Discover POCI, the index of open citations from PubMed 

We’re happy to announce POCI, the OpenCitations Index of PubMed open PMID-to-PMID citations, an RDF dataset containing details of all the citations from publications bearing PubMed Identifiers (PMIDs) to other PMID-identified publications, harvested from the National Institutes of Health Open Citations Collection (NIH-OCC). The citations available in POCI are treated as first-class data entities, with accompanying properties including the citations timespan, modelled according to the OpenCitations Data Model. 

Currently, POCI’s December 2022 release contains 717,654,703 citations from 26,024,862 bibliographic resources, and is based on the dump of NIH Open Citation Collection dated November 2022. 

Citation URLs

Each citation (i.e. an individual of the class cito:Citation) is identified by an URL structured as follows:

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

Open Citation Identifiers

Each Open Citation Identifier [[OCI]] has a simple structure: the lower-case letters “oci” followed by a colon, followed by two numbers separated by a dash (e.g. https://w3id.org/oc/index/poci/ci/01600102060800080706-016002060909030401), in which the first number identifies the citing work and the second number identifies the cited work.

For citations in which the citing and cited works are identified by PMIDs, which includes all the POCI citations, the OCI is created in the following manner, as explained more fully here. Each converted numeral part of OCI is prefixed by a 0160, which indicates that NIH is the supplier of the original metadata of the citation (as indicated at http://opencitations.net/oci).

OCIs can be resolved using the OpenCitations OCI Resolution Service.

Access to POCI data

All the data in POCI:

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. The current indexes available in OpenCitations are:  

All the OpenCitations Indexes have six characteristics in common, summarized here: https://opencitations.net/index   

Cite this article as: Chiara Di Giambattista, "Discover POCI, the index of open citations from PubMed ," in OpenCitations blog, 27/12/2022, https://opencitations.hypotheses.org/3246.

 

Discover DOCI, the index of open citations from DataCite

We’re excited to introduce DOCI, the OpenCitations Index of Datacite open DOI-to-DOI citations, a new tool containing citations derived from publications bearing DataCite DOIs to other DOI-identified publications, harvested from DataCite. The citations available in DOCI are treated as first-class data entities, with accompanying properties including the citations timespan, modelled according to the OpenCitations Data Model

Currently, DOCI’s December 2022 release contains 169,822,752 citations from 1,753,860  citing resources, and is based on the last dump of DataCite dated 22 October 2021 provided by the Internet Archive

Citation URLs

Each citation (i.e. an individual of the class cito:Citation) is identified by an URL structured as follows:

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

Open Citation Identifiers

Each Open Citation Identifier [[OCI]] has a simple structure: the lower-case letters “oci” followed by a colon, followed by two numbers separated by a dash (e.g. https://opencitations.net/index/doci/ci/080010504060836132137200707121027-080010504060836161221130313.html), in which the first number identifies the citing work and the second number identifies the cited work.

For citations in which the citing and cited works are identified by DOIs, which includes all the DOCI citations, the OCI is created in the following manner, as explained more fully here. Each case-insensitive DOI is first normalized to lower case letters. Then, after omitting the initial doi:10. prefix, the alphanumeric string of the DOI is converted reversibly to a pure numerical string using the simple two-numeral lookup table for numerals, lower case letters and other characters presented at https://github.com/opencitations/oci/blob/master/lookup.csv. Finally, each converted numeral is prefixes by a 080, which indicates that DataCite is the supplier of the original metadata of the citation (as indicated at http://opencitations.net/oci).

OCIs can be resolved using the OpenCitations OCI Resolution Service.

Access to DOCI data

All the data in DOCI:

More information is available at https://opencitations.net/index/doci. 

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. The current indexes available in OpenCitations are: 

All the OpenCitations Indexes have six characteristics in common, summarized here: https://opencitations.net/index  

Follow OpenCitations on Mastodon

OpenCitations has happily joined the open-source social media platform joinmastodon.org.

Mastodon is “a free and open-source software developed by a non-profit organization”, with the aim of favouring interoperability and bringing social media interaction “back in the hands of the people”.

We look forward to recreating there our wide network of connections, and getting in touch with new people, projects and institutions in a different virtual environment.

Follow us at https://scicomm.xyz/@opencitations !

Tutorial: how to process COCI’s zipped CSV dump without decompressing it

Blog post by Ivan Heibi (Universiy of Bologna) and Arcangelo Massari (University of Bologna).

OpenCitations publishes the COCI dataset after each new release in three main formats: CSV, N-Triples, and Scholix (see https://opencitations.net/download#coci). The CSV format is the most popular and downloaded one due to its comprehensive data organization (i.e. tabular format) and smaller size (compared to the other formats provided). Therefore, this is also the format we suggest using for a local process of the entire COCI dataset. 

The CSV dumps of COCI are uploaded on Figshare. You can check and download the last dump released from https://doi.org/10.6084/m9.figshare.6741422. The dump consists of one main ZIP file, including other smaller ZIP archives (one for each release) containing the actual CSV files (Figure 1).

