Two billion citation links in Crossref help research travel further - Crossref

You are Crossref - Crossref

Crossref runs open infrastructure to link research objects, entities, and actions—creating a lasting and reusable scholarly record that underpins open science. Together with our >25,000 members in 167 countries, we drive metadata exchange and support 2.1 billion monthly API queries, facilitating global research communication, for the benefit of society.

Crossref: The sustainable source of community-owned scholarly metadata

This paper describes the scholarly metadata collected and made available by Crossref, as well as its importance in the scholarly research ecosystem. Containing over 106 million records and expanding at an average rate of 11% a year, Crossref’s metadata has become one of the major sources of scholarly data for publishers, authors, librarians, funders, and researchers. The metadata set consists of 13 content types, including not only traditional types, such as journals and conference papers, but also data sets, reports, preprints, peer reviews, and grants. The metadata is not limited to basic publication metadata, but can also include abstracts and links to full text, funding and license information, citation links, and the information about corrections, updates, retractions, etc. This scale and breadth make Crossref a valuable source for research in scientometrics, including measuring the growth and impact of science and understanding new trends in scholarly communications. The metadata is available through a number of APIs, including REST API and OAI-PMH. In this paper, we describe the kind of metadata that Crossref provides and how it is collected and curated. We also look at Crossref’s role in the research ecosystem and trends in metadata curation over the years, including the evolution of its citation data provision. We summarize the research used in Crossref’s metadata and describe plans that will improve metadata quality and retrieval in the future.

How Grobid uses Crossref metadata to enrich bibliographic information - Crossref

Crossref members register around 37,000 records daily. All this metadata is openly shared with the global scholarly community through our free APIs. We receive over two billion calls to our API each month, signalling its use by hundreds of tools and services worldwide, supporting the discovery and assessment of scholarly metadata. As part of a new series in our blog, we will spotlight the diversity of tools and integrations that rely on Crossref metadata to serve the scholarly community. In this initial post, we chat to Luca Foppiano, the developer and maintainer of Grobid1, to learn about how Grobid uses Crossref metadata to improve the extraction and enrichment of bibliographic information from scholarly PDFs.

Scholarly blogs and their place in the research nexus - Crossref

If you are reading this blog on our website, you may have noticed that alongside each post we now list a Crossref DOI link, which was not the case a few months ago (though we have retroactively added DOIs to all older posts too). You can find the persistent link for this post right above this paragraph. Go on, click on it, we’ll wait.

SemRepo.org

SemRepo is a large-scale RDF knowledge graph of GitHub repositories linked to scientific research. SemRepo captures fine-grained repository-level metadata (e.g., contributors, issues, programming languages) and interlinks this with external scholarly knowledge graphs: repositories to publications in LPWC, repository authors to their profiles in SemOpenAlex, and research artifacts (e.g., datasets, experiments) are linked via MLSea.

The Research Nexus vision for a more connected scholarly community

Crossref envisions “a rich and reusable open network of relationships connecting research organizations, people, things, and actions; a scholarly record that the global community can build on forever, for the benefit of society”. This Research Nexus expands on the importance of research objects being persistently and uniquely identified. The scholarly community has an established practice of connecting things such as citations to others’ work and it is increasingly critical to identify relationships beyond citations, bringing together published work, unpublished work, institutions, individuals, and identifying the actions that they take e.g., funding, publishing, creating, modifying, citing, and sharing. The Research Nexus brings together metadata and relationships to build a joined-up picture of the scholarly ecosystem and helps everyone identify these relationships and how they change through time. This vision is possible if all parts of the scholarly ecosystem (and beyond) work together, including various scholarly infrastructure organizations.

SemRepo: A Knowledge Graph for Research Software and Its Scholarly Ecosystem

We present SemRepo, an RDF knowledge graph comprising over 81 million triples describing nearly 200,000 GitHub repositories associated with scientific research. SemRepo captures repository-level metadata, such as contributors, issues, and programming languages, and interlinks this information with external scholarly knowledge graphs. In particular, repository authors are linked to their profiles in SemOpenAlex, repositories are connected to scholarly publications in LPWC, and research artifacts, such as datasets and experiments, are linked via MLSea-KG. This integration enables queries that span publications and their scholarly artifacts, which are typically fragmented across separate platforms. SemRepo supports analyses that are difficult to perform with existing resources in isolation, including provenance reconstruction across repositories and publications, as well as the systematic identification of risks to research reproducibility and software sustainability. By unifying research software with its scholarly context in a single graph, SemRepo provides an important infrastructure for large-scale analysis of software within the broader scientific research ecosystem.

How Grobid uses Crossref metadata to enrich bibliographic information - Crossref | Luca F.

