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The challenges in curating research data: one case study.

Friday, April 28th, 2017

Research data (and its management) is rapidly emerging as a focal point for the development of research dissemination practices. An important aspect of ensuring that such data remains fit for purpose is identifying what curation activities need to be associated with it. Here I revisit one particular case study associated with the molecular structure of a product identified from a photolysis reaction[1] and the curation of the crystallographic data associated with this study.

This particular dataset (CSD, dataDOI: 10.5517/cctnx5j) is associated with an article entitled “Single-Crystal X-ray Structure of 1,3-Dimethylcyclobutadiene by Confinement in a Crystalline Matrix“.[1] Data for crystal structures supporting a research article is required (at least in part) to be deposited into the Cambridge structure database (internal reference MUWMEX) and for which a significant level of curation is performed. Although the definition of the term curation has evolved over the last few years, here I take it to include the following:

  1. Identification of appropriate metadata describing the data. For molecules, this would include any identifiers such as the name of the molecule and the connectivities of the atoms constituting that molecule.
  2. The submission of this metadata to a suitable aggregator, such as e.g. DataCite and its inclusion in any other databases associated with the data. These two tests are part of the FAIR data guidelines[2], covering the F (findable) and A (accessible).
  3. Performing any validation tests for the data that can be identified. With crystal structure data in CIF format, this is defined by the utility checkCIF and helps to ensure the I (inter-operable) of FAIR. The R refers in part to the licenses under which the data can be re-used.

On (it has to be said rare) occasions, these procedures can lead to a disparity between the author’s conclusions arrived on the basis of their acquired data and the metadata identified by the independent curators. This difference is most obviously illustrated in this case study by the chemical names inferred by the curation process for the structure represented by the data in the CSD:

  • chemical name: “tetrakis(Guanidinium) 25,26,27,28-tetrahydroxycalix(4)arene-5,11,17,23-tetrasulfonate 1,5-dimethyl-2-oxabicyclo[2.2.0]hex-5-en-3-one clathrate trihydrate“
  • chemical name synonym: “tetrakis(Guanidinium) tetra-p-sulfocalix(4)arene 1,3-dimethylcyclobutadiene carbon dioxide clathrate trihydrate“.

Only the synonym agrees with the title given by the original authors in their publication.[1] One might indeed strongly argue that these two names are not in fact synonyms, since they refer to quite different chemical structures with different atom connectivities. A search of the database for the sub-structure corresponding to 1,3-dimethylcyclobutadiene does not reveal any hits and so the information implied by this synonym is not recorded in the index created for the CSD database.

I asked the scientific editors of the CSD for some guidance on the curation procedures applied to crystal structure datasets and they have kindly allowed me to quote some of this.

  1. “In cases such as this, we as editors are sometimes faced with conflicting information and have to try our best to strike a balance between the data presented in the CIF, a published interpretation and our knowledge based on the information already in the CSD”.
  2. “In areas where there is a particular conflict between these, we often would include a comment (usually in the Remarks or Disorder field as appropriate)”. For this particular dataset, one finds the following under the Disorder field:
    • “Under UV radiation the clathrated pyrone molecule converts to a disordered mixture of square-planar 1, 3-dimethylcyclobutadiene and rectangular-bent 1, 3-dimethylcyclobutadiene in van der Waals contact with a carbon dioxide molecule. The ratio of the square-planar to rectangular-bent 1, 3-dimethylcyclobutadiene clathrate is modelled with occupancies 0.6292:0.3708”.
    • It is not entirely obvious however whether this last comment originates from the original authors or from the data curators. It does not resolve the difference between the assigned chemical name and the indicated chemical name synonym.
  3. “In the case of MUWMEX, I think that the editor produced a diagram (below) which seems chemically reasonable based on the crystallographic data with which we were provided and tried to cover the situation regarding disorder, van der Waals contacts etc in the ‘Disorder’ field. At this point, it is left to the CSD user to decide for themselves.”

We have arrived at a point where the CSD user must indeed decide what the species described by this dataset actually is. Ideally, the best recourse would be to acquire the original data in full and repeat the crystallographic analysis. This is an aspect of the curation of crystallographic data that is not conducted as part of the current processes, which would require as a minimum a superset known as the hkl information to be present in the data. Again, to quote the CSD scientific editors:

  1. “With regard to your question: Is there any mechanism in the Conquest search to identify structures where the hkl information is present? I understand that it is not currently possible to do this in ConQuest. It is, however, possible … to access structure factor data (where available) using Access Structures.”

For MUWMEX, the hkl information is not present in the CSD dataset and in 2010 when the structure was published would have to be obtained directly from the authors. By 2016 however, its presence in deposited datasets was becoming far more common. It is worth pointing out that even the hkl information is not the complete data recorded for the experiment.  That is represented by the original image files recording the X-ray diffractions. This latter is hardly ever available as FAIR data even nowadays.

