Available via license: CC BY 4.0
Content may be subject to copyright.
Citation: Vrana, R. Research Data
Management in the Croatian
Academic Community: A Research
Study. Publications 2024,12, 16.
https://doi.org/10.3390/
publications12020016
Academic Editors: Iva Grabari´c
Andonovski, Nikolina Peša Pavlovi´c
and Jadranka Stojanovski
Received: 3 March 2024
Revised: 21 April 2024
Accepted: 13 May 2024
Published: 15 May 2024
Copyright: © 2024 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
publications
Article
Research Data Management in the Croatian Academic
Community: A Research Study
Radovan Vrana
Faculty of Social Sciences and Humanities Zagreb, University of Zagreb, 10000 Zagreb, Croatia;
rvrana@ffzg.unizg.hr
Abstract: This paper presents the results of an empirical research study of Croatian scientists’ use and
management of research data. This research study was carried out from 28 June 2023 until 31 August
2023 using an online questionnaire consisting of 28 questions. The answers of 584 respondents
working in science were filtered out for further analysis. About three-quarters of the respondents
used the research data of other scientists successfully. Research data were mostly acquired from
colleagues from the same department or institution. Roughly half of the respondents did not ask
other scientists directly for their research data. Research data are important to the respondents mostly
for raising the quality of research. Repeating someone else’s research by using their research data
is still a problem. Less than one-third of the respondents provided full access to their research data
mostly due to their fear of misuse. The benefits of research data sharing were recognized but few of
the respondents received any reward for it. Archiving research data is a significant problem for the
respondents as they dominantly use their own computers prone to failure for that activity and do not
think about long-term preservation. Finally, the respondents lacked deeper knowledge of research
data management.
Keywords: research data; research data sharing; research data reuse; research data management; Croatia
1. Introduction
The last three decades have been marked by some major developments and innova-
tions in digital technologies, providing support to newly created paradigms of e-science
and open science and to activities related to research data such as creating, collecting,
storing, sharing and use of the data, making modern science increasingly digital and
data-intensive [
1
]. Since the 1990s and the advent of e-science, research data have become
a popular topic of many theoretical and practical scientific endeavors. During the same
period, scientific and professional papers and books have been published describing the
positive impact of research data on the development of modern science. Consequently,
the value of research data has increased and, for many, they have become a new currency
in science [
2
]. Additionally, the increased quantity of research data created a strong de-
mand for suitable infrastructure: hardware, software, data management policies and other
supporting documents and, of course, human resources for supervising the process of re-
search data management. Suitable infrastructure, as the most crucial component, provides
support to scientists, helping them focus on research itself rather than on technical and
administrative aspects of research like manipulating large quantities of data.
Although science has been technically and structurally advancing through the second
part of the 20th and the beginning of the 21st century, activities related to research data in
digital format still lack the ease and speed with which researchers could create, collect large,
complex datasets and manage them so they could be openly available. Due to the lack of
adequate human (professional), technical (infrastructural) and document (guidelines and
policies) support, researchers fall behind in the development and acquisition of knowledge
and skills necessary to ensure data quality, integrity, shareability, discoverability and reuse
Publications 2024,12, 16. https://doi.org/10.3390/publications12020016 https://www.mdpi.com/journal/publications
Publications 2024,12, 16 2 of 19
over time [
3
]. Furthermore, researchers are not always able to spend enough time on
research data management, while at the same time they neglect other activities. “Managing
research data is time-consuming, costly and tedious, requiring additional resources that
are not available to all researchers” [
4
] (p. 23). To achieve better and easier research data
management with the aim to provide open access to research data resulting in broader
availability and accessibility of research data and better cooperation between individual
researchers, academic institutions and society must build uniform support to all researchers,
which includes necessary technical infrastructure (individual, institutional and national),
research data management policies and guidelines and training for researchers in research
data management procedures. All these activities require a choice of good technical
resources, time, good organization, professionally trained human resources (in IT and
information sciences) and substantial long-term funding. The outcome of these activities
will create a difference between research communities, those who are able to manage
research data and those who are not.
This paper focuses on the management and use of data created by the Croatian
scientists working in all scientific disciplines. Research data are “at the very heart of
the knowledge life cycle and are a central ingredient to the scholarships of discovery,
integration, teaching, engagement” etc. [
5
] (p. 345). The aim of data management is not only
to facilitate the long-term preservation of research data but also to make these data available
for two crucial activities: possible sharing and possible reuse by interested scientists. The
latter activity adds most value to research data in general. Providing access to research
data is achieved mostly by sharing research data with other people, directly upon their
request or by providing access in institutional general purpose digital repositories or in
specialized data repositories.
2. Materials and Methods
2.1. Research Data Management, Data Sharing, Data Reuse
In a growing data-intensive world, it has become difficult to assess the current volume
of data created by vast number of industrial, personal and scientific endeavors, let alone
predict future volumes of created data. Yet, there is a projection according to which, by 2025,
global data volume will reach 175 zetabytes [
6
], a volume that will require meticulously
created management procedures; otherwise, data will be lost, regardless of their origin
or purpose. The main origin of data today is digital technology, the proliferation of
which resulted in the production of big volumes of data. In the academic community,
data have become an integral part of daily research processes and are more frequently
counted as a standard research output [
7
], thus becoming a strategic commodity as well as
a starting point for activities such as data sharing and data reuse (secondary analyses and
new research).
Data sharing has become a symbol of open science. It promotes the transparency
of research [
8
], provides evidence of a proper research, adds content to services such as
data archiving, enables reuse (secondary analysis and comparative research), generates
new research questions and results in new findings from the same data, thus creating
value for money and added value [
9
–
11
]. It can inspire new and multiple perspectives [
12
]
and enables the quality of the research through seamlessness and documentation [
13
,
14
].
Provision of data for the research quality control was a focus of the paper by Askarov,
Doucouliagos, Doucouliagos and Stanley [
14
], in which the authors investigated publication
bias and questionable research practices and they found out that data sharing and reuse
may have reduced inflated statistical significance and decreased publication bias. Statistical
significance was produced by authors wanting to make their paper appear more significant
than it was, to be able to publish their research. Data sharing may be a tool to suppress
these issues, at least partially.
Data sharing has been a holy grail of modern science. In 2023, data sharing is still not
a universal activity in science, and it is burdened by barriers presented by individuals or
institutions. As Khan, Thelwall and Kousha [
7
] point out, there have been many studies of
Publications 2024,12, 16 3 of 19
data sharing and reuse in narrow contexts but there are no substantial science-wide research
studies in this topic due to disciplinary differences. For instance, in the biotechnology
sector, “individual privacy as well as intellectual property concerns often stand in the way
of sharing data” [
15
]. Other problems include the time needed to share data effectively and
fact that shared data are rarely used [7].
