Posts Tagged ‘Knowledge representation’

The “Accessible” in FAIR (data).

Thursday, April 18th, 2019

In a previous post, I looked at the Findability of FAIR data in common chemistry journals. Here I move on to the next letter, the A = Accessible.

The attributes of A[1] include:

  1. (meta)data are retrievable by their identifier using a standardized communication protocol.
  2. the protocol is open, free and universally implementable. 
  3. the protocol allows for an authentication and authorization procedure.
  4. metadata are accessible, even when the data are no longer available. 
  5. The metadata should include access information that enables automatic processing by a machine as well as a person.

Items 1-2 are covered by associating a DOI (digital object identifier) with the metadata. Item 3 relates to data which is not necessarily also OPEN (FAIR and OPEN are complementary, but do not mean the same).

Item 4 mandates that a copy of the metadata be held separately from the data itself; currently the favoured repository is DataCite (and this metadata way well be duplicated at CrossRef, thus providing a measure of redundancy). It also addresses an interesting debate on whether the container for data such as a ZIP or other compressed archive should also contain the full metadata descriptors internally, which would not directly address item 4, but could do so by also registering a copy of the metadata externally with eg DataCite.

Item 4 also implies some measure of separation between the data and its metadata, which now raises an interesting and separate issue (introduced with this post) that the metadata can be considered a living object, with some attributes being updated post deposition of the data itself. Thus such metadata could include an identifier to the journal article relating to the data, information that only appears after the FAIR data itself is published. Or pointers to other datasets published at a later date. Such updating of metadata contained in an archive along with the data itself would be problematic, since the data itself should not be a living object.

Item 5 is the need for Accessibility to relate both to a human acquiring FAIR data and to a machine. The latter needs direct information on exactly how to access the data. To illustrate this, I will use data deposited in support of the previous post and for which a representative example of metadata can be found at (item 4) a separate location at:
data.datacite.org/application/vnd.datacite.datacite+xml/10.14469/hpc/5496

This contains the components:

  1. <relatedIdentifier relatedIdentifierType="URL" relationType="HasMetadata" relatedMetadataScheme="ORE"schemeURI="http://www.openarchives.org/ore/
    ">https://data.hpc.imperial.ac.uk/resolve/?ore=5496</relatedIdentifier>
  2. <relatedIdentifier relatedIdentifierType="URL" relationType="HasPart" relatedMetadataScheme="Filename" schemeURI="filename://aW5wdXQuZ2pm">https://data.hpc.imperial.ac.uk/resolve/?doi=5496&file=1</relatedIdentifier>

Item 6 is an machine-suitable RDF declaration of the full metadata record. Item 7 allows direct access to the datafile. This in turn allows programmed interfaces to the data to be constructed, which include e.g. components for immediate visualisation and/or analysis. It also allows access on a large-scale (mining), something a human is unlikely to try. 

It would be fair to say that the A of FAIR is still evolving. Moreover, searches of the DataCite metadata database are not yet at the point where one can automatically identify metadata records that have these attributes. When they do become available, I will show some examples here.

Added: This search: https://search.test.datacite.org/works?
query=relatedIdentifiers.relatedMetadataScheme:ORE shows how it might operate.

References

  1. 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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The "Accessible" in FAIR (data).

Thursday, April 18th, 2019

In a previous post, I looked at the Findability of FAIR data in common chemistry journals. Here I move on to the next letter, the A = Accessible.

The attributes of A[1] include:

  1. (meta)data are retrievable by their identifier using a standardized communication protocol.
  2. the protocol is open, free and universally implementable. 
  3. the protocol allows for an authentication and authorization procedure.
  4. metadata are accessible, even when the data are no longer available. 
  5. The metadata should include access information that enables automatic processing by a machine as well as a person.

Items 1-2 are covered by associating a DOI (digital object identifier) with the metadata. Item 3 relates to data which is not necessarily also OPEN (FAIR and OPEN are complementary, but do not mean the same).

Item 4 mandates that a copy of the metadata be held separately from the data itself; currently the favoured repository is DataCite (and this metadata way well be duplicated at CrossRef, thus providing a measure of redundancy). It also addresses an interesting debate on whether the container for data such as a ZIP or other compressed archive should also contain the full metadata descriptors internally, which would not directly address item 4, but could do so by also registering a copy of the metadata externally with eg DataCite.

