Introducing InTRePIDs – In-Text Reference Pointer Identifiers

Rationale

Readers of this blog will be familiar with Open Citation Identifiers (OCIs), described in an earlier post and formally defined in [1]. OCIs enable bibliographic citations, treated as first class information entities, to be uniquely identified and referenced, and are used to identify the >624 million individual citations indexed in the latest release of COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations, as described in a recent post.

However, COCI and similar citation indexes do not provide any information about where within the citing paper a citation is generated, the textual contexts of the in-text reference pointers, or the reasons for including different in-text reference pointers denoting the same reference at different points within the text.

As explained in the preceding post describing the Open Biomedical Citations in Context Corpus funded by the Wellcome Trust and under development by OpenCitations, deep citation analysis requires a more nuanced approach to citations, which acknowledges that each in-text reference pointer that denotes a bibliographic reference in the reference list of a citing publication instantiates its own citation, as shown in Figure 1.

Figure 1. Citations between a citing paper and a cited paper instantiated both by the inclusion of a bibliographic reference within the reference list of the citing paper and by the inclusion within the text of the citing paper of one or more in-text reference pointers denoting that reference.

The pointer citations clearly involve the same cited publication as does the reference citation itself, but each has its own unique characteristics: the location and textual context of its in-text reference pointer within the text of the citing publication, and its particular rhetorical function which is determined by that context.

If the reference citation is open (as defined in [2]) and identified by an OCI, each in-text reference pointer related to that citation can be identified uniquely using an In-Text Reference Pointer Identifier (InTRePID).

InTRePIDs facilitate in-depth scholarship on in-text reference pointer locations and citation functions, and fine-grained analysis of the relationships between publications, by making it possible

  • to identify each in-text reference pointer with a unique PID,
  • to distinguish references that are cited only once from those that are cited multiple times,
  • to see which references are cited together (e.g. in the same sentence or within an in-text reference pointer list),
  • to determine from which section(s) of the article references are cited (e.g. Introduction, Methods, Discussion), and, potentially,
  • to determine the rhetorical function of the citations from analysis of their textual contexts, by the application of natural language processing, machine learning and artificial intelligence techniques to conduct sentiment analysis on the citation contexts.

Definition of an InTRePID

An InTRePID is composed of two parts separated by an oblique stroke

intrepid:<oci-numerals>/<ordinal><total>

where

  • <oci-numerals> is the numerical part of the OCI uniquely identifying the particular open citation to which the in-text reference pointer and its denoted bibliographic reference relate. Thus an InTRePID can be assigned for any in-text reference pointer that relates to an open citation for which a valid OCI has been assigned;
  • <ordinal> identifies the nth occurrence of an in-text reference pointer within the text of the citing paper relating to that citation; and
  • <total> defines the total number of in-text reference pointers denoting that bibliographic reference within the citing paper.

For example, intrepid:070433-070475/46 is a valid InTRePID for an in-text reference pointer defined within the OpenCitations Citations in Context Corpus.

A formal definition document for the InTRePID is given in [3].

Exemplar in-text reference pointers

Consider the following citing paper:

Zou, J. et al. (2020). Phenotypic and genotypic correlates of penicillin susceptibility in nontoxigenic Corynebacterium diphtheriae, British Columbia, Canada, 2015–2018. Emerging Infectious Diseases, 26: 97-103. https://doi.org/10.3201/eid2601.191241

This paper contains six in-text reference pointers denoting Reference 13 in the reference list:

13. Lowe, C. et al. (2011). Cutaneous diphtheria in the urban poor population of Vancouver, British Columbia, Canada: a 10-year review. J. Clinical Microbiology 49: 2664-2666. https://doi.org/10.1128/JCM.00362-11

The InTRePIDs for these pointers are recorded within the OpenCitations Biomedical Citations in Context Corpus, together with the corpus identifiers and DOIs of the citing and cited papers, as shown in the excerpt presented in Figure 2.

Figure 2. An excerpt from the OpenCitations Biomedical Citations in Context Corpus, showing highlighted the InTRePIDs for the six in-text reference pointers within Zou, J. et al. (2020) denoting Reference 13, the reference to Lowe, C. et al. (2011), together with the internal corpus identifiers for each in-text reference pointer, and the corpus identifiers and DOIs for the citing and cited papers.