Successful research data sharing has technical and financial prerequisites. Technical
prerequisites (personal computers, high-bandwidth Internet connections, capable storage
servers) must be met so that the interested researchers can access data. Continuous funding
is another prerequisite for facilitating future data reuse. The costs are related to datasets’
size, complexity, different data types, metadata and documentation about the data, data
quality, reuse value of data (so called “hot” and “cold” data) [
16
] and access control to
data [
17
]. There are also two big categories of data that should be considered—qualitative
and quantitative; both require different kinds of preparation for use [
9
]. Data manage-
ment exists to ensure that data are “findable and accessible to both designated users and
reusers, on a day-to-day basis” [
16
] (p. 1). However, in real life, researchers encounter dif-
ferent problems like diverse devices and diverse virtual places that contain digital content
fragmentally, due to the fast accumulations of digital content shared [18].
The second important activity, research data reuse (a result or consequence of data
sharing) includes the reproduction and verification of past research, making the results
of funded research available; it supports efficiency and collaboration in research [
10
],
interdisciplinarity and enables more frequent citation [
7
]. It helps in generating new ques-
tions based on the existing data and, most generally, make the advancement of science
possible [
19
]. One of the biggest if not the biggest goal of research data reuse is to intro-
duce reproducibility as a research quality criterion by acknowledging “the strengths and
weaknesses of different ways of validating results and learn as much as possible from
the methodological precepts that guide different parts of science” [
20
] (p. 142). Despite
many efforts and initiatives in the scientific community and outside of it, reproducibility of
research remains a substantial and unsolved problem in modern science [21].
Today, digital data management and related activity—data reuse—have become inter-
connected and essential activities in academia and elsewhere as the amount of data grows
exponentially with and projection that the amount of data will only grow in near future.
2.2. Data Sharing Benefits, Motivation and Obstacles
The benefits resulting from the sharing the research data for the research community
include data discovery, reuse, validation and verification of data [
5
]. The motives to share
data include journal publishing pressure, normative pressure, perceived effort, pressure by
funding agencies, perceived career benefits and scholarly altruism [22].
There are also reasons why researchers do not like to share their research data. Fecher,
Friesike and Hebing [
10
] pointed out that research data are still not common knowledge
because researchers have fears and doubts about data sharing including endangering the
right to publish their research results first and the possibility of data misuse. In addition,
publications that publish research data lack sufficient formal recognition for this activity,
and there are also no incentives for sharing research data, so researchers remain incentivized
not to share data. The lack of incentives for acknowledging and crediting researchers for
sharing their own data and citing others’ research data remains an unsolved problem [
23
].
Moreover, the authors of research data claim that other researchers cannot use their data
properly because data can only exist in the specific context of their creation and, without
the methodologies and ontologies adopted at the time of creation, other researchers cannot
adapt data into their new research (projects) [
23
]. Lin Yoong, Turon, Grady, Hodder and
Wolfenden [
24
] (p. 18) singled out the following problems to data sharing: “review access
(e.g., located behind a paywall), logistical issues (e.g., lack of metadata, standardized
reporting of measures, challenges with accessibility and data quality), motivational barriers
(e.g., lack of author incentives), legal impediments (e.g., issues around ownership of data,
intellectual property [IP]) and ethical concerns”. In addition to the previously mentioned
Publications 2024,12, 16 4 of 19
reasons (which sometimes overlap between authors), Mallasvik and Martins [
12
] compiled
additional reasons like unavailability of funds or poor levels of knowledge regarding
adequate formats and reusability, lack of recognition for those who willingly engage
in research data sharing, high financial costs, uncertainties surrounding loss of control
intellectual property rights and potential threats to national security.
Lewis [
25
] listed the rewards of good management of research data including signifi-
cant potential benefits for academic research itself:
•
The ability to share research data, minimizing the need to repeat work in the laboratory,
field or library;
•
Ensuring that research data gathered at considerable cost are not lost or inadvertently
destroyed;
•
The retrieval, comparison and co-analysis of data from multiple sources can lead to
powerful new insights;
•
The ability to verify or repeat experiments and verify findings, particularly important
amid growing national and international concern about research integrity;
•
New research themes—and in particular cross-disciplinary themes—can emerge from
the re-analysis of existing data or comparisons with new data; increasingly, data
may become the starting point for new research as well as represent an output from
current research.
•
If at least some of these issues were resolved, researchers would be prepared to share
data if there were rewards for doing so [26].
2.3. Data Storing and Archiving
A prerequisite of achieving high reproducibility of research is reliable, long-term
access to digital content [
27
], which is often missing or neglected. This long-terms access
to research data is now commonly required by publishers, academic institutions and
research project funding agencies [
7
,
13
,
28
,
29
]. However, research data are not always
widely available for reuse as data sharing is not fully appreciated by researchers across all
disciplines [2], restricting the reuse of data.
To be able to share research data, one must store and possibly archive them (long-term
storage). Data archiving has become essential since the early days of personal computers
and continues to be one of the challenges for modern science. The main goal of data
archiving is to make quantitative and qualitative research data available for reuse.
Research data can come in different types: in open or controlled forms that include
raw data, research data, sensitive data, government data and big data [30].
Sometimes, data are used only once and only by authors who collected data, inter-
preted and used them in an article, a report or a conference paper. After the publication
of a journal article, conference paper or a book chapter, data may become hard to find as
they were stored on scientist’s personal computer, external hard disk and elsewhere where
data are not widely available (online). Nowadays, in the era of Big Data, data storing and
archiving has become even more important because data can be easily lost and therefore
become unavailable. This is especially true for older data collected, described and stored in
places other than institutional, national or other digital repositories.
Furthermore, as previously noted, “scientific publications increasingly require that the
data relevant to an article be available through archives” [
13
] (p. 6). On the personal level,
personal computing users continue to experience catastrophic personal data loss [31].
3. Research Study
3.1. Research Design
The purpose of this research study was to explore current sentiments of the Croatian
scientists about three aspects of use and management of research data, which were also
research questions this study aimed to answer:
Q1: Do the Croatian scientists use research data of other scientists?
Q2: Do the Croatian scientists offer access to their research data?
Publications 2024,12, 16 5 of 19
Q3: What are the modes of storing research data?
The main hypothesis of this research is that the Croatian scientists are only moderately
ready for data sharing and data reuse, which also only partially fulfils their research mission
in society.
3.2. Research Instrument
To collect data about the current sentiment of the Croatian scientists about other
scientists’ and their own research data, an online questionnaire (Appendix A) was designed.
The questionnaire consisted of 25 closed-type questions and three open-type questions, a
total of 28 questions.
The research was initiated by sending invitations to the mailing list of the Croatian sci-
entists and by asking scientists to send the same invitation to their colleagues (snowballing).
The research used a convenience sampling method. This type of sampling was used as
it was not feasible to divide members of the mailing list according to their age, research
experience or other criteria. The findings, especially the demographics data, helped in
differentiating scientists who participated in this research.