Item 4 also implies some measure of separation between the data and its metadata, which now raises an interesting and separate issue (introduced with this post) that the metadata can be considered a living object, with some attributes being updated post deposition of the data itself. Thus such metadata could include an identifier to the journal article relating to the data, information that only appears after the FAIR data itself is published. Or pointers to other datasets published at a later date. Such updating of metadata contained in an archive along with the data itself would be problematic, since the data itself should not be a living object.

Item 5 is the need for Accessibility to relate both to a human acquiring FAIR data and to a machine. The latter needs direct information on exactly how to access the data. To illustrate this, I will use data deposited in support of the previous post and for which a representative example of metadata can be found at (item 4) a separate location at:
data.datacite.org/application/vnd.datacite.datacite+xml/10.14469/hpc/5496

This contains the components:

  1. <relatedIdentifier relatedIdentifierType="URL" relationType="HasMetadata" relatedMetadataScheme="ORE"schemeURI="http://www.openarchives.org/ore/
    ">https://data.hpc.imperial.ac.uk/resolve/?ore=5496</relatedIdentifier>
  2. <relatedIdentifier relatedIdentifierType="URL" relationType="HasPart" relatedMetadataScheme="Filename" schemeURI="filename://aW5wdXQuZ2pm">https://data.hpc.imperial.ac.uk/resolve/?doi=5496&file=1</relatedIdentifier>

Item 6 is an machine-suitable RDF declaration of the full metadata record. Item 7 allows direct access to the datafile. This in turn allows programmed interfaces to the data to be constructed, which include e.g. components for immediate visualisation and/or analysis. It also allows access on a large-scale (mining), something a human is unlikely to try. 

It would be fair to say that the A of FAIR is still evolving. Moreover, searches of the DataCite metadata database are not yet at the point where one can automatically identify metadata records that have these attributes. When they do become available, I will show some examples here.

Added: This search: https://search.test.datacite.org/works?
query=relatedIdentifiers.relatedMetadataScheme:ORE shows how it might operate.

References

  1. 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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A search of some major chemistry publishers for FAIR data records.

Friday, April 12th, 2019

In recent years, findable data has become ever more important (the F in FAIR). Here I test that F using the DataCite search service.

Firstly an introduction to this service. This is a metadata database about datasets and other research objects. One of the properties is relatedIdentifier which records other identifiers associated with the dataset, being say the DOI of any published article associated with the data, but it could also be pointers to related datasets.

One can query thus:

  1. https://search.datacite.org/works?query=relatedIdentifiers.relatedIdentifier:*
    which retrieves the very healthy looking 6,179,287 works.
  2. One can restrict this to a specific publisher by the DOI prefix assigned to that publisher:
    ?query=relatedIdentifiers.relatedIdentifier:10.1021*
    which returns a respectable 210,240 works.
  3. It turns out that the major contributor to FAIR currently are crystal structures from the CCDC. One can remove them from the search to see what is left over:
    ?query=(relatedIdentifiers.relatedIdentifier:10.1021*)+NOT+(identifier:*10.5517*) 
    and one is down to 14,213 works, of which many nevertheless still appear to be crystal structures. These may be links to other crystal datasets.

I have performed searches 2 and 3 for some popular publishers of chemistry (the same set that were analysed here).

Publisher Search 2 Search 3
ACS 210,240 14,213
RSC 138,147 1,279
Elsevier 185,351 56,373
Nature 12,316 8,104
Wiley 135,874 9,283
Science 3,384 2,343

These publishers all have significant numbers of datasets which at least accord with the F of FAIR. A lot of data sets may not have metadata which in fact points back to a published article, since this can be something that has to be done only when the DOI of that article appears, in other words AFTER the publication of the dataset. So these numbers are probably low rather than high.

How about the other way around? Rather than datasets that have a journal article as a related identifier, we could search for articles that have a dataset as a related identifier?

  1. ?query=(identifier:*10.1039*)+AND+(relatedIdentifiers.relatedIdentifier:*)
    returns rather mysterious nothing found. It might also be that there is no mapping of this search between the CrossRef and DataCite metadata schemas.
  2. And just to show the searches are behaving as expected:
    ?query=(relatedIdentifiers.relatedIdentifier:10.1021*)+AND+(identifier:*10.5517*)
    returns 196,027 works.

It will also be of interest to show how these numbers change over time. Is there an exponential increase? We shall see.

