On FAIR Data Principles of Institutional Data and Information ofUniversities
MORI Masao (1), OISHI Tetsuya (2), TAKATA Eiichi (3), OGASHIWA Kahori (4),SHIRATORI Naruhiko (5), TAJIRI Shintaro (6)Tokyo Institute of Technology, Japan (1)Tokyo Institute of Technology, Japan (2)Kobe University, Japan (3)Teikyo University, Japan (4)Kaetsu University, Japan (5)Yokohama College of Commerce, Japan (6)Abstract
In scientific research, it costs a lot to reproduce experiments succeeded in the past. For efficiency ofresearch, scientific researchers come to reuse research data of the past research. To do so, it isnecessary not only to archive research data, but research data should be arranged confirming to FAIR
data principles. FAIR is acronym of “findability”, “accessibility”, “interoperability” and “re-usability”. To
realize FAIR data principle, fulfillment of metadata and persistent identifier on data is significant. On
the other hands, institutional data on education and institutional management are coming to berecognized significant for university management to secure fundamental information of universities. In
that context the FAIR data principle is effective to facilitate collecting institutional data as well.
However, generally speaking, as the technical situations of institutional information are different fromuniversity to university, it is difficult to acquire a general resolution. We study the problem to makeinstitutional data and information to be abstract using ontology engineering, and consider the design ofinstitutional data and information that meets FAIR data principles.
Keywords: FAIR data principle, open science, institutional research, ontology;

1. Introduction
FAIR Data Principle (Fig1) has
been proposed by FORCE11, thecommunity for research academics,
librarian and research funders,aiming at good data management
for scientific research data [1].
From 2011, FORCE11 started todiscuss and output the guide line in2016. EU adopted FAIR Data
Principle for Open Data Policy
Pilot.
“FAIR” is an acronym for four
attributes of scientific research
data; to be Findable, to beAccessible, to be Interoperable andto be Re-usable. In order to realize
the principles, research data need
to be indexed by PID (persistentidentifier), to be enriched with metadata, to be deployed on database
Fig. 1 FAIR Data Principles
system, and to be on networks.
Institutional Research is aiming at supporting decision making and planning for universities andresearch institutions by analyzing data analysis and supplying strategic information. The data andinformation are required to be comprehensive, exhausted, period-continuous and indexed as possible.
As FAIR Data Principles meet these requirements, FAIRness is necessary principles on institutional
data and information for institutional research.
This study gives a method to describe metadata of institutional information and data using ontology ininformation science.

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2. Ontology in Information ScienceA “Domain knowledge” is a system of knowledge containing, for instance, within computer software orbusiness procedure. Ontology in information science is a technique to describe formal representationof concepts and relationships between those concepts in domain knowledge. For instance, CommonEuropean Research Information Format (CERIF) has defined an ontology in the form of diagram like(Fig.2), but normally ontology is described in XML format.

Fig. 2 Ontology of CERIF 1.3 (Quoted from [3])In ontology engineering, concepts in domain knowledge are called ‘Class’. Class has rank, which iscalled ‘Relation’. ‘Is-a relation’ expresses relation between upper class and lower class. In some case,a set of classes is described as a class, then we have ‘part-of relation’ which means relationshipbetween set-class of classes and its element-class. Attribute of class is expressed ‘attribute-ofrelation’.
If the relationship between the classes is kept to the minimum necessary, the designer may arbitrarilyadd. Generally, it is said that there is no unique way to create an ontology for any knowledge system,
and from various viewpoints utilizing the ontology to be constructed, it is only necessary to make itappropriate system according to necessity [2]. Therefore, the ontology of university information shown
here is one possibility, not a suggestion as the only solution.
For example, in the ontology of CERIF in Fig. 2, theclass cerif:Publication (publication) is a upper classof cerif:ResultEntity (is-a relation), and arrows are
drawn from the lower class to the upper class.
Furthermore, cerif:ResultEntity is one of the items
in cerif:Classification (classification of researchinformation) (is-class fied-by relationship) (see Fig.
3). The last is-classified-by relationship is anadditionally introduced class relation. This is an
example in which relationship can be flexibly
introduced in ontology engineering. This ontology
represents the following description:

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Fig. 3 Part of ontology of CERIF

Research results consist of papers, publications, products and patents. The research outcome is one
item of research information, and information on basic object (BaseEntity) and infrastructure(InfrastructureEntity) are also included as items of research information.
We can see that ontology engineering is used to expresses concepts and relations efficiently in thedomain knowledge of CERIF.

3. Construction of The Ontology for Institutional Data : example
In this section, as an example of anontology, we prototype an ontology ofinformation on education and its outcome.
The following six items are listed aspossible classes.
1.
Student
2.
Faculty3.
Department
4.
Lesson5.
Curriculum
6.
GradeLesson and Curriculum are clearly an is-arelationship. Looking at the ontology ofCERIF, engagement of Organization and
Person are comprehended by introducing
an upper class called BaseEntity.
Following this, it is conceivable that forFig. 4 Example of ontology on institutional information
Student, Faculty and Department wecan construct unification-class by introducing upper classes like PersonnelEntity. In summary, we canpropose the ontology as shown in Fig. 4. Solid arrows indicate. The relationship between the classes
mentioned above is shown, and the broken line shows what is undefined due to the relationship of theclass which needs to be considered.

4. Conclusion
In this paper, in order to apply FAIR Data Principles to institutional data and information, it isnecessary to describe those metadata. The method of ontology engineering is helpful to understandthe structure of metadata. As an advanced example, we introduced CERIF ontology. Based on theontology structure used there, we examined the method to be carried out in this research in the future.

References[1] FORCE11, “The fair data principles”, https://www.force11.org/group/fairgroup/fairprinciples, 2016[2] Natalya F. Noy and Deborah L. McGuinness, “Ontology Development 101: A Guide to CreatingYour First Ontology”, Stanford Knowledge Systems Laboratory Technical report KSL-01-05, 2001
[3] Iván Ruiz Rube, Keith G Jeffery, Jan Dvorak, Brigitte Joerg, Geert van Grootel, Miguel ÁngelSicilia, “CERIF Ontology 1.3”, 2013.

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