Of these six in-text reference pointers, having InTRePIDs intrepid:070433-070475/1-6 to intrepid:070433-070475/6-6, the first and the fourth of these, together with their document locations, their embedding sentences, their in-text reference pointer lists, and their InTRePIDs, chosen as examples, are as follows:

Introduction. “Nontoxigenic strains have been shown to have epidemic potential, causing infections in persons afflicted by homelessness, alcohol abuse, and injection drug use (9,13–15).” (intrepid:070433-070475/1-6)

Discussion. “We also noted ST5 and ST32 in our review from downtown Vancouver during 1998–2007 (13).” (intrepid:070433-070475/4-6)

The first of these discusses those people most susceptible to diphtheria infection, while the other discusses which multilocus sequence types (STs) of C. diphtheriae were found, thus relating to the organism causing the infection rather than to the infected individuals. The rhetorical function of these two in-text reference pointers is quite distinct.

To permit this information to be recorded within the OpenCitations Citations in Context Corpus, extensions were required to the OpenCitations Data Model, a new extended version of which was recently published [4], as described in a related blog post.

The OpenCitations InTRePID Resolution Service

To support the use of InTRePIDs to identify in-text reference pointers, OpenCitations has recently developed an InTRePID Resolution Service (currently in ‘beta’ in its development cycle), which is running at http://opencitations.net/intrepid. A screenshot of this service is shown in Figure 3.

Figure 3. A screenshot of the user interface of the InTRePID Resolution Service.

In addition to using the Web user interface shown in Figure 3, InTRePIDs can be entered into this resolution service in the form of resolvable URIs, e.g.

http://opencitations.net/intrepid/070433-070475/4-6

As shown in Figure 4, the OpenCitations InTRePID Resolution service returns metadata concerning the in-text reference pointer identified by the InTRePID, and the bibliographic reference that it denotes, from which further information about the citation and the citing and cited publications may be obtained by following the links provided.

Figure 4. A screenshot of the Web page displaying metadata returned by the InTRePID Resolution Service.

Note that as well as rendering this information in HTML on a web page, the resolution service can also provide it in a variety of machine-readable formats.

Conclusion

InTRePIDs, which enable the identification of individual in-text reference pointers, and the InTRePID Resolution Service, are new services from OpenCitations that will facilitate scholarship on the textual contexts and rhetorical functions of such in-text reference pointers, and of the citations that they instantiate.

InTRePIDs were first announced on 30th January 2020 at PIDapalooza 2020 in Lisbon, the Open Festival of Persistent Identifiers.

References

[1] Silvio Peroni and David Shotton (2019): Open Citation Identifier: Definition. Figshare. https://doi.org/10.6084/m9.figshare.7127816.v2

[2] Silvio Peroni and David Shotton (2018). Open Citation: Definition. Figshare. https://doi.org/10.6084/m9.figshare.6683855

[3] David Shotton, Marilena Daquino and Silvio Peroni (2020). In-Text Reference Pointer Identifier: Definition. Figshare. https://doi.org/10.6084/m9.figshare.11674032

[4] Marilena Daquino, Silvio Peroni and David Shotton (2019). The OpenCitations Data Model. Version 2.0. Figshare. https://doi.org/10.6084/m9.figshare.3443876

Cite this article as: davidshotton, "Introducing InTRePIDs – In-Text Reference Pointer Identifiers," in OpenCitations blog, 30/01/2020, https://opencitations.hypotheses.org/1011.

Citations as First-Class Data Entities: Open Citation Identifiers

Requirements for citations to be treated as First-Class Data Entities

In my introductory blog post, I listed five requirements for the treatment of citations as first-class data entities.  The fourth of these requirements is that they must be identifiable using a global persistent identifier scheme.

At the recent PIDapalooza Conference on persistent identifiers, held in Girona, Spain, I launched the Open Citation Identifier (abbreviated OCI, in line with DOI), the new persistent identifier for citations [1].

In this post, I describe the Open Citation Identifier scheme, created and operated by OpenCitations, which supports the assignment of Open Citation Identifiers not only to the citations present in the OpenCitations Corpus (OCC) but also to open citations present in other bibliographic databases.

Structure and syntax of the Open Citation Identifier

Each OCI has a simple structure: oci:number-number, where “oci:” is the identifier prefix.

OCIs for citations stored within the OpenCitations Corpus are constructed by combining the OpenCitations Corpus local identifiers for the citing and cited bibliographic resources, separating them with a dash.  (For definition of OCC local identifiers, see the OpenCitations Data Model).

