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http://dx.doi.org/10.3743/KOSIM.2020.37.1.153

An Investigation of Intellectual Structure on Data Papers Published in Data Journals in Web of Science  

Chung, EunKyung (이화여자대학교 사회과학대학 문헌정보학과)
Publication Information
Journal of the Korean Society for information Management / v.37, no.1, 2020 , pp. 153-177 More about this Journal
Abstract
In the context of open science, data sharing and reuse are becoming important researchers' activities. Among the discussions about data sharing and reuse, data journals and data papers shows visible results. Data journals are published in many academic fields, and the number of papers is increasing. Unlike the data itself, data papers contain activities that cite and receive citations, thus creating their own intellectual structures. This study analyzed 14 data journals indexed by Web of Science, 6,086 data papers and 84,908 cited references to examine the intellectual structure of data journals and data papers in academic community. Along with the author's details, the co-citation analysis and bibliographic coupling analysis were visualized in network to identify the detailed subject areas. The results of the analysis show that the frequent authors, affiliated institutions, and countries are different from that of traditional journal papers. These results can be interpreted as mainly because the authors who can easily produce data publish data papers. In both co-citation and bibliographic analysis, analytical tools, databases, and genome composition were the main subtopic areas. The co-citation analysis resulted in nine clusters, with specific subject areas being water quality and climate. The bibliographic analysis consisted of a total of 27 components, and detailed subject areas such as ocean and atmosphere were identified in addition to water quality and climate. Notably, the subject areas of the social sciences have also emerged.
Keywords
data journal; data paper; citation anlaysis; network; co-citation analysis; bibliographic coupling analysis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 McKiernan, E. C., Bourne, P. E., Brown, C. T., Buck, S., Kenall, A., Lin, J., ... & Spies, J. R. (2016). Point of view: How open science helps researchers succeed. Elife, 5, e16800. https://doi.org/10.7554/eLife.16800   DOI
2 Pampel, H., Vierkant, P., Scholze, F., Bertelmann, R., Kindling, M., Klump, J., ... & Dierolf, U. (2013). Making research data repositories visible: the re3data. org registry. PloS one, 8(11), e78080. https://doi.org/10.1371/journal.pone.0078080   DOI
3 Silvello, G. (2018). Theory and practice of data citation. Journal of the Association for Information Science and Technology, 69(1), 6-20. https://doi.org/10.1002/asi.23917   DOI
4 Kim, Ji Hyun (2019). A study on the current status of data journals and the establishment of data journal policies. Korea Institute of Science and Technology Information.
5 Lee, Jae Yun (2006). WNET Software Package.
6 Chung, EunKyung (2019). An investigation on scientific data for data journal and data paper. Journal of the Korean Society for Information Management, 36(1), 117-135. https://doi.org/10.3743/KOSIM.2019.36.1.117   DOI
7 Borgman, C. L. (2016). Data citation as a bibliometric oxymoron. In Sugimoto, C. (Eds), Theories of informetrics and scholarly communication (pp. 93-116). Berlin: De Gruyter Mouton.
8 Akers, K. G., & Doty, J. (2013). Disciplinary differences in faculty research data management practices and perspectives. International Journal of Digital Curation, 8(2), 5-26. https://doi.org/10.2218/ijdc.v8i2.263   DOI
9 Belter, C. W. (2014). Measuring the value of research data: A citation analysis of oceanographic data sets. PLoS One, 9(3), e92590. https://doi.org/10.1371/journal.pone.0092590   DOI
10 Borgman, C. L. (2015). Big data, little data, no data. Cambridge, MA: MIT Press.
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   DOI
12 Castelli, D., Manghi, P., & Thanos, C. (2013). A vision towards scientific communication infrastructures. International Journal on Digital Libraries, 13(3-4), 155-169. https://doi.org/10.1007/s00799-013-0106-7   DOI
13 Chavan, V., & Penev, L. (2011). The data paper: a mechanism to incentivize data publishing in biodiversity science. BMC bioinformatics, 12(15), S2. https://doi.org/10.1186/1471-2105-12-S15-S2   DOI
14 Costello, M. J., Michener, W. K., Gahegan, M., Zhang, Z. Q., & Bourne, P. E. (2013). Biodiversity data should be published, cited, and peer reviewed. Trends in Ecology & Evolution, 28(8), 454-461. doi.org/10.1016/j.tree.2013.05.002   DOI
15 Huang, X., Hawkins, B. A., & Qiao, G. (2013). Biodiversity data sharing: Will peer-reviewed data papers work?. BioScience, 63(1), 5-6. https://doi.org/10.1525/bio.2013.63.1.2   DOI
16 Goodman, A., Pepe, A., Blocker, A. W., Borgman, C. L., Cranmer, K., Crosas, M., ... & Hogg, D. W. (2014). Ten simple rules for the care and feeding of scientific data. PLoS Computational Biology. 10(4). https://doi.org/10.1371/journal.pcbi.1003542
17 Gorgolewski, K., Margulies, D. S., & Milham, M. P. (2013). Making data sharing count: a publication-based solution. Frontiers in neuroscience, 7, 9. https://doi.org/10.3389/fnins.2013.00009   DOI