I hope I have here illustrated at least some of the challenging aspects of curating scientific data and the issues that can arise when derived metadata (in this case the name and the atom connectivities of a molecule) reveal conflicts with the original interpretations. This for an area of chemistry where both the data deposition and its curation is a very mature subject, having operated for ~52 years now. It is still a process that requires the intervention of skilled curators of the data, but perhaps even more importantly it reveals the need to identify even more strictly what the provenance of the interpretations is. Should the CSD curation rest merely at the stage of teasing out and flagging inconsistencies and allowing the user to then take over to resolve the conflicts? Should it be more active, in re-analyzing data for each entry where conflicts have been detected? Perhaps the latter is not practical now, but it might be in the near future. What is certain is that with increasing availability of FAIR data these sorts of issues will increasingly come to the fore. And not just for the very well understood case of crystallographic data but for many other types of data.

References

  1. Y. Legrand, A. van der Lee, and M. Barboiu, "Single-Crystal X-ray Structure of 1,3-Dimethylcyclobutadiene by Confinement in a Crystalline Matrix", Science, vol. 329, pp. 299-302, 2010. https://doi.org/10.1126/science.1188002
  2. M.D. Wilkinson, M. Dumontier, I.J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J. Boiten, L.B. da Silva Santos, P.E. Bourne, J. Bouwman, A.J. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C.T. Evelo, R. Finkers, A. Gonzalez-Beltran, A.J. Gray, P. Groth, C. Goble, J.S. Grethe, J. Heringa, P.A. ’t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S.J. Lusher, M.E. Martone, A. Mons, A.L. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M.A. Swertz, M. Thompson, J. van der Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao, and B. Mons, "The FAIR Guiding Principles for scientific data management and stewardship", Scientific Data, vol. 3, 2016. https://doi.org/10.1038/sdata.2016.18

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Posted in Chemical IT, crystal_structure_mining | 5 Comments »

Supporting information: chemical graveyard or invaluable resource for chemical structures.

Friday, March 31st, 2017

Nowadays, data supporting most publications relating to the synthesis of organic compounds is more likely than not to be found in associated “supporting information” rather than the (often page limited) article itself. For example, this article[1] has an SI which is paginated at 907; almost a mini-database in its own right! Here I ponder whether such dissemination of data is FAIR (Findable, accessible, interoperable and re-usable).[2]

I am going to use this article as my starting point.[3] One of the compounds discussed there is shown below; it is not explicitly discussed in the main body of the article. So how findable is it?

  1. A search of Scifinder (Chemical abstracts) using the structure above reveals one hit, the source being the expected one.[3]
  2. A search of Reaxys (used to be Beilstein) reveals no hits in their own database, but one hit is noted in …
  3. Pubchem, where it occurs as substance 163835830. The source is again cited correctly[3]. One of the properties reported is the InChI key: JSLVVAICXSKSEQ-UHFFFAOYSA-N. This is the same key generated from the structure drawing programs Chemdraw or ChemDoodle.
  4. Google on the other hand finds nothing for JSLVVAICXSKSEQ-UHFFFAOYSA-N.[4]
  5. I also tried Google Scholar but again with no luck.

So supporting information does appear to be indexed by both Chemical Abstracts and Pubchem; it is thankfully not a graveyard![5] The chemical databases do return valuable additional information about the molecule, such as e.g. its InChI key and much else besides. Given that presumably the open PubChem resource IS indexed by Google, it must be a policy somewhere that prevents e.g. JSLVVAICXSKSEQ-UHFFFAOYSA-N from being found.

I suppose the next question might be Supporting information: chemical graveyard or invaluable resource for chemical spectra? I confess here that this post was in fact inspired by a previous one on the topic of the provenance of NMR spectra. And perhaps also with some input from the concept of sonification of spectra, in which an instrumental spectrum is converted into a sound signature to allow blind people access to such information.‡ I wonder whether a sonified unique digital signature could be used to search for spectra, somewhat in the manner that InChI helped in tracking down (or not) the molecule above? I think it would be reasonable to say that e.g. NMR spectra as embedded in say a 907 page supporting information document are likely to be very much less FAIR[2]. The solution there of course is better provenance and better metadata, as I previously mulled.

‡I cannot help but wonder what a carbonyl group sounds like!