For example, oci:2544384-7295288 is a valid OCI for the citation between two papers stored within the OpenCitations Corpus, the first number being the OCC local identifier for the citing bibliographic resource [2], and the second being the OCC local identifier for the cited bibliographic resource [3], these bibliographic resource local identifiers being unique within the OCC.  [Note: Supplier prefixes are omitted from OCC local identifiers of bibliographic resources ingested into the OpenCitations Corpus prior to February 2018, but will be included within all OCC local identifiers of bibliographic resources ingested into Corpus after that date.]

OCIs for external resources identifies by numerical identifiers

OCIs can also be created for bibliographic resources described in an external bibliographic database, if they are similarly identified there by identifiers having a unique numerical part.  For example, the OCI for the citation that exists between Wikidata resources Q27931310 (the citing resource, [4]) and Q22252312 (the cited resource, [5]) is oci:0102793131001022252312, where “010” is the assigned OCC supplier prefix for Wikidata.  (The colours here and below are added simply for clarity.)

The OCC supplier prefix consist of a positive number (following the pattern “nnn”, where “nnn” is a string of numerals of variable length which includes no zeros), enclosed between two zeros (e.g. “0420”).  The list of all assigned OCC supplier prefixes is given at https://github.com/opencitations/oci/blob/master/suppliers.csv.

OCIs for citations between resources identified by DOIs

OCIs can also be created for bibliographic resources described in external bibliographic database such as Crossref or DataCite where they are identified by alphanumeric Digital Object Identifiers (DOIs), rather than purely numerical strings.

To achieve this, each case-insensitive DOI is first normalized to lower case letters. Then, after omitting the initial “doi:10.” prefix, the alphanumeric string of the DOI is converted reversibly to a pure numerical string using the simple two-numeral lookup table for numerals, lower case letters and other characters presented at https://github.com/opencitations/oci/blob/master/lookup.csv. For example, using this lockup table, “1” becomes “01”, “2” becomes “02”, “a” becomes “10”, “b” becomes “11”, and “/” becomes “36”.  To the resulting number, the appropriate OCC supplier prefix is then added, to clearly identify its provenance.

A citation documented in Crossref exists between the two publications [3] and [6], which are there identified by the DOIs doi:10.1108/jd-12-2013-0166 and doi:10.1371/journal.pcbi.1000361.  We can thus create an OCI for this Crossref citation by using numerical representations of the two DOIs. These numerical representations are:

0200101000836191363010263020001036300010606

and

02001030701361924302723102137251211183701000000030601

where the initial “020” in each case is the assigned OCC supplier prefix for Crossref.

From these two numerical representations of DOIs, the OCI for the Crossref citation between these two paper is easily constructed, and is:

oci:0200101000836191363010263020001036300010606-02001030701361924302723102137251211183701000000030601

While this is long for an identifier, it should be remembered that it will be processed computationally, and is not intended for human readability.

In this way, Crossref OCIs can be assigned to all ~350 million open references within Crossref in which the cited paper as well as the citing paper has a DOI [7].

OCIs for the same citation recorded within different databases

If a citation is recorded in more than one bibliographic database, a separate OCI can be created for each instance, each OCI having a distinct supplier prefix and being specific to that database.

Thus, in addition to the Crossref OCI created from DOIs and described above for the citation from [3] to [6], a Wikidata OCI exists for the same citation recorded within Wikidata, having the form oci:01024260641-01021092566.

Upon resolution of an OCI, the Open Citation Identifier Resolution Service will pull metadata only from the database specified by the supplier prefix of the OCI.  Details of the Open Citation Identifier Resolution Service are given in the next blog post.

It is important to note that an OCI can only be used to specify a citation between a citing and a cited publication which is actually recorded within a bibliographic database.  For this reason, the OCI “oci:7295288-3962641” shown below the second diagram in the introductory blog post to this series is presently invalid.  While the OpenCitations Corpus has metadata describing both bibliographic resources [3] and [6], it has not yet ingested the reference list for the first bibliographic resource [3] (which has the OCC local identifier 7295288), having information about it only from a reference within a third paper, with no information about the references [3] itself contains.  As a result, at present OCC has no record that a citation actually exists between [3] and the second bibliographic resource [6] (which has the OCC local identifier 3962641).

Representing OCIs in RDF

To permit the description of OCIs in RDF, “oci” has been added as a new member of the class datacite:ResourceIdentifierScheme within the DataCite Ontology.

