Additional 31 million citations in COCI

We are proud to announce that COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations, has just been extended with more than 31 million additional citations.

As introduced in an earlier blog post and an open-access article recently published on Scientometrics, COCI is our first OpenCitations Index of open citations. In COCI, we have applied the concept of citations as first-class data entities, each identified using a unique persistent Open Citation Identifier (OCI). COCI indexes the contents of one of the major databases of open scholarly citation information, namely Crossref, and renders and makes available this information in machine-readable RDF and in other formats.

The fourth release of COCI contains more than 655 million DOI-to-DOI citation links between more than 55 million bibliographic entities. The additional 31 million citations added in the new release come from the reprocessing of previous dumps of Crossref  data. In particular, we retrieved all the citations that involve references in citing articles that were in the Crossref ‘Limited’ set when we downloaded it in October 2018. Such citing articles currently appear in the Crossref ‘Closed’ dataset due to more recent restrictive policy decisions taken by their publishers.

Finally, we wish to remind you that all the bibliographic and citation data in COCI:

OpenCitations described

OpenCitations is an infrastructure organization for open scholarship dedicated to the publication of open bibliographic and citation data. We at OpenCitations are proud to announce the publication, in the first issue of Quantitative Science Studies, of a canonical paper in which we introduce and describe OpenCitations and outline its achievements and goals [1].

Here, I outline the contents of our paper, and provide definitive links on the topics described. Many of these topics have been the subjects of earlier blog posts.

This paper appears in the first Special Issue of QSS, dedicated to the description of the bibliometric data sources that lie at the heart of scientometric research, which aims to characterize the most important data sources currently available and to show how they differ in various dimensions, for instance in the data they provide, their level of openness, and their support for making research reproducible. The first three papers in this special issue cover the most important commercial bibliographic data sources: Web of Science (Clarivate Analytics), Scopus (Elsevier), and Dimensions (Digital Science), while the remaining three articles describe open data sources: Microsoft Academic, Crossref and OpenCitations.

In the introduction to our own paper, we describe the origins of OpenCitations, discuss the growth and benefits of open science, and introduce the Semantic Web techniques used at OpenCitations for recording and publishing our data. We then go on to describe OpenCitations’ services and data, namely Open Citation Identifiers, the OpenCitations Data Model, the SPAR (Semantic Publishing and Referencing) Ontologies, the OpenCitations Corpus, and the OpenCitations Indexes of citation data, of which the first and largest is COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations, that currently holds information on over 624 million citations. We conclude our survey of OpenCitations’ services and data by outlining the generic open source software developed at OpenCitations, including OSCAR, the OpenCitations RDF Search Application for searching over RDF datasets, LUCINDA, OSCAR’s associated OpenCitations RDF Resource Browser, and RAMOSE, OpenCitations’ application for creating REST APIs over SPARQL endpoints, thus opening Semantic Web datasets to those not familiar with SPARQL, the RDF query language.

In the second half of the paper, we describe OpenCitations as an organization in terms of its compliance with the principles for the sustainability of open infrastructures proposed by Bilder, Lin and Neylon (2015) [2], and report the selection of OpenCitations by the Global Sustainability Coalition for Open Science Services (SCOSS) as an open infrastructure organization worthy of crowd-funding support by the stakeholder community. We then provide usage statistics for our datasets and web site, and describe the adoption of OpenCitations data and services by the community, before concluding with a forward look at our proposed developments of OpenCitations activities.

References

[1] Silvio Peroni and David Shotton (2020). OpenCitations, an infrastructure organization for open scholarship. Quantitative Science Studies 1 (1): 428-444. https://doi.org/10.1162/qss_a_00023

[2] Geoffrey Bilder, Jennifer Lin and Cameron Neylon (2015). Principles for open scholarly infrastructures. Figshare. https://doi.org/10.6084/m9.figshare.1314859

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

The Open Biomedical Citations in Context Corpus: Progress Report

The creation of the Open Biomedical Citations in Context Corpus (CCC) is the goal of a one-year project funded by the Wellcome Trust. The aim is to create a new open corpus of bibliographic and citation data that contain detailed information about individual in-text reference pointers in biomedical journal articles. The project is led by Professor Silvio Peroni of the Research Centre for Open Scholarly Metadata (University of Bologna), is being undertaken by Dr Marilena Daquino (University of Bologna), and actively involves the Oxford e-Research Centre (University of Oxford), the École de Bibliothéconomie et des Sciences de l’Information (Université de Montréal), and the Centre for Science and Technology Studies (CWTS), (Leiden University).

An in-text reference pointer is a textual device (e.g. “[1]”, or “(Peroni and Shotton 2012)”) that appears in the main text of a citing work and denotes a bibliographic reference listed in the Bibliography section of the citing work. While a single in-text reference pointer uniquely denotes a single bibliographic reference, it can occur together with one or more other pointers, forming an in-text reference pointer list that denotes several references (e.g. “[5-13]”, or “(Peroni and Shotton 2012; Peroni and Shotton 2019)”). In-text reference pointers may appear in several places within the same citing publication (e.g. Introduction, Methods, Discussion), may occur within different document components (e.g. body text, figure captions, tables), and may address the cited publication for different purposes (e.g. as the source of an experimental protocol, as a data source, or for general background information).

Unfortunately, current citation indexes contain no information about in-text reference pointers, such as the number of times a particular work is referenced in the citing work, the text of the sentences in which they occur, or the rhetorical purpose of such citations.

Having data at the level of individual in-text reference pointers offers many new opportunities, enabling one: (1) to distinguish between works that are referenced just once in a citing publication and those that are referenced multiple times, and thereby (potentially) to distinguish when a citation is fundamental for the understanding or the development of the citing work, or merely incidental; (2) to see which in-text reference pointers occur together (e.g. in the same sentence or the same paragraph), thus, potentially, to infer similarities between the co-cited publications; and (3) to determine in which specific sections of the publication these in-text references occur (e.g. Introduction, Methods, Results), and thus, potentially, by means of textual analysis of the citation contexts, to retrieve the rhetorical functions of the citations – i.e. the reason why an author cites another work. 

The goal of the CCC Project is to provide stakeholders with an exemplar Linked Open Data corpus, created from the open access biomedical research literature, that is tailored for such deep citation analyses. The corpus will be a new member of the collection of OpenCitations datasets, and will be accompanied by services for accessing and querying data.

In the CCC Project, we have achieved or are currently dealing with the following developments:

  • Extending the OpenCitations Data Model (OCDM). The OpenCitations Data Model has been extended and enriched with new terms and relations to represent bibliographic entities related to in-text reference pointers, such as the in-text reference pointers themselves, in-text reference pointer lists, discourse elements (e.g. sections, paragraphs, sentences), and annotations on citations, bibliographic references and in-text reference pointers. In addition, the provenance layer of the data model has been revised to provide meaningful provenance information in a more compact way. A revised version of the OCDM including these terms was published on November 8, 2019, and it is available on Figshare [1].
  • Extending the OpenCitation harvesting and data re-engineering pipeline. The CCC Project leverages existing OpenCitations technologies for building this new corpus, using as input articles from the Open Access Subset of biomedical literature hosted by Europe PubMed Central (EPMC) and encoded in XML. The OpenCitations pipelines for knowledge extraction (i.e. the software called BEE) and for data re-engineering (i.e. the software called SPACIN) have been enhanced so as to harvest relevant information from the full-text of the XML sources provided by EPMC, rather than just the reference lists,  and to transform these data into RDF according to the revised OCDM. The source code of the new pipeline is available on GitHub.
  • Creating InTRePID, a new persistent identifier for in-text reference pointers. Different in-text reference pointers denoting the same bibliographic reference have distinct logical, rhetorical and textual contexts wherein they occur. To permit them to be identified individually and handled properly, we have recently developed a new persistent identifier, the In-Text Reference Pointer Identifier (InTRePID), for identifying individual in-text reference pointers relating to an open bibliographic citation. The InTRePID is based on the Open Citation Identifier (OCI), currently being used to identify the >624 million citations present in the new release of COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations, described in the previous blog post. The formal definition of an InTRePID is available on Figshare [2]. In addition, an InTRePID Resolution Service has been developed (currently in beta-testing) to facilitate the retrieval of the metadata relating to in-text reference pointers. At the moment, a subset of the CCC corpus is available online for testing the InTRePID Resolution Service.
  • Development of services for accessing and querying the Citations in Context Corpus. Along with the development of the CCC itself, we are also developing services for querying data within the CCC. In particular, we are currently working to extend the RAMOSE software to provide an API for accessing the CCC triplestore. This CCC API will permit users to access the CCC corpus and retrieve detailed information about in-text reference pointers and their related annotations in a variety of human- and machine-readable formats. The source code of the API Manager is available on GitHub. The configuration file for querying the CCC corpus is still in the process of development.

Moreover, we are currently working to evaluate the content data quality of the CCC corpus and to develop reconciliation activities with information stored in Crossref. Specifically, by means of new validation methods, we are testing whether the extracted in-text reference pointers are complete (i.e. determining that all the in-text reference pointers for a particular bibliographic reference have been correctly extracted from the text), and that in-text reference pointer lists (e.g. “[5-13]”) have been correctly parsed to extract all the implicit pointers (in this case “[6]”, “[7]”, “[8]”, “[9]”, “[10]”, “[11]” and “[12]”), and to associate them correctly with the appropriate bibliographic references that they denote. This activity is fundamental, in order to address the diverse citation styles adopted by different journals and to overcome possible incoherencies in the publishers’ XML markup of the articles. Secondly, whenever a DOI is not specified for the citing or cited publications in the full-text of the citing publication, a text search using the Crossref API is performed in order to match possible candidates and supply the missing DOI. This reconciliation process itself can be error-prone since recommended matches are obtained by means of a non-transparent scoring mechanism. Therefore we are currently testing the application of a scoring threshold that will eliminate false positives and provide us only with correct results.

The deployment of the enhanced OpenCitations pipeline for populating the CCC corpus automatically is planned to start in the next weeks. For more details and to provide suggestions, please contact us!

References

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

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

More than 624 million citations now available on COCI

COCI is the OpenCitations Index of Crossref open DOI-to-DOI citations, all released as CC0 material, and is described in the article

Heibi I, Peroni S, Shotton D (2019). Software review: COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations. Scientometrics 121(2): 1213-1228. https://doi.org/10.1007/s11192-019-03217-6

COCI is our first OpenCitations Index of open citations, in which we have applied the concept of citations as first-class data entities, each identified using a unique persistent Open Citation Identifier (OCI), to index the contents of one of the major databases of open scholarly citation information, namely Crossref, and to render and make available this information in machine-readable RDF.

We are now proud to announce the third release of COCI, which contains more than 624 million DOI-to-DOI citation links coming from both ‘the ‘Open’ and the ‘Limited’ sets of Crossref reference data. This represents an increase of 40% in the number of indexed citations, compared with the second release of COCI on 12th November 2018, which indexed more than 445 million citations. The data model used for this third release of COCI is the updated revision of the OpenCitation Data Model, published on 8 November 2019 and available at https://doi.org/10.6084/m9.figshare.3443876.

This new release of COCI has been created using new software developed specifically for this purpose, which is available on our GitHub repository under an open ISC license. This software automates the process of creating an OpenCitations Index compliant with the OpenCitations Data Model and creates the citation data and related provenance information in three different formats: CSV, N-Triples (RDF), and Scholix. The support for Scholix – a high-level interoperability framework supported by Crossref, DataCite, Europe PubMed Central, OpenAIRE and others  – has recently been added to provide an additional format for the exchange of information about the links between scholarly literature and datasets.