Finally, we have not really explored adherence to eg the AIR of FAIR.  That is for another post.

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Questions about the (metadata) components of a scientific article.

Monday, April 8th, 2019

The conventional procedures for reporting analysis or new results in science is to compose an “article”, augment that perhaps with “supporting information” or “SI”, submit to a journal which undertakes peer review, with revision as necessary for acceptance and finally publication. If errors in the original are later identified, a separate corrigendum can be submitted to the same journal, although this is relatively rare. Any new information which appears post-publication is then considered for a new article, and the cycle continues. Here I consider the possibilities for variations in this sequence of events.

The new disruptors in the processes of scientific communication are the “data“, which can now be given a separate existence (as FAIR data) from the article and its co-published “SI”. Nowadays both the “article+SI” and any separate “data” have another, mostly invisible component, the “metadata“. Few authors ever see this metadata. For the article, it is generated by the publisher (as part of the service to the authors), and sent to CrossRef, which acts as a global registration agency for this particular metadata. For the data, it is assembled when the data is submitted to a “data repository”, either by the authors providing the information manually, or by automated workflows installed in the repository or by a combination of both. It might also be assembled by the article publisher as part of a complete metadata package covering both article and data, rather than being separated from the article metadata. Then, the metadata about data is registered with the global agency DataCite (and occasionally with CrossRef for historical reasons).‡ Few depositors ever inspect this metadata after it is registered; even fewer authors are involved in decisions about that metadata, or have any inputs to the processes involved in its creation.

Let me analyse a recent example.

  1. For the article[1] you can see the “landing page” for the associated metadata as https://search.crossref.org/?q=10.1021/acsomega.8b03005 and actually retrieve the metadata using https://api.crossref.org/v1/works/10.1021/acsomega.8b03005, albeit in a rather human-unfriendly manner.† This may be because metadata as such is considered by CrossRef as something just for machines to process and not for humans to see!
  2. Citation #22 in the above does have its own metadata record, obtainable using: 
    https://data.datacite.org/application/vnd.datacite.datacite+xml/10.14469/hpc/4751

    • This has an entry
      <relatedIdentifier relatedIdentifierType="DOI" relationType="IsReferencedBy">10.1021/acsomega.8b03005</relatedIdentifier>
      which points back to the article.[1]
  3. To summarise, the article noted above[1] has a metadata record that does not include any information about the references/citations (apart from an ambiguous count). A human reading the article can however can easily identify one citation pointing to the article data, which it turns out DOES have a metadata record which both human and machine can identify as pointing back to the article. Let us hope the publisher (the American  Chemical  Society) corrects this asymmetry in the future; it can be done as shown here![2]

For both types of metadata record, it is the publisher that retains any rights to modify them. Here however we encounter an interesting difference. The publishers of the data are, in this case, also the authors of the article! A modification to this record was made post-publication by this author so as to include the journal article identifier once it had been received from the publisher,[1] as in 2 above. Subsequently, these topics were discussed at a workshop on FAIR data, during which further pertinent articles[3], [4], [5] relating to the one discussed above[1] were shown in a slide by one of the speakers. Since this was deemed to add value to the context of the data for the original article, identifiers for these articles were also appended to the metadata record of the data.

This now raises the following questions:

  1. Should a metadata record be considered a living object, capable of being updated to reflect new information received after its first publication?
  2. If metadata records are an intrinsic part of both a scientific article and any data associated with that article, should authors be fully aware of their contents (if only as part of due diligence  to correct errors or to query omissions)?
  3. Should the referees of such works also be made aware of the metadata records? It is of course enough of a challenge to get referees to inspect data (whether as SI or as FAIR), never mind metadata! Put another way, should metadata records be considered as part of the materials reviewed by referees, or something independent of referees and the responsibility of their publishers?
  4. More generally, how would/should the peer-review system respond to living metadata records? Should there be guidelines regarding such records? Or ethical considerations?

I pose these questions because I am not aware of much discussion around these topics; I suggest there probably should be!

‡Actually CrossRef and DataCite exchange each other’s metadata. However, each uses a somewhat different schema, so some components may be lost in this transit. †JSON, which is not particularly human friendly.

