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Comparing paper level classifications across different methods and systems: an investigation of Nature publications

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Abstract

The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different approaches across three different systems. These were: Web of Science (WoS) subject categories (SCs) provided by InCites, which are based on the disciplinary affiliation of the majority of a paper’s references; Fields of Research (FoR) classification provided by Dimensions, which are derived from machine learning techniques; and subjects classification provided by Springer Nature, which are based on author-selected subject terms in the publisher’s tagging system. The results show, first, that the single category assignment in InCites is not appropriate for a large number of papers. Second, only 27% of papers share the same fields between FoR classification in Dimensions and subjects classification in Springer Nature, revealing great inconsistencies between these machine-determined versus human-judged approaches. Being aware of the characteristics and limitations of the ways we categorize research publications is important to research management.

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Notes

  1. The document types, article and letter, are defined on the Nature website. Both are peer-reviewed research papers published in Nature.

  2. ANZSRC FoR classification have now been updated to 2020 and are significantly different to the 2008 version. However, since our data was exported from Dimensions in which only FoR 2008 is available, we applied FoR 2008 in this study.

  3. https://www.nature.com/nature/browse-subjects.

  4. https://incites.help.clarivate.com/Content/Research-Areas/oecd-category-schema.htm.

  5. https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/1297.02008?OpenDocument.

  6. Authors are required to choose the most relevant subject categories (including the top-level subject areas, the second level subjects, and the more fine-grained levels of specific subject terms) when submitting their manuscripts to Springer Nature. These fine-grained subject terms are presented on the webpage of each paper.

References

  • Abramo, G., D’Angelo, C. A., & Zhang, L. (2018). A comparison of two approaches for measuring interdisciplinary research output: The disciplinary diversity of authors vs the disciplinary diversity of the reference list. Journal of Informetrics, 12(4), 1182–1193.

    Article  Google Scholar 

  • Ahlgren, P., Chen, Y., Colliander, C., et al. (2020). Enhancing direct citations: A comparison of relatedness measures for community detection in a large set of PubMed publications. Quantitative Science Studies, 1(2), 714–729.

    Google Scholar 

  • Baharudin, B., Lee, L. H., & Khan, K. (2010). A review of machine learning algorithms for text-documents classification. Journal of Advances in Information Technology, 1, 4–20.

    Article  Google Scholar 

  • Ballesta, S., Shi, W., Conen, K. E., et al. (2020). Values encoded in orbitofrontal cortex are causally related to economic choices. Nature, 588(7838), 450–453.

    Article  Google Scholar 

  • Bornmann, L. (2018). Field classification of publications in Dimensions: A first case study testing its reliability and validity. Scientometrics, 117(1), 637–640.

    Article  Google Scholar 

  • Boyack, K. W., Newman, D., Duhon, R. J., et al. (2011). Clustering more than two million biomedical publications: Comparing the accuracies of nine text-based similarity approaches. Plos One, 6(3), e18029.

    Article  Google Scholar 

  • Carley, S., Porter, A. L., Rafols, I., et al. (2017). Visualization of disciplinary profiles: Enhanced science overlay maps. Journal of Data and Information Science, 2(3), 68–111.

    Article  Google Scholar 

  • Chapman, H. N., Fromme, P., Barty, A., et al. (2011). Femtosecond X-ray protein nanocrystallography. Nature, 470(7332), 73–77.

    Article  Google Scholar 

  • Dehmamy, N., Milanlouei, S., & Barabási, A.-L. (2018). A structural transition in physical networks. Nature, 563(7733), 676–680.

    Article  Google Scholar 

  • Eykens, J., Guns, R., & Engels, T. C. E. (2019). Article level classification of publications in sociology: An experimental assessment of supervised machine learning approaches. In: Proceedings of the 17th International Conference on Scientometrics & Informetrics, Rome (Italy), 2–5 September, 738–743.

  • Glänzel, W., & Debackere, K. (2021). Various aspects of interdisciplinarity in research and how to quantify and measure those. Scientometrics. https://doi.org/10.1007/s11192-11021-04133-11194

    Article  Google Scholar 

  • Glänzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56(3), 357–367.

    Article  Google Scholar 

  • Glänzel, W., Thijs, B., & Huang, Y. (2021). Improving the precision of subject assignment for disparity measurement in studies of interdisciplinary research. FEB Research Report MSI_2104, 1–12.

