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Jihyun Kim 6 Articles
Data sharing attitudes and practices of researchers in Korean government research institutes: a survey-based descriptive study
Jihyun Kim, Hyekyong Hwang, Youngim Jung, Sung-Nam Cho, Tae-Sul Seo
Sci Ed. 2023;10(1):71-77.   Published online February 16, 2023
DOI: https://doi.org/10.6087/kcse.299
  • 1,810 View
  • 232 Download
  • 3 Web of Science
  • 3 Crossref
AbstractAbstract PDF
Purpose: This study explored to what extent and how researchers in five Korean government research institutes that implement research data management practices share their research data and investigated the challenges they perceive regarding data sharing.
Methods
The study collected survey data from 224 respondents by posting a link to a SurveyMonkey questionnaire on the homepage of each of the five research institutes from June 15 to 29, 2022. Descriptive statistical analyses were conducted.
Results
Among 148 respondents with data sharing experience, the majority had shared some or most of their data. Restricted data sharing within a project was more common than sharing data with outside researchers on request or making data publicly available. Sharing data directly with researchers who asked was the most common method of data sharing, while sharing data via institutional repositories was the second most common method. The most frequently cited factors impeding data sharing included the time and effort required to organize data, concerns about copyright or ownership of data, lack of recognition and reward, and concerns about data containing sensitive information.
Conclusion
Researchers need ongoing training and support on making decisions about access to data, which are nuanced rather than binary. Research institutes’ commitment to developing and maintaining institutional data repositories is also important to facilitate data sharing. To address barriers to data sharing, it is necessary to implement research data management services that help reduce effort and mitigate concerns about legal issues. Possible incentives for researchers who share data should also continue to be explored.

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Citations to this article as recorded by  
  • Korean scholarly journal editors’ and publishers’ attitudes towards journal data sharing policies and data papers (2023): a survey-based descriptive study
    Hyun Jun Yi, Youngim Jung, Hyekyong Hwang, Sung-Nam Cho
    Science Editing.2023; 10(2): 141.     CrossRef
  • Data sharing and data governance in sub-Saharan Africa: Perspectives from researchers and scientists engaged in data-intensive research
    Siti M. Kabanda, Nezerith Cengiz, Kanshukan Rajaratnam, Bruce W. Watson, Qunita Brown, Tonya M. Esterhuizen, Keymanthri Moodley
    South African Journal of Science.2023;[Epub]     CrossRef
  • Identifying key factors and actions: Initial steps in the Open Science Policy Design and Implementation Process
    Hanna Shmagun, Jangsup Shim, Jaesoo Kim, Kwang-Nam Choi, Charles Oppenheim
    Journal of Information Science.2023;[Epub]     CrossRef
Status and factors associated with the adoption of data sharing policies in Asian journals
Jihyun Kim, Seo Young Bai
Sci Ed. 2022;9(2):97-104.   Published online August 19, 2022
DOI: https://doi.org/10.6087/kcse.274
  • 3,039 View
  • 298 Download
  • 2 Web of Science
  • 1 Crossref
AbstractAbstract PDF
Purpose: This study investigated the current status and factors associated with adopting data sharing policies in Asian journals. Data sharing policies vary by country and region, and few studies have examined the trends and factors related to these policies in journals across the Asian region.
Methods
The 2020 Scimago Journal and Country Rank was used to download data about 1,143 Asian journals indexed in Web of Science. Excluding 40 journals inaccessible via the Internet or without English-language websites and author guidelines, 1,103 journals were analyzed through descriptive statistical analyses and the chi-square test.
Results
Of the 1,103 journals, 325 (29.5%) had data sharing policies, showing a moderate level of policy adoption among Asian journals. The results of the chi-square test suggested that the impact factor and publisher type (whether a publisher was commercial) were significantly associated with the presence of data sharing policies in journals, but subject categories were not identified as a significant factor. Regarding the strength of data sharing policies, most journals provided policies that only encouraged data sharing.
Conclusion
Policies only encouraging data sharing are unlikely to lead to actual data sharing; thus, considering varying levels of policy strength and effective ways to induce authors’ compliance with the policies is important. Further research needs to examine other factors affecting the presence or strength of data sharing policies.

