Twinning data science with information science in schools of library and information science

Pages1243-1257
Published date08 October 2018
Date08 October 2018
DOIhttps://doi.org/10.1108/JD-02-2018-0036
AuthorLin Wang
Subject MatterLibrary & information science,Records management & preservation,Document management,Classification & cataloguing,Information behaviour & retrieval,Collection building & management,Scholarly communications/publishing,Information & knowledge management,Information management & governance,Information management,Information & communications technology,Internet
Twinning data science with
information science in schools of
library and information science
Lin Wang
Department of Management, Tianjin Normal University, Tianjin, China
Abstract
Purpose As an emerging discipline, data science represents a vital new current of school of library and
information science (LIS) education. However, it remains unclear how it relates to information science within
LIS schools. The purpose of this paper is to clarify this issue.
Design/methodology/approach Mission statement and nature of both data science and information
science are analyzed by reviewing existing work in the two disciplines and drawing DIKW hierarchy. It looks
at the ways in which information science theories bring new insights and shed new light on fundamentals of
data science.
Findings Data science and information science are twin disciplines by nature. The mission, task and nature
of data science are consistent with those of information science. They greatly overlap and share similar
concerns. Furthermore, they can complement each other. LIS school should integrate both sciences and
develop organizational ambidexterity. Information science can make unique contributions to data science
research, including conception of data, data quality control, data librarianship and theory dualism. Document
theory, as a promising direction of unified information science, should be introduced to data science to solve
the disciplinary divide.
Originality/value The results of this paper may contribute to the integration of data science and
information science within LIS schools and iSchools. It has particular value for LIS school development
and reform in the age of big data.
Keywords Information science, Documents, Documentation, Data science, Disciplinary relationship,
LIS school
Paper type Conceptual paper
1. Introduction
Accompaniedwith big data and its applications, data sciencehas emerged as a new discipline
in recent years. Schools of library and information science (LIS) in general, and iSchools in
particular (Zuo et al., 2017), in the USA have seen fast growth of data science and attempt to
absorb this highlyrelevant discipline. Some LIS schoolsbegin to launch masters programs of
data science or add more courses of data science in the current curriculum development. By
doing these, they tend to address the problem of a talent shortage in data science
professionals,which is estimated at as many as 140,000190,000 people withdeep knowledge
of data analytics in the USA in 2018 (Manyika et al., 2011). Department of Information and
Library Science at Indiana University in Bloomington and School of Information Sciences at
University of Illinois at Urbana Champaign have offered a data science specialization within
Master of Library Science and Master of Information Science degrees. Information school
in University of Washington includes courses such as Introduction to Data Science,
Programming for Data Science and Visualization and Data Curation in its course catalogs.
School of Information studies in Syracuse University also offers a graduate-level program of
data science. All of these indicate that data science has become a new growth point of LIS
school education.LIS schools play a leading role of promoting data science education across
many disciplines (Song and Zhu, 2017). However, big data does not mean a big potential for
LIS school automatically. Only if we theoretically clarify the relationship and overlaps
between data science and information science, can data science become an integral part of
information sciences and produce the most effective education in LIS schools.
Journal of Documentation
Vol. 74 No. 6, 2018
pp. 1243-1257
© Emerald PublishingLimited
0022-0418
DOI 10.1108/JD-02-2018-0036
Received 28 February 2018
Revised 29 May 2018
8 June 2018
Accepted 21 June 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0022-0418.htm
1243
Twinning data
science with
information
science

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