The document discusses how big data and data analytics can be leveraged in research management. It provides examples of how tools like SciVal and the Elsevier Fingerprint Engine use massive amounts of research data from Scopus to generate insights like identifying areas of research strength, benchmarking institutions, exploring potential collaborations, and identifying overlapping research areas between countries that could indicate new partnership opportunities. These types of analyses can highlight properties of research systems that could inform strategies at institutional, national, or regional levels.

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Alberto Zigoni
Customer Consultant
Research Solutions Sales
Big Data and the pursuit of
African “indigenuity”
10 July, 2014

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Technology, Research and GDP are linked
Source: World Bank (World Development Indicators) and SciVal

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Data generation is growing exponentially
• “Big Data, for better or worse:
90% of world's data generated
over last two years”
ScienceDaily, May 22, 2013
• “From 2005 to 2020, the digital
universe will grow by a factor of
300”
IDC's Digital Universe Study, sponsored by
EMC, December 2012
• “23% of the information in the
digital universe […] would be
useful for Big Data if it were
tagged and analyzed”
- 3% tagged
- 0.5% analyzed
Ibid.

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Leveraging Big Data in Research Management
Identify areas of research
strength
Benchmark institutions and
countries
Explore collaborations
Identify potential partners

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SciVal: powerful data analytics
4
• Scopus data
• 1996 onwards
• Weekly update
Query around 75 trillion
metric values
Using advanced data analytics super-computer technology, SciVal allows
you to instantly process an enormous amount of data to generate powerful
data visualizations on-demand, in seconds.
hpccsystems.com/

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Elsevier Fingerprint Engine: advanced text mining

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Modeling a research area: ICT4D (Information &
Communication Technology for Development)

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Semantic Map – Earth Sciences
Data source: Elsevier Fingerprint Engine, Earth Sciences Thesaurus

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Semantic Map - Medicine
Data source: Elsevier Fingerprint Engine, Medicine Thesaurus

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SciVal: Performance overview

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SciVal: Benchmark contributing institutions in Africa
The size of the bubble represents
the share of academic / corporate
collaborations

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SciVal: Potential institutional collaborations

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Collaboration between SADC countries
Data Source: Elsevier Fingerprint Engine
The additional links in the semantic network represent potential new collaborations
based on overlapping areas of research.
Co-authorship network Semantic network

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Summary
Scientific Research is a major contributor of relevant data in the “digital
universe”
Big Data can be effectively used for Research Management purposes
Key Enabling Technologies:
• High Performance Computing infrastructure
• Text Mining software to process unstructured data
Analyses on Big data can highlight “emergent properties” of a research system
that can be considered to inform research strategies at institutional, country or
regional level.

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www.elsevier.com/research-intelligencewww.elsevier.com/research-intelligence
Thanks!
Alberto Zigoni
Customer Consultant
Research Solutions Sales
a.zigoni@elsevier.com