Assessment of radiation damage behaviour in a large collection of empirically optimized datasets highlights the importance of unmeasured complicating effects.

The radiation damage behaviour in 43 datasets of 34 different proteins collected over a year was examined, in order to gauge the reliability of decay metrics in practical situations, and to assess how these datasets, optimized only empirically for decay, would have benefited from the precise and automatic prediction of decay now possible with the programs RADDOSE [Murray, Garman & Ravelli (2004). J. Appl. Cryst. 37, 513-522] and BEST [Bourenkov & Popov (2010). Acta Cryst. D66, 409-419]. The results indicate that in routine practice the diffraction experiment is not yet characterized well enough to support such precise predictions, as these depend fundamentally on three interrelated variables which cannot yet be determined robustly and practically: the flux density distribution of the beam; the exact crystal volume; the sensitivity of the crystal to dose. The former two are not satisfactorily approximated from typical beamline information such as nominal beam size and transmission, or two-dimensional images of the beam and crystal; the discrepancies are particularly marked when using microfocus beams (<20 µm). Empirically monitoring decay with the dataset scaling B factor (Bourenkov & Popov, 2010) appears more robust but is complicated by anisotropic and/or low-resolution diffraction. These observations serve to delineate the challenges, scientific and logistic, that remain to be addressed if tools for managing radiation damage in practical data collection are to be conveniently robust enough to be useful in real time.