Design and validation of a conceptual model regarding impact of open science on healthcare research processes

Abstract

Introduction
The development and use of digital tools in various stages of research highlight the importance of novel open science methods for an integrated and accessible research system. The objective of this study was to design and validate a conceptual model of open science on healthcare research processes.

Methods
This research was conducted in three phases using a mixed-methods approach. The first phase employed a qualitative method, namely purposive sampling and semi-structured interview guides to collect data from healthcare researchers and managers. Influential factors of open science on research processes were extracted for refining the components and developing the proposed model; the second phase utilized a panel of experts and collective agreement through purposive sampling. The final phase involved purposive sampling and Delphi technique to validate the components of the proposed model according to researchers’ perspectives.

Findings
From the thematic analysis of 20 interview on the study topic, 385 codes, 38 sub-themes, and 14 main themes were extracted for the initial proposed model. These components were reviewed by expert panel members, resulting in 31 sub-themes, 13 main themes, and 4 approved themes. Ultimately, the agreed-upon model was assessed in four layers for validation by the expert panel, and all the components achieved a score of > 75% in two Delphi rounds. The validated model was presented based on the infrastructure and culture layers, as well as supervision, assessment, publication, and sharing.

Conclusion
To effectively implement these methods in the research process, it is essential to create cultural and infrastructural backgrounds and predefined requirements for preventing potential abuses and privacy concerns in the healthcare system. Applying these principles will lead to greater access to outputs, increasing the credibility of research results and the utilization of collective intelligence in solving healthcare system issues.

Full text

RESEARCH Open Access
© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,
sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and
the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included
in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available
in this article, unless otherwise stated in a credit line to the data.
Zarghani et al. BMC Health Services Research (2024) 24:309
https://doi.org/10.1186/s12913-024-10764-z BMC Health Services Research
*Correspondence:
Leila Nemati-Anaraki
nematianaraki.l@iums.ac.ir; lnemati@yahoo.com
Full list of author information is available at the end of the article
Abstract
Introduction The development and use of digital tools in various stages of research highlight the importance of
novel open science methods for an integrated and accessible research system. The objective of this study was to
design and validate a conceptual model of open science on healthcare research processes.
Methods This research was conducted in three phases using a mixed-methods approach. The rst phase employed a
qualitative method, namely purposive sampling and semi-structured interview guides to collect data from healthcare
researchers and managers. Inuential factors of open science on research processes were extracted for rening
the components and developing the proposed model; the second phase utilized a panel of experts and collective
agreement through purposive sampling. The nal phase involved purposive sampling and Delphi technique to
validate the components of the proposed model according to researchers’ perspectives.
Findings From the thematic analysis of 20 interview on the study topic, 385 codes, 38 sub-themes, and 14 main
themes were extracted for the initial proposed model. These components were reviewed by expert panel members,
resulting in 31 sub-themes, 13 main themes, and 4 approved themes. Ultimately, the agreed-upon model was
assessed in four layers for validation by the expert panel, and all the components achieved a score of > 75% in
two Delphi rounds. The validated model was presented based on the infrastructure and culture layers, as well as
supervision, assessment, publication, and sharing.
Conclusion To eectively implement these methods in the research process, it is essential to create cultural and
infrastructural backgrounds and predened requirements for preventing potential abuses and privacy concerns in
the healthcare system. Applying these principles will lead to greater access to outputs, increasing the credibility of
research results and the utilization of collective intelligence in solving healthcare system issues.
Keywords Conceptual model, Open science, Open research, Openness in science, Openness in research, Validation
Design and validation of a conceptual
model regarding impact of open science
on healthcare research processes
MaryamZarghani1, LeilaNemati-Anaraki2,3* , ShahramSedghi2,4 , Abdolreza NorooziChakoli5 and
AnisaRowhani-Farid6
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 2 of 12
Zarghani et al. BMC Health Services Research (2024) 24:309
Introduction
e transformation of information carriers, digital
media, and internet tools has created new opportunities
for the dissemination and sharing of scientic informa-
tion, giving rise to the broader concept of open science
[1]. Open science aims to take advantage of diverse meth-
ods to remove barriers to sharing scientic research
[2–4], bringing about fundamental changes in how
research is conducted, communicated, published, its
results evaluated, researchers collaborated, and scientic
works shared [5]. Open science has been recognized as
a tool for participatory research management [6]. Euro-
pean Commission has introduced open science as a new
approach to scientic processes, which is based on col-
laborative work and innovative methods of knowledge
dissemination through digital technologies [3]. Funda-
mentally, open science aims to enhance public access to
data, analyses, and ndings with historical roots. David
(1994) suggested that open science likely began during
the scientic revolution in 17th century, when printed
versions of scientic results were intended for public
access [3], implicitly seeking to bridge the gap between
science and society through new methods and greater
alignment with democratic values and rights as well as
promoting access to publicly-funded knowledge and the
development of open tools [7].
Given the importance of open science methods and
tools in research processes in various elds, many
researches have been conducted in this regard. However,
most of these studies have focused only on one dimen-
sion of various subject areas or one dimension of open
science. e highlighted topics include principles and
methods of open science in research teams [8], the gap
between science and practice in open science [9], open
science opportunities in knowledge sharing [10], the
relationship between open science policies and research
methods [11], clinical data sharing [12], strengthen-
ing open science in research process [13], the concept
and aspects of open science [3]. In addition, some stud-
ies have examined a number of approaches for applying
these principles to maximize the value of open science
and minimize its adverse eects on the progress of sci-
ence in practice [8]. For accelerating the dissemination
and development of new treatments in neurodegen-
erative disorders, a new strategy called “Open Science”
model has been used experimentally by the Montreal
Neurological Institute (MNI) and partners to remove
the barriers of many universities and companies [9].
