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...
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Drowning in information – the need of macroscopes for research funding

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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

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  • 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.
  • 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

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