References

  1. A. Barba, S. Dominguez, C. Cobas, D.P. Martinsen, C. Romain, H.S. Rzepa, and F. Seoane, "Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data", ACS Omega, vol. 4, pp. 3280-3286, 2019. https://doi.org/10.1021/acsomega.8b03005
  2. S. Arkhipenko, M.T. Sabatini, A.S. Batsanov, V. Karaluka, T.D. Sheppard, H.S. Rzepa, and A. Whiting, "Mechanistic insights into boron-catalysed direct amidation reactions", Chemical Science, vol. 9, pp. 1058-1072, 2018. https://doi.org/10.1039/c7sc03595k
  3. T. Monaretto, A. Souza, T.B. Moraes, V. Bertucci‐Neto, C. Rondeau‐Mouro, and L.A. Colnago, "Enhancing signal‐to‐noise ratio and resolution in low‐field NMR relaxation measurements using post‐acquisition digital filters", Magnetic Resonance in Chemistry, vol. 57, pp. 616-625, 2018. https://doi.org/10.1002/mrc.4806
  4. D. Barache, J. Antoine, and J. Dereppe, "The Continuous Wavelet Transform, an Analysis Tool for NMR Spectroscopy", Journal of Magnetic Resonance, vol. 128, pp. 1-11, 1997. https://doi.org/10.1006/jmre.1997.1214
  5. U.L. Günther, C. Ludwig, and H. Rüterjans, "NMRLAB—Advanced NMR Data Processing in Matlab", Journal of Magnetic Resonance, vol. 145, pp. 201-208, 2000. https://doi.org/10.1006/jmre.2000.2071

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“Richer metadata makes content more useful”

Saturday, February 16th, 2019

The title of this post comes from the site www.crossref.org/members/prep/ Here you can explore how your favourite publisher of scientific articles exposes metadata for their journal.

Firstly, a reminder that when an article is published, the publisher collects information about the article (the “metadata”) and registers this information with CrossRef in exchange for a DOI. This metadata in turn is used to power e.g. a search engine which allows “rich” or “deep” searching of the articles to be undertaken. There is also what is called an API (Application Programmer Interface) which allows services to be built offering deeper insights into what are referred to as scientific objects. One such service is “Event Data“, which attempts to create links between various research objects such as publications, citations, data and even commentaries in social media. A live feed can be seen here.

So here are the results for the metadata provided by six publishers familiar to most chemists, with categories including;

  1. References
  2. Open References
  3. ORCID IDs
  4. Text mining URLs
  5. Abstracts

RSC

ACS

Elsevier

Springer-Nature

Wiley

Science

One immediately notices the large differences between publishers. Thus most have 0% metadata for the article abstracts, but one (the RSC) has 87%! Another striking difference is those that support open references (OpenCitations). The RSC and Springer Nature are 99-100% compliant whilst the ACS is 0%. Yet another variation is the adoption of the ORCID (Open Researcher and Collaborator Identifier), where the learned society publishers (RSC, ACS) achieve > 80%, but the commercial publishers are in the lower range of 20-49%. 

To me the most intriguing was the Text mining URLs. From the help pages, “The Crossref REST API can be used by researchers to locate the full text of content across publisher sites. Publishers register these URLs – often including multiple links for different formats such as PDF or XML – and researchers can request them programatically“. Here the RSC is at 0%, ACS is at 8% but the commercial publishers are 80+%. I tried to find out more at e.g. https://www.springernature.com/gp/researchers/text-and-data-mining but the site was down when I tried. This can be quite a controversial area. Sometimes the publisher exerts strict control over how the text mining can be carried out and how any results can be disseminated.  Aaron Swartz famously fell foul of this.

I am intrigued as to how, as a reader with no particular pre-assembled toolkit for text mining, I can use this metadata provided by the publishers to enhance my science. After all, 80+% of articles with some of the publishers apparently have a mining URL that I could use programmatically. If anyone reading this can send some examples of the process, I would be very grateful. 

Finally I note the absence of any metadata in the above categories relating to FAIR data. Such data also has the potential for programmatic procedures to retrieve and re-use it (some examples are available here[1]), but apparently publishers do not (yet) collect metadata relating to FAIR. Hopefully they soon will.

References

  1. A. Barba, S. Dominguez, C. Cobas, D.P. Martinsen, C. Romain, H.S. Rzepa, and F. Seoane, "Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data", ACS Omega, vol. 4, pp. 3280-3286, 2019. https://doi.org/10.1021/acsomega.8b03005

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"Richer metadata makes content more useful"

Saturday, February 16th, 2019

The title of this post comes from the site www.crossref.org/members/prep/ Here you can explore how your favourite publisher of scientific articles exposes metadata for their journal.