However, in the mentioned study, it has been attempted
to identify all aspects of open science that inuence the
research process as tools and methods promoting and
facilitating the research process in the eld of health and
determine how to use open science methods and tools at
each stage of research process in healthcare, including
publication, distribution, evaluation and eectiveness of
research.
e methods of open science have been clearly eec-
tive in the dissemination and access to information in
medicine; studies have often focused on methods such as
open data, publication of research details, open referee-
ing, and open research repositories in the organization
[12, 14–17]. To maintain the principles of research, ethics
and issues such as privacy in the health system should be
taken into account in infrastructure and open publishing
laws in research organizations and legislative organiza-
tions of dierent countries [18]. Despite recognizing the
extensive applications of open science tools in scientic
processes, the proponents of open science hold diverse
viewpoints on how traditional openness to research out-
puts should be interpreted [19]. Dierent denitions,
objectives, and commitments have been proposed for
utilizing repositories, databases, researcher communica-
tion, and open science tools [19]. Substantial variations
exist among scientic elds, countries, and stakeholders’
groups regarding open science methods and concepts
in relation to policies and program directions [4]. us,
challenges and opportunities for implementing open sci-
ence policies in various countries require further investi-
gation and study [3].
erefore, considering the direct relationship between
the method of publishing research outputs, as well as
publishing rules, infrastructure and culture governing
the subject areas, a specic conceptual framework should
be provided to use the open science tools and methods
according to the nature of information. e application
of open science tools in healthcare system to optimize
research outputs for treatment processes, management
decisions, and public knowledge enhancement is of high
importance [20, 21]. Universities and research centers
must address approaches to create value for stakehold-
ers at social, national, and international levels by employ-
ing modern technology tools similar to that presented in
open science practices to tackle multifaceted challenges.
Given this gap, our study aims to identify and validate
the inuential components of open science on research
processes of the healthcare system by using a conceptual
model enhancing the understanding of dimensions asso-
ciated with it for beneting researchers, policymakers,
and healthcare managers. Since open science introduces
novel concepts of applicable technologies and innova-
tions in research processes, investigating the implemen-
tation of open science methods in research processes of
the healthcare system necessitates exploring a conceptual
model, which could lead to the formulation of relevant
policies, legal conditions for publishing and retrieving
various research outputs within the framework of open
science for universities and research centers related to
the healthcare sector. is conceptual framework is
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 3 of 12
Zarghani et al. BMC Health Services Research (2024) 24:309
based on an exploratory method, which was conducted
by interview, expert panel and Delphi method and pre-
sented the eective and important factors in the imple-
mentation of open science in health system under the
conceptual model.
Methodology
e current study falls under the category of exploratory
research in terms of nature and applied research in terms
of research type, which adopts an inductive strategy that
show in ow chart of the study desing (Fig. 1). It also uti-
lizes a qualitative data approach by employing a thematic
analysis method. To formulate the model components, a
three-step process of framework coding was employed.
is process involved structuring organized concepts
(main themes and subthemes obtained from combin-
ing and summarizing codes) and comprehensive con-
cepts (themes encompassing the impact of open science
on research process) within the healthcare system with
respect to validation purposes (reliability and credibil-
ity of themes), for which two methods were utilized. e
rst method involved communicative validation, mean-
ing referring back to the participants (interviewees) for
verication [22]. e second method was expert valida-
tion, which utilized expert panels and Delphi technique.
Furthermore, for validating the stability of themes, two
methods were applied: repeatability and generalizability.
e former was achieved through an agreement process
between the two coders (i.e., the researcher and a col-
laborator) regarding coding [23]. is approach aimed
to resolve inconsistencies arising from the coding review
process. Regarding generalizability, eorts were made
to involve various academic and executive stakeholders
related to research topic as much as possible. It means
that sampling should be done regularly and comprehen-
sively based on the agreement of experts [24].
Phase I
To identify inuential components and develop an initial
model, a qualitative method was employed through semi-
structured interviews (Appendix 1 & Appendix 2) among
academic experts and managers in the eld of research
and technology within Deputy of Research and Technol-
ogy of MOHME in dierent universities. e sampling
method was purposeful and snowball, and the individu-
als needed to meet one of the following criteria: research-
ers with at least three years of research experience and
involvement; academic members or managers who had
served in a managerial or executive role in Deputy of
Research and Technology within MOHME for at least
one semester and were available and willing to cooperate.
According to these criteria, interviews continued until
data saturation, ultimately resulting in 20 interviews.
Data saturation refers to the point where new data on the
research topic is no longer obtained during interviews
Fig. 1 Flow chart of the study design
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 4 of 12
Zarghani et al. BMC Health Services Research (2024) 24:309
and the data becomes repetitive. e interviews began in
early July 2021 and continued until mid-November 2021.
During the rst ve interviews, initial direction and
examination determined the number of questions, timing
of interviews, and nal interview guidance. Each inter-
view was allotted a time between 40 and 90min. In the
review done by participants to conrm validity, a portion
of the text along with initial codes was sent to some of
them to compare and validate the coherence of emerg-
ing ideas from the data with their own content. In the
next step, to control data validity, the method of agree-
ment rate between two coders was employed. Five initial
interviews were coded in parallel, and the codes were dis-
cussed to reach an agreement. e data analysis method
in this phase was the framework analysis. After each
interview, the interview was rst heard multiple times by
the researcher (the one conducting the interview). en,
the text was transcribed using Microsoft Word (version
13) and read multiple times, and initial semantic units
were identied. e transcribed les were transferred to
MAXQDA software (version 20), and the determination
of initial codes and their analysis was performed. e
thematic analysis method was used to categorize codes,
extract and classify sub-themes and main themes. After
analyzing the data, a list of inuential components of
open science was prepared according to the perspectives
of participants, which should be applied in the research
processes of healthcare system. is list was used to
develop the initial model.
Phase II
is stage of research was designed according to consen-
sus among experts. e preliminary proposed model was
developed based on the components that were extracted
from the rst stage of the study. Expert panel members
were selected using purposeful and available sampling
methods. e panel consisted of ve research team mem-
bers, three researchers with research experience in the
eld of open science and four healthcare system execu-
tives related to research and technology. e research
was conducted in the workplace of experts using online
sessions. In this step, a form designed according to main
components and subcomponents was utilized to assess
the position of each component in the proposed model
considering the experts’ opinions (Appendix 3). e
expert panel guidelines were sent electronically and in
print to panel members. A one-month time frame was
allocated for panel members to complete and review
the form. After this period, follow-ups were conducted,
both in-person and online, to collect the forms. Once all
panel members had submitted their forms, the summa-
rized opinions were entered into the data collection form.
