|
|
|
| The Impact of Interdisciplinarity: Distinct Effects on Usage and Citation |
| Zhang Lin1,2,3, Sun Beibei1, Wang Xianwen4, Huang Ying1,3 |
1.School of Information Management, Wuhan University, Wuhan 430072
2.Department of Management and Economics, North China University of Water Resource and Electric Power, Zhengzhou 450046
3.Centre for R&D Monitoring (ECOOM) and Department of MSI, KU Leuven, Leuven B- 3000
4.WISE Lab, Dalian University of Technology, Dalian 116024 |
|
|
|
|
Abstract With the advantages interdisciplinary research (IDR) offers in promoting comprehensive problem-solving in social development, governments are giving serious attention and extensive support to IDR. However, how to effectively identify and evaluate interdisciplinary outputs still remains an unresolved important question for scientific policy makers. On the basis of traditional citation data, this paper further introduces the emerging usage data (html page views, xml downloads, and pdf downloads) downloaded from PLoS platform to comprehensively evaluate the impact of IDR outputs. Taking scientific papers published in PLoS Computational Biology during 2009-2013 as an example, we reached the following threeconclusions First, there is a positive relationship between interdisciplinarity and impact. For papers with higher interdisciplinarity, the corresponding citation and usage data are significantly higher than those with lower interdisciplinarity. Second, there is an interactive effect between citation data and usage data, and the latter shows a slightly increasing trend which corresponds to the “peak” of citations. Third, interdisciplinarity also has a significant effect on correlations between usage data and citation data. This study attempts to explore the impact of IDR outputs by using both usage data and citation data, thereby opening up a new perspective for IDR evaluations.
|
|
Received: 31 October 2019
|
|
|
|
1 张琳. 国内外“交叉科学”研究现状及评述[J]. 科技管理研究, 2013, 33(12): 251-254.
2 王前, 李丽, 高成锴. 跨学科同行评议的合理性研究[J]. 科学学研究, 2013, 31(12): 1792-1795.
3 AbramoG, D’AngeloC A, DiCosta F. Do interdisciplinary research teams deliver higher gains to science?[J]. Scientometrics, 2017, 111(1): 317-336.
4 ZhouP, LeydesdorffL. Fractional counting of citations in research evaluation: A cross- and interdisciplinary assessment of the Tsinghua University in Beijing[J]. Journal of Informetrics, 2011, 5(3): 360-368.
5 LarivièreV, GingrasY. On the relationship between interdisciplinarity and scientific impact[J]. Journal of the American Society for Information Science and Technology, 2010, 61(1): 126-131.
6 LevittJ M, ThelwallM. Is multidisciplinary research more highly cited? A macrolevel study[J]. Journal of the American Society for Information Science and Technology, 2008, 59(12): 1973-1984.
7 RiniaE J, van LeeuwenT N, van RaanA F J. Impact measures of interdisciplinary research in physics[J]. Scientometrics, 2002, 53(2): 241-248.
8 ChenS J, ArsenaultC, LarivièreV. Are top-cited papers more interdisciplinary?[J]. Journal of Informetrics, 2015, 9(4): 1034-1046.
9 Yegros-YegrosA, RafolsI, D’EsteP. Does interdisciplinary research lead to higher citation impact? The different effect of proximal and distal interdisciplinarity[J]. PLoS ONE, 2015, 10(8): e0135095.
10 RafolsI, LeydesdorffL, O’HareA, et al. How journal rankings can suppress interdisciplinary research: A comparison between Innovation Studies and Business & Management[J]. Research Policy, 2012, 41(7): 1262-1282.
11 PriemJ, TaraborelliD, GrothP, et al. Altmetrics: A manifesto[EB/OL]. (2010-10-26). http://altmetrics.org/manifesto.
12 BinfieldP. PLoS ONE: Background, future development, and article-level metrics[C]// Proceedings of the 13th International Conference in Electronic Publishing, Milan, Italy, 2009: 69-86.
13 BornmannL. Usefulness of altmetrics for measuring the broader impact of research: A case study using data from PLOS and F1000Prime[J]. Aslib Journal of Information Management, 2015, 67(3): 305-319.
14 何星星, 武夷山. 基于文献利用数据的期刊论文定量评价研究[J]. 情报杂志, 2012, 31(8): 98-102.
15 王贤文, 方志超, 王虹茵. 连续、动态和复合的单篇论文评价体系构建研究[J]. 科学学与科学技术管理, 2015, 36(8): 37-48.
16 PiwowarH. Value all research products[J]. Nature, 2013, 493(7431): 159.
17 WangJ, ThijsB, Gl?nzelW. Interdisciplinarity and impact: Distinct effects of variety, balance, and disparity[J]. PLoS ONE, 2015, 10(5): e0127298.
18 van NoordenR. Interdisciplinary research by the numbers[J]. Nature, 2015, 525(7569): 306-307.
19 尚海茹, 冯长根, 孙良. 用学术影响力评价学术论文——兼论关于学术传承效应和长期引用的两个新指标[J]. 科学通报, 2016, 61(26): 2853-2860.
20 WangX W, WangZ, XuS M. Tracing scientist’s research trends realtimely[J]. Scientometrics, 2013, 95(2): 717-729.
21 BornmannL, MarxW. How should the societal impact of research be generated and measured? A proposal for a simple and practicable approach to allow interdisciplinary comparisons[J]. Scientometrics, 2014, 98(1): 211-219.
22 PorterA L, RoessnerJ D, CohenA S, et al. Interdisciplinary research: Meaning, metrics and nurture[J]. Research Evaluation, 2006, 15(3): 187-196.
23 StirlingA. A general framework for analysing diversity in science, technology and society[J]. Journal of the Royal Society: Interface, 2007, 4(15): 707-719.
24 LeydesdorffL, RafolsI. Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations[J]. Journal of Informetrics, 2011, 5(1): 87-100.
25 ZhouQ J, RousseauR, YangL Y, et al. A general framework for describing diversity within systems and similarity between systems with applications in informetrics[J]. Scientometrics, 2012, 93(3): 787-812.
26 ZhangL, RousseauR, Gl?nzelW. Diversity of references as an indicator of the interdisciplinarity of journals: Taking similarity between subject fields into account[J]. Journal of the Association for Information Science and Technology, 2016, 67(5): 1257-1265.
27 LeydesdorffL, BornmannL, ZhouP. Construction of a pragmatic base line for journal classifications and maps based on aggregated journal-journal citation relations[J]. Journal of Informetrics, 2016, 10(4): 902-918.
28 CassiL, ChampeimontR, MeschebaW, et al. Analysing institutions interdisciplinarity by extensive use of Rao-Stirling diversity index[J]. PLoS ONE, 2017, 12(1): e0170296.
29 MarxW, BornmannL. Change of perspective: Bibliometrics from the point of view of cited references—a literature overview on approaches to the evaluation of cited references in bibliometrics[J]. Scientometrics, 2016, 109(2): 1397-1415.
30 RapoportB I. Metabolic factors limiting performance in marathon runners[J]. PLoS Computational Biology, 2010, 6(10): e1000960.
31 BrodyT, HarnadS, CarrL. Earlier Web usage statistics as predictors of later citation impact[J]. Journal of the American Society for Information Science and Technology, 2006, 57(8): 1060-1072.
32 熊泽泉, 段宇锋. 论文早期下载量可否预测后期被引量?——以图书情报领域期刊为例[J]. 图书情报知识, 2018(4): 32-42.
33 王贤文. 科学计量大数据及其应用[M]. 北京: 科学出版社, 2016. |
|
|
|
