Posted on August 10th 2022 by Chiara Di Giambattista
More than a year ago, Ginny Hendricks, Director of Member & Community Outreach for Crossref, and a valued member of the OpenCitations International Advisory Board, published on the Crossref blog the post “The road ahead: our strategy through 2025”. In order to describe all Crossref’s principles and activities, Ginny presented the Crossref strategic planning framework as a diagram summarizing Crossref’s statements, key messages and truths. The clarity and immediacy of the diagram were such that we adapted it to present OpenCitations’ own statements and goals. The resulting poster “OpenCitations – what does the future hold?” was presented by our Director David Shotton at the OASPA2021 conference, and can be found in this blog post.
Although the poster offered a wide overview of OpenCitations values, unique traits, benefits and plans, it differed slightly from Ginny’s original diagram, in particular because it lacked a “Mission Statement”, scattering the relevant information within the “Values” and “Principles” boxes. Indeed, at that time (September 2021), we didn’t have a clearly defined Mission Statement.
Nevertheless, the creation of that poster was crucial in helping us start to articulate more clearly the purpose and meaning of OpenCitations. As David underlined in his post “From little acorns…a retrospective on OpenCitations”, since 2018 OpenCitations activities have progressively increased and, with them, the number of related journal articles, conference papers and technical definitions. OpenCitations’ involvement in international networks and collaborations (such as SCOSS and the OpenAIRE-Nexus project), together with our need of identifying and reaching out to new stakeholders to assure OpenCitations’ development and sustainability, has made it necessary to publicly define OpenCitations’ mission, unique strengths and next developmental steps.
which together provide a summary of why we exist and where we are heading.
We are particularly proud of the definition of OpenCitations’ primary mission, namely
“to harvest and openly publish accurate and comprehensive metadata describing the world’s academic publications and the scholarly citations that link them, and to preserve ongoing access to this information by secure archiving.”
The Mission Statement also presents brief descriptions of the OpenCitations context, our vision, our value proposition and our relationship with the community and stakeholders.
The Uniqueness of OpenCitations provides the answer to the question ‘Why choose to use OpenCitations?’, and is a detailed presentation of OpenCitations’ benefits.
OpenCitations – Present Status and Future Plans summarizes OpenCitations’ ongoing activities, that can be quickly visualized on our public roadmap. It also introduces the OpenCitations Working Groups, served by the members of the OpenCitations International Advisory Board, which are currently working on the themes of governance evolution and community building, with the common purpose of driving OpenCitations along the path from being a ‘sustainable infrastructure’ (in POSI terms) to being an enduring community led andfinancially sustained infrastructure.
In fulfilling our mission and reaching our goals, the support and vital interest of our community members is fundamental. We request that you, as a member of our community, provide us with feedback on these documents and the ideas they contain, or indeed to ask for clarifications, to help us improving our mission and our communications to explain it. You can reach us here: email@example.com.
Cite this article as: Chiara Di Giambattista, "New documents that present OpenCitations’ mission, unique benefits, present status and future plans," in OpenCitations blog, 10/08/2022, https://opencitations.hypotheses.org/2498.
Now that OpenCitations is hosting over one billion freely available scholarly bibliographic citations, this is perhaps an opportune moment to look back to the start of this initiative. A little over eleven years ago, on 24 April 2010, I spoke at the Open Knowledge Foundation Conference, OKCon2010, in London, on the topic
OpenCitations: Publishing Bibliographic Citations as Linked Open Data
I reported that, earlier that same week, I had applied to Jisc for a one-year grant to fund the OpenCitations Project (opencitations.net). Jisc (at that time ‘The JISC’, the Joint Information Systems Committee) was tasked by the UK government, among other things, to support research and development in information technology for the benefit of the academic community.
The purpose of that original OpenCitations R&D project was to develop a prototype in which we:
harvested citations from the open access biomedical literature in PubMed Central;
described and linked them using CiTO, the Citation Typing Ontology ;
encoded and organized them in an RDF triplestore; and
I told those at the conference that in this demonstration project, with limited JISC funding, we could not hope to “boil the whole ocean”, but that nevertheless there would be substantial benefits from even partial coverage of citation data from the scholarly literature:
We could show the way and establish best practice.