Figure 1. The contents of the COCI CSV dataset (after the August 2022 release)

It is possible to process this data without unzipping the internal archives, thus saving a lot of disk space. In this tutorial, we will see how to achieve this in Python. Same process could be done in other programming languages.

Processing the COCI dump using Python

Step 1) Downloading the COCI dump

First, you need to download the last CSV dump release of COCI from https://doi.org/10.6084/m9.figshare.6741422 and decompress only the external archive. After this operation, you should have a folder containing the internal ZIP files such as in Figure 1.

Note: It is beneficial to decompress the external archive because doing so does not increase the space occupied on the disk (compressing archives results in a compression rate of 0%) and because working on nested archives would significantly increase RAM requirements. 

Step 2) Working with the ZIP files

Python provides the built-in zipfile module, whose ZipFile class allows you to create, read, write, edit and list the contents of a ZIP file. Given as input the path of the root directory containing all the ZIP files (FOLDER_PATH), the process elaborates each of these files on a different iteration. Each cycle initializes a ZipFile object by specifying the path to the ZIP file (archive_path).

from zipfile import ZipFile
import os

for archive_name in os.listdir(FOLDER_PATH):        
archive_path = os.path.join(FOLDER_PATH, archive_name)
with ZipFile(archive_path) as archive:     # ...

Step 3) Accessing the ZIP files

Use the namelist() method to return the list of CSV files contained in each archive. Then to open the inner CSV files, simply cycle through the list of names and feed them to the open() method of the ZipFile instance, i.e. archive in the example below.

from zipfile import ZipFile
import os

for archive_name in os.listdir(FOLDER_PATH):
    archive_path = os.path.join(FOLDER_PATH, archive_name)
    with ZipFile(archive_path) as archive:
        for csv_name in archive.namelist():
with archive.open(csv_name) as csv_file:       # ...

Step 4) Reading the CSVs

The .open() method returns a buffer. To read the CSV file as a list of dictionaries (i.e. represent each row of the CSV in dictionary format, e.g., {“column1″:”val1”, “column2″:”val2”}) we need to transform the buffer using the TextIOWrapper class and read it using the DictReader class of csv. Then we convert the result of DictReader into a list. 

from io import TextIOWrapper
from zipfile import ZipFile
import os

for archive_name in os.listdir(FOLDER_PATH):
    archive_path = os.path.join(FOLDER_PATH, archive_name)
    with ZipFile(archive_path) as archive:
        for csv_name in archive.namelist():
with archive.open(csv_name) as csv_file:
reader = csv.DictReader(io.TextIOWrapper(csv_file))
rows = list(reader)
# ...

Step 5) Processing the CSVs content

Now you can go through each row of the list and process the citation data as you want. The following example prints the citing and cited entity of each citation in the dump. 

from io import TextIOWrapper
from zipfile import ZipFile
import os

for archive_name in os.listdir(FOLDER_PATH):
    archive_path = os.path.join(FOLDER_PATH, archive_name)
    with ZipFile(archive_path) as archive:
        for csv_name in archive.namelist():
with archive.open(csv_name) as csv_file:
reader = csv.DictReader(io.TextIOWrapper(csv_file))
rows = list(reader)
# Process the CSV here
for r in rows:
print("Citing entity:",r["citing"])
print("Cited entity:",r["cited"])

Cite this article as: Arcangelo Massari, "Tutorial: how to process COCI’s zipped CSV dump without decompressing it," in OpenCitations blog, 30/09/2022, https://opencitations.hypotheses.org/2940.

 

Additional 48 million citations in COCI, including references from IEEE 

We announce the August 2022 release of COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations, which is based on open references to works with DOIs within the Crossref dump dated August 2022. This new release extends COCI with more than 48 million additional citations, giving a total number of more than 1.36 billion DOI-to-DOI citation links. 

This release includes citations from the articles published over the last four years by IEEE, whose bibliographic references were opened in June 2022. 

A fundamental role in pushing the commercial publishers to open their citation data was played by Crossref’s recent announcement to change its reference distribution policy, by making all its metadata open.  

Besides IEEE, COCI already includes the citation data derived from Elsevier (open via Crossref since December 2020) and from the last articles published by the American Chemical Society (whose references were opened in February 2021) 

You can find more information about COCI in our open-access article  

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

Finally, just a reminder that the bibliographic and citation data in COCI:  

    • can be queried using the OpenCitations Indexes SPARQL endpoint;  
    • can be retrieved by using the COCI REST API;  
    • can be searched by using the OpenCitations Indexes Search Interface;  
    • are also available as dumps on Figshare in CSV, N-Triples, and Scholix; and  
    • can be freely re-used for any purpose.

      Cite this article as: Chiara Di Giambattista, "Additional 48 million citations in COCI, including references from IEEE ," in OpenCitations blog, 31/08/2022, https://opencitations.hypotheses.org/2732.

