Journal of Korean Library and Information Science Society (한국도서관정보학회지)

Korean Library and Information Science Society (한국도서관정보학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis and Modeling of Essential Concepts and Process for Peer-Reviewing Data Paper

데이터논문 동료심사를 위한 핵심 개념 분석과 프로세스 모델링

  • 안성수 (경상국립대학교 항공우주및소프트웨어공학부) ;
  • 조성남 (한국과학기술정보연구원) ;
  • 정영임 (한국과학기술정보연구원)
  • Received : 2023.08.22
  • Accepted : 2023.09.20
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

A data paper describing research data helps credit researchers producing the data while helping other researchers verify previous research and start new research by reusing the data. Publishing a data paper and depositing data to a public data repository are increasing with these benefits. A domestic academic society that plans to publish data papers faces challenges, including timely acquiring tremendous knowledge concerning data paper structures and templates, peer review policy and process, and trustworthy data repositories, as a data paper has different characteristics, unlike a research paper. However, the need for more research and information concerning the critical elements of data paper and the peer-review process makes it difficult to operate for data paper review and publication. To address these issues, we propose essential concepts of the data paper and the data paper peer-review, including the process model of the peer-review with in-depth analysis of five data journals' data paper templates, articles, and other guides worldwide. Academic societies intending to publish or add data papers as a new type of paper may establish policies and define a peer-review process by adopting the proposed conceptual models, effectively streamlining the preparation of data paper publication.

연구데이터를 서술하는 데이터논문은 데이터를 생산한 연구자에게 연구논문과 같은 학술적 성과로 인정받을 수 있게 하고 다른 연구자가 데이터논문에서 공유한 데이터를 활용하여 기존 연구를 재현하거나 새로운 연구를 시작할 수 있도록 도움을 준다. 이러한 장점으로 데이터논문의 동료심사, 출판, 인용은 지속적으로 증가하고 있다. 데이터논문을 출판하고자 하는 학술단체는 연구논문과 다른 데이터논문의 핵심 개념, 데이터논문 심사 절차, 데이터논문 출판과 관계된 정보시스템 등을 이해하고 데이터논문에 포함할 구성요소 정의, 동료심사 절차 수립, 그리고 데이터저장소와 연계 등 세부 사항을 결정하는 것이 필요하다. 하지만, 데이터논문의 동료심사와 관련하여 다루어야 할 정보가 방대하고, 데이터논문 출판에 관한 연구 및 체계적인 정보가 부족하여 국내 학술단체는 데이터논문의 동료심사 업무에 어려움을 겪고 있다. 이러한 문제를 해결하는 데 도움이 될 수 있도록, 본 논문은 국내외 다섯 종 데이터학술지의 데이터논문 양식, 동료심사 사례를 조사·분석하여 데이터논문의 핵심 구성요소를 포함한 데이터논문 동료심사의 핵심 개념을 제안하고 프로세스를 모델링하였다. 데이터논문을 신규로 출판하거나, 기존의 연구논문과 함께 데이터논문을 심사하여 출판하고자 하는 학술단체는 본 논문에서 제시한 데이터논문의 핵심 개념과 동료심사 프로세스를 활용하여 데이터논문의 양식 개발, 체계적인 동료심사 프로세스 정립, 출판 정책 개발 등의 학술지업무에 신속하고 효과적으로 적용할 수 있을 것으로 기대된다.