Despite partial coverage, all key papers would most likely be cited several times.
The overall topological structure of the citation network would be revealed.
We would create a ‘benchmark’ corpus of high-quality RDF citation data that could be used to develop analytical and visualization tools.
We could show the value of open citation data in helping scholars to discover full text articles of all types, and thus encourage subscription-access publishers to release their reference metadata.
The important thing, I said, was to make a start!
The Jisc OpenCitations Project
That JISC grant application was funded, and the project, to last for a year with modest funding of £100K, started in my lab in the Department of Zoology at Oxford University on 1st June 2010, and was subsequently extended for a further six months.
Using data from the Open Access subset of PubMed Central, we created the first prototype release of the OpenCitations Corpus of linked bibliographic citation data, containing 6,529,815 independent bibliographic records of both citing and cited entities, comprising references to ~20% of all post-1980 articles recorded in PubMed, including those to all the most important highly cited papers in every field of biomedical endeavour.
This achievement was almost entirely the result of the excellent work by our chief data wrangler Alex Dutton, whose skill and natural feel for linked data did wonders for this project. Ben O’Steen, Graham Klyne and Alistair Miles made important contributions.
The project also resulted in many other development, described here, most which were developed or at least initiated during a short but wonderfully productive collaboration with Silvio Peroni, who spent six months with me in 2010 as a doctoral student intern from the University of Bologna, to which he subsequently returned to complete his thesis and develop his academic career.
the deconstruction and re-development of the original version of CiTO into a suite of orthogonal and complementary ontologies covering the whole domain of scholarly publishing – the SPAR (Semantic Publishing and Referencing) Ontologies [2, 3];
the mapping of various existing metadata schemas into RDF using SPAR, including the DataCite Metadata Schema, and subsequently JATS, now the default NISO standard for XML markup of scholarly documents) ; and
After the Jisc funding ended and I, after a long career in biological teaching and research, formally retired from the Department of Zoology at the Oxford University, members of the initial OpenCitations team moved on to other things. Like so many grant-funded academic project whose initial financial support had dried up, OpenCitations could have foundered at that stage, as an interesting prototype but with too little content to be useful. However, the concept of providing an open alternative to proprietary citation indexes was too important to abandon. But how could it be transitioned into something enduring and useful, particularly when as a matter of principle one had decided that the citation data should be made freely available, thus precluding income generation by charging for ‘premium’ services or the formation of a commercial spin-off?
Finally, I realized that something radical needed to be done to move OpenCitations forward. I had maintained a lively collaboration with Silvio Peroni at the University of Bologna, resulting between 2011 and 2014 in the publication of 18 articles and conference papers concerning the SPAR ontologies, ontology development, documentation and visualization, and related topics, and in 2015 I invited him to start working with me directly on OpenCitations. It was the best decision I could have made. We decided to take the initial concept and re-implement it from the bottom up. OpenCitations gave Silvio a major computer science project to which he could apply his considerable talent, and soon resulted in the development of a revised RDF data model for describing citation data, the OpenCitations Data Model (OCDM)  and a suite of new software tools to harvest, organise and publish citations at linked open data . The credit for almost all the subsequent conceptual and technical developments within OpenCitations, which have incrementally led to our present situation, is due to Silvio Peroni, and the scholarly community is indebted to him for the intelligence, skill and diligent application he has given to OpenCitations over the past six years. I am truly honoured to have Silvio as co-Director of OpenCitations, and wish to take this opportunity to acknowledge his contributions and to thank him publicly.