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  • Journal metrics, document network, and conceptual and social structures of the Korean Journal of Anesthesiology from 2017 to July 2022: a bibliometric study
    Sun Huh
    Korean Journal of Anesthesiology.2023; 76(1): 3.     CrossRef
Korean researchers’ motivations for publishing in data journals and the usefulness of their data: a qualitative study
Jungyeoun Lee, Jihyun Kim
Sci Ed. 2021;8(2):145-152.   Published online August 20, 2021
DOI: https://doi.org/10.6087/kcse.246
  • 3,606 View
  • 184 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDF
Purpose: This study investigated the usefulness and limitations of data journals by analyzing motivations for submission, review and publication processes according to researchers with experience publishing in data journals.
Methods
Among 79 data journals indexed in Web of Science, we selected four data journals where data papers accounted for more than 20% of the publication volume and whose corresponding authors belonged to South Korean research institutes. A qualitative analysis was conducted of the subjective experiences of seven corresponding authors who agreed to participate in interviews. To analyze interview transcriptions, clusters were created by restructuring the theme nodes using Nvivo 12.
Results
The most important element of data journals to researchers was their usefulness for obtaining credit for research performance. Since the data in repositories linked to data papers are screened using journals’ review processes, the validity, accuracy, reusability, and reliability of data are ensured. In addition, data journals provide a basis for data sharing using repositories and data-centered follow-up research using citations and offer detailed descriptions of data.
Conclusion
Data journals play a leading role in data-centered research. Data papers are recognized as research achievements through citations in the same way as research papers published in conventional journals, but there was also a perception that it is difficult to attain a similar level of academic recognition with data papers as with research papers. However, researchers highly valued the usefulness of data journals, and data journals should thus be developed into new academic communication channels that enhance data sharing and reuse.

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  • Development and validation of the motivation to publish scale-scientific articles (EMP-AC) for Peruvian university students
    Oscar Mamani-Benito, Julio Torres-Miranda, Edison Effer Apaza-Tarqui, Madona Tito-Betancur, Wilter C. Morales-García, Josué Edison Turpo-Chaparro
    Frontiers in Education.2023;[Epub]     CrossRef
  • Korean scholarly journal editors’ and publishers’ attitudes towards journal data sharing policies and data papers (2023): a survey-based descriptive study
    Hyun Jun Yi, Youngim Jung, Hyekyong Hwang, Sung-Nam Cho
    Science Editing.2023; 10(2): 141.     CrossRef
Data journals: types of peer review, review criteria, and editorial committee members’ positions
Sunkyung Seo, Jihyun Kim
Sci Ed. 2020;7(2):130-135.   Published online August 20, 2020
DOI: https://doi.org/10.6087/kcse.207
  • 6,910 View
  • 158 Download
  • 3 Web of Science
  • 3 Crossref
AbstractAbstract PDFSupplementary Material
Purpose: This study analyzed the peer review systems, criteria, and editorial committee structures of data journals, aiming to determine the current state of data peer review and to offer suggestions.
Methods
We analyzed peer review systems and criteria for peer review in nine data journals indexed by Web of Science, as well as the positions of the editorial committee members of the journals. Each data journal’s website was initially surveyed, and the editors-in-chief were queried via email about any information not found on the websites. The peer review criteria of the journals were analyzed in terms of data quality, metadata quality, and general quality.
Results
Seven of the nine data journals adopted single-blind and open review peer review methods. The remaining two implemented modified models, such as interactive and community review. In the peer review criteria, there was a shared emphasis on the appropriateness of data production methodology and detailed descriptions. The editorial committees of the journals tended to have subject editors or subject advisory boards, while a few journals included positions with the responsibility of evaluating the technical quality of data.
Conclusion
Creating a community of subject experts and securing various editorial positions for peer review are necessary for data journals to achieve data quality assurance and to promote reuse. New practices will emerge in terms of data peer review models, criteria, and editorial positions, and further research needs to be conducted.

