Subject analysis of LIS data archived in a Figshare using co-occurrence analysis
| Date | 08 April 2019 |
| DOI | https://doi.org/10.1108/OIR-12-2017-0369 |
| Pages | 256-264 |
| Published date | 08 April 2019 |
| Author | Jane Cho |
| Subject Matter | Library & information science,Information behaviour & retrieval,Collection building & management,Bibliometrics,Databases,Information & knowledge management,Information & communications technology,Internet,Records management & preservation,Document management |
Subject analysis of LIS data
archived in a Figshare using
co-occurrence analysis
Jane Cho
Department of Library and Information Science, Institute of Social Science,
Incheon National University, Incheon, Republic of Korea
Abstract
Purpose –Based on the data from Figshare repositories, the purpose of this paper is to analyze which
research data are actively produced and shared in the interdisciplinary field of library and information
science (LIS).
Design/methodology/approach –Co-occurrence analysis was performed on keywords assigned to
research data in the field of LIS, which were archived in the Figshare repository. By analyzing the keyword
network using the pathfinder algorithm, the study identifies key areas where data production is actively
conducted in LIS, and examines how these results differ from the conventional intellectual structure of LIS
based on co-citation or bibliographic coupling analysis.
Findings –Four majordomains –Open Access, ScholarlyCommunication, DataScience and Informatics–and
15 sub-domains were created. The keywords with the highest global influence appeared as follows, in
descending order:“open access,”“scholarly communication”and “altmetrics.”
Originality/value –This is the first study to understand the key areas that actively produce and utilize data
in the LIS field.
Keywords Figshare, LIS, Research data, Pathfinder network
Paper type Research paper
1. Background and purpose
Genome big dataanalysis is a new technology paradigmused to analyze and predict the cause
of diseases, and to help develop customized medicines. Data analysis in the field of
biotechnology is being applied as a core technologyleading the Fourth Industrial Revolution
in medicine and healthcare. In academic research, the opening of research data not only
improves the transparency of research through the reproduction and verification of research
results, but also promotes scientific development through sharing and reusing.
The articles or research reports analyzing the data were submitted and published as final
research results, but the research data itself have not been submitted and published, but
have been retained by researchers. However, diverse policies to open data are being
announced in the Open Science paradigm, with the aim of increasing the verifiability and
reusability of research results. The OECD Principles and Guidelines for Access to Research
Data from Public Funding stipulate that publicly funded research data are public good and
should be publicly available (Organization for Economic Co-operation and Development,
2007). They also encourage people to submit a plan to expand public access to research data
from government agencies whose development spending is greater than $100m per year
(The Office of Science and Technology Policy, 2013). In total, 14 US federal agencies,
including Department of Health and Human Services, Education and Commerce, require
researchers to submit a data management plan (DMP) for research data (STEPI, 2017). In
Japan, the Japan Institute of Science and Technology announced an open science policy
recommending the establishment of a DMP on research data produced by public research in
April 2017 ( Japan Science and Technology Agency, 2017).
These tides have inspired the creation of the Research Data Alliance, a community
organized around the aim of developing data sharing technologies and policy. Another
organization is DataCite (www.datacite.org/whycitedata), which aims to create a
Online Information Review
Vol. 43 No. 2, 2019
pp. 256-264
© Emerald PublishingLimited
1468-4527
DOI 10.1108/OIR-12-2017-0369
Received 27 December 2017
Revised 18 May 2018
17 July 2018
Accepted 11 August 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1468-4527.htm
256
OIR
43,2
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