Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases (2012.07675v3)

Abstract: Growth of science is a prevalent issue in science of science studies. In recent years, two new bibliographic databases have been introduced which can be used to study growth processes in science from centuries back: Dimensions from Digital Science and Microsoft Academic. In this study, we used publication data from these new databases and added publication data from two established databases (Web of Science from Clarivate Analytics and Scopus from Elsevier) to investigate scientific growth processes from the beginning of the modern science system until today. We estimated regression models that included simultaneously the publication counts from the four databases. The results of the unrestricted growth of science calculations show that the overall growth rate amounts to 4.10% with a doubling time of 17.3 years. As the comparison of various segmented regression models in the current study revealed, the model with five segments fits the publication data best. We demonstrated that these segments with different growth rates can be interpreted very well, since they are related to either phases of economic (e.g., industrialization) and / or political developments (e.g., Second World War). In this study, we additionally analyzed scientific growth in two broad fields (Physical and Technical Sciences as well as Life Sciences) and the relationship of scientific and economic growth in UK. The comparison between the two fields revealed only slight differences. The comparison of the British economic and scientific growth rates showed that the economic growth rate is slightly lower than the scientific growth rate.

• The paper employs latent piecewise growth curve models with cross-database data to reveal an overall scientific growth rate of 4.10% and a doubling time of 17.3 years.
• It identifies five distinct growth phases that align with major historical events like the Industrial Revolution and post-World War II expansion.
• The study contrasts discipline-specific trends and economic indicators, highlighting differences between fields and between scientific output and GDP growth rates.

Assessing Scientific Growth Using a Latent Piecewise Growth Curve Model

The paper by Bornmann, Haunschild, and Mutz investigates the growth of scientific publications utilizing data from four prominent bibliographic databases: Web of Science, Scopus, Dimensions, and Microsoft Academic. Through the application of latent piecewise growth curve models, the research seeks to offer nuanced insights into the temporal dynamics of scientific growth, drawing on segmented regression analyses that suitably address distinct historical stages.

Methodology and Data Sources

The paper draws on publication data through the implementation of advanced statistical modeling, specifically latent piecewise growth curves, to assess publication growth trends. The choice of four diverse databases allows for a cross-validation of growth trends and mitigates potential biases inherent in individual data sources. These databases differ in their historic coverage and document type inclusivity, thereby providing a comprehensive overview of scientific output over an extended period.

Key Findings

1. Growth Patterns and Rates:
   • The analysis reveals an overall growth rate across the four databases of 4.10%, amounting to a doubling time of 17.3 years. The paper identifies five distinct growth phases correlating with historical, economic, and political events. For instance, periods of significant scientific expansion align with the Industrial Revolution and the post-World War II era.
2. Segment-specific Growth:
   • Segmented growth analysis underscores phases such as the pre-industrial period with moderate growth, the Industrial Revolution reflecting accelerated growth, and the post-World War II period which again favored exponential growth. These findings correspond to known historical contexts that have either impeded or facilitated scientific output.
3. Differences Across Fields:
   • When narrowing focus to specific disciplines, such as Physical and Technical Sciences against Life Sciences, the research finds slight variances in growth dynamics, with a 5.51% growth rate in Physical and Technical Sciences compared to 5.07% in Life Sciences.
4. Economic Contextualization:
   • An ancillary analysis compared UK’s scientific and economic growth indicators, concluding that the growth rate of scientific publications (4.97%) surpassed the GDP growth rate (3.05%). This comparison provides insights into the interplay between macroeconomic factors and scientific productivity.

Implications for Future Scientific Research

This work holds implications for understanding how historical, political, and socioeconomic factors drive scientific expansion. The distinct growth phases and their alignment with major historical events suggest that geopolitical stability, economic resources, and technological advances are primary catalysts behind scientific productivity.

Additionally, the research underscores the potential limitations in assuming homogeneous growth across disciplines or geographic regions. The differential growth rates between fields such as Life Sciences and Physical Sciences, and between UK and global outputs, suggest tailored approaches when making future predictions about scientific growth.

Speculations on Future Developments

Speculating on future developments, the paper implies that while the overall growth of scientific literature may continue in an exponential trajectory, localized political or economic disruptions could alter this pattern. Thus, a forward-looking perspective that considers present geopolitical instability, digital transformation, and resource allocation will be essential in forming accurate assessments of future scientific output.

Conclusion

In synthesizing growth data over centuries through an innovative latent piecewise growth curve framework, this research enhances understanding of scientific publication trends across major global databases. By revealing the nuanced interplay between broader historical contexts and scientific productivity, the paper adds a layer of complexity to models of scientific output, promoting informed policymaking and resource allocation in research sectors.