To maintain the condentiality of opinions, they were
coded and entered into the form, which was subsequently
sent to panel members again with a one-week window
for review. In an online session using Google Meet,
each component was discussed, and a consensus-based
approach was used to conrm the results. rough the
review of all components listed in the expert panel guide-
lines regarding the proposed model, the experts’ opinions
regarding the acceptance or rejection of each proposed
component were evaluated. Final analysis was per-
formed by assessing each component based on consen-
sus through collective agreement and utilizing the Likert
scale. If there was unanimity regarding a component, it
was incorporated into the nal model. In cases of dier-
ence of opinions among the experts, the majority opin-
ion prevailed, leading to revisions and corrections of the
component in question.
Phase III
is stage involved the Delphi method, and the par-
ticipants were managers and researchers of Ministry of
Health who had also participated in the rst stage, as well
as the activists of the eld of open science in MOHME
who were invited to evaluate the model. e research
sample was selected using purposeful and available sam-
pling methods. In addition, the diversity of participants in
this stage contributed to better evaluation and improved
the quality of the model. e sample size for this stage
ranged from 20 to 30 participants. In the rst Del-
phi round, 24 participants took part, and in the second
round, there were 21 participants. To qualify as the study
sample, individuals needed to meet at least two of the fol-
lowing criteria: being a faculty member and researcher at
one of the medical sciences universities under MOHME,
having research or managerial experience in research
processes, or being specialists in librarianship and medi-
cal information with research experience in open science
or having at least three years of active record in research
management. e research environment was the work-
place of research community members. A structured
questionnaire based on the main components and sub-
components extracted from interview analysis in the sec-
ond phase was used for data collection (Appendix 4).
Implementation process of delphi approach
Selection of experts
In studies employing the Delphi method, the sample
size varies from 10 to 50 people, which was shown in the
study of Campbell and Cantrill [25]. Agumba and Haupt
identied 30 experts, out of whom 20 participated in
completing the questionnaires [26]. In Rowe and Wrigh
analysis of Delphi studies, it has been shown that the
number of experts varies from 4 to 21 [27], and Wouden-
berg stated that he considered between 5 and 20 experts
[28]. Based on these references and considering the nec-
essary population size for Delphi studies, the sample size
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 5 of 12
Zarghani et al. BMC Health Services Research (2024) 24:309
was determined to be between 20 and 30 participants in
this study. Experts were selected using purposive sam-
pling. Based on the inclusion criteria explained in the
sampling section, the experts at least met two of the con-
ditions for participation. Agumba and Haupt required
experts to meet at least three of eight entry criteria [26],
while Rogers and Lopez were satised with two out of
ve inclusion criteria [29]. Consequently, 30 experts were
rst identied and provided with the questionnaire, 24
of whom expressed willingness to participate and took
part in validating the model components. Ultimately,
the research sample included 24 experts, all of whom
were educators and researchers with over three years of
research and executive experience.
Development and validation of questionnaire
irteen main themes and 31 sub-themes that were
approved by experts as components of the proposed
model in the second step were the basis of the closed
structured questionnaire design for this step. e rst
phase analysis and evaluation by the expert panel in the
second phase served as the basis for constructing the
structured questionnaire for this stage. According to Hsu
and Sandford, the use of a closed questionnaire is more
appropriate than an open one because a simpler response
process and shorter completion time increases the like-
lihood of greater expert participation [30]. If the mem-
bers participating in the study are representative of the
relevant eld of knowledge, it can guarantee the validity
of the content [1]. Also, the Delphi approach should not
be judged with quantitative methods, but rather transfer-
ability, reliability, applicability and conrmability criteria
should be considered for the validity and reliability of the
results [31]. Since the structured Delphi questionnaire
was prepared based on expert panel in the second phase,
including representatives from the healthcare knowledge
domain and open science practitioners and had also been
reviewed by research team as well as some of the par-
ticipating experts in the third phase, its face validity was
conrmed.
Criterion for achieving consensus
e term “consensus” refers to the agreement on an idea
for participants to reach a common ground on a specic
topic, rather than nding a correct answer [32]. Research
using Delphi method has also shown that there is no spe-
cic criterion for achieving consensus. A common crite-
rion in these studies is that at least 60% of respondents
should agree on the component under consideration,
which occurs with 50–90% probability [32, 33]. Compo-
nents with agreement levels below this rate are consid-
ered not to have reached consensus and move on to the
next phase [34]. However, achieving 100% agreement is
not feasible due to diverse political, social, economic,
and scientic backgrounds of individuals [35]. A decision
about consensus is made when a certain percentage of
votes fall within a specic range [30]. In previous studies,
a consensus range of 51–100% has been reported [36, 37].
In this study, the criterion for achieving consensus for
each component was based on research, considering that
at least 60% of participants should agree on the impor-
tance of the component. Accordingly, responses were
scored on a ve-point Likert scale, ranging from one to
ve. e acceptance threshold for each component was
a score higher than 75% or > 75% agreement based on
the total opinions about it (very much and much). Com-
ponents that scored between 50% and 75% underwent
revisions and were re-entered into the validation cycle
for reevaluation. Components that scored < 50% were
excluded from the study. e Delphi process was con-
ducted in two rounds to conrm the components. Delphi
iterations refer to the process of systematically (and in
writing) repeating a series of steps using questionnaires
with the aim of reaching consensus on opinions [38]. In
terms of the number of iterations, articles have reported
2 to 10 rounds [37]. e decision about the number of
rounds is somewhat practical or empirical and depends
on available time and the nature of the initial question
[37]. In this study, a panel of experts was used for validat-
ing the components of the proposed model. As a result,
Delphi iterations were implemented in two rounds to val-
idate the components.
Data analysis
Analysis methods are determined based on Delphi’s
objective, the structure of iterations, the type of ques-
tions, and the number of participants [30, 38]. Descrip-
tive statistics such as mean, median, and measures
of dispersion are commonly used [39]. In this study,
descriptive statistics was utilized for analyzing the results
of the rst and second rounds, including frequency and
percentage for ranking the ndings. After collecting
questionnaires in the rst Delphi round, the proposed
components were applied, and the results of the rst
phase along with the revised questionnaire were sent
again to study participants. is process continued until
consensus was reached on the options. Data analysis in
the validation phase was done using descriptive statistics
(frequency, percentage), and the responses were scored
on a ve-point Likert scale. e acceptance criterion for
each component was a score > 75%. Components that
scored 50–75% underwent revisions and were re-entered
into the validation cycle. Components that scored < 50%
were excluded from the study.