Firstly, a reminder that when an article is published, the publisher collects information about the article (the “metadata”) and registers this information with CrossRef in exchange for a DOI. This metadata in turn is used to power e.g. a search engine which allows “rich” or “deep” searching of the articles to be undertaken. There is also what is called an API (Application Programmer Interface) which allows services to be built offering deeper insights into what are referred to as scientific objects. One such service is “Event Data“, which attempts to create links between various research objects such as publications, citations, data and even commentaries in social media. A live feed can be seen here.

So here are the results for the metadata provided by six publishers familiar to most chemists, with categories including;

  1. References
  2. Open References
  3. ORCID IDs
  4. Text mining URLs
  5. Abstracts

RSC

ACS

Elsevier

Springer-Nature

Wiley

Science

One immediately notices the large differences between publishers. Thus most have 0% metadata for the article abstracts, but one (the RSC) has 87%! Another striking difference is those that support open references (OpenCitations). The RSC and Springer Nature are 99-100% compliant whilst the ACS is 0%. Yet another variation is the adoption of the ORCID (Open Researcher and Collaborator Identifier), where the learned society publishers (RSC, ACS) achieve > 80%, but the commercial publishers are in the lower range of 20-49%. 

To me the most intriguing was the Text mining URLs. From the help pages, “The Crossref REST API can be used by researchers to locate the full text of content across publisher sites. Publishers register these URLs – often including multiple links for different formats such as PDF or XML – and researchers can request them programatically“. Here the RSC is at 0%, ACS is at 8% but the commercial publishers are 80+%. I tried to find out more at e.g. https://www.springernature.com/gp/researchers/text-and-data-mining but the site was down when I tried. This can be quite a controversial area. Sometimes the publisher exerts strict control over how the text mining can be carried out and how any results can be disseminated.  Aaron Swartz famously fell foul of this.

I am intrigued as to how, as a reader with no particular pre-assembled toolkit for text mining, I can use this metadata provided by the publishers to enhance my science. After all, 80+% of articles with some of the publishers apparently have a mining URL that I could use programmatically. If anyone reading this can send some examples of the process, I would be very grateful. 

Finally I note the absence of any metadata in the above categories relating to FAIR data. Such data also has the potential for programmatic procedures to retrieve and re-use it (some examples are available here[1]), but apparently publishers do not (yet) collect metadata relating to FAIR. Hopefully they soon will.

References

  1. A. Barba, S. Dominguez, C. Cobas, D.P. Martinsen, C. Romain, H.S. Rzepa, and F. Seoane, "Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data", ACS Omega, vol. 4, pp. 3280-3286, 2019. https://doi.org/10.1021/acsomega.8b03005

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Examples please of FAIR (data); good and bad.

Sunday, May 6th, 2018

The site fairsharing.org is a repository of information about FAIR (Findable, Accessible, Interoperable and Reusable) objects such as research data.

A project to inject chemical components, rather sparse at the moment at the above site, is being promoted by workshops under the auspices of e.g. IUPAC and CODATA and the GO-FAIR initiative. One aspect of this activity is to help identify examples of both good (FAIR) and indeed less good (unFAIR) research data as associated with contemporary scientific journal publications.

Here is one example I came across in 2017.[1]. The data associated with this article is certainly copious, 907 pages of it, not including data for 21 crystal structures! The latter is a good example of FAIR, being offered in a standard format (CIF) well-adapted for the type of data contained therein and for which there are numerous programs capable of visualising and inter-operating (i.e. re-using) it. The former is in PDF, not a format originally developed for data and one could argue is closer to the unFAIR end of the spectrum. More so when you consider this one 907-page paginated document contains diverse information including spectra on around 60 molecules. Thus the spectra are all purely visual; they are obviously data but in a form largely designed for human consumption and not re-use by software. The text-based content of this PDF does have numerous pattens, which lends itself to pattern recognition software such as OSCAR, but patterns are easily broken by errors or inexperience and so we cannot be certain what proportion of this can be recovered. The metadata associated with such a collection, if there is any at all, must be general and cannot be easily related to specific molecules in the collection. So I would argue that 907 pages of data as wrapped in PDF is not a good example of FAIR. But it is how almost all of the data currently being reported in chemistry journals is expressed. Indeed many a journal data editor (a relatively new introduction to the editorial teams) exerts a rigorous oversight over the data presented as part of article submissions to ensure it adheres to this monolithic PDF format.