Our work on OpenCitations at that stage, summarized in , would not have been possible without the enthusiastic support of Silvio’s senior colleague Fabio Vitali and of the Department of Computer Science and Engineering at the University of Bologna, which not only provided a stimulating environment for Silvio’s post-doctoral work, but also supplied computing services and infrastructure at no charge to OpenCitations. It was also greatly helped by Professor David De Roure of Oxford University, who gave me an academic home and a formal affiliation within the Oxford e-Research Centre after my retirement from the Department of Zoology, which enabled me to continue to hold research grants.
A significant breakthrough came in January 2018 with our decision to treat citations as first-class data entities, each with its own persistent identifier (PID), the Open Citations Identifier (OCI) . This gave Silvio the freedom to envision a new kind of database, a citation index in which each citation had its own metadata, including citation timespan, citation categorization (e.g. self-citation), and of course the DOIs of the citing and cited publications. The creation of this new index was possible only with the incredible effort by Ivan Heibi, who served as a Research Fellow in the project funded by the Alfred P. Sloan Foundation at that time, and who was entirely responsible for developing the first version of the code necessary for creating such a database. Having harvested all the open references from Crossref metadata dumps, Silvio and Ivan created COCI, the OpenCitations Index of Crossref DOI-to-DOI Citations, which immediately became our principal source of open citations, the original OpenCitations Corpus being retained as a ‘sandbox’ in which to experiment with new data representations, for example those required for the Open Biomedical Citations in Context Corpus. Access to COCI was facilitated by Silvio’s development of a REST API, using his software tool RAMOSE (Restful API Manager Over SPARQL Endpoints), which enables the easily configurable deployment of a REST API over any SPARQL endpoint to an RDF triplestore . We were able to organize our all data, both ‘traditional’ and new, and to encode it in RDF, thanks to the comprehensive OpenCitations Data Model , itself based on our SPAR Ontologies , which we evolved as necessary to accommodate new data representation requirements.
During this period we published a number of definitions, conference papers and journal articles documenting these advances, details of which can be found here. Of these, the most recent canonical publication describing OpenCitations as an infrastructure for open scholarship, and its datasets, tools, services and activities, is Peroni and Shotton (2020) . We also established the Research Centre for Open Scholarly Metadata at the University of Bologna, primarily to handle administrative, financial and academic aspects of OpenCitations activities.
The problem remained: how to sustain the OpenCitations infrastructure financially. We were greatly helped by Bilder, Lin and Neylon’s formulation of the Principles of Open Scholarly Infrastructures (POSI) , in which they clearly pointing out that reliance solely on grant funding for specific projects was not the answer. OpenCitations compliance with POSI is described here. We were thus immensely grateful that SPARC Europe and other institutions had the wisdom to establish SCOSS (The Global Sustainability Coalition for Open Science Services) to facilitate the crowd-sourced financial support of useful open infrastructures by the scholarly community, including academic libraries, government agencies and other stakeholders. OpenCitations applied for SCOSS support in 2019, which led to the selection of OpenCitations for support in the SCOSS second round.
The donations we are now starting to receive from such stakeholders, and the new staff that this funding has recently allowed us to hire, signal the start of our transition from a financially vulnerable academic project to a sustainable open scholarly infrastructure of real value to the community.
The work of opening more of the global citation graph now requires two things:
that each publisher takes responsibility for ensuring that the references from all of its journal articles and books are submitted, together with all other bibliographic metadata, to open scholarly bibliographic metadata aggregators such as Crossref and DataCite, from which they can be indexed into open citation indexes of sufficient quality, depth of detail and breadth of coverage that these offer genuine alternatives to the expensive proprietary citation indexing services upon which the academic community presently relies; and
that the entire scholarly stakeholder community re-directs a fraction of the enormous sums currently spent on its subscriptions to proprietary bibliographic services in order to support Open Science infrastructures such as OpenCitations that making citations and other forms of scholarly metadata and objects freely available.
