Putting FAIR Principles in the Context of Research Information:

FAIRness for CRIS and CRIS for FAIRness

Otmane Azeroual

1 a

, Joachim Sch

opfel

2 b

, Janne P

onen

3 c

and Anastasija Nikiforova

4,5 d

German Centre for Higher Education Research and Science Studies (DZHW), Sch

utzenstraße 6A, 10117 Berlin, Germany

GERiiCO Laboratory, University of Lille, 59653 Villeneuve-d’Ascq, France

Federation of Finnish Learned Societies, Snellmaninkatu 13, 00170 Helsinki, Finland

Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia

European Open Science Cloud (EOSC) Task Force “FAIR metrics and data quality”, 1050 Brussels, Belgium

ﬁ

Keywords:

CRIS, FAIR, Findability, Data Management, Knowledge, Information Management, Interoperability,

Research Data Management, Research Information System, Open Science.

Abstract:

Digitization in the research domain refers to the increasing integration and analysis of research information

in the process of research data management. However, it is not clear whether it is used and, more impor-

tantly, whether the data are of sufﬁcient quality, and value and knowledge could be extracted from them. FAIR

principles (Findability, Accessibility, Interoperability, Reusability) represent a promising asset to achieve this.

Since their publication, they have rapidly proliferated and have become part of (inter-)national research fund-

ing programs. A special feature of the FAIR principles is the emphasis on the legibility, readability, and un-

derstandability of data. At the same time, they pose a prerequisite for data for their reliability, trustworthiness,

and quality. In this sense, the importance of applying FAIR principles to research information and respec-

tive systems such as Current Research Information Systems (CRIS), which is an underrepresented subject for

research, is the subject of the paper. Supporting the call for the need for a ”one-stop-shop and register-once-

use-many approach”, we argue that CRIS is a key component of the research infrastructure landscape, directly

targeted and enabled by operational application and the promotion of FAIR principles. We hypothesize that

the improvement of FAIRness is a bidirectional process, where CRIS promotes FAIRness of data and infras-

tructures, and FAIR principles push further improvements to the underlying CRIS.

1 INTRODUCTION

Today, more and more data and information - both

produced, collected, and available from the past -

stored for decades in paper form, are being digitized,

which is also the case for the research domain. How-

ever, although digitization refers to making data avail-

able in an electronic and machine-readable format for

further use, making it signiﬁcantly more efﬁcient, it

is not clear whether the data are of sufﬁcient quality

for further use, and they can be transformed into value

and knowledge. In other words, digitization does not

necessarily involve data quality management, while

data require quality management as they are often af-

https://orcid.org/0000-0002-5225-389X

https://orcid.org/0000-0002-4000-807X

https://orcid.org/0000-0003-1649-0879

https://orcid.org/0000-0002-0532-3488

fected by data quality issues of various nature ((Fer-

raris et al., 2018), (Ivanovi

c et al., 2019), (Corte-Real

et al., 2020), (Nikiforova, 2020), (Azeroual et al.,

2022)). Here, FAIR principles become a promising

asset.

The FAIR principles were originally developed as

guidelines or recommendations for the effective and

efﬁcient management of research data and steward-

ship as part of a new open science policy framework,

with a “speciﬁc emphasis on enhancing the ability of

machines to automatically ﬁnd and use the data” in

data repositories (Wilkinson et al., 2016). Since then,

FAIR principles have become central element in the

debate and implementation of open science policies,

and they are increasingly being applied to other “dig-

ital objects” (Wittenburg, 2019) of different levels

such as institutional repositories and large infrastruc-

tures such as European Open Science Cloud (EOSC)

Azeroual, O., Schöpfel, J., Pölönen, J. and Nikiforova, A.

Putting FAIR Principles in the Context of Research Information: FAIRness for CRIS and CRIS for FAIRness.

DOI: 10.5220/0011548700003335

In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 3: KMIS, pages 63-71

ISBN: 978-989-758-614-9; ISSN: 2184-3228

and the German NFDI (Nationale Forschungsdatenin-

frastruktur – the mandated organisation for Germany

in the EOSC), but also to metadata, identiﬁers, cata-

logs, software, or scientiﬁc practice. The FAIR prin-

ciples have been applied in the handling of electronic

theses and dissertations (Ivanovi

c et al., 2019) and as

criteria for assessing technological behaviors (Mor-

nati, 2019).

