High-quality model curation provides insights by organizing biological knowledge-fragments. We aim to integrate published results about circadian clocks in Drosophila melanogaster while exploring economies of scale in model curation. Clocks govern rhythms of gene-expression that impact fitness, health, cancer, memory, mental functions, and more. Human clock insights have been pioneered in flies. Flies simplify investigating complex gene regulatory networks, which express proteins cyclically using environmentally entrained interlocking feedback loops. Simulations could simplify research further, but currently few models test their quality directly against experimentally observed time series scattered across publications. We designed FlyClockbase for robust efficient access to such scattered data for biologists and modelers, prioritizing simplicity and openness to encourage experimentalists to preserve more annotations and raw-data. Such details could multiply long-term value for modelers interested in meta-analyses, parameter estimates, and hypothesis testing. Currently FlyClockbase contains over 400 wildtype time series of core circadian components systematically curated from 86 studies published between 1990 and 2015. Using FlyClockbase, we show that PERIOD protein amount peak time variance unexpectedly exceeds that of TIMELESS. We hypothesize, PERIOD's exceedingly more complex phosphorylation rules are responsible. Human error analysis improved data quality and revealed significance-degrading outliers, possibly violating presumed absence of wildtype heterogeneity or lab evolution. We found PCR-measured peak time variances exceed those from other methods, pointing to initial count stochasticity. Our trans-disciplinary analyses demonstrate how compilers with more biology-friendly logic could simplify, guide, and naturally distribute biological model curation. Resulting quality increases and cost reductions benefit curation-dependent grand challenges like personalizing medicine.