A great advantage of the new software is that it will now enable us to extend COCI (and any other OpenCitations Index) by means of incremental additions, rather than having to re-create the entire index at each update. This should enable us to release index updates more frequently than hitherto, thus keeping the index more closely in synchrony with the latest reference data released by Crossref. Note that we are currently run the software on previous dumps of Crossref data so as to retrieve all the citations that involve references in citing articles that were in the ‘Limited’ set when we downloaded it, but that currently appear in the Crossref ‘Closed’ data set due to more recent restrictive policy decisions taken by their publishers.

Finally, we wish to remind you that all the bibliographic and citation data in COCI:

COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations

Abstract

In this paper, we present COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations (http://opencitations.net/index/coci). COCI is the first open citation index created by OpenCitations, in which we have applied the concept of citations as first-class data entities, and it contains more than 445 million DOI-to-DOI citation links derived from the data available in Crossref. These citations are described in RDF by means of the new extended version of the OpenCitations Data Model (OCDM). We introduce the workflow we have developed for creating these data, and also show the additional services that facilitate the access to and querying of these data by means of different access points: a SPARQL endpoint, a REST API, bulk downloads, Web interfaces, and direct access to the citations via HTTP content negotiation. Finally, we present statistics regarding the use of COCI citation data, and we introduce several projects that have already started to use COCI data for different purposes.

Introduction

The availability of open scholarly citations [21] is a public good, of significant value to the academic community and the general public. In fact, citations not only serve as an acknowledgment medium [16], but also can be characterised topologically (by defining the connected graph between citing and cited entities and its evolution over time [19]), sociologically (such as for identifying odd conduct within or elitist access paths to scientific research [18]), quantitatively by creating citation-based metrics for evaluating the impact of an idea or a person [17], and financially by defining the scholarly value of a researcher within his/her own academic community [20]. The Initiative for Open Citations (I4OC, https://i4oc.org) has dedicated the past two years to persuading publishers to provide open citation data by means of the Crossref platform (https://crossref.org), obtaining the release of the reference lists of more than 43 million articles (as of February 2019), and it is this change of behaviour by the majority of academic publishers that has permitted COCI to be created.

OpenCitations (http://opencitations.net) is a scholarly infrastructure organization dedicated to open scholarship and the publication of open bibliographic and citation data by the use of Semantic Web (Linked Data) technologies, and is a founding member of I4OC. It has created and maintains the SPAR (Semantic Publishing and Referencing) Ontologies (http://www.sparontologies.net) [22] for encoding scholarly bibliographic and citation data in RDF, and has previously developed the OpenCitations Corpus (OCC) of open downloadable bibliographic and citation data recorded in RDF [4].

In this paper, we introduce a new dataset made available a few months ago by OpenCitations, namely COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations (https://w3id.org/oc/index/coci). This dataset, launched in July 2018, is the first of the indexes proposed by OpenCitations (https://w3id.org/oc/index), in which citations are exposed as first-class data entities with accompanying properties (i.e. individuals of the class cito:Citation as defined in CiTO [7]) instead of being defined simply as relations among two bibliographic resources (via the property cito:cites). Currently COCI, contains more than 445 million DOI-to-DOI citation links made available under a Creative Commons CC0 public domain waiver, that can be accessed and queried through a SPARQL endpoint, an HTTP REST API, by means of searching/browsing Web interfaces, by bulk download in different formats (CSV and N-Triples), or by direct access via HTTP content negotiation.

The rest of the paper is organized as follows. In Section 2 we introduce some of the main RDF datasets containing scholarly bibliographic metadata and citations. In Section 3, we provide some details on the rationale and the technologies used to describe citations as first-class data entities, which are the main foundation of the development of COCI. In Section 4, we present COCI, including the workflow process developed for ingesting and exposing the open citation data available and other tools used for accessing these data. In Section 5, we show the scale of the community uptake of COCI since its launch by means of quantitative statistics on the use of its related services and by listing existing projects that are using it for specific purposes. Finally, in Section 6, we conclude the paper sketching out related and upcoming projects.

Related works

We have noticed a recent growing interest within the Semantic Web community for creating and making available RDF datasets concerning the metadata of scholarly resources, particularly bibliographic resources. In this section, we briefly introduce some of the most relevant ones.

ScholarlyData (http://www.scholarlydata.org) [1] is a project that refactors the Semantic Web Dog Food so as to keep the dataset growing in good health. It uses the Conference Ontology, an improvement version of the Semantic Web Conference Ontology, to describe metadata of documents (5,415, as of March 31, 2019), people (more than 1,100), and data about academic events (592) where such documents have been presented.

Another important source of bibliographic data in RDF is OpenAIRE (https://www.openaire.eu) [3]. Created by funding from the European Union, its RDF dataset makes available data for around 34 million research products created in the context of around 2.5 million research projects.

While important, these aforementioned datasets do not provide citation links between publications as part of their RDF data. In contrast, the following datasets do include citation data as part of the information they make available.

In 2017, Springer Nature announced SciGraph (https://scigraph.springernature.com) [2], a Linked Open Data platform aggregating data sources from Springer Nature and other key partners managing scholarly domain data. It contains data about journal articles (around 8 millions, as of March 31, 2019) and book chapters (around 4.5 millions), including their related citations, and information on around 7 million people involved in the publishing process.

The OpenCitations Corpus (OCC, https://w3id.org/oc/corpus) [4] is a collection of open bibliographic and citation data created by ourselves, harvested from the open access literature available in PubMed Central. As of March 31, 2019, it contains information about almost 14 million citation links to more than 7.5 million cited bibliographic resources.

WikiCite (https://meta.wikimedia.org/wiki/WikiCite) is a proposal, with a related series of workshops, which aims at building a bibliographic database in Wikidata [10] to serve all Wikimedia projects. Currently Wikidata hosts (as of March 29, 2019) more than 170 million citations.

Biotea (https://biotea.github.io) [5] is an RDF datasets containing information about some of the articles available in the Open Access subset of PubMed Central, that have been enhanced with specialized annotation pipelines. The last released dataset includes information extracted from 2,811 articles, including data on their citations.

Finally, Semantic Lancet [6] proposes to build a dataset of scholarly publication metadata and citations (including the specification of the citation functions) starting from articles published by Elsevier. To date it includes bibliographic metadata, abstract and citations of 291 articles published in the Journal of Web Semantics.

Indexing citations as first-class data entities

Citations are normally defined simply as links between published entities (from a citing entity to a cited entity). However, an alternative richer view is to regard each citation as a data entity in its own right, as illustrated in Figure 1. This alternative approach permits us to endow a citation with descriptive properties, such as those ones introduced in Table 11.

Figure 1. Two different ways of describing citations: as a relation between two bibliographic entities (top), or as an individual first-class data entitiy in its own right where the citing entity and the cited entity are among its attributed data.

The advantages of treating citations as first-class data entities are:

  • all the information regarding each citation is available in one place, since such information is defined as attributes of the citation itself;
  • citations become easier to describe, distinguish, count and process, and it becomes possible to distinguish separate citations within the citing entity to the cited entity, enabling one to count how many times, from which sections of the citing entity, and (in principle) for what purposes a particular cited entity is cited within the source paper;
  • if available in aggregate, citations described in this manner are easier to analyse using bibliometric methods, for example to determine how citation time spans vary by discipline.

We have appropriately extended the OpenCitations Data Model (OCDM, http://opencitations.net/model) [23] so as to define each citation as a first-class entity in machine-readable manner. In particular, we have used the class cito:Citation defined in the revised and expanded Citation Typing Ontology (CiTO, http://purl.org/spar/cito) [7], which is part of the SPAR Ontologies [22]. This class allows us to define a permanent conceptual directional link from the citing bibliographic entity to a cited bibliographic entity, that can be accompanied by additional ontological terms for defining specific attributes, as introduced in Table 1.

Characteristic Description CiTO entity
citing entity The bibliographic entity which acts as source for the citation. Object property cito:hasCitingEntity.
cited entity The bibliographic entity which acts as target for the citation. Object property cito:hasCitedEntity.
citation creation date The date on which the citation was created. This has the same numerical value as the publication date of the citing bibliographic resource, but is a property of the citation itself. When combined with the citation time span, it permits that citation to be located in history. Data property cito:hasCitationCreationDate, one of xsd:date, xsd:gYearMonth, or xsd:gYear as datatype value.
citation timespan The temporal characteristic of a citation, namely the interval between the publication date of the cited entity and the publication date of the citing entity. Data property cito:hasCitationTimespan, xsd:duration as datatype value.
type A classification of the citation according to particular dimensions, e.g. whether or not it is a self-citation. Property rdf:type associated with one or more subclasses of cito:Citation – in particular, for example cito:AuthorSelfCitation (i.e. citing and the cited entities have at least one author in common) and cito:JournalSelfCitation (i.e. citing and the cited entities are published in the same journal).

Table 1. List of characteristics that can be associated with a citation when it is described as first-class data entity, using the properties and classes available in CiTO for their definition in RDF.

So as to identify each citation precisely, when described as first-class data entity and included in an open dataset, we have also developed the Open Citation Identifier (OCI) [24], which is a new globally unique persistent identifier for citations. OCIs are registered in the Identifiers.org platform (https://identifiers.org/oci) and recognized as persistent identifiers for citations by the EU FREYA Project (https://www.project-freya.eu) [25]. Each OCI has a simple structure: the lower-case letters oci followed by a colon, followed by two sequences of numerals separated by a dash, where the first sequence is the identifier for the citing bibliographic resource and the second sequence is the identifier for the cited bibliographic resource. For example, oci:0301-03018 is a valid OCI for a citation defined within the OpenCitations Corpus, while oci:02001010806360107050663080702026306630509-02001010806360107050663080702026305630301 is a valid OCI for a citation included in Crossref. It is worth mentioning that OCIs are not opaque identifiers, since they explicitly encode directional relationships between identified citing and cited entities, the provenance of the citation, i.e. the database that contains it, and the type of identifiers used in that database to identify the citing and cited entities. In addition, we have created the Open Citation Identifier Resolution Service (http://opencitations.net/oci), which is a resolution service for OCIs based on the Python application oci.py available at https://github.com/opencitations/oci. Given a valid OCI as input, this resolution service is able to retrieve citation data in RDF (either as RDF/XML, Turtle or JSON-LD), or in Scholix, JSON or CSV formats. A more detailed explanation of OCIs and related material is available in [24].

At OpenCitations, we define an open citation index as a dataset containing citations that complies with the following requirements:

  • the citations contained are all open, according to the definition provided in [21];
  • the citations are all treated as first-class data entities;
  • each citation is identified by an Open Citation Identifier (OCI) [24];
  • the citation data are recorded in RDF according to the OpenCitations Data Model (OCDM) [23], where the OCI of a citation is embedded in the IRI defining it in RDF;
  • each citation defines the attributes shown in Table 1.

COCI: ingestion workflow, data, and services

COCI, the OpenCitations Index of Crossref open DOI-to-DOI references, is the first citation index to be published by OpenCitations, in which we have applied the concept of citations as first-class data entities, introduced in the previous section, to index the contents of one of the major open databases of scholarly citation information, namely Crossref (https://crossref.org), and to render and make available this information in machine-readable RDF under a CC0 waiver. Crossref contains metadata about publications (mainly academic journal articles) that are identified using Digital Object Identifiers (DOIs). Out of more than 100 million publications recorded in Crossref, Crossref also stores the reference lists of more than 43 million publications deposited by the publishers. Many of these references are to other publications bearing DOIs that are also described in Crossref, while others are to publications that lack DOIs and do not have Crossref descriptions. Crossref organises such publications with associated reference lists according to three categories: closed, limited and open. These categories to publications for which the reference lists are not visible to anyone outside the Crossref Cited-by membership, are visible only to them and to Crossref Metadata Plus members, or are visible to all users, respectively2.

