  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),
$R_{UEA}$, 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
reference dataset. We study six sunspot data series including $R_{UEA}$ and the
new "backbone" data series $R_{BB}$, recently generated by Svalgaard and
Schatten (2016) by employing both regression and daisy-chaining. We show that
all six can be used with a continuity model to reproduce the main features of
the open solar flux variation for 1845-2014, as reconstructed from geomagnetic
activity data. However, some differences can be identified that are consistent
with tests using a basket of other proxies for solar magnetic fields. Using
data from a variety of sunspot observers, we illustrate problems with the
method employed in $R_{BB}$ which cause it to increasingly overestimate sunspot
numbers going back in time and we recommend using $R_{UEA}$ because it employs
more robust procedures that avoid such problems.