  • Gläser, J., Glänzel, W., & Scharnhorst, A. (2017). Same data—Different results? Towards a comparative approach to the identification of thematic structures in science. Scientometrics, 111(2), 981–998.

    Article  Google Scholar 

  • Gómez-Núñez, A. J., Batagelj, V., Vargas-Quesada, B., et al. (2014). Optimizing SCImago Journal & Country Rank classification by community detection. Journal of Informetrics, 8(2), 369–383.

    Article  Google Scholar 

  • Gómez-Núñez, A. J., Vargas-Quesada, B., & de Moya-Anegón, F. (2016). Updating the SCImago journal and country rank classification: A new approach using Ward’s clustering and alternative combination of citation measures. Journal of the Association for Information Science and Technology, 67(1), 178–190.

    Article  Google Scholar 

  • Haunschild, R., Schier, H., Marx, W., et al. (2018). Algorithmically generated subject categories based on citation relations: An empirical micro study using papers on overall water splitting. Journal of Informetrics, 12(2), 436–447.

    Article  Google Scholar 

  • Janssens, F., Zhang, L., Moor, B. D., et al. (2009). Hybrid clustering for validation and improvement of subject-classification schemes. Information Processing & Management, 45(6), 683–702.

    Article  Google Scholar 

  • Kandimalla, B., Rohatgi, S., Wu, J., et al. (2021). Large scale subject category classification of scholarly papers with deep attentive neural networks. Frontiers in Research Metrics and Analytics, 5, 31.

    Article  Google Scholar 

  • Klavans, R., & Boyack, K. W. (2017). Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge? Journal of the Association for Information Science and Technology, 68(4), 984–998.

    Article  Google Scholar 

  • Leydesdorff, L., & Bornmann, L. (2016). The operationalization of “fields” as WoS subject categories (WCs) in evaluative bibliometrics: The cases of “library and information science” and “science & technology studies.” Journal of the Association for Information Science and Technology, 67(3), 707–714.

    Article  Google Scholar 

  • Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60(2), 348–362.

    Article  Google Scholar 

  • Liu, X., Glänzel, W., & De Moor, B. (2012). Optimal and hierarchical clustering of large-scale hybrid networks for scientific mapping. Scientometrics, 91(2), 473–493.

    Article  Google Scholar 

  • McGillivray, B., & Astell, M. (2019). The relationship between usage and citations in an open access mega-journal. Scientometrics, 121(2), 817–838.

    Article  Google Scholar 

  • Milojević, S. (2020). Practical method to reclassify Web of Science articles into unique subject categories and broad disciplines. Quantitative Science Studies, 1(1), 183–206.

    Article  Google Scholar 

  • Nam, S., Jeong, S., Kim, S.-K., et al. (2016). Structuralizing biomedical abstracts with discriminative linguistic features. Computers in Biology and Medicine, 79, 276–285.

    Article  Google Scholar 

  • Park, I.-U., Peacey, M. W., & Munafò, M. R. (2014). Modelling the effects of subjective and objective decision making in scientific peer review. Nature, 506(7486), 93–96.

    Article  Google Scholar 

  • Porter, A. L., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719–745.

    Article  Google Scholar 

  • Rafols, I., & Leydesdorff, L. (2009). Content-based and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and indexer effects. Journal of the American Society for Information Science and Technology, 60(9), 1823–1835.

    Article  Google Scholar 

  • Roach, N. T., Venkadesan, M., Rainbow, M. J., et al. (2013). Elastic energy storage in the shoulder and the evolution of high-speed throwing in Homo. Nature, 498(7455), 483–486.

    Article  Google Scholar 

  • Rutishauser, U., Ross, I. B., Mamelak, A. N., et al. (2010). Human memory strength is predicted by theta-frequency phase-locking of single neurons. Nature, 464(7290), 903–907.

    Article  Google Scholar 

  • Shu, F., Julien, C.-A., Zhang, L., et al. (2019). Comparing journal and paper level classifications of science. Journal of Informetrics, 13(1), 202–225.

    Article  Google Scholar 

  • Shu, F., Ma, Y., Qiu, J., et al. (2020). Classifications of science and their effects on bibliometric evaluations. Scientometrics, 125(3), 2727–2744.

    Article  Google Scholar 

  • Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.

