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A refinement strategy for identification of scientific software from bioinformatics publications

Author

Listed:
  • Lu Jiang

    (Nanjing Agricultural University
    Chengdu Library and Information Center of Chinese Academy of Sciences)

  • Xinyu Kang

    (Chengdu University of Technology)

  • Shan Huang

    (Sun Yat-Sen University)

  • Bo Yang

    (Nanjing Agricultural University
    Nanjing Agricultural University)

Abstract

In the field of bioinformatics, a large number of classical software becomes a necessary research tool. To measure the influence of scientific software as one kind of important intellectual products, a few strategies have been proposed to identify the software names from full texts of papers to collect the usage data of packages in bioinformatics research. However, the performance of these strategies is limited because of the highly imbalance of data in the full texts. This study proposes EnsembleSVMs-CRF, a two-step refinement strategy based on ensemble learning that gradually increases the sentences that contain software mentions to improve the performance of named entity recognition. The experiment on the bioinformatics corpus shows that the performance of EnsembleSVMs-CRF, in terms of the local F1 (78.81%) and the global F1-A (73.49%), is superior to the rule-based bootstrapping method and direct CRF. Application of this strategy to the articles published between 2013 and 2017 in 27 bioinformatics journals extracted 8,239 unique packages. The most popular 50 packages thus identified demonstrate that most of them are professional software which generally requires inter-discipline knowledge, rather than programming skill. Meanwhile, we found that researchers in bioinformatics tend to use free scientific software, and the application of general software is increasing compared with professional software.

Suggested Citation

  • Lu Jiang & Xinyu Kang & Shan Huang & Bo Yang, 2022. "A refinement strategy for identification of scientific software from bioinformatics publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3293-3316, June.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:6:d:10.1007_s11192-022-04381-y
    DOI: 10.1007/s11192-022-04381-y
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    References listed on IDEAS

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