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  • Dissemination effect of data papers on scientific datasets
    Hong Jiao, Yuhong Qiu, Xiaowei Ma, Bo Yang
    Journal of the Association for Information Science and Technology.2024; 75(2): 115.     CrossRef
  • The data paper as a sociolinguistic epistemic object: A content analysis on the rhetorical moves used in data paper abstracts
    Kai Li, Chenyue Jiao
    Journal of the Association for Information Science and Technology.2022; 73(6): 834.     CrossRef
  • Korean researchers’ motivations for publishing in data journals and the usefulness of their data: a qualitative study
    Jungyeoun Lee, Jihyun Kim
    Science Editing.2021; 8(2): 145.     CrossRef
An analysis of data paper templates and guidelines: types of contextual information described by data journals
Jihyun Kim
Sci Ed. 2020;7(1):16-23.   Published online February 20, 2020
DOI: https://doi.org/10.6087/kcse.185
  • 6,186 View
  • 176 Download
  • 8 Web of Science
  • 10 Crossref
AbstractAbstract PDF
Purpose: Data papers are a promising genre of scholarly communication, in which research data are described, shared, and published. Rich documentation of data, including adequate contextual information, enhances the potential of data reuse. This study investigated the extent to which the components of data papers specified by journals represented the types of contextual information necessary for data reuse.
Methods
A content analysis of 15 data paper templates/guidelines from 24 data journals indexed by the Web of Science was performed. A coding scheme was developed based on previous studies, consisting of four categories: general data set properties, data production information, repository information, and reuse information.
Results
Only a few types of contextual information were commonly requested by the journals. Except data format information and file names, general data set properties were specified less often than other categories of contextual information. Researchers were frequently asked to provide data production information, such as information on the data collection, data producer, and related project. Repository information focused on data identifiers, while information about repository reputation and curation practices was rarely requested. Reuse information mostly involved advice on the reuse of data and terms of use.
Conclusion
These findings imply that data journals should provide a more standardized set of data paper components to inform reusers of relevant contextual information in a consistent manner. Information about repository reputation and curation could also be provided by data journals to complement the repository information provided by the authors of data papers and to help researchers evaluate the reusability of data.

