A paper presentation at the International Conference on Communication, Electrical and Computer Networks (ICCECN 2020) University of Malaya, Malaysia.

1. James Oluwaseyi Hodonu-Wusu, Olayiwola Taofeek
Tokunbo, Peter Alaba, Lazarus, G. Nneka, Fadhil Mukhlif,
Shapla, Khanam, & Hellen Auma Mbotela.
2nd February 2020
BY

2. PRESENTATIONOUTLINE
1. INTRODUCTION
2. OBJECTIVES
3. LITERATURE REVIEW
4. METHODOLOGY
5. SOFTWARE USED IN ANALYSING THE
DATA
6. RESULTS & DISCUSSIONS
7. CONCLUSION
8. REFERENCES

3. INTRODUCTION •Scientometrics has been
one of the most essential
methods for the
evaluation of scientific
findings. According to
[Lolis, 2009]
•Scientometric indicators have
become important to the
academic community in order
to assess and predict the trends
of a given area of research.
•Scientometrics
has a symbiosis
relationship with
Bibliometrics.
Bibliometric analysis
refers to mixture of
several frameworks,
tools and procedures
to study and analyze
citations of scholarly
publication [Hodonu-
Wusu, et al., 2018].
Scientometrics has
been used in life
sciences [Williams, etal.,
2019], Education [20],
Preventing Sciences
[21], Library Science
[22], [23] and [24], but
none has investigated
the trends of open
science and big data
analytics as a field.
As research system is spreading,
and the scientific landscapes are
changing toward openness. Open
Science is a movement that makes
repositories available online and
freely accessible via the internet
[Foster, 2015].
Big Data on the other hand, is
the explosion of large – scale
streaming data that is
beginning to change scientific
research topology which has
the potential to impact
researchers, their funders as
well as impacts the society on
the availability of the
information within their reach.
Today, new discoveries are
trending towards research
openness and researchers need
to be aware of this fact, and key
into the vision and mission of
open science initiatives which
among others is to provide
resources in order to boost the
growth of scientific knowledge.

4. • The aim of this paper is to present the
research trends in respect to the productivity
of researchers using Web of Science
database, top authors in open science and
big data analytical study, Geographical
distribution on the topic, most productive
research institutions, top journals on open
science and big data, subject areas in the
literature, publication year, document type,
and funders agencies.
RESEARCH OBJECTIVES
LITERATURE REVIEW

5. Six Vs of Big
Variety: Different types of data
(structure and unstructured data)
Veracity: degree of trusting the
data
Velocity : Speed at which data is
generated
Data
Volume: Amount/Size of the data
Value: Usefulness of the data
collected
Variability: Use and Reused of
data
Big Data
Volume
Variety
Velocity
Varacity
Value
Variability
LITERATURE REVIEW CONT’D
Dimension
of Big Data
Analytics
Descriptive
Big Data
Analytics
Diagnostics
Big Data
Analytics
Predictive
Big Data
Analytics
Prescriptive
Big Data
Analytics
Types/Dimension of Big
Data Analytics

6. BIG DATA ANALYTICS USES CASES
Text
Analysis
e.g. Social
Media Data
Human
Resources
Analytics
Customer
Lifetime
Value:
Health
Care
Analytics
Institutional/
Funders
Analytics
Open
Government
Analytics
Lewis, 2015

7. The data was collected from the Web of Science Core
Collection Consisting of:
Methodology
• The Social Sciences Citation Index “SSCI”
• Science Citation Index Expanded “SCI EXPANDED”
• Conference Proceeding Citation Index – Science “CPCI-S”
• Conference Proceeding Citation Index – Social Science & Humanities “CPCI-SSH”
• Arts & Humanities Citation Index – “A&HCI”
• Emerging Sources Citation Index “ESCI” that comes From Standard and Acceptable articles
and quality
2009-2019Duration
• Boolean Search of Keywords ”OPEN SCIENCE,” “BIG DATA” “BIG
DATA ANALYTICS”
664 ARTICLESRETURNS OF SEARCH

8. SOFTWARE USED IN ANALYSING THE DATA

9. SEARCH RESULTS
ANALYZE RESULTS &
CITATION REPORTS
SOFTWARE USING
FOR ANALYSING
• Rstudio Software
• Gephi

10. RESULTS & DISCUSSION
• ANALYSIS BASED ON WEB OF SCIENCE CATEGORIES
• ANALYSIS BASED ON KEYWORDS
• ANALYSIS BASED ON AUTHORS
• ANALYSIS BASED ON COUNTRIES
• ANALYSIS BASED ON PUBLICATION TYPES AND YEARS
• ANALYSIS BASED ON RESEARCH AREA
• ANALYSIS BASED ON DOCUMENT TYPE
• ANALYSIS BASED ON JOURNALS
• ANALYSIS BY ORGANIZATION

