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.
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.
Visual analytics of science

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.
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.
Impact EV
Impact of SSH projects in FP6, FP7 and beyond

7.
Impact EV
http://www.ub.edu/sior/index.php
http://www.orcid-casrai-2015.org/ May 18-19

8.
Impact EV
ImpactEV WP 3 team

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.
Baseline statisticsHenk van den Berg

11.
Baseline statisticsHenk van den Berg

12.
Baseline statistics
Linda Reijnhoudt

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.
Summary

15.
Challenge
Summary
Datamine the 344 reports and see which projects they cover,
methods they use and results they produce.

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.
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