Figure 2. The diagram of the data model adopted to define the new class for defining citations as first-class data entities, which forms part of the OpenCitations Data Model. This model uses terms from the Citation Typing Ontology (CiTO, http://purl.org/spar/cito) for describing the data, and from the Provenance Ontology (PROV-O, http://www.w3.org/ns/prov) to define the citation’s provenance.

Followed the first release of COCI on June 4, 2018, the most recent version of COCI, released on November 12, 2018, contains more that 445 million DOI-to-DOI citations included in the open and the limited datasets of Crossref reference data3. All the citation data in COCI and their provenance information, described according the Graffoo diagram [27] presented in Figure 2, are included in two distinct graphs – https://w3id.org/oc/index/coci/ and https://w3id.org/oc/index/coci/prov/ respectively – released under a CC0 waiver, and compliant with the FAIR data principles [26].

An example of a citation included in COCI is shown in the following excerpt (in Turtle), where the OCI is embedded as part of the IRI of the citation (without the oci: prefix) after the ci/ (meaning citation according to the OpenCitations Data Model [23]):

@prefix cito: <http://purl.org/spar/cito/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://w3id.org/oc/index/coci/ci/02001010806360107050663080702026306630509-02001010806360107050663080702026305630301>
  a 
    cito:Citation,
    cito:JournalSelfCitation ;
  cito:hasCitationCreationDate "2013"^^xsd:gYear ;
  cito:hasCitationTimeSpan "P1Y"^^xsd:duration ;
  cito:hasCitingEntity <http://dx.doi.org/10.1186/1756-8722-6-59> ;
  cito:hasCitedEntity <http://dx.doi.org/10.1186/1756-8722-5-31> ;
  prov:generatedAtTime "2018-11-01T05:47:54+00:00"^^xsd:dateTime ;
  prov:hadPrimarySource <https://api.crossref.org/works/10.1186/1756-8722-6-59> ;
  prov:wasAttributedTo <https://w3id.org/oc/index/coci/prov/pa/1> .

In the following subsections we introduce the ingestion workflow developed for creating COCI, we provides some figures on the citations it contains, and we list the resources and services we have made available to permit access to and querying of the dataset.

Ingestion workflow

We processed all the data included in the October 2018 JSON dump of Crossref data, available to all the Crossref Metadata Plus members. The ingestion workflow, summarised in Figure 3, was organised in four distinct phases, and all the related scripts developed and used are released as open source code according to the ISC License and downloadable from the official GitHub repository of COCI at https://github.com/opencitations/coci.

Figure 3. A flowchart scheme describing the workflow to build COCI. It is divided in four phases: (1) global data generation, (2) CSV generation, (3) conversion into RDF, and (4) updating the triplestore.

Phase 1: global data generation. We parse and process the entire Crossref bibliographic database to extract all the publications having a DOI and their available list of references. Through this process three datasets are generated, which are used in the next phase:

  • Dates, the publication dates of all the bibliographic entities in Crossref and of all their references if they explicitly specify a DOI and a publication date as structured data – e.g. see the fields DOI and year in the array reference in https://api.crossref.org/works/10.1007/978-3-030-00668-6_8. Where the same DOI is encountered multiple times, e.g. as a proper item indexed in Crossref and also as a reference in the reference list of another article deposited in the Crossref, we use the full publication date defined in the indexed item.
  • ISSN: the ISSN (if any) and publication type (journal-article, book-chapter, etc.) of each bibliographic entity identified by a DOI indexed in Crossref.
  • ORCID: the ORCIDs (if any) associated with the authors of each bibliographic entity identified by a DOI indexed in Crossref.

Phase 2: CSV generation. We generate a CSV file such that each row represents a particular citation between a citing entity and a cited entity according to the data available in the Crossref dump, by looking at the DOI identifying the citing entity and all the DOIs specified in the reference list of such a citing entity according to the Crossref data. In particular, we execute the following four steps for each citation identified:

  1. We generate the OCI for the citation by encoding the DOIs of the citing and cited entities into numerical sequences using the lookup table available at https://github.com/opencitations/oci/blob/master/lookup.csv, which are prefixed by the supplier prefix 020 to indicate Crossref as the source of the citation.
  2. We retrieve the publication date of the citing entity from the Dates dataset and assign it as citation creation date.
  3. We retrieve the publication date of the cited entity (from the Dates dataset) and we use it, together with the publication date of the citing entity retrieved in the previous step, to calculate the citation timespan.
  4. We use the data contained in the ISSN and ORCID datasets to establish whether the citing and cited entity have been published in the same journal and/or have at least one author in common, and in these cases we assign the appropriate self-citation type(s) to the citation.

Simultaneously with the creation of the CSV file of citation data, we generate a second CSV file containing the provenance information for each citation (identified by its OCI generated in the aforementioned Step 1). These provenance data include the agent responsible for the generation of the citation, the Crossref API call that refers to the data of the citing bibliographic entity containing the reference used to create the citation, and the creation date of the citation.

Phase 3: converting into RDF. The CSV files generated in the previous phase are then converted into RDF according to the N-Triples format, following the OWL model introduced in Figure 2, where the DOIs of the citing and cited entities become DOI URLs starting with http://dx.doi.org/4, while the IRI of the citation includes its OCI (without the oci: prefix), as illustrated in the example given in the previous section.

Phase 4: updating the triplestore. The final RDF files generated in Phase 3 are used to update the triplestore used for the OpenCitations Indexes.

Data

COCI was first created and released on July 4, 2018, and most recently updated on November 12, 2018. Currently, it contains 445,826,118 citations between 46,534,705 bibliographic entities. These are stored by means of 2,259,134,894 RDF statements (around 5 RDF statements per citation) for describing the citation data, and 1,337,478,354 RDF statements (3 statements per citation) for describing the related provenance information. Of the citations stored, 29,755,045 (6.7%) are journal self-citations, while 250,991 (0.06%) are author self-citations. The number of identified author self-citations, based on author ORCIDs, is a significant underestimate of the true number, mainly due to the sparsity of the data concerning the ORCID author identifiers within the Crossref dump. Journal entities (i.e. journals, volumes, issues, and articles) are the type of the bibliographic entities that are mostly cited, with over 420 million citations.

We also classify the cited documents according to their publishers – Table 2 shows the ten top publishers of citing and cited documents, calculated by looking at the DOI prefixes of the entities involved in each citation. As we can see, Elsevier is by far the publisher having the majority of cited documents. It is also the largest publisher that is not participating in the Initiative for Open Citations by making its publications’ reference lists open at Crossref – which is highlighted by the very limited amount of outgoing citations recorded in COCI. Its present refusal to open its article reference lists in Crossref, contrary to the practice of most of the major scholarly publishers, is contributing significantly to the invisibility of Elsevier’s own publications within the corpora of open citation data such as COCI that are increasingly being used by the scholarly community for discovery, citation network visualization and bibliometric analysis, as we introduce below in the section entitled Section 5.

Publisher Outgoing citations Incoming citations
Springer Nature 79,860,827 52,257,862
Wiley 76,819,685 48,174,542
Elsevier 2,853,739 96,310,027
Informa UK Limited 41,433,917 14,975,989
Institute of Electrical and Electronics Engineers (IEEE) 30,114,985 20,940,703
American Physical Society (APS) 15,729,297 16,065,862
SAGE Publications 15,933,805 7,915,082
Ovid Technologies (Wolters Kluwer Health) 9,971,274 12,840,293
Oxford University Press (OUP) 9,891,000 11,466,659
AIP Publishing 10,130,022 8,455,097

Table 2. A classification of the COCI citations according to the publishers of the cited (incoming citations) and citing (outgoing citations) documents. The table shows the top ten publishers by the overall amount of incoming and outgoing to/from their published works. Those publishers shown in italics are not participating in the Initiative for Open Citations by making their publications’ reference lists open at Crossref – see https://i4oc.org for additional information.

Resources and services

The citation data in COCI can be accessed in a variety of convenient ways, listed as follows.

Open Citation Index SPARQL endpoint. We have made available a SPARQL endpoint for all the indexes released by OpenCitations, including COCI, which is available at https://w3id.org/oc/index/sparql. When accessed with a browser, it shows a SPARQL endpoint editor GUI generated with YASGUI [8]. Of course, this SPARQL endpoint can additionally be queried using the REST HTTP protocol, e.g. via curl. In order to access to COCI data, the graph https://w3id.org/oc/index/coci/ must be specified in the SPARQL query.

COCI REST API. Citation data in COCI can be retrieved by using the COCI REST API, available at https://w3id.org/oc/index/coci/api/v1. The rationale of making a REST API available in addition to the SPARQL endpoint was to provide convenient access to the the citation data included in COCI for Web developers and users who are not necessarily experts in Semantic Web technologies. This REST API, as are all the other REST APIs made available by OpenCitations, has been implemented by means of RAMOSE, the Restful API Manager Over SPARQL Endpoints (https://github.com/opencitations/ramose), which is a Python application that allows one to simply create a REST API over any SPARQL endpoint by means of a simple configuration file that execute a SPARQL query dependently of the particular API call specified. The configuration file for the COCI API is available at https://github.com/opencitations/api/blob/master/coci_v1.hf. Currently, the COCI REST API makes available four operations, that will retrieve either (a) the citation data for all the references of a given DOI (operation: references), or (b) the citation data for all the citations received by a given DOI (operation: citations), or (c) the citation data for the citation identified by an OCI (operation: citation), or (d) the metadata for the articles identified by the specified DOIs (operation: metadata). It is worth mentioning that the latter operation strictly depends on live API calls to external services, namely the Crossref API (https://api.crossref.org), the DataCite API (https://api.datacite.org), and the Unpaywall API (http://api.unpaywall.org), to gather the metadata of the requested articles, such as the title, the authors, and the journal name, that are not explicitly included within the OpenCitations Index triplestore.

Searching and browsing interfaces. We have additionally developed a user-friendly text search interface (https://w3id.org/oc/index/search), and a browsing interface (e.g. https://w3id.org/oc/index/browser/coci/ci/02001010806360107050663080702026306630509-02001010806360107050663080702026305630301), that can be used to search citation data in all the OpenCitations Indexes, including COCI, and to visualise and browse them, respectively. These two interfaces have been developed by means of OSCAR, the OpenCitations RDF Search Application (https://github.com/opencitations/oscar) [9], and LUCINDA, the OpenCitations RDF Resource Browser (https://github.com/opencitations/lucinda), that provide a configurable layer over SPARQL endpoints that permit one easily to create Web interfaces for querying and visualising the results of SPARQL queries.

Data dumps. All the citation data and provenance information in COCI are available as dumps stored in Figshare (https://figshare.com) in both CSV and N-Triples formats, while a dump of the whole triplestore is available on The Internet Archive (https://archive.org). The links to these dumps are available on the download page of the OpenCitations website (http://opencitations.net/download#coci).

Direct HTTP access. All the citation data in COCI can be accessed directly by means of the HTTP IRIs of the stored resources (via content negotiation, e.g. https://w3id.org/oc/index/coci/ci/02001010806360107050663080702026306630509-02001010806360107050663080702026305630301).

Quantifying the use of COCI citation data

In the past months, we have monitored the accesses to COCI data since its launch in July 2018. The statistics and graphics we show in this section highlight two different aspects: the quantification of the use of COCI data – and related services – and the community uptake, i.e. the use of COCI data for specific reuses within cross-community projects and studies. All the data of the charts described in this section are freely available for download from Figshare [15].