Early this year Crossref and I talked about how Grobid users rely on their API, and the numbers are big: in Q1 2026 alone, GROBID clients sent more than 100 million requests to consolidate extracted bibliographic data. Crossref offers three tiers: public (anonymous, low throughput), polite (identified by email, higher throughput) and Metadata Plus (paid, for large-scale use). GROBID’s integration dated back to 2014, and it turned out it needed an update. Since 0.9.0, GROBID lets you join the polite pool or use your Metadata Plus key. Consolidation is more reliable, and it’s fairer to a shared, open service. Luis M. Montilla and I had a conversation about how Grobid uses the Crossref metadata to enrich references extracted from PDFs, that eventually become a blog post: 🔗 https://lnkd.in/egxWRAV6 And there’s a next step: for really large volumes, a local service may be more suitable than the API. On 22 October at 15:15 UTC, I’ll present biblio-glutton in the Community Highlights session of the Crossref Annual Meeting. Biblio-glutton is a local metadata search service for scholary publications using the Crossref public dump and data from other services: Pubmed, HAL, OpenAlex, etc... I built with Patrice Lopez in 2016. It provides provide lookup, structured, and unstructured metadata search. The owners of the Crossref Metadata Plus subscription can keep it in sync with regular background jobs so the data is kept up-to-date. 🔗 Annual Meeting (online, free; register for Session 2): https://lnkd.in/ew699KCw 🔗 biblio-glutton: https://lnkd.in/eR6KvmJM

Manuscript submission systems and metadata completeness in Crossref: Patterns and associations

The importance of open research information, particularly publication metadata, is widely recognised. Crossref is one of the most important infrastructures for registering open metadata as part of DOI record registration. It is widely known, however, that the metadata of many publications is far from complete, with many publishers making certain metadata openly available, but failing to do so for other metadata elements. Publishers’ ability to register this metadata with Crossref depends on their capacity to capture and retain this data in their production workflows. Manuscript submission systems are an important, yet largely overlooked, factor in the extent to which publishers make metadata available through Crossref. In this paper, we present the results of an analysis investigating the relation between the level of metadata that publishers deposit with Crossref and the submission systems that they deploy for their journals. We have looked at the 153 publishers with the largest amounts of publications in Crossref and concentrate on the four most commonly used systems: Editorial Manager, ScholarOne, Open Journal Systems (OJS) and eJournalPress. We show that some submission systems appear better suited to capturing certain metadata elements. However, there are always cases where publishers using the same system differ widely in the level of metadata they register, suggesting that technology is not the only prohibiting factor and other considerations are at play.

Schema 5.5 now available: adding CRediT, new record types for blogs and posters, and more - Crossref

Research is rarely limited to a single contributor performing a single role. Behind every research output are people contributing in various ways: software development, data analyses, methodology design, and much more. Often, the same person contributes in several of these ways. Until now, Crossref metadata could only capture part of that picture, but this is changing with Schema 5.5.

relational citation system — Deborah Khodanovich

cargo.site

Incorrect Citation Association for Articles in Online-Only Springer Nature Journals

We show that citation metrics of journal articles in many of the online-only Springer Nature journals and associated ones are distorted, going back to articles from 2001. We find that most likely due to an API response error, there are many incorrect references which typically lead to Article Number 1 of a given Volume. Among others, the issue affects journals such as Scientific Reports, Nature Communications, Communications journals, Cell Death & Disease, Light: Science & Applications, as well as many BMC, Discovery and npj journals. Beyond the negative effect of introducing incorrect reference information, this distorts the citation statistics of articles in these journals, with a few articles being massively over-cited compared to their peers, while many lose citations; e.g. both in Scientific Reports and in Nature Communications, 5 of the 10 top cited articles have article numbers of 1. We validate the distorted statistics by assessing data from multiple scientific literature databases: Crossref, OpenCitations, Semantic Scholar, and the journals' websites. The issue primarily arises from the inconsistent transition from page-based referencing of articles to article number-based referencing, as well as the improper handling of the change in the publisher's article metadata API. It seems that the most pressing problem has been present since approximately 2011, which we estimate affects the citation count of millions of authors.

Citing Less Critically: LLMs Reshape the Rhetoric and Reach of Scientific Citation

Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentioning). As large language models (LLMs) increasingly assist scientific writing, whether they reproduce citations with the same rhetorical intent as humans remains unclear. We introduce a masked-citation task to compare human and LLM-generated citation behavior. For each citation context, an LLM generates a replacement citation sentence, producing a counterfactual corpus directly comparable to human citation. We analyze what, whom, and how models cite, using an LLM-as-a-judge to classify citation intent and a 20-million-edge coauthorship network to measure social distance between cited authors. Across six popular LLMs and 1,746 top NLP conference papers (63k+ contexts, 132k+ citations), three patterns emerge: (1) Compared with human citation, LLMs cite significantly less critically; (2) LLMs over-cite popular and older papers, a tendency amplified for contrasting citations where human writing more often draws on recent, niche work; (3) Whereas humans often cite within their close social network, especially for supporting citations, LLMs tend to draw on more socially distant authors. Together, these differences are double-edged: LLM citation reaches beyond a scholar's close collaborators while being less critical and amplifying visibility bias, reshaping the rhetoric and reach of scientific citation.

Attribution, Provenance, Reference, Citation, and AI for Research Applications – Understanding the Differences - The Scholarly Kitchen

Building robust citation and attribution into generative AI systems are foundational to usage, credit and trust. We need to expect more from AI.

Guest Post — Scholarly AI Search Shortcomings and the Need for Better Metadata - The Scholarly Kitchen

AI scholarly search tools often miss important literature due to incomplete metadata. Better full-text-derived metadata could significantly improve discovery.