Research data management and research data literacy in Slovenian science
| DOI | https://doi.org/10.1108/JD-03-2018-0042 |
| Pages | 24-43 |
| Date | 14 January 2019 |
| Published date | 14 January 2019 |
| Author | Polona Vilar,Vlasta Zabukovec |
| Subject Matter | Library & information science,Records management & preservation,Document management,Classification & cataloguing,Information behaviour & retrieval,Collection building & management,Scholarly communications/publishing,Information & knowledge management,Information management & governance,Information management,Information & communications technology,Internet |
Research data management and
research data literacy in
Slovenian science
Polona Vilar and Vlasta Zabukovec
Department of Library and Information Science and Book Studies,
University of Ljubljana, Ljubljana, Slovenia
Abstract
Purpose –The purpose of this paper is to investigate the differences between scientific disciplines (SDs) in
Slovenia in research data literacy (RDL) and research data management (RDM) to form recommendations
regarding how to move things forward on the institutional and national level.
Design/methodology/approach –Purposive sample of active researchers was used from widest possible
range of SD. Data were collected from April 21 to August 7, 2017, using 24-question online survey
(5 demographic, 19 content questions (single/multiple choice and Likert scale type). Bivariate (ANOVA) and
multivariate methods (clustering) were used.
Findings –The authors identified three perception-related and four behavior-related connections; this gave
three clusters per area. First, perceptions –skeptical group, mainly social (SocS) and natural sciences (NatS):
no clear RDM and ethical issues standpoints, do not agree that every university needs a data management
plan (DMP). Careful group, again including mainly SocS and NatS: RDM is problematic and linked to ethical
dilemmas, positive toward institutional DMPs. Convinced group, mainly from humanities (HUM), NatS,
engineering (ENG) and medicine and health sciences (MedHeS): no problems regarding RDM, agrees this is an
ethical question, is positive toward institutional DMP’s. Second, behaviors –sparse group, mainly from
MedHeS, NatS and HUM, some agricultural scientists (AgS), and some SocS and ENG: do not tag data sets
with metadata, do not use file-naming conventions/standards. Frequent group –many ENG, SocS, moderate
numbers of NatS, very few AgS and only a few MedHeS and HUM: often use file-naming conventions/
standards, version-control systems, have experience with public-domain data, are reluctant to use metadata
with their RD. Slender group, mainly from AgS and NatS, moderate numbers of ENG, SocS and HUM, but no
MedHeS: often use public-domain data, other three activities are rare.
Research limitations/implications –Research could be expanded to a wider population, include other
stakeholders and use qualitative methods.
Practical implications –Results are useful for international comparisons but also give foundations and
recommendations on institutional and national RDM and RDL policies, implementations, and how to bring
academic libraries into the picture. Identified differences suggest that different educational, awareness-raising
and participatory approaches are needed for each group.
Originality/value –The findings offer valuable insight into RDM and RDL of Slovenian scientists, which
have not yet been investigated in Slovenia.
Keywords Slovenia, Researchers, Research data management, Data literacy, International studies,
Research data literacy
Paper type Research paper
Introduction and literature survey
In the context of the efforts for Open Science, there is increasing importance of research data
management (RDM) for researchers, their institutions as well as supporting research
infrastructure (libraries, technological support, IT, etc.). Data-intensive science (Hey and
Hey, 2006; Lynch, 2009) poses raising demands on making studies robust and reproducible
(Smalheiser, 2017). This presents certain requirements on the data being produced and used
in research, such as data quality, activities with this data, accompanied by the required
competences, such as data citation, data management, data curation, data literacy, etc.
(Koltay, 2015a, b, 2016a, b). Adequate management of data resulting from research work is
becoming key precondition for effective data exchange within certain scientific community
as well as publication beyond the project group (Sesartic and Töwe, 2016). Consequently,
Journal of Documentation
Vol. 75 No. 1, 2019
pp. 24-43
© Emerald PublishingLimited
0022-0418
DOI 10.1108/JD-03-2018-0042
Received 14 March 2018
Revised 26 July 2018
Accepted 26 July 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0022-0418.htm
24
JD
75,1
this improves the visibility of scientific work, citations, etc. (Piwowar and Vision, 2013;
Piwowar et al., 2007), as well as verification and transparency. For these reasons, we now see
initiatives and demands of, e.g., scientific publishers for publication of data together with or
even before the publication of research papers (Briney et al., 2017). Besides effective data
interchange there are also issues of effective long-term preservation, which has to be based
on good metadata to ensure long-term viability, renderability and interpretability of data.
International science, besides publicizing, also more and more intensely encourages
re-use of data, especially data from publicly funded research, to achieve better use of such
data, increased benefit for science and community, etc. This requires researchers to follow
various international policies and guidelines (Hickson et al., 2016) both on national and
institutional level. For example, European Commission has within its program Horizon
2020 established a pilot program Open Research Data Pilot (www.openaire.eu/what-is-the-
open-research-data-pilot). All project proposals have to include a part with the description
of data management plan (DMP), i.e. an accurate and realistic description of their
dissemination, exchange and preservation. In this context, in 2015 Slovenia has proposed
a national strategy of open access to scientific publications and research data (www.mizs.
gov.si/fileadmin/mizs.gov.si/pageuploads/Znanost/doc/Zakonodaja/Strategije/National_
strategy_for_open_access_21._9._2015.pdf), followed by an action plan and a pilot
program (www.mizs.gov.si/fileadmin/mizs.gov.si/pageuploads/Znanost/doc/Odprti_
dostop/Akcijski_nacrt_-_POTRJENA_VERZIJA.pdf) for establishing this access. For
the time these documents remain on a strategic level and are not based on empirical
findings and as such do not give precise directions and recommendations. Neither the
strategy nor the action plan mention behavior and competences of researchers or other
stakeholders; the process of RDM is also not mentioned.
Whyte and Tedds (2011) defined RDM as “organization of data from their entry in the
research cycle to dissemination to archiving of important results,”while according to
Schneider (2013), it is about “processing all types of raw, i.e. primary, data which are the
results of any research process”and this process encompasses all activities from data
creation and analysis within their so-called “lifecycle.”This denotes organization, protection,
preservation of data as well as data sharing and distribution. Organization of data means
naming conventions, data sets version control, data description, etc. Data protection
concerns areas such as saving, backup, privacy ensurance, while data distribution denotes
ensuring access to data, data exchange among researchers, etc. Koltay (2015a, b, 2016a)
mentioned the concept of data governance, denoting decision making and authority based
on agreed models defining who can perform a certain activity, when and under which
conditions and circumstances, which methods may be used, etc.
Institutions often establish data services which are, as Tenopir et al. (2012) defined, an
entire spectrum of information and technical services offered by an institution (in its name
often its library) to researchers for management of their data. As financing bodies
increasingly view research data as an asset, this consequently brings more and more
frequent demands that DMPs be included in project proposals (Pryor, 2012, p. 4). This is a
new competence area for both researchers, who prepare these proposals, and for service
providers. It has also led to a new area, i.e., the evaluation of RDM plans (see Van Loon
et al., 2017). As stated by Van Loon et al. (2017), the problem of the quality of research plans
is often connected to inadequate competences of researchers, who prepare them, in areas
such as data description, data governance during the project lifecycle, maintenance and
preservation of data, as well as data sharing after the project ends. Researchers are,
understandably, besides research itself, best acquainted with exchange or sharing of their
results. Van Loon et al. (2017) also found that different scientific disciplines (SDs) show
different gaps, which implies the need for differentiated education/training and help services
(however, these need to follow the investigation of these differences).
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RDM and RDL
in Slovenian
science
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