Quantitative analysis

Figure 4 shows the number of accesses made between July 2018 and February 2019 (inclusive) to the various COCI services described above – the search/browse interfaces, the REST API, SPARQL queries, and others (e.g. direct HTTP access to particular citations and visits to COCI webpages in the OpenCitations website). We have excluded from all these counts all accesses made by automated agents and bots. As shown, the REST API is, by far, the most used service, with extensive usage recorded in the last four months, following the announcement of the second release of COCI. This is reasonable, considering that the REST API has been developed exactly for accommodating the needs of generic Web users and developers, including (and in particular) those who are not expert in Semantic Web technologies. There is just one exception in November 2018, where the SPARQL endpoint was used to retrieve quite a large amount of citation data. After further investigation, we noticed a large proportion of the SPARQL calls were coming from a single source (according to the IP data stored in our log), which probably collected citation data for a specific set of entities.

Figure 4. The number of accesses to COCI-related services since July 2018 to February 2019. The scale used in the y-axis is logarithmic.

Figure 5 shows a particular cut of the figures given in Figure 4, which focuses on the REST API accesses only. In particular, we analysed which operations of the API were used the most. According to these figures, the most used operation is metadata (which was first introduced in the API in August 2018) which allows one to retrieve all the metadata describing certain publications. In contrast to the other API operations, this metadata search accepts one or more DOIs as input. The least used operation was citation, which allows one to retrieve citation data given an OCI, which should not be surprising, considering the currently limited knowledge of this new identifier system for citations.

Figure 5. The number of access made to each different COCI REST-API operation since the release of COCI on July 2018. Classified into 4 categories (requested resource): references, citations, citation, and metadata, as defined in the text.Note again the logarithmic scale of the y-axis.

In addition, we have also retrieved data about the views and downloads (as of March 29, 2019) of all the dumps uploaded to Figshare and to the Internet Archive. The CSV data dump received 1,321 views and 454 downloads, followed by the N-Triples data dump with 316 views and 93 downloads. The CSV provenance information dump has 166 and 127 downloads, while the N-Triples provenance information dump had 95 views and 34 downloads. Finally, the least accessed dump was that of the entire triplestore available in the Internet Archive, uploaded for the very first time in November 2018, that had only 3 views.

Community uptake

The data in COCI has been already used in various projects and initiatives. In this section, we list all the tools and studies doing this of which we are aware.

VOSviewer (http://www.vosviewer.com) [11] is a software tool, developed at the Leiden University’s Centre for Science and Technology Studies (CWTS), for constructing and visualizing bibliometric networks, which may include journals, researchers, or individual publications, and may be constructed based on citation, bibliographic coupling, co-citation, and co-authorship relations. Starting from version 1.6.10 (released on January 10, 2019), VOSviewer can now directly use citation data stored in COCI, retrieved by means of the COCI REST API.

Citation Gecko (http://citationgecko.com) is a novel literature mapping tool that allows one to map a research citation network using some initial seed articles. Citation Gecko is able to leverage citation links between seed papers and other papers to highlight papers of possible interest to the user, for which it uses COCI data (accessed via the REST API) to generate the citation network.

OCI Graphe (https://dossier-ng.univ-st-etienne.fr/scd/www/oci/OCI_graphe_accueil.html) is a Web tool that allows one to search articles by means of the COCI REST API, that are then visualised in a graph showing citations to the retrieved articles. It enriches this visualisation by adding additional information about the publication venues, publication dates, and other related metadata.

Zotero [12] is a free, easy-to-use tool to help users collect, organize, cite, and share research. Recently, the Open Citations Plugin for Zotero (https://github.com/zuphilip/zotero-open-citations) has been released, which allows users to retrieve open citation data extracted from COCI (via its REST API) for one or more articles included in a Zotero library.

COCI data, downloaded from the CSV dump available on Figshare, have been also used in at least two bibliometric studies. In particular, during the LIS Bibliometrics 2019 Event, Stephen Pearson presented a study (https://blog.research-plus.library.manchester.ac.uk/2019/03/04/using-open-citation-data-to-identify-new-research-opportunities/) run on publications by scholars at the University of Manchester which used COCI to retrieve citations between these publications so as to investigate possible cross-discipline and cross-department potential collaborations. Similarly, COCI data were used to conduct an experiment on the latest Italian Scientific Habilitation [13] (the national exercise that evaluates whether a scholar is appropriate to receive an Associate/Full Professorship position in an Italian university), which aimed at trying to replicate part of the outcomes of this evaluation exercise for the Computer Science research field by using only open scholarly data, including the citations available in COCI, rather than citation data from subscription services.

Conclusions

In this paper, we have introduced COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations. After an initial introduction of the notion of citations as first-class data entities, we have presented the ingestion workflow that has been implemented to create COCI, have detailed the data COCI contains, and have described the various services and resources that we have made available to access COCI data. Finally, we have presented some statistics about the use of COCI data, and have mentioned the tools and studies that have adopted COCI in recent months.

COCI is just the first open citations index that OpenCitations will make available. Using the experience we have gathered by creating it, we now plan the release of additional indexes, so as to extend the coverage of open citations available through the OpenCitations infrastructure. The first of these, recently released, is CROCI (https://w3id.org/oc/index/croci) [14], the Crowdsourced Open Citations Index, which contains citations deposited by individuals. CROCI is designed to permit scholars proactively to fill the open citations gap in COCI resulting from four causes: (a) the failure of many publishers using Crossref DOIs to deposit reference lists of their publications at Crossref, (b) the failure of some publishers that do deposit their reference lists to make these reference lists open, in accordance with the recommendations of the Initiative for Open Citations; (c) the absence from ~11% of Crossref reference metadata of the DOIs for cited articles which in fact have been assigned DOIs (https://www.crossref.org/blog/underreporting-of-matched-references-in-crossref-metadata/), a problem that Crossref are currently working hard to rectify; and (d) the existence of citations to published entities that lack Crossref DOIs. In the near future, we plan to extend the number of indexes by harvesting citations from other open datasets including Wikidata (https://www.wikidata.org), DataCite (https://datacite.org), and Dryad (https://datadryad.org). In addition, we plan to extend and generalise the current software developed for COCI, so as to facilitate most frequent updates of the indexes.

Acknowledgements

We gratefully acknowledge the financial support provided to us by the Alfred P. Sloan Foundation for the OpenCitations Enhancement Project (grant number G‐2017‐9800).

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  26. Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. DOI: https://doi.org/10.1038/sdata.2016.18
  27. Falco, R., Gangemi, A., Peroni, S., Shotton, D., Vitali, F. (2014). Modelling OWL Ontologies with Graffoo. In The Semantic Web: ESWC 2014 Satellite Events: 320–325. DOI: https://doi.org/10.1007/978-3-319-11955-7_42

Footnotes

1. An in-depth description about the definition and use of citations as first-class data entities can be found at https://opencitations.hypotheses.org/816. [back]
2. Additional information on this classification of Crossref reference lists is available at https://www.crossref.org/reference-distribution/.[back]
3. We have access to the limited dataset since we are members of the Crossref Metadata Plus plan.[back]
4. We are aware that the current practice for DOI URLs is to use the base https://doi.org/ instead of http://dx.doi.org/. However, when one tries to resolve a DOI URL owned by Crossref by specifying an RDF format (e.g. Turtle) in the accept header of the request, the bibliographic entity is actually defined using the old URL structure starting with http://dx.doi.org/. For this reason, since COCI is derived entirely from Crossref data, we decided to stay with the approach currently used by Crossref.[back]

The OpenCitations Enhancement Project – final report

The OpenCitations Enhancement Project
Final report for the Alfred P. Sloan Foundation

Report period: 1st May 2017 – 30 November 2018.
Report written: 30th December 2018

Background

OpenCitations (http://opencitations.net) is a scholarly infrastructure organization dedicated to open scholarship and the publication of open bibliographic and citation data by the use of Semantic Web (Linked Data) technologies, and engaged in advocacy for semantic publishing and open citations [Peroni and Shotton, 2018b]. It provides the OpenCitations Data Model [Peroni and Shotton, 2018d], the SPAR (Semantic Publishing and Referencing) Ontologies [Peroni and Shotton, 2018e] for encoding scholarly bibliographic and citation data in RDF, and open software of generic applicability for searching, browsing and providing APIs over RDF triplestores. It has developed the OpenCitations Corpus (OCC) [Peroni et al., 2017] of open downloadable bibliographic and citation data recorded in RDF, and a system and resolution service for Open Citation Identifiers (OCIs) [Peroni and Shotton, 2018c], and it is currently developing a number of Open Citation Indexes using the data openly available in third-party bibliographic databases.

The Directors of OpenCitations are David Shotton, Oxford e-Research Centre, University of Oxford (david.shotton@opencitations.net), and Silvio Peroni, Digital Humanities Advanced Research Centre, Department of Classical Philology and Italian Studies, University of Bologna (silvio.peroni@opencitations.net). We are committed to open scholarship, open data, open access publication, and open source software. We espouse the FAIR data principles developed by Force11, of which David Shotton was a founding member (https://www.force11.org/group/fairgroup/fairprinciples), and the aim of the Initiative for OpenCitations (I4OC, https://i4oc.org), of which both David Shotton and Silvio Peroni were founding members, to promote the availability of citation data that is structured, separable, and open.

Project personnel and roles

Ivan Heibi – Research Fellow

Ivan Heibi was appointed to the 12 months Research Fellowship position funded by the Sloan Foundation.

Ivan has been responsible for the development of new visualization and programming interfaces for exploring and making sense of the citation data included in the OCC and in the new OpenCitations Indexes, for the main part of the scripts related to the population and regular maintenance of COCI (The OpenCitations Index of Crossref open DOI-to-DOI citations, the first of the OpenCitations Indexes), and for conference presentations and paper writing.

Silvio Peroni – Lead Applicant and Principal Investigator

Silvio has been responsible for project management, for interview, appointment and supervision of the work of Ivan Heibi, for all aspects of software coding and technical developments required for the OpenCitations Corpus (OCC), the OpenCitations Indexes, and the Open Citation Identifier Resolution Service, for the ordering and management of new Sloan-funded hardware, and for conference presentations, paper writing and other forms of outreach and dissemination (e.g. blog and social networks).

David Shotton – Consultant Co-Investigator

David has been responsible for project management, interaction with publishers, conference presentations, paper writing, other forms of outreach and dissemination (e.g. blog and social networks), for web site and data model revision, and for independent usability evaluation, stress-testing and design feedback of new user interfaces and applications.

Project management

Project management has been straightforward, as should be the case, given the small size of our team. It has involved more than 1500 e-mail exchanges between David Shotton and Silvio Peroni, about 500 e-mail exchanges between Silvio Peroni and Ivan Heibi since November 2017, two dozen or so video conferences, some of which have involved collaborators, and an extended face-to-face meeting during the WikiCite 2017 Conference in Vienna at the start of the project and the Workshop on Open Citations 2018 in Bologna.

We have together harmoniously developed the concept of and vision for OpenCitations as an infrastructure organization, the structure and content of the OpenCitations web site (http://opencitaitons.net), the classes and properties of our supporting SPAR ontologies (http://www.sparontologies.net), and the community of collaborators and users of our developments. This has involved outreach and dissemination at a number of international research conferences, involvement with publishers through the Initiative for Open Citations, and associated publications.

We have studied and preliminarily tested a new scalable architecture centred on one powerful independent physical server, that both stores and handles all the data in the Corpus and in the new OpenCitations Indexes, and also offers adequate performance for query services. This server is supplemented by 30 additional small physical machines, Raspberry Pi 3Bs, working in parallel, each in charge of ingesting a defined set of reference lists and feeding the ingested data to the central server for further processing and storage as RDF in our Blazegraph triplestore.

Current status of the OpenCitations services

Currently, we release two different datasets – the OpenCitations Corpus and COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations – and several interfaces so as to make these data queryable from different access points.