New documents that present OpenCitations’ mission, unique benefits, present status and future plans

Posted on August 10th 2022 by Chiara Di Giambattista

More than a year ago, Ginny Hendricks, Director of Member & Community Outreach for Crossref, and a valued member of the OpenCitations International Advisory Board, published on the Crossref blog the post “The road ahead: our strategy through 2025”. In order to describe all Crossref’s principles and activities, Ginny presented the Crossref strategic planning framework as a diagram summarizing Crossref’s statements, key messages and truths. The clarity and immediacy of the diagram were such that we adapted it to present  OpenCitations’ own statements and goals. The resulting poster “OpenCitations – what does the future hold?” was presented by our Director David Shotton at the OASPA2021 conference, and can be found in this blog post.

Although the poster offered a wide overview of OpenCitations values, unique traits, benefits and plans, it differed slightly from Ginny’s original diagram, in particular because it lacked a “Mission Statement”, scattering the relevant information within the “Values” and “Principles” boxes. Indeed, at that time (September 2021), we didn’t have a clearly defined Mission Statement.

Nevertheless, the creation of that poster was crucial in helping us start to articulate more clearly the purpose and meaning of OpenCitations. As David underlined in his post “From little acorns…a retrospective on OpenCitations”, since 2018 OpenCitations activities have progressively increased and, with them, the number of related journal articles, conference papers and technical definitions. OpenCitations’ involvement in international networks and collaborations (such as SCOSS and the OpenAIRE-Nexus project), together with our need of identifying and reaching out to new stakeholders to assure OpenCitations’ development and sustainability, has made it necessary to publicly define OpenCitations’ mission, unique strengths and next developmental steps.

After numerous revisions, aided by wise advice from members of the OpenCitations Advisory Board members, we’re now happy to publish the following three OpenCitations documents:

OpenCitations Mission Statement,

The Uniqueness of OpenCitations   and

OpenCitations – Present Status and Future Plans,

which together provide a summary of why we exist and where we are heading.

We are particularly proud of the definition of OpenCitations’ primary mission, namely

to harvest and openly publish accurate and comprehensive metadata describing the world’s academic publications and the scholarly citations that link them, and to preserve ongoing access to this information by secure archiving.

The Mission Statement also presents brief descriptions of the OpenCitations context, our vision, our value proposition and our relationship with the community and stakeholders.

The Uniqueness of OpenCitations provides the answer to the question ‘Why choose to use OpenCitations?’, and is a detailed presentation of OpenCitations’ benefits.

OpenCitations – Present Status and Future Plans summarizes OpenCitations’ ongoing activities, that can be quickly visualized on our public roadmap. It also introduces the OpenCitations Working Groups, served by the members of the OpenCitations International Advisory Board, which are currently working on the themes of governance evolution and community building, with the common purpose of driving OpenCitations along the path from being a ‘sustainable infrastructure’ (in POSI terms) to being an enduring community led and financially sustained infrastructure.

In fulfilling our mission and reaching our goals, the support and vital interest of our community members is fundamental. We request that you, as a member of our community, provide us with feedback on these documents and the ideas they contain, or indeed to ask for clarifications, to help us improving our mission and our communications to explain it. You can reach us here: contact@opencitations.net.

Thank you!

Cite this article as: Chiara Di Giambattista, "New documents that present OpenCitations’ mission, unique benefits, present status and future plans," in OpenCitations blog, 10/08/2022, https://opencitations.hypotheses.org/2498.

Performing live time-traversal queries on RDF datasets

Guest post by Arcangelo Massari, University of Bologna

In this post, Arcangelo Massari, who recently graduated in Digital Humanities and Digital Knowledge under Professor Silvio Peroni at the University of Bologna, shares the results of his master thesis.

A particular problem in information retrieval is that of obtaining data from an evolving dataset, independent of the time at which that item of data was added, changed or removed. To permit such time-independent queries to be performed over evolving RDF datasets, I have developed two new pieces of open source software, time-agnostic-library [1] and time-agnostic-browser [2], that are now available from the OpenCitations GitHub repository.

The time-agnostic-library is a Python library to perform live time-traversal queries on RDF datasets. Time-traversal means being agnostic about time: a SPARQL query that is not run on the current state of the collection but over its entire history or over a specified timespan of that history [3]. This tool allows materializations – obtaining all versions of an entity over time, or its status at a given time. Furthermore, SPARQL queries can be performed to get the delta between two or more versions of one or more resources. Thereby, the time-agnostic-library realizes all the retrieval functionalities described in the taxonomy by Fernández et al. [3].

To complement this query software, the time-agnostic-browser is a web application built on top of the time-agnostic-library to achieve the same results via a graphical user interface.