Execution of delphi rounds
When necessary information regarding the research topic
is available, a structured questionnaire can be used to
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 6 of 12
Zarghani et al. BMC Health Services Research (2024) 24:309
improve responses [30]. Since the information regarding
tool design was obtained in previous steps of this study,
a structured questionnaire was used. In the rst Delphi
round, a total of 30 questionnaires were sent to the iden-
tied individuals through e-mail and in-person channels.
After two weeks, 24 questionnaires were returned to the
research group following repeated follow-ups. At the end
of the rst round, responses were collected and sum-
marized. e results of this round indicated that there
was a consensus of over 75% on 13 main components
and 29 sub-components listed in the questionnaire. Two
sub-components did not reach consensus in this stage,
so the second round of Delphi was initiated. In the Sec-
ond Delphi Round, the feedback received from the rst
round along with revisions of components that were not
approved was sent to 24 participants of the rst round.
ey were asked to provide their opinions and reasons
for agreement or disagreement with the components.
After collecting questionnaires in this round and analyz-
ing them, all 31 sub-components and 13 main compo-
nents achieved a consensus with a score exceeding 75%.
Results
From the analysis of interviews in the rst stage using
thematic analysis, a structured collection of 385 codes,
38 sub-themes, 14 main themes, and 3 major themes was
extracted. e initial proposed model concerning the
impact of open science on health research processes was
formed based on the semantic relationship between these
components for presentation to the expert panel. Table1
presents the structured collection of themes, as well as
main components, and sub-components extracted from
qualitative data related to the interviews.
In the second phase, to review and rene the titles
of extracted components and the semantic relation-
ships established between them in the proposed model
according to experts’ opinions, the model was evaluated
and reviewed by experts using the data collection form
(Appendix 3). e summary of experts’ opinions that
aimed at revising, rening the titles, and establishing
semantic relationships between the proposed model’s ini-
tial components indicated a collective agreement on most
of the components. Furthermore, summarizing experts’
opinions and applying them to the proposed model led
to the renement and enhancement of components.
e modied titles of the components were as follows:
“Enhancing Factors of Trust in Research Outputs,” “Pub-
lishing Peer-Reviewed Results and Other Outputs in Sci-
entic Networks,” “Publishing Research Outputs in the
Scientic Language,” “Disseminating Research Outputs
to the Public,” “Enhancing Participation in All Research
Stages,” “Increasing Public Involvement in Data Collec-
tion,” “Strengthening the Knowledge Cycle and Trust
in Research,” “Leveraging Innovative Communication
Table 1 Extracted concepts from qualitative interview data
Major
themes
Main themes Sub-themes
Publishing &
sharing
Open access to a
variety of research
outputs
✓ Publishable research items
✓ Access to maximum output
✓ Sharing dierent data
Conditions of ac-
cess to outputs
✓ Access conditions to outputs
✓ User-oriented access level
Transparency and
reproducibility of
factors of research
credibility
✓ Research reproducibility
✓ Research transparency
Channels of pub-
lishing and sharing
outputs
✓ Informal channels for publish-
ing research output
✓ Formal channels for publishing
research output
✓ Publishing modes of the results
for the public
Citizens’ participa-
tion in research
stages
✓ Participation in all stages of
research
✓ Knowledge cycle and research
credibility
Infrastructural
and cultural
Infrastructure-
tools for registra-
tion & sharing
✓ Tools for recording and sharing
research cases
✓ Data publishing infrastructure
✓ Library for open-research man-
agement and publishing
Management
and protection
infrastructure
✓ Research stages’ management
platform and system
✓ Data publishing protocol
✓ Protective infrastructure
Culturalization and
education
✓ Transparency culture
✓ Educating the principles of
open-science
✓ Educational and culturalization
requirements
Formation of
extensive scientic
communications
✓ Extensive research
collaborations
✓ Communication paths
✓ New communication tools
Publishing costs ✓ Citizens’ participation in re-
search budgets
✓ Adjustment of publication costs
Monitor-
ing and
evaluation
Legislation and
guidelines
✓ Facilitating the intellectual
property of research
✓ Rules and mechanisms of open
research
Ethical principles
in the research
process
✓ Organizational monitoring of
open-research process
✓ Institutionalization of research
ethics
✓ Ethical considerations in pub-
lishing data
Supportive
policies
✓ Research budget transparency
✓ Organizational support
✓ Executive and incentive policies
Open-research
evaluation process
✓ Open peer review of articles
✓ Research eciency
✓ Research evaluation indicator
✓ Supervisory Working Group
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 7 of 12
Zarghani et al. BMC Health Services Research (2024) 24:309
Tools,” “Public Participation in Research Funding,”
“Mechanisms and Guidelines for Open Research,” “Facili-
tating Intellectual Property Conditions for Research,” and
“Promoting Ethical Principles in the Research Process.”
Semantic congruence according to expert opinions
and reevaluation of codes and components led to the
integration of eight components as follows: “Publish-
able Research Topics,” “Publishing Research Outputs in
the Scientic Language,” “Infrastructure and Tools for
Sharing Outputs,” “Protective Infrastructure and Data
Sharing,” “Training in Open Science Principles,” “Ethical
Considerations in Publishing Outputs,” “Supportive and
Encouraging Policies,” and “Evaluation Indicators.” Based
on the revisions suggested, new concepts emerged dur-
ing the re-review of codes and component meanings:
“Transparency of Research’s Scientic and Technical
Process,” “Transparency of Research’s Managerial and
Financial Process,” “Impact of Open Science on Regula-
tory Processes,” “Impact of Open Science on Evaluation
Processes,” and “Open Peer Review.”
According to the overall opinion, open science is con-
sidered an eective factor in reducing research barriers.