What is more, an investigation conducted by PwC

EU Services in 2018 (European Commission and In-

novation, 2019) showed that the annual cost of not

having FAIR data to a minimum of C10.2 billion per

year and 16.9 billion in lost innovation opportunities

(Masuzzo, 2022), where the actual costs are likely to

be signiﬁcantly higher due to unquantiﬁable elements

such as the value of improved research quality and

other indirect positive spill-over effects of FAIR re-

search data. They found that the impact on innova-

tion, would account for over 60% of the likely cost

of not having FAIR research data, while the mini-

mum true cost of not having FAIR research data, en-

compassing indicators such as – “time spent”, “li-

cense costs”, “research duplication”, “cost of storage”

and “research retraction” – accounts for the remain-

ing 40%. These indicators, however, represent three

areas applicable to all sectors (i.e. academic, pri-

vate, public, non-proﬁt) and can be described as (1)

impact on research activities, (2) impact on collabo-

ration, and (3) impact on innovation. Therefore the

topic of FAIRness becomes increasingly important.

This can also be seen as one of the reasons, why

in the European Commission Open Science policy,

FAIR and open data sharing is one of the eight pil-

lars of Open Science (Commission, 2021). While

in the emerging EOSC, which is represented by one

of the authors of this study, the FAIRsFAIR project

addresses “the development and concrete realization

of an overall knowledge infrastructure on academic

quality data management, procedures, standards, met-

rics and related matters, based on the FAIR princi-

ples”, as a kind of general reference / guide to best

practices in Higher Education and Research (FAIRs-

FAIR, 2022). As outlined in the UNESCO Recom-

mendation on Open Science, FAIRness has become

an essential feature of what has been called “open

science culture” over the past couple of years. Pro-

moting a culture of open science requires, for exam-

ple, the development of rewards and incentives that

“give value to all relevant research activities and sci-

entiﬁc outputs including high-quality FAIR data and

metadata” (UNESCO, 2021). At the moment, how-

ever, EOSC have begun their own research to de-

ﬁne and introduce guidelines to application of FAIR

principles to digital objects not necessarily limited to

the research domain, thereby expanding the scope to

the entire digital environment and all data and data-

based objects (also referred to as ”digital objects”),

including research objects such as scientiﬁc articles

and software, available on the Internet, while the main

focus remains on the research domain. In this study,

however, we refer to the research domain and FAIR-

ness of research information systems (RIS).

Research information management systems or

Current Research Information Systems (hereafter

CRIS) are seen as “core elements of the technologi-

cal solution since they provide rich additional meta-

data on datasets and put the datasets and their meta-

data into their proper context, and so signiﬁcantly en-

hance the FAIRness of datasets” (Terheggen and Si-

mons, 2016). In other words, CRIS are not just data

repository, but a key component of the research in-

frastructure landscape, which is directly targeted and

involved by the operational application and promo-

tion of the FAIR principles. This involvement can be

described at three levels constituting a set of proposi-

tions of our study on which we will elaborate within

this paper:

1. research information management systems

(CRIS) are helpful to assess the FAIRness of

research data and data repositories;

2. research information management systems

(CRIS) contribute to the FAIRness of other

research infrastructure;

3. research information management systems

(CRIS) can be improved through the application

of the FAIR principles.

This study is exploratory in nature. First, we pro-

vide an overview of the challenges associated with

research data and research information management,

then we describe each level based on a review of rele-

vant studies, and conclude with some perspectives for

further research, thereby raising an awareness of this

topic and making a call for other researchers to refer

to it.

2 RESEARCH INFORMATION:

WHY DOES IT MATTER?

Research information or an information about re-

search is a crucial resource for research institutions

that is dynamic in nature and growing in size. This is

also due to digitization, namely the fact that the way

researchers work has changed, and more and more

data are being digitized and stored electronically in-

stead of paper-based archives. But precisely because

these data, i.e. research information, is not only the

KMIS 2022 - 14th International Conference on Knowledge Management and Information Systems

basis of scientiﬁc knowledge processes, but is also re-

lated to other data, research data management (RDM)

is becoming more and more important to enable and

ensure comprehensive access to data stocks so that re-

search results can not only be veriﬁed and interpreted,

but it can also be understood how these results were

obtained and how they can be made usable and ac-

tionable.