You can also visit this article in Chemistry World (rsc.li/2HG7lTk) for an alternative view of what could be regarded as rather more FAIR data. The article has citations to the FAIR components, which is not published as part of the article or indeed by the journal itself but is held separately in a research data repository. You will find that at doi: 10.14469/hpc/3657 where examples of computational, crystallographic and spectroscopic data are available.

The workshop I allude to above will be held in July. Can I ask anyone reading this blog who has a favourite FAIR or indeed unFAIR example of data they have come across to share these here. We also need to identify areas simply crying out for FAIRer data to be made available as part of the publishing process beyond the types noted above. I hope to report back on both such feedback and the events at this workshop in due course.

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

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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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Metametadata: data about data about (chemical) data.

Saturday, April 16th, 2016

Scientists are familiar with the term data, at least in a scientific or chemical context, but appreciating metadata (meaning "after", or "beyond") is slightly more subtle, in the sense of using it to mean data about data. The challenge lies in clarifying where the boundary between data and its metadata lies and in specifying and controlling the vocabulary used for these metadata descriptions. Items in a chemical metadata dictionary might include e.g. subject classifications such as Organic Molecular Chemistry or identifiers such as InChIkey. But what could metametadata be? Here I briefly show some examples by way of illustration.

Let me start by defining a data repository as a store of both data and the metadata describing it. The metadata is to be exposed in a standard manner which allows it to be aggregated by other agencies. Nowdays, it is becoming common to identify such a data object together with its metadata using a persistent identifier, or DOI. But to decide if any particular repository and the data objects contained therein is generally useful to you, you need information about the metadata itself. Technically, this is defined using a schema[1] describing the metadata (which might e.g. identify any dictionaries used); hence metametadata. Now you need to store the metametadata and so I introduce the concept of a registry which does this. This metametadata object is itself assigned a DOI‡ and here I list these DOIs for a personal selection of some chemically oriented examples, in this case deriving from the largest registry of research data repositories re3data.org. You can search for your own entry at their site: http://service.re3data.org/search.

Data repository The repository metametadata DOI♣ Badge
Figshare 10.17616/R3PK5R[2] NH3-8
Zenodo 10.17616/R3QP53[3] NH3-8
Cambridge structure database 10.17616/R36011[4] NH3-8
Crystallographic open database 10.17616/R37S31[5] NH3-8

Oxford University Research Archive

10.17616/R3Q056[6] NH3-8

Open Notebook Science

10.17616/R3859D[7] NH3-8

Usefulchem

10.17616/R3Z89N[8] NH3-8

Chemotion

10.17616/R34P5T[9] NH3-8

Chemspider

10.17616/R38P4P[10] NH3-8

Chemical Database Service

10.17616/R36P42[11] NH3-8

Imperial College HPC data repository.

r3d100011965[12],[13] NH3-8

Imperial College SPECTRa repository.[14]

10.17616/R30316[15] NH3-8

Not all of the repositories listed in the table above assign formal DOIs to their data collections, meaning that the metadata for their entries cannot be aggregated in a searchable manner using e.g. search.datacite.org/ui (or search.datacite.org/api for the machine version). Currently, the metametadata does not fully carry this information, an aspect which I gather will be rectified in a future revision of the re3data schema.[1]

Importantly, both metadata and (repository) metametadata can be searched using APIs (application programmer interface), ensuring that the entire flow of meta information can be subject to automated software analysis rather than just visual inspections by a human.This should allow a rich and open infrastructure for handling research objects or data to be built up using hierarchical metadata. The examples above indeed show that the chemical space is already the largest component of the Natural Sciences space.

NH3-8

Although the edifice is still largely in its infancy, already I think we can start to see an alternative open approach emerging to "Googling" for data, or the even older traditional bespoke (i.e. non-open) services offered by commercial human-based abstractors of chemical metadata.

‡This DOI is information about the metametadata, and hence it is metametametadata, or m3data. Sorry! ♣The citations at the foot of this post are generated entirely automatically (by a WordPress plugin called Kcite) from the m3data associated with each entry, i.e. the DOI listed. Were the persistent identifier for the entry ever to be changed, this would propagate automatically to the citation, unlike the static entries in the table.

