The interdisciplinarity of research data: How widely is shared research data reused in the STEM fields?

被引:2
|
作者
Park, Hyoungjoo [1 ]
机构
[1] Chungnam Natl Univ, Dept Lib & Informat Sci, Daejeon, South Korea
来源
JOURNAL OF ACADEMIC LIBRARIANSHIP | 2022年 / 48卷 / 04期
关键词
Research data; Interdisciplinarity; Data citation; Data sharing; Data reuse; DATA CITATION; DIVERSITY; SCIENTISTS; KNOWLEDGE; INDICATOR; VARIETY; SCIENCE;
D O I
10.1016/j.acalib.2022.102535
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
The aim of this study was to examine the interdisciplinarity of research data in the science, technology, engineering, and mathematics (STEM) fields. The findings revealed that interdisciplinarity was not distributed evenly across journals serving the STEM fields. Based on the diversity of the references as measured by the Gini coefficient index, the mathematical sciences showed the greatest inequality, followed by astronomy/physics, the earth sciences, the biological sciences, and technology. Based on the number of Essential Science Indicator (ESI) fields, the biological sciences showed the greatest variety, followed by the earth sciences, technology, the mathematical sciences, chemistry, and computing, while engineering showed no variety. Lastly, based on the Leydesdorff interdisciplinarity formula outcomes, the earth sciences showed the greatest diversity, but earth sciences articles were cited in articles in fewer fields than biological sciences articles. This study contributes to the study of interdisciplinary data citation for data sharing and reuse in STEM fields with respect to the measurement of the balance, variety, and diversity of research data.
引用
收藏
页数:10
相关论文
共 50 条
  • [31] Various aspects of interdisciplinarity in research and how to quantify and measure those
    Wolfgang Glänzel
    Koenraad Debackere
    [J]. Scientometrics, 2022, 127 : 5551 - 5569
  • [32] How is Big Data reshaping preclinical aging research?
    Maria Emilia Fernandez
    Jorge Martinez-Romero
    Miguel A. Aon
    Michel Bernier
    Nathan L. Price
    Rafael de Cabo
    [J]. Lab Animal, 2023, 52 : 289 - 314
  • [33] HOW TO COLLECT DATA IN A QUALITATIVE FIELD RESEARCH.
    Demidenko, S. Yu.
    [J]. SOTSIOLOGICHESKIE ISSLEDOVANIYA, 2021, (09): : 161 - 164
  • [34] Research on big data audit based on financial shared service model
    Hong, Jiang
    Wang, Zhichong
    [J]. ANNALS OF OPERATIONS RESEARCH, 2023, 326 (01) : 621 - 621
  • [35] How is Big Data reshaping preclinical aging research?
    Fernandez, Maria Emilia
    Martinez-Romero, Jorge
    Aon, Miguel
    Bernier, Michel
    Price, Nathan
    de Cabo, Rafael
    [J]. LAB ANIMAL, 2023, 52 (12) : 281 - 281
  • [36] How can qualitative research face open data?
    Perez-Soria, Judith
    [J]. RECERCA-REVISTA DE PENSAMENT & ANALISI, 2022, 27 (02):
  • [37] How to analyze tumor stage data in clinical research
    Hu, Zhi-De
    Zhou, Zhi-Rui
    Qian, Shi
    [J]. JOURNAL OF THORACIC DISEASE, 2015, 7 (04) : 566 - 575
  • [38] The importance of a research data statement and how to develop one
    Ford, E. David
    [J]. ANNALES ZOOLOGICI FENNICI, 2009, 46 (02) : 82 - 92
  • [39] HOW TO DESIGN A RESEARCH STUDY, AND TO COLLECT AND ANALYSE DATA?
    Kisely, S.
    [J]. AUSTRALIAN AND NEW ZEALAND JOURNAL OF PSYCHIATRY, 2015, 49 : 26 - 27
  • [40] Data Mining: How Research Meets Practical Development?
    Wu, Xindong
    Yu, Philip S.
    Piatetsky-Shapiro, Gregory
    Cercone, Nick
    Lin, T.Y.
    Kotagiri, Ramamohanarao
    Wah, Benjamin W.
    [J]. Knowledge and Information Systems, 2003, 5 (02) : 248 - 261