The invitation for participation in this research was sent by e-mail to the mailing list of
the Croatian scientists administered at the “Ru ¯
der Boškovi´c” Research Institute in Zagreb,
Croatia. The mailing list included 15,000 scientists, postgraduate students, professional
associates, librarians and other employees from the system of science and higher education
of the Republic of Croatia, who at some point during participation in various conferences,
workshops, through consent in the Croatian scientific bibliography “CROSBI” [
32
] or
through the application form subscribed to the mailing list and received the contents sent to
it. According to the Croatian Research Information System [
33
], which quoted the Register
of scientists and artists of the Republic of Croatia created by the Ministry of science and
education of the Republic of Croatia [
34
], 29641 scientists were listed in the register on
19 April 2024.
The research started on 28 June 2023, and the access to the questionnaire was closed
on 31 August 2023.
A total of 650 respondents participated in this research study by answering ques-
tions in the online questionnaire. Since only the respondents working in science and on
science-related jobs were relevant to this research study (assistant, senior assistant, assistant
professor, associate professor, full professor, full professor tenure, full professor retired,
professor emeritus, scientific associate, scientific advisor, Ph.D. students), a total of 584 of
650 who participated in the study were filtered out) and their answers were further ana-
lyzed. It must be noted that not all respondents answered every question in sections of
questionnaire related to them, so the number of answers per question varies from question
to question. That means that 584 respondents equal approx. 1.97% of all Croatian scientists
listed in the register of scientists and artists of the Republic of Croatia.
4. Findings
The findings are divided into four sections (A–D) in accordance with the structure of
the questionnaire.
4.1. Use and Non-Use of Other Researchers’ Data
The first section (Section A) of the research aimed at identifying whether the respon-
dents used other scientists’ research data or not.
A total of 584 respondents answered the initial questions about using or not using
other scientists’ research data. A total of 428 respondents (73.3%) used the research data of
other scientists while 156 respondents (26.7%) did not use other scientists’ research data.
As an addition to this question, the scientists in this research study were asked about
their years of service. The scientists had been working in science for 20 years (median).
These data about years of service were compared to the number of respondents who
used research data of other scientists (not all respondents provided their years of service
Publications 2024,12, 16 6 of 19
by choice): 1–10 years of service, 82 respondents used other scientists’ research data;
11–20 years
of service, 185 respondents used other scientists’ research data; 21–30 years
of service, 114 respondents used other scientists’ research data; 31–40 years of service
65 respondents used other scientists’ research data and 41+ years of service, 16 respondents
used other scientists’ research data. The years of service for the respondents who did not
use other scientists’ research data were the following: 1–10 years of service, 35 respondents
did not use other scientists’ research data; 11–20 years of service, 59 respondents did not
use other scientists’ research data; 21–30 years of service, 35 respondents did not use other
scientists’ research data; 31–40 years of service, 23 respondents did not use other scientists’
research data and 40+ years of service, three respondents did not use other scientists’
research data. Again, not all respondents provided data on their years of service.
The number of scientists in this research study who claimed they used other scientists’
research data is high. It is impossible to back up such results with actual numbers of
the exchanged research data because researchers use other scientists’ data from different
resources, sometimes internally (within the same lab, department or university) and fre-
quently there is no exact proof of these activities. This is the reason why this result should
be investigated further by conducting interviews with scientists.
A total of 153 respondents (3 out of the total of 156 who did not use other scientists’
data left this question unanswered) did not use other scientists’ research data for the
following reasons (multiple answers were possible). Table 1shows the detailed reasons
for not using other scientists’ data. Predefined reasons were offered to the respondents.
About three-quarters of the respondents (from those who did not use other scientists’
data) did not have a need for other scientists’ data, while other respondents encountered
different obstacles like paywall, membership, passwords or licenses. Such obstacles have
usually been met by scientists in recent decades and they have caused the inability to access
published papers and books. Now they have been extended to research data.
Table 1. Not using other scientists’ data (N = 153).
Not Using Other Scientists’ Data Respondents (N) Percentage %
I did not have need for other scientists’ research data
120 78.4
Access to research data was restricted by a paywall 16 10.5
Access to research data was restricted by
membership in an academic institution, etc. 18 11.8
Access to research data was restricted by password 11 7.2
User interface was too complex 6 3.9
I had to agree to licenses and other terms 8 5.2
User interface used a language I did not know 1 0.7
Other reasons 12 7.8
After answering this question, the respondents were asked to go to section D of the
questionnaire related to storing research data and providing access to them.
4.2. Sending Requests for Research Data to Other Scientists
The next section (section B) of the questionnaire was dedicated to sending requests for
research data of other scientists.
Direct contact with other scientists for gaining access to their research data was not
the first choice of the respondents (N = 182) (Table 2). Another big block of results started
with scientists who sent 2–3 requests (N = 163), while the rest of the respondents sent much
less requests.
The respondents were also asked to list the sources from whom or from which they
received research data (Table 3), suggesting that they used multiple resources for finding
research data before accessing them.
Publications 2024,12, 16 7 of 19
Table 2. Sending requests for data of other scientists (N = 428).
Sending Requests for Data of Other Scientists Respondents (N) Percentage %
Did not send a single request for research data to
other scientists 182 42.5
Sent one such request 52 12.1
Sent 2–3 requests 163 38.1
Sent 6–9 requests 31 7.2
Sent 10 or more requests 34 7.9
Table 3. Information on sources of research data (multiple answers were possible) (N = 419).
Sending Requests for Data of Other Scientists
Respondents (N)
Percentage %
Scientific publications (articles in magazines, books
and proceedings) 379 90.5
Colleagues with whom I work directly (in the same
department, etc.) 279 66.6
Colleagues working in scientific institutions
outside Croatia 270 64.4
Digital repositories outside Croatia 270 64.4
Digital repositories in Croatia 203 13.7
Colleagues working in other scientific institution in
Croatia 196 27.3
Colleagues with whom I work in the same institution 182 43.4
Other sources not listed here 16 3.8
Next, the respondents were asked to estimate the number of their own requests sent
to other scientists to gain access to research data (Table 4).
Table 4. Estimation of the number of their requests sent to other scientists (N = 420).
Estimation of the Number of Their Requests Sent
to Other Scientists Respondents (N) Percentage %
Did not send requests to other scientists 165 29.1
Sent one request 49 8.6
Sent 2–5 requests 142 25.0
Sent 6–9 requests 30 5.3
Sent 10 or more requests 34 5.99
Not all the delivered requests were answered positively: 0—16 respondents;
1—30 respondents; 2—22 respondents; 3—24 respondents; 4—five respondents; 5—eight
respondents; 6—three respondents; 7—one respondent; 10—six respondents; 15—one
respondent. Some respondents added their own answers instead of choosing pre-defined
answers: “a few”—four respondents; “less than half”—two respondents; “half”—three
respondents; “almost all”—22 respondents; “all”—116 respondents
The next four questions were oriented toward estimating the importance of research
data for different phases of research in general: acquiring ideas for new research (N = 580),
preparation of research (N = 575), execution of research (N = 580) and verifying the quality
of research (N = 579).
Figure 1shows the importance of research data in four aspects of research: developing
ideas for new research, preparation of research, implementation of research and finally,
increasing quality of research. The respondents considered increasing quality of research
as the most important aspect of research data over other aspects of use of research data.