11. ANALYSIS
BASED ON
WEB OF
SCIENCE
CATEGORIES

12. ANALYSIS
BASED ON
KEYWORDS

13. ANALYSIS BASED ON MOST CITED AUTHORS AND YEAR

14. ANALYSIS BASED ON COUNTRIES

15. ANALYSIS BASED ON RESEARCH AREAS/YEAR OF PUBLICATION

16. ANALYSIS BASED ON DOCUMENT TYPE
0 50 100 150 200 250 300 350 400 450
ARTICLE
PROCEEDINGS PAPER
REVIEW
BOOK CHAPTER
EDITOTIAL MATERIAL
EARLY ACCESS
DATA PAPER
BOOK CHAPTER
MEETING ABSTRACT
CORRECTION
RETRACTED PUBLICATION
RECORD COUNT % OF 664
DOCUMENT TYPE
RECORD
COUNT % OF 664
ARTICLE 375 56.732
PROCEEDINGS PAPER 192 29.047
REVIEW 76 11.498
BOOK CHAPTER 22 3.328
EDITOTIAL MATERIAL 20 3.026
EARLY ACCESS 9 1.362
DATA PAPER 4 0.605
BOOK CHAPTER 2 0.303
MEETING ABSTRACT 2 0.303
CORRECTION 1 0.155
RETRACTED PUBLICATION 1 0.155

17. ANALYSIS
BASED ON
ORGANIZATIO
N/RESEARCH
INSTITUTIONS

18. ANALYSIS
BASED ON TOP
JOURNALS

19. CONCLUSION
Top Research
funders/Institutions are
Chinese Academy,
University of Cambridge,
University of Winconsin,
University Calif Berkeley,
Indiana University etc
Top Research disciplines/areas
are in Computer Science
Information Systems, Computer
Science Theory Methods,
Engineering, Electrical and
Electronics among Others
Most of the
influential
journals are
PLoS ONE,
SCIENTOMETRI
CS, BIG DATA
& SOCIETY,
GIGASCIENCE.
It was found out that the top three
major problems of data are data
preservation (data curation), which
includes accountability for publicly
funded research, inspiration for
scientific advancements and
reanalysis of previously generated
data
Due to the increase in digital
interference which led the big
data era, scholarly
communication and data-
driven researchers have
become popular.
This research is a first attempt
to carry out a Scientometric
study about Open Science and
Big Data Analytics.

20. REFERENCES
[1] . Lewis, S. C., and Westlund, O. (2015). Big data and journalism. Epistemology, expertise, economics and ethics. Digital Journalism 3(3):
447-66. Doi:10.1080/21670811.2014.976418.
[2]. Parasie, S. (2015). Data –driven revolution? Epistemological tensions in investigative journalism in the age of “big data”. Digital
Journalism, 3(3). 364-80.
[3]. Ferreras-Rodriguez, E, M. (2012). Nuevos perfiles professionals: El periodista de datos. In Actas –IV conngreso International Latina de
Communicacion Social –IV CILCS –Universidad de La Laguna, Diciembre 2012, 1-19.
[4]. Kuipers, T., & Van der Hoeven, J. (2009). Insight into digital preservation of research output in Europe: Survey report (D3.4). Didcot, UK:
PARSE.Insight p.37. Retrieved from http://www.parse-insight.eu/downloads/PARSE-Insight_D3-SurveyReport_final_hq.pdf
[5]. Sclater, N (2017). Learning Analytics Explained. Routledge.
[6] Lolis, S. F. & et. al. (2009). Scientometric analysis of energetic ecology: Primary production of aquatic macrophytes. Maringá, 31 (4),
363-369.
[7]. Hodonu-Wusu., James Oluwaseyi., and Gift Nneka Lazarus. “Major Trends in LIS Research: A Bibliometric Analysis.” Library Philosophy
& Practice. (2018).
[8]. Mukhlif, Fadhil., James Oluwaseyi Hodonu-Wusu, Kamarul, Arinffin Bin Noordin, and Zarinah Mohd Kasirun. “Major trends in device to
device communications research: A bibliometric analysis.” In 2018 IEEE Student Conference on Research and Development (SCOReD),
pp.1-6. IEEE, 2018.
[9] Williams, Jason, J., Drew, Jennifer C, Pauley, Mark A. & etal., (2019). Barriers to integration of bioinformatics into undergraduate life
sciences education: A national study of US life sciences faculty uncover significant barriers to integrating bioinformatics into undergraduate
instruction. PLoS ONE 14(11):e0224288.
[10]. Riahi, Y., and Riahi, Sara (2018). Big data and Big Data Analytics: Concepts, Types and Technologies. International Journal of Research
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