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데이터 관리와 공유에 대한 대학 연구자들의 인식에 관한 연구

A Study on the Perceptions of University Researchers on Data Management and Sharing

한국문헌정보학회지 / 한국문헌정보학회지, (P)1225-598X; (E)2982-6292
2015, v.49 no.3, pp.413-436
https://doi.org/10.4275/KSLIS.2015.49.3.413
김지현 (이화여자대학교)
  • 다운로드 수
  • 조회수

초록

본 연구는 국내 대학에 소속된 연구자들을 대상으로 데이터 관리 현황과 데이터 공유 및 재이용에 대한 경험과 인식을 조사하는 것을 목적으로 하였다. 이를 위해 본 연구에 선행하여 수행된 설문조사의 응답자 중 후속 인터뷰에 동의한 13명을 대상으로 반구조화된 인터뷰를 수행하였다. 참여자들은 다양한 유형과 포맷의 데이터를 생성 또는 수집하고 있었으며 데이터 기록화를 수행하는 연구자들은 소수에 불과하였으나 이들은 그 중요성을 인식하고 있었다. 데이터가 유용한 기간을 데이터가 논문 출판에 활용될 수 있는 기간으로 인식하는 연구자들이 대다수이었으나, 데이터가 유용하다고 인식하는 기간 이상으로 데이터를 보존하려는 연구자들이 많아 데이터의 저장과 보존에 대한 연구자들의 요구가 적지 않은 것으로 나타났다. 데이터의 공유와 재이용은 개인적인 연구 모임이나 연구 팀 등 잘 알고 있는 사람들과의 범위 내에서 이루어지고 있었다. 정부연구비 지원을 받는 과제의 데이터는 오픈 액세스로 공개해야 한다는 논리에 전적으로 찬성하는 연구자들도 있는 반면 부분적으로 동의하거나 반대하는 의견도 있었다. 다수의 참여자들이 연구아이디어의 도용, 표절, 논문 출판의 주도권 문제 등 연구데이터 공유에 대한 우려를 나타내고 있었으며 이를 완화할 수 있는 유인책이 마련될 필요가 있다.

keywords
Data Management, Data Sharing, Data Reuse, University Researchers, 데이터관리, 데이터공유, 데이터재이용, 대학 연구자

Abstract

This study aimed to investigate data management practices of university researchers in Korea, as well as their experiences and perceptions of data sharing and reuse. For this purpose, it performed semi-structured interviews of 13 researchers who agreed to participate in interviews followed by a survey conducted prior to this study. The interview participants created or collected research data with various types and formats, and only a few conducted data documentation while they recognized its significance. The majority of participants perceived the period that data would be useful as the period that data can be employed for publications. However, most participants wanted to preserve data beyond the period that data would be considered useful and it indicates they have no small need for data storage and preservation. Participants usually shared data with those whom they have known, such as a personal research group or a research team. While some completely agree with the principle that publicly-funded data should be open to the public, others partially agreed or disagreed with it. Most participants were concerned about being scooped, plagiarism, and maintaining the first right to publish and incentives to mitigate the concerns would be necessary.

keywords
Data Management, Data Sharing, Data Reuse, University Researchers, 데이터관리, 데이터공유, 데이터재이용, 대학 연구자

참고문헌

1.

김문정, 김성희. 2015. 과학기술분야 연구자의 연구데이터 공유의 영향요인에 대한 연구. 한국문헌정보학회지, 49(2), 313-334.

2.

김은정, 남태우. 2012. 연구데이터 수집에 영향을 미치는 요인 분석. 정보관리학회지, 29(2), 27-44.

3.

Akers, K. G., and Doty, J. 2013. Disciplinary differences in faculty research data management practices and perspectives. International Journal of Digital Curation, 8(2), 5-26.

4.

Alexogiannopoulos, E., McKenney, S., and Pickton, M. 2010. Research Data Management Project: a DAF investigation of research data management practices at The University of Northampton. Northampton: The University of Northampton. [online] [cited 2015. 7. 15.]<http://nectar.northampton.ac.uk/2736/1/Alexogiannopoulos20102736.pdf>

5.

Association of Research Libraries. 2014. Open Scholarship. Washington D. C., US: Association of Research Libraries. [online] [cited 2015. 7. 15.]<http://www.arl.org/focus-areas/open-scholarship#.VaXCyKNWFMt>

6.

Borgman, C. L. 2012. The conundrum of sharing research data. Journal of the American Society for Information Science and Technology, 63(6), 1059-1078.

