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First, Open Access, then Open (and FAIR) Data, now Open Citations.

Saturday, February 3rd, 2018

The topic of open citations was presented at the PIDapalooza conference and represents a third component in the increasing corpus of open scientific information.

David Shotton gave us an update on  Citations as First Class data objects – Citation Identifiers and introduced (me) to the blog where he discusses this topic. The citations or bibliography has long been regarded as an essential, and until recently inseparable, component at the end of a scientific article. It is also a component easily susceptible to “game play“. Authors can be tempted to self-cite themselves, possibly to excess and perhaps worse, to cite their friends and colleagues for other than purely scientific reasons. There are other issues. Thus to infer the context of any particular citation, one has to read the text where it is cited and this too can be subjected to game play. One may have to “read between the lines” to try to judge whether the citation is being cited favourably as supporting any case being made, or instead to indicate disagreement with the cited authors. An article that is being cited because one disagrees with the conclusions therein may still go on to contribute to the cited author’s “h-index” of esteem. So there are various aspects of citations that deserve improvement, or certainly development and evolution.

Shotton told us that many publishers are now releasing article citations as open (CC0) data in their own right, as urged to do so on the Initiative for Open Citations site. A corpus of some 13 million of these are now available  as RDF triples with a SPARQL end-point. This latter means that semantic searches of the corpus can be undertaken. So what are the benefits? Worthy aspirations such as to explore connections between knowledge fields, and to follow the evolution of ideas and scholarly disciplines (similar in fact to the new Dimensions product I discussed in the previous post). When I probed into the various sites linked above, I had in mind to identify some clear scientific outcomes of making them available in this manner, perchance even in the field of chemistry. When I succeed I will follow-up on this post, but at the moment I am not yet in a position to illustrate these benefits with chemical stories. If anyone reading this post has such, please let us know! 

I will conclude here by noting much discussion at universities of the future of the scientific article itself; whether it should be increasingly mandated as GOLD Open Access (made so by payment of an article processing charge, or APC, by its authors), or whether journals should retain the hybrid publishing models where only a proportion of articles are GOLD, and the remainder are paid for by subscription fees for licensing access to the non-GOLD articles in the journal. Meanwhile, in what seems sometimes as a separate conversation, the article itself is being dis-assembled into components such as open and/or FAIR data, open citations, infographics, social media and yes, even blogs. Are these two evolutions headed in different directions? Certainly, I think the future is not what it used to be!

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PIDapalooza 2018. A conference like no other!

Tuesday, January 23rd, 2018

Another occasional conference report (day 1). So why is one about “persistent identifiers” important, and particularly to the chemistry domain?

The PID most familiar to most chemists is the DOI (digital object identifier). In fact there are many; some 60 types have been collected by ORCID (themselves purveyors of researcher identifiers). They sometimes even have different names; in life sciences they tend to be known instead as accession numbers. One theme common to many (probably not all) is that they represent sources of metadata about the object being identified. Further information if which allows you (or a machine) to decide if acquiring the full object is worthwhile. So in no particular order, here are some of the things I learnt today.

  1. Mark Hahnel noted the recent launch of the Dimensions resource which links research data with other research activities; I have not yet had a chance to learn its capabilities, but it seems an interesting alternative to other stalwarts such as eg Google Scholar etc.

    You can try this example: https://app.dimensions.ai/discover/publication?search_text=10.6084&search_type=kws&full_search=true which retrieves articles in which the data repository with prefix 10.6084 (Figshare) is cited. Try also the prefix 10.14469 which is the Imperial College repository.

  2. Andy Mabbett talked about the deployment and use of persistent identifiers (the Q numbers) in Wikidata, which increasingly underpin the basis for the various flavours of Wikipedia. He also noted their use of some 50 different identifiers.
  3. Johanna McEntyre noted some 5M published articles in life sciences which reference 1M+ ORCID identifiers, easily the domain with the fastest uptake of this type. Also noted was the new FREYA project; aiming to connect open identifiers for discovery, access and use of research resources.
  4. Tom Gillespie talked about RRID, or Research Resource Identifiers. Included in this are hardware, including instruments and with around 6000 RRIDs systematized so far. They argue this area promotes both the A and I of FAIR (accessible and inter-operable). Of course A and I mean many things to many people.
  5. Several other presentations talked about the finer detail of metadata, such as sub-classifications into e.g. descriptive/admin/technical, but I did rather miss demos showing how search queries of such fine-grained metadata could be constructed.

Apart from the presentations themselves, PIDapalooza is unusual for some other activities. Thus you could go get your PIDnails done, with a selection of 8 or so tasteful logos to choose from. There will be tattoos tomorrow (this is a conference for younger people after all). I may grab a photo or two to provide evidence!

 

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FAIR data ⇌ Raw data.

Thursday, December 7th, 2017

FAIR data is increasingly accepted as a description of what research data should aspire to; Findable, Accessible, Inter-operable and Re-usable, with Context added by rich metadata (and also that it should be Open). But there are two sides to data, one of which is the raw data emerging from say an instrument or software simulations and the other in which some kind of model is applied to produce semi- or even fully processed/interpreted data. Here I illustrate a new example of how both kinds of data can be made to co-exist.

I will start with a recent publication[1] with the title Crystallographic Snapshot of an Arrested Intermediate in the Biomimetic Activation of CO2. The nature of this intermediate caught the eye of another research group, who responded with their own critique[2]‡ along with the comment “However, since we have no access to the original crystallographic data …” They might have been referring to the semi-processed data (containing the so-called hkl structure factors) but they may also have been alluding to the raw image data captured directly from the diffractometer cameras. That traditionally has not been available via the CSD (Cambridge structural database), but would be required for a complete re-analysis of the crystal structure. Now the first example of how both FAIR (processed) data and raw data can co-exist has appeared.

The latest version of the CSD database shows an entry resulting from the following publication[3] and the deposited data has its own DOI there (10.5517/ccdc.csd.cc1n9ppb). That entry in turn has a DOI pointer to the Raw data (10.14469/hpc/2300) held in a different location and the pointer is reciprocated (⇌) with the latter pointing back to the former. Both datasets point to the original article, thus completing a holy triangle.†

There is more. The Raw dataset (10.14469/hpc/2300) declares it is a member of a superset, called Crystal structure data for Synthesis and Reactions of Benzannulated Spiroaminals; Tetrahydrospirobiquinolines (10.14469/hpc/2297) where you can find information about six other related structures. That collection is in turn a member of a superset called Synthesis and Reactions of Benzannulated Spiroaminals; Tetrahydrospirobiquinolines (10.14469/hpc/2099) where DOIs to other types of data associated with this project can be found, such as Computational data (10.14469/hpc/2098) and NMR data (10.14469/hpc/2294). Although a human can with some determination follow these associations up, down and across, the system is designed to also be followed by automated algorithms that could traverse this web quickly and efficiently.

