Henry Rzepa's blog

FAIR is one of those acronyms that spreads rapidly, acquires a life of its own and can mean many things to different groups. A two-day event has just been held in Amsterdam to bring some of those groups from the chemical sciences together to better understand FAIR. Here I note a few items that caught my attention.

1. Fairsharing.org was the basis for several presentations. It serves as “a curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies.” It promotes establishing metrics which strive to quantify how FAIR any given resource is.[1] Any site which achieves a good FAIR metric can be described as a FAIR data point (a term new to me), and which can serve as an exemplar of what FAIR data aspires to.
2. Intrigued, I offered this page and hope to establish its FAIR metric in the near future, if only to understand how to improve its “score” so that future pages can be improved. It is based on the following Figure[2] which appeared in a recent article and appears to be a publishing “first” in as much as the figure contains hyperlinks directly to the data sources upon which it is based. The putative FAIR data point takes this a little further by wrapping the figure with visualisation tools which take the FAIR data and convert it to interactive models with the help of an added toolbox.
3. Another topic for discussion was spectroscopy and a veritable file format for its distribution, JCAMP-DX. One emerging theme is the idea of promoting two types of spectral distribution. The first is the use of a common standard format (JCAMP-DX) which strives to eliminate much of the proprietary character associated with data emerging from instruments. At the other extreme is to to offer to readers the raw instrumental data,[3] which has the advantage of having none of the inevitable loss of information when transforming to standard formats. The downside is that it almost always can only be processed using proprietary software provided by the instrument vendor. One way of avoiding this is Mpublish (the topic of an earlier blog) and we heard interesting updates on progress from MestreLabs, the originators of this procedure. It is still my hope that more vendors (both of instruments and of software) will adopt such a model.
4. A further topic was metadata, which is at the heart of each of the terms in FAIR (F = findable, A = accessible, I = interoperable and R = re-usable), which are all defined in part at least by the metadata associated with any item. The state of metadata associated with research data is often dire, and often too little resource has been assigned to its improvement. I presented an example of how richer metadata might be injected. The below is a snippet of the metadata associated with one entry in a data repository (download the metadata here and open the file with a text editor). An advantage of doing this is that rich searches against these terms become enabled.
5. Finally, I note events such as e.g. Harnessing FAIR data are starting to spring up. This one is at Queen Mary University of London on 3rd September 2018, for which “PhDs and Post Docs from a range of disciplines” are welcomed, they of course being the pre-eminent generators of  data and often the ones in charge of making it “FAIR”.

References

1. M.D. Wilkinson, S. Sansone, E. Schultes, P. Doorn, L.O. Bonino da Silva Santos, and M. Dumontier, "A design framework and exemplar metrics for FAIRness", Scientific Data, vol. 5, 2018. https://doi.org/10.1038/sdata.2018.118
2. S. Arkhipenko, M.T. Sabatini, A.S. Batsanov, V. Karaluka, T.D. Sheppard, H.S. Rzepa, and A. Whiting, "Mechanistic insights into boron-catalysed direct amidation reactions", Chemical Science, vol. 9, pp. 1058-1072, 2018. https://doi.org/10.1039/c7sc03595k
3. J.B. McAlpine, S. Chen, A. Kutateladze, J.B. MacMillan, G. Appendino, A. Barison, M.A. Beniddir, M.W. Biavatti, S. Bluml, A. Boufridi, M.S. Butler, R.J. Capon, Y.H. Choi, D. Coppage, P. Crews, M.T. Crimmins, M. Csete, P. Dewapriya, J.M. Egan, M.J. Garson, G. Genta-Jouve, W.H. Gerwick, H. Gross, M.K. Harper, P. Hermanto, J.M. Hook, L. Hunter, D. Jeannerat, N. Ji, T.A. Johnson, D.G.I. Kingston, H. Koshino, H. Lee, G. Lewin, J. Li, R.G. Linington, M. Liu, K.L. McPhail, T.F. Molinski, B.S. Moore, J. Nam, R.P. Neupane, M. Niemitz, J. Nuzillard, N.H. Oberlies, F.M.M. Ocampos, G. Pan, R.J. Quinn, D.S. Reddy, J. Renault, J. Rivera-Chávez, W. Robien, C.M. Saunders, T.J. Schmidt, C. Seger, B. Shen, C. Steinbeck, H. Stuppner, S. Sturm, O. Taglialatela-Scafati, D.J. Tantillo, R. Verpoorte, B. Wang, C.M. Williams, P.G. Williams, J. Wist, J. Yue, C. Zhang, Z. Xu, C. Simmler, D.C. Lankin, J. Bisson, and G.F. Pauli, "The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research", Natural Product Reports, vol. 36, pp. 35-107, 2019. https://doi.org/10.1039/c7np00064b