 

References

  1. J. Rücknagel, P. Vierkant, R. Ulrich, G. Kloska, E. Schnepf, D. Fichtmüller, E. Reuter, A. Semrau, M. Kindling, H. Pampel, M. Witt, F. Fritze, S. Van De Sandt, J. Klump, H. Goebelbecker, M. Skarupianski, R. Bertelmann, P. Schirmbacher, F. Scholze, C. Kramer, C. Fuchs, S. Spier, and A. Kirchhoff, "Metadata Schema for the Description of Research Data Repositories", 2015. https://doi.org/10.2312/re3.008
  2. Re3data.Org., "figshare", 2012. https://doi.org/10.17616/r3pk5r
  3. Re3data.Org., "Zenodo", 2013. https://doi.org/10.17616/r3qp53
  4. Re3data.Org., "The Cambridge Structural Database", 2013. https://doi.org/10.17616/r36011
  5. Re3data.Org., "Crystallography Open Database", 2013. https://doi.org/10.17616/r37s31
  6. Re3data.Org., "Oxford University Research Archive", 2014. https://doi.org/10.17616/r3q056
  7. Re3data.Org., "ONSchallenge", 2013. https://doi.org/10.17616/r3859d
  8. Re3data.Org., "UsefulChem", 2014. https://doi.org/10.17616/r3z89n
  9. Re3data.Org., "chemotion", 2013. https://doi.org/10.17616/r34p5t
  10. Re3data.Org., "ChemSpider", 2013. https://doi.org/10.17616/r38p4p
  11. Re3data.Org., "Chemical Database Service", 2012. https://doi.org/10.17616/r36p42
  12. https://doi.org/
  13. H. Rzepa, "Imperial College High Performance Computing Service Data Repository Metadata Schema", 2016. https://doi.org/10.14469/hpc/382
  14. J. Downing, P. Murray-Rust, A.P. Tonge, P. Morgan, H.S. Rzepa, F. Cotterill, N. Day, and M.J. Harvey, "SPECTRa: The Deposition and Validation of Primary Chemistry Research Data in Digital Repositories", Journal of Chemical Information and Modeling, vol. 48, pp. 1571-1581, 2008. https://doi.org/10.1021/ci7004737
  15. Re3data.Org., "SPECTRa Project", 2013. https://doi.org/10.17616/r30316

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Personal web pages on digital repositories.

Saturday, June 20th, 2015

The university sector in the UK has quality inspections of its research outputs conducted every seven years, going by the name of REF or Research Excellence Framework. The next one is due around 2020, and already preparations are under way! Here I describe how I have interpreted one of its strictures; that all UK funded research outputs (i.e. research publications in international journals) must be made available in open unrestricted form within three months of the article being accepted for publication, or they will not be eligible for consideration in 2020.

At the outset, I should say that one infrastructure to help researchers adhere to the guidelines is being implemented in the form of the Symplectic system. This allows a researcher to upload the final accepted version of a manuscript. At Imperial College, a digital repository called Spiral serves this purpose and also acts as the front end for collecting informative metadata to enhance discoverability. The final accepted version is then converted by the publisher into a version-of-record. This contains styling unique to the publisher and the content is subjected to further scrutiny by the authors as proof corrections. In an ideal world, these latter changes should also be faithfully propagated back to the final accepted version, as would all the supporting information associated with the article. Since most authors do not exactly enjoy the delights of proof corrections, this final reconciliation of the two versions may not always be assiduously undertaken.

I became concerned about the existence of two versions of any given scientific report and that the task of ensuring total fidelity in the content of both versions may negatively impact on the author’s time. Much better if the publisher could grant permission for the author to archive the version-of-record into a digital repository.

Some experiments were needed, and I decided to start them in reverse, by archiving my oldest publications. Since Symplectic now provides a system to do this, I began by using it. Symplectic identifies each publisher’s policies for archival, of which the most liberal are known as ROMEO GREEN. To quote from the definition, this colour allows the author to “archive pre-print and post-print or publisher’s version/PDF“. In an afternoon I had processed most of my ROMEO green articles. You know how it is sometimes, you do not read the fine print! And so the library soon informed me that archival of ROMEO GREEN was in fact only permitted on the author’s “personal web page”. Spiral, as an institutional repository, does not apparently constitute a personal web page for me and so none of my Symplectic submissions could be accepted for archival there.

