Hi all, The EXAFS Divination Dataset is here: http://www.xafs.org/EXAFS_Divination_Set You may well be asking, "What is the EXAFS Divination Dataset"? It's a set of data collected on mixtures of iron compounds, mostly oxides of one sort or another. My research assistants have mixed random amounts of the compounds together--they know how much they used of each, but I don't, you don't, and (if you're a mentor to those learning XAFS), your students don't either. Thus, if you wish, you can try your hand at analyzing the data, knowing that when you're done, you can find out what the honest-to-goodness right answer is (how refreshing!). Or you can make your students try it, either as practice or to assess their current skills. My own motivation is to find out how accurate XAFS analysis really is for this kind of problem. Some researchers have attacked that question from the bottom up, evaluating the uncertainties inherent in each step of analysis. I'd like to complement that by looking at the issue from the top down, using these double-blind conditions to determine how much accuracy we can get in practice. So although you're free to use the dataset as you will (and there are a bunch of standards in there), if you'd like to know the correct phase identifications and answers, then I'd like to know the answers you got (including things like nearest-neighbor bond length if you got them), your level of expertise, an estimate of the time it took, and a brief (or not brief, if you prefer) description of the methods you used. (Note: If you do that, please don't respond to this email, as your findings will be posted to the entire list! Email to me directly at SCalvin.mailaps.org or SCalvin.slc.edu. Likewise, please don't use this list to discuss your attempts to fit samples in the dataset, as that will compromise my experiment.) If I end up publishing this study, I won't do so in a way that allows people to identify which analysis was done by whom. I will include you in the acknowledgments if you so choose. Some details of the dataset: The set consists of raw data files from X-11B at the NSLS. There are multiple scans for each sample and standard, and the data quality varies from moderate to good. Each sample and standard was measured at a different random temperature between 303 and 403 K; this reduces (but does not eliminate) the utility of methods like linear combinations of standards. There are seven standards: iron metal, Fe2O3, Fe3O4, FeO, alpha-FeOOH, gamma-FeOOH, iron(II) oxalate hydrate. Matt--you can put these standards in your library if you'd like, although temperature information will necessarily be missing until my study is complete (I don't even know the temperatures they were measured at yet). The first part of the dataset consists of mixtures where the constituents are known, but the fractions aren't. These problems are presumably pretty easy. In fact, if you or a student of yours wanted to take a very quick stab at these using linear combination methods, that's fine--just let me know that's what you did when you send the request for the answers. It will be interesting to see how far off linear combination methods are when the temperatures of the samples and standards are signficantly different. The second part consists of 2-3 standards mixed together, but you don't know which ones or how much of each. The third part consists of 1-2 standards that are specified, and one mystery compound that is not, except to say that it is a fairly simple organic compound (salt, probably) of iron. I don't yet know what it is; I had a colleague in the chemistry department pick something and order it for me, and then my students prepped it. The fourth part is 1-2 standards and the mystery compound, none of which are specified. Feel free to attack any parts of this you want, in any order, with any degree of seriousness. If you tell me that you eyeballed sample B5 and it looks like 60% iron metal and 40% Fe2O3, that's fine. :) I want a sense of how well various techniques work, not necessarily everyone's best effort. On the other hand, if you consider it a matter of personal pride to do as well as you can, then by all means... --Scott Calvin Sarah Lawrence College