References

  1. J.M. Lopchuk, K. Fjelbye, Y. Kawamata, L.R. Malins, C. Pan, R. Gianatassio, J. Wang, L. Prieto, J. Bradow, T.A. Brandt, M.R. Collins, J. Elleraas, J. Ewanicki, W. Farrell, O.O. Fadeyi, G.M. Gallego, J.J. Mousseau, R. Oliver, N.W. Sach, J.K. Smith, J.E. Spangler, H. Zhu, J. Zhu, and P.S. Baran, "Strain-Release Heteroatom Functionalization: Development, Scope, and Stereospecificity", Journal of the American Chemical Society, vol. 139, pp. 3209-3226, 2017. https://doi.org/10.1021/jacs.6b13229
  2. M.D. Wilkinson, M. Dumontier, I.J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J. Boiten, L.B. da Silva Santos, P.E. Bourne, J. Bouwman, A.J. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C.T. Evelo, R. Finkers, A. Gonzalez-Beltran, A.J. Gray, P. Groth, C. Goble, J.S. Grethe, J. Heringa, P.A. ’t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S.J. Lusher, M.E. Martone, A. Mons, A.L. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M.A. Swertz, M. Thompson, J. van der Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao, and B. Mons, "The FAIR Guiding Principles for scientific data management and stewardship", Scientific Data, vol. 3, 2016. https://doi.org/10.1038/sdata.2016.18
  3. G.M.S. Yip, Z. Chen, C.J. Edge, E.H. Smith, R. Dickinson, E. Hohenester, R.R. Townsend, K. Fuchs, W. Sieghart, A.S. Evers, and N.P. Franks, "A propofol binding site on mammalian GABAA receptors identified by photolabeling", Nature Chemical Biology, vol. 9, pp. 715-720, 2013. https://doi.org/10.1038/nchembio.1340
  4. S.J. Coles, N.E. Day, P. Murray-Rust, H.S. Rzepa, and Y. Zhang, "Enhancement of the chemical semantic web through the use of InChI identifiers", Organic & Biomolecular Chemistry, vol. 3, pp. 1832, 2005. https://doi.org/10.1039/b502828k
  5. M. Karthikeyan, and R. Vyas, "ChemEngine: harvesting 3D chemical structures of supplementary data from PDF files", Journal of Cheminformatics, vol. 8, 2016. https://doi.org/10.1186/s13321-016-0175-x

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The provenance of scientific data – establishing an audit trail.

Thursday, March 30th, 2017

In an era when alternative facts and fake news afflict us, the provenance of scientific data becomes ever more important. Especially if that data is available as open access and exploitable by others for both valid scientific reasons but potentially also by those with other motives. Here I consider the audit trail that might serve to establish data provenance in one typical situation in chemistry, the acquisition of NMR instrumental data. 

Here I describe how such data is generated in my department; details may vary elsewhere.

  1. The prospective user of the NMR service is allocated a service ID. In our case, that ID relates to the research group rather than to individual researchers. This ID is parochial, it does not reference any other information about the user in the institute. Only the service manager has the information to associate this ID with real users and this information is normally not distributed.
  2. When a sample is submitted, this ID is used to create a new folder containing the data as a sub-folder of the group ID and located on the NMR data servers.
  3. The dataset itself‡ contains a number of files that contain an audit trail (names such as audita.txt, auditp.txt) with the fields: ##AUDIT TRAIL= $$ (NUMBER, WHEN, WHO, WHERE, PROCESS, VERSION, WHAT). Typically, none of these files have propagated the original user ID under which the data was collected; to do so would require a programmatic connection between the local authentication systems and the spectrometer software used, a connection that is normally missing. Thus the first break in the provenance trail.
  4. In principle other audit trails can be inferred from these files, such as the unique identity of the instrument provided by its manufacturer. Further information such as e.g. the probe used to collect the data (probes can be readily changed over) or any calibration data used in setting up the instrument for the data collection are by and large not recorded. To my knowledge, although an instrument can have a unique serial number, such serial numbers of swappable components such as probes are not recorded by the collection software. Thus the second break in the provenance trail.
  5. This data then needs to be processed by further software. In this case we use the MestreNova system for this task. Each dataset has editable assigned properties; below I show those that can be associated with the spectrum (accessed with MestreNova using Edit/Properties). All this comes from the information collected by the instrument. The user’s identity can be inserted into the “title” field, the display of which is off by default. 
  6. There is also a section for parameters, a synonym for which might be metadata and accessed using this program from View/Tables/Parameters. If Author was entered as a parameter in the dataset by the spectrometer software, the Mnova document would retrieve that information. Equally, an ORCID identifier for the author entered at the time of data collection and thus stored in the dataset could be read by Mnova, stored and displayed if configured to do so. It would be fair to say however that this option is rarely if indeed ever systematically implemented by NMR instrument data collection software and so is never propagated to the data processing software (as highlighted in red below). Thus a third break in the provenance trail.
    This is also an alternative and this time formal metadata field that can be populated, by default as shown below with the type of spectrum and nucleus. These properties are not controlled in the sense of only allowing those terms that are present in a specified dictionary. The jargon for such control is a metadata schema. This is not used here, since dissemination of this information is not intended; the software accepts whatever information it is given. 
    There are thus several opportunities to collect the identity of the experimenter and thus attribute provenance to the collected data, but this does very much depend on the will of researchers, institutions or publishers to enforce specific policies around this. The fourth break in the provenance trail.
  7. The dataset can then be uploaded (DOI: 10.14469/hpc/1291), at which stage provenance can finally be added using the ORCID credentials of the person publishing the dataset, who of course may or may not be the person who actually recorded the data! The full metadata for this specific collection can be seen at data.datacite.org/10.14469/hpc/1291. Or to put it another way, this is the first point in the provenance chain where the metadata is controlled by a schema and is also discoverable in a standard programmatic manner, i.e. the preceding link. The provenance is now formally associated with the ORCID identifier using the DataCite metadata schema. You should be aware that a local policy† is that access to the repository at https://data.hpc.imperial.ac.uk is only allowed by cross-authentication with http://orcid.org/ using the user’s ORCID. This identifier is then automatically propagated to the metadata held at e.g. data.datacite.org/10.14469/hpc/1095. Currently however, none of any metadata originally recorded in either the instrumental file set or the processed MestreNova file is forwarded on to the metadata record held at DataCite; again loss of information and potentially of provenance. 
  8. The peer-reviewed article resulting from the interpretation of this data however can be associated with the provenance introduced in the previous stage; see data.datacite.org/10.14469/hpc/1267  and the IsReferencedBy property. 

