Scientometrics as Big Data Science:

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1 Scientometrics as Big Data Science: On integration of data sources and the problem of different types of classification systems Henk F. Moed (Elsevier, The Netherlands) With: Marc Luwel (Hercules Foundation, Brussels) Cinzia Daraio (Univ La Sapienza, Rome) OECD Workshop, Paris, 25 March 2014

2 Short CV Henk F. Moed Years Position Staff member at Centre for Science and Technology Studies (CWTS), Leiden Univ Full Professor of Research Assessment Methodologies at Leiden University 2010 Sept 2012 As from Sept 2012 As from July 2014 Elsevier, SciVal Dept. Senior Scientific Advisor Elsevier, AGRM Dept. Head of Informetric Research Group Elsevier (2 days/week) and visiting professor at academic institutions

3 Contents 1 Sciento/Informetrics as big data science 2 General trends and important topics 3 Need for concordance of different classification systems and for consistency 4 The need for standards in scientometrics

4 Contents 1 Sciento/Informetrics as big data science 2 General trends and important topics 3 Need for concordance of different classification systems and for consistency 4 The need for standards in scientometrics

5 Journal full text data Journal articles + citations Journal usage data Books Conference Procs Acad. Library Catalogs OECD research input data Unit of assessment Sci Personnel Info Syst Patents Trade jrnls Social media Newspapers

6 Contents 1 Sciento/Informetrics as big data science 2 General trends and important topics 3 Need for concordance of different classification systems and for consistency 4 The need for standards in scientometrics

7 Trends Biblio/sciento/informetrics as big data science More (Large) datasets electronically available Combination of large datasets More interest in research assessment, metrics Multi-dimensional approaches, integral views Linking components within a system Comparing, benchmarking Emphasis on accountability, productivity, societal impact

8 Important topics in research assessment 1. Institutional performance and rankings 2. Societal and technological impact 3. National/regional scientific development 4. From data to models

9 UNESCO Report on HE in SE Asia PRE- DEVELOPMENT Low research activity without clear policy or structural funding of research. A bibliometric model for capturing the state of scientific development BUILDING- UP Collaborations with developed countries are established. National researchers enter international scientific networks. CONSOLIDATION AND EXPANSION The country develops its own scientific infrastructure. The amount of funds available for research increases. INTERNATIONALISATI ON Research institutions in the country start functioning as fully fledged partners, and increasingly take the lead in international collaborations.

10 Publications & Doctoral Enrollment The number of publications generated within a country increases in almost linear fashion with doctoral enrolment. This suggests that doctoral students play a key role in the production of a country s publication output. # Publications (Scopus) # Doctoral enrolments

11 Contents 1 Sciento/Informetrics as big data science 2 General trends and important topics 3 Need for concordance of different classification systems and for consistency (with Marc Luwel) 4 The need for standards in scientometrics

12 Need for concordance tables (Science related) Type of analysis 1 Output - Input 2 Research Teaching 3 Science Technology 4 Science Industry Concordances needed Journal (WoS, Scopus) input stats (OECD); funding data (NSF) Research Teaching Subject concordance tables Journal Patent classification Journal industrial Sector

13 OECD data on # FTE Research vs. Scopus # Authors Country OECD # FTE Res All (2007) OECD # FTE Res Gov+HE (2007) SCOPUS # Authors (2007) Ratio # authors / # FTE Res All Ratio # authors / # FTE Res Gov+HE DEU 290, , , UK 254, , ,

14 Inconsistencies in data on # FTE Res? Country OECD # FTE Res All (2007) OECD # FTE Res Gov+HE (2007) SCOPUS # Authors (2007) Ratio # authors / # FTE Res All Ratio # authors / # FTE Res Gov+HE DEU 290, , , Differences in ratios Scopus/ OECD between {ITA, NLD} and {DEU, UK} almost a factor 2 UK 254, , , ITA 93,000 56, , NLD 49,700 23,800 46,

15 Contents 1 Sciento/Informetrics as big data science 2 General trends and important topics 3 Need for concordance of different classification systems and for consistency 4 The need for standards in scientometrics

16 Standards? 1 Mapping techniques: towards a winner like the Mercator projection? (with Felix Moya) 2 Indicators: depend upon object, aspect and objective of assessment 3 Need for user-oriented state-of-the-art reports on practices and methods, their pros and cons 4 Need for concordance tables and consistency of classification systems (C. Daraio project)

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