 Peroni S, Lapeyre DA and Shotton D (2012) From Markup to Linked Data: Mapping NISO JATS v1.0 to RDF using the SPAR (Semantic Publishing and Referencing) Ontologies. Proc. 2012 JATS Conference, National Library of Medicine, Bethesda, Maryland, USA (October 2012): 16-17. http://www.ncbi.nlm.nih.gov/books/NBK100491/
 Silvio Peroni, David Shotton, Fabio Vitali (2017). One Year of the OpenCitations Corpus: Releasing RDF-based scholarly citation data into the Public Domain. In The Semantic Web – ISWC 2017 (Lecture Notes in Computer Science Vol. 10588, pp. 184–192). Springer, Cham. https://doi.org/10.1007/978-3-319-68204-4_19
JATS, the Journal Article Tag Suite, defines a vocabulary of XML elements and attributes used to describe the content and metadata of journal articles. In this post, I describe the mapping of JATS to RDF, so that publishers’ XML article metadata encoded using JATS might become part of the web of linked data.
This work, that makes extensive use of the SPAR (Semantic Publishing and Referencing) Ontologies, was undertaken by Silvio Peroni and myself over the past summer, guided by Deborah Aleyne Lapeyre of Mulberry Technologies Inc., Maryland, the company that wrote the JATS specification on behalf of the National Library of Medicine.
It is the successor to the National Library of Medicine (NLM) DTD, which for many years has been a de facto standard for the XML markup of scholarly journal articles, widely used by many academic publishers within their routine publication workflows, and also used as the ingest format for PubMed Central. The last version of the NLM DTD, Version 3.0, was released in 2008, and JATS, although rebranded and now an official NISO standard, can be thought of simply as the next version of it.
As did the NLM DTD, JATS contains three tag sets, the Journal Archiving and Interchange Tag Set, the Journal Publishing Tag Set, and the Article Authoring Tag Set, intended for slightly different purposes. The Journal Publishing Tag Set is a moderately prescriptive Tag Set, optimized to regularize and control the sequence of the XML content.
As regular readers of this blog will know, the Resource Description Framework (RDF) is the key enabling technology for the Semantic Web, also know as the web of linked data. By defining statements about entities and their relationships in RDF syntax using publicly available ontologies, such statements can be combined into interconnected information networks (RDF graphs) in which the truth content of each original statement is maintained, thereby creating a web of linked data, the Semantic Web.
The SPAR (Semantic Publishing and Referencing) Ontologies are a suite of complementary and orthogonal OWL 2 DL ontologies described in this and subsequent posts in this blog. These ontologies were created specifically to permit RDF descriptions of bibliographic entities, citations, reference collections and library catalogues, the structural and rhetorical component parts of documents, and roles, statuses and workflows in publishing.
Mapping JATS to RDF
Silvio Peroni and I decided to map the metadata elements of the JATS Journal Publishing Tag Set to RDF. For this purpose, we were given pre-publication access to JATS v1.0, enabling us to undertake our mapping work in July, and to revise it during August and September 2012. I present this work at the 2012 JATS Conference at the National Library of Medicine on the NIH campus in Bethesda, Maryland on 16th October 2012.
Since JATS is a large standard, containing 246 elements and 134 attributes, we chose to map only the JATS metadata entities that describe an article (e.g. <journal-meta> for metadata about the journal in which the article was published, such as the name of the journal), and to leave aside (possibly for a later mapping exercise using DoCO, the Document Components Ontology) those entities describing the textual and graphical structure and content of the article (e.g. <title>, <body>, <fig>, <table>).
Detailed description and examples
Within the JATS2RDF mapping document we created, our mappings are presented in tabular form, with one table for each of the principle JATS metadata elements <article>, <article-meta>, <journal-meta>, <contrib>, and <ref-list>, plus their principle contained elements and attributes. In all, we created 242 separate XML to RDF mapping statements.
Within each table there are three columns, the first giving the JATS element or attribute name, the second showing an exemplar XML usage from the JATS specification, and the third documenting our mapping of that usage to RDF.
