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The Implications of Current Practices Relating to the Sharing, Reuse, and Citation of Research Software for the Future of Research

연구소프트웨어의 공유, 재사용 및 인용과 관련된 현재 관행의 의미

  • Park, Hyoungjoo (Department of Library and Information Science, Chungnam National University) ;
  • Wolfram, Dietmar (School of Information Studies, University of Wisconsin - Milwaukee)
  • Received : 2021.11.15
  • Accepted : 2021.12.16
  • Published : 2021.12.30

Abstract

The purpose of this research is to explore the phenomenon of the sharing, reuse, and citation of research software. These practices are playing an increasingly important role in scholarly communication. The researchers found that the citation and reuse of research software are currently uncommon or at least not reflected in the Data Citation Index (DCI). Such citation was observed, however, for the newer software in a number of prominent repositories. The repositories Comprehensive R Archive Network (CRAN) and Zenodo received the most formal software citations. The researchers observed both formal and informal forms of citation when researchers reused software. The latter form involves mentioning research software in passing in the main text of articles, while formal citations appear in the references section. In addition, our comparative analysis helps to explain the phenomenon of self-citation of research software.

이 연구의 목적은 연구소프트웨어의 공유, 재사용, 인용 현황을 분석하는 것이다. 학술커뮤니케이션에서 연구소프트웨어는 최근 들어 더욱 중요한 역할을 하고 있다. 현재 연구소프트웨어의 인용이 일반적인 관행이 아니거나, 적어도 데이터인용색인(DCI)이 연구소프트웨어의 인용과 재사용을 제대로 인덱싱하지 못하는 것으로 관찰되었다. 소프트웨어인용은 주요 레포지토리(prominent repositories)에서 발견되었다. 소프트웨어인용이 많은 레포지토리는 CRAN(Comprehensive R Archive Network)과 Zenodo였다. 연구소프트웨어가 재사용되는 경우, 비공식 소프트웨어인용(informal software citation)과 공식 소프트웨어인용(formal software citation)이 동시에 관찰되었다. 비공식 소프트웨어인용은 연구소프트웨어가 논문의 본문에서는 언급되지만 참고문헌에는 없는 경우였고, 공식 소프트웨어인용은 참고문헌에도 있는 경우였다. 또한, 이 연구의 결과는 연구소프트웨어의 자기 인용(self-citation) 현황을 설명했다.