So you can now see that a crystal structure held in the CSD could be the starting point for a journey of FAIR data discovery, in manner that has not hitherto been possible. How quickly the CSD will become populated by links to Raw (and other) data remains to be seen. I have not yet discovered any mechanism for specifying a CSD query which stipulates that Raw data must be available, but no doubt this will come.

To end, back to the Biomimetic Activation of CO2 referred to at the start. With no access to the original data, recourse was made to computational modelling.[2] Which where  I came in, since I wanted access to the original (computational) data. Sadly it did not appear to be available with the article,[2] in much the same manner as the original complaint. Perhaps, when FAIR data becomes fully accepted as part of how science is done nowadays, such complaints will become ever rarer!

‡In fact the original authors did respond[4] with an acknowledgement that their original conclusions were not correct.

†Almost. The article [3] cites DOI: 10.14469/hpc/2099 (Ref 28), but it does not cite DOI: 10.5517/ccdc.csd.cc1n9ppb because the latter had not been minted yet at the time the final proofs were corrected, and there is no mechanism to add it at a later stage.

References

  1. S.L. Ackermann, D.J. Wolstenholme, C. Frazee, G. Deslongchamps, S.H.M. Riley, A. Decken, and G.S. McGrady, "Crystallographic Snapshot of an Arrested Intermediate in the Biomimetic Activation of CO<sub>2</sub>", Angewandte Chemie International Edition, vol. 54, pp. 164-168, 2014. https://doi.org/10.1002/anie.201407165
  2. J. Hurmalainen, M.A. Land, K.N. Robertson, C.J. Roberts, I.S. Morgan, H.M. Tuononen, and J.A.C. Clyburne, "Comment on “Crystallographic Snapshot of an Arrested Intermediate in the Biomimetic Activation of CO<sub>2</sub>”", Angewandte Chemie International Edition, vol. 54, pp. 7484-7487, 2015. https://doi.org/10.1002/anie.201411654
  3. J. Almond-Thynne, A.J.P. White, A. Polyzos, H.S. Rzepa, P.J. Parsons, and A.G.M. Barrett, "Synthesis and Reactions of Benzannulated Spiroaminals: Tetrahydrospirobiquinolines", ACS Omega, vol. 2, pp. 3241-3249, 2017. https://doi.org/10.1021/acsomega.7b00482
  4. S.L. Ackermann, D.J. Wolstenholme, C. Frazee, G. Deslongchamps, S.H.M. Riley, A. Decken, and G.S. McGrady, "Corrigendum: Crystallographic Snapshot of an Arrested Intermediate in the Biomimetic Activation of CO<sub>2</sub>", Angewandte Chemie International Edition, vol. 54, pp. 7470-7470, 2015. https://doi.org/10.1002/anie.201504197

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PIDapalooza 2018: the open festival for persistent identifiers.

Tuesday, November 14th, 2017

PIDapalooza is a new forum concerned with discussing all things persistent, hence PID. You might wonder what possible interest a chemist might have in such an apparently arcane subject, but think of it in terms of how to find the proverbial needle in a haystack in a time when needles might look all very similar. Even needles need descriptions, they are not all alike and PIDs are a way of providing high quality information (metadata) about a digital object.  

The topics for discussion along with descriptions are now available at https://pidapalooza18.sched.com/list/descriptions/ and yes, before you ask, the event has its own PID (DOI: 10.5438/11.0002). Check out the speakers at https://pidapalooza18.sched.com/directory/speakers. I will be telling some stories from chemistry, and who knows, even some of the posts on this blog might feature. And if you do not brush up on the topic, no doubt your librarian, your funding body and your publisher will be telling you about it soon!

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VSEPR Theory: Octet-busting or not with trimethyl chlorine, ClMe3.

Sunday, November 12th, 2017

A few years back, I took a look at the valence-shell electron pair repulsion approach to the geometry of chlorine trifluoride, ClF3 using so-called ELF basins to locate centroids for both the covalent F-Cl bond electrons and the chlorine lone-pair electrons. Whereas the original VSEPR theory talks about five “electron pairs” totalling an octet-busting ten electrons surrounding chlorine, the electron density-based ELF approach located only ~6.8e surrounding the central chlorine and no “octet-busting”. The remaining electrons occupied fluorine lone pairs rather than the shared Cl-F regions. Here I take a look at ClMe3, as induced by the analysis of SeMe6.

The difference between ClF3 and ClMe3 is that octet-excess electrons (two in this case) in the former can relocate into fluorine lone pairs by occupying in effect anti-bonding orbitals and hence end up not contributing to the central atom valence shell.‡ With ClMe3 the methyl groups cannot apparently sustain such lone pairs, at least not distinct from the Cl-C bond region. So might we get an octet-busting example with this molecule? A ClMe3 calculation (ωb97xd/6-311++g(d,p)) reveals a molecule with all real vibrational modes (i.e. a minimum, FAIR data DOI: 10.14469/hpc/3241) and ELF (FAIR data DOI 10.14469/hpc/3242)† basins as shown below:

Density-derived approach: Two of the C-Cl bonds each exhibit two ELF basins; one disynaptic basin (0.94e) and one monosynaptic basin (0.20e) closer to the chlorine. The former pair integrate to 1.88e, density which largely arises from carbon (natural charge -0.84) and which contribute to a total integration for these carbons of 7.17e. The latter pair contributes to a total chlorine integration of 7.19e. The angle subtended at chlorine for the two 2.68e “lone pair” basins is 141°. Thus an inner, octet-compliant, valence-shell for chlorine is revealed, plus an expanded-octet outer one into which the two additional electrons go. The latter contribute to forming an octet-compliant carbon valence shell, but may be considered as not contributing to the valence shell of the other atom of the pair, the chlorine. An endo lone-pair rather than the more usual exo lone-pair if you will. These results reveal that the molecular feature we know as a (single) “bond” may in fact have more complex inner structures or zones, something we do not normally consider bonds as having. In this model, these zones are not invariably considered as shared between both the atoms comprising the bond.