The primary purpose of these developments is to offer a system for browsing the provenance [4] of RDF statements across time: who produced them, when, where the information was taken from, and what changes were made compared to the previous state of the resource. Knowledge of such information is essential because data changes over time, either because of the natural evolution of concepts or due to the correction of mistakes. Indeed, the latest version of knowledge may not be the most accurate. Such phenomena are particularly tangible in the Web of Data, as highlighted in a study by the Dynamic Linked Data Observatory, which noted the modification of about 38% of the nearly 90,000 RDF documents monitored for 29 weeks, and the permanent disappearance of 5% of them [5] (Figure 1).

Figure 1. Donut chart showing the results of the study conducted by the Dynamic Linked Data Observatory on the evolution of RDF documents [5].

Additionally, the truthfulness of data cannot be assessed without provenance records and a system to query them. In fact, the truth value of an assertion on the Web is never absolute, as demonstrated by Wikipedia, which in its official policy on the subject states: “The threshold for inclusion in Wikipedia is verifiability, not truth.” [6]. The Semantic Web does not alter that condition, and trustworthiness has to be evaluated by each application by probing the context of the statements [7]. It is a challenging task and thus, in the Semantic Web Stack, trust is the highest and most complex level to satisfy, subsuming all the previous ones (Figure 2).

Figure 2.The Semantic Web layers [7]. Trust is the uppermost level of the stack, subsuming all the others.

Notwithstanding these premises, at present the most extensive RDF datasets – DBPedia [8], Wikidata [9], Yago [10], and the Dynamic Linked Data Observatory [11] – do not use RDF to track changes and record the provenance of such changes. Instead, they all adopt backup-based archiving policies. Some of them, such as Yago 4, record provenance but not changes. As far as citation databases are concerned, OpenCitations is the only infrastructure to implement change-tracking mechanisms and to record full RDF provenance records for each data entity. Among the leading players in this field, neither Web of Science nor Scopus have adopted similar solutions.

In accordance with the OpenCitations Data Model (OCDM) [12], a provenance snapshot is generated by OpenCitations every time a bibliographical entity is created or modified. Each snapshot (prov:Entity) records the responsible agent (prov:wasAttributedTo), the generation time (prov:generatedAtTime), the invalidation time (prov:invalidatedAtTime), the primary source (prov:hadPrimarySource), and a link to the previous snapshot (prov:wasDerivedFrom), using terms from the Provenance Ontology. In addition, OCDM introduced a system to simplify restoring an entity’s status at a given time, by saving the delta between two versions as a SPARQL update query (prov:hasUpdateQuery) [13] (Figure 3). This approach enables one to restore an entity to a specific timepoint (snapshot) in a straightforward way by applying the inverse operations, i.e., deletions instead of additions, etc.

Figure 3. Provenance in the OpenCitations Data Model.

This solution is concretely used in all the datasets related to the OpenCitations infrastructure, such as COCI, an open index containing almost 1.2 billion DOI-to-DOI citation links derived from the open reference data available in Crossref [14]. It is important to note that this OpenCitations provenance model is generic and reusable in any other context. Since the time-agnostic-library leverages OCDM, it too is generic and can be used for any RDF dataset that tracks changes and provenance as OpenCitations does.

The time-agnostic-library is released under the ISC license and is downloadable through pip [1]. Test-driven development was adopted as a software development process during its creation [15]. It makes three main classes available to the user: AgnosticEntity, VersionQuery, and DeltaQuery, for materializations, version queries, and delta queries, respectively (Listing 1).

Listing 1. Code template to achieve materializations, time-traversal queries, and delta queries.

All three operations can be performed over the entire available history of the dataset, or by specifying a time interval via a tuple in the form (START, END).

The time-agnostic-browser [2] is also released under the ISC license and can be run as a Flask application. It is organized into two macro-sections: “Explore” and “Query”. In the former, a text input accepts a URI. By submitting it, the entire history of the corresponding resource is displayed. In the latter, a text area receives a SPARQL query, which is resolved on all dataset states. Its main added value is hiding the triples and the complexity of the underlying RDF model: predicate URIs, as well as subjects and objects, appear in a human-readable format. Moreover, all the entities are displayed as links, providing shortcuts to reconstruct the history of the related resources (Figure 4).

Figure 4. Graphical user interface of an entity history reconstruction through the time-agnostic-browser.

The efficiency of time-agnostic-library was measured with two types of benchmarks [16], one on execution times and the other on the amount of computer memory (RAM) required by ten different use cases, each repeated ten times to produce significant results and avoid outliers. In light of these benchmarks, time-agnostic-library has proven effective for any materialization. Regarding structured queries, they are swift if all subjects are known or deductible. On the other hand, the presence of unknown subjects in the user’s SPARQL query involves the identification of all present and past entities that satisfy that pattern, and so requires a more significant amount of time and resources. Specifically, all materializations and the cross-version structured query with known subjects required about half a second and about 50 MB of RAM; conversely, with unknown subjects, 581 seconds and 519 MB of RAM are required. It can be concluded that the proposed software can be used effectively in all cases where the subject is known, that is, for any materialization or formulated SPARQL queries without isolated triple patterns containing unknown subjects.