Citations

Citations to this article as recorded by  
  • Dissemination effect of data papers on scientific datasets
    Hong Jiao, Yuhong Qiu, Xiaowei Ma, Bo Yang
    Journal of the Association for Information Science and Technology.2024; 75(2): 115.     CrossRef
  • Korean scholarly journal editors’ and publishers’ attitudes towards journal data sharing policies and data papers (2023): a survey-based descriptive study
    Hyun Jun Yi, Youngim Jung, Hyekyong Hwang, Sung-Nam Cho
    Science Editing.2023; 10(2): 141.     CrossRef
  • The data paper as a sociolinguistic epistemic object: A content analysis on the rhetorical moves used in data paper abstracts
    Kai Li, Chenyue Jiao
    Journal of the Association for Information Science and Technology.2022; 73(6): 834.     CrossRef
  • A Preliminary Analysis of Geography of Collaboration in Data Papers by S&T Capacity Index
    Pei‐Ying Chen, Kai Li, Chenyue Jiao
    Proceedings of the Association for Information Science and Technology.2022; 59(1): 642.     CrossRef
  • Taxonomy 2.0: computer-aided identification tools to assist Antarctic biologists in the field and in the laboratory
    Thomas Saucède, Marc Eléaume, Quentin Jossart, Camille Moreau, Rachel Downey, Narissa Bax, Chester Sands, Borja Mercado, Cyril Gallut, Régine Vignes-Lebbe
    Antarctic Science.2021; 33(1): 39.     CrossRef
  • Korean researchers’ motivations for publishing in data journals and the usefulness of their data: a qualitative study
    Jungyeoun Lee, Jihyun Kim
    Science Editing.2021; 8(2): 145.     CrossRef
  • Document Network and Conceptual and Social Structures of Clinical Endoscopy from 2015 to July 2021 Based on the Web of Science Core Collection: A Bibliometric Study
    Sun Huh
    Clinical Endoscopy.2021; 54(5): 641.     CrossRef
  • A Survey of Exclusively Data Journals and How They Are Indexed by Scientific Databases
    Kai Li, Chuyi Lu, Chenyue Jiao
    Proceedings of the Association for Information Science and Technology.2021; 58(1): 771.     CrossRef
  • Three-stage publishing to support evidence-based management practice
    Juan A. Marin-Garcia
    WPOM-Working Papers on Operations Management.2021; 12(2): 56.     CrossRef
  • The role of the data paper in scholarly communication
    Chenyue Jiao, Peter T. Darch
    Proceedings of the Association for Information Science and Technology.2020;[Epub]     CrossRef
Overview of disciplinary data sharing practices and promotion of open data in science
Jihyun Kim
Sci Ed. 2019;6(1):3-9.   Published online February 20, 2019
DOI: https://doi.org/10.6087/kcse.149
  • 5,836 View
  • 192 Download
  • 8 Web of Science
  • 7 Crossref
AbstractAbstract PDF
The present study specifies the historical development of data sharing practices in three disciplines—oceanography, ecology, and genomics—along with the evolving progress of movements—e-Science, cyberinfrastructure, and open science—that expedite data sharing in more diverse disciplines. The review of these disciplinary data-sharing practices and the movements suggests opportunities and challenges that would serve as a basis for implementing data-sharing practices. The increasing need for large-scale and interdisciplinary research provides momentum for initiating data sharing. In addition, the development of data repositories and standards for metadata and data format facilitates data sharing. However, challenges need to be addressed, in regard to conflicting issues of patenting data, concerns about privacy and confidentiality, and informed consent that adequately enables data sharing. It is also necessary to consider the needs of the various stakeholders involved in data sharing to incentivize them to improve its impact.

Citations

Citations to this article as recorded by  
  • Korean scholarly journal editors’ and publishers’ attitudes towards journal data sharing policies and data papers (2023): a survey-based descriptive study
    Hyun Jun Yi, Youngim Jung, Hyekyong Hwang, Sung-Nam Cho
    Science Editing.2023; 10(2): 141.     CrossRef
  • Deep learning-empowered crop breeding: intelligent, efficient and promising
    Xiaoding Wang, Haitao Zeng, Limei Lin, Yanze Huang, Hui Lin, Youxiong Que
    Frontiers in Plant Science.2023;[Epub]     CrossRef
  • Corporate Data Sharing, Leakage, and Supervision Mechanism Research
    Haifei Yu, Xinyu He
    Sustainability.2021; 13(2): 931.     CrossRef
  • Scientific Cooperation: Supporting Circumpolar Permafrost Monitoring and Data Sharing
    Troy J. Bouffard, Ekaterina Uryupova, Klaus Dodds, Vladimir E. Romanovsky, Alec P. Bennett, Dmitry Streletskiy
    Land.2021; 10(6): 590.     CrossRef
  • Understanding Research Data Repositories as Infrastructures
    Ceilyn Boyd
    Proceedings of the Association for Information Science and Technology.2021; 58(1): 25.     CrossRef
  • Bringing Code to Data: Do Not Forget Governance
    Christine Suver, Adrian Thorogood, Megan Doerr, John Wilbanks, Bartha Knoppers
    Journal of Medical Internet Research.2020; 22(7): e18087.     CrossRef
  • Recent trends in medical journals’ data sharing policies and statements of data availability
    Sun Huh
    Archives of Plastic Surgery.2019; 46(06): 493.     CrossRef

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