All in all, the “research information” term stands

for “technical data, processes, methods, inven-

tions, compositions-of-matter, and biological materi-

als, equipment, instruments, apparatuses, devices, ar-

ticles of manufacture or component parts(s) thereof

developed under or resulting from the performance

of Research under the Research Agreement and im-

provements thereof, except for improvements on new

subject matter not funded by Licensee. In the event

that computer software or information management

systems are created or developed in the performance

of Research under the Research Agreement such com-

puter software or information management systems

shall be treated as Research Information” (Insider,

2022).

In order to ensure the effectiveness, visibility and

acceptance of RDM in the long term, research infor-

mation should be sustainable and usable by a wide

range of science and society (Flores et al., 2015).

For instance, the OECD has published guidelines on

access to research data for public funding (OECD,

2007), and the Alliance of German Science Organi-

zations has adopted principles for handling research

data (Society, 2010). The EU funding program Hori-

zon 2020 has introduced FAIR data management

(Commission, 2020a) guidelines / recommendations,

and the new Horizon Europe program conﬁrms that

research data management cannot be “opt-out” and

that projects generating research data must manage

their data responsibly and in line with FAIR princi-

ples (Commission, 2020c).

One of the main goals of open science is to in-

crease the transparency and accessibility of scientiﬁc

results (Paic, 2021). The FAIR data principles set the

standard for the sustainable use of research data, in-

sofar metadata are crucial for the later interpretabil-

ity, interoperability and traceability of data, including

quality assurance and control measures.

The ever-increasing relevance of data for research

requires new and efﬁcient strategies for processing

and handling research information and research data.

For this purpose, the National Research Data Infras-

tructure (NFDI) is currently building a digital knowl-

edge repository for the development, networking and

use of research data in Germany (Wachtler et al.,

2021). This includes, for example, making data col-

lection or RDM transparent and understandable. This

is important given that in universities and research in-

stitutions it is often unclear what methods are used to

collect the data or how their trustworthiness is ensured

(Guba and Lincoln, 1989), (Azeroual and Sch

opfel,

2021). In this sense, the context of the data is of in-

terest, so information about the collection strategy, as

well as their structure, is essential.

In addition, the data must be of a certain quality

in order to be further used, including but not limited

for research ((Ferraris et al., 2018), (Ivanovi

c et al.,

2019), (Corte-Real et al., 2020), (Nikiforova, 2020),

(Azeroual et al., 2022)). The data quality of research

information, however, is in most cases ensured man-

ually, which obviously comes with a lot of effort,

where automated or at least semi-automated quality

assurance processes signiﬁcantly reduce these efforts.

This applies in particular to automatic plausibility and

completeness checks directly at the time of data entry

with data proﬁling and data cleansing / cleaning, ma-

chine learning processes for text recognition (such as

text and data mining), conspicuous data point or data

anomaly detection, and statistical processes for data

normalization. However, they must be applicable in

practice, where some of the above can be solved by

designing a data schema or deﬁning data entry con-

straints or with validation checks – although even they

are lacking in most cases, while some of them turn

out to be less primitive in their development and im-

plementation.

The exchange of information and knowledge is

one of the pillars of science. With the increasing dig-

itization in science and the spread of the philosophy

of open science, research information is also becom-

ing more accessible to the public. These data are in

various open and proprietary formats in various data

repositories. Metadata are used to describe them, the

scope of which is mostly determined by the capabil-

ities of the data portals or specialized communities.

The quality of the metadata recording depends on the

professional competence, including data literacy, and

completeness of individual curators (Tammaro et al.,

2019).

With the growing volume of data and the emerging

cross-disciplinary challenges, the need for a consis-

tent, interoperable framework for documenting data

that can reliably ﬁnd, ﬁlter and compare research in-

formation of different origins is becoming increas-

ingly evident. Largely supported by Artiﬁcial Intelli-

gence (AI), Big Data analytics and Machine Learning

(ML) methods require data to be not only accessible

to humans, but also be machine-readable and under-

standable (Nguyen et al., 2018). This requires a selec-

tion of standardized, long-life readable data formats,

Putting FAIR Principles in the Context of Research Information: FAIRness for CRIS and CRIS for FAIRness

as well as clear coding of the metadata descriptions,

standardizing the two attribute ﬁelds and their permis-

sible content (attribute values). The four FAIR data

principles, introduced by RDM, specify goals, but do

not provide solutions for this non-trivial task.