The resolvable URL for any citation identified by a OCI has the form “https://w3id.org/oc/virtual/ci/nnn-mmm”, where nnn-mmm represents the OCI with its “oci:” prefix removed. Currently, we are able to return the RDF description of all the citations contained in the OpenCitations Corpus and Wikidata. We are working to extend the coverage so as to include other datasets, e.g. Crossref.

References

[1]     David Shotton (2018). Citations as first-class data entities. Open Citation Identifiers.  Conference presentation. PIDapalooza 2018, Girona, 23-23 January 2018. https://doi.org/10.6084/m9.figshare.5844972

[2]     Armen Yuri Gasparyan, Marlen Yessirkepov et al. (2015). Preserving the integrity of citations and references by all stakeholders of science communication.  J. Korean Med. Sci. 30:1545-1552. (English.)  https://doi.org/10.3346/jkms.2015.30.11.1545

[3]     Silvio Peroni, Alexander Dutton, Tanya Gray and David Shotton (2015). Setting our bibliographic references free: towards open citation data. Journal of Documentation, 71 (2): 253-277.  https://doi.org/10.1108/jd-12-2013-0166

[4]     Daniel K. Bricker, Eric B. Taylor et al. (2012). A Mitochondrial Pyruvate Carrier Required for Pyruvate Uptake in Yeast, Drosophila, and Humans. Science 337: 96-100.
https://doi.org/10.1126/science.1218099

[5]     Douglas Hanahan and Robert A. Weinberg (2011). Hallmarks of cancer: the next generation.  Cell 144: 646–674.  https://doi.org/10.1016/j.cell.2011.02.013

[6]     David Shotton, Katie Portwin, Graham Klyne and Alistair Miles (2009).  Adventures in semantic publishing: exemplar semantic enhancement of a research article. PLoS Computational Biology 5: e1000361. http://dx.doi.org/10.1371/journal.pcbi.1000361

[7]     Daniel Ecer (2017). Crossref Data Notebook (updated). Available at https://elifesci.org/crossref-data-notebook

Cite this article as: davidshotton, "Citations as First-Class Data Entities: Open Citation Identifiers," in OpenCitations blog, 12/03/2018, https://opencitations.hypotheses.org/826.

 

Pensoft Journals policy and author guidelines on data publication and citation

In a recent blog post, Heather Piwowar, in discussing the advantages of citing datasets in the reference list of the article, said “No journals have standardized on this approach so far”. However, Pensoft Journals, a publisher that specializes in publishing biodiversity and biological systematics papers, and that has taken the lead in promoting the publication of datasets with DOIs, has exactly such a policy.

Recently, in response to my Data Citation Best Practice Discussion Document [1] discussed in the preceding blog post, I was invited to work with Pensoft Journals to contribute to and help revise their now-published Data Publishing Policies and Guidelines for Biodiversity Data [2].  This 34-page paper has a three-page section on how to cite data in Pensoft Journals.

While recognising that citations of Genbank and similar bioinformatics datasets are by custom made by placing the database accession number somewhere in the text, with no entry in the reference list of the article, we make the following generic recommendation:

“Data citations may relate either to the author’s own data, or to data created and published by others (“third-party data”). In the former case, the dataset may have been previously published, or may be published for the first time in association with the article that is now citing it. All these types of data should, for consistency, be cited in the same manner.

“As is the norm when citing another research article, any citation of a data publication, including a citation of one’s own data, should always have two components:

  • An in-text citation statement containing an in-text reference pointer that directs the reader to a formal data reference in the paper’s reference list.

and

  • A formal data reference within the article’s reference list.

“The data reference in the article’s reference list should contain the minimal components recommended in the DataCite Metadata Kernel v2.0 specification. In DataCite terms: Creator PublicationYear Title Publisher Identifier; alternatively (but meaning the same thing): Author PublicationYear Title DataRepositoryName DOI. These components should be presented in whatever format and punctuation style the journal specifies for its references. The following example demonstrates in general terms what is required.

“In-text citation:

This paper uses data from the [name] data repository at http://dx.doi.org/***** (Jones et al. 2008a), first described in Jones et al. 2008b.

“Data reference in reference list:

Jones A, Bloggs B, Smith C (2008a). Title of data package. Repository name. doi:*****.

