Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases
Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases is a dataset published in Humanities and Social Sciences Communications (2021). On theSindex it has a DataRank of 1.5, placing it in the top 38.1% of the data-sharing corpus. It has been cited 334 times, with 39 citing works in its 1-hop citation network. Its calibrated FAIR score is 30/100.
Abstract
AbstractGrowth 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, models with four or five segments fit 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.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.557
From this paper's citation signal
Citation Network Contribution
0.908
From 26 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Growth rates of modern science: A bibliometric analysis based on the number of publications and cited referencesJournal of the Association for Information Science and Technology20151,488 citationsDataRank 1.1
- Analysis of logistic growth modelsMathematical Biosciences20021,084 citationsDataRank 17.1
- Researchers’ Individual Publication Rate Has Not Increased in a CenturyPLOS ONE2016139 citationsDataRank 0.741
- Empirical analysis of recent temporal dynamics of research fields: Annual publications in chemistry and related areas as an exampleJournal of Informetrics202215 citationsDataRank 0.416
- Skewed distributions of scientists’ productivity: a research program for the empirical analysisScientometrics20249 citationsDataRank 0.345
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 38% comes from its base citations and 62% from the citation network (26 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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