An Assessment of Sunspot Number Data Composites over 1845-2014
Mike Lockwood, Mathew J. Owens, Luke A. Barnard, Ilya G. Usoskin

TL;DR
This paper evaluates different sunspot data composites from 1845 to 2014, highlighting the advantages of the $R_{UEA}$ composite over others due to its robust calibration method that avoids common pitfalls like regressions and daisy-chaining.
Contribution
It compares multiple sunspot data series and advocates for the $R_{UEA}$ composite, demonstrating its reliability and robustness over traditional methods.
Findings
$R_{UEA}$ avoids regressions and daisy-chaining, reducing calibration errors.
All six data series can reproduce solar flux variations, but some show overestimation issues.
$R_{UEA}$ is recommended for its robust calibration procedures.
Abstract
New sunspot data composites, some of which are radically different in the character of their long-term variation, are evaluated over the interval 1845-2014. The method commonly used to calibrate historic sunspot data, relative to modern-day data, is "daisy-chaining", whereby calibration is passed from one data subset to the neighbouring one, usually using regressions of the data subsets for the intervals of their overlap. Recent studies have illustrated serious pitfalls in these regressions and the resulting errors can be compounded by their repeated use as the data sequence is extended back in time. Hence the recent composite data series by Usoskin et al. (2016), , is a very important advance because it avoids regressions, daisy-chaining and other common, but invalid, assumptions: this is achieved by comparing the statistics of "active day" fractions to those for a single…
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