Functionality and holdings

As of 29th December 2018, the OpenCitations Corpus (OCC) contains information about 13,964,148 citation links to 7,565,367 cited resources, ingested from 326,743 citing bibliographic resources obtained from the Open Access corpus of Europe PubMed Central and from the citation data imported from the EXCITE project. The main part of the development effort in the past months has been spent in implementing ingestion strategies that allow partners to provide us citation data, stored according the OpenCitations Data Model [Peroni and Shoton, 2018b], so as to be added directly into the Corpus. In September 2018, we successfully completed the ingestion of the initial data coming from the EXCITE project (citations from social sciences scholarly papers published by German publishers), and we are actively interacting with the LOC-DB project and the Venice Scholar Index so as to add their data to the OCC as well. In ongoing work, we are also collaborating with arXiv and EXCITE to harvest all the references from all PDF documents in the arXiv ePrints collection, to record these in RDF according to the OpenCitations Data Model, and to ingest them into the OpenCitations Corpus, as well as creating a new Index of these citations.

The first of these OpenCitations Indexes is COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations, an RDF dataset containing details of all the citations that are specified by the open references to DOI-identified works present in Crossref, as of the latest COCI update. COCI does not index Crossref references that are closed, nor Crossref references to entities that lack DOIs. These citations are treated as first-class data entities, with accompanying properties including the citations timespan and possible kinds of self-citation characteristics, modelled according to the index data model described in the OpenCitations Indexes page. COCI was launched in July 2018, and the most recent update of COCI is dated 12 November 2018. It presently contains 449,840,503 citations between 46,534,705 bibliographic resources. COCI is the first citation index released by OpenCitations, being a bibliographic index recording citations between publications that permits the user to establish which later documents cite earlier documents, and to create citation graphs of these citations.).

While full coverage of the scholarly citation graph depicted by the aforementioned datasets (or as full as practically possible) is required for the calculation of certain bibliometric indicators such as journal impact factors and individual h-indexes (Hirsch numbers), partial coverage while OCC grows is still of value, since it includes citations of all the most important biomedical papers obtained from the Open Access Subset off PubMed Central. These can be easily recognized by their large number of inward citation links, and can be used to explore the development of disciplines and research trends. In addition, COCI, with its wider scope, has sufficient coverage to be used for large-scale bibliometrics analysis.

Purchase of new hardware, and testing and development of new software

Because of unavoidable academic teaching commitments for Silvio Peroni, the installation of the new Sloan-funded hardware for OCC, purchased in autumn 2017, had to be postponed. All the services (old and new) of OpenCitations were successfully transferred to our new server in October 2018. In order to implement such transition, the ingestion process of the OCC was halted, so as to allow Silvio Peroni to properly test the new hardware and to extend the existing ingestion software so as to be usable within the new parallel processing architecture. The final tests are currently running, and we will recommence the full ingestion process of the OCC using the new hardware configuration, with its greatly enhanced ingest rate, in January 2019. In the meantime, the new infrastructure has been used to allow us to create COCI, so far the largest RDF dataset of open citation data available worldwide.

In addition, we have completed the transition of all the OpenCitations software from the old GitHub repository (i.e. https://github.com/essepuntato/opencitations) to a new GitHub organization, namely https://github.com/opencitations. This organisation includes several repositories which permit third parties to initiate the whole suite of OpenCitations software on a local machine. This is of key importance for the resilience of this open source project.

User interfaces

SPARQL, the query language used to interrogate RDF triplestores, is a quite powerful language. However, one needs appropriate skills with Semantic Web technologies to master it for solving even easy search tasks. Thus normal web users are unable to use appropriately such technologies if not appropriately instructed, leaving all these technologies in the hands of a limited number of experts. The datasets made available by OpenCitations suffered similar issues.

Initially, the goal we had was to develop ad-hoc user interfaces to abstract the complexities of the SPARQL endpoints into well-designed Web interfaces that anyone could use. During the development, though, we thought it would be better to develop generic frameworks for building customizable interfaces that allow one to expose, in a more human-understandable way, RDF data stored in any RDF triplestore and accessible through any SPARQL-endpoint, so as to forster reuse of such software in contexts that, in principle, might go far beyond the OpenCitations domain.

To this end we developed three different open software applications:

  • OSCAR [Heibi et al., 2018a] [Heibi et al., 2018b], a Javascript application for creating textual search interfaces to RDF data;
  • LUCINDA, another Javascript application for creating Web browsers over RDF data;
  • RAMOSE, a Python application that permits one to easily create and serve a conventional HTTP REST API over a SPARQL endpoint.

All these tools provide a configurable mechanism – by means of one single textual configuration file – that allows one to generate Web-based interfaces to any SPARQL endpoint. In practice, these tools are flexible Web-interface makers to RDF data, and as such represent a significant advance available to the entire community.

All these applications have been used to produce several interfaces to all the datasets released by OpenCitations. In particular, we have created user-friendly textual search interfaces (via OSCAR) both for the OCC (see the search box now on the OpenCitations home page, and the related search page) and for COCI (see the related search page). We have additionally developed browsing applications (via LUCINDA) to permit humans an easier navigation of all the entities included in the OCC (e.g. see the bibliographic resource br/1791056) and in COCI (e.g. see the citation oci:02001010806360107050663080702026306630509-02001010806360107050663080702026305630301). Finally, we have also implemented REST HTTP APIs (via RAMOSE) for simplifying the queries to both datasets, the OCC and COCI, by Web developers with no expertise in Semantic Web technologies.

In addition, in order to demonstrate its flexibility, we have also created two web pages using OSCAR, LUCINDA, and RAMOSE for permitting similar tasks (text query, browsing, and REST APIs) on the scholarly data in Wikidata / WikiCite – another project recently funded by the Alfred P. Sloan Foundation. These interfaces have been introduced in two distinct event: during the hack day of the Workshop on Open Citations 2018 and during the WikiCite 2018 Conference.

A further prototypical interface / service has been recently proposed so as to try to gather additional open citation data to include in the OCC, involving users of the scholarly domain such as editors and researchers. This application is called BCite [Daquino et al., 2018]. BCite is designed to provide a full workflow for citation discovery, allowing users to specify the references as provided by the authors of an article, to retrieve them in the required format and style, to double-check their correctness, and, finally, to create new open citation data according to the OpenCitations Data Model [Peroni and Shotton, 2018d], so as to permit their future integration into the OCC. While presently only a prototype, we received several commendations for this tool, and we are currently studying funding strategies to develop a full standalone application that can be used by anyone and that allows users to directly interact with the OCC, so as to upload new data into the Corpus.

Open Citation Identifiers

During the reporting period, it became increasingly evident to us that citations deserved treating as First Class Data Entities, which would give the following advantages:

  • All the information regarding each citation would be available in one place.
  • Citations become easier to describe, distinguish, count and process.
  • If available in aggregate, citations become easier to analyze using bibliometric methods, for example to determine how citation time spans vary by discipline.

Four developments were required to make this possible:

  • The metadata describing the citation must be definable in a machine-readable manner.
  • Such metadata must be storable, searchable and retrievable.
  • Each citation must be identifiable, using a globally unique Persistent Identifier.
  • There must be a Web-based resolution service that takes the identifier as input and returns a description of the citation.

To this end, we have achieved:

  • the first requirement by the addition of appropriate classes and properties to CiTO, the Citation Typing Ontology, and by the addition of a new member of the class datacite:ResourceIdentifierScheme in the DataCite Ontology, namely Open Citation Identifier;
  • the second requirement by modifying the OpenCitations Data Model [Peroni and Shotton, 2018d] so that citations can be properly described using these new ontology terms within the OpenCitations Corpus;
  • the third requirement by creating the syntax for this new Open Citation Identifier (OCI) [Peroni and Shotton, 2018c], and enabling the creation of such identifiers, both to specify citations within the OpenCitations Corpus, and also (importantly) to specify citations described in Wikidata (by QIDs) and in Crossref (by DOIs); and
  • the fourth requirement by creating a resolving service for OCIs at http://opencitations.net/oci and additional software (in Python) for retrieving information about a particular citation identified by an OCI.

To date, OCIs have been actively used both in the OCC and in COCI to identify all the citations they contain. In addition, we now plan to create and publish additional OpenCitations Indexes of all the citations DataCite, Wikidata and (of course) the OCC, which we hope will be of great benefit to bibliometricians in their analysis of citation networks, self-citation, etc., and in the calculation of citation metrics. OCIs have been recognised by the EU Project FREYA as unique global identifiers for bibliographic citations.

Collaborations and Users

OpenCitations Data Model

We are collaborating with the following groups and academic projects, both to promote the use of the OpenCitations Data Model (OCDM), and to provide a publication venue for the citation data that they are liberating from the scholarly literature:

  • Matteo Romanello of the Digital Humanities Laboratory at the University of Lausanne is using OCDM for modelling citations of the classical literature within ancient Venetian documents in the context of the Venice Scholar Index, and is currently working on producing a dataset of citation data compliant with OCDM so as to be ingested in the OCC.
  • Two DFG-funded German projects that are extracting citations from Social Science publications:
    • The Linked Open Citations Database (LOC-DB) at the University of Mannheim is using OCDM to model their data, with the aim of producing them accordigly with such model so as to be ingested in the OCC.
    • Steffen Staab (University of Koblenz) and Philipp Mayr (GESIS) are running the EXCITE Project, which uses OCDM to model their citation data, and already adopted the OCC as their publication platform. In fact, in September 2018, ~1 million citations coming from the EXCITE Project were successfully ingested in the OCC.
  • Sergey Parinov is technically leading CitEcCyr, which is an open repository of citation relationships obtained from research papers in the Russian language and Cyrillic script. This project intends to model its citations using the OCDM, and will use the OpenCitations Corpus as its publication platform.

Users of OpenCitations data

The following project and organizations have let us know that they are using data from the OCC and COCI:

  • Wikidata includes alignments between several bibliographic entries with OCC resources;
  • OpenAIRE imported OCC metadata about articles into their LOD database;
  • Daniel Ecer and Lisa Knoll of eLife performed analytics on the OCC data;
  • Ontotext demonstrated SPARQL query federation between Springer Nature LOD and OCC;
  • Anna Kamińska published a bibliometrics case study of PLOS ONE articles in OCC;
  • Daniel Himmelstein processed OpenCitations data to create DOI-to-DOI citation tables;
  • Thiago Nunes and Daniel Schwabe are using OCC to exemplify their XPlain framework;
  • Antonina Dattolo and Marco Corbatto are using the OCC as source for VisualBib framework;
  • Nees Jan van Eck and Ludo Waltman extended VOSviewer so as to use data in the OCC + COCI, that will be officially published in the next release of the tool;
  • Barney Walker developed Citation Gecko, a graph-based citation discovery tool based on the OCC and COCI for retrieving citation data about the papers;
  • Philipp Zumstein developed a Zotero plugin that gives information about open citations using COCI;
  • Dominique Rouger developed a Web application that provides a visual graph representation of citation links in COCI.

Website statistics

From May 2017 to November 2018, the official OpenCitations website has been accessed ~5.8M times – we have excluded from this list the hits done by well-known spiders and crawlers. It is worth mentioning that the pages related to the data available and the services for querying them (i.e. “/corpus”, “/sparql”, and “/index” in the following diagram) have together gained a very high percentage of the overall accesses, showing that the main reason people access the OpenCitations website is to explore and use the data in the OCC and in COCI. It is also clear how the introduction of COCI brought additional accesses to the OpenCitations services, and the trend is increasing – e.g. in December 2018 (not shown in the following diagram) we got more than 200M accesses to “/index”, mainly related to the use of the COCI REST APIs.

Community outreach

From May 2017 to November 2018, the documents (i.e. [Peroni and Shotton, 2018b] [Peroni and Shotton, 2018c] [Peroni and Shotton, 2018d]) and the dumps of the OCC and of COCI available on Figshare (see http://opencitations.net/download) have been viewed 37,960 times and downloaded 3,842 times. The figure below summarizes how many views and downloads such resources have received month by month. For example, the latest version of COCI in CSV has been downloaded 239 times since its release in November (see https://doi.org/10.6084/m9.figshare.6741422.v3).