“Article reference in reference list:

Jones A, Saul D, Smith C (2008b). Title of journal article. Journal Volume: Pages. doi:###. ”

Pensoft also recommends that the in-text data citation statement in Pensoft journals should be included in the body of the paper, in a separate section named Data Resources situated after the Material and Methods section.  More details are given in the paper [2].

Furthermore, Pensoft has reached an agreement for cooperation in data hosting and developing of data publishing workflows with GBIF, the Global Biodiversity Information Facility, with the Dryad Data Repository and with the Consortium for Barcode of Life.

Clearly, these Pensoft data citation recommendations, which work fine for on-line journals without a numerical limit on the number of citations, would not be feasible in journal articles with a strict limit to the number of citations, which is why Heather’s emphasis of exploring alternative ways for data citation in such cases is important.

[1]     David Shotton (2011) Data Citation Best Practice Discussion Document. Google Docs. https://docs.google.com/document/d/1kF8-faB72l4dKTLEyx6Z5cIabk68GrJ9GraCtWnK0qQ/edit?hl=en_GB&authkey=CPPW46wL#.  

[2]     Penev L, Mietchen D, Chavan V, Hagedorn G, Remsen D, Smith V, Shotton D (2011). Pensoft Data Publishing Policies and Guidelines for Biodiversity Data. Pensoft Publishers, http://www.pensoft.net/J_FILES/Pensoft_Data_Publishing_Policies_and_Guidelines.pdf.

Cite this article as: davidshotton, "Pensoft Journals policy and author guidelines on data publication and citation," in OpenCitations blog, 30/06/2011, https://opencitations.hypotheses.org/156.

How to cite data

As an approach towards developing best practice for data citation, I recently wrote a Data Citation Best Practice Discussion Document that is available on Google Docs, and that I have now slightly revised to Version 2 [1].

In that document, I first compared what is recommended by DataCite [2] and by Altman and King [3] with what currently practised by the Dryad Data Repository and what presently occurs ‘in the wild’ in a handful of journal articles that reference Dryad datasets.  I then proposed some ‘internal’ recommendations for Dryad to adopt, and concluded with draft Data Citation Best Practice Recommendations.  As I say in the preface to the document:

“Since Dryad is pioneering data management in terms of data resources that are linked to journal articles, it is to be hoped that by first developing citation best practice in the Dryad context we can thereby catalyse its wider spread.  If we can thus agree what such best practice should be among the Dryad community and implement such best practice proposals, we can then promote such practices within the wider scholarly community.”

I realized that much of the confusion and disagreement concerning the best method of citing data resources within earlier e-mail threads resulted from a conflation of ideas about two entities which in the conventional citation of journal articles are quite distinct:

  • the in-text citation containing an in-text reference pointer, e.g. “this paper builds upon the work of Jones et al. [15].”     and
  • the actual reference to Jones et al. within the article’s reference list, e.g. “[15] Jones A, Bloggs B and Smith C (2008). Title. JournalName
    14:132-134. doi:*****.”

Thus, in an e-mail I wrote on 27 April, where I said

“Excellent, but what we really want is for the data citations to be included in the reference list along with the bibliographic citations, following the DataCite model: Creator (PublicationYear): Title. Version. Publisher. ResourceType. Identifier “

. . . I should also have stressed the need for explicit in-text citations that denote such references.

All that is explained within the Google Docs paper.  In that paper I also proposed having a separate Data Resources section within the body text of a journal article, in which data resource citations can be gathered.  That does not preclude these resources also being cited, where appropriate, within the Methods and Materials or Results sections of the paper, but is designed to put data resource citations “on the map”, so to speak, as important new publication performative acts.

It is not appropriate, in my mind, for data citations to be included in the Acknowledgements section of a paper, which is designed for acknowledging contributions to the work from people and funding agencies, even if Thomson Reuters has developed methods to parse such entries, since they also have well-established mechanisms for harvesting proper (data) references from the reference list.

All the ontological terms required to mark up in-text reference pointers and their textual contexts, references, reference lists, etc., to permit automated detection and harvesting of data citations and references, are available as RDF within the SPAR (Semantic Publishing and Referencing) Ontologies (http://purl.org/spar/), which were designed precisely to facilitate such work.

Since writing my Data Citation Best Practice Discussion Document, I was invited (on a purely voluntary non-commercial basis, I should add!) to work with Pensoft Journals, a publisher that specialises in publishing biodiversity and biological systematics papers, and that has taken the lead in promoting the publication of datasets with DOIs, to contribute to and help revise their now-published Data Publishing Policies and Guidelines for Biodiversity Data [4].  This 34-page paper has a three-page section on how to cite data in Pensoft Journals, which I discuss in the next blog post, and which I am pleased to say includes all the recommendations discussed above.