A uniform research structure cannot be proposed for
all organizations. e sub-component “Unied Form of
Open Research Structure” was removed. e initial cod-
ing was also reviewed again. Applying the suggestions
received from experts led to reconsideration of the ini-
tial proposed model. Ultimately, the proposed model was
selected for nal evaluation using Delphi method, which
consisted of 31 sub-components, 13 main components,
and 4 super-components as shown in (Fig.2).
Fig. 2 Conceptual model of the impact of open science on research processes in healthcare system of Iran
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 8 of 12
Zarghani et al. BMC Health Services Research (2024) 24:309
e results of nal stage of the study, which aimed at
validating the proposed model regarding the impact of
open science on research processes of the healthcare
system, were obtained using the classical Delphi method
and quantitative descriptive statistics. Participants of
this stage consisted of 13 males (54.2%) and 11 females
(45.8%). All the participants (100%) had more than ve
years of research experience. According to the opinions
of participants in the rst round of Delphi, all sub-com-
ponents, main components, and super-components (a
total of 31 sub-components, 13 main components, and 4
super-components) reached a consensus except for two
sub-components, namely “Transparency of Manage-
rial and Financial Process of Research” and “Publishing
Research Outputs to the Public”. e acceptance or rejec-
tion of each component depended on the total opinions
received (very much and much) and required a score of
> 75%.
e second round of Delphi was conducted to reeval-
uate the two components that could not achieve a
score > 75%. Accordingly, a questionnaire was designed
for the participants to assess the impact of these two
components on research process in the second round
of Delphi. e questionnaire was sent to 24 participants
who took part in the rst round. After analyzing the data
from this stage, these two components also reached a
consensus with a score > 75%.
All components of open science that were eective on
research processes of healthcare system reached a con-
sensus in both rounds of the Delphi process. Table2 cat-
egorizes the importance levels of the main components
according to opinions of Delphi study participants into
three levels. Four components, namely “Enhancing Trust
Factors in Research Outputs,” “Mechanisms and Guide-
lines for Open Research,” “Promoting Ethical Principles
in the Research Process,” and “Open Research Evaluation
Process” achieved 100% consensus among participants.
Additionally, three components, including “Formation of
Extensive Scientic Communication,” “Managing Publi-
cation Costs,” and “Supportive Policies,” were ranked sec-
ond with over 90% agreement. Components that ranked
third also obtained consensus with over 80% agreement.
is indicates the importance of all components in the
proposed model and the need for proper implementation
of each.
In the presented model, the impact of open science
components on research processes is structured in four
main layers, forming the foundation for open research
policy. is model, which is derived from analysis of
interviews and expert opinions relevant to research topic,
created a structure leading to open research policy. In the
rst layer, namely the broadest layer, the necessary hard-
ware and software equipment for implementing open sci-
ence research methods should be provided, as along with
issues such as specialized human resources, technical
infrastructure, software, systems, and tools needed for
conducting research in an open manner, as well as path-
ways for sharing, which should be taken into account.
e educational principles required for fostering open
science culture are considered in this layer, too. e
second layer is essential for determining the necessary
principles and strategies for implementing open science
Table 2 Ranking of main themes of open science aecting research processes in the healthcare system
Rank Main themes Importance Sum total/
percentage
Collective
agreement/
percentage
Very- low
(F/P)
Low
(F/P)
Me-
dium
(F/P)
Much
(F/P)
Very-
much
(F/P)
1 Increasing trust factors in research outputs 0 (0) 0 (0) 0 (0) 5 (8/20) 19 (2/79) 24 (100) 24 (100)
Mechanism and guidelines of open research 0 (0) 0 (0) 0 (0) 8 (3/33) 16 (7/66) 24 (100) 24 (100)
Promoting compliance with ethical principles in the
research process
0 (0) 0 (0) 0 (0) 5 (8/20) 19 (2/79) 24 (100) 24 (100)
Open research evaluation process 0 (0) 0 (0) 0 (0) 7 (2/29) 17 (8/70) 24 (100) 24 (100)
2 The formation of extensive scientic communication 0 (0) 0 (0) 1 (2/4) 6 (25) 17 (8/70) 24 (100) 23 (95/8)
Managing publishing costs 0 (0) 0 (0) 1 (2/4) 6 (25) 17 (8/70) 24 (100) 23 (95/8)
Supportive policies 0 (0) 0 (0) 1 (2/4) 3 (5/12) 20 (3/83) 24 (100) 23 (95/8)
Strengthening the infrastructure - management tools
and sharing research outputs
0 (0) 0 (0) 1 (2/4) 6 (25) 17 (8/70) 24 (100) 23 (95/8)
Output distribution and sharing channels 0 (0) 0 (0) 2 (3/8) 6 (25) 16 (7/66) 24 (100) 22 (91/7)
Open access to all types of research output 0 (0) 0 (0) 2 (3/8) 5 (8/20) 17 (8/70) 24 (100) 22 (91/6)
Culturalization based on education 0 (0) 1
(2/4)
1 (2/4) 5 (8/20) 17 (8/70) 24 (100) 22 (91/6)
3 Level of access to outputs 1 (2/4) 0 (0) 2 (3/8) 8 (3/33) 13 (2/54) 24 (100) 21 (87/5)
Participation of citizens in research stages 0 (0) 1
(2/4)
2 (3/8) 11
(8/45)
10 (7/41) 24 (100) 21 (87/5)
1Frequency/Percent
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 9 of 12
Zarghani et al. BMC Health Services Research (2024) 24:309
research. In this layer, the laws, ethical principles in open
research and policies are determined; it is a fundamental
step towards creating an open research policy and plays
a role in all stages of research. e third layer is based
on open peer review, research eciency, and evaluation
indicators related to pre- and post-publication evalu-
ation of research results, as well the impact of research
from various aspects, which should be measured based
on quantitative and qualitative indicators. e fourth
layer is related to the process of publishing and sharing
of research outputs addressing publishable aspects of
research, access principles and conditions, transparency
and reproducibility processes of open research. Addi-
tionally, pathways for accessing research outputs and
participation of citizens are dened in this layer. For the
establishment of this layer, previous layers must be sys-
tematically and eectively dened and supported. e
proper formation of these four layers will lead to an open
research policy for health system research, resulting in
better issue identication, transparent process execution
and responsiveness of research, as well as eective utili-
zation of outputs by relevant stakeholders.
