A look back at a pilot of the citation typing ontology

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.

Anthropic’s new Citations API

Here’s a new API-only feature from Anthropic that requires quite a bit of assembly in order to unlock the value: Introducing Citations on the Anthropic API. Let’s talk about what …

DITA Open Toolkit

The open-source publishing engine for content authored in the Darwin Information Typing Architecture

The anatomy of a nanopublication

As the amount of scholarly communication increases, it is increasingly difficult for specific core scientific statements to be found, connected and curated. Additionally, the redundancy of these statements in multiple fora makes it difficult to determine attribution, quality and provenance. To tackle these challenges, the Concept Web Alliance has promoted the notion of nanopublications (core scientific statements with associated context). In this document, we present a model of nanopublications along with a Named Graph/RDF serialization of the model. Importantly, the serialization is defined completely using already existing community-developed technologies. Finally, we discuss the importance of aggregating nanopublications and the role that the Concept Wiki plays in facilitating it.

Stackable Citation Knowledge — Live Demo: Annotate While Reading (CiTeX 2026)

This screencast is the pre-recorded demo segment from the talk   "Stackable Citation Knowledge: Building on Nanopublications for   Climate and Biodiversity Research", delivered remotely at the   CiTeX 2026 Workshop on Citation Extraction and Parsing (DIPF   Leibniz Institute, Frankfurt, 28-29 May 2026) by Anne Fouilloux   (LifeWatch ERIC) and Jean Iaquinta (Vitenhub AS).     The demo walks through the third production path for citation-   typed nanopublications: an expert reader annotates a citation   found in someone else's paper, rather than the citing author   declaring intent (Author path) or an LLM-extraction pipeline   inferring it retroactively (Extract path). The annotation is   published as a signed CiTO Citation nanopub on the open Science   Live network — ORCID-attributed, immutable via Trusty URI, and   contestable by counter-nanopubs.     Worked example: while reading Wernberg et al. 2016 (Science,   353:169-172, "Climate-driven regime shift of a temperate marine   ecosystem"), the reader infers that Wernberg cites reference 11 -   Poloczanska et al. 2013 (Nature Climate Change, 3:919-925, "Global   imprint of climate change on marine life") - as the global   authority for climate-driven marine species redistribution. The   annotation is published to Science Live with relation   cito:citesAsAuthority and a one-sentence rationale tying it to the   authors' planned Mediterranean Iberian extension.     This is the alternative to LLM-based citation intent extraction:   same downstream signed nanopub, human expert reader instead of a   language model in the loop. Complements OpenCitations, WikiCite,   and CiTO-extraction tools (GROBID, CEC, GRAPHIA, OFFZIB) by   providing a citable target for any typed-citation output.     Tools used:   - Science Live Zotero plugin (Zotero 7+, plugin v1.0.6)   - Science Live platform (platform.sciencelive4all.org)   - Citation with CiTO template (built on CiTO and FaBiO ontologies)     Related materials:   - Talk repository (Slidev source, demo script, references):     https://github.com/ScienceLiveHub/citex2026-stackable-citations   - Live deck:     https://sciencelive4all.org/citex2026-stackable-citations/   - CiTeX 2026 workshop:     https://sites.google.com/view/workshop-on-citation-extractio/

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.

Introducing Citations on the Anthropic API | Claude

Claude can now cite specific passages from your documents, delivering verifiable responses with built-in source tracking. Update: Now available in Amazon Bedrock. (June 30, 2025) Today, we're launching Citations, a new API feature that lets Claude ground its answers in source documents.

A Novel Kuhnian Ontology for Epistemic Classification of STM Scholarly Articles

Despite rapid gains in scale, research evaluation still relies on opaque, lagging proxies. To serve the scientific community, we pursue transparency: reproducible, auditable epistemic classification useful for funding and policy. Here we formalize KGX3 as a scenario-based model for mapping Kuhnian stages from research papers, prove determinism of the classification pipeline, and define the epistemic manifold that yields paradigm maps. We report validation across recent corpora, operational complexity at global scale, and governance that preserves interpretability while protecting core IP. The system delivers early, actionable signals of drift, crisis, and shift unavailable to citation metrics or citations-anchored NLP. KGX3 is the latest iteration of a deterministic epistemic engine developed since 2019—originating as Soph.io (2019–2020), advanced as iKuhn (2020–2024), and field-tested through Preprint Watch.

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.

Semantic Scholar | AI-Powered Research Tool

Semantic Scholar uses groundbreaking AI and engineering to understand the semantics of scientific literature to help Scholars discover relevant research.

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.

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

We’ve recently reached an important milestone for the research nexus: the works in our metadata corpus are now connected with over 2 billion citation links! This is a great opportunity to share a dedicated dataset and discuss why these are important for science.

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.