[1]     David Shotton (2011) Data Citation Best Practice Discussion Document. Google Docs. https://docs.google.com/document/d/1kF8-faB72l4dKTLEyx6Z5cIabk68GrJ9GraCtWnK0qQ/edit?hl=en_GB&authkey=CPPW46wL#.

[2]    The DataCite Metadata Kernel version 2.0 (2011). http://datacite.org/schema/DataCite-MetadataKernel_v2.0.pdf.

[3]    Micah Altman and Gary King (2007). A proposed standard for the scholarly citation of quantitative data. D-Lib Magazine. 13. http://www.dlib.org/dlib/march07/altman/03altman.html.

[4]     Penev L, Mietchen D, Chavan V, Hagedorn G, Remsen D, Smith V, Shotton D (2011). Pensoft Data Publishing Policies and Guidelines for Biodiversity Data. Pensoft Publishers, http://www.pensoft.net/J_FILES/Pensoft_Data_Publishing_Policies_and_Guidelines.pdf.

Cite this article as: davidshotton, "How to cite data," in OpenCitations blog, 30/06/2011, https://opencitations.hypotheses.org/153.

Questions of granularity – Dryad’s use of DataCite DOIs for data citation, and the Annotation Ontology

DataCite is an international organisation, founded in 2009, which promotes the use of DOIs (Digital Object Identifiers) for published datasets, in order to establish easier access to research data, to increase acceptance of research data as legitimate contributions in the scholarly record, and to support data archiving to permit results to be verified and re-purposed for future study.

Its founding members were the British Library; the Technical Information Center of Denmark; TU Delft Library; the National Research Council’s Canada Institute for Scientific and Technical Information (NRC-CISTI); California Digital Library; Purdue University; and the German National Library of Science and Technology. Since its foundation, it has been joined by several other leading organisations from around the world, and it therefore provides a stable basis for the ongoing use of DOIs for data.

This recent availability of DOIs from DataCite for the identification of data entities has made all the difference to data repositories wishing to give unique global identifiers to their data holdings, since DOIs are widely recognised and respected throughout the academic world, because of their widespread prior use for identifying journal articles, made possible by CrossRef.

However, in their recent discussion paper Data Citation and Linking, published on 8th June 2011, Alex Ball and Monica Duke of UKOLN at the University of Bath ask:

“At what granularity should data be made citable? If single datasets are given identifiers, what about collections of datasets, or subsets of data?”

Individual data files and metadata documents will, of course, have their own unique internal identifiers within any data repository, but may not have externally resolvable identifiers such as DOIs.  Practice varies.

This post is to explain how DOIs are employed in the Dryad Data Repository, that specializes in publishing data linked to peer-reviewed biological journal articles, since it is both elegant and addresses at least some of the issues raised by Alex and Monica.

The Dryad DOI usage policy is described at https://www.nescent.org/wg_dryad/DOI_Usage, and involves assigning unique DOIs to each version of every data package, and to each version of every data file, in a principled and easy-to-understand manner. In summary:

  • Each data package is given a DataCite DOI, which can be versioned by adding “.2”, “.3”, etc. after the original DOI to create new DOIs for new versions of the same data package.
  • Within each data package, each data file has a unique DOI defines by suffixing the data package DOI with “/1”, “/2”. etc., with versions indicated as for data packages.

Thus the third version of the second data file in the second version of a Dryad data package would have a DOI of the form doi:10.5061/dryad.1234.2/2.3.

One might argue that it would result in an awfully large number of DOIs if a single data package was made up of thousands of data files. True, but numbers themselves are limitless and free, and the cost of a DataCite DOI is small relative to the cost of data creation and preservation. The real problem at present is lack of identifiable, citable data entities within repositories – to have so many that the cost of DOIs becomes an issue should be regarded as an achievement, not a problem!

Dryad does not have a mechanism for assigning identifiers to a portion of a data file (“a subset of data”), and DOIs are probably not the correct identifiers for that purpose, since they are primarily designed for citation and resource discovery.