Publications 2024,12, 16 8 of 19
Publications 2024, 12, x FOR PEER REVIEW 8 of 21
Table 4. Estimation of the number of their requests sent to other scientists (N = 420).
Estimation of the Number of Their
Requests Sent to Other Scientists
Respondents (N)
Percentage %
Did not send requests to other scien-
tists
165
29.1
Sent one request
49
8.6
Sent 2–5 requests
142
25.0
Sent 6–9 requests
30
5.3
Sent 10 or more requests
34
5.99
Not all the delivered requests were answered positively: 0—16 respondents; 1—30
respondents; 2—22 respondents; 3—24 respondents; 4—ve respondents; 5—eight re-
spondents; 6—three respondents; 7—one respondent; 10—six respondents; 15—one re-
spondent. Some respondents added their own answers instead of choosing pre-dened
answers: “a few”—four respondents; “less than half”—two respondents; “half”—three
respondents; “almost all”—22 respondents; “all”—116 respondents
The next four questions were oriented toward estimating the importance of research
data for dierent phases of research in general: acquiring ideas for new research (N = 580),
preparation of research (N = 575), execution of research (N = 580) and verifying the quality
of research (N = 579).
Figure 1 shows the importance of research data in four aspects of research: develop-
ing ideas for new research, preparation of research, implementation of research and -
nally, increasing quality of research. The respondents considered increasing quality of re-
search as the most important aspect of research data over other aspects of use of research
data.
Figure 1. Importance of research data for dierent phases of research (1—least important; 5—most
important).
In the last question in this section, the respondents were asked whether they ever
tried to repeat the scientic research of another scientist based on their available research
data (Table 5.).
1 2 3 4 5
ideas 10 26 78 174 139
preparation 315 94 158 148
execution 930 130 141 111
quality 1 5 32 121 262
0
50
100
150
200
250
300
ideas preparation execution quality
Figure 1. Importance of research data for different phases of research (1—least important; 5—
most important).
In the last question in this section, the respondents were asked whether they ever tried
to repeat the scientific research of another scientist based on their available research data
(Table 5).
Table 5. Succes of repeating scientific research of another scientist (N = 423).
Success of Repeating Scientific
Research of Another Scientist Respondents (N) Percentage %
No 253 59.8
Yes, I succeeded/I managed to get identical results 95 23.2
Yes, but I could not get identical results 75 17.7
The issue of reproducibility of research in science is a problem that has been well
described in the scientific literature. Scientists involved in repeating one’s research would
most certainly like to obtain identical results to those in the original research/analysis.
This was the last question in this section that was aimed only at scientists who use
other scientists’ research data.
The next section (section C) was oriented toward the frequency of use of other scientists’
research data as a template for new research, comparison with other research and quality
control of research.
4.3. Providing Access to Own Research Data
The remaining part of the research study results included all the respondents, regard-
less of whether they used other scientists’ research data or not.
The respondents were asked to estimate the number of requests they received for their
own research data in the last five years (Table 6).
Table 6. Number of requests they received for their research data in the last five years (N = 584).
Number of Requests They Received for Their
Research Data in the Last Five Years Respondents (N) Percentage %
One request 56 9.6
2–5 requests 183 31.3
6–9 requests 46 7.9
More than 10 requests 49 5.0
Did not receive any request 254 43.5
Publications 2024,12, 16 9 of 19
Almost half of the respondents did not receive any request from other scientists for
their research data, while over half of the respondents (cumulatively) received one or
more requests.
The next part of this research study was oriented toward providing access to research
data for other scientists (the second part of the questionnaire).
In addition to the information found in Table 7, the respondents (N = 459) were
divided according to their working experience (years of services), regardless of the mode
of providing access to research data: 1–10 years of service, 82 respondents opened access
to their research data; 11–20 years of service, 169 respondents opened access to their
research data; 21–30 years of service, 127 respondents opened access to their research data;
31–40 years
of service, 69 respondents opened access to their research data and 40+ years of
service, nine respondents opened access to their research data. Next, the respondents who
did not open access to their research data (N = 152) were divided according to their years
of service: 1–10 years of service, 35 respondents did not open access to their research data;
11–20 years
of service, 60 respondents did not open access to their research data;
21–30 years
of service, 36 respondents did not open access to their research data;
31–40 years
of service,
19 respondents did not open access to their research data and 40+ years of service, two
respondents did not open access to their research data.
Table 7. Providing access to research data for other scientists (N = 640).
Providing Access to Research Data for Other Scientists
Respondents (N)
Percentage %
Access to all their research data without restrictions 186 29.1
Did not provide access to research data 157 24.5
Partial access to their research data without restrictions 148 23.1
Access to their research data in some other way not listed
in pre-defined answers 120 18.8
Partial access to research data under certain conditions
(IP address, password, etc.) 21 3.3
Access to all their research data with some restrictions
(IP address, password, etc.) 11 1.7
While scientists shared their research data partially or fully, they also identified prob-
lems they encountered or feared encountering during the sharing of their own research
data with other scientists (Table 8).
Table 8. Research data sharing problems (N = 430) (multiple answers were possible).
Research Data Sharing Problems
Respondents (N)
Percentage %
Fear of misuse of your research data in the form of
idea theft 135 31.4
Excessive consumption of time 128 29.8
Fear of misuse of data of your research data in the form of
theft of authorship 112 26.0
Lack of research data sharing infrastructure 110 25.6
Technical problems when sharing scientific research data 106 27.0
Lack of knowledge about how to share information about
other scientists’ data 99 23.0
Fear of misuse of your research data for
commercial purposes 70 16.3
Costs of storing and sharing research data with others 44 10.2
There was no problem 27 6.3
Law restrictions and GDPR 5 1.2
Instead of research data, published works were shared 4 0.9
Improper or lack of citations 3 0.7
Research data were published in journals 3 0.7
Publications 2024,12, 16 10 of 19
Research data sharing depends on the good will of scientists, but it also depends on a
potential system of recognition received for this activity.
Receiving recognition for sharing research data is one of the most important moments
in scientists’ careers, as it acknowledges scientists’ efforts in certain areas. Table 9shows
that almost none of the respondents received a recognition for sharing their research data,
which does not motivate scientists to start sharing their research data in the future.
Table 9. Recognition received for sharing research data (N = 488).
Recognition Received for Sharing
Research Data Respondents (N) Percentage %
Received recognition or an award once 9 1.8
Received recognition several times 22 4.5
Never received recognition 457 93.7
The results presented in Table 10 indicate the awareness of practical aspects of opening
access to research data like researchers’ visibility, creating partnerships, transparency,
quality and citations, which are benefits found at the top of the list (first five choices).
Advancement in academic career and receiving recognition for opening/providing access
to research data were least selected by the respondents. The complete lack of recognition
or inadequate recognition for researchers for opening access to their research data was
indicated in the previous question, and the same answer in this question was ranked very
low and perceived to be less important by the respondents in this research study.
