This figure displays that most papers in the SciSciNet data source4 (n = 39,888,199) and most patents in the PatentsView data source (n = 2,926,923) with CD 5 = 1 have zero references. a, Our analysis shows that PatentsView contains 142,362 patents with CD 5 = 1 between 1980 and 2010, of which 78% appear in the database with zero references. b, Within the category of patents with CD 5 = 1, the relative frequency of patents with zero references is stable between 1980 and 2010. c, The relative frequency of patents with CD 5 index exactly equal to one and zero references is decreasing over time. Therefore, a substantial part of the reported decline in the disruptiveness of technological knowledge over time can be attributed to a relatively increasing metadata quality over time. It is also intriguing to note how well the shape of this curve resembles the shape of the top curve shown in Fig. 1f. d, SciSciNet4 shows a similar behaviour with 8,861,343 papers having CD 5 = 1 between 1944 and 2011, of which 97% appear in the database with zero references. e, Within the category of papers with CD 5 = 1, the relative frequency of papers with zero references is stable between 1944 and 2011. f, The relative frequency of papers with CD 5 index exactly equal to one and zero references is decreasing over time. Therefore, a substantial part of the observed decline in the disruptiveness of scientific knowledge over time can be attributed to a relatively increasing metadata quality over time. It is also intriguing to note how well the shape of this curve resembles the shape of the top curve shown in Extended Data Fig. 1c.
Dataset artefacts can partially drive the measured decline in disruption
Why This Matters
This study highlights that the perceived decline in technological and scientific disruptiveness over time may be significantly influenced by improvements in metadata quality, such as the completeness of references in patents and papers. Recognizing this bias is crucial for accurately assessing innovation trends and the true impact of new knowledge in the tech industry. It underscores the importance of data quality in research metrics and innovation analysis.
Key Takeaways
- Metadata quality improvements influence disruptiveness metrics.
- Most patents and papers with low disruptiveness have zero references.
- Accurate innovation assessment requires considering data artefacts and metadata completeness.
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