Now imagine if there was a common thread in all the stages of acquiring, processing and publishing this scientific data based on the ORCID. 

  1. Providing an ORCID could be made an essential requirement of access to the instrument.
  2. This information would be propagated to the dataset …
  3. by inclusion in one or more of the audit trail files.
  4. At this stage, further persistent identifiers associated with the instrument manufacturer could be added, which help identify not only the instrument used, but sub-components such as the changeable probe. This would allow access to any calibration curves or probe sensitivity and other aspects.
  5. The ORCID and other relevant information could be picked up by the software used to convert the data into spectra and propagated into the metadata containers for this software …
  6. where its use is controlled by a specified schema.
  7. At this stage, the ORCID and information such as the nucleus recorded, the sample temperature etc can be propagated on to the final metadata records.
  8. And the reader of the article describing this work would have a formally defined provenance audit trail they could follow back to the start of the experiment or forward to a published article. In this case, the data claims provenance (acquired from peer review) from the article, but it should also work in reverse with the article claiming provenance from the data on which it is based. The indexing of this bidirectional exchange is one of the exciting features that we should see emerging from CrossRef (holders of metadata about articles) and DataCite (holders of metadata about research data) in the near future.

We are clearly a little way from having the infrastructures described above for establishing such data audit trails. To do so will require cooperation from instrument manufacturers, at least in the example as charted above, as well as researchers, institutions, publishers, peer-reviewers and funding bodies. The first step would be to ensure that all scientists who intend collecting, processing and publishing data should claim an ORCID. That remark is directed specifically at undergraduate, postgraduate and post-doctoral researchers, not just at their supervisor or their PI (principal investigator). At a point when the discussion about alternate facts and perhaps even alternate data risks a general loss of confidence in science, we should be pro-active in establishing trust in the scientific processes.

‡ You can see an example obtained by this process at DOI: 10.14469/hpc/1095

† This requirement is a strong driver for the uptake of ORCID amongst our student population.

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A nice example of open data (in London).

Sunday, March 5th, 2017

Living in London, travelling using public transport is often the best way to get around. Before setting out on a journey one checks the status of the network. Doing so today I came across this page: our open data from Transport for London. 

  1. I learnt that by making TFL travel data openly available, some 11,000 developers (sic!) have registered for access, out of which some 600 travel apps have emerged.
  2. The data is in XML, which makes it readily inter-operable.[1]
  3. This encourages crowd-sourced innovation.
  4. They have taken the trouble to produce an API (application programmable interface) which allows rich access to the data and information about e.g. AccidentStats, AirQuality, BikePoint, Journey, Line, Mode, Occupancy, Place, Road, Search, StopPointVehicle.

Chemists could learn some lessons here! Of course, there are quite a few chemical databases with APIs that are examples of open data, but the “ESI” (electronic supporting information) sources which almost all published articles rely upon to disseminate data are clearly struggling to cope. Take for example this recent article[2], where much of the data has been dropped into the inevitable PDF “coffin” and which is a breathtaking 907 pages long. To give the authors their due, they also provide 20 CIF files which ARE good sources of data. Rarely commented on, but clearly missing from the information associated with this (indeed most) articles is the metadata about the data. Thus the metadata for these CIF files amounts to just e.g. 229. To find out the context, one has to scour the article (or the 907 pages of the ESI) to identify compound 229 (I strongly suspect it’s a molecule because of the implied semantics of the term, not because its been explicitly declared). You will not find the metadata at e.g. data.datacite.org which is one open aggregator and global search engine based on deposited metadata.

I have commented elsewhere on this blog that other types of data could also be enhanced in the manner that CIF crystallographic files represent. For example the Mpublish NMR project,‡ examples of which are shown here, and for which typical data AND its metadata can be seen at DOI: 10.14469/hpc/1053. I fancy that if this method had been adopted,[2] those 907 pages might have shrunk somewhat, although of course not entirely. But my hope is that gradually the innovative chemistry community will find ways of exhuming more and more data from the PDF coffin and in the process reducing the paginated lengths of the PDF-based ESI further, perchance eventually even to zero?

If you are yourself preparing an article and sweating over the ESI at this very moment, do please take a look at the Mpublish method and how perhaps it can help make your NMR data at least more useful to others.