Table 10. Benefits of opening access to research data to other researchers (N = 457) (multiple answers
were possible).
Benefits of Opening Access to Research Data to Other
Researchers
Respondents (N)
Percentage (%)
Increasing your visibility in the scientific community 348 76.2
Creating partnerships in the preparation and
implementation of scientific research 324 70.9
Increasing the transparency of scientific research 306 67.0
Increasing the quality of new scientific research 277 60.6
Increasing the citation of your scientific papers and
scientific research data 249 54.5
Inspiration for starting new scientific research 237 51.9
Increasing the responsibility of scientists for conducting
scientific research and presenting the results of
that research
204 44.6
Confirming the accuracy of your scientific research results
201 44.0
Reducing the burden on scientists by using already
existing scientific research data 134 29.3
Spotting mistakes in the scientific research of
other scientists 117 25.6
Obtaining funds and/or equipment for the
implementation of new research 84 19.0
Advancement to a higher scientific title based on storage
activities and sharing of scientific research data 68 14.9
Receiving an award or recognition 34 7.4
Other 6 1.3
I see no benefit in providing access to my research data 5 1.1
To be able to provide access to research data, researchers must be offered some type of
training/education about the process of research data management.
Based on the results in Table 11, researchers should be offered education about research
data management if the scientific community in general expects them to share their research
data on a wider scale.
Publications 2024,12, 16 11 of 19
Table 11. Training/education about the process of research data management (N = 455).
Training/Education about the Process of Research
Data Management
Respondents
(N)
Percentage
(%)
Live lectures 24 5.3
Live workshops 24 5.3
University course 4 0.9
Live course 11 2.4
Webinars 71 15.6
Did not receive any of education about research data management 321 70.6
The next section (section D of these findings and the third part of the questionnaire) in
the research study was dedicated to data archiving, a general and crucial precondition for
data sharing and data reuse.
4.4. Research Data Storing and Archiving
The first question in this section was about devices or places for storing research data.
Table 12 suggests that the respondents store their research data most frequently on their
own computer at work, which is potentially very dangerous in the case of computer failure.
Additionally, they stored research data on network drives in the cloud, which can also
be dangerous if the network connection is lost, in the case of a security breach or due to
another type of failure to access the data. Storing research data on offline external drives
(and possibly on different locations) is a much better solution than storing data only on
one’s computer at work or on a shared computer. Digital repositories are not so popular,
while there is a growing number of general types of repositories and data-only repositories
worldwide available to scientists from different countries.
Table 12. Storing research data (N = 428) (multiple answers were possible).
Storing Research Data Respondents (N) Percentage (%)
On your own computer at work 255 59.6
On a network drive in the cloud 126 29.4
On an external hard drive 114 26.6
On an external SSD 48 11.2
In the institutional digital repository 46 10.8
On a shared computer at work where they store the
contents of scientific research 30 7.0
I do not independently store scientific research data 8 1.9
On optical media 6 1.4
On a shared computer at work where they store the
contents of scientific research 3 0.7
File format is a highly important element in research data archiving. The choice
of data file format (Table 13) depends on the area of science in which different devices
are used for the acquisition of data (by taking a record of a phenomenon, by recording
measurements, etc.). Table 13 shows the great versatility of file formats. Some of them are
standardized and appear in all areas of science, while some can be found only in particular
areas of science and relate to some type of laboratory instrument paired with a computer,
etc.
Time spent on data archiving is one of the biggest time eaters when considering a
scientist’s (monthly) workload. The results in Table 14 show that more than one-third of
the respondents spend less than one hour monthly on data archiving, and that more than
three-quarters of the respondents (cumulatively) spend up to 5 h monthly on data archiving.
While the time spent on data archiving may differ from one research area to another and
may depend on the type of research, the results show that more than three-quarters of the
respondents do spend up to 5 h a month on data archiving, which is a positive result.
Publications 2024,12, 16 12 of 19
Table 13. File formats of research data (N = 428).
Research Data File Formats
Respondents (N) Percentage (%)
Text files (TXT, DOC, DOCX, ODF, XML, PDF itd.) 399 93.2
Still pictures (TIFF, JPEG, PNG, SVG itd.) 268 62.6
Datasets (TSV, CSV, XLS, XLSX, XML, JSON itd.) 200 46.7
Video (MPEG4, MPEG2, QuickTime) 65 15.2
Geospatial data (ESRI, OGC, Geo PDF, GEOTIFF, etc.) 44 10.3
Sound (PCM, AIFF, MP3 itd.) 41 9.6
Other formats and applications related to specific formats like:
.fasta, .abi; databases; BIN, CCS, MAT; simulator files and
program code;
Dwg, Excell, grib, NetCDF and other binary files; LOG,
FCHK, CIF, SDF, TRJ, mgf, raw NMR specters (FID); NVvivo
software; opj; various output formats of specific software;
different programming languages; spss (multiple answers);
SQL (dump); various database backup formats; statistical
software files; Web GIS baza databases; XLS SPW
25 5.8
None of these formats 5 1.2
Table 14. Time spent on research data archiving (N = 623).
Time Spent on Research Data Archiving Respondents (N) Percentage (%)
Less than an hour 251 40.3
1–5 h 248 40.0
6–10 h 56 9.0
More than 10 h 32 5.1
I do not spend any time at all archiving the data of
my scientific research 58 9.3
Data archiving requires knowledge about online or offline storage systems, file formats,
metadata creation for data description and institutional policies for data archiving. The
Croatian scientists were asked to rate their knowledge about research data archiving. A
total of 644 respondents provided answers on a scale from 1 (no knowledge at all) to
5 (excellent knowledge). The results in Table 15 indicate that they acquired a certain
amount of knowledge about data archiving but there is still space to reach a more advanced
level of knowledge.
Table 15. Rating one’s own knowledge about research data archiving (N = 644).
Research Data Archiving Level of Knowledge Respondents (N) Percentage (%)
1 42 6.5
2 173 26.9
3 271 42.1
4 129 20.0
5 29 4.5
5. Discussion
Empirical research studies of this type are a good base for solving different problems
in research data management and could enable comparative analyses of conditions for
work in different areas of science.
The results of this research study on the Croatian scientists identified several problems
in use and management of research data. Roughly three-quarters of the respondents
claimed they used other scientists’ research data, which is a very good result. Still, they
ran into different obstacles like paywalls, memberships in institutions or technical issues,
which are all globally present problems that remain unsolved. Therefore, they require
additional efforts on the side of different stakeholders involved in scientific endeavors.
Publications 2024,12, 16 13 of 19
Some obstacles, like paywalls, require cooperation with information aggregators in the
commercial sector and are subject to long-term negotiations with commercial publishers.
Technical issues are more easily solved but can sometimes be expensive.
Generally, some of the encountered problems are local and infrastructural and can
be solved rather easily (e.g., digital research data repositories), while other problems are
global and require more money, time and effort to be solved on the international level (e.g.,
publishing fees and research data storing as a supplement to books and articles).