Discussion
Open science can have a signicant impact on various
research processes. By providing an integrated digi-
tal research structure, it can facilitate broader access to
outputs and increase participation in various research
stages, fostering interactions among researchers and
stakeholders within academic, industrial, and policy-
making structures. In some cases, open science can be
likened to a double-edged sword. On one hand, it could
be constructive and transformative, while on the other, it
might create challenges such as privacy concerns and the
lack of protection for stakeholders’ rights. Nevertheless,
the application of open science methodologies in health
system research is both constructive and advantageous,
with its benets potentially outweighing the drawbacks.
For this purpose, it is necessary to utilize the unique
opportunities of open science to enhance knowledge and
science derived from research through a specic perspec-
tive and plan. is would lead to knowledge democrati-
zation and proper utilization in various societal strata,
alongside increased community awareness and appropri-
ate utilization of research outputs. Consequently, open
science methodologies play a pivotal role in quality man-
agement of science. As a result, this support will lead to a
win-win situation [40].
Whereas the use of these technologies has led to chal-
lenges in some cases, the potential and actual benets
have been so impressive that newer measures should
be taken to apply these technologies correctly. One of
the goals of organizational science is contributing to
evidence-based development in problem solving. Since
studies such as clinical trials and cohorts in the eld of
medical sciences are looking for a scientic and practical
basis in the direction of evidence-based medicine, the use
of open science methods in these research processes to
discover and test evidence-based actions can be bene-
cial for doctors [20]. One of the most prominent advan-
tages of open science in healthcare system is providing
conditions for maximum public access to scientic out-
puts in an understandable language free from complex-
ity. Utilizing diverse scientic discourse methods through
various media outlets should be considered in this
regard. Nonetheless, for proper utilization of research
outputs to create conducive conditions, the need for a
cycle of credible and transparent knowledge circulation
arises. And a well-established knowledge cycle based on
sharing outputs across dierent research stages enhances
trust in research structures, fosters greater participation,
and ultimately amplies the impact of research across
dierent societal domains. To fulll these requirements,
various dimensions of open science provide this crucial
opportunity to researchers and stakeholders, yielding
signicant cost-eectiveness for institutions and univer-
sities [13]. An open science research policy comprises
scientic dissemination channels, participation, uni-
versity relationships, research quality and coherence,
transparency, repeatability, requirements for transparent
scientic processes, and a system for alignment and eval-
uation [6]. is system is achievable based on values of
openness, fair sharing, resource accessibility, education
of research outputs, and acceptance of open culture [41].
erefore, an open science platform should have several
properties, including categorizing multiple versions of
data and codes, supporting multiple data access schemes,
especially for sensitive data, exible metadata manage-
ment and standards in evolution, connecting organiza-
tional and external data, supporting object identiers
such as DOI, facilitating internal and external scientic
collaboration and participation [36]. ese characteris-
tics enable the digital support of all research steps within
the framework of open science.
Models of open access and open data dissemination are
rapidly becoming open scientic methods that inuence
the entire research ecosystem, including production,
communication, and reuse of research results. Utilizing
technological innovations for the dissemination of scien-
tic content is vital for sustainability of scientic journals
and publishers [42]. Nevertheless, in the current context,
these practices are not widely adopted because insuf-
cient knowledge on utilizing these practices, potential
misuse of research, imposing high publication costs on
researchers, and so forth have led to negative reactions
towards the application of these methods. Also, the
lengthy process of open peer reviews and the dissemina-
tion of evaluation feedback have not been favorable for
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 10 of 12
Zarghani et al. BMC Health Services Research (2024) 24:309
researchers. Appropriate policies with clear mechanisms
are needed to create desirability and condence among
stakeholders for conducting research within the frame-
work of open science; for example, encouraging factors
and preventive measures against potential misuse. Trans-
parency and openness in research require cultural trans-
formation. Enhancing transparency and openness should
not only be embraced by scientists and researchers, but
also by budget-providing institutions and even those
beyond the research and innovation sector [43]. More-
over, the budgetary mechanism for publishing research
outputs plays a crucial role in this stage. Most academics
support the principle of making knowledge freely avail-
able to everyone, but the use of open access publications
among academics is still limited due to relevant policies
[44]. Additionally, legal and ethical issues in research
have prompted the development of new tools and meth-
ods for addressing these matters. European Commission
has deemed the implementation of open science pro-
cesses as a task for universities to free themselves from
these conditions [45].
Limitations
Due to their nature, qualitative studies have limitations.
It has been attempted to reduce these limitations with
the measures taken for validity and reliability of the study
as follows. Some participants were not willing to coop-
erate in the interview when the purpose of the research
was explained to them and they were assured that their
information would remain condential. e time and
place of the interview was determined according to their
wishes. In addition, the timetable to conduct this study
was arranged according to the communication restric-
tions imposed by COVID-19, which caused the time to
collect and carry out various stages of the study to be lon-
ger than usual. In order to solve this limitation, remind-
ers were sent via e-mail, as well as face-to-face and
telephone follow-ups to receive comments. e diversity
and geographical dispersion of participants was another
issue that caused a lot of time to follow up and receive
information. An attempt was made to use auxiliary forces
in dierent geographical areas of Iran to follow up and
receive information.
Conclusion
e conceptual model presented based on the nd-
ings of this study has shown that to apply open science
methods in dierent stages of research in the health sys-
tem, it is essential to cultivate a culture of open research
and ethical issues through formal and informal educa-
tion or repeated communication within universities
and research centers, which reaches various stakehold-
ers. e technical infrastructure should also be estab-
lished, which has already been provided to a considerable
extent in research libraries through monitoring software
of research centers and universities. Access conditions
should be reconsidered based on the type of research and
the target audience. Another important nding based
on this model was that the laws and policies for imple-
menting open research in the healthcare system should
be formulated through university research councils and
ethics committees, so that the support of higher-level
organizations and lawmakers, as well as necessary laws
are enacted and enforced. Additionally, principles and
assessment processes must consider various aspects such
as eectiveness, problem-solving, participation and col-
laboration in dierent projects, as well as transparency
enhancements. Based on the conditions and processes
outlined in dierent layers of this model, maximal dis-
semination and sharing of various research outputs will
result in the greatest degree of research application in
healthcare system and various strata of society.