We provide specific examples of each mapping, giving alternatives where appropriate. To show the style employed, the mappings for the first four elements in the JATS <ref-list> element table:
Automating the mapping to RDF
By means of an Extensible Stylesheet Language Transformation (XSLT) transform that we have also created (downloadable from http://purl.org/spar/jats2rdf/xslt), this JATS2RDF mapping now permit the JATS metadata elements and their attributes, from documents marked up in XML using the NISO-JATS Journal Publishing Tag Library v1.0, to be converted automatically to RDF, enabling this information to be published to the Semantic Web as linked open data in a manner that is unambiguous and universally understood.
We hope that this ability to express JATS metadata descriptions in RDF will promote the use of JATS to a wider community.
Publications arising from this work
Our paper describing our JATS to RDF mapping is available from the National Library of Medicine:
Peroni S, Lapeyre DA and Shotton D (2012) From Markup to Linked Data: Mapping NISO JATS v1.0 to RDF using the SPAR (Semantic Publishing and Referencing) Ontologies. Proc. 2012 JATS Conference, National Library of Medicine, Bethesda, Maryland, USA, 16-17 October 2012. http://www.ncbi.nlm.nih.gov/books/NBK100491/.
Our JATS2RDF mapping document, entitled JATS2RDF (v1.2): a mapping of JATS metadata to RDF, is available in PDF format from http://purl.org/spar/jats2rdf.
The purpose of mapping DataCite metadata elements to ontology terms is to enable DataCite metadata to be published in RDF as Open Linked Data, enabling these metadata to be understood programmatically and integrated automatically with similar data from elsewhere.
In the previous blog post, I described the updated and expanded version of the DataCite Ontology, version 0.6.1, that I created with Silvio Peroni to conform to the DataCite Metadata Kernel, v2.2 published in July 2012. The revised ontology now provides the eleven classes and five properties required to cover all the items in v2.2 of the DataCite Metadata Schema – not just the core DataCite metadata elements, but all of them – that were not conveniently covered by terms in other ontologies.
In this post, I describe how we have used this revised DataCite Ontology to create a new revised DataCite2RDF mapping document. This now replaces the previous mapping document, described in an earlier post, that was a partial mapping of the DataCite Metadata Kernel v2.0 using the original version of the DataCite Ontology.
Wherever possible in this new DataCite2RDF mapping, we have used commonly used vocabularies, including:
The mapping document is structured in tabular form, with three columns: the first containing the DataCite ID, the second containing the name of the DataCite property, and the third containing the ontology entities used in mapping each of the DataCite metadata elements. All the metadata elements of the DataCite Metadata Kernel version 2.2 are included, both mandatory and optional, and both major and supplementary. For each, we provide not only the ontology terms, but also a specific exemplar of the usage of that term in an RDF statement, giving alternatives where appropriate. To show the style employed, the mappings for the first three DataCite metadata elements are shown in the following table:
Equivalent ontology class or property
datacite:PrimaryIdentifier (A sub-class of datacite:ResourceIdentifier that uses a datacite:IdentiferScheme that is restricted to datacite:doi, an individual in the datacite:ResourceIdentifierScheme)Exemplar usage:
To facilitate our mapping, the object properties compiles and isCompiledBy, that are required for the DataCite relationType controlled list, have now been included in version 2.2 of CiTO (created 3 July 2012) as cito:compiles and cito:isCompiledBy. The use of the mini-ontology CiTO4Data, that contained only those properties, has consequently been deprecated.
In several instances, we propose alternative mappings, depending upon whether one wishes to use a data property that has a literal (e.g. text, number, date) as its object, or to use an object property that has a URI as its object. As explained more fully in the mapping document itself, our recommended best practice is to use DCMI metadata terms (dcterms:) as object properties in preference over Dublin Core metadata elements (dc:) as data properties, unless one specifically needs to use a literal as the object of an RDF triple.
A presentation related to this work, that was given at a DataCite meeting held at the British Library on 6 July 2012, is available here.
The next blog post describes a DataCite Metadata Input Form based on this new DataCite2RDF mapping that Tanya Gray and I have created. This is a Web input tool that permits easy entry of metadata compliant with the DataCite Metadata Kernel v2.2. The metadata can be saved in an XML file, and can be automatically mapped to RDF by employing an XSLT that uses this mapping.