Time to rethink the experiment. Firstly, I very much wanted the reprints to be held by a proper digital repository rather than a conventional web page. Why? I wanted my reprints to adhere as much as possible to FAIR: findable, accessible, interoperable and re-usable. Well, at least the first two of those (the last two relate more to data). A repository is designed to hold metadata in a formal and standards-based manner and metadata helps achieve FAIR. So I asked the Royal Society of Chemistry (as a ROMEO GREEN publisher) whether a personal web page hosted on a digital repository would qualify. I was soon informed  that I had proposed a neat solution here, and they couldn’t see an issue.

Now, all I had to do is find a repository where I could create such a personal web page. The chemistry department at Imperial College has for ten years hosted a DSpace repository called SPECTRa[1] which already has the functionality for individuals to create personal collections. I had also picked up on the increasing attention being given to Zenodo, like the World-Wide Web itself an offshoot of CERN (of large Hadron Collider fame) and born from the need for researchers to more permanently archive the outputs of their researches. These outputs include software, videos, images, presentations, posters, publications and (most obviously for CERN) datasets. I thought I would include them in my experiment as well. There results are summarised below.

DSpace-SPECTRa Zenodo
Community Henry Rzepa personal web page
reprint collection		 Rzepa personal computational
chemistry data and reprint page
Collection Royal Society of Chemistry reprints
Publication 10042/195577 10.5281/zenodo.18758[2]
Thesis 10044/1/20860[3] 10.5281/zenodo.18777[4]
Dataset 10.14469/ch/191342[5] 10.5281/zenodo.18632[6]
Harvesting OAI-ORE OAI-PMH

The last line of this table includes a link to another design feature of a repository, facilitating the ability to harvest the content. The ContentMine project (“The right to read is the right to mine!“) has shown how such harvesting of facts from the literature can be automated on a vast scale, and (IMHO) represents an example of those disruptive innovations that have the power to change the world forever. It also enshrines the idea that scientific facts funded by the public purse should be capable of being openly liberated from their containers. A harvestable repository seems an ideal container for achieving this.

My experiment is part of what might be seen as the increasingly subtle interplay between:

  • scientific authors, whose creative endeavour research is and without whom scientific publishers would not exist
  • publishers who create a business model from the content freely given them by authors but also (especially if a commercial publisher) need to be accountable to their shareholders.
  • the funding councils, many of whom now wish the outcomes of the research they fund to be openly available to all
  • the local libraries/administrators who have to adhere to/enforce all the rules contractually handed down to them by publishers whose direct customers they are, but who also need to serve their community of readers and authors.
  • researchers who would rather do research than fret about the above, and who would rather spend limited resources doing that research rather than diverting an increasing amount of their attention into the above system.
  • readers, who need unimpeded access to the research endeavours of others, but often have little influence on the policies and actions of all the other stakeholders, since they are  NOT considered customers (of the publishers).
  • etc. etc.

My experiment was in part designed to explore these rules, their interpretations and their boundaries. For the time being at least I seem to have found an arrangement that allows me to distribute versions-of-record of my own work, thanks to a generous and far-sighted learned society publisher. Watch this space!

References

  1. J. Downing, P. Murray-Rust, A.P. Tonge, P. Morgan, H.S. Rzepa, F. Cotterill, N. Day, and M.J. Harvey, "SPECTRa: The Deposition and Validation of Primary Chemistry Research Data in Digital Repositories", Journal of Chemical Information and Modeling, vol. 48, pp. 1571-1581, 2008. https://doi.org/10.1021/ci7004737
  2. H.S. Rzepa, and B.C. Challis, "The Mechanism Of Diazo-Coupling To Indoles And The Effect Of Steric Hindrance On The Rate Limiting Step", Zenodo, 1975. https://doi.org/10.5281/zenodo.18758
  3. H.S. Rzepa, "Hydrogen transfer reactions of indoles", 1974. http://doi.org/10044/1/20860
  4. H.S. Rzepa, "Hydrogen Transfer Reactions Of Indoles", Zenodo, 1974. https://doi.org/10.5281/zenodo.18777
  5. H.S. Rzepa, "C 25 H 34 Cl 1 N 3 O 1", 2015. https://doi.org/10.14469/ch/191342
  6. H.S. Rzepa, A. Lobo, M.S. Andrade, V.S. Silva, and A.M. Lourenco, "Chiroptical properties of streptorubin B – the synergy between theory and experiment.", 2015. https://doi.org/10.5281/zenodo.18632

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