The FAIR data principles describe an ecosystem

of data, metadata, scientiﬁc software, but also work-

ﬂows, metrics, and the need for continued funding of

infrastructures that are consistent with the implemen-

tation of the FAIR principles (e.g. repositories) (Are-

folov et al., 2021). The unique identiﬁcation of data

(items), people, institutions, projects, as well as poli-

cies, guidelines, standards and data repositories are

indispensable tools in such a system. The question

implies from the above - whether and how can CRIS

contribute to promotion of FAIRness?

3 CRIS AS AN ASSET IN

ASSESSING FAIRness

The FAIR data principles provide a comprehensive

framework and guidance on the criteria that well-

preserved data must meet, as well as on the stan-

dardization of all data schemes (Mayer et al., 2021).

The quality of data depends not only on the accuracy

of the measurement and recording methods and, of

course, on their scientiﬁc relevance, but increasingly

on the quality of their processing and storage (Na-

tional Academy of Sciences (US) et al., 2009). The

FAIR principles can be seen as the gold standard for

data quality (“ﬁt of use”), generally recognized as a

generic standard (Hasselbring et al., 2020).

Several tools have been developed over the years

to assess the FAIRness of research data and data

repositories, such as the Australian Research Data

Commons (ARDC) FAIR Data self-assessment tool,

the Dutch DANS FAIRdat tool or the EUDAT Fair

Data Checklist (FOSTER, 2020). Moreover, the re-

sults obtained using different tools tend to differ sig-

niﬁcantly with a very vague understanding on what

should be done to improve the result if another tool

assessed the level of FAIRness as sufﬁcient. All in

all, such “ad-hoc” tools may be more or less useful

for the follow-up of local FAIR programs or for train-

ing FAIR principles, thereby developing FAIR liter-

acy. However, they create new information silos and

in most cases are not linked to professional assess-

ment systems such as CRIS.

Open data practices and the FAIRness of research

data have become essential characteristics of research

performance. The European Commission’s Open Sci-

ence Monitor includes national level information on

open research data (Commission, 2020b). “Us-

ing the FAIR data principles” is one of the evaluation

criteria proposed in OS-CAM, the Open Science

Career Assessment Matrix (O’Carroll et al., 2017),

(European Commission and Innovation, 2021). The

CRIS can and even should collect, aggregate and

integrate structured and carefully curated information

on research data and its FAIRness to support the

monitoring and assessment of this element.

For the purpose of this study, we selected euro-

CRIS repository - as a major source used by euro-

CRIS community, which participants represent prac-

titioners dealing with the actual national, regional and

international CRIS - as a source for conducting a

extracting the most relevant studies with an explicit

elaboration of FAIR principles and their implementa-

tion within CRIS. All in all, after a systematic liter-

ature review, three studies were found to be relevant

to illustrate this approach. In other words, all rele-

vant studies available in euroCRIS repository were se-

lected and studied. The low number of relevant stud-

ies, however, points out the limited body of knowl-

edge on this topic, thereby making this study unique

and constituting a call for action. Let us elaborate on

them. The scope of the research, however, was ex-

tended by referring to other relevant projects and ini-

tiatives found around the world, which list was iden-

tiﬁed using a snowballing approach, i.e., referring to

the projects covered in the selected studies, or based

on our own experience dealing with this topic at both

regional, national and international levels and repre-

senting different communities.

According to (Lindel

ow, 2019), in 2017, the gov-

ernment of Sweden gave the Swedish Research Coun-

cil and the National Library of Sweden parallel as-

signments to propose criteria and a method for assess-

ing how well research data and scholarly publications

produced by Swedish organizations comply with the

FAIR principles, based on the assumption that “the

products of research must meet the FAIR principles

as far as possible”. Suggested criteria include (1)

metadata quality (richness), (2) licensing and persis-

tent identiﬁers, (3) openness, (4) accessibility and (5)

standard vocabularies. The aim is to provide an “over-

all picture of FAIRness” of national research results,

through the collected metadata.

Authors of (Miniberger and Reding, 2017) de-

scribed how the implementation of a commercial

CRIS at the University of Vienna and the creation of

a national network of CRIS managers from all Aus-

trian universities (FIS/CRIS Austria) contributed to

the visibility, ﬁndability, accessibility and interoper-

ability of research information, through the develop-

ment of standards (including identiﬁers and data mod-

KMIS 2022 - 14th International Conference on Knowledge Management and Information Systems

els) and shared strategies. More recently, this network

developed a tool that enables tracking and monitoring

of the transition to open access based on data stored

in local CRIS which is interoperable and connected

with OpenAIRE (Danowski et al., 2020).

