1. Title Data handling Zbigniew DAUTER Zbyszek(friends) Zbyniuś(MD) Darling(MD sometimes) Zygmunt(GGD) Speedcheck(KSW) Zibi(USA)

2. Name confusion Confusing name (continuation) data or da’ta data was collected data were collected (Polish is much easier)

3. Handle with care Data Handle with care Fragile !Do not abuse h k l

4. H,k,l,I,sigma h k l I  (I) very easyunfortunately to collect these are also (KSW) necessary

5. Uncomplete data If data are not complete, there are problems in: - direct methods substructure solution - Patterson methods substructure solution Molecular Replacement - Fourier maps additional features

6. Non-random missing refl. Missing reflections are never random unfinished rotation range - missing wedge unhappy Kevin’s duck in orthorhombic symmetry 90 o is not always enough - only between two axial orientations

7. 622 symmetry In 622 symmetry if rotated around 6-fold 30 o is enough if rotated around 2-fold 90 o is necessary

8. Blind region normally not serious, except if - symmetry is low (P1) - resolution is very high (atomic)

9. Overloads Overloaded profiles detector dynamic range, e.g. 2 16 limit (low resolution pass may be necessary) small number of missing strongest reflections ruins Molecular Replacement (Gideon Davies, 93)

10. Accuracy criteria R merge (R sym, R int ) = __________________ I/  (I) - generally > 2, or 50% of reflections > 3  Redundancy – the higher the better, but beware of radiation damage  hkl  i | - I i |  hkl  i I i Data accuracy criteria

11. Improved Rmerge R merge - bad criterion from statistical point of view (depends on redundancy) Improved forms (sadly, rarely used): R meas = ____________________ (Diederichs & Karplus, 97) R r.i.m. = ______________________ (Weiss & Hilgenfeld, 97) R p.i.m. = __________________ (Weiss & Hilgenfeld, 97)  hkl n  i | - I i |  hkl (n-1)  i I i  hkl n 1/2  i | - I i |  hkl (n-1) 1/2  i I i  hkl  i | - I i |  hkl (n-1) 1/2  i I i

12. Sigmas I/  (I) is much better criterion 2-D detectors do not measure individual X-ray quanta but something proportional therefore counting statistics is not valid  ’s must be corrected for detector “gain” t-plot = _________ average = 0, s.d. = 1  2 criterion - agreement with expectations - I i  (I)

13. Invitation to York Why was I invited to speak here ? Because I learned from the masters ! I was a postdoc in York 1981-1985

14. Woolfson First in Physics with Michael Woolfson

15. Guy & Bakers Then in Chemistry Learning from the masters (Mirka too)

16. Zygmunt + Casio We had excellent computing facilities Zygmunt refining haemoglobin

17. Long computations Long computations – Mirka helped too only rarely the message would appear: “something wrong… consult the programmer” (EJD)

18. Colin & Eleanor Cd- and Pb- Insulin data collected on a Hilger-Watts diffractometer result - 5 punched paper tapes R3 became my favoured space group - importance of reflection 472 SSM – (Sort, Scale and Merge) _ Colin Reynolds and EJD

19. Disappointme nts Later disappointments… Synchrotrons – everything too fast… Autoindexing – those people (Rossmann, Kabsch, Otwinowski, Leslie) spoiled the fun of fitting diffraction patterns by hand… Automatization – now data processing is like a black box Well, not completely !

20. conclusion Conclusion

21. Poor and sick It is better to be rich and healthy than poor and sick or to have complete and accurate data rather than otherwise gift from Thomas Schneider (has Italian wife)

22. Cato the Elder after Cato the Elder in the Roman senate: „et memento delenda est Carthago” data collection involves a lot of technical problems but is not a technicality - last experimental step, later only calculations it is good to engage the brain

23. Thanks I learned it in York Thank you Eleanor Thank you Guy