‡I understand an article describing this project is in preparation. If you cannot wait, this recent application of the Mpublish project has some details.[3]

References

  1. P. Murray-Rust, and H.S. Rzepa, "Chemical Markup, XML, and the Worldwide Web. 1. Basic Principles", Journal of Chemical Information and Computer Sciences, vol. 39, pp. 928-942, 1999. https://doi.org/10.1021/ci990052b
  2. J.M. Lopchuk, K. Fjelbye, Y. Kawamata, L.R. Malins, C. Pan, R. Gianatassio, J. Wang, L. Prieto, J. Bradow, T.A. Brandt, M.R. Collins, J. Elleraas, J. Ewanicki, W. Farrell, O.O. Fadeyi, G.M. Gallego, J.J. Mousseau, R. Oliver, N.W. Sach, J.K. Smith, J.E. Spangler, H. Zhu, J. Zhu, and P.S. Baran, "Strain-Release Heteroatom Functionalization: Development, Scope, and Stereospecificity", Journal of the American Chemical Society, vol. 139, pp. 3209-3226, 2017. https://doi.org/10.1021/jacs.6b13229
  3. 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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Open science and the chemistry lab of the future.

Thursday, February 9th, 2017

The title refers to an upcoming symposium on the topic on 22-24 May, 2017.  I quote here some of the issues tabled for discussion:

  • Which data do we want to save, how and why and how long?
  • What really needs to be reproducible?
  • Are current reporting standards being used sufficiently?
  • Are the current procedures for depositing data too onerous for scientists?
  • Will technology, through increasing automation, fix most of the problems?
  • Is bureaucracy killing creativity in science?
  • Have we got a reproducibility crisis?
  • If we save and share data routinely, what is the future of the publication?
  • Are funding agencies causing science to be too short term in their quest for value for money?
  • Are chemists repeating too many experiments?
  • What can chemistry learn from other areas and what can they learn from chemistry?

For more information, visit www.beilstein-institut.de/en/symposia/open-science. If you have your own questions,  or indeed comments at this stage, do append them as a comment.  I don’t know what “social media” will be used to allow people to participate (science by Twitter feed?) and whether recordings will be made, but after the event I will update here with any further interesting news.

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Revisiting (and maintaining) a twenty year old web page. Mauveine: The First Industrial Organic Fine-Chemical.

Thursday, February 2nd, 2017

Almost exactly 20 years ago, I started what can be regarded as the precursor to this blog. As part of a celebration of this anniversary, I revisited the page to see whether any of it had withstood the test of time. Here I recount what I discovered.

The site itself is at www.ch.ic.ac.uk/motm/perkin.html  and has the title “Mauveine: The First Industrial Organic Fine-Chemical” It was an application of an earlier experiment[1] to which we gave the title “Hyperactive Molecules and the World-Wide-Web Information System“. The term hyperactive was supposed to be a play on hyperlinking to the active 3D models of molecules built using their 3D coordinates. The word has another, more negative, association with food additives such as tartrazine – which can induce hyperactivity in children – and we soon discontinued the association. This page was cast as a story about a molecule local to me in two contexts; the first being that the discoverer of mauveine, W. H. Perkin, had been a student at what is now the chemistry department at Imperial College. The second was the realization that where we lived in west London was just down the road from Perkin’s manufacturing factory. Armed with (one of the first) digital cameras, a Kodak DC25, I took some pictures of the location and added them later to the web page. The page also included two sets of 3D coordinates for mauveine itself and alizarin, another dyestuff associated with the factory. These were “activated” using HTML to make use of the then very new Chime browser plugin; hence the term hyperactive molecule.

This first effort, written in December 1995, soon needed revision in several ways. I note that I had maintained the site in 1998, 2001, 2004 and 2006. This took the form of three postscripts to add further chemical context and more recent developments and in replacing the original Chime code for Java code to support the new Jmol software (Chime itself had been discontinued, probably around 2001 or possibly 2004). With the passage of a further ten years, I now noticed that the hyperactive molecules were no longer working; the original Jmol applet was no longer considered secure by modern browsers and hence deactivated. So I replaced this old code with the latest version (14.7.5 as JmolAppletSigned.jar) and this simple fix has restored the functionality. The coordinates themselves were invoked using the HTML applet tag, which amazingly still works (the applet tag had replaced an earlier one, which I think might have been embed?).  A modern invocation would be by using e.g. the JSmol Javascript based tool and so perhaps at some stage this code will indeed need further revision when the Java-based applet is permanently disabled.

You may also notice that the 3D coordinates are obtained from an XML document, where they are encoded using CML (chemical markup language[2]), which is another expression from the family that HTML itself comes from. That form may well last rather longer than earlier formats – still commonly used now – such as .pdb or .mol (for an MDL molfile). 

Less successful was the attempt to include buttons which could be used to annotate the structures with highlights. These buttons no longer work and will have to be entirely replaced in the future at some stage.

