Computational implementation and formalism of FAIR data stewardship principles

Publication Date19 Apr 2020
AuthorKushal Ajaybhai Anjaria
SubjectLibrary & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Metadata,Information & knowledge management,Information & communications technology,Internet
Computational implementation and
formalism of FAIR data
stewardship principles
Kushal Ajaybhai Anjaria
Institute of Rural Management, Anand, India
Purpose The progress of life science and social science research is contingent on effective modes of data
storage, data sharing and data reproducibility. In the present digital era, data storage and data sharing play a
vital role. For productive data-centric tasks, findable, accessible, interoperable and reusable (FAIR) principles
have been developed as a standard convention. However, FAIR principles have specific challenges from
computational implementation perspectives. The purpose of this paper is to identify the challenges related to
computational implementations of FAIR principles. After identification of challenges, this paper aims to solve
the identified challenges.
Design/methodology/approach This paper deploys Petri net-based formal model and Petri net algebra to
implement and analyze FAIR principles. The proposed Petri net-based model, theorems and corollaries may
assist computer system architects in implementing and analyzing FAIR principles.
Findings To demonstrate the use of derived petri net-based theorems and corollaries, existing data
stewardship platforms FAIRDOM and Dataverse have been analyzed in this paper. Moreover, a data
stewardship model –“Datalectionhas been developed and conversed about in the present paper. Datalection
has been designed based on the petri net-based theorems and corollaries.
Originality/value This paper aims to bridge information science and life science using the formalism of
data stewardship principles. This paper not only provides new dimensions to data stewardship but also
systematically analyzes two existing data stewardship platforms FAIRDOM and Dataverse.
Keywords Translational informatics, Data stewardship, Petri net, Datasets, Open research,
Data-driven research
Paper type Research paper
1. Introduction
Ease of access and reusability of data have been considered as vital aspects of health and
biomedical research because of research integrity, reproducibility, research opportunity,
research data analysis and re-analysis (Munaf
oet al., 2017). In biomedical research, most of
the journals and funding agencies mandate data sharing up to various degrees (PLOS
Medicine Editors, 2016). The biomedical and health data sharing were lacking standard
protocols before 2014. Moreover, before 2014, there were conditional constraints, privacy
considerations and limitations for data sharing. The limitations were not only for humans,
but even more so for machines.
A workshop was held in Leiden, The Netherlands, in 2014, named Jointly Designing a
Data Fairportto make data sharing and data stewardship standards (Wilkinson et al., 2016).
The workshop was concluded with a draft formulation of a set of foundational principles for
data stewardship. The fundamental principles elucidated that data should be findable,
accessible, interoperable and reusable (FAIR). These principles were commonly known as
FAIR principles. The FAIR (findability, accessibility, interoperability and reusability)
guiding principles for data stewardship have quickly gained traction and are set to become
the next major thing in the research related to bioinformatics. The FAIR principles set
FAIR data
Conflict of interest statement: On behalf of all authors, the corresponding author states that there is no
conflict of interest
The current issue and full text archive of this journal is available on Emerald Insight at:
Received 4 September 2019
Revised 13 January 2020
Accepted 29 February 2020
Data Technologies and
Vol. 54 No. 2, 2020
pp. 193-214
© Emerald Publishing Limited
DOI 10.1108/DTA-09-2019-0164
essential pre-conditions for data access, data sharing and data reuse. The details about each
aspect of FAIR principles have been described in the Table 1:
FAIR principles are considered as a complete framework for biological and healthcare
data sharing. However, from computer science perspectives, some challenges need to be
addressed. From Table 1, essential elements from computer science and data science
perspectives can be derived, which play a vital role in FAIR implementation. These elements
are related to a common platform like the internet, metadata, machine and human
accessibility of data, authentication and authorization. If FAIR principles need to be
practically implemented, the aforementioned elements generate challenges from computer
science and data science perspectives. The next sub-section elaborates on various challenges
from computer science and digital data science perspectives related to the implementation of
FAIR principles. The primary objective of the proposed work is to solve challenges and
provide solutions for the practical implementation of FAIR principles from computer science
1.1 FAIR principles: challenges for practical implementation from computer science
Boeckhout et al. (2018) have discussed challenges related to FAIR principles in detail from
healthcare and human genetics perspectives. Gregory et al. (2018) have discussed FAIR data
sharing challenges from social informatics perspectives. In the present work, the challenges
related to FAIR implementation have been derived and discussed from computer science
perspectives. Following is the list of challenges that computer science research community is
facing during the implementation of FAIR principles:
(1) Data sharing: FAIR principles state that there should be a standardized protocol for
data sharing. The FAIRprinciple does not explicitly specify howdata sharing will be
facilitated. Duringthe practical implementation of FAIR principles, if the way of data
sharing and data sharingprotocols are not identified, thenit is complicated to achieve
accessibilityand interoperability aspects.Moreover, without data sharingprotocols, it
No Principle Explanation
1 Findability (1) Data should be assigned a globally unique identifier
(2) A plentiful amount of metadata should be used to identify the data uniquely
(3) Data and related metadata should be indexed or registered with the searchable
resource, especially on World Wide Web (WWW)
2 Accessibility (1) Once the required data are found, there should be a standardized protocol to
access the data
(2) The protocol should be universal. Humans and machines should both be able
to access data using the protocol
(3) The protocol should provide authentication and authorization mechanism, if
3 Interoperability (1) Attach metadata or other qualified references with data that describe on which
platforms data or a procedure to acquire data is working
(2) Describe formal and broadly accessible language for knowledge
4 Reusability (1) Dataand metadata should be posted on a universal platform with clear data
usage license information
(2) Data and metadata should be posted on a universal platform with relevant
community standards
Table 1.
Details about an
individual aspect of
FAIR principles
(Wilkinson et al., 2016)

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