As part of the AT2OA (Austrian Transition to

Open Access) project, a sub-project deals with the de-

velopment of a concept for monitoring the Open Ac-

cess (OA) publication output in Austria. Authors of

(Danowski et al., 2020) build their feasibility study on

the analysis of international best practice models and

aim to demonstrate the added value and feasibility of

OA monitoring at national level. However, the idea of

(Danowski et al., 2020) should also serve to (further)

develop OA monitoring in other countries and also to

support OA monitoring in an international context.

Based on experience with the Flemish research in-

formation system (FRIS), an application proﬁle for

research data was presented in (Vancauwenbergh,

2021), including various aspects of metadata such

as description, discovery, contextualisation, coupling

users, software and computing resources to data, re-

search proposal, funding, project information, re-

search outputs, outcomes, impact etc., which allows

assessing FAIRness and compliance with open sci-

ence policy. The author concluded that convergence

to a common metadata model and interoperation/ in-

teroperability across multiple metadata models are

two conditions for developing such an application.

This approach may also allow for the “FAIR label-

ing” of research infrastructures that comply with the

FAIR principles.

All in all, what three initiatives have in common is

that CRIS is used to assess different levels of FAIR-

ness and FAIRness of the object under assessment at

different levels, as part of the global open science

assessment. This assessment is primarily based on

the collection and processing / handling of metadata.

Regarding researcher evaluation, for example, the

Finnish and Norwegian national policies on respon-

sible assessment of researchers indicate the national

and/or local CRIS as a potential source of documen-

tation of open data practices (of Finnish Learned So-

cieties, 2020), (Norway, 2016).

In general, the initiatives discussed above conﬁrm

that CRIS has the potential and capacity at the insti-

tutional, regional or national level to contribute to the

monitoring of open science policies and, in particu-

lar, to the follow-up of projects aimed at improving

the FAIRness of research data, research repositories

and other related research infrastructures. In addition,

it has been clearly recognized that CRIS has the po-

tential to support and facilitate more responsible re-

search assessment systems to reward and incentivize

researchers for open science practices, including open

and FAIR data.

4 CONTRIBUTING TO THE

FAIRness OF RESEARCH

INFRASTRUCTURES

The current body of knowledge, i.e. scientiﬁc litera-

ture on the topic, suggests that CRIS can contribute

to improving the FAIRness of other research infras-

tructures, such as data repositories or publication plat-

forms.

One of the main reasons for this potential is the

central position of CRIS in the research information

ecosystem (Donohue et al., 2018). In short, CRIS

obtains data from a wide range of external and inter-

nal sources such as scientometric databases, library

catalogs, human resources, ﬁnance, project manage-

ment etc., and provides the data or information in

standardized formats to other infrastructures and re-

search information tools. This potential is not a gen-

eral and common characteristic of all types of CRIS

- it rather depends on the degree of standardization

of the CRIS data model and the CRIS format. In

other words, since CRIS require and depend on ex-

change with (many) other infrastructures, their qual-

ity, effectiveness and performance are affected by the

standardization of data and procedures. For this rea-

son, they have a kind of standardizing impact on other

infrastructures, both down-stream (output) and up-

stream (input).

All in all, one of the goals of CRIS is to sup-

port the monitoring and evaluation of research perfor-

mance at the national, institutional or even individual

levels, which usually requires and therefore indirectly

facilitates the production and collection of rich, com-

plete, structured, comprehensive, comparable, inter-

operable data. As an example that one of the authors

came across in Finland, the need to integrate meta-

data from the institutional CRIS into the national sys-

tem forced the development of deﬁnitions, standards

and procedures at national level and their further im-

plementation locally, thus contributing to standardiza-

tion.