The final part of the maintenance (which I had probably also done with the earlier versions) was to re-validate the HTML code. Checking that a web page has valid HTML was always a behind-the-scenes activity which I remember doing when constructing the ECTOC conferences also back in 1995 and doing so probably does prolong the longevity of a web page. This requires “tools-of-the-trade” and I use now (and indeed did also back in 1995 or so) an industrial strength HTML editor called BBedit. To this is added an HTML validation tool, the installation of which is described at https://wiki.ch.ic.ac.uk/wiki/index.php?title=It:html5 I re-ran this again† and so this 2017 version should be valid for a little while longer at least. The page itself now has not just a URL but a persistent version called a DOI (digital object identifier), which is 10.14469/hpc/2133[3]. In theory at least, even if the web server hosting the page itself becomes defunct, the page could – if moved – be found simply from its DOI. The present URL-based hyperlink of course is tied to the server and would not work if the server stopped serving.

To complete this revisitation, I can add here a recent result‡. Back in 1995, I had obtained the 3D coordinates of mauveine using molecular modelling software (MOPAC) together with a 2D structure drawing package (ChemDraw) because no crystal structure was available. Well, in 2015 such structures were finally published.[4] Twenty years on from the original “hyperactive” models, their crystal structures can be obtained from their assigned DOI, much in the same manner as is done for journal articles: Try DOI: 10.5517/CC1JLGK4[5] or DOI: 10.5517/CC1JLGL5[6].

At some stage, web archaeology might become a fashionable pursuit. Twenty year old Web pages are actually not that common and it would be of interest to chart their gradual decay as security becomes more important and standards evolve and mature. One might hope that at the age of 100, they could still be readable (or certainly rescuable). During this period, the technology used to display 3D models within a web page has certainly changed considerably and may well still do so in the future. Perhaps I will revisit this page in 2037 to see how things have changed!

†The old code can still be seen at www.ch.ic.ac.uk/motm/perkin-old.html

‡It should really be postscript 4.

References

  1. O. Casher, G.K. Chandramohan, M.J. Hargreaves, C. Leach, P. Murray-Rust, H.S. Rzepa, R. Sayle, and B.J. Whitaker, "Hyperactive molecules and the World-Wide-Web information system", Journal of the Chemical Society, Perkin Transactions 2, pp. 7, 1995. https://doi.org/10.1039/p29950000007
  2. P. Murray-Rust, and H.S. Rzepa, "Chemical Markup, XML, and the Worldwide Web. 1. Basic Principles", Journal of Chemical Information and Computer Sciences, vol. 39, pp. 928-942, 1999. https://doi.org/10.1021/ci990052b
  3. H. Rzepa, "Molecule of the month: Mauveine.", Imperial College London, 2017. https://doi.org/10.14469/hpc/2133
  4. M.J. Plater, W.T.A. Harrison, and H.S. Rzepa, "Syntheses and Structures of Pseudo-Mauveine Picrate and 3-Phenylamino-5-(2-Methylphenyl)-7-Amino-8-Methylphenazinium Picrate Ethanol Mono-Solvate: The First Crystal Structures of a Mauveine Chromophore and a Synthetic Derivative", Journal of Chemical Research, vol. 39, pp. 711-718, 2015. https://doi.org/10.3184/174751915x14474318419130
  5. Plater, M. John., Harrison, William T. A.., and Rzepa, Henry S.., "CCDC 1417926: Experimental Crystal Structure Determination", 2016. https://doi.org/10.5517/cc1jlgk4
  6. Plater, M. John., Harrison, William T. A.., and Rzepa, Henry S.., "CCDC 1417927: Experimental Crystal Structure Determination", 2016. https://doi.org/10.5517/cc1jlgl5

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OpenCon (2016)

Friday, November 25th, 2016

Another conference, a Cambridge satellite meeting of OpenCon, and I quote here its mission: “OpenCon is a platform for the next generation to learn about Open Access, Open Education, and Open Data, develop critical skills, and catalyze action toward a more open system of research and education” targeted at students and early career academic professionals. But they do allow a few “late career” professionals to attend as well!

I could only attend the morning session, for which the keynote speaker was Erin McKiernanorcid The presentation was entitled How open science helps researchers succeed, presented as an exploration of an article written by Erin and colleagues with the same name and published in eLife[1] Erin has created a support page at http://whyopenresearch.org to augment the presentation and it’s well worth a visit.

One striking point made was the assertion that Open publications get more citations! 
Open publications get more citations

As with many metrics of the impacts of the science publication processes, a citation itself lacks the context of why it was made (see this post for further discussion), but the expectation is that a citation is “good”. From my perspective as a chemist, I did wonder why molecular science was missing from the graphic above. Do open chemistry publications also get more citations?

Which brings me to another point made during the talk, the increasingly controversial aspect of (journal) impact factors and the pressure placed on early career researchers to publish only in those with “high” impact factors, and for their careers to be assessed at least in part based on these and the anticipated “h-index”. The audience was indeed encouraged to go visit http://www.ascb.org/Dora/ (Declaration on Research Assessment, or Putting science into the assessment of research). Have you signed it yet?