7.

Borgman, C. L. 2015. Big Data, Little Data, No Data: Scholarship in the Networked World. Cambridge, MA: MIT Press.

8.

Borgman, C. L., Wallis, J. C., and Enyedy, N. 2007. Little science confronts the data deluge:habitat ecology, embedded sensor networks, and digital libraries. International Journal on Digital Libraries, 7(1-2), 17-30.

9.

Corti, L. et al. 2014. Managing and sharing research data: A guide to good practice. London:SAGE Publications.

10.

Cragin, M. H. et al. 2010. Data sharing, small science and institutional repositories. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 368(1926), 4023-4038.

11.

Ekmekcioglu, C., and Rice, R. 2009. Edinburgh Data Audit Implementation Project. Scotland:The University of Edinburgh. [online] [cited 2015. 7. 15.]<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.175.6624&rep=rep1&type=pdf>

12.

Georgia Tech Library. 2013. Cataloging Metadata Template. Atalanta: Georgia Tech Library. [online] [cited 2015. 7. 15.] <http://d7.library.gatech.edu/research-data/metadata>

13.

Goben, A., and Salo, D. 2013. Federal research: Data requirements set to change. College and Research Libraries News, 74(8), 421-425. [online] [cited 2015. 7. 15.]<http://crln.acrl.org/content/74/8/421.full#ref-17>

14.

Karasti, H., Baker, K. S., and Millerand, F. 2010. Infrastructure Time: Long-term Matters in Collaborative Development." Computer Supported Cooperative Work-the Journal of Collaborative Computing, 19(3-4), 377-415.

15.

Kim, Y., and Stanton, J. M. 2012. Institutional and individual influences on scientists’ data sharing practices. Journal of Computational Science Education, 3(1), 47-56.

16.

Lynch, C. 2008. Big data: How do your data grow? Nature, 455(7209), 28-29.

17.

Molloy, J. C. 2011. The open knowledge foundation: open data means better science. PLoS Biology, 9(12). [online] [cited 2015. 7. 15.]<http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001195>

18.

National Science Foundation. 2013. Chapter VI - Other Post Award Requirements and Considerations. National Science Foundation. Ann Arbor: National Science Foundation. [online] [cited 2015. 7. 15.]<http://www.nsf.gov/pubs/policydocs/pappguide/nsf13001/aag_6.jsp#VID4>

19.

Open Exeter Project Team. 2012. Summary Findings of the Open Exeter Data Asset Framework (DAF) Survey. Exeter: The University of Exeter. [online] [cited 2015. 7. 15.]<http://hdl.handle.net/10036/3689>

20.

Parsons, T., Grimshaw, S., and Williamson, L. 2013. Research data management survey:report. Nottingham: The University of Nottingham. [online] [cited 2015. 7. 15.]<http://eprints.nottingham.ac.uk/1893/>

21.

Saltz, J. et al. 2006. caGrid: Design and implementation of the core architecture of the cancer biomedical informatics grid. Bioinformatics, 22(15), 1910-1916.

22.

Scaramozzino, J. M., Ramírez, M. L., and McGaughey, K. J. 2011. A study of faculty data curation behaviors and attitudes at a teaching-centered university. College & Research Libraries, 73(4), 349-365.

23.

Wallis, J. C., Rolando, E., and Borgman, C. L. 2013. If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology. PloS one, 8(7). [online][cited 2015. 7. 15.]<http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0067332>

24.

Wellcome Trust. 2014. Establishing Incentives and Changing Cultures to Support Data Access. London: Wellcome Trust. [online] [cited 2015. 7. 15.]<http://www.wellcome.ac.uk/stellent/groups/corporatesite/@msh_peda/documents/web_do cument/wtp056495.pdf>

25.

Westra, B. 2010. Data services for the sciences: A needs assessment. Ariadne, 64. [online][cited 2015. 7. 15.] <http://www.ariadne.ac.uk/issue64/westra>

26.

Whyte, A., and Pryor, G. 2011. Open science in practice: Researcher perspectives and participation. International Journal of Digital Curation, 6(1), 199-213.

27.

Williams, S. C. 2013. Data sharing interviews with crop sciences faculty: why they share data and how the library can help. Issues in Science and Technology Librarianship, 72. [online] [cited 2015. 7. 15.]<http://www.istl.org/13-spring/refereed2.html?utm_source=dlvr.it&utm_medium=twitter>

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