Keywords

References

  1. Ajiferuke, I., Lu, K., & Wolfram, D. (2010). A comparison of citer and citation-based measure outcomes for multiple disciplines. Journal of the American Society for Information Science and Technology, 61(10), 2086-2096. http://doi.org/10.1002/asi.21383
  2. Allen, A. (2021). Citation method, please? A case study in astrophysics. arXiv. Available: https://arxiv.org/pdf/2111.12574.pdf
  3. American Association for the Advancement of Science (2016). Science journals: editorial policies. Available: https://www.science.org/content/page/sciencejournalseditorialpolicies#research-standards
  4. American Astronomical Society (2016). Policy statement on software. Available: https://journals.aas.org/news/policy-statement-on-software
  5. Astrophysics Source Code Library. [n.d.]. Welcome to the ASCL. Available: https://ascl.net
  6. Bassett, P. G. (1997). Framing Software Reuse: Lessons from the Real World. Upper Saddle River: Yourdon Press.
  7. Bietz, M. J., Baumer, E. P. S., & Lee, C. P. (2020). Synergizing in cyberinfrastructure development. Computer Supported Cooperative Work, 19, 245-281. http://doi.org/10.1007/s10606-010-9114-y
  8. Clarivate Analytics (2020). Data Citation Index help. Available: http://images.webofknowledge.com/WOKRS517B4/help/DRCI/index.html
  9. Clarivate Analytics (2021). Data Citation Index. Available: https://clarivate.com/webofsciencegroup/solutions/webofscience-data-citation-index
  10. Druskat, S., Spaaks, J. H., Chue Hong, N., Haines, R., & Baker, J. (2019). Citation file format (CFF) - specifications. Zenodo. http://doi.org/10.5281/zenodo.3515946
  11. Du, C., Cohoon, J., Lopez, P., & Howison, J. (2021). Softcite dataset: a dataset of software mentions in biomedical and economic research publications. Journal of the Association for Information Science and Technology. http://doi.org/10.1002/asi.24454
  12. FAIR for Research Software Working Group (2021). FAIR for research software (FAIR4RS) WG. Available: https://www.rd-alliance.org/groups/fair-4-research-software-fair4rs-wg
  13. Garcia, F., Bertoa, M. F., Calero, C., Vallecillo, A., Ruiz, F., Piattini, M., & Genero, M. (2006). Towards a consistent terminology for software measurement. Information and Software Technology, 48(8), 631-644. http://doi.org/10.1016/j.infsof.2005.07.001
  14. Goble, C. (2014). Better software, better research. IEEE Internet Computing, 18(5), 4-8. http://doi.org/10.1109/MIC.2014.88
  15. Hannay, J. E., MacLeod, C., Singer, J., Langtangen, H. P., Pfahl, D., & Wilson, G. (2009). How do scientists develop and use scientific software? 2009 ICSE Workshop on Software Engineering for Computational Science and Engineering. Vancouver: IEEE. http://doi.org/10.1109/SECSE.2009.5069155
  16. He, N. & Nahar, V. (2016). Reuse of scientific data in academic publications: an investigation of Dryad digital repository. Aslib Journal of Information Management, 68(4), 478-494. http://doi.org/10.1108/AJIM-01-2016-0008
  17. Henry, E. & Faller, B. (1995). Large-scale industrial reuse to reduce cost and cycle time. IEEE Software, 12(5), 47-53. http://doi.org/10.1109/52.406756
  18. Henry, V., Bandrowski, A. E., Pepin, A.-S., Gonzalez, B. J., & Desfeux, A. (2014). OMICtools: an informative directory for multi-omic data analysis. Database, 2014, 1-5. http://doi.org/10.1093/database/bau069
  19. Hong, N. C. (2014). Minimal information for reusable scientific software. Proceedings of the 2nd Workshop on Working towards Sustainable Scientific Software. Available: http://www.research.ed.ac.uk/portal/files/16773670/MinimalInfoScientificSoftware.pdf
  20. Howison, J. & Bullard, J. (2016). Software in the scientific literature: problems with seeing, finding, and using software mentioned in the biology literature. Journal of the Association for Information Science and Technology, 67(9), 2137-2155. http://doi.org/10.1002/asi.23538
  21. Howison, J. & Herbsleb, J. D. (2011). Scientific software production: incentives and collaboration. Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, 513-522. http://doi.org/10.1145/1958824.1958904
  22. Hwang, L., Pauloo, R., & Carlen, J. (2020). Assessing the impact of outreach through software citation for community software in geodynamics. Computing in Science & Engineering, 22(1), 16-25. http://doi.org/10.1109/MCSE.2019.2940221
  23. Jones, M. B., Boettiger, C., Mayes, A. C., Smith, A., Slaughter, P., Niemeyer, K., Gil, Y., Fenner, M., Nowak, K., Hahnel, M., Coy, L., Allen, A., Crosas, M., Sands, A., Chue Hong, N., Cruse, P., Katz, D. S., & Goble, C. (2017). CodeMeta. KNB Data Repository. http://doi.org/10.5063/schema/codemeta-2.0