This positive effect has been described in various

research information projects and systems such as the

implementation of PURE at the University of Vienna

(Miniberger and Reding, 2017), some DSpace-CRIS

projects in Italy, Cyprus, Australia and Hongkong

(Mornati et al., 2018), CRIS of Radboud University in

The Netherlands (Jetten et al., 2018), (Jetten and Si-

mons, 2019) and, more generally, the DSpace, Fedora

Putting FAIR Principles in the Context of Research Information: FAIRness for CRIS and CRIS for FAIRness

and Vivo implementations around the world (Dono-

hue et al., 2018). The Dutch project shows that local

infrastructure and support are vital for a data man-

agement policy to work, that “we need to get away

from the silo- and closed vault-thinking” in order to

move to a “one-stop-shop and register-once-use-many

approach” based on a standard data model and for-

mat, with the argument that connecting shared (possi-

bly, ‘dark’) local data repositories to CRIS will make

them FAIR. In this sense, (Ivanovi

c et al., 2019) de-

scribe how English metadata in the local CRIS im-

prove the ﬁndability of Serbian theses and disserta-

tions deposited in the institutional Electronic Theses

and Dissertations (ETD) repository. Following (Van-

cauwenbergh, 2021), the need for standard data and

metadata will also improve the interoperability of re-

search infrastructures, which is another FAIR guiding

principle.

The preferred standard data model and format of

all these projects is the Common European Research

Information Format (CERIF), ﬂexible enough to in-

clude new elements, semantics and relations, and able

to provide standard output to other infrastructures,

thus increasing their FAIRness. A Swedish study

explores how CERIF can improve the FAIRness of

open repositories (Engelman et al., 2019). (Engelman

et al., 2019) ﬁnd that the “CERIF model is used to rep-

resent research information and to transfer it between

repositories (...). When CERIF is employed in rele-

vant archive processes, a FAIR compliant archive is

easier to achieve”, with an “archival structure based

on a cfProject tree (...), archived objects (...) repre-

sented by cfResult entities and their descriptive meta-

data (...) given in attached Cerif entities (...)”.

5 IMPROVING THE FAIRness OF

CRIS

In other words, while FAIR is typically discussed at

three levels - (1) digital object, which refers to dataset,

videos, journals, books etc., (2) metadata about this

object on elementary level, including title, creator,

identiﬁer, etc., and (3) metadata records with the ref-

erence to the body of metadata element on the object

in a speciﬁc database (Engelman et al., 2019), we sug-

gest refer not only to data and information, but also to

the upper level of the data or information management

systems, i.e. CRIS. Thus, our third proposition is that

CRIS itself can be improved by following and / or ap-

plying FAIR principles on it. In other words, CRIS is

not only suitable to improve the FAIRness of research

data management, but the FAIR principles are also

beneﬁcial for the further development of sustainable

and FAIR CRIS. Indeed, the Science Europe Posi-

tion Statement on Research Information Systems sug-

gests that “research information systems should fos-

ter the ﬁndability, accessibility, interoperability, and

reusability of the data that they store by implement-

ing the FAIR Guiding Principles for research activity

data” (Europe, 2016).

Two levels can be distinguished here. First, the

need for standard data and metadata, especially per-

sistent identiﬁers, requires tools capable of producing

and processing, handling them, and this is a strong

argument in favor of CRIS as the central system

(middleware) in the research infrastructure ecosys-

tem. Secondly, this need also calls for more stan-

dardization of CRIS, improved data models and for-

mats especially the long tail of less standardized re-

search information systems (cf. the large diversity and

heterogeneity of CRIS revealed in the OCLC survey

(Bryant et al., 2021).

The positive impact of the FAIR principles on

the CRIS infrastructures is less documented, proba-

bly because most of the research is focused on “good

examples” or “best practices” and highly exemplary

projects, with a high degree of standardization. How-

ever, the standard format can improve CRIS interop-

erability, although this is not sufﬁcient – CRIS should

(also) prefer open identiﬁer systems “to make things

ﬁndable” and link information on source data and

rights information, for instance, to support access and

facilitate reuse (Tatum and Brown, 2018).

The interconnection of infrastructures based on

the FAIR principles is another example of an im-

provement in CRIS, which must fulﬁll certain techni-

cal requirements based on the FAIR principles. This

“need for upgrading” is illustrated in (Mornati et al.,

2018) that describes a list of FAIR requirements for

joining the European OpenAIRE community. In other

words, only CRIS meeting these criteria will be con-

nected to OpenAIRE – another almost mandatory way

to improve (and enforce) the FAIRness of CRIS.

