Posts Tagged ‘Records management’
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:
- (meta)data are retrievable by their identifier using a standardized communication protocol.
- the protocol is open, free and universally implementable.
- the protocol allows for an authentication and authorization procedure.
- metadata are accessible, even when the data are no longer available.
- 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:
- <relatedIdentifier relatedIdentifierType="URL" relationType="HasMetadata" relatedMetadataScheme="ORE"schemeURI="http://www.openarchives.org/ore/
">https://data.hpc.imperial.ac.uk/resolve/?ore=5496</relatedIdentifier>
- <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
- 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
Tags:Academic publishing, automatic processing, Data management, Digital Object Identifier, EIDR, FAIR data, Findability, Identifiers, Information, Information architecture, Information science, Knowledge, Knowledge representation, metadata, mining, Open Archives Initiative, RDF, Records management, representative, standardized communication protocol, Technical communication, Technology/Internet, Web design, Written communication, XML
Posted in Chemical IT | No Comments »
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:
- (meta)data are retrievable by their identifier using a standardized communication protocol.
- the protocol is open, free and universally implementable.
- the protocol allows for an authentication and authorization procedure.
- metadata are accessible, even when the data are no longer available.
- 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:
- <relatedIdentifier relatedIdentifierType="URL" relationType="HasMetadata" relatedMetadataScheme="ORE"schemeURI="http://www.openarchives.org/ore/
">https://data.hpc.imperial.ac.uk/resolve/?ore=5496</relatedIdentifier>
- <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
- 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
Tags:Academic publishing, automatic processing, Data management, Digital Object Identifier, EIDR, FAIR data, Findability, Identifiers, Information, Information architecture, Information science, Knowledge, Knowledge representation, metadata, mining, Open Archives Initiative, RDF, Records management, representative, standardized communication protocol, Technical communication, Technology/Internet, Web design, Written communication, XML
Posted in Chemical IT | No Comments »
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.
- 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!
-
- This metadata indicates “references-count":22, which is a bit odd since 37 are actually cited in the article. It is not immediately obvious why there is a difference of 15 (I am querying this with the editor of the journal). None of the references themselves are included in the metadata record, because the publisher does not currently support liberation using Open References, which makes it difficult to track the missing ones down.
- Of the 37 citations listed in the article itself,[1] #22, #24 and #37 are different, being citations to different data sources. The first of these, #22 is an explicit reference to its data partner for the article.
- An alternative method of invoking a metadata record;
https://data.datacite.org/application/vnd.datacite.datacite+xml/10.1021/acsomega.8b03005
retrieves a sub-set of the article metadata available using the CrossRef query,‡ but again with no included references and again nothing for the data citation #22.
- 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]
- 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:
- Should a metadata record be considered a living object, capable of being updated to reflect new information received after its first publication?
- 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)?
- 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?
- 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
- 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
- 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
- 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
- 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
- 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
Tags:Academic publishing, American Chemical Society, author, Business intelligence, Company: DataCite, CrossRef, data, Data management, DataCite, editor, EIDR, Information, Information science, JSON, Knowledge representation, Metadata repository, Records management, Technology/Internet, The Metadata Company
Posted in Chemical IT | No Comments »
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;
- References
- Open References
- ORCID IDs
- Text mining URLs
- 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
- 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
Tags:Aaron Swartz, Academic publishing, API, Business intelligence, CrossRef, data, Data management, Elsevier, favourite publisher, Identifiers, Information, Information science, Knowledge, Knowledge representation, metadata, mining, ORCiD, PDF, Pre-exposure prophylaxis, Publishing, Publishing Requirements for Industry Standard Metadata, Records management, Research Object, Scholarly communication, Scientific literature, search engine, social media, Technical communication, Technology/Internet, text mining, Written communication, XML
Posted in Interesting chemistry | 1 Comment »
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;
- References
- Open References
- ORCID IDs
- Text mining URLs
- 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
- 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
Tags:Aaron Swartz, Academic publishing, API, Business intelligence, CrossRef, data, Data management, Elsevier, favourite publisher, Identifiers, Information, Information science, Knowledge, Knowledge representation, metadata, mining, ORCiD, PDF, Pre-exposure prophylaxis, Publishing, Publishing Requirements for Industry Standard Metadata, Records management, Research Object, Scholarly communication, Scientific literature, search engine, social media, Technical communication, Technology/Internet, text mining, Written communication, XML
Posted in Interesting chemistry | 1 Comment »