic spaces and libraries
  • 23. Knowledge Space Lab Almila Akdag Sahal, Cheng Gao, Krzysztof Suchecki, Andrea Scharnhorst Different knowledge representations
  • 24. Dynamic Phenomena time time How do changes in classification influence relational data ? How do changes in relations change the classification? Courtesy K. Suchecki
  • 25. 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 http://www.scimaps.org/flat/exhibit_info/#7
  • 26. Evolution of the Wikipedia category level – top topical level http://arxiv.org/abs/1203.0788 Advances in Complex Systems
  • 27. ! ! Complexity insights as pointers where to look !
  • 28. 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! Lambiotte, Ausloos, 2005!, PhysRefE, Physics World
  • 29. ANALYZING THE DYNAMICS OF INFORMATION AND KNOWLEDGE LANDSCAPES Possible use-cases 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?
  • 30. 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
  • 32. www.knowescape.org TD1201 Chair: A. Scharnhorst ViceChair: S. Fortunato WG 1: Phenomenology A.Slavic/S. Thurner WG 2: Theory P. Ahrweiler/P. Richmond WG 3: Visual analytics A. Akdag/R. Lambiotte WG 4: Data curation C. Gueret/P.Mutschke
  • 33. Pointers • LinkedIn • Academia.edu • ResearchGate • Mendeley -> KnowEscape • Arxiv.org • Slideshare • www.knowescape.org • www.easy.dans.nl -> wikipedia link networks
  • 34. eResearch DANS Thank you for your attention! For more information please contact Andrea.scharnhorst@dans.knaw.nl Leen Breure Enhanced publications, eHistory Dirk Roorda Queries as annotations, CLARIN, Circulation of knowledge Peter Doorn eHistory, Clarin, Dariah, Clariah Director of DANS Rene van Horik Sustainability and permanence, multi-media sources, APARSEN, NEDIMAH Frank van der Most Scientific careers and cultures of data sharing, ACUMEN Albert Moroño Peñuela Semantic web, CEDAR Linda Reijnhoudt NARCIS, Visualizations Katy Börner Indiana University Visiting fellow DANS-KNAW Christophe Guéret Semantic web, complex networks CEDAR, PI: WikiReg Ashkan Askpour History, information sciences, IISH CEDAR Cristian Dinu WikiReg Marat Charlaganov WikiReg