In general, the ndings of this research have shown that
open science methods can be highly eective in improv-
ing the research process and beneting from its outputs,
which requires providing sucient background, knowl-
edge and skills to apply each of them in dierent stages of
research. In line with the ndings of this study, it is sug-
gested that the organizations in charge of health system
should review research guidelines and communication
processes between research stakeholders. And in con-
nection with the inuential factors in cultural processes,
infrastructure and supervision help implement the
open research process by forming specialized working
groups consisting of people active in the eld of research,
observing ethics in research, evaluation and validation
of studies, as well as knowledge translation groups. e
principles of transparency and scientic openness by
research organizations and universities should be con-
sidered as a codied and strategic plan because it will
cause positive consequences, including increasing the
amount of scientic credibility, widespread participa-
tion of dierent people in research, and beneting more
from the scientic knowledge produced. Also, to create
an organizational culture based on the results obtained
in the policy department, it is suggested that the prin-
ciples of scientic openness should be considered as an
aspect of research activities of organizations and uni-
versities. Considering the importance of the type of data
and research outputs in health system and privacy pro-
tection, openness and open access to research results
can be dened according to the type of studies. And the
tools and services that provide the conditions of scien-
tic openness should be dened as one of the strategies
of organizations and universities because open science
accelerates the conditions for creating a culture of scien-
tic openness in organizations. According to the neces-
sity of open research topic in future studies, open science
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 11 of 12
Zarghani et al. BMC Health Services Research (2024) 24:309
should focus on the following topics. Compilation of
open research evaluation principles based on new indi-
cators in the health system, presenting a user model to
apply each of the open science methods in health system
research, the eect of teaching necessary skills to apply
open science methods by researchers and research sup-
porting organizations, compilation of ethical principles
and adjustment of intellectual property in health system
researches, compilation of the conditions of access to
information and data of health system with an emphasis
on privacy and biosecurity issues. With the identication
of these factors, the research stakeholders will proceed to
widely use open science methods in a safer intellectual
environment.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12913-024-10764-z.
Supplementary Material 1: Inductive interview guideline
Supplementary Material 2: Informed consent form
Supplementary Material 3: A tool for collecting experts’ opinions in the
second step to modify the initial coding and the proposed model
Supplementary Material 4: A tool for collecting experts’ opinions in the
third step for evaluation of the proposed model
Acknowledgements
We appreciate Iran University of Medical Sciences for nancial support with
grant NO: IUMS/SHMIS_99-2-37-18607.
Author contributions
The interviews were collected by “M.Z”; implementation, analysis by “L. NA,
S.S and A.NC”. The rst draft of the manuscript was written by “ M. Z”. Writing
- review and editing nal of the manuscript was written by"M. Z, A. RF, L. N
A” and all authors commented on previous versions of the manuscript. All
authors read and approved the nal manuscript.
Funding
This work was supported by Partial nancial from Iran University of Medical
Sciences with grant NO: IUMS/SHMIS_99-2-37-18607.
Data availability
The datasets formed and analysed during the current study are available from
the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study procedure was approved by Medical Ethics Committee of Iran
University of Medical Sciences [date: Jul 2020, ID: IR.IUMS.REC.1399.462] as a
doctoral dissertation titled “Developing an conceptual model for open science
in health system research processes”. The current study included only those
who supplied their informed consent. For this purpose, informed consent
form (Additional le2.ICF) was completed by all participants after explanation
of the objectives of study. Information from all the participants was private
and nameless; there was no personal information that could link the answers
with any of the participants in the present study. All methods in the study
were in accordance with relevant regulations and guidelines (General Ethical
Guidance for Medical Research with Human Participants in the Islamic
Republic of Iran).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1Medical Library and Information Sciences, School of Health
Management and Medical Information Science, Iran University of Medical
Sciences, Tehran, Iran
2Department of Medical Library and Information Sciences, School of
Health Management and Medical Information Science, Iran University of
Medical Sciences, Rashid Yasmin Street, Upper than Mirdamad St., Tehran,
Iran
3Health Management and Economics Research Center, Iran University of
Medical Sciences, Tehran, Iran
4Health Management and Economics Research Center, Health
Management Research Institute, Iran University of Medical Sciences,
Tehran, Iran
5Department of Information Science & Knowledge Studies, Shahed
University, Tehran, Iran
6Department of Pharmaceutical Health Services Research, University of
Maryland School of Pharmacy, Baltimore, Maryland, USA
Received: 23 October 2023 / Accepted: 21 February 2024
References
1. Nosek BA, Alter G, Banks GC, Borsboom D, Bowman SD, Breckler SJ, et al.
Promoting open Res Cult Sci. 2015;26(3486242):1422–5.
2. Banks GC, Field JG, Oswald FL. Answers to 18 questions about Open Science
practices. J Bus Psychol. 2019;34(3):257–70.
3. OECD. Making Open Science a Reality. Paris: OECD Science, Technology and
Industry Policy Papers.; 2015. Report No.: No. 25.
4. Qian D, Eunjung S. S. C. Open and inclusive collaboration in science: a frame-
work e. Sci Technol Policy Inst 2018.
5. Commission E. Evaluation of Research Careers fully acknowledging Open
Science practices rewards, incentives and/or recognition for researchers
practicing Open Science. European Commission B-1049. Brussels: European
Commission; 2017.
6. Lyon L, Transparency. The emerging third dimension of open science and
open data. LIBER Q. 2016;25(4):153–71.
7. Commission E. Six Recommendations for Implementation of FAIR Practice.
FAIR in Practice Task Force of the European Open Science Cloud FAIR Working
Group; 2020. Report No.: KI-01-20-580-EN-N.
8. Vicente-Saez R, Gustafsson R, VdB L. The dawn of an open exploration era:
emergent principles and practices of open science and innovation of univer-
sity research teams in a digital world. Technological Forecast Social Change.
2020;1(156):120037.