Other software solutions for such problems have been proposed. Table 1 shows the list of available software to perform materializations and time-traversal queries on RDF datasets. As can be observed, time-agnostic-library is the only one to support all retrieval functionalities without requiring pre-indexing processes. This feature makes it particularly suitable for use in scenarios with large amounts of data that often change over time. Moreover, compared to the approach of Im, Lee and Kim [17] and OSTRICH [18], the OpenCitations Data Model only requires storing the current state of the dataset, rather than the original one, allowing one to query the latest version, without additional computational effort to first re-create the original version.

SoftwareVersion materializationDelta materializationSingle-version structured queryCross-version structured querySingle-delta structured queryCross-delta structured queryLive
PromptDiff [19]+++
SemVersion [20]+++
Im, Lee, & Kim, 2012 [17]+++++
R&Wbase [21]++++
x-RDF-3X [22]+++
v-RDFCSA [23]++++++
OSTRICH [18]+++
Tanon & Suchanek, 2019 [24]++++++
time-agnostic-library[1]+++++++
Table 1. Comparative between time-agnostic-library and preexisting software to achieve materializations and time traversal queries on RDF datasets. (Scroll right to see Columns 6-8).

The OpenCitations Data Model and the time-agnostic-library software are the pre-requisites that will allow OpenCitations to involve third parties, for example members of staff in academic libraries, in the submission, curation and updating of OpenCitations bibliographic and citation data. At this stage, all entities in COCI have a single snapshot — the one made at the time of creation. However, since these entities may become modified, corrected or enriched over time, it is imperative to have appropriate software tools available for use by curators. With the time-agnostic-library software and its associated time-agnostic-browser, it will be possible for a curator to explore the entire history of the changes within an RDF dataset, to know when they were made, based on which source, and by which responsible agent, thus ensuring the reliability and verifiability of data, and facilitating any necessary further changes.

References

[1] A. Massari, time-agnostic-library. 2021. Available: https://archive.softwareheritage.org/swh:1:snp:d7fd1754377f45d16afb61efc770815b5a3c8f83

[2] A. Massari, time-agnostic-browser. 2021. Available: https://archive.softwareheritage.org/swh:1:dir:337f641375cca034eda39c2380b4a7878382fc4c

[3] J. D. Fernández, A. Polleres, and J. Umbrich, ‘Towards Efficient Archiving of Dynamic Linked’, in DIACRON@ESWC, Portorož, Slovenia: Computer Science, 2015, pp. 34–49.

[4] December, ‘Provenance XG Final Report’. 2010. Available: http://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/

[5] T. Käfer, A. Abdelrahman, J. Umbrich, P. O’Byrne, and A. Hogan, ‘Observing Linked Data Dynamics’, in The Semantic Web: Semantics and Big Data, vol. 7882, P. Cimiano, O. Corcho, V. Presutti, L. Hollink, and S. Rudolph, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 213–227. doi: 10.1007/978-3-642-38288-8_15

[6] S. L. Garfinkel, ‘Wikipedia and the Meaning of Truth’, MIT Technology Review, 2008, [Online]. Available: https://stephencodrington.com/Blogs/Hong_Kong_Blog/Entries/2009/4/11_What_is_Truth_files/Wikipedia%20and%20the%20Meaning%20of%20Truth.pdf

[7] M.-R. Koivunen and E. Miller, ‘Semantic Web Activity’, W3C, Nov. 02, 2001. https://www.w3.org/2001/12/semweb-fin/w3csw

[8] F. Orlandi and A. Passant, ‘Modelling provenance of DBpedia resources using Wikipedia contributions’, Journal of Web Semantics, vol. 9, no. 2, pp. 149–164, Jul. 2011, doi: 10.1016/j.websem.2011.03.002.

[9] P. Dooley and B. Božić, ‘Towards Linked Data for Wikidata Revisions and Twitter Trending Hashtags’, in Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services, Munich Germany, Dec. 2019, pp. 166–175. doi: 10.1145/3366030.3366048.

[10] Yago Project, ‘Download data, code, and logo of Yago projects’, Yago, 2021. https://yago-knowledge.org/downloads (accessed Sep. 24, 2021).

[11] J. Umbrich, M. Hausenblas, A. Hogan, A. Polleres, and S. Decker, ‘Towards Dataset Dynamics: Change Frequency of Linked Open Data Sources’, in Proceedings of the WWW2010 Workshop on Linked Data on the Web, Raleigh, USA, 2010. Available: http://ceur-ws.org/Vol-628/ldow2010_paper12.pdf

[12] M. Daquino, S. Peroni, and D. Shotton, ‘The OpenCitations Data Model’, p. 836876 Bytes, 2020, doi: 10.6084/M9.FIGSHARE.3443876.V7.

