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At the risk of sounding like another "poll", I have a pragmatic question for the methods development community: Hypothetically, assume that there was a website where you could download the original diffraction images corresponding to any given PDB file, including "early" datasets that were from the same project, but because of smeary spots or whatever, couldn't be solved. There might even be datasets with "unknown" PDB IDs because that particular project never did work out, or because the relevant protein sequence has been lost. Remember, few of these datasets will be less than 5 years old if we try to allow enough time for the original data collector to either solve it or graduate (and then cease to care). Even for the "final" dataset, there will be a delay, since the half-life between data collection and coordinate deposition in the PDB is still ~20 months. Plenty of time to forget. So, although the images were archived (probably named "test" and in a directory called "john") it may be that the only way to figure out which PDB ID is the "right answer" is by processing them and comparing to all deposited Fs. Assume this was done. But there will always be some datasets that don't match any PDB. Are those interesting? What about ones that can't be processed? What about ones that can't even be indexed? There may be a lot of those! (hypothetically, of course). Anyway, assume that someone did go through all the trouble to make these datasets "available" for download, just in case they are interesting, and annotated them as much as possible. There will be about 20 datasets for any given PDB ID. Now assume that for each of these datasets this hypothetical website has two links, one for the "raw data", which will average ~2 GB per wedge (after gzip compression, taking at least ~45 min to download), and a second link for a "lossy compressed" version, which is only ~100 MB/wedge (2 min download). When decompressed, the images will visually look pretty much like the originals, and generally give you very similar Rmerge, Rcryst, Rfree, I/sigma, anomalous differences, and all other statistics when processed with contemporary software. Perhaps a bit worse. Essentially, lossy compression is equivalent to adding noise to the images. Which one would you try first? Does lossy compression make it easier to hunt for "interesting" datasets? Or is it just too repugnant to have "modified" the data in any way shape or form ... after the detector manufacturer's software has "corrected" it? Would it suffice to simply supply a couple of "example" images for download instead? -James Holton MAD Scientist