However, we must keep in mind that the FAIR-

ness of research information management infrastruc-

tures has its speciﬁc limitations due to the nature of

the data and the potential impact of their reuse. Some

of the data can be personal data and protected by pri-

vacy laws such as General Data Protection Regulation

(GDPR), other information may be conﬁdential for

other reasons, e.g. being highly ﬁnancial and of in-

terest for competitors etc. Thus, for ethical and legal

reasons, the accessibility of CRIS data must be con-

trolled and respect the above, i.e. it cannot be a guide-

line and require an openness of all data, following the

H2020 Program Guidelines on FAIR Data of “as open

as possible, as restricted as necessary” (Landi et al.,

KMIS 2022 - 14th International Conference on Knowledge Management and Information Systems

2020) and distinguishing FAIR and open data, while

the preferred option is a combination of both.

6 DISCUSSION AND

CONCLUSIONS

This paper focuses on FAIR data principles and their

potential and real contribution to the quality and the

reusability of research information with the further

possibility of creating knowledge from it and an ef-

ﬁcient knowledge management, which is only pos-

sible if the list of the discussed prerequisites is met.

It is crucial that the research information is available

in such a way that it can be found, accessed, linked

and reused as easily as possible (for authorized users)

thereby being as FAIR as possible. Essentially, it is a

quality requirement for data management as a prereq-

uisite for being able to use the data, which are often

obtained with great effort, which quality is not pre-

served when the data are stored and further processed

thereby losing their value.

Research information is not just research data, and

research information management systems such as

CRIS are not just repositories for research data. They

are much more complex, alive, dynamic, interactive

and multi-stakeholder objects. However, in the real-

world they are not directly subject to the FAIR re-

search data management guiding principles. But as

described above, CRIS are part of the research infras-

tructure ecosystem and are linked to data repositories,

where the idea of CRIS partly overlaps with the main

goal of FAIR principles, where the original scope of

CRIS is more limited. At the same time, the scope

of the FAIR principles has been extended recently to

include a broader variety of “digital objects”, infras-

tructures and content. For both reasons, CRIS can

(and already does) improve the FAIRness of research

infrastructures and data through the evaluation (mon-

itoring) and standardization of data and metadata.

In this paper we have raised a discussion on this

topic showing that the improvement of FAIRness is a

dual or bidirectional process, where CRIS promotes

and contributes to the FAIRness of data and infras-

tructures, and FAIR principles push for further im-

provement in the underlying CRIS data model and

format, positively affecting the sustainability of these

systems and underlying artifacts. CRIS are beneﬁcial

for FAIR, and FAIR is beneﬁcial for CRIS. Never-

theless, as pointed out by (Tatum and Brown, 2018),

the impact of CRIS on FAIRness is mainly focused

on the (1) ﬁndability (“F” in FAIR) through the use

of persistent identiﬁers and (2) interoperability (“I”

in FAIR) through standard metadata, while the im-

pact on the other two principles, namely accessibil-

ity and reusability (“A” and “R” in FAIR) seems to

be more indirect, related to and conditioned by meta-

data on licensing and access. Paraphrasing the state-

ment that “FAIRness is necessary, but not sufﬁcient

for ‘open’” (Tatum and Brown, 2018), our conclu-

sion is that “CRIS are necessary but not sufﬁcient for

FAIRness”.

In terms of rewards and incentives, the FAIRness

of CRIS data, as recommended by European Com-

mission, is critical to ensure the “independence and

transparency of the data, infrastructure and criteria

necessary for research assessment and for determin-

ing research impacts” (European Commission and In-

novation, 2021). While CRIS has great potential to

support more responsible assessments with reliable,

comprehensive, well-structured and comparable qual-

itative and quantitative data and metrics on research,

they still offer only limited support for the evaluation

of a broad range of open science practices other than

publications (Mustajoki et al., 2021).

For further research, more case studies are needed

to explore the potential of research information man-

agement to monitor FAIR projects and infrastructures

at the local, regional, national and international levels.

More empirical evidence needs to be presented on the

real and speciﬁc impact of CRIS on the development

of FAIR data repositories and other research infras-

tructures, with a particular focus on standardization.

Finally, further development of CRIS data models and

formats should focus on FAIR principles, especially

ﬁndability and interoperability, in an explicit way. At

the same time, the ethical and legal aspects of acces-

sibility of CRIS data require further investigation to

get a full picture of what it really means to apply the

FAIR principles to research information management.

While this is a research currently conducted by the

European Open Science Cloud Task Force by means

of both surveys, interviews, case studies and other ac-

tivities, it can and should be supplemented with other

independent and use-case based studies.

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