Another manifestation of the modern trend to analyse impact metrics is the site Impactstory.org. This is a scripted resource that starts from your ORCID identifier and (optionally) your Twitter account (yes, apparently Tweets matter!) to derive a more complex alternative metric of a individual’s impacts. I had not tried this one before and so I submitted my ORCID and my Twitter account, and watched as the system went off to http://orcid.scopusfeedback.com (Scopus is an Elsevier product) to attempt to create my profile. It ground for quite a while, reporting initially that I had no publications! This was followed by an unexpected error; I did not get my impact back! But this experiment served to highlight one aspect that was discussed at the meeting; data and other research objects. The graphic above refers only to the citation of journal articles, it does not yet include the citation of data. However ORCID DOES include data and research objects as works.  And because the granularity of my data and research objects is very fine (one molecule = one work), I have quite a few. In fact ~200,000! ORCID gets to about 8000 before it gives up. I suspect http://orcid.scopusfeedback.com queries ORCID, gets back ~8000 entries and crashes. No doubt the programmer tasked with implementing this resource did not anticipate that any individual could accumulate 8000+ entries! Or probably factor in that the vast majority of these would of course not be journal articles but data. If the site gets back to me about the crash I experienced, I will update here.

Simon Deakin was the next speaker with (open) data as the focus and the worries many researchers have in being scooped by others who have re-used your open data without proper attributions. The discussion teased out that if data is properly deposited, it will indeed have full associated metadata and in particular a date stamp that could help protect an author’s interests.

It was really good to meet so many early career researchers who espouse the open ethos. Perhaps, in 20 years time,  another graphic akin to the one above might demonstrate that open researchers get more promotions!

References

  1. E.C. McKiernan, P.E. Bourne, C.T. Brown, S. Buck, A. Kenall, J. Lin, D. McDougall, B.A. Nosek, K. Ram, C.K. Soderberg, J.R. Spies, K. Thaney, A. Updegrove, K.H. Woo, and T. Yarkoni, "How open science helps researchers succeed", eLife, vol. 5, 2016. https://doi.org/10.7554/elife.16800

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Pidapalooza!

Thursday, November 10th, 2016

This is sent from the Pidapalooza event in Reykjavik, Iceland, and is a short collection of notable things I learnt or which attracted my attention.

Firstly, what IS PIDapalooza[1]? Well, it’s all about persistent identifiers, but don’t let that put you off! Another way of putting it is that it’s a way of finding things scientific on the Web. Not just publications, but conferences, social media, teaching, research datasets, infrastructure, grants, organizations, instruments, scientific objects and samples and no doubt much more. These (will) live in an inter-connected eco-system, and so the idea goes, will become an integral part of how a scientist accumulates and disseminates information nowadays. Yes, the conference itself has its own PID: 10.5438/11.0001  and the individual talks will also appear as both a collection and with their own  PID in the near future.

  1. The first example comes from WikiData, a collection of carefully curated data, from which can be dynamically assembled say a periodic table of the elements. All the data here is included from other objects, and everything is referenced by its PID. Since it’s all assembled from data, if say the name of element 118 is assigned, then it will automatically be absorbed into this presentation.
  2. This next example proved highly contentious, but is included here anyway. It is templated PIDs, as in http://doi.org/10.5446/12780#t=00:20.00:27 which allows navigation to a particular part of an object referenced by the PID. In this case a time code for a movie, but it might be say an active site in a protein, or a key atom or group in a molecular complex for example.  This might never happen (for reasons only the computer scientists currently understand!) but it does show one way in which the humble DOI might evolve.
  3. http://typeregistry.org exists for registering data types. It has almost no chemistry at the moment, but perhaps it should have! 
  4. There was a great deal about  ORCIDs, and the ways in which uses of this particular  PID are evolving.  For example, the next big effort is to use the ORCID system for organisations.  You will find my ORCID at the top of this post.
  5. PIDs are also being mooted for instruments. The idea is that instrumental capabilities, settings, calibration etc are often an integral part of the data acquisition for a project. So if data is generated using such a device, why not quote its  PID in any derived article so that others can more easily replicate a particular experiment in their own laboratory.
  6. A quote by one of the speakers was attributed to Bill Gates around 1997 “We need  banking. We don’t need banks anymore” (think how this might apply to 2016. Was he correct?).  This was followed by straw men such as: “We need publications. We don’t need publishers anymore”. Or “We need archiving. We don’t need libraries anymore”. Just like Gates’ own quote, the reality is of course far more complex.
  7. And PID fatigue;  I hope you are not getting too much of that at the moment.

There are lots more I have learnt which I need to fix/enhance/address in our own experiments in the use of PIDs in chemistry, so I have better get on with it now!

References

  1. ORCID., DataCite., Crossref., and California Digital Library., "PIDapalooza 2016", 2016. https://doi.org/10.5438/11.0001

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The 2016 Bradley-Mason prize for open chemistry.

Tuesday, October 4th, 2016

Peter Murray-Rust and I are delighted to announce that the 2016 award of the Bradley-Mason prize for open chemistry goes to Jan Szopinski (UG) and Clyde Fare (PG).