Orbital-derived approach: NBO analysis (FAIR data DOI: 10.14469/hpc/3241) reveals the chlorine electronic configuration as [core]3S(1.83)3p(4.67)4S(0.01)3d(0.03)5p(0.02,) showing very little population of the Rydberg shells (4s, 3d, 5p) occurs (0.13e in total). This method of partitioning the electrons allocates a chlorine Wiberg bond index of 2.00 and the methyl carbon bond index of  3.83. If the regular valence of Cl is taken as 1, then the central chlorine can be regarded as non-Rydberg hypervalent (the electrons in the 0.94e basins are taken as contributing to the chlorine bond index).

The carbon-halogen bond internal structures simplify for Br (DOI: 10.14469/hpc/3248, 10.14469/hpc/3250) and I (DOI: 10.14469/hpc/3249, 10.14469/hpc/3247); for each only a single ELF basin is located and the NBO Br and I bond indices are respectively 2.10 and 2.1. This is not due to incursion of  Rydberg hypervalence (Br: [core]4S(1.83)4p(4.46)5S(0.02)4d(0.03)6p( 0.01); I: [core]5S(1.82)5p(4.29)6S(0.02)5d(0.02)6p(0.01) ) but of a merging of the carbon and halogen valence basin such that the ELF contributions to each cannot be deconvoluted. In each case the NBO bond indices of ~2 suggest hypervalency for the halogen.

What have we learnt?  That the shared electron (covalent) bond can have complex internal features, such as two discrete basins for the apparently shared electrons. How one partitions these electrons can influence the value one obtains for the total shared electron count and hence whether the octet is retained or expanded for main group elements such as the halogens. And finally, that hypervalence and hyper-coordination are related in the orbital model at least. Thus along the series MenI (n= coordination number 1,3,5,7), the values of the Wiberg bond index at the halogen progress as 1.0, 2.1, 3.1 (DOI: 10.14469/hpc/3236) and 4.01 (DOI: 10.14469/hpc/3238), or one extra atom bond index per electron pair.  Given this, it seems useful to retain the distinction between the terms hypervalence and hyper-coordination, but also recognize that we still may have much to learn about the former.

‡See the previous post on SeMe6 for a more detailed discussion.

† The FAIR Data accompanying this blog post is organised in a new way here. All the calculations are collected together with an over-arching DOI: 10.14469/hpc/3252 associated with this post, with individual entries accessible directly using the DOIs given above. The post itself has a  DOI: 10.14469/hpc/3255 and the two identifiers are associated with each-other via their respective metadata.  A set of standards (https://jats.nlm.nih.gov) with implementation guidelines for e.g. repositories, authors and publishers-editors  are expected in the future to establish infra-structures for cross-linking narratives/stories with the data on which they are based.

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Two stories about Open Peer Review (OPR), the next stage in Open Access (OA).

Thursday, October 5th, 2017

We have heard a lot about OA or Open Access (of journal articles) in the last five years, often in association with the APC (Article Processing Charge) model of funding such OA availability. Rather less discussed is how the model of the peer review of these articles might also evolve into an Open environment. Here I muse about two experiences I had recently.

Organising the peer review of journal articles is often now seen as the single most important activity a journal publisher can undertake on behalf of the scientific community; the very reputation of the journal depends on this process being conducted responsibly, thoroughly and with integrity by the selected reviewers. Reviewers conduct this process voluntarily, mostly anonymously, without remuneration or recognition and often with short deadlines for completion. After one such process, I recently received an interesting follow-up email from the journal, suggesting I register my activity with Publons.com, a site set up to register and give non-anonymous credit for reviewing activities. I should say that Publons is a commercial company, set up in 2012 to to “address the static state of peer-reviewing practices in scholarly communication, with a view to encourage collaboration and speed up scientific development”. Worthy aims, but like many a .com company nowadays, one might ask what the back-story might be. Thus many of the Internet giants, Google, Facebook, Twitter etc, do have back-stories, which often underpin their business models, but which may only emerge years after their founding. With only a hazy idea of what Publons’ back-story might be, I went ahead and registered my reviewing activity.

After doing so, I then accessed my entry. You only learn that I have reviewed for a particular journal, but nothing about the actual process itself. I did not really think that this experiment had done much to encourage collaboration and speed up scientific development. It might be useful for early career researchers to get their name exposed however.

I can almost understand why the review itself might not be publicly displayed, but as a result you learn nothing about the factual basis of the review and whether it might have been conducted responsibly, thoroughly and with integrity. Instead, I now suspect that the presence of my name on this site might merely encourage other publishers to deluge me with requests for further (freely donated) refereeing.

Discussing this at lunch, a colleague (thanks Ed!) reminded me of a veritable journal called Organic Syntheses. Here, authors submit a synthetic procedure and open identified “checkers” are invited to repeat the procedure and comment on it. The two roles are kept separate (i.e. the checkers do not become co-authors), but they could get credit for their activity. Thus if you view a typical recent entry[1] you will see a full biography and affiliation of the checkers given at the end, with footnotes often describing their own observations if they differ from those of the authors. 

This set me thinking whether an open peer review process might also contain such an element of checking, as well as informed comment, nay opinion, about the article itself and the conclusions it makes. The opportunity arose when I was contacted by an author who was about to submit a computational article to a journal. This journal allowed open peer review. If I agreed to review, my name would be attached to the article if accepted for publication. I undertook this on the basis that I would use this review to conduct some limited checking of the computations and other assumptions underpinning the conclusions in the submitted article. I also wanted this open process to include the data on which my review was based. Most importantly if anyone wished to replicate my replication, the barriers to doing so should be as low as is possible. Shortly thereafter, I received a formal invitation from the journal and I set about my task. Crucially, all my own calculations supporting the review were archived in a data repository, albeit under embargo. In my cover letter I included the DOI for my data and the embargo access code, so that the authors (and the editor of the journal if they so wished) could inspect the data against which I wrote my review.

Then followed standard procedures, whereby the authors took my comments into consideration, revised the article and the final version was indeed accepted and published.[2] You will find the two referees/checkers listed, although unlike Organic Syntheses,  there is no bibliographic information about them or their affiliation. I did ask the journal if they could at least link my ORCID identifier to my name, but that request was refused. If my name had been a common one, then disambiguating it into a unique identity could be a challenge. There was also no mechanism to associate my identity on the journal with any data on which I had based my review. Really, the only open aspect of this process was just my (potentially ambiguous) name, nothing else. No follow-up was received from the journal to add the review to Publons. 

