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>In the past few years, there have been almost no structures retracted >due to gross errors and the checks being used by structural biology >community seemed to working quite well - what can we learn from this >tragic and sad error ? First of all, Happy Holidays to all of us :) Please feel free not to read on, nothing that is written below is new or original. --- In my personal opinion, this isn't about the science of crystallography - the issue at hand concerns human behavior and psychology. We learn (not that we don't know this already) that human beings make mistakes. Neither fancy software, nor Draconian deposition/verification measures will save us from the consequences of human error. For example, the recently deep-fried Mars probe was checked, double-checked, and then checked a couple more times for good measure - and it still ended up thinly spread over a patch of Mars geography due to a unit conversion error! We can help minimize the fallout if we spend more time thinking about the results - it is generally likely that errors are discovered if enough time is spent (you don't have to even look for them most of the time, just spend time looking *in general*. Unfortunately, time is something that almost none of us have. During the last couple of centuries scientific research has transitioned from the (relatively) sane realm of knowledge and discovery into the frenetic and highly competitive world of finance and politics. Both industrial and academic scientists are now under the gun to deliver 'stunning' results quickly - deadlines, competition, and burn-out are common phenomena on both sides of the fence (in fact it seems that the industrial scientists are more relaxed now than the academic ones!). 1) if the same kind of error, followed by a retraction, happened to a less 'hot' structure - would we be mass-emailing about it now? While the answer to this question *should be* a resounding 'yes' - it probably is a 'no' in real life. 2) What is the likelihood that this kind of catastrophic event can happen to a 'run of the mill' structure? Would the authors just abandon a low resolution dataset in favor of doing something else (something that gets the grant money)? Would they spend more time trying to get better data, or analyzing the data they have? Would the referees demand more proof? Artem
