Putting FAIR Principles in the Context of Research Information:
FAIRness for CRIS and CRIS for FAIRness
Otmane Azeroual
1 a
, Joachim Sch
2 b
, Janne P
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
CRIS, FAIR, Findability, Data Management, Knowledge, Information Management, Interoperability,
Research Data Management, Research Information System, Open Science.
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.
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-
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
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
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
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.
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-
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,
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
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-
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.,
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?
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.
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-
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-
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
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 (...)”.
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.
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
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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