Jan’s open chemistry derives from a final year project looking at why atom charges derived from quantum chemical calculation of the electronic density represent chemical information well, but the electrostatic potential (ESP) generated from these charges is very poor and conversely charges derived from the computed electrostatic potential are incommensurate with chemical information (such as the electronegativity of atoms). He has developed a Python program called ‘repESP’ in which ‘compromise’ charges are generated which attempt to reconcile the physical world-view (fitting the ESP) with chemical insight provided by NPA (Natural Population Analysis). Jan was the main driver to making his code open source, “opening his supervisor’s eyes” to the various flavours of open source licences. To ensure that all subsequent improvements to the program remain available to anyone, the source code has been released under a ‘copyleft’ licence (GPL v3) and is maintained by Jan on GitHub, where Jan looks forward to helping new users and collaborating with contributors.

Clyde has made various contributions to opensource chemistry over the period of his PhD, with the focus mainly on utilities to improve quantum chemical research and the enhancement of a popular machine learning library with a method that has been successful in chemometrics, creation of an opensource channel for teaching chemists programming and data analysis and creation of a tool to help encourage open sourcing software development. Cclib is the most popular library for parsing quantum chemical data from output files and Clyde has contributed patches for the Atomic simulation environment which enables control of quantum chemical codes from a unified python interface. He was responsible for the construction of a computational chemistry electronic notebook published to github and which is now under active development by others as well. This aims to encapsulate computation chemical research projects, both for the sake of reproducibility and for the sake of organising and keeping track of quantum chemical research. Alongside this platform he created an enhanced Gaussian calculator for the Atomic Simulation Environment that enables automatic construction of ONIOM input files, also now under active development. He also made contributions to scikit learn, the most popular python machine learning framework, implementing a kernel for Kernel Ridge Regression that has become the most successful kernel for regression over molecular properties. He was part of the team that won the 2014 sustainable software conference prize for creation of the opensource healthchecker software as part of Sustain. He has argued for opensource as a platform for teaching resources and created the Imperial Chemistry github user account, which is now run by the department. Materials for the Imperial Chemistry Data Analysis and Programming workshops implemented as Python Notebooks are now available through this account and continue under active development.

Criteria for the award will include judging the submission on its immediate accessibility via public web sites, what is visible and re-usable in this way and of evidence of either community formation/engagement or re-use of materials by people other than the proposer.

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Chemistry preprint servers (revisited).

Tuesday, August 16th, 2016

This week the ACS announced its intention to establish a “ChemRxiv preprint server to promote early research sharing“. This was first tried quite a few years ago, following the example of especially the physicists. As I recollect the experiment lasted about a year, attracted few submissions and even fewer of high quality. Will the concept succeed this time, in particular as promoted by a commercial publisher rather than a community of scientists (as was the original physicists model)?

The RSC (itself a highly successful commercial publisher) has picked up on this and run its own commentary. You will find quotes from yours truly there, along with Peter Murray-Rust, a long time ardent promoter of community driven open science. One interesting aspect is that the ACS runs around 50 journals, and the decision on whether each will accept preprints for publication will (shortly = next few weeks) be made by the individual editors. I wonder if the eventual list of those supporting the project will bring any surprises (bets on J. Am. Chem. Soc. preprints anyone)?

But I want to pick up on the declared aspiration “to promote early research sharing“. Here I couple research sharing with data sharing. If you share your research, you should also share the data resulting from that research. We are now entering a new era of data sharing (in part as a result of mandation by various funding bodies) and so one has to ask whether a pre-print server will encourage people to create and share FAIR data (data which is findable, accessible, inter-operable and re-usable) as a model to replace the current one of “supporting information” held in enormous PDF files (mostly unFAIR on at least three counts). This question is indeed posed in the RSC commentary. What I would like to see happen are projects such as that described here, which create what were described as “first class research objects”, and which I think amply fulfil the criteria of being FAIR. So, will ChemRxiv preprint servers help promote such FAIR data sharing as part of early research sharing? We will find out soon.

The ACS supports OA (Open Access) sharing of articles, provided the authors pay (or arrange payment of) the appropriate APC or article processing charge. These charges are complex, being subject to various discounts (for example if you as an author are an ACS member or not) but are generally not insignificant (> $1000). I wondered whether preprints might be subject to an APC, and so I asked the ACS. The response was “we don’t anticipate any submission or usages fees at this time“. I think that means free at point of submission, and free at point of readership “at this time“.

Finally, let me now summarise as I understand the current family of “research publications”:

  1. The preprint
  2. The final author version as submitted to a journal
  3. The “version of record” (VoR) as published by the journal
  4. Any FAIR published data associated with the article

All four of these are attempts at “research sharing”. Each may be located in a different location, and each may have its own DOI. And of course we cannot easily know how much overlap there is between each of them. Thus, how might 1-3 differ in terms of the story or “narrative” of scientific claims? Does 4 agree or support 1-3? Does 4 agree with perhaps data subsets contained in 1-3? If keeping abreast of the current research literature is a challenge, imagine having to cope with/reconcile up to four versions of each “publication”! 

Lots of food for thought here. We have not heard the last of these themes. 

 

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