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
Drowning in information – the need of macroscopes for research funding
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
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
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
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.
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
Notes de l'éditeur
Science maps are nowadays an almost standard tool when it comes to quantitative studies of science
In flow monitoring – punctual monitoring – use cases – well developed – bibliometric specialists
Project oriented monitoring – ideally each project funded would use maps to display the literature it bases on, to encourage communication in interdisciplinary projects, we are far away from this
Creating macroscopes – overview maps – and this is even more rare event – for the WoS/Scopus maps of science exist – for other information spaces they have been created as explorations -> Atlas Katy
In the following, I take a current project we are engaged in, to demonstrate how visuals can be used, but actually it is all about data!
Large literature review about the different definitions of impact, and impact studies for SSH
Questionnaire as classical social science method: Impact Online questionnaire:
Response -> Telephone interviews 1220 telephone contacts, and about 72 in-depth interviews
Analysis of final project reports by close reading -> SESAM database
Baseline statistics : mixture of bibliometrics, webometrics, baseline statistics
One very interesting outcome so far is a webinterface to submit your project and gain visibility with achieved impact
ORCID as platform to further promote ImpactEV results and aspirations
One of the problems: object of analysis, partly also moving target
how many projects: FP6-FP7 SSH, 272 or 222 depending on which calls are included
When HERA, Norface, ERC and other funding streams are added we end with 439
For 134 from FP7 a bibliometric analysis for researchers was conducted
It is not so that information is not collected by the EC, it is collected at too many places, some times under restrictions, and different data sharing practices can be a real problem!
Luckily, there is also an ‘underground’ open data movement at work
It is not so that information is not collected by the EC, it is collected at too many places, some times under restrictions, and different data sharing practices can be a real problem!
Luckily, there is also an ‘underground’ open data movement at work
From the first two sources we downloaded data, cleaned them and created a database
From the open data we already get quite some information: budgets, duration, participating countries/institutions
These graphs show some of the distributions one can extract from the database, they not surprising, this kind of baseline statistics is often shown in quantitative explorations of projects.
came with a list of 238 URLs for 218 of 222 projects.
The same holds for the webometric analysis.
Explorations using SCI2tool, the increase in budget per project in the last years is interesting.
came with a list of 238 URLs for 218 of 222 projects.
So with a platform as Openaire, we are almost there.