    Article  MathSciNet  Google Scholar 

  • Szomszor, M., Adams, J., Pendlebury, D. A., et al. (2021). Data categorization: Understanding choices and outcomes. The Global Research Report from the Institute for Scientific Information.

  • Tannenbaum, C., Ellis, R. P., Eyssel, F., et al. (2019). Sex and gender analysis improves science and engineering. Nature, 575(7781), 137–146.

    Article  Google Scholar 

  • Thijs, B., Zhang, L., & Glänzel, W. (2015). Bibliographic coupling and hierarchical clustering for the validation and improvement of subject-classification schemes. Scientometrics, 105(3), 1453–1467.

    Article  Google Scholar 

  • Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: Guaranteeing well-connected communities. Scientific Reports, 9(1), 5233.

    Article  Google Scholar 

  • Van Eck, N. J., Waltman, L., Van Raan, A. F. J., et al. (2013). Citation analysis may severely underestimate the impact of clinical research as compared to basic research. PLoS ONE, 8(4), e0062395.

    Google Scholar 

  • Waltman, L., & van Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 63(12), 2378–2392.

    Article  Google Scholar 

  • Waltman, L., & Van Eck, N. J. (2013). Source normalized indicators of citation impact: An overview of different approaches and an empirical comparison. Scientometrics, 96(3), 699–716.

    Article  Google Scholar 

  • Waltman, L., & van Eck, N. J. (2019). Field normalization of scientometric indicators. In W. Glänzel, H. F. Moed, U. Schmoch, et al. (Eds.), Springer Handbook of Science and Technology Indicators (pp. 281–300). Springer.

  • Waltman, L., Van Eck, N. J., & Noyons, E. C. M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635.

    Article  Google Scholar 

  • Zhang, L., Janssens, F., Liang, L., et al. (2010). Journal cross-citation analysis for validation and improvement of journal-based subject classification in bibliometric research. Scientometrics, 82(3), 687–706.

    Article  Google Scholar 

  • Zhang, L., Rousseau, R., & Glänzel, W. (2016). Diversity of references as an indicator of the interdisciplinarity of journals: Taking similarity between subject fields into account. Journal of the Association for Information Science and Technology, 67(5), 1257–1265.

    Article  Google Scholar 

  • Zhang, L., Sun, B., Chinchilla-Rodríguez, Z., et al. (2018). Interdisciplinarity and collaboration: On the relationship between disciplinary diversity in departmental affiliations and reference lists. Scientometrics, 117(1), 271–291.

    Article  Google Scholar 

  • Zhang, L., Sun, B., Jiang, L., et al. (2021a). On the relationship between interdisciplinarity and impact: Distinct effects on academic and broader impact. Research Evaluation, 30(3), 256–268.

    Article  Google Scholar 

  • Zhang, L., Sun, B., Shu, F., et al. (2021b). Comparing paper level classifications across different methods and systems: An investigation on Nature publications. In: Proceedings of the 18th International Conference on Scientometrics and Informetrics, Leuven (Belgium), 12–15 July, 1319–1324.

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Acknowledgements

The present study is an extended version of an article presented at the 18th International Conference on Scientometrics and Informetrics, Leuven (Belgium), 12–15 July 2021 (Zhang et al. 2021b). The authors would like to acknowledge support from the National Natural Science Foundation of China (Grant Nos. 71974150, 72004169, 72074029), the National Laboratory Centre for Library and Information Science at Wuhan University, and the project “Interdisciplinarity & Impact” (2019–2023) funded by the Flemish Government.

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Authors and Affiliations

  1. School of Information Management, Wuhan University, Wuhan, China

    Lin Zhang, Beibei Sun & Ying Huang

  2. Center for Science, Technology & Education Assessment, Wuhan University, Wuhan, China

    Lin Zhang, Beibei Sun & Ying Huang

  3. Department of MSI & ECOOM, KU Leuven, Leuven, Belgium

    Lin Zhang, Beibei Sun & Ying Huang

  4. Chinese Academy of Science and Education Evaluation, Hangzhou Dianzi University, Hangzhou, China

    Fei Shu

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Correspondence to Lin Zhang.

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The first author (Lin Zhang) is the Co-Editor-in-Chief of Scientometrics.

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Zhang, L., Sun, B., Shu, F. et al. Comparing paper level classifications across different methods and systems: an investigation of Nature publications. Scientometrics 127, 7633–7651 (2022). https://doi.org/10.1007/s11192-022-04352-3

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