Almost half of the respondents did not obtain data by sending requests to other
scientists to access their data, whether they did not have a need for research data or
did not receive any answer from the scientists to whom they sent requests. This latter
problem could be solved by storing data in open data repositories to alleviate the problem
of spending too much time on direct communication with other scientists.
The use of other scientists’ research data by a larger number of scientists is practical,
as a single researcher cannot discover all the problems present in some areas of research.
Also on the practical side, most of the research data that the respondents use in their
analyses or new research come from their colleagues from the same department or institu-
tion, who are easily accessible and are reliable sources of research data. Research data also
come from colleagues from outside the country with varying but mostly positive outcomes.
This research study showed that the Croatian scientists consider research data to be im-
portant in several cases: for obtaining ideas for new research, its preparation and execution,
which is expected (if one uses research data as a starting point). The most important quality
of research data for the respondents is that they are a means to increase research quality as
they view research data as tools for verifying and increasing research quality. Regarding
the problem of bad reproducibility of someone’s research, it has been extensively addressed
in the scientific literature but the solution to this problem has not been found yet. One
evident reason is the need for hyperproduction of scientific output in the form of published
books and articles as proof of one’s scientific abilities. The hyperproduction diminishes the
research quality and focuses on bureaucratic criteria for academic advancement. A grow-
ing number of research studies therefore remain irreproducible due to different problems
related to low quality, which was partially confirmed by this research study. A significant
number of researchers in this research study did not try to reproduce someone’s research by
using his/her research data, while one-quarter of the respondents claim that they managed
to reproduce research and obtain the exact same results. Clearly, the results are not good.
Archiving data is another big and important topic in global science covered by this
research study. The Croatian scientists who participated in this research study store
research data on their own desktop computer, which could lead to a disaster in case
of hardware failure. There are also other technical solutions for storing research data other
than desktop computers like cloud infrastructure and external drives, each with its own
problems, but they are still more reliable for long-term storing/archiving research data
than desktop computers used by one or even many scientists. One possible solution is that
cloud infrastructure could be better marketed to scientists.
Knowledge about data archiving has not yet reached a desirable level at which sci-
entists will have advanced knowledge on this topic. Their knowledge is currently at an
intermediate level. In Croatia, scientists have an opportunity to store research data in the
national digital repository system, specially made for the Croatian academic community.
The proper storing of research data enables data sharing and data reuse. However,
data sharing is not a straightforward process since there are many administrative, legal and
technical obstacles and fears like misuse of research data, excessive consumption of time
while sharing data to other people, idea theft, etc. These are very serious problems that are
present globally in academic communities and remain unresolved. The same problems are
present in the Croatian academic community and were recognized by the scientists partici-
pating in this research study. Another area that can be further researched is the benefits
of opening research data to other scientists. The benefits were also clearly recognized by
Publications 2024,12, 16 14 of 19
the respondents, but are hardly present in their daily work, and this is especially true for
receiving reward for data sharing, which is a practically non-existent occurrence.
Finally, education on how to manage research data has become necessary, yet close
to three-quarters of the respondents did not receive any form of education. Education in
research data management will help them to overcome obstacles and spend less time on
research data management.
This research study confirmed the hypothesis according to which the Croatian scien-
tists who participated in this research study are only moderately ready for data sharing
and data reuse, which also only partially fulfils their research mission in society. They will
have to put more effort to achieve better results in research data management to make data
sharing and data reuse more easily doable, but they should also receive recognition and
rewards for doing so. This study also provided answers to all three research questions, as
discussed in this part of the paper.
6. Conclusions
Research studies on research data management have now become common. While
there is global interest in making research data easily obtainable, there are also local
particularities that still block easy data sharing and reuse. Today, we know more about the
role of research data in designing and executing new research than we knew yesterday, but
we still need more marketing on research data management processes and infrastructures
supporting scientists. Science is in transition toward open science, and every development
that helps scientists to better and more broadly communicate the results of their research is
valuable. This research study on the Croatian academic community shows that the Croatian
scientists are partially ready for open science. The presumption is (which should be further
researched) that those scientists who are collaborating with their colleagues outside Croatia
and sharing and using the same datasets they created are more open to the idea of data
sharing and data reuse and are probably actively participating in open science.
Funding: This research received no external funding.
Data Availability Statement: The original contributions presented in the study are included in the
article, further inquiries can be directed to the corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Questionnaire
(1)
In general, until the moment of your participation in this research, have you used
data (regardless of their type) of scientific research by other scientists for the purposes
of your scientific work?
(a)
Yes, I use data from other scientists and scientists from their scientific research
(go to question 3)
(b)
No, I do not use the data of other scientists from their scientific research (mark
the reasons for a negative answer in the next question)
(2)
If your answer to the previous question was negative, please choose the reasons for
not accessing scientific research data of other scientists. AFTER THIS QUESTION GO
TO QUESTION 14!
(a) Access to scientific research data required a password;
(b) The interface of scientific research data sources is too complex to use;
(c) The scientific research data source interface uses a language I do not know;
(d)
To access the data, I have to agree to license and other conditions that are
unacceptable to me;
(e)
To access scientific research data, it is necessary to be a member of a certain
institution, professional society, etc.;
(f) I had no need to use the data of other scientists’ scientific research;
Publications 2024,12, 16 15 of 19
(g) Access to scientific research data requires payment;
(h) Other:
Use of scientific research data
(3)
Among the answers offered, choose the sources of scientific research data (EXCEPT
DATA FROM YOUR OWN RESEARCH) that you use in your daily scientific work
(it is possible to mark several answers).
(a)
The information I receive from the closest colleagues with whom I work directly
(in the department, etc.);
(b)
Information I receive from other colleagues in the same scientific institution
where I work;
(c)
Information I receive from colleagues in other scientific institutions in Croatia;
(d)
Information I receive from colleagues in scientific institutions outside Croatia;
(e) Data that I find in digital repositories of scientific information in Croatia;
(f)
Data that I find in digital repositories of scientific information outside of Croatia;
(g)
Information that I find in scientific literature (articles in magazines, books
and proceedings);
(h) Other:
(4)
According to your estimation, in the last 5 years, how many requests have you sent to
a scientist/scientists to give you access to the data of their scientific research?
(a) One;
(b) 2–5 requests;
(c) 6–9 requests;
(d) 10 or more requests;
(e) No, so far I have not sent a request to other scientists.
(5)
If you sent one or more requests, how many of your requests were answered posi-
tively? The importance of scientific research data
(6)
Mark the IMPORTANCE that the scientific research data of other scientists have on
your consideration of ideas for starting your new research? They have no importance;
1 2 3 4 5 They are extremely important.
(7)
Mark the IMPORTANCE that the scientific research data of other scientists have in
the preparation of your scientific research? They have no importance; 1 2 3 4 5 They
are extremely important.
(8)
Mark the IMPORTANCE that the scientific research data of other scientists have in
the implementation of your scientific research? They have no importance; 1 2 3 4 5
They are extremely important.