In the past nineteen months, the posts published by the official Twitter account of OpenCitations have been engaged by 599,200 distinct Twitter accounts, the Twitter profile (@opencitations) has been visited 12,719 times and has been mentioned in 565 tweets written by others, and it has collected an additional 1,925 followers. The diagram below shows all these statistics month by month.

Although we made relatively few new blog posts in the reporting period (there have been twenty more since then), from May to November 2018, the blog dedicated to OpenCitations (http://opencitations.wordpress.com) received 19,454 visits by 15,184 distinct users. As shown in the diagram below, the biggest peaks in terms of visits has been in July 2018 (the month when we launched COCI) and in September 2018 (the month of the Workshop on Open Citations).

Users of the SPAR Ontologies

The SPAR Ontologies [Peroni and Shotton, 2018e] are in use by about 40 other projects and organizations, including:

  • The United States Global Change Information System, which encodes federal information relating to climate change, makes extensive use of SPAR ontology terms.
  • The United Nations Document Ontology (UNDO) has been specifically aligned with FaBiO.
  • Wikidata has many classes that have been alighted with FaBiO or CiTO.
  • DBPedia’s DataID ontology uses the FaBiO and DataCite ontologies.
  • W3C’s Data on the Web Best Practices: Dataset Usage Vocabulary uses SPAR Ontologies.

For the full list, see http://www.sparontologies.net/uptake.

To date, as far as we are aware, more than 677 papers have been published that cite or use one or more of the SPAR ontologies. For the full list, see http://www.sparontologies.net/uptake#publications.

OpenCitations and the Initiative for Open Citations

OpenCitations and the Initiative for Open Citations, despite the similarity of title, are two distinct organizations. The primary purpose of OpenCitations is to host and build the OpenCitations Corpus (OCC) and the OpenCitations Indexes, as long as additional service to browse, query, and analyse citation data. In contrast, the Initiative for Open Citations (I4OC, https://i4oc.org) is separate and independent organization, whose founding was spearheaded by Dario Taraborelli of the WikiMedia Foundation. OpenCitations is one of several founding members of the Initiative for Open Citations, as documented at https://i4oc.org/#founders. I4OC is a pressure group to promote the unrestricted availability of scholarly citation data, but does not itself host citation data.

Because open reference lists are necessary for the population of OCC, we at OpenCitations have devoted considerable effort to promoting I4OC’s aims, and we host the I4OC web site on behalf of that community.

Within a short space of time, I4OC has persuaded most of the major scholarly publishers to open their reference lists submitted to Crossref, so that the proportion of all references submitted to Crossref that are now open has risen from 1% to over 50%. These are now available for OpenCitations to harvest into the OpenCitations Corpus and publish in RDF, as well as for others to harvest and use as they wish [Shotton, 2018].

Publications during the reporting period

Scholarly papers

Marilena Daquino, Ilaria Tiddi, Silvio Peroni, David Shotton (2018). Creating Open Citation Data with BCite. In Emerging Topics in Semantic Technologies – ISWC 2018 Satellite Events: 83-93. DOI: https://doi.org/10.3233/978-1-61499-894-5-83, OA at http://ceur-ws.org/Vol-2184/paper-01.pdf

Ivan Heibi, Silvio Peroni, David Shotton (2018). Enabling text search on SPARQL-endpoints through OSCAR. Submitted for publication to Data Science – Methods, Infrastructure, and Applications. OA at at https://w3id.org/people/essepuntato/papers/oscar-datascience2019/

Ivan Heibi, Silvio Peroni, David Shotton (2018). OSCAR: A Customisable Tool for Free-Text Search over SPARQL Endpoints. In Semantics, Analytics, Visualization – 3rd International Workshop, SAVE-SD 2017, and 4th International Workshop, SAVE-SD 2018, Revised Selected Papers: 121-137. DOI: https://doi.org/10.1007/978-3-030-01379-0_9, OA at https://w3id.org/people/essepuntato/papers/oscar-savesd2018.html

Silvio Peroni, David Shotton (2018). OpenCitations: enabling the FAIR use of open citation data. In Proceedings of the GARR Conference 2017 – The data way to Science – Selected Papers. DOI: https://doi.org/10.26314/GARR-Conf17-proceedings-19

Silvio Peroni, David Shotton (2018). The SPAR Ontologies. In Proceedings of the 17th International Semantic Web Conference (ISWC 2018): 119-136. DOI: https://doi.org/10.1007/978-3-030-00668-6_8

Silvio Peroni, David Shotton, Fabio Vitali (2017). One year of the OpenCitations Corpus: Releasing RDF-based scholarly citation data into the Public Domain. In Proceedings of the 16th International Semantic Web Conference (ISWC 2017): 184-192. DOI: https://doi.org/10.1007/978-3-319-68204-4_19, OA at https://w3id.org/people/essepuntato/papers/oc-iswc2017.html

David Shotton (2018). Funders should mandate open citations. Nature 553: 129. https://doi.org/10.1038/d41586-018-00104-7

Additional documents

Silvio Peroni, David Shotton (2018). Open Citation: Definition. Figshare. DOI: https://doi.org/10.6084/m9.figshare.6683855.v1

Silvio Peroni, David Shotton (2018). Open Citation Identifier: Definition. Figshare. DOI: https://doi.org/10.6084/m9.figshare.7127816.v1

Silvio Peroni, David Shotton (2018). The OpenCitations Data Model. Figshare. DOI: https://doi.org/10.6084/m9.figshare.3443876.v5

Blog posts

6 May 2017: Querying the OpenCitations Corpus

15 May 2017: The Sloan Foundation funds OpenCitations

24 Nov 2017: Milestone for I4OC – open references at Crossref exceed 50%

24 Nov 2017: Elsevier references dominate those that are not open at Crossref

28 Nov 2017: Openness of non-Elsevier references

9 Jan 2018: The new Crossref reference distribution policy

9 Jan 2018: Barriers to comprehensive reference availability

15 Jan 2018: Funders should mandate open citations

16 Jan 2018: Oxford University Press opens its references!

29 Jan 2018: OpenCitations and the Initiative for Open Citations: A Clarification

19 Feb 2018: Citations as First-Class Data Entities: Introduction

22 Feb 2018: Citations as First-Class Data Entities: Citation Descriptions

25 Feb 2018: Citations as First-Class Data Entities: The OpenCitations Data Model

4 Mar 2018: Citations as First-Class Data Entities: The OpenCitations Corpus

12 Mar 2018: Citations as First-Class Data Entities: Open Citation Identifiers

15 Mar 2018: Citations as First-Class Data Entities: The Open Citation Identifier Resolution Service

23 Mar 2018: Early adopters of the OpenCitations Data Model

17 Apr 2018: Workshop on Open Citations

12 Jul 2018: COCI, the OpenCitations Index of Crossref open DOI-to-DOI references

19 Nov 2018: New release of COCI: 450M DOI-to-DOI citation links now available

Conference presentations and other outreach

Workshop on Open Citations 2018

OpenCitations, the EXCITE Project and Europe PubMed Central ran the first Workshop on Open Citations (Twitter: @workshop_oc) at the University of Bologna in Bologna, Italy, on 3-5 September 2018. It was organised as follows:

  • Day One and Day Two: Formal presentations and discussions on the creation, availability, uses and applications of open bibliographic citations, and of bibliometric studies based upon them;
  • Day Three: A Hack Day on Open Citations to see what services can be prototyped using large volumes of open citation data.

The workshop involved 60 participants, including researchers, computer scientists, scholarly publishers, academic administrators, research funders and policy makers. The workshop was organised around the following topics:

  • Opening up citations: Initiatives, collaborations, methods and approaches for the creation of open access to bibliographic citations;
  • Policies and funding: Strategies, policies and mandates for promoting open access to citations, and transparency and reproducibility of research and research evaluation;
  • Publishers and learned societies: Approaches to, benefits of, and issues surrounding the deposit, distribution, and services for open bibliographic metadata and citations;
  • Projects: Metrics, visualizations and other projects. The uses and applications of open citations, and bibliometric analyses and metrics based upon them.

All the sessions were recorded and are available on the official YouTube channel of the University of Bologna, and are linked (together with slides) at the website of the workshop – https://workshop-oc.github.io.

A further Workshop on Open Citations is being planned for autumn 2019.

Presentations

We have made conference presentations on OpenCitations, the Initiative for Open Citations, and Open Citation Identifiers at the following international conferences and workshops:

WikiCite Conference 2017, Vienna, 23 May 2017, https://www.slideshare.net/essepuntato/opencitations (Silvio Peroni and David Shotton)

COASP 9, 9th Conference of Open Access Scholarly Publishing, Lisbon, 20 September 2017, https://www.slideshare.net/essepuntato/the-initiative-for-open-citations-and-the-opencitations-corpus (David Shotton)

SemSci 2017, 1st International Workshop on Enabling Open Semantic Science, Vienna, 21 October 2017, https://w3id.org/people/essepuntato/presentations/the-open-citations-revolution.html (Silvio Peroni)

ISWC 2017, 16th International Semantic Web Conference, Vienna, 24 October 2017, https://w3id.org/people/essepuntato/presentations/oc-iswc2017.html (Silvio Peroni)

FORCE 2017, Research Communication and e-Scholarship Conference, Berlin, 27 October 2017, http://w3id.org/people/essepuntato/presentations/oc-force2017.html (Silvio Peroni)

Linked Open Citation Database (LOC-DB) Workshop, Mannheim, 7 November 2017, https://locdb.bib.uni-mannheim.de/wordpress/wp-content/uploads/2017/10/Shotton-LOC-DB-Mannheim.pdf (David Shotton)

GARR Conference 2017, Venice, 16 November 2017, https://www.eventi.garr.it/it/documenti/conferenza-garr-2017/presentazioni-2/232-conf2017-presentazione-peroni/file (Silvio Peroni)

OpenCon 2017, Oxford, 1 December 2017, https://doi.org/10.6084/m9.figshare.5844981.v1 (David Shotton)

PIDapalooza Conference of Persistent Identifiers, Girona, 24 January 2018, https://doi.org/10.6084/m9.figshare.5844972.v2 (David Shotton)

2018 International Workshop on Semantics, Analytics, Visualisation: Enhancing Scholarly Dissemination, Lyon, 24 April 2018, https://doi.org/10.6084/m9.figshare.7531577.v1 (Ivan Heibi)

Workshop on Open Citations 2018, Bologna, 3 September 2018, https://workshop-oc.github.io/presentations/D1S3_David_Shotton.pdf (David Shotton)

Workshop on Open Citations 2018, Bologna, 4 September 2018, https://docs.google.com/presentation/d/1mybQmjhFY6kLtTE1TdONaxsl0nSjmRGOSCnFMTwfzWQ/edit?usp=sharing (Silvio Peroni)

The 5th Conference on Scholarly Publishing in the Context of Open Science (PUBMET 2018), Zadar, 20 September 2018, https://doi.org/10.6084/m9.figshare.7110653.v3 (Silvio Peroni)

The 17th International Semantic Web Conference (ISWC 2018), Monterey, 12 October 2018, https://doi.org/10.6084/m9.figshare.7151759.v1 (Silvio Peroni)

WikiCite Conference 2018, Berkeley, 27 November 2018, https://doi.org/10.6084/m9.figshare.7396667.v1 (Ivan Heibi)

A further presentation on Open Citation Identifiers will be given at the 2019 PIDapalooza Conference of Persistent Identifiers in Dublin in January 2019.

Tweets

We have tweeted about the project and related matters under the names @opencitations, @dshotton, @essepuntato, @ivanHeiB, @workshop_oc, and @i4oc_org.