The next stage was to contact the author who had originally set the process under way to ask them if they would mind my releasing the data on which my review had been based. They agreed, as also they did to my telling this story. The overall outcome is thus a published article with the reviewers (if not their reviews or any supporting evidence for their review) openly named. In this specific case, there is also an open dataset with a formal link back to the article in the form of a DOI (10.14469/hpc/2640, although I suspect this aspect is unique, even precedent setting), but one driven by the reviewer and not the journal. It would be nice to have bidirectional links between both article and the review data, but I do not know any publishers currently operating such a mechanism (if anyone knows such, please tell).

Now to the broader questions about the process described above. I think that the aspiration to encourage collaboration and speed up scientific development may indeed have been promoted by this association between article and the data assembled by the reviewer. Whether the final article was improved as a result of the processes described here I will leave the authors to comment if they wish. As with the checkers employed by Organic Syntheses, such a review process takes not just time, but resources. Resources that currently have to be freely donated by the reviewers and their host institution and which clearly cannot become expensive, time-consuming or onerous. That was not the case as it happens here; my contributions were facilitated by my having sufficient expertise to perform the tasks I undertook really quite quickly.

I will raise one more issue; that of whether to add my review to the dataset which is now openly available. In fact it is not included, in part because it related to the initially submitted version of the MS. The final MS version has been revised and so many of the comments in my review may only make sense if you have the first version to hand. It would be perhaps unreasonable to make the first drafts of manuscripts routinely available (although historians of science would probably love that!) alongside the reviews of that first draft. But I could also see a case for doing so if the community agreed to it. One to discuss for the future I think. There is also the associated issue of what should happen to any dataset associated with a review in the event that the final article is rejected and not accepted. Should the data remain permanently under embargo and the reviewer’s identity permanently anonymous? Perhaps opening up even such datasets might nevertheless  encourage collaboration and speed up scientific development, but I fancy some would consider that a step too far!

References

  1. J. Zhu, "Preparation of N-Trifluoromethylthiosaccharin: A Shelf-Stable Electrophilic Reagent for Trifluoromethylthiolation", Organic Syntheses, vol. 94, pp. 217-233, 2017. https://doi.org/10.15227/orgsyn.094.0217
  2. L. Li, M. Lei, Y. Xie, H.F. Schaefer, B. Chen, and R. Hoffmann, "Stabilizing a different cyclooctatetraene stereoisomer", Proceedings of the National Academy of Sciences, vol. 114, pp. 9803-9808, 2017. https://doi.org/10.1073/pnas.1709586114

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Posted in Chemical IT, General | 5 Comments »

Accessing (raw) chemical data: a peek into the CIF format.

Friday, July 21st, 2017

There is much focus at the moment on how to ensure experimental replicability in e.g. the molecular sciences. An important aspect of that is having access to FAIR data; data which is findable, accessible, inter-operable and re-usable. One of the “gold standards” in chemistry is the data associated with crystal structures. Here I take an inside peek into the standard file-type for carrying crystal structure data, the CIF file (the Crystallographic Information File).

CIF is a tightly managed format, with utility tools such as checkCIF to validate the files and check for errors. It is also what is called a processed data format, created from structural analysis of the raw image data that emerges from a diffractometer, and is therefore what might be described as a lossy format. Discussing these aspects with our crystallographer here (thanks Andrew!), I began to realise that there are at least three distinctly different versions of a CIF file, each carrying a different degree of data loss.

I am going to take as my illustration of this structure[1] known by three different identifiers; AZUJOW, CCDC 1406199 or DOI: 10.5517/ccdc.csd.cc1j6888

  1. The CIF originates with the authors and this version is 449KB in size. I have deposited it and the other two at DOI: 10.14469/hpc/2752  for you to inspect and compare them. This file is relatively large since it contains the so-called structure factors or hkl information, a snippet of which looks like: 
    _shelx_hkl_file 
; 
   0   0   1 108882. 1066.19   2 
   0   0   2 320.055 130.609   2 
   0   0   3 18538.0 806.608   2 
   0   0   4 173192. 2808.03   2
  2. This information is removed using a utility known as shredcif to produce a second version, known as the name_x.cif version and reducing the size to 27KB. This retains information about properties such as thermal ellipsoids and bond length and angle information but loses the hkl information.
  3. After the CIF is submitted to CSD, it emerges as AZUJOW.cif, which is now just 7KB in size and is now missing the bond lengths and angles etc.

The original raw image data for this structure is not publicly available, but you can see a set of structures for which it IS available at DOI:10.14469/hpc/2297  (published as [2] and where the file sizes are typically 200-600 MB (they can get much larger). 

So a CIF can vary in data content between 7- 449KB, and the original “raw” data can be ten thousand  times larger than this! To acquire all the flavours, you have to access both the CSD and contact the original authors (unless of course the latter have deposited their versions in an open data repository, as above). 

Fortunately for most chemical applications, even the “lossiest” of the CIF formats is more than adequate. But for the gold standard in chemical data, you should be aware that you may still be losing access to a lot of original data in the CIF formats and of course to all of the raw diffractometer data. I think it fair to say however that there is now momentum to increasingly retain as much of this data as is possible for posterity.

References

  1. A. Toscani, K.A. Jantan, J.B. Hena, J.A. Robson, E.J. Parmenter, V. Fiorini, A.J.P. White, S. Stagni, and J.D.E.T. Wilton-Ely, "The stepwise generation of multimetallic complexes based on a vinylbipyridine linkage and their photophysical properties", Dalton Transactions, vol. 46, pp. 5558-5570, 2017. https://doi.org/10.1039/c6dt03810g
  2. J. Almond-Thynne, A.J.P. White, A. Polyzos, H.S. Rzepa, P.J. Parsons, and A.G.M. Barrett, "Synthesis and Reactions of Benzannulated Spiroaminals: Tetrahydrospirobiquinolines", ACS Omega, vol. 2, pp. 3241-3249, 2017. https://doi.org/10.1021/acsomega.7b00482

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How to search data repositories for FAIR chemical content and data: SubjectScheme

Thursday, June 8th, 2017

As data repositories start to flourish, it is reasonable to ask questions such as what sort of chemistry can be found there and how can I find it? Here I give an updated[1] worked example of a digital repository search for chemical content and also pose an important issue for the chemistry domain.

Firstly, I should say this search is restricted just to those data repositories that submit indexing terms (metadata) to DataCite, which is the agency that will be used to conduct the searches. Each type of metadata is defined by a prefix or operator field (much in the same way that an advanced Google search can be prefixed with an operator, e.g. author:♥). I will use just two such DataCite field prefixes† here as exemplars (there are many more).