[13] S. Peroni, D. Shotton, and F. Vitali, ‘A Document-inspired Way for Tracking Changes of RDF Data’, in Detection, Representation and Management of Concept Drift in Linked Open Data, Bologna, 2016, pp. 26–33. Available: http://ceur-ws.org/Vol-1799/Drift-a-LOD2016_paper_4.pdf

[14] I. Heibi, S. Peroni, and D. Shotton, ‘Software review: COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations’, Scientometrics, vol. 121, no. 2, pp. 1213–1228, Nov. 2019, doi: 10.1007/s11192-019-03217-6.

[15] K. Beck, Test-driven development: by example. Boston: Addison-Wesley, 2003.

[16] A. Massari, ‘time-agnostic-library: benchmark results on execution times and RAM’. Zenodo, Oct. 05, 2021. doi: 10.5281/ZENODO.5549648.

[17] D.-H. Im, S.-W. Lee, and H.-J. Kim, ‘A Version Management Framework for RDF Triple Stores’, Int. J. Softw. Eng. Knowl. Eng., vol. 22, pp. 85–106, 2012.

[18] R. Taelman, M. V. Sande, and R. Verborgh, ‘OSTRICH: Versioned Random-Access Triple Store’, in Companion Proceedings of the Web Conference 2018, 2018, pp. 127–130. Available: https://core.ac.uk/download/pdf/157574975.pdf

[19] N. F. Noy and M. A. Musen, ‘Promptdiff: A Fixed-Point Algorithm for Comparing Ontology Versions’, in Proc. of IAAI, 2002, pp. 744–750.

[20] M. Völkel, W. Winkler, Y. Sure, S. Kruk, and M. Synak, ‘SemVersion: A Versioning System for RDF and Ontologies’, 2005.

[21] M. V. Sande, P. Colpaert, R. Verborgh, S. Coppens, E. Mannens, and R. V. Walle, ‘R&Wbase: Git for triples’, 2013.

[22] T. Neumann and G. Weikum, ‘x-RDF-3X: Fast Querying, High Update Rates, and Consistency for RDF Databases’, Proceedings of the VLDB Endowment, vol. 3, pp. 256–263, 2010.

[23] A. Cerdeira-Pena, A. Farina, J. D. Fernandez, and M. A. Martinez-Prieto, ‘Self-Indexing RDF Archives’, in 2016 Data Compression Conference (DCC), Snowbird, UT, USA, Mar. 2016, pp. 526–535. doi: 10.1109/DCC.2016.40.

[24] T. Pellissier Tanon and F. Suchanek, ‘Querying the Edit History of Wikidata’, in The Semantic Web: ESWC 2019 Satellite Events, vol. 11762, P. Hitzler, S. Kirrane, O. Hartig, V. de Boer, M.-E. Vidal, M. Maleshkova, S. Schlobach, K. Hammar, N. Lasierra, S. Stadtmüller, K. Hose, and R. Verborgh, Eds. Cham: Springer International Publishing, 2019, pp. 161–166. doi: https://doi.org/10.1007/978-3-030-32327-1_32.

Cite this article as: Chiara Di Giambattista, "Performing live time-traversal queries on RDF datasets," in OpenCitations blog, 29/11/2021, https://opencitations.hypotheses.org/1427.

Coverage of open citation data approaches parity with Web of Science and Scopus

Guest blog post by Alberto Martín-Martín, Facultad de Comunicación y Documentación, Universidad de Granada, Spain <albertomartin@ugr.es>

In this post, as a contribution to Open Access Week, Alberto Martín-Martín shares his comparative analysis of COCI and other sources of open citation data with those from subscription services, and comments on their relative coverage.

Comprehensive bibliographic metadata is essential for the development of effective understanding and analysis across all phases of the research workflow. Commercial actors have historically filled the role of infrastructure providers of bibliographic and citation data, but their choice of subscription-based business models and/or restrictive user licenses has significantly limited how users and other parties can access, build upon, and redistribute the information available on those platforms. Locking bibliographic and citation metadata behind these barriers is problematic, as it hinders innovation and is an obstacle to reproducibility.

Fortunately, the process of digital transformation that scientific communication is currently undergoing is providing us with the tools to get closer to the ideal of science as a public good. One of the most successful initiatives in this area is Crossref, arguably the single most critical piece of research metadata infrastructure currently in existence. I consider the best thing about it to be its commitment to openness. Not only is Crossref responsible for minting many of the DOIs that are assigned to academic publications, but it also publishes metadata about these publications (for over 120+ million records in their latest public data file) without imposing any access or reuse limitations.

Crossref metadata has already boosted innovation in a variety of academic-oriented tools. New discovery services such as Dimensions, The Lens, and Scilit all take advantage of Crossref metadata to keep their indexes up to date with the latest publications. The open-source reference manager Zotero is able to pull metadata associated with a given DOI from Crossref’s servers, providing an easy way to populate one’s personal reference collection that is more reliable than using Google Scholar. The Unpaywall database uses Crossref metadata (among other data sources) to keep track of which documents are Open Access, and this data is in turn used by Unsub, a service that helps libraries make more informed decisions about their journal subscriptions.