(9)
In general, do you consider publicly available scientific research data of other sci-
entists IMPORTANT for increasing the quality of scientific research? They have no
importance; 1 2 3 4 5 They are extremely important. The purpose of using scientific
research data
(10)
How often do you use the scientific research data of other scientists to control the
quality of their scientific research?
(a) Constantly;
(b) Most often;
(c) Sometimes;
(d) Rarely;
(e) Never.
(11)
How often do you use the scientific research data of other scientists for comparison
with the results of your own research?
(a) Constantly;
(b) Often;
(c) Usually;
(d) Sometimes;
Publications 2024,12, 16 16 of 19
(e) Rarely;
(f) Never.
(12)
How often do you take the data of scientific research of other scientists as a template
for formulating research questions in your scientific research?
(a) Constantly;
(b) Most often;
(c) Sometimes;
(d) Rarely;
(e) Never.
(13)
Have you ever tried to repeat the scientific research of another scientist based on the
available data of their scientific research?
(a) Yes, but I could not get identical results;
(b) Yes, I succeeded/I managed to get identical results;
(c) No.
Enabling access to YOUR scientific research data to OTHER scientists
(14)
According to your estimation, in the last 5 years, how many requests have you
received from another scientist (or more) to grant them access to the data of your
scientific research?
(a) One request;
(b) 2–5 requests;
(c) 6–9 requests;
(d) 10 requests and more;
(e) No, I have not received any request.
(15)
If you received one or more requests, how many of these requests did you respond
to positively?
(16)
Have you provided access to the data of your scientific research at THIS MOMENT?
(a) Yes, I offer open access to ALL data from my scientific research;
(b) Yes, I offer open access to ONLY ONE PART of my scientific research data;
(c)
Yes, I offer access to ALL the data of my scientific research with some restriction
(password, IP address, etc.);
(d)
Yes, I offer access to ONLY ONE part of the data of my scientific research with
some restriction (password, IP address, etc.);
(e)
I offer some form of access to the data of my scientific research that is not
listed here;
(f)
No, I have not provided access to my research data (partially or in full) (go to
question 19!).
(17)
If the answer to the previous question was POSITIVE, please answer whether you
have ever received any form of recognition or award for providing access to the data
of your scientific research to other scientists?
(a) Yes, but only once;
(b) Yes, more than once;
(c) No, never.
(18)
What problems have you encountered when sharing data from your own
scientific research?
(a) Excessive consumption of time;
(b) Costs of storing and sharing scientific research data with others;
(c) Technical problems when sharing scientific research data;
(d) Lack of knowledge about how to share information with others;
(e) Lack of data sharing infrastructure;
(f) Fear of data misuse of your scientific research in the form of idea theft;
(g)
Fear of data misuse of your scientific research in the form of theft of authorship;
Publications 2024,12, 16 17 of 19
(h) Fear of misuse of your scientific research data for commercial purposes;
(i) Other:
(19)
Providing access to the data of your scientific research to other scientists can be
useful for:
(a) Increasing your visibility in the scientific community;
(b)
Creating partnerships in the preparation and implementation of
scientific research;
(c) Obtaining funds and/or equipment for the implementation of new research;
(d) Receiving an award or recognition;
(e)
Advancement to a higher scientific title based on storage activities and sharing
of scientific research data;
(f) Increasing the transparency of scientific research;
(g)
Increasing the responsibility of scientists for conducting scientific research and
presenting the results of that research;
(h) Increasing the quality of new scientific research;
(i)
Reducing the workload of scientists by using already existing scientific re-
search data;
(j) Inspiration for starting new scientific research;
(k) Spotting mistakes in the scientific research of other scientists;
(l) Increasing the citation of your scientific papers and scientific research data;
(m) Confirming the accuracy of the results of your scientific research;
(n) I see no benefit in providing access to the data of my scientific research;
(o) Other:
(20)
In the last 5 years, have you participated in any form of education dedicated to the
management of scientific research data?
(a) Yes, a live course;
(b) Yes, a live workshop;
(c) Yes, a live lecture;
(d) Yes, Webinar;
(e) Yes, a university course;
(f) No.
Storage and archiving of scientific research data
(21) Where do you most often INDEPENDENTLY store the data of your scientific research?
(a) On your own computer at work;
(b)
On a shared computer at work where they store the contents of
scientific research;
(c) To a network drive in the cloud;
(d) In the institution’s digital repository;
(e) To an external hdd;
(f) To an external SSD;
(g) On optical media;
(h) I do not independently store scientific research data.
(22)
What file formats do you use to store the data of your scientific research?
(a) Text files (TXT, DOC, DOCX, ODF, XML, PDF, etc.);
(b) Images (TIFF, JPEG, PNG, SVG, etc.);
(c) Video (MPEG4, MPEG2, QuickTime;
(d) Sound (PCM, AIFF, MP3, etc.);
(e) Datasets (TSV, CSV, XLS, XLSX, XML, JSON, etc.);
(f) Spatial data (ESRI, OGC, Geo PDF, GEOTIFF, etc.);
(g) None of the above formats;
(h) Other:
Publications 2024,12, 16 18 of 19
(23)
According to your estimation, how much time do you spend PER MONTH on archiv-
ing the data of your scientific research?
(a) Less than an hour;
(b) 1–5 h;
(c) 5–10 h;
(d) More than 10 h;
(e) I do not spend any time at all archiving the data of my scientific research.
(24)
Rate your level of knowledge about data archiving of scientific research. Completely
without knowledge; 1 2 3 4 5 Excellent knowledge. Data on research participants
(25)
In which field of science do you work?
(a) Natural Sciences;
(b) Technical sciences;
(c) Biomedicine and healthcare;
(d) Biotechnical sciences;
(e) Social sciences;
(f) Humanities;
(g) Artistic area;
(h) Interdisciplinary fields of science;
(i) Interdisciplinary fields of art;
(j) Other:
(26)
Mark your gender.
(a) Male;
(b) Female;
(c) I do not want to state.
(27)
How much work experience do you have in jobs related to science?
(28)
What is your current associate, scientific, scientific–teaching or artistic–teaching title?
(Scientific–teaching professions choose only one answer!)
(a) Scientific associate;
(b) Senior research associate;
(c) Scientific adviser;
(d) Scientific advisor in permanent position;
(e) Assistant;
(f) Senior assistant;
(g) Assistant professor;
(h) Associate professor;
(i) Full-time teacher;
(j) Full professor in a permanent position;
(k) Other:
References
1.
Tenopir, C.; Rice, N.M.; Allard, S.; Baird, L.; Borycz, J.; Christian, L.; Grant, B.; Olendorf, R.; Sandusky, R.J. Data sharing,
management, use, and reuse: Practices and perceptions of scientists worldwide. PLoS ONE 2020,15, e0229003. [CrossRef]
2.
Zenk-Möltgen, W.M.; Akdeniz, E.; Katsanidou, A.; Naßhoven, V.; Balaban, E. Factors influencing the data sharing behavior of
researchers in sociology and political science. J. Doc. 2018,74, 1053–1073. [CrossRef]
3.
