DANS is an institute of KNAW and NWO
Data Archiving and Networked ServicesData Archiving and Networked Services
Drowning i...
Andrea Scharnhorst – “science located”
•Head of Research&Innovation at DANS and scientific coordinator of the
Computationa...
Visual analytics of science
Internet Science - EINS
Akdag Salah, A., Wyatt, S., Passi, S., & Scharnhorst, A. (2013). Mapping
EINS - An exercise in map...
Ref: Linda Reijnhoudt, Michael J. Stamper, Katy Börner, Chris Baars, and Andrea Scharnhorst (2012)
NARCIS: Network of Expe...
Impact EV
Impact of SSH projects in FP6, FP7 and beyond
Impact EV
http://www.ub.edu/sior/index.php
http://www.orcid-casrai-2015.org/ May 18-19
Impact EV
ImpactEV WP 3 team
Data source
Baseline statistics projects
https://open-data.europa.eu/en/data/dataset/cordisfp6projects https://open-
data....
Baseline statisticsHenk van den Berg
Baseline statisticsHenk van den Berg
Baseline statistics
Linda Reijnhoudt
SummaryThe main problem are not the visuals but the data!
In reports about FP’s and other funding streams on the European ...
Summary
Challenge
Summary
Datamine the 344 reports and see which projects they cover,
methods they use and results they produce.
ANALYZING THE DYNAMICS OFANALYZING THE DYNAMICS OF
INFORMATION AND KNOWLEDGEINFORMATION AND KNOWLEDGE
Browse a collection
...
Informa on Professionals/
Informa on Scien sts
Social Scien sts
Computer Scien sts
Physics/Mathema cs
Digital Humani es
In...
Nächste SlideShare
Wird geladen in …5
×

Drowning in information – the need of macroscopes for research funding

335 Aufrufe

Veröffentlicht am

Andrea Scharnhorst (2015) Drowning in information – the need of macroscopes for research funding. Presentation at the international conference: PLANNING, PREDICTION, SCENARIOS - Using Simulations and Maps - 2015 Annual EA Conference - 11–12 May 2015 Bonn

Veröffentlicht in: Bildung
  • Als Erste(r) kommentieren

  • Gehören Sie zu den Ersten, denen das gefällt!

Drowning in information – the need of macroscopes for research funding

  1. 1. DANS is an institute of KNAW and NWO Data Archiving and Networked ServicesData Archiving and Networked Services Drowning in information – the need of macroscopes for research funding Andrea Scharnhorst PLANNING, PREDICTION, SCENARIOS Using Simulations and Maps 2015 Annual EA Conference 11–12 May 2015
  2. 2. Andrea Scharnhorst – “science located” •Head of Research&Innovation 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
  3. 3. Visual analytics of science
  4. 4. 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 Visual analytics of science
  5. 5. Ref: Linda Reijnhoudt, Michael J. Stamper, Katy Börner, Chris Baars, and Andrea Scharnhorst (2012) NARCIS: Network of Experts and Knowledge Organizations in the Netherlands. Poster presented at the Third annual VIVO conference, August 22 - 24, 2012 Florida, USA, http://vivoweb.org/conference2012 Visual analytics of science
  6. 6. Impact EV Impact of SSH projects in FP6, FP7 and beyond
  7. 7. Impact EV http://www.ub.edu/sior/index.php http://www.orcid-casrai-2015.org/ May 18-19
  8. 8. Impact EV ImpactEV WP 3 team
  9. 9. Data source Baseline statistics projects https://open-data.europa.eu/en/data/dataset/cordisfp6projects https://open- data.europa.eu/en/data/dataset/cordisfp7projects websites of SSH projects Project information Contractor information Henk van den Berg
  10. 10. Baseline statisticsHenk van den Berg
  11. 11. Baseline statisticsHenk van den Berg
  12. 12. Baseline statistics Linda Reijnhoudt
  13. 13. SummaryThe main problem are not the visuals but the data! In reports about FP’s and other funding streams on the European level, we find a lot of project baseline statistics. But those are on different aggregation levels. This is why we need access to data directly and more explorations of the open data already available. (see http://ec.europa.eu/research/evaluations/pdf/archive/fp7_monitoring_reports/7th_fp7_monitoring_report.pdf#view=fit&pagemode=none as an example of a decent Bread-and-butter project analytics; see https://open-data.europa.eu/en/apps for open data and applications build on them) There are different portals into RI on European level, but they all monitor specific aspects (e.g. openaire.eu) and often come without visuals overviews. An observatory of European funding would need to start from there. Analytics (statistical, visual) is always question driven. Many projects have been funded to look into specific calls/programme and evaluate them, partly also also using inf vis. The problem is not a tailored approach to evaluation but that there is no overview of those studies. We need in an observatory two layers: - Baseline information on projects and –Information which of those projects figured in which evaluative study. Otherwise, there is a big risk of repetition.
  14. 14. Summary
  15. 15. Challenge Summary Datamine the 344 reports and see which projects they cover, methods they use and results they produce.
  16. 16. ANALYZING THE DYNAMICS OFANALYZING THE DYNAMICS OF INFORMATION AND KNOWLEDGEINFORMATION AND KNOWLEDGE 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?
  17. 17. 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 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

×