Barriers and facilitators to research data sharing: a lifecycle perspective
| Date | 08 July 2024 |
| Pages | 1546-1569 |
| DOI | https://doi.org/10.1108/JD-03-2024-0048 |
| Published date | 08 July 2024 |
| Subject Matter | Library & information science,Records management & preservation,Document management,Classification & cataloguing,Information behaviour & retrieval,Collection building & management,Scholarly communications/publishing,Information & knowledge management,Information management & governance,Information management,Information & communications technology,Internet |
| Author | Zilong He,Wei Fang |
Barriers and facilitators
to research data sharing:
a lifecycle perspective
Zilong He and Wei Fang
School of Management, Northwestern Polytechnical University, Xi’an, China
Abstract
Purpose –This study investigates the multifaceted barriers and facilitators affecting research data sharing
across the research data lifecycle. It aims to broaden the understanding of data sharing beyond the publication
phase, emphasizing the continuous nature of data sharing from generation to reuse.
Design/methodology/approach –Employing a mixed-methods approach, the study integrates the Theory
of Planned Behavior, the Technology Acceptance Model, and the Institutional Theory to hypothesize the
influence of various factors on data sharing behaviors across the lifecycle. A questionnaire survey and
structural equation modeling are utilized to empirically test these hypotheses.
Findings –This study identifies critical factors influencing data sharing at different lifecycle stages, including
perceived behavioral control, perceived effort, journal and funding agency pressures, subjective norms,
perceived risks, resource availability, and perceived benefits. The findings highlight the complex interplay of
these factors and their varying impacts at different stages of data sharing.
Research limitations/implications –This study illuminates the dynamics of research data sharing,
offering insights while recognizing its scope might not capture all disciplinary and cultural nuances. It
highlights pathways for stakeholders to bolster data sharing, suggesting a collaborative push towards open
science, reflecting on how strategic interventions can bridge existing gaps in practice.
Practical implications –This study offers actionable recommendations for policymakers, journals, and
institutions to foster a more conducive environment for data sharing, emphasizing the need for support
mechanisms at various lifecycle stages.
Originality/value –This study contributes to the literature by offering a comprehensive model of the
research data lifecycle, providing empirical evidence on the factors influencing data sharing across this
continuum.
Keywords Research data sharing, Data lifecycle, Data reuse, Data sharingbarriers, Data sharing facilitators
Paper type Research paper
1. Introduction
Research data sharing has emerged as a cornerstone of scientific progress, fostering
collaboration, reproducibility, and the efficient reuse of data to accelerate discovery.
Traditionally, research data sharing has been viewed from a narrow perspective, focusing
primarily on the data publication stage. The data publication stage marks the transition of
data from a personal resource, confined within individual or close-knit research groups, to a
public asset accessible to the broader scientific community (Smith, 2009;Lawrence et al.,
2011). This phase is pivotal as it represents the entire process by which research data
becomes publicly accessible. This includes the submission of data to repositories, the
publication of datasets as standalone resources, and the integration of data with articles or as
supplements to enhance transparency and reproducibility. However, viewing research data
sharing merely as a publication checkpoint fails to capture the dynamic nature of data as it
transitions through different phases of research. In recent years, a broader understanding of
data sharing as a continuous process spanning the entire research data lifecycle is gaining
JD
80,6
1546
This research was funded by Projects of the National Social Science Foundation of China grant number
[22BGL011]: Research on the Influence Mechanism and Implementation Path of Key Core Technology
Breakthroughs under the New National System.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0022-0418.htm
Received 3 March 2024
Revised 10 June 2024
Accepted 16 June 2024
Journal of Documentation
Vol. 80 No. 6, 2024
pp. 1546-1569
© Emerald Publishing Limited
0022-0418
DOI 10.1108/JD-03-2024-0048
prominence (Si et al., 2023). This holistic approach recognizes data sharing as a
comprehensive management concept that permeates all stages, enabling researchers to
harness the full potential of scientific data and bridge the gap between data resources and
value realization (El Arass and Souissi 2018;Wissik and
Dur
co, 2015). Each stage has distinct
challenges and opportunities for sharing, which are often overlooked when the focus is
narrowed to the publication alone.
Significant progress has been made in understanding factors that impact the willingness
and behavior towards research data sharing, yet existing research predominantly focuses on
the data publication stage, neglecting potential influences at other lifecycle stages. This
narrow perspective limits the findings’relevance to only the publication phase, overlooking
the comprehensive realization of data sharing practices. For example, journal pressure is
often cited as a significant driver during the data publication stage. However, this pressure
alone may not result in effective data sharing if earlier stages like data generation are poorly
handled. Therefore, the main research questions for this study are:
RQ1. What are the critical stages necessary in the research data lifecycle, from the
perspectives of both researchers and data repositories, to facilitate effective data
sharing?
RQ2. What are the key barriers and facilitators to data sharing at each stage of the
lifecycle?
To answer these questions, our study expands the scope of research data sharing by
summarizing existing knowledge into a refined model of the research data lifecycle. This
model identifies five key stages: data generation, data processing and analysis, data
publication, repository management, and data reuse and citation. Based on this framework
and the established theories, including the theory of planned behavior and the technology
acceptance model, we propose a comprehensive research model that hypothesizes the
influence of various factors on data sharing behavior across the entire lifecycle. These factors
include perceived behavioral control, perceived effort, journal pressure, funding agency
pressure, subjective norms, perceived risk, perceived resource availability, perceived
benefits, and data sharing willingness, highlighting the unique challenges and opportunities
for effective data sharing at each stage. For instance, the data generation stage involves
planning and collecting data in a manner that supports future sharing, while the data
processing and analysis stage requires documenting and annotating data transformations
to ensure reproducibility. The data publication stage focuses on making data publicly
available, often driven by journal and funding agency requirements. Repository management
involves maintaining and securing data within repositories, and data reuse and citation
emphasize the benefits of data sharing for future research. Based on this framework and
established theories including the theory of planned behavior and the technology acceptance
model, we propose a comprehensive research model that hypothesizes the influence of
various factors on data sharing behavior across the entire lifecycle. Through an empirical
investigation using a questionnaire survey and structural equation modeling, we aim to
examine the sharing drivers and their intricate mechanisms in each stage of the lifecycle.
The main contributionsof this study are threefold.Firstly, it broadens theunderstanding of
researchdata sharing by examiningthe entire lifecycle, not justthe publication stage, revealing
the continuousnature and various challengesof data sharing acrossdifferent stages. Secondly,
it identifiesand dissects the key barriersand facilitators to data sharingat each lifecycle stage,
providing targeted insights forimproving data sharing practices.Lastly, this study integrates
the theoryof planned behavior,the technology acceptancemodel, and the institutionaltheory to
analyze data sharing behaviors, offering a robust framework to predict and enhance the
effectiveness of data management strategies. These contributions advance the field by
Journal of
Documentation
1547
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