We commend the use of this mapping to all who wish to encode DataCite metadata in RDF, and welcome feedback on this work.
These properties permitted us to provide identifier descriptions required by DataCite that could not be achieved using other Ontologies. We did this using the following type of construction:
[ a prism:doi ; literal:hasLiteralValue "***" ] .
in which the object property relates to a blank node defining something that is a DOI and that has the particular literal value specified.
In July 2012, to permit an updating and expansion of the DataCite2RDF mapping, to conform to the DataCite Metadata Kernel, v2.2 published in July 2012, we undertook a complete revision of the DataCite Ontology to permit us to create mappings not just for the core DataCite metadata elements, but for them all.
Note that the four original specific object properties have been replaced by the single object property datacite:hasIdentifier, and that the method of defining the identifier has been changed. Now, rather than the object property relating to a blank node in which the identifier is defined as a literal, the object property datacite:hasIdentifier has as its object a member of the class datacite:Identifier, or of one of its three sub-classes, datacite:PersonalIdentifier, datacite:FunderIdentifier or datacite:ResourceIdentifier, as shown in the following diagram:
The exact nature of the identifier is then defined using the second DataCite object property datacite:usesIdentifierScheme that has as its object the class datacite:IdentifierScheme or one of its three sub-classes: datacite:PersonalIdentifierScheme, datacite:FunderIdentifierScheme or datacite:ResourceIdentifierScheme.
This provides a robust method for defining identifiers, since each specific identifier is defined as an individual member of its appropriate identifier scheme class. Using the new DataCite Ontology, these three types of identifier scheme can be used as follows:
As need arises, new identifiers can be added later as new members of each class, without having to modify the structure of the DataCite Ontology. We have already added to the DataCite specification by adding three members, datacite:local-resource-identifier-scheme, datacite:local-personal-identifier-scheme and datacite:local-funder-identifier-scheme, to permit the use of local identifiers , and have requested that DataCite include such local identifier schemes in their next release (version 2.3) of the DataCite Metadata Kernel.
Version 2.2 of the DataCite Metadata Kernel has a property “Description”, with four permitted values: ‘abstract’, ‘other’, ‘series information’ and ‘table of content’. The DataCite team recognises that this rather rag-bag collection of values makes use of the Description property highly problematic, and has referred this matter to its metadata committee for re-consideration.
Nevertheless, to complete our development of the DataCite Ontology, thus permitting mapping of all the DataCite Metadata Kernel v2.2 metadata properties, we have created a new class, datacite:DescriptionType, and two final DataCite object properties, datacite:hasDescription and datacite:hasDescriptionType. These allows us to link an entity to another item representing an entity description of a particular type. This is defined using the property datacite:hasDescriptionType, which must have as its object one of the members of the class datacite:DescriptionType, i.e datacite:abstract, datacite:other, datacite:series-information and datacite:table-of-content. In this way it is possible to associate written documents (e.g. journal articles or ‘data articles’) as descriptions of datasets, as shown in the following excerpt:
:this-dataset a fabio:Dataset ;
datacite:hasDescription [ a fabio:JournalArticle ;
datacite:hasDescriptionType datacite:other ] .
We expect the membership of this class datacite:DescriptionType will expand once the DataCite Metadata Kernel v2.3 is published.