Historically, citation indexing has been a functionality available only from a few subscription-based data sources (most notably Web of Science and Scopus), or from free but largely restricted sources (e.g., Google Scholar). In recent years, however, commercial exclusivity over citation data has been waning. Digital publishing workflows make it easier for publishers to deposit the list of cited references along with the rest of the metadata when they register a new document in Crossref, and many are already doing it. Crossref’s policy is to make these lists of references publicly available by default, although publishers can elect to prevent their public release. From this, it follows that if most publishers deposited their reference lists in Crossref and consented to make them open, a comprehensive open citation index, one that is free of the restrictions present in traditional platforms, could be built.

The Initiative for Open Citations (I4OC) is an advocacy group that has been working since 2017 to achieve this precise goal, and it has already managed to convince a large number publishers (over two thousand) to open the references they deposit in CrossRef. In the first half of 2021, Elsevier, the American Chemical Society, and Wolters Kluwer joined this group, so that today all the major scholarly publishers now support I4OC and have open references at Crossref, with the exception of IEEE (the Institute of Electrical and Electronics Engineers). Thanks to the efforts of I4OC and the collaboration of publishers, 88% of the publications for which publishers have deposited references in CrossRef are now open. This has allowed organizations such as OpenCitations (one of the founding members of I4OC) to create a non-proprietary citation index using these data, namely COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations. Other open citation indexes such as the NIH Open Citation Collection (NIH-OCC) and Refcat have also been recently released.

How do such open citation indexes compare to long-established indexes? In 2019, I set out with colleagues to analyze the coverage of citations contained within the most widely used academic bibliographic data sources (Web of Science, Scopus, and Google Scholar) to a selected corpus of 2,515 highly-cited English-language documents published in 2006 from 252 subject categories, and to compare this to the coverage provided by some of the more recent data sources (Microsoft Academic, Dimensions, and COCI). At that time, COCI was the smallest of the six indexes, containing only 28% of all citations. For comparison, Web of Science contained 52%, and Scopus contained 57%.

There are a number of reasons for those differences: first, at that point some of the larger commercial publishers including Elsevier, IEEE, and ACS, which routinely deposit references in Crossref, had not yet opened them. Second, many smaller publishers still do not deposit their reference lists in Crossref. Third, COCI only captures citation relationships between documents that have DOIs, thus missing citations to publications that lack them. Finally, while for our study data collection from all sources was carried out during May/June of 2019, COCI at that time had not been updated since November 2018, which increased its disadvantage when compared to other data sources with more frequent updates.

Since Elsevier is the largest academic publisher in the world, its recent opening of references at Crossref resulted in a significant increase in the total number of openly available Crossref references. The most recent version of COCI (dated 3 September 2021, and based on open references to works with DOIs within the Crossref dump dated August 2021) now contains both the processed references from Elsevier, and the references in the most recently published articles by ACS (the complete backfile of ACS references will appear in future versions of COCI).

Given these significant developments, how much has the picture changed? To find this out, I updated our 2019 analysis using the version of COCI released on September 3rd 2021 and the NIH-OCC dataset released in the same month. To carry out a reasonably fair comparison while reusing the data extracted in 2019 from the other sources, I employed the same corpus of target documents, and only used citations in which the citing document was published before the end of June 2019. The intention was to learn how much the coverage of open citation data has grown as a result of the subsequent opening of reference lists in Crossref that were not public in 2019, and similar efforts.  The results of this new comparison are published in [1].

The combination of COCI’s and NIH-OCC’s September 2021 releases contained more than 1.62 million citations to our sample corpus of documents from all areas, a 91% increase over the 0.85 million citations that we were able to recover in 2019 from COCI alone. Considering the citations available in all data sources, 53% of all citations are now available from these two open sources under CC0 waivers, up from the 28% we found in 2019. This coverage now surpasses the 52% found by Web of Science, and is much closer to the 54% found by Dimensions, and the 57% covered by Scopus. The relative overlap between COCI and the other data sources has also significantly increased: in 2019 COCI found 47% of the citations available in Web of Science, whereas now open citation data sources find 87% of the WoS citations. In the case of Scopus, in 2019 COCI found 44% of the citations available in Scopus: the percentage available from open sources has now increased to 81%. The number of citations found by COCI but not present in the other data sources has also widened slightly. These data are presented graphically in Figure 1.

Fig. 1. Percentage of citations found by each database, relative to all citations (first row), and relative to the number of citations found by the other databases (subsequent rows).

Where are these new citations coming from? Well, as we might expect, references from articles published in Elsevier journals comprise the lion’s share of the newly found citations in open data sources (close to half of all new citations), as shown in Figure 2. But there are also some IEEE citations here. This is because until recently reference lists from IEEE publications were available in the ‘limited’ Crossref category to members of Crossref Metadata Plus, a paid-for service that provides a few additional advantages over the free services Crossref provides. As a member of Crossref Metadata Plus, OpenCitations obtained these reference lists while they were available and included them in COCI. Subsequently, IEEE decided to make their references completely closed, explaining why references from more recent IEEE publications are not included in COCI.