A more appropriate method for identifying portions of a data file, or of any other digital object or document, is to use the Annotation Ontology (AO) developed by Paolo Ciccarese of Harvard University, described at http://code.google.com/p/annotation-ontology/wiki/Homepage. AO can be used to identify and annotate portions of a wide variety of resources such as HTML, PDF, Word, Excel, XML documents, images, videos, databases, web services, experimental data and metadata files. Paolo is currently working with a group in Harvard that focuses on biodiversity, who are using OA to address databases and data, and he anticipates publishing version 2.0 of AO in September.

Cite this article as: davidshotton, "Questions of granularity – Dryad’s use of DataCite DOIs for data citation, and the Annotation Ontology," in OpenCitations blog, 30/06/2011, https://opencitations.hypotheses.org/148.

DataCite2RDF – Mapping DataCite Metadata Scheme Terms to ontologies

The DataCite Metadata Kernel version 2.0 [1] specifies the minimal metadata, and optional metadata, that should accompany a DataCite DOI for the identification of a published data entity. Within the Metadata Kernel document there is an XML mapping of these metadata terms, using DCMI Metadata Terms, and an example encoded in XML.

Silvio Peroni and I recently published a mapping of the DataCite metadata elements to RDF using ontology terms [2], in order to enable data repositories to publish DataCite metadata in RDF as Open Linked Data, enabling these metadata to be understood programmatically and integrated automatically with similar data from elsewhere.

Our mapping covers all the main terms, and the Relation Type sub-properties that describe the relationship of the related resource to the resource being registered, but does not address DataCite sub-terms, e.g. 2.2.1 nameIdentifierScheme.

Wherever possible, commonly used Dublin Core Elements, DCMI (Dublin Core Metadata Initiative) Metadata Terms, FOAF (Friend of a Friend Vocabulary) and PRISM (Publishing Requirements for Industry Standard Metadata) terms have been used.

These have been supplemented, as appropriate, by terms:

from FRBR (Functional Requirements for Bibliographic Records),

from the following SPAR (Semantic Publishing and Referencing) Ontologies:

                CiTO, Citation Typing Ontology

                FaBiO, FRBR-aligned Bibliographic Ontology, and

CiTO4Data, an extension of CiTO for datasets that provides the properties cito4data:compiles and cito4data:isCompiledBy that the DataCite Metadata Kernel requires;

and from a new DataCite Ontology (http://purl.org/spar/datacite/) that we created to provide the following four object properties lacking in other Ontologies:

                    datacite:hasPrimaryIdentifier

                    datacite:hasAlternateIdentifier

                    datacite:hasRelatedIdentifier

                    datacite:hasPersonalIdentifier

Use of DCMI Metadata Terms in RDF

An object property has a class or a URI as its object, while a data property has a literal (e.g. text, number, date) as its object, and may have a W3C XML Schema Definition Language (XSD) datatype qualifier, e.g. ^^xsd:date. (See http://www.w3.org/TR/xmlschema11-2/).

Many Dublin Core properties are not formally specified to be one or another, leading to potential confusion. In the following mapping, Dublin Core Elements are always used as data properties, while Dublin Core Metadata Initiative Metadata Terms are used either as data properties or as object properties, as helpfully specified by the Max Planck Digital Library in their document entitled How to use DCMI Metadata as linked data.  

In our DataCite2RDF mapping document [2], alternative mappings are given where appropriate, separated by semi-colons.  Both dc: and dcterms: properties are listed.  Preferred terms are shown bold.  RDF statements are given in Turtle notation.

Accompanying this DataCite2RDF mapping document, we published as Google docs both an RDF mapping of the DataCite XML example, and an RDF mapping of the metadata for a Dryad repository holding, showing how DataCite2RDF can be used for real data.

We welcome feedback on these documents: <david.shotton@zoo.ox.ac.uk> and <speroni@cs.unibo.it>.

[1]    The DataCite Metadata Kernel version 2.0 (2011). http://datacite.org/schema/DataCite-MetadataKernel_v2.0.pdf.

[2]     David Shotton and Silvio Peroni (2011). Mapping DataCite Metadata Scheme Terms (v2.0) to ontologies (DataCite2RDF). Google docs. https://docs.google.com/document/d/1paJgvmCMu3pbM4in6PjWAKO0gP-6ultii3DWQslygq4/edit?authkey=CMeV3tgF&hl=en_GB.

Cite this article as: davidshotton, "DataCite2RDF – Mapping DataCite Metadata Scheme Terms to ontologies," in OpenCitations blog, 30/06/2011, https://opencitations.hypotheses.org/139.

 

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