Future sustainability

While presently the OpenCitations Corpus has only partial coverage, our aim is that OpenCitations should become a comprehensive source of open citation information from all disciplines of scholarly endeavour, used on a daily basis by scholars worldwide, to equal or better the commercial offerings from Clarivate Analytics (Web of Science) and Elsevier (Scopus).

We also wish to develop effective graphical user interfaces to explore the citation network, and analytical tools over our open data. Since the OCC and COCI data are all open and available for others also to build such tools, we anticipate that such developments will best be undertaken collaboratively, under some open community organization, and indeed such development is currently being undertaken in collaboration with colleagues from CWTS at the University of Leiden, famous for their development of VOSviewer.

In order to fully support open scholarship, OpenCitations need to mature from being an academic research and development project to become a recognised scholarly infrastructure service such as PubMed. We wish to avoid becoming a commercial company, and see our development better served by being ‘adopted’ by a major established scholarly institution such as national or university library or an internationally recognised centre providing scholarly bibliographic services, that has already shown a commitment to open scholarship, where the interaction between that institution and OpenCitations would be mutually beneficial. To this end, we are currently in the mid-phase of negotiations with two institutions.

Conclusions

The grantees wish to express their deep gratitude to the Alfred P. Sloan Foundation for financial support enabling them to undertake the OpenCitations Enhancement Project, without which the rapid developments reported here would not have been possible.

References

Marilena Daquino, Ilaria Tiddi, Silvio Peroni, David Shotton (2018). Creating Open Citation Data with BCite. In Emerging Topics in Semantic Technologies – ISWC 2018 Satellite Events: 83-93. DOI: https://doi.org/10.3233/978-1-61499-894-5-83, OA at http://ceur-ws.org/Vol-2184/paper-01.pdf

Ivan Heibi, Silvio Peroni, David Shotton (2018a). Enabling text search on SPARQL-endpoints through OSCAR. Submitted for publication to Data Science – Methods, Infrastructure, and Applications. OA at at https://w3id.org/people/essepuntato/papers/oscar-datascience2019/

Ivan Heibi, Silvio Peroni, David Shotton (2018b). OSCAR: A Customisable Tool for Free-Text Search over SPARQL Endpoints. In Semantics, Analytics, Visualization – 3rd International Workshop, SAVE-SD 2017, and 4th International Workshop, SAVE-SD 2018, Revised Selected Papers: 121-137. DOI: https://doi.org/10.1007/978-3-030-01379-0_9, OA at https://w3id.org/people/essepuntato/papers/oscar-savesd2018.html

Silvio Peroni, Alexander Dutton, Tanya Gray, David Shotton (2015). Setting our bibliographic references free: towards open citation data. Journal of Documentation, 71: 253-77. DOI: https://doi.org/10.1108/JD-12-2013-0166, OA at http://speroni.web.cs.unibo.it/publications/peroni-2015-setting-bibliographic-references.pdf

Silvio Peroni, David Shotton (2018a). OpenCitations: enabling the FAIR use of open citation data. In Proceedings of the GARR Conference 2017 – The data way to Science – Selected Papers. DOI: https://doi.org/10.26314/GARR-Conf17-proceedings-19

Silvio Peroni, David Shotton (2018b). Open Citation: Definition. Figshare. DOI: https://doi.org/10.6084/m9.figshare.6683855.v1

Silvio Peroni, David Shotton (2018c). Open Citation Identifier: Definition. Figshare. DOI: https://doi.org/10.6084/m9.figshare.7127816.v1

Silvio Peroni, David Shotton (2018d). The OpenCitations Data Model. Figshare. DOI: https://doi.org/10.6084/m9.figshare.3443876.v5

Silvio Peroni, David Shotton (2018e). The SPAR Ontologies. In Proceedings of the 17th International Semantic Web Conference (ISWC 2018): 119-136. DOI: https://doi.org/10.1007/978-3-030-00668-6_8

Silvio Peroni, David Shotton, Fabio Vitali (2016a). Building Citation Networks with SPACIN. Knowledge Engineering and Knowledge Management – EKAW 2016 Satellite Events, EKM and Drift-an-LOD, Revised Selected Papers: 162-166. DOI: https://doi.org/10.1007/978-3-319-58694-6_23, OA at https://w3id.org/oc/paper/spacin-demo-ekaw2016.html

Silvio Peroni, David Shotton, Fabio Vitali (2016b). Freedom for bibliographic references: OpenCitations arise. In Proceedings of 2016 International Workshop on Linked Data for Information Extraction (LD4IE 2016): 32-43. http://ceur-ws.org/Vol-1699/paper-05.pdf

Silvio Peroni, David Shotton, Fabio Vitali (2017). One year of the OpenCitations Corpus: Releasing RDF-based scholarly citation data into the Public Domain. In Proceedings of the 16th International Semantic Web Conference (ISWC 2017): 184-192. DOI: https://doi.org/10.1007/978-3-319-68204-4_19, OA at https://w3id.org/people/essepuntato/papers/oc-iswc2017.html

David Shotton (2013). Open citations. Nature, 502: 295-297. https://doi.org/10.1038/502295a

David Shotton (2018). Funders should mandate open citations. Nature 553: 129. https://doi.org/10.1038/d41586-018-00104-7

New release of COCI: 445M DOI-to-DOI citation links now available

As introduced in a previous blog post, COCI is the OpenCitations Index of Crossref open DOI-to-DOI references, all released as CC0 material. It is our first OpenCitations Index of open citations, in which we have applied the concept of citations as first-class data entities to index the contents of one of the major databases of open scholarly citation information, namely Crossref, and to render and make available this information in machine-readable RDF.

We are now proud to announce a new release of COCI, the second, which now contains almost 445 million DOI-to-DOI citation links coming from both ‘the ‘Open’ and the ‘Limited’ sets of Crossref reference data.  This represents an increase of 42% in the number of indexed citations, compared with the initial release of COCI on 4th June 2018, which indexed 316,243,802 citations involving 45,145,889 bibliographic resources. In addition, the data model for COCI has now been extended so as to state directly the presence of journal self-citations and author self-citations.

Extended data model

The previous data model used for storing the citation data in COCI – which is itself a subset of the OpenCitations Data Model – has been extended so as to keep track of two particular types of self-citation, as shown in the following figure.

The new data model used in COCI for describing its citation data, which includes classes for describing two kinds of self-citations, i.e. journal self-citations and author self-citations.

Generally speaking, a self-citation is citation in which the citing and the cited entities have something significant in common with one another, over and beyond their subject matter. The two kinds of self-citations we are now tracking are:

  • journal self-citation (class cito:JournalSelfCitation), i.e. a citation in which the citing and the cited entities are published in the same journal. This information has been obtained by comparing the ISSNs of the journals where two journal articles related by a citation have been published, as provided by Crossref. If they share the same ISSN, then the citation is described as journal self-citation;
  • author self-citation (class cito:AuthorSelfCitation), i.e. a citation in which the citing and the cited entities have at least one author in common. This information has been obtained by comparing the ORCIDs associated to the authors of a citing bibliographic entity with the ORCIDs of the authors of the cited entity. In this case, if any ORCID is shared, then the citation is described as author self-citation.  This categorization excludes authors bearing the same name where the ORCIDs are not known, since, while these instances may be author self-citations, they may alternatively merely represent name coincidences of distinct individuals.

It is worth mentioning that, while the ISSN information are usually present in the data returned by Crossref, the presence of ORCID id data associated with the authors of the various paper represented in Crossref is presently very limited, so that the number of recorded author self-citations in COCI is likely to be a considerable underestimate.

In this new release, COCI contains 445,826,118 citations, of which 30,114,696 are recorded as journal self-citations and 251,699 are recorded as author self-citations.

Extended REST API

The REST API for querying COCI has been extended so as to return information about the aforementioned self-citations. In particular, the response to the operations “references” and “citations” now has two more fields, i.e. “journal_sc” and “author_sc”, that are set to “yes” if the citation returned is a journal self-citation or an author self-citation respectively, or “no” otherwise.

Using the capabilities of the REST API, it is also possible to keep in or exclude from the result set those citations that are (or are not) one of the aforementioned types of self-citation. For instance, the following call

https://w3id.org/oc/index/coci/api/v1/citations/10.1002/pol.1987.140251103?filter=journal_sc:yes

returns all the citations having the article with DOI “10.1002/pol.1987.140251103” that are journal self-citations.

Conclusions

In this blog post we have introduced the second release of COCI, the OpenCitations Index of Crossref open DOI-to-DOI references, a citation index which now contains almost 450 million open citations created from the ‘Open’ and ‘Limited’ references included within Crossref.

As a reminder, all the data in COCI:

We plan soon to extend the OpenCitations Indexes by adding indexes of citations coming from other source datasets, including Wikidata and DataCite.

COCI, the OpenCitations Index of Crossref open DOI-to-DOI references

In a previous series of blog posts we proposed the treatment of bibliographic citations as first-class data entities, permitting citations to be endowed with descriptive properties. In doing so, we outlined some specific requirements, namely that the citations should be machine readable, should conform to a specific data model (in this case the OpenCitations Data Model), should be stored in an accessible database under an open license, and should be identified using global persistent identifiers (specifically Open Citation Identifiers) which are resolvable using an identifier resolution service (namely the Open Citation Identifier Resolution Service).

In this blog post, we introduce COCI, the OpenCitations Index of Crossref open DOI-to-DOI references1, our first open citation index, in which we have applied the concept of citations as first-class data entities to index the contents of one of the major open databases of scholarly citation information, namely Crossref, and to render and make available this information in machine-readable RDF.

Crossref contains metadata about publications (mainly academic journal articles) that are identified using Digital Object Identifiers (DOIs).  For about half of these publications Crossref also stores the reference lists of these articles submitted by the publishers (for discussion of why this is not true for all the publications, see this previous blog post).  Many of these references are to other publications bearing DOIs that are also described in Crossref, while others are to publications that lack DOIs and do not have Crossref descriptions.

COCI is an index of all the open DOI-to-DOI citations present in Crossref, and presently includes more than 300 million citations, obtained by parsing the open reference lists of the articles deposited there. COCI is available at http://opencitations.net/index/coci, and is released under a CC0 waiver.  COCI does not index Crossref references that are not open, nor Crossref open references to entities that lack DOIs.

What is an open citation index?

A citation index is a bibliographic index recording citations between publications, allowing the user to establish which later documents cite earlier documents. Several citation indexes are already available, some of which are freely accessible but not downloadable (e.g. Google Scholar), while others can be accessed only by paying significant access fees (e.g. Web of Science and Scopus). An open citation index contains only data about open citations, as defined in [1].

OpenCitations is a scholarly infrastructure organization dedicated to the promotion of semantic publishing by the use of semantic web (linked data) technologies, and engaged in advocacy for semantic publishing and open citations. It provides the OpenCitations Data Model and the SPAR (Semantic Publishing and Referencing) Ontologies for encoding scholarly bibliographic and citation data in RDF, and open software of generic applicability for searching, browsing and providing REST APIs over RDF triplestores. It has developed the OpenCitations Corpus (OCC) of open downloadable bibliographic and citation data recorded in RDF, and a system and resolution service for Open Citation Identifiers (OCIs), and it is currently developing a number of Open Citation Indexes using the data openly available in third-party bibliographic databases.

These Open Citation Indexes have the following characteristics in common:

  1. The citations they contain are all open [1].
  2. The citations are treated as first-class data entities;
  3. Each citation is identified by an Open Citation Identifier (OCI), which has a simple structure: the lower-case letters “oci” followed by a colon, followed by two numbers separated by a dash (e.g. oci:1-18);
  4. The citation metadata are recorded in RDF, based on the OpenCitations Data Model [2];
  5. The RDF statements for each citation record the basic properties shown in the following figure, which is based on the Citation Typing Ontology (CiTO) for describing the data, and the Provenance Ontology (PROV-O) for the provenance information.

The data model used for describing the citation data included in any Open Citation Index.
The data model used for describing the citation data included in any Open Citation Index.

Parsing the Crossref collection

Over the past few months, we have parsed the entire Crossref bibliographic database to extract all the DOI-to-DOI citations included in the dataset, as well as additional information about each citation, specifically its creation date (i.e. the publication date of the citing entity) and the citation time span (i.e. the interval between the publication date of the cited entity and the publication date of the citing entity, to an accuracy determined by these publication dates as recorded in Crossref). These data for the open citations are now made available in COCI.

Each citation is described as an individual of the class cito:Citation and is identified by an URL structured as follows:

https://w3id.org/oc/index/coci/ci/[[OCI]]

The parameter [[OCI]] refers to the numerical part of the Open Citation Identifier (OCI) assigned to the citation, i.e. two numbers separated by a dash, in which the first number identifies the citing work and the second number identifies the cited work. For instance:

https://w3id.org/oc/index/coci/ci/02001010806360107050663080702026306630509-02001010806360107050663080702026305630301

For citations extracted from Crossref in which the citing and cited works are identified by DOIs, which includes all the COCI citations, the OCI is created in the following manner:

  1. Each case-insensitive DOI is first normalized to lower case letters.
  2. 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.
  3. Finally, each converted numeral is prefixes by a “020”, which indicates that Crossref is the supplier of the original metadata of the citation (as indicated at http://opencitations.net/oci)

Currently COCI contains 316,243,802 citations and 45,145,889 bibliographic resources. We plan to update COCI at least every six months as more open DOI-to-DOI citations appear in Crossref.

How to access the citation data in COCI

All the data in COCI are available for inspection, download and reuse in the following ways.

SPARQL endpoint

By querying the COCI SPARQL endpoint at https://w3id.org/oc/index/coci/sparql. If you access this URL with a browser, a GUI will be shown, in which is an editable text box that enables the user to compose and execute a SPARQL query. In addition, the COCI SPARQL endpoint can be queried using the REST protocol, e.g. (via curl):

curl -L -H "Accept: text/csv" "https://w3id.org/oc/index/coci/sparql?query=PREFIX%20cito%3A%20%3Chttp%3A%2F%2Fpurl.org%2Fspar%2Fcito%2F%3E%0ASELECT%20%3Fcitation%20%3Fcreation%20%7B%20%3Fcitation%20a%20cito%3ACitation%20%3B%20cito%3AhasCitationCreationDate%20%3Fcreation%20%7D%20LIMIT%201"

The above GET call executes the following simple SPARQL query:

PREFIX cito: <http://purl.org/spar/cito/>
SELECT ?citation ?creation { 
    ?citation a cito:Citation ; 
        cito:hasCitationCreationDate ?creation 
} 
LIMIT 1

This query returns the IRI of one citation accompanied by its creation date in CSV format, as shown as follows:

citation,creation
https://w3id.org/oc/index/coci/ci/02001000002360105020963000103015801090909000259040238024003010138381018136310232701044203370037122439026315-02001000002361027293701070800030100060007,1999-02

Instead, the following SPARQL query should be used to get the information about a particular citation given its OCI:

PREFIX oci: <https://w3id.org/oc/index/coci/ci/>
PREFIX cito: <http://purl.org/spar/cito/>
SELECT DISTINCT ?citing ?cited ?creation ?timespan {
oci:02001010806360107050663080702026306630509-02001010806360107050663080702026305630301 a cito:Citation ;
    cito:hasCitingEntity ?citing ;
    cito:hasCitedEntity ?cited ;
    cito:hasCitationCreationDate ?creation ;
    cito:hasCitationTimeSpan ?timespan
}

In this case, the query will returns the DOI URLs of the citing and cited entities, accompanied by the creation date and the timespan of the citation:

citing,cited,creation,timespan
http://dx.doi.org/10.1186/1756-8722-6-59,http://dx.doi.org/10.1186/1756-8722-5-31,2013,P1Y

It is worth mentioning that the results can be also returned in JSON (using “Accept: application/json” in the header of the request) or XML (using “Accept: application/xml” in the header of the request). For example, accessing the long URL starting with “https” of the above request with a browser will return an XML document describing the same result.

REST API

Citation information may also be retrieved by using the COCI REST API, available and documented at https://w3id.org/oc/index/coci/api/v1, which has been implemented by means of RAMOSE (the Restful API Manager Over SPARQL Endpoints). Specifically, the COCI REST API makes available a mechanism for getting the citation data:

  • either for all the references to other works appearing in the particular bibliographic entity identified by the input DOI, e.g. https://w3id.org/oc/index/coci/api/v1/references/10.1186/1756-8722-6-59;
  • or for all the references appearing in other works to the particular bibliographic entity identified by the input DOI, e.g. https://w3id.org/oc/index/coci/api/v1/citations/10.1186/1756-8722-5-31;
  • or for the citation identified by the input Open Citation Identifier (OCI), e.g. https://w3id.org/oc/index/coci/api/v1/citation/02001010806360107050663080702026306630509-0200101080636102704000806).

If you would like to suggest an additional operation to be included in this API, please use the issue tracker of the COCI API available on GitHub.

Search and browsing interfaces

An interface providing a free text search over the contents of COCI is available at http://opencitations.net/index/coci/search. It allows one to search for citation data according to the same operational principles implemented in the REST API discussed above. However, in this case, the result are returned in tabular form through a web interface implemented by means of OSCAR, the OpenCitations RDF Search Application, e.g. http://opencitations.net/index/coci/search?text=10.1186%2F1756-8722-6-59&rule=citingdoi.

Each OCI returned by the search interface is a clickable link that opens a new descriptive page detailing that citation. These web pages are created by LUCINDA, which is a Javascript-based RDF data browser developed for exposing the statements contained in an RDF triplestore as descriptive human-readable HTML pages, e.g. http://opencitations.net/index/coci/browser/ci/02001010806360107050663080702026306630509-02001000002361222131237020000060000020201.

Data dumps

The dump of all the citation data available in COCI, including their provenance information, is downloadable from Figshare. These data are available in CSV and N-Triples formats, and each dump has a DOI assigned so as to be citable. Download links are available at http://opencitations.net/download#coci. A new dump will be made each time COCI is udpated.

By content negotiation

Using the HTTP URI of the individual citations, it is possible to access their representations in different formats: HTML, RDF/XML, Turtle, and JSON-LD. This is possible through a content negotiation mechanism that disambiguates in which format the information about a citation should be returned, by looking at the “Accept” header declared in the request. For instance, accessing the URL https://w3id.org/oc/index/coci/ci/02001010806360107050663080702026306630509-02001000002361222131237020000060000020201 will return the citation data in HTML, while the GET request below will return the same information in Turtle:

curl -L -H "Accept: text/turtle" "https://w3id.org/oc/index/coci/ci/02001010806360107050663080702026306630509-0200100000236122213123702000"

Conclusions

In this blog post we have introduced COCI, the OpenCitations Index of Crossref open DOI-to-DOI references, a citation index which contains more than 310 million open citations created from the ‘Open’ references included in Crossref. We plan soon to extend COCI to additionally include those DOI-to-DOI citations extracted from the ‘Limited’ set of Crossref reference data.

References

  1. Silvio Peroni and David Shotton (2018). Open Citation: Definition. figshare. DOI: https://doi.org/10.6084/m9.figshare.6683855
  2. Silvio Peroni and David Shotton (2018). The OpenCitations Data Model. figshare. DOI: https://doi.org/10.6084/m9.figshare.3443876

Footnotes

  1. At Crossref’s request, we have changed the originally proposed description of COCI from “the Crossref Open Citation Index (COCI)” to “COCI, the OpenCitations Index of Crossref open DOI-to-DOI references”, to make clear that COCI is an OpenCitations index and to avoid any implication that COCI is a Crossref service. We apologize for the initial ambiguity of our original wording and any confusion this may have caused.

Citations as First-Class Data Entities: The Open Citation Identifier Resolution Service

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 fifth and final of these requirements is that there must be a Web-based identifier resolution service that takes the citation identifier as input and returns a description of the citation.

At the recent PIDapalooza Conference on persistent identifiers, held in Gerona, Spain, I described the Open Citation Identifier Resolution Service, the new resolution service for Open Citation Identifiers created and operated by OpenCitations [1].

In this post, I describe this Open Citation Identifier Resolution Service, which supports the resolution of Open Citation Identifiers not only of the citations documented in the OpenCitations Corpus (OCC), but also of open citations recorded in other bibliographic databases.

What is the Open Citation Identifier Resolution Service

The Open Citation Identifier Resolution Service runs on the OpenCitations server, presenting itself to the user as a web page with the URI http://opencitations.net/oci.

When a user enters a valid OCI and clicks the “Look up citation” button, this activates the resolution service, which, after a brief delay, returns information about the citation itself and about the citing and cited bibliographic resources, as shown in the following screen image (which for clarity omits the provenance data associated with this citation).

This information can optionally be returned to the user in a variety of other formats: RDF/XML, Turtle or JSON-LD.

Clicking on the links provided will return additional metadata held by the OpenCitations Corpus for the citing and the cited documents.  In the near future, this service will be integrated with LUCINDA, the forthcoming OCC browse interface, to present this information in a more user-friendly fashion.

Using the Resolution Service with citations in an external resource via a SPARQL endpoint

The Open Citation Identifier Resolution Service currently works for citations between bibliographic resources both within the OpenCitations Corpus and within external bibliographic databases, provided that the external service uses bibliographic resource identifiers having a unique numerical part, and provides a SPARQL endpoint to makes available information about bibliographic resources and the references they contain.

It can therefore resolve OCIs identifying citations within Wikidata, such as oci:01027931310-01022252312, where, as explained in the previous blog post, “010” is the assigned OCC supplier prefix for Wikidata.

Entering this OCI in the Open Citation Identifier Resolution Service pulls live data from the Wikidata SPARQL endpoint and returns the following information about that citation, as shown in the following screen image (which, again, omits for clarity the provenance data associated with that citation):

Clicking on the links provided here returns information about the relevant Wikidata entities.

Citing paper:

Cited paper:

How the Resolution Service works

The bibliographic database supplying the metadata for a particular citation identified by an OCI is specified by the assigned OCC supplier prefix that forms part of the OCI, as described in the previous blog post. Each OCI is thus specific for and unique within a particular bibliographic database.

The resolution service takes the OCI entered into the search box, recognises the supplier prefix specifying the bibliographic database holding the citation information, parses the OCI into the database identifiers for the citing and cited entities, and then sends an appropriate SPARQL query to interrogate the SPARQL endpoint of the relevant database. When that database has returned information about the citation itself and about the citing and cited bibliographic resources, this is displayed to the user as shown in screen images above – or in other RDF formats (Turtle, JSON-LD, RDF/XML) according to the request.

It is important to realize that no other databases are contacted during this resolution process, and that the quality and accuracy of the metadata retrieved by the Open Citation Identifier Resolution Service is the responsibility of the database hosting that citation.  The OCI Resolution Service does no more than retrieve this information, and does nothing to address possible errors or omissions in the metadata coming from the hosting database.

Using the Resolution Service with external citations via a REST API

While the resolution service presently works only to retrieve information from bibliographic databases having a SPARQL endpoint, we plan soon to extend this resolution service to work with information supplied by a bibliographic database via a REST API.

Coupled with the ability to create OCIs by numerical conversions of Digital Object Identifiers (DOIs), as explained in the previous blog post, the Open Citation Resolution Service could then be used to pull metadata live from the Crossref REST API for any of the ~350 million Crossref open references in which the cited paper as well as the citing paper has a DOI, and for which an OCI can thus be created.

Watch this space!

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

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