  1. media: This specifies the media type for the data being searched. For restriction to chemistry one takes advantage of the chemical/x- media type, as described previously.[2]
  2. SubjectScheme: This is a new declaration, as specified in the DataCite V4 metadata schema.[3] The subject scheme in effect declares a subject-specific term, and is designed to be used by domains such as chemistry.

This latter is best illustrated by one specific example of a search which I will dissect here:
https://search.datacite.org/works?query=media:chemical\/x\-gaussian*+SubjectScheme:inchikey+subject:XZYDALXOGPZGNV-UHFFFAOYSA-M+media:chemical\/x\-mnpub*‡

  1. https://search.datacite.org/works?query= queries the DataCite MDS† (metadata store).
  2. media:chemical\/x\-gaussian* defines a media type which contains the string chemical/x-gaussian, with the * being a wild-card which allows any characters to follow this string. This now is specifying any data repository where Gaussian files have been deposited and assigned this media type.
  3. + represents a Boolean AND operator.
  4. SubjectScheme:inchikey restricts a subject search to a subjectScheme having the value inchikey, whilst
  5. subject:XZYDALXOGPZGNV-UHFFFAOYSA-M defines the value of the subject itself.
  6. media:chemical/x-mnpub completes the search definition, this relating to the mandatory additional presence of an Mpublish[4] file indicating (spectroscopic, probably NMR) data readable by the MestreNova program.

One hit with these restrictions has doi: 10.14469/HPC/2635 and clicking the button on the landing page for this object labelled metadata resolves to e.g.
https://data.datacite.org/application/vnd.datacite.datacite+xml/10.14469/hpc/2635,
and downloads the metadata record for this object. Part of this record looks a bit like:

This brings me to the important issue for the chemistry domain, which is to agree upon a core set of SubjectSchemes for implementation in data repositories with domain-specific chemical content. The two subjects above, the InChI and the InChIKey seem obvious candidates for inclusion. But how the list is extended and how the SubjectScheme is specified are now matters for the community to discuss. Perhaps the IUPAC GoldBook is one starting point for the SubjectScheme URIs. Watch this space.

‡The \ syntax indicates an “escaped” character. Thus in chemicalx\-gaussian a \ ensured that the following / is treated as part of the search string, and not as part of the search syntax. Likewise \- ensures the minus character is part of the string and not a syntactic negation. The current list of characters requiring escaping is + - & | ! ( ) { } [ ] ^ " ~ * ? : \ /

† The documentation lists common fields, but there are far more specified in V4 of their schema. The ones you see used here are not (yet?) documented at https://search.datacite.org/help.html

♥ This Google page has a rich plethora of powerful searches, which I suggest almost no-one knows about!

References

  1. H.S. Rzepa, A. Mclean, and M.J. Harvey, "InChI As a Research Data Management Tool", Chemistry International, vol. 38, pp. 24-26, 2016. https://doi.org/10.1515/ci-2016-3-408
  2. H.S. Rzepa, P. Murray-Rust, and B.J. Whitaker, "The Application of Chemical Multipurpose Internet Mail Extensions (Chemical MIME) Internet Standards to Electronic Mail and World Wide Web Information Exchange", Journal of Chemical Information and Computer Sciences, vol. 38, pp. 976-982, 1998. https://doi.org/10.1021/ci9803233
  3. DataCite Metadata Working Group., "DataCite Metadata Schema Documentation for the Publication and Citation of Research Data v4.0", DataCite e.V., 2016. https://doi.org/10.5438/0012
  4. M.J. Harvey, A. McLean, and H.S. Rzepa, "A metadata-driven approach to data repository design", Journal of Cheminformatics, vol. 9, 2017. https://doi.org/10.1186/s13321-017-0190-6

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Research data: Gravitational waves as an example from the astrophysics community.

Friday, June 2nd, 2017

In 2016, the world heard that gravitational waves had been detected and now a third instance is reported.‡ Given that the data associated with these detections are perhaps amongst the most important instances in recent times, I thought I might take a peek at how it was managed.

  1. The original report in 2016[1] cited (Ref 116) data as DOI: 10.7935/K5MW2F23.
  2. The second instance[2] also cites (Ref 89) a DOI: 10.7935/K5H41PBP
  3. Today a third instance was reported[3] with data cited (Ref 180) as DOI: 10.7935/K53X84K2

Each of these datasets is beautifully presented, with interactive components making the data very accessible and indeed FAIR. Recently, guidelines for how to cite data have emerged and the FORCE11 organisation has interpreted them for authors, publishers and journal editors, and repository administrators and it is good that the data for these detections has adhered to these principles. When implemented, it will identify e.g. Refs 116, 89 or 180 in the above articles with attributes that will trigger something called EventData with the objective that it retrieves and exposes the activity that occurs around research data objects and brings to light links between publications and data, citations, software, reuse, documentation, etc. At the moment I presume that these three data citations do not yet trigger any EventData. However, there are plans to for both CrossRef and DataCite to implement search procedures to find such EventData. When this service goes live, I intend to explore its potential; watch this space.

I did one other check on each of the datasets:

These download the metadata associated with each dataset. Ideally this could contain a host of information about the data and in particular its creators (i.e. the ORCID of each of the many authors describing how the data was acquired and interpreted). I show just the last as an example in full (the others are similar). Its pretty minimal.

Now there is an area that this otherwise magnificent example of open data sharing could improve upon. In a forthcoming post, I will show how rich metadata population of a chemistry dataset can be used to enhance it and also to trigger EventData as noted above.

‡For a famous chemical controversy involving gravitational fields, see here.