  24. Kats, D. S., Bouquin, D., Chue Hong, N. P., Hausman, J., Jones, C., Chivvis, D., Clark, T., Crosas, M., Druskat, S., Fenner, M., Gillespie, T., Gonzalez-Beltran, A., Gruenpeter, M., Habermann, T., Haines, R., Harrison, M., Henneken, E., Hwang, L., Jones, M. B., Alastair, J., Kelly A. A., Kennedy, D. N., Leinweber, K., Rois, F., Robinson, C. B., Todorov, I., Wu, M., & Zhang, Q. (2019). Software citation implementation challenges. Available: https://arxiv.org/ftp/arxiv/papers/1905/1905.08674.pdf
  25. Keswani, R., Joshi, S., & Jatain, A. (2014). Software reuse in practice. Proceedings of the Fourth International Conference on Advanced Computing & Communication Technologies, 159-162. http://doi.org/10.1109/Acct.2014.57
  26. Li, K., Lin, X., & Greenberg, J. (2016). Software citation, reuse and metadata considerations: an exploratory study examining LAMMPS. Proceedings of the 82nd Annual Meeting of the Association for Information Science and Technology, 1-10. http://doi.org/10.1002/pra2.2016.14505301072
  27. Lu, K., Ajiferuke, I., & Wolfram, D. (2014). Extending citer analysis to journal impact evaluation. Scientometrics, 100(1), 245-260. http://doi.org/10.1007/s11192-014-1274-y
  28. Makitalo, N., Taivalsaari, A., Kiviluoto, A., & Capilla, R. (2020). On opportunistic software reuse. Computing, 102, 2385-2408. http://doi.org/10.1007/s00607-020-00833-6.
  29. Malone, J., Brown, A., Lister, A. L., Ison, J. Hull, D., Parkinson, H., & Stevens, R. (2014). The software ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation. Journal of Biomedical Semantics, 5(25). http://doi.org/10.1186/2041-1480-5-25
  30. Morisio, M., Ezran, M., & Tully, C. (2002). Success and failure factors in software reuse. IEEE Transaction on Software Engineering, 28(4), 340-357. http://doi.org/10.1109/TSE.2002.995420
  31. Nangia, U. & Katz, D. S. (2017). Track 1 paper: surveying the U.S. national postdoctoral association regarding software use and training in research. Workshop on Sustainable Software for Science: Practice and Experiences. http://doi.org/10.5281/zenodo.814220
  32. Pan, X., Yan, E., & Hua, W. (2016). Disciplinary differences of software use and impact in scientific literature. Scientometrics, 109, 1593-1610. http://doi.org/s11192-016-2138-4 https://doi.org/10.1007/s11192-016-2138-4
  33. Pan, X., Yan, E., Cui, M., & Hua, W. (2018). Examining the usage, citation, and diffusion patterns of bibliometric mapping software: a comparative study of three tools. Journal of Informetrics, 12(2), 481-493. http://doi.org/10.1016/j.joi.2018.03.005
  34. Pan, X., Yan, E., Wang, Q., & Hua, W. (2015). Assessing the impact of software on science: a bootstrapped learning of software entities in full-text papers. Journal of Informetrics, 9(4), 860-871. http://doi.org/10.1016/j.joi.2015.07.012
  35. Park, H. & Wolfram, D. (2017). An examination of research data sharing and re-use: implications for data citation practice. Scientometrics, 111(1), 443-461. http://doi.org/10.1007/s11192-017-2240-2
  36. Park, H. & Wolfram, D. (2019). Research software citation in the Data Citation Index: current practices and implications for research software sharing and reuse. Journal of Informetrics, 13, 574-582. http://doi.org/10.1016/j.joi.2019.03.005
  37. Piwowar, H. A., Day, R. S., & Fridsma, D. B. (2007). Sharing detailed research data is associated with increased citation rate. PLoS ONE, 2(3), e308. http://doi.org/10.1371/journal.pone.0000308
  38. Robinson-Garcia, N., Jimenez-Contreras, E., & Torres-Salinas, D. (2015). Analyzing data citation practices using the data citation index. Journal of the Association for Information Science and Technology, 67(12), 2964-2975. http://doi.org/10.1002/asi.23529
  39. SciCodes Consortium (2021). SCICODES: consortium of scientific registries and repositories. Available: https://scicodes.net/2021/04/09/welcome-to-scicodes
  40. Smith, A. M., Katz, D. S., Niemeyer, K. E., & FORCE11 Software Citation Working Group. (2016). Software citation principles. PeerJ Computer Science, 2(e86). http://doi.org/10.7717/peerj-cs.86
  41. Socias, S. M., Morin, A., Tomony, M. A., & Sliz, P. (2015). AppCiter: a web application for increasing rates and accuracy of scientific software citation. Structure, 23(5), 807-808. http://doi.org/10.1016/j.str.2015.04.005
  42. Springer Nature. [n.d.]. Reporting standards and availability of data, materials, code and protocols. Available: https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards
  43. Yang, B., Huang, S., Wang, X., & Rousseau, R. (2018). How important is scientific software in bioinformatics research? A comparative study between international and Chinese research communities. Journal of the Association for Information Science and Technology. http://doi.org/10.1002/asi.24031