The first of these was an invited contribution with the title Enriching Scientific Citations to Facilitate Knowledge Discovery, given to publishers at the Scientific, Technical and Medical Publishers Innovations Seminar 2010 entitled “Flows in Flux: how publishing technologies change the researcher’s life”, held in London, UK, on 3rd December 2010:
Further details of the Open Citations Corpus and the SPAR ontologies, and their applications are, of course, given in this Open Citations and Semantic Publishing blog, for which the following two posts provide the best introduction for those coming here for the first time:
To accompany today’s publication in D-Lib Magazine of the article The Five Stars of Online Journal Articles – a framework for article evaluation highlighted in the previous post, I have today also published The Five Stars Ontology, a simple ontology written in OWL 2 DL that forms part of SPAR, a suite of Semantic Publishing and Referencing Ontologies. It is intended for use by publishers and others wishing to encode Five Stars ratings, such as those exemplified in the D-Lib article, in machine-readable form, so they can accompany other machine-readable metadata for the article. To exemplify this, the following RDF graph, shown in turtle notation, gives the Five Stars ratings for the D-Lib article itself:
fivestars:hasPeerReviewRating “3”^^xsd:nonNegativeInteger ;
fivestars:peerReviewRatingComment “Post-publication responsive
peer review of the preprint.” ;
fivestars:hasOpenAccessRating “4”^^xsd:nonNegativeInteger ;
fivestars:openAccessRatingComment “Gold/libre open access
without author fee!” ;
fivestars:hasEnhancedContentRating “1”^^xsd:nonNegativeInteger ;
fivestars:enhancedContentRatingComment “Plentiful Web links in
text and to all references. No additional semantic
enhancement of text.” ;
fivestars:hasAvailableDatasetsRating “0”^^xsd:nonNegativeInteger ;
fivestars:availableDatasetsRatingComment “Not applicable.” ;
fivestars:hasMachine-readableMetadataRating “1”^^xsd:nonNegativeInteger ;
markup in HTML only.” ;
fivestars:hasOverallFiveStarsRating “9”^^xsd:nonNegativeInteger ;
fivestars:overallFiveStarsRatingComment “The nature of this
article, being a position paper rather than a research
paper with primary research data, has influenced the
overall rating obtained.” .
As part of the Open Citations Project, Alex Dutton recently completed a graphing plug-in for the Open Citations web site, that permits users to generate different kinds of graphs of citation networks by querying the Open Citation Corpus for a particular article, and either display the network of papers citing that article (input citations), papers cited by that article (output citations), or both. These can be displayed on screen in the web browser in a variety of layouts, or conveniently downloaded in a number of useful formats.
THIS IS SOOOOO COOL!
Having survived the preparation and posting of the JISC Open Citations Project Final Blog Post last night, minutes before the midnight deadline, I’m now like a kid with a new train set, playing with this display tool and exploring the citation networks present in the Open Citation Corpus, something I have dreamed of doing for two years now.
Remember first that in the Open Citations Corpus we have some 200,000 citing articles – those within the Open Access Subset (OASS) of Pubmed Central – citing ~3.4 million papers out there in the big wide world, which are only recipients of citations. The consequence of this limited corpus is that the majority of citation chains are of length one – from a paper in the OASS to a paper outside the OASS. Not very interesting. Add to this the fact that PubMed Central is new – over 90% of the papers in the Open Access Subset were published in the 21st Century, and 77% of them in the last 5 years. Thus there are only a very few citation from articles within the OASS to other articles within the OASS. That means that the maximum length of our citation chains, at present, to three or four on links the input side – a selected article may be cited by a chain of three or four other OASS articles, and three or four on the output side – the selected article may cite other OASS articles in addition to non-OASS articles, and these in turn will cite others. However, in most cases, the citations chains are much shorter.
Let’s start with something familiar – the article in PLoS Neglected Tropical Diseases by Reis et al. (2008)  that I used for our semantic publishing exemplar . Its inward citation graph, limited to a citation chain length of two links, created by and copied from the Open Citations Project web site, looks like this:
I, of course, cited the Reis et al. (2008) paper  in our 2009 Adventures paper  that we based upon it, and also in my first paper on CiTO in 2010 , which also cites the Adventures paper. Reis et al. (2008) is also cited by Fink et al. (2010) , who also cited our Adventures paper, and by Bourhy et al. (2010) , another PLoS Neglected Tropical Diseases paper in the OASS, which in turn is cited by Galloway and Levett (2010) , while our Adventures paper is also cited by Gerner and Nenadic (2010) .
