2026 March 31

Voices from Crossref Metadata Sprint in São Paulo

This year, we placed a spotlight on the Latin American community, hosting the second Crossref Metadata Sprint in São Paulo, Brazil from 4 - 6 March 2026. In our first tri-lingual event, we brought together 31 participants from Argentina, Brazil, Colombia, Ecuador, and Mexico. Our goal was to foster community co-creation using the open scholarly metadata. The Sprint was an opportunity to pose questions, share ideas, collaborate on research, and propose innovative solutions that enhance the use of metadata in scholarly communication and beyond.

Read on for more details about the content of the Sprint, and the resulting projects. You can also register to join our Sprint Showcase call on 22nd April to hear directly from the team about their creations.

2026 March 30

DOI resolution and deposit outage on 17 March 2026

On 17 March 2026, we experienced an outage that affected DOI resolution for Crossref DOIs and the deposit of metadata records by Crossref members. In this summary, we outline what happened, the impact on our community, and the steps we are taking to strengthen our systems and processes as a result.

2026 March 24

Strengthening support for data citations and saying goodbye to Event Data

We’re excited to announce a new data citation API endpoint and are seeking your feedback. The new service makes existing data citation relationships in our metadata available, thereby surfacing this part of the research nexus. At the same time, we’ve decided that it’s time to move on from Event Data.

2026 March 19

On metadata enrichment

Metadata is communication; it can tell a story about research and paint a picture for others to respond to and learn from, across the world and throughout the forthcoming generations. Metadata can feel technical with words like ‘infrastructure’ and ‘schema’, and sometimes, like tech in general, it comes with hyperbole. But metadata really is part art (storytelling and pictures) and part science (structured models and standards) with both aspects being equally important, and requiring people as well as systems. That necessary combination of human and machine involvement also makes metadata challenging.