9. Aguinis H, Banks GC, Rogelberg SG, Cascio WFJOB. HD. P. actionable recom-
mendations for narrowing the science-practice gap in open science. Organ
Behav Hum Decis Process. 2020;158:27–35.
10. Potterbusch M, Lotrecchiano G. Shifting paradigms in information ow: an
open science framework (OSF) for knowledge sharing teams. Int J Emerg
Transdisciplin. 2018;21.
11. Dai Q, Shin E. C. S. Open and inclusive collaboration in science: a framework.
the OECD Going Digital project; 2018.
12. JS. R. Clinical research data sharing: what an open science world means for
researchers involved in evidence synthesis. J Syst Reviews 2016;5 (1):1–4.
13. Pontika N, Knoth P, Cancellieri M. PearceS. Fostering Open Science to
Research using a Taxonomy and an eLearning Portal. InProceedings of the
15th international conference on knowledge technologies and data-driven
business 2015 p. 1–8.
14. Hrynaszkiewicz I. Publishers’ Responsibilities in Promoting Data Quality and
Reproducibility. Handb Exp Pharmacol. 2019/11/07;257:319–48. ed2020.
15. Austin C, Bloom T, Dallmeier-Tiessen S, Khodiyar V, Murphy F, Nurnberger
A, et al. Key components of data publishing: using current best prac-
tices to develop a reference model for data publishing. Int J Digit Libr.
2017;18(2):77–92.
16. Payne P, Lele O, Johnson B, Holve E. Enabling Open Science for Health
Research: Collaborative Informatics Environment for Learning on Health
Outcomes (CIELO). J Med Internet Res. 2017;19(7):e276.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 12 of 12
Zarghani et al. BMC Health Services Research (2024) 24:309
17. Xas V, Labude M. Asian Bioeth Rev. 2019;11(3):255–73. Openness in Big Data
and Data Repositories.
18. Besançon L, Peier-Smadja N, Segalas C, Jiang H, Masuzzo P, Smout C
et al. Open science saves lives: lessons from the COVID-19 pandemic.
2021;21(1):1–18.
19. Ayris P, de López A. MK. Open science and its role in universities: a roadmap
for cultural change. Leuven:: LERU Oce; 2019.
20. Guzzo RA, Nalbantian HR, Schneider B. Open science, closed doors: the perils
and potential of open science for research in practice. Industrial Organiza-
tional Psychol. 2022;15(4):495–515.
21. Han C, Chaineau M, Chen CX-Q, Beitel LK, Durcan TMJFN. Open science
meets stem cells: a new drug discovery approach for neurodegenerative
disorders. 2018;12:47.
22. Tahzibi K, Keshishyan Siraki G. Design and validation of the optimal model of
Corona Environmental Crisis Management on International Security. iauh-
ipsj. 2022;2(1):155–77.
23. Sarukhani B. Research methods in social sciences. Tehran, Institute of
Humanities and Cultural Studies, 2 vols. 2015.
24. Bhattacharya K. Fundamentals of qualitative research: a practical guide. Taylor
& Francis; 2017.
25. Campbell SM. Consensus methods in prescribing research. J J Clin Pharm
Ther. 2001;26(1):5–14.
26. Agumba JN. Validating and identifying health and safety performance
improvement indicators: experience of using delphi technique. J J Econ
Behav Stud. 2015;7(3):14–22.
27. Rowe G, Wright GJI. The Delphi technique as a forecasting tool: issues and
analysis. 1999;15(4):353–75.
28. Woudenberg, FJTf. change s. An evaluation of Delphi. 1991;40(2):131– 50.
29. Rogers MR. Identifying critical cross-cultural school psychology competen-
cies. J J School Psychol. 2002;40(2):115–41.
30. Hsu CC. Minimizing non-response in the Delphi process: how to respond to
non-response. PARE. 2007;12(1):17.
31. Manca DP, Varnhagen S, Brett MP, Allan GM, Szafran O, Ausford A, et al.
Rewards and challenges of family practice: web-based survey using the
Delphi method. J Can Family Physician. 2007;53(2):277–86.
32. Linstone HA. M. T. The delphi method. Addison-Wesley Reading, MA; 1975.
33. Murphy MK, Black NA, Lamping DL, McKee CM, Sanderson CF, Askham J,
et al. Consensus development methods, and their use in clinical guideline
development. J Health Technol Assess. 1998;2(3):i–88.
34. Cottam H, Roe M. Outsourcing of trucking activities by relief organisations. J
Humanitarian Assistance. 2004;1(1):1–26.
35. Current validity of the Delphi method in social sciences. J Technol Forecas
Social Change. 2006;73(5):467–82.
36. Chu HC. A Delphi-based approach to developing expert systems with the
cooperation of multiple experts. J Expert Syst Appl. 2008;34(4):2826–40.
37. Fry M. Using the Delphi technique to design a self-reporting triage survey
tool. J Accid Emerg Nurs. 2001;9(4):235–41.
38. Skulmoski GJ, Hartman FT, Krahn J. The Delphi method for graduate research.
J Inform Technol Education: Res. 2007;6(1):1–21.
39. Rahmani A, Vaziri Nezhad R, Ahmadi Nia H. Methodological principles and
applications of the Delphi Method: a narrative review. J J Rafsanjan Univ Med
Sci. 2020;19(5):515–38.
40. Toelch U, Ostwald D. Digital open science teaching digital tools for reproduc-
ible and transparent research. PLoS Biol. 2018;16(7).
41. Bezuidenhout L, Quick R, Shanahan H. Ethics when you least expect it: a
Modular Approach to Short Course Data Ethics instruction. Sci Eng Ethics.
2020;26(4):2189–213.
42. Penev L. From Open Access to Open Science from the viewpoint of a schol-
arly publisher. RIO. 2017;3.
43. Lacey J, Coates R, Herington M. Open science for responsible innovation in
Australia: understanding the expectations and priorities of scientists and
researchers. J Responsible Innov. 2020;7(3):427–49.
44. Zhu Y. Do new forms of scholarly communication provide a pathway to open
science? [Electronic Thesis or Dissertation]: University of Manchester; 2015.
45. Rowhani-Farid A. Towards a culture of open science and data sharing in
health and medical research. Queensland University of Technology; 2018.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional aliations.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com