The following image shows this graph as it was originally created within the Open Citations web page:
Since Reis et al. (2008) has a reference list containing 52 references, its output citation graph is much more complex, even when limited to a citation chain length of 2, since several of its cited papers are also members of the Open Access Subset. The following figure shows the whole output citation network a citation chain length of 2, which is too demagnified to be legible.
The next figure shows a close-up of part of the previous diagram – the output citation network of Reis et al. (2008), again showing the Reis et al. (2008) paper in red, and one of the key papers it cites, Maciel et al. (2010) , a slightly earlier paper from the same research group, forming a second key node in the top right of the diagram.
Clearly, there is lots of information that can be extracted from these graphs, particularly when we display them in a tool like GraphViz that permits interactions with the data. While the Open Citations web site simply displays such citation graphs created using one of several layout algorithms selected by the user, the raw data can also be downloaded in a variety of formats including GraphViz, GraphML and SVG, while the resulting network images can be downloaded in as PNG, JPEG and PDF images, and the underlying RDF metadata can be downloaded as RDF/XML. N-triples, Notation3 and Turtle.
Having used our new Open Citations web site and its network display interface for a short while, I am already aware of many shortcomings and limitations that we will attempt to improve upon in the next few days. However, we would very much like to hear from you – as a user of the Open Citations web site – both to learn what you like about what we have done and to hear what you find to be shortcomings of the functionality and new features that you would like to see implemented, which we will record as user stories to input into our next round of development. These can either be recorded as comments on this blog post, or can be e-mailed with the subject line “Open Citations web site” either to me <firstname.lastname@example.org> or to Alex Dutton <Alexander.email@example.com>, who is the person who deserves all the credit for the present system. We look forward to hearing from you.
 Reis RB, Ribeiro GS, Felzemburgh RDM, Santana FS, Mohr S, Melendez SXTO, Queiroz A, Santos AC, Ravines RR, Tassinari WS, Carvalho MS, Reis MG, Ko AI(2008). Impact of environment and social gradient on Leptospira infection in urban slums. PLoS Negl Trop Dis2(4): e228. doi:10.1371/journal.pntd.0000228.
 Shotton D, Portwin K, Klyne G, Miles A (2009). Adventures in semantic publishing: exemplar semantic enhancements of a research article. PLoS Comput Biol5:e1000361. doi:10.1371/journal.pcbi.1000361.
Reis et al. (2008)  cites an earlier paper from Albert Ko’s research group, Ko et al. (1999) .
In conventional parlance, as the following diagram shows, the word “reference” can mean either what is found in the text, what is found in the reference list, the act of citation, or the object of the citation itself, as in the sentence “All the references you will need to prepare for the journal club are on Kevin’s desk”.
This situation does not make for unambiguous machine-readable encoding, so to improve the situation we have, in the SPAR (Semantic Publishing and Referencing) Ontologies, introduced a more principled way of referring to these items and actions, as the second diagram illustrates:
This permits us to create RDF describing all aspect of the citation process. Within the body of the text we have an in-text citation containing an in-text reference pointer, while the actual bibliographicreference that the in-text reference pointer denotes is to be found within the article’s reference list. That bibliographic reference references the cited article, while the whole performative act of including the reference in the article constitutes the act of citation.
Are we all clear now?
 Reis RB, Ribeiro GS, Felzemburgh RDM, Santana FS, Mohr S, Melendez AXTO, Queiroz A, Santos AC, Ravines RR, Tassinari WS, Carvalho MS, Reis MG, Ko AI (2008) Impact of Environment and Social Gradient on Leptospira Infection in Urban Slums. PLoS Negl Trop Dis 2(4): e228. doi:10.1371/journal.pntd.0000228
 Ko AI, Reis MG, Ribeiro Dourado CM, Johnson WD Jr, Riley LW and the Salvador Leptospirosis Study Group. (1999) Urban epidemic of severe leptospirosis in Brazil. Lancet 354: 820–825. doi:10.1016/S0140-6736%2899%2980012-9.
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.
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.
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.
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 .
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.
 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#.