Whitmire, A.L.; Boock, M.; Sutton, S.C. Variability in academic research data management practices: Implications for data services
development from a faculty survey. Program 2015,49, 382–407. [CrossRef]
4.
Rehnert, M.; Takors, R. FAIR research data management as community approach in bioengineering. Eng. Life Sci. 2023,
23, e2200005. [CrossRef]
5.
Makani, J. Knowledge management, research data management, and university scholarship: Towards an integrated institutional
research data management support-system framework. Vine 2015,45, 344–359. [CrossRef]
6.
Zinieris, M. Data: A Small Four-Letter Word Which Has Grown Exponentially to Such a Big Value. Available online: https:
//www2.deloitte.com/cy/en/pages/technology/articles/data-grown-big-value.html# (accessed on 12 April 2023).
7.
Khan, N.; Thelwall, M.; Kousha, K. Data sharing and reuse practices: Disciplinary differences and improvements needed. Online
Inf. Rev. 2023, ahead-of-print. [CrossRef]
Publications 2024,12, 16 19 of 19
8.
Parks, N.; Fischer, T.; Blankenburg, C.; Scalfani, V.; McEwen, L.; Herres-Pawlis, S.; Neumann, S. The current landscape of author
guidelines in chemistry through the lens of research data sharing. Pure Appl. Chem. 2023,95, 439–450. [CrossRef]
9.
Childs, S.; McLeod, J.; Lomas, E.; Cook, G. Opening research data: Issues and opportunities. Rec. Manag. J. 2014,24, 142–162.
[CrossRef]
10.
Fecher, B.; Friesike, S.; Hebing, M.; Linek, S.; Sauermann, A. A Reputation Economy: Results from an Empirical Survey on Academic
Data Sharing. Discussion Papers of DIW Berlin 1454, DIW Berlin, German Institute for Economic Research. Available online:
https://www.econstor.eu/handle/10419/107687 (accessed on 12 April 2023). [CrossRef]
11.
Elman, C.; Kapiszewski, D.; Vinuela, L. Qualitative Data Archiving: Rewards and Challenges. PS Political Sci. Politics 2010,43,
23–27. [CrossRef]
12.
Mallasvik, M.L.; Martins, J.T. Research data sharing behaviour of engineering researchers in Norway and the UK: Uncovering the
double face of Janus. J. Doc. 2021,77, 576–593. [CrossRef]
13.
Joye, D. Qualitative or Quantitative? Data Archiving in Documentation, Research and Teaching. Qual. Social. Res. 2005,6.
[CrossRef]
14. Askarov, Z.; Doucouliagos, A.; Doucouliagos, H.; Stanley, T.D. The Significance of Data-Sharing Policy. J. Eur. Econ. Assoc. 2023,
21, 1191–1226. [CrossRef]
15. Data sharing in the age of deep learning. Nat. Biotechnol. 2023,41, 433. [CrossRef] [PubMed]
16.
Pernet, C.; Svarer, C.; Blair, R.; Van Horn, J.D.; Poldrack, R.A. On the Long-term Archiving of Research Data. Neuroinform 2023,21,
243–246. [CrossRef] [PubMed]
17. Perry, A.; Netscher, S. Measuring the time spent on data curation. J. Doc. 2022,78, 282–304. [CrossRef]
18.
Sinn, D.; Kim, S.; Syn, S.Y. Personal digital archiving: Influencing factors and challenges to practices. Libr. Hi Tech. 2017,35,
222–239. [CrossRef]
19. Borgman, C.L. The conundrum of sharing research data. J. Am. Soc. Form. Sci. Technol. 2012,63, 1059–1078. [CrossRef]
20.
Leonelli, S. Rethinking Reproducibility as a Criterion for Research Quality. In Symposium on Mary Morgan: Curiosity, Imagination,
and Surprise (Research in the History of Economic Thought and Methodology, Vol. 36B); Emerald Publishing Limited: Bingley, UK, 2018;
pp. 129–146. [CrossRef]
21.
Limani, F.; Hajra, A.; Ferati, M.; Radevski, V. Research data repository requirements: A case study from universities in North
Macedonia. Int. J. Knowl. Content Dev. Technol. 2023,13, 75–100. [CrossRef]
22.
Kim, Y.; Stanton, J.M. Institutional and Individual Factors Affecting Scientists’ Data Sharing Behaviors: A Multilevel Analysis. J.
Assn Inf. Sci. Tec. 2016,67, 776–799. [CrossRef]
23.
Horton, L.; Katsanidou, A. Purposing your survey: Archives as a market regulator, or how can archives connect supply and
demand? IASSIST Q. 2012,35, 18. [CrossRef]
24.
Lin Yoong, S.; Turon, H.; Grady, A.; Hodder, R.; Wolfenden, L. The benefits of data sharing and ensuring open sources of
systematic review data. J. Public. Health 2022,44, e582–e587. [CrossRef] [PubMed]
25.
Lewis, M.J. Libraries and the management of research data. In Envisioning Future Academic Library Services; McKnight, S., Ed.;
Facet Publishing: London, UK, 2010; pp. 145–168. [CrossRef]
26.
Kling, R.; Spector, L. Academic Rewards for Scholarly Research Communication via Electronic Publishing. CSI Working Paper.
Available online: https://scholarworks.iu.edu/dspace/handle/2022/167 (accessed on 12 April 2023).
27. Faniel, I.M.; Frank, R.D.; Yakel, E. Context from the data reuser’s point of view. J. Doc. 2019,75, 1274–1297. [CrossRef]
28.
Evertsz, N.; Bull, S.; Pratt, B. What constitutes equitable data sharing in global health research? A scoping review of the literature
on low-income and middle-income country stakeholders’ perspectives. BMJ Glob. Health 2023,8, e010157. [CrossRef] [PubMed]
29.
Wright, B.; Brunner, O.; Nebel, B. On the Importance of a Research Data Archive. In Proceedings of the AAAI Conference on
Artificial Intelligence, New Orleans, LA, USA, 2–7 February 2018; p. 32. [CrossRef]
30.
Zarghani, M.; Nemati-Anaraki, L.; Sedghi, S.; Noroozi Chakoli, A.; Row-hani-Farid, A. The Application of Open Science Potentials
in Research Processes: A Comprehensive Literature Review. Libri 2023,73, 167–186. [CrossRef]
31.
Cocciolo, A.; Yanni, M. Personal Digital Archiving and Catastrophic Data Loss: Is it Getting Better? Preserv. Digit. Technol. Cult.
2022,51, 133–139. [CrossRef]
32. Croatian Scientific Bibliography “CROSBI”. Available online: https://www.croris.hr/crosbi/ (accessed on 19 April 2024).
33. Croatian Research Information System. Available online: https://www.croris.hr/?lang=en (accessed on 19 April 2024).
34.
Register of Scientists and Artists of the Republic of Croatia. Available online: https://mzo.gov.hr/en (accessed on 19 April 2024).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