Keywords

References

  1. Han, Jong-Gyu (2023). GEO current state of data publishing in earth system science. The Spring Conference of GeoAIData Society, 88-88.
  2. Hwang, Hyekyong, Jung, Youngim, Cho, Sung-Nam, Seo, Tae-Sul, & Kim, Jihyun (2023). A study on awareness and experience of data publishing by scientists. Journal of Korean Library and Information Science Society, 54(1), 45-68. https://doi.org/10.16981/kliss.54.1.202303.45
  3. Jeong, Yong-il, Ro, Ji-Yoon, Cho, Sung-Nam, & Ahn, Sungsoo (2022). A study on science and technology scholarly societies' understanding on open peer review. The Journal of the Korea Contents Association, 22(10), 59-73. https://doi.org/10.5392/JKCA.2022.22.10.059
  4. Jung, Youngim & Cho, Sung-Nam (2022). Analysis of Current Data Paper Publication, Data Publication Seminar, Seoul, National Library of Korea.
  5. Jung, Youngim, Kwon, Ohseok, Kim, Kidong, Kim, Sohyeong, Seo, Tae-Sul, & Kim, Suntae (2020). A study on the strategies for publishing data journals in the field of ecology: focused on K institution. Journal of Korean Library and Information Science Society, 51(4), 83-100. https://doi.org/10.16981/kliss.51.4.202012.83
  6. Kim, Kidong (2023). GEO DATA: current state of data peer review. The Spring Conference of GeoAIData Society, 89-89.
  7. Seo, Tae-Sul(2022). Towards data publishing from data sharing. KCSE Newsletter, 41, 9-12.
  8. Yi, Hyun Jung, Jung, Youngim, & Hwang, Hyekyong (2023). Survey: data sharing/data paper policies by editors of Korean scholarly journals. The Spring Conference of GeoAIData Society, 82-82.
  9. Ahn, R., Supakkul, S., Zhao, L., Kolluri, K., Hill, T., & Chung, L. (2021). Validating business problem hypotheses: a goal-oriented and machine learning-based approach. In International Conference on Big Data (pp. 17-33). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-96282-1_2
  10. Booch, G., Rumbaugh, J., & Jacobson, I. (2005). Unified Modeling Language User Guide, The 2nd Edition, Addison-Wesley Object Technology Series.
  11. Candela, L., Castelli, D., Manghi, P., & Tani, A. (2015). Data journals: a survey. Journal of the Association for Information Science and Technology, 66(9), 1747-1762. https://doi.org/10.1002/asi.23358
  12. Carpenter, T. (2017). What constitutes peer review of data? A survey of peer review guidelines. The Scholarly Kitchen.
  13. Chavan, V. & Penev, L. (2011). The data paper: a mechanism to incentivize data publishing in biodiversity science. BMC bioinformatics, 12, 1-12. https://doi.org/10.1186/1471-2105-12-S15-S2
  14. Elmasri, R. & Navathe, S. B. (2015). Fundamentals of Database Systems (Seventh edition). Pearson.
  15. Gomes, D. G., Pottier, P., Crystal-Ornelas, R., Hudgins, E. J., Foroughirad, V., Sanchez-Reyes, L. L., ... & Gaynor, K. M. (2022). Why don't we share data and code? perceived barriers and benefits to public archiving practices. Proceedings of the Royal Society B, 289(1987), https://doi.org/10.1098/rspb.2022.1113
  16. Kim, S. Y., Yi, H. J., & Huh, S. (2019). Current and planned adoption of data sharing policies by editors of Korean scholarly journals. Science Editing, 6(1), 19-24. https://doi.org/10.6087/kcse.151
  17. Lawrence, B., Jones, C., Matthews, B., Pepler, S., & Callaghan, S. (2011). Citation and peer review of data: moving towards formal data publication. International Journal of Digital Curation, 6(2), 4-37. https://doi.org/10.2218/ijdc.v6i2.205
  18. Mylopoulos, J., Chung, L., & Yu, E. (1999). From object-oriented to goal-oriented requirements analysis. Communications of the ACM, 42(1), 31-37. https://doi.org/10.1145/291469.293165
  19. Tedersoo, L., Kungas, R., Oras, E., Koster, K., Eenmaa, H., Leijen, A., ... & Sepp, T. (2021). Data sharing practices and data availability upon request differ across scientific disciplines. Scientific Data, 8(1), 192. https://doi.org/10.1038/s41597-021-00981-0
  20. White, S. A. & Bock, C. (2011). BPMN 2.0 Handbook: Methods, Concepts, Case Studies and Standards in Business Process Management Notation (Second Edition). Future Strategies Inc., Book Division.
  21. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., ... & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 1-9. https://doi.org/10.1038/sdata.2016.18