This project is created for providing the main source code of our latest manuscript "Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities" that submitted to JASIST (The Journal of the Association for Information Science and Technology).
We proposed a novel knowledge-entity-based disruption indicator that differs from prior ones (Funk & Owen-Smith, 2017; Wu et al., 2019; Bornmann et al., 2020). This indicator quantifies the change of knowledge elements directly created and inspired by a focal paper to its evolutionary trajectory.
The effectiveness of the proposed indicators was evaluated against four standard scientific breakthrough datasets linked to a large-scale PubMed dataset (Liang et al., 2021). We evaluated the ability of our indicator in distinguishing the standard scientific breakthroughs from non-breakthrough papers in the PubMed dataset and compared the performance of our indicator with several baseline disruption indicators.
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disruption indicators.ipynbincludes the main source codes for five disruption indicators (three baseline indicators that proposed by previous studies and two new indicators that proposed in our research) -
sample_datafile provides five sample datasets for the calculation of disruption indicator. It should be noted that files with a ".dat" suffix should be loaded using the code in the "data preparation" section in the "disruption Indicators.ipynb" file.
figures and tables.ipynbincludes data processing and plotting code for the main figures and tables in our paper.
Bornmann, L., Devarakonda, S., Tekles, A., & Chacko, G. (2020). Are disruption index indicators convergently valid? The comparison of several indicator variants with assessments by peers. Quantitative Science Studies, 1(3), 1242–1259. https://doi.org/10.1162/qss_a_00068
Funk, R. J., & Owen-Smith, J. (2017). A dynamic network measure of technological change. Management Science, 63(3), 791–817. https://doi.org/10.1287/mnsc.2015.2366
Liang, Z., Mao, J., Lu, K., & Li, G. (2021). Finding citations for PubMed: a large-scale comparison between five freely available bibliographic data sources. Scientometrics, 126(12), 9519–9542. https://doi.org/10.1007/s11192-021-04191-8
Wu, L., Wang, D., & Evans, J. A. (2019). Large teams develop and small teams disrupt science and technology. Nature, 566(7744), 378–382. https://doi.org/10.1038/s41586-019-0941-9
Shiyun Wang1,2, Yaxue Ma3*, Jin Mao1,2*, Yun Bai1,2, Zhentao Liang1,2, Gang Li1
1, Center for Studies of Information Resources, Wuhan University, Wuhan, Hubei, China
2, School of Information Management, Wuhan University, Wuhan, Hubei, China
3, School of Information Management, Nanjing University, Nanjing, Jiangsu, China
Shiyun Wang. Email: wangsy2@whu.edu.cn
Yaxue Ma. Email: mayaxue@nju.edu.cn
Jin Mao. Email: danveno@163.com