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. 2018;117(1):641-645.
doi: 10.1007/s11192-018-2854-z. Epub 2018 Jul 27.

Response to the letter 'Field classification of publications in Dimensions: a first case study testing its reliability and validity'

Christian Herzog  1 , Brian Kierkegaard Lunn  1
Affiliations

Response to the letter 'Field classification of publications in Dimensions: a first case study testing its reliability and validity'

Christian Herzog et al. Scientometrics. 2018.

Abstract

With Dimensions, Digital Science provides the research community a new approach on research related information, bringing formerly siloed content types such as grants, patents, clinical trials with publications and citations together, making it as openly available as possible (see app.dimensions.ai). Due to the different content types, (controversial) journal based classifications were not an option since it would not allow to categorise grants etc. Hence Digital Science opted for applying a categorisation approach using machine learning and based on the content of the documents and well established classification systems for which a training set was available. The implementation at launch was a first step and requires to be improved-although we observe a reliability comparably to manual coding for grants, the implementation at launch comes with some shortcomings as observed by Bornmann (2018), mostly due to challenges with the training set coverage. To overcome the shortcomings of the initial categorization approach we implemented an improvement process with the research community and Lutz Bornmann's analysis presented a great opportunity to provide more transparency and insights in the ongoing improvements.

Keywords: Bibliometrics; Digital Science; Dimensions; Lutz Bornmann; Machine learning; Research classification.

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References

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