Fig 2. The increases between 2019 and 2021 of citations indexed by open sources (COCI + NIH-OCC) from the articles of different publishers

There can be no doubt that open citation data is of benefit to the entire academic community. Thanks to COCI, NIH-OCC, and similar initiatives, and despite some setbacks, we are already witnessing how open infrastructure can help us develop models and practices that are better aligned with the opportunities that our current digital environment offers and the challenges that our society faces.

Conclusion: The coverage of citation data available under CC0 waivers from open sources is now comparable to that from subscription sources such as Web of Science and Scopus, offering a viable alternative upon which to base open and reproducible metrics of academic performance.

Reference:

[1]  Martín-Martín, A., Thelwall, M., Orduna-Malea, E. et al. Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations. Scientometrics 126, 871–906 (2021). https://doi.org/10.1007/s11192-020-03690-4

Cite this article as: davidshotton, "Coverage of open citation data approaches parity with Web of Science and Scopus," in OpenCitations blog, 27/10/2021, https://opencitations.hypotheses.org/1420.

Open Access Tage 2021: valuable insights from the libraries in the German-speaking region 

On September 27, OpenCitations’ director Silvio Peroni, together with Niels Stern (DOAB/OAPEN) and James MacGregor (PKP), held the online workshop “How Open Infrastructure Benefits Libraries” during the Open Access Tage 2021. Open-Access-Tage (Open Access Days) are the annual central platform for the steadily growing Open Access and Open Science community from Germany, Austria and Switzerland, and are aimed at all those involved with the possibilities, conditions and perspectives of scientific publishing.  

The workshop gathered three of the SCOSS-supported infrastructures to discuss how Open Infrastructures (OIs) could encourage the engagement of university libraries, and how they could be beneficial game-changing alternative to commercial infrastructures. This theme, which was also been presented during the last LIBER conference, was here discussed under a new perspective, by involving the specific case of the libraries from the German-speaking region. Their point of view particularly emerged during the second part of the workshop, during which the participants were divided into two breakout rooms to discuss two questions each. These are the answers and comments that emerged from the discussions.  

1. What would prevent or encourage libraries in the German-speaking region to support open infrastructures? 

The three main concepts held to be crucial in this field were transparency, promotion and governance.  

Transparency: German libraries and public institutions often deal with strict funding limitations relating to donations. It is therefore crucial for OIs (a) to present in a clear way how libraries can get involved and the money needed, (b) to communicate what they do and how they can add value to libraries compared to other services, and (c) to clarify the direct return and benefits on investments. These points would make it easier to recommend OIs internally, especially when people from subject-specific institutions are interested in subject-independent OIs. Point (b) leads to the Promotion issue: Open Infrastructures should promote themselves non only on a global level, by communicating their impact in the open research movement as against non-transparent propitiatory services, but also at a local level, by providing information about the usage (and the value) of their services at an institutional and/or national level. This case-by-case narration (with attention to the specific benefits) would make it easier for the institutions to evaluate the sustainability of the investment. An incentive to donate is being actively involved in the community governance, i.e.through a board membership.  

Nevertheless, is also necessary for libraries to “take courage” when investing in such OIs, and, when possible, to overcome administrative boundaries by forming consortia. Finally, of particular note was a desire to see locally-managed sub-communities that could speak specifically to the German (or whichever) language environment, much as ORCID arranges itself.  

2. Community Governance. What kind of involvement do you want to see, how do you want to be involved? 

Some common problems which prevent the institutions from being involved are (a) a general concern about the fact that negotiations with publishers are typically the main focus of OA discussions – leaving little time to focus on OIs and other open initiatives, and (b) a lack of time, or of guidelines, for evaluating the different infrastructures to invest in. This is why SCOSS was appreciated as an intermediary in the decision process, because of its own rigorous evaluation and selection mechanismThe community-funding approach proposed by SCOSS thus seems to be the preferred way by which to support OIs.  

Regarding community governance, one idea could be to involve interested scholars in the governance of the open infrastructures (with the library acting as an interface between the open infrastructure and the scholars) rather than only involving library staff – although this idea was argued against in the second group, as researchers are often percieved as too busy to be functional in operational infrastructure groups. What also emerged from this second question is an interest in community involvement on different levels, for example as a community of practices or through discussion boards, mailing lists, periodic meet-ups, workshops, newsletters, etc. The community could also be articulated into local sub-communities, as in the successful case of ORCID and ORCID_DE.  

Cite this article as: Chiara Di Giambattista, "Open Access Tage 2021: valuable insights from the libraries in the German-speaking region ," in OpenCitations blog, 07/10/2021, https://opencitations.hypotheses.org/1394.
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