References

  1. B. Abbott, R. Abbott, T. Abbott, M. Abernathy, F. Acernese, K. Ackley, C. Adams, T. Adams, P. Addesso, R. Adhikari, V. Adya, C. Affeldt, M. Agathos, K. Agatsuma, N. Aggarwal, O. Aguiar, L. Aiello, A. Ain, P. Ajith, B. Allen, A. Allocca, P. Altin, S. Anderson, W. Anderson, K. Arai, M. Arain, M. Araya, C. Arceneaux, J. Areeda, N. Arnaud, K. Arun, S. Ascenzi, G. Ashton, M. Ast, S. Aston, P. Astone, P. Aufmuth, C. Aulbert, S. Babak, P. Bacon, M. Bader, P. Baker, F. Baldaccini, G. Ballardin, S. Ballmer, J. Barayoga, S. Barclay, B. Barish, D. Barker, F. Barone, B. Barr, L. Barsotti, M. Barsuglia, D. Barta, J. Bartlett, M. Barton, I. Bartos, R. Bassiri, A. Basti, J. Batch, C. Baune, V. Bavigadda, M. Bazzan, B. Behnke, M. Bejger, C. Belczynski, A. Bell, C. Bell, B. Berger, J. Bergman, G. Bergmann, C. Berry, D. Bersanetti, A. Bertolini, J. Betzwieser, S. Bhagwat, R. Bhandare, I. Bilenko, G. Billingsley, J. Birch, I.A. Birney, O. Birnholtz, S. Biscans, A. Bisht, M. Bitossi, C. Biwer, M. Bizouard, J. Blackburn, C. Blair, D. Blair, R. Blair, S. Bloemen, O. Bock, T. Bodiya, M. Boer, G. Bogaert, C. Bogan, A. Bohe, P. Bojtos, C. Bond, F. Bondu, R. Bonnand, B. Boom, R. Bork, V. Boschi, S. Bose, Y. Bouffanais, A. Bozzi, C. Bradaschia, P. Brady, V. Braginsky, M. Branchesi, J. Brau, T. Briant, A. Brillet, M. Brinkmann, V. Brisson, P. Brockill, A. Brooks, D. Brown, D. Brown, N. Brown, C. Buchanan, A. Buikema, T. Bulik, H. Bulten, A. Buonanno, D. Buskulic, C. Buy, R. Byer, M. Cabero, L. Cadonati, G. Cagnoli, C. Cahillane, J.C. Bustillo, T. Callister, E. Calloni, J. Camp, K. Cannon, J. Cao, C. Capano, E. Capocasa, F. Carbognani, S. Caride, J.C. Diaz, C. Casentini, S. Caudill, M. Cavaglià, F. Cavalier, R. Cavalieri, G. Cella, C. Cepeda, L.C. Baiardi, G. Cerretani, E. Cesarini, R. Chakraborty, T. Chalermsongsak, S. Chamberlin, M. Chan, S. Chao, P. Charlton, E. Chassande-Mottin, H. Chen, Y. Chen, C. Cheng, A. Chincarini, A. Chiummo, H. Cho, M. Cho, J. Chow, N. Christensen, Q. Chu, S. Chua, S. Chung, G. Ciani, F. Clara, J. Clark, F. Cleva, E. Coccia, P. Cohadon, A. Colla, C. Collette, L. Cominsky, M. Constancio, A. Conte, L. Conti, D. Cook, T. Corbitt, N. Cornish, A. Corsi, S. Cortese, C. Costa, M. Coughlin, S. Coughlin, J. Coulon, S. Countryman, P. Couvares, E. Cowan, D. Coward, M. Cowart, D. Coyne, R. Coyne, K. Craig, J. Creighton, T. Creighton, J. Cripe, S. Crowder, A. Cruise, A. Cumming, L. Cunningham, E. Cuoco, T.D. Canton, S. Danilishin, S. D’Antonio, K. Danzmann, N. Darman, C. Da Silva Costa, V. Dattilo, I. Dave, H. Daveloza, M. Davier, G. Davies, E. Daw, R. Day, S. De, D. DeBra, G. Debreczeni, J. Degallaix, M. De Laurentis, S. Deléglise, W. Del Pozzo, T. Denker, T. Dent, H. Dereli, V. Dergachev, R. DeRosa, R. De Rosa, R. DeSalvo, S. Dhurandhar, M. Díaz, L. Di Fiore, M. Di Giovanni, A. Di Lieto, S. Di Pace, I. Di Palma, A. Di Virgilio, G. Dojcinoski, V. Dolique, F. Donovan, K. Dooley, S. Doravari, R. Douglas, T. Downes, M. Drago, R. Drever, J. Driggers, Z. Du, M. Ducrot, S. Dwyer, T. Edo, M. Edwards, A. Effler, H. Eggenstein, P. Ehrens, J. Eichholz, S. Eikenberry, W. Engels, R. Essick, T. Etzel, M. Evans, T. Evans, R. Everett, M. Factourovich, V. Fafone, H. Fair, S. Fairhurst, X. Fan, Q. Fang, S. Farinon, B. Farr, W. Farr, M. Favata, M. Fays, H. Fehrmann, M. Fejer, D. Feldbaum, I. Ferrante, E. Ferreira, F. Ferrini, F. Fidecaro, L. Finn, I. Fiori, D. Fiorucci, R. Fisher, R. Flaminio, M. Fletcher, H. Fong, J. Fournier, S. Franco, S. Frasca, F. Frasconi, M. Frede, Z. Frei, A. Freise, R. Frey, V. Frey, T. Fricke, P. Fritschel, V. Frolov, P. Fulda, M. Fyffe, H. Gabbard, J. Gair, L. Gammaitoni, S. Gaonkar, F. Garufi, A. Gatto, G. Gaur, N. Gehrels, G. Gemme, B. Gendre, E. Genin, A. Gennai, J. George, L. Gergely, V. Germain, A. Ghosh, A. Ghosh, S. Ghosh, J. Giaime, K. Giardina, A. Giazotto, K. Gill, A. Glaefke, J. Gleason, E. Goetz, R. Goetz, L. Gondan, G. González, J.G. Castro, A. Gopakumar, N. Gordon, M. Gorodetsky, S. Gossan, M. Gosselin, R. Gouaty, C. Graef, P. Graff, M. Granata, A. Grant, S. Gras, C. Gray, G. Greco, A. Green, R. Greenhalgh, P. Groot, H. Grote, S. Grunewald, G. Guidi, X. Guo, A. Gupta, M. Gupta, K. Gushwa, E. Gustafson, R. Gustafson, J. Hacker, B. Hall, E. Hall, G. Hammond, M. Haney, M. Hanke, J. Hanks, C. Hanna, M. Hannam, J. Hanson, T. Hardwick, J. Harms, G. Harry, I. Harry, M. Hart, M. Hartman, C. Haster, K. Haughian, J. Healy, J. Heefner, A. Heidmann, M. Heintze, G. Heinzel, H. Heitmann, P. Hello, G. Hemming, M. Hendry, I. Heng, J. Hennig, A. Heptonstall, M. Heurs, S. Hild, D. Hoak, K. Hodge, D. Hofman, S. Hollitt, K. Holt, D. Holz, P. Hopkins, D. Hosken, J. Hough, E. Houston, E. Howell, Y. Hu, S. Huang, E. Huerta, D. Huet, B. Hughey, S. Husa, S. Huttner, T. Huynh-Dinh, A. Idrisy, N. Indik, D. Ingram, R. Inta, H. Isa, J. Isac, M. Isi, G. Islas, T. Isogai, B. Iyer, K. Izumi, M. Jacobson, T. Jacqmin, H. Jang, K. Jani, P. Jaranowski, S. Jawahar, F. Jiménez-Forteza, W. Johnson, N. Johnson-McDaniel, D. Jones, R. Jones, R. Jonker, L. Ju, K. Haris, C. Kalaghatgi, V. Kalogera, S. Kandhasamy, G. Kang, J. Kanner, S. Karki, M. Kasprzack, E. Katsavounidis, W. Katzman, S. Kaufer, T. Kaur, K. Kawabe, F. Kawazoe, F. Kéfélian, M. Kehl, D. Keitel, D. Kelley, W. Kells, R. Kennedy, D. Keppel, J. Key, A. Khalaidovski, F. Khalili, I. Khan, S. Khan, Z. Khan, E. Khazanov, N. Kijbunchoo, C. Kim, J. Kim, K. Kim, N. Kim, N. Kim, Y. Kim, E. King, P. King, D. Kinzel, J. Kissel, L. Kleybolte, S. Klimenko, S. Koehlenbeck, K. Kokeyama, S. Koley, V. Kondrashov, A. Kontos, S. Koranda, M. Korobko, W. Korth, I. Kowalska, D. Kozak, V. Kringel, B. Krishnan, A. Królak, C. Krueger, G. Kuehn, P. Kumar, R. Kumar, L. Kuo, A. Kutynia, P. Kwee, B. Lackey, M. Landry, J. Lange, B. Lantz, P. Lasky, A. Lazzarini, C. Lazzaro, P. Leaci, S. Leavey, E. Lebigot, C. Lee, H. Lee, H. Lee, K. Lee, A. Lenon, M. Leonardi, J. Leong, N. Leroy, N. Letendre, Y. Levin, B. Levine, T. Li, A. Libson, T. Littenberg, N. Lockerbie, J. Logue, A. Lombardi, L. London, J. Lord, M. Lorenzini, V. Loriette, M. Lormand, G. Losurdo, J. Lough, C. Lousto, G. Lovelace, H. Lück, A. Lundgren, J. Luo, R. Lynch, Y. Ma, T. MacDonald, B. Machenschalk, M. MacInnis, D. Macleod, F. Magaña-Sandoval, R. Magee, M. Mageswaran, E. Majorana, I. Maksimovic, V. Malvezzi, N. Man, I. Mandel, V. Mandic, V. Mangano, G. Mansell, M. Manske, M. Mantovani, F. Marchesoni, F. Marion, S. Márka, Z. Márka, A. Markosyan, E. Maros, F. Martelli, L. Martellini, I. Martin, R. Martin, D. Martynov, J. Marx, K. Mason, A. Masserot, T. Massinger, M. Masso-Reid, F. Matichard, L. Matone, N. Mavalvala, N. Mazumder, G. Mazzolo, R. McCarthy, D. McClelland, S. McCormick, S. McGuire, G. McIntyre, J. McIver, D. McManus, S. McWilliams, D. Meacher, G. Meadors, J. Meidam, A. Melatos, G. Mendell, D. Mendoza-Gandara, R. Mercer, E. Merilh, M. Merzougui, S. Meshkov, C. Messenger, C. Messick, P. Meyers, F. Mezzani, H. Miao, C. Michel, H. Middleton, E. Mikhailov, L. 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Pascucci, A. Pasqualetti, R. Passaquieti, D. Passuello, B. Patricelli, Z. Patrick, B. Pearlstone, M. Pedraza, R. Pedurand, L. Pekowsky, A. Pele, S. Penn, A. Perreca, H. Pfeiffer, M. Phelps, O. Piccinni, M. Pichot, M. Pickenpack, F. Piergiovanni, V. Pierro, G. Pillant, L. Pinard, I. Pinto, M. Pitkin, J. Poeld, R. Poggiani, P. Popolizio, A. Post, J. Powell, J. Prasad, V. Predoi, S. Premachandra, T. Prestegard, L. Price, M. Prijatelj, M. Principe, S. Privitera, R. Prix, G. Prodi, L. Prokhorov, O. Puncken, M. Punturo, P. Puppo, M. Pürrer, H. Qi, J. Qin, V. Quetschke, E. Quintero, R. Quitzow-James, F. Raab, D. Rabeling, H. Radkins, P. Raffai, S. Raja, M. Rakhmanov, C. Ramet, P. Rapagnani, V. Raymond, M. Razzano, V. Re, J. Read, C. Reed, T. Regimbau, L. Rei, S. Reid, D. Reitze, H. Rew, S. Reyes, F. Ricci, K. Riles, N. Robertson, R. Robie, F. Robinet, A. Rocchi, L. Rolland, J. Rollins, V. Roma, J. Romano, R. Romano, G. Romanov, J. Romie, D. Rosińska, S. Rowan, A. Rüdiger, P. Ruggi, K. Ryan, S. Sachdev, T. Sadecki, L. Sadeghian, L. Salconi, M. Saleem, F. Salemi, A. Samajdar, L. Sammut, L. Sampson, E. Sanchez, V. Sandberg, B. Sandeen, G. Sanders, J. Sanders, B. Sassolas, B. Sathyaprakash, P. Saulson, O. Sauter, R. Savage, A. Sawadsky, P. Schale, R. Schilling, J. Schmidt, P. Schmidt, R. Schnabel, R. Schofield, A. Schönbeck, E. Schreiber, D. Schuette, B. Schutz, J. Scott, S. Scott, D. Sellers, A. Sengupta, D. Sentenac, V. Sequino, A. Sergeev, G. Serna, Y. Setyawati, A. Sevigny, D. Shaddock, T. Shaffer, S. Shah, M. Shahriar, M. Shaltev, Z. Shao, B. Shapiro, P. Shawhan, A. Sheperd, D. Shoemaker, D. Shoemaker, K. Siellez, X. Siemens, D. Sigg, A. Silva, D. Simakov, A. Singer, L. Singer, A. Singh, R. Singh, A. Singhal, A. Sintes, B. Slagmolen, J. Smith, M. Smith, N. Smith, R. Smith, E. Son, B. Sorazu, F. Sorrentino, T. Souradeep, A. Srivastava, A. Staley, M. Steinke, J. Steinlechner, S. Steinlechner, D. Steinmeyer, B. Stephens, S. Stevenson, R. Stone, K. Strain, N. Straniero, G. Stratta, N. Strauss, S. Strigin, R. Sturani, A. Stuver, T. Summerscales, L. Sun, P. Sutton, B. Swinkels, M. Szczepańczyk, M. Tacca, D. Talukder, D. Tanner, M. Tápai, S. Tarabrin, A. Taracchini, R. Taylor, T. Theeg, M. Thirugnanasambandam, E. Thomas, M. Thomas, P. Thomas, K. Thorne, K. Thorne, E. Thrane, S. Tiwari, V. Tiwari, K. Tokmakov, C. Tomlinson, M. Tonelli, C. Torres, C. Torrie, D. Töyrä, F. Travasso, G. Traylor, D. Trifirò, M. Tringali, L. Trozzo, M. Tse, M. Turconi, D. Tuyenbayev, D. Ugolini, C. Unnikrishnan, A. Urban, S. Usman, H. Vahlbruch, G. Vajente, G. Valdes, M. Vallisneri, N. van Bakel, M. van Beuzekom, J. van den Brand, C. Van Den Broeck, D. Vander-Hyde, L. van der Schaaf, J. van Heijningen, A. van Veggel, M. Vardaro, S. Vass, M. Vasúth, R. Vaulin, A. Vecchio, G. Vedovato, J. Veitch, P. Veitch, K. Venkateswara, D. Verkindt, F. Vetrano, A. Viceré, S. Vinciguerra, D. Vine, J. Vinet, S. Vitale, T. Vo, H. Vocca, C. Vorvick, D. Voss, W. Vousden, S. Vyatchanin, A. Wade, L. Wade, M. Wade, S. Waldman, M. Walker, L. Wallace, S. Walsh, G. Wang, H. Wang, M. Wang, X. Wang, Y. Wang, H. Ward, R. Ward, J. Warner, M. Was, B. Weaver, L. Wei, M. Weinert, A. Weinstein, R. Weiss, T. Welborn, L. Wen, P. Weßels, T. Westphal, K. Wette, J. Whelan, S. Whitcomb, D. White, B. Whiting, K. Wiesner, C. Wilkinson, P. Willems, L. Williams, R. Williams, A. Williamson, J. Willis, B. Willke, M. Wimmer, L. Winkelmann, W. Winkler, C. Wipf, A. Wiseman, H. Wittel, G. Woan, J. Worden, J. Wright, G. Wu, J. Yablon, I. Yakushin, W. Yam, H. Yamamoto, C. Yancey, M. Yap, H. Yu, M. Yvert, A. Zadrożny, L. Zangrando, M. Zanolin, J. Zendri, M. Zevin, F. Zhang, L. Zhang, M. Zhang, Y. Zhang, C. Zhao, M. Zhou, Z. Zhou, X. Zhu, M. Zucker, S. Zuraw, J. Zweizig, and . , "Observation of Gravitational Waves from a Binary Black Hole Merger", Physical Review Letters, vol. 116, 2016. https://doi.org/10.1103/physrevlett.116.061102
  2. B. Abbott, R. Abbott, T. Abbott, M. Abernathy, F. Acernese, K. Ackley, C. Adams, T. Adams, P. Addesso, R. Adhikari, V. Adya, C. Affeldt, M. Agathos, K. Agatsuma, N. Aggarwal, O. Aguiar, L. Aiello, A. Ain, P. Ajith, B. Allen, A. Allocca, P. Altin, S. Anderson, W. Anderson, K. Arai, M. Araya, C. Arceneaux, J. Areeda, N. Arnaud, K. Arun, S. Ascenzi, G. Ashton, M. Ast, S. Aston, P. Astone, P. Aufmuth, C. Aulbert, S. Babak, P. Bacon, M. Bader, P. Baker, F. Baldaccini, G. Ballardin, S. Ballmer, J. Barayoga, S. Barclay, B. Barish, D. Barker, F. Barone, B. Barr, L. Barsotti, M. Barsuglia, D. Barta, J. Bartlett, I. Bartos, R. Bassiri, A. Basti, J. Batch, C. Baune, V. Bavigadda, M. Bazzan, M. Bejger, A. Bell, B. Berger, G. Bergmann, C. Berry, D. Bersanetti, A. Bertolini, J. Betzwieser, S. Bhagwat, R. Bhandare, I. Bilenko, G. Billingsley, J. Birch, I.A. Birney, O. Birnholtz, S. Biscans, A. Bisht, M. Bitossi, C. Biwer, M. Bizouard, J. Blackburn, C. Blair, D. Blair, R. Blair, S. Bloemen, O. 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FAIR Research data: Gravitational waves as an example from the astrophysics community.

Friday, June 2nd, 2017

In 2016, the world heard that gravitational waves had been detected and now a third instance is reported.‡ Given that the data associated with these detections are perhaps amongst the most important instances in recent times, I thought I might take a peek at how it was managed.

  1. The original report in 2016[1] cited (Ref 116) data as DOI: 10.7935/K5MW2F23.
  2. The second instance[2] also cites (Ref 89) a DOI: 10.7935/K5H41PBP
  3. Today† a third instance was reported[3] with data cited (Ref 180) as DOI: 10.7935/K53X84K2
  4. Sixth instance in 2017[4], Data (Ref 80) DOI: 10.7935/K5B8566F.
  5. A complete list of events is available at losc.ligo.org/events/

Each of these datasets is beautifully presented, with interactive components making the data very accessible and indeed FAIR. Recently, guidelines for how to cite data have emerged and the FORCE11 organisation has interpreted them for authors, publishers and journal editors, and repository administrators and it is good that the data for these detections has adhered to these principles. When implemented, it will identify e.g. Refs 116, 89 or 180 in the above articles with attributes that will trigger something called EventData with the objective that it retrieves and exposes the activity that occurs around research data objects and brings to light links between publications and data, citations, software, reuse, documentation, etc. At the moment I presume that these three data citations do not yet trigger any EventData. However, there are plans to for both CrossRef and DataCite to implement search procedures to find such EventData. When this service goes live, I intend to explore its potential; watch this space.

I did one other check on each of the datasets:

These download the metadata associated with each dataset. Ideally this could contain a host of information about the data and in particular its creators (i.e. the ORCID of each of the many authors describing how the data was acquired and interpreted). I show just the last as an example in full (the others are similar). Its pretty minimal.

Now there is an area that this otherwise magnificent example of open data sharing could improve upon. In a forthcoming post, I will show how rich metadata population of a chemistry dataset can be used to enhance it and also to trigger EventData as noted above.

‡For a famous chemical controversy involving gravitational fields, see here.

References

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