Good governance is important and something that Crossref thinks about regularly so the board frequently discusses the topic, and this year even more so. At the November 2017 meeting there was a motion passed to create an ad-hoc Governance Committee to develop a set of governance-related questions/recommendations.

We’ve mentioned why data citation is important to the research community. Now it’s time to roll up our sleeves and get into the ‘how’. This part is important, as citing data in a standard way helps those citations be recognised, tracked, and used in a host of different services. This week A Data Citation Roadmap for Scientific Publishers was published in Scientific Data.

Matching (or resolving) bibliographic references to target records in the collection is a crucial algorithm in the Crossref ecosystem. Automatic reference matching lets us discover citation relations in large document collections, calculate citation counts, H-indexes, impact factors, etc. At Crossref, we currently use a matching approach based on reference string parsing. Some time ago we realized there is a much simpler approach.

At Crossref Labs, we often come across interesting research questions and try to answer them by analyzing our data. Depending on the nature of the experiment, processing over 100M records might be time-consuming or even impossible. In those dark moments we turn to sampling and statistical tools. But what can we infer from only a sample of the data? Imagine you are cooking soup.

A couple of weeks ago we shared with you that data citation is here, and that you can start doing data citation today. But why would you want to? There are always so many priorities, why should this be at the top of the list? I’m sure you heard this before—data sharing and data citation are important for scientific progress.

Our LIVE Annual Meeting is back in North America for the first time since 2015, and with just 10 days to go, there’s a lot going on in preparation. As you’d expect with a How good is your metadata?

There has been a steady increase in the growth of our membership in Latin America—and in Brazil in particular—over the past few years. We currently have more than 800 Brazil-based members; some as individual members, but most are sponsored by another organization.

But actually, sometimes it is about the money. As a not-for-profit membership organization that is obsessed with persistence, we have a duty to remain sustainable and manage our finances in a responsible way. Our annual audit is incredibly thorough, and our outside auditors and Board-based Audit committee consistently report that we’re in good shape. Our Membership &

Best practices seem to be having a moment. In the ten years since the Books Advisory Group first created a best practice guide for books, the community beyond Crossref has developed or updated at least 17 best practice resources, as collected here by the Metadata 2020 initiative.

Over 100 Million unique scholarly works are distributed into systems across the research enterprise 24/7 via our APIs at a rate of around 633 Million queries a month. Crossref is broadcasting descriptions of these works (metadata) to all corners of the digital universe.

As part of our blog series highlighting some of the tools and services that use our API, we asked Michael Parkin—Data Scientist at the European Bioinformatics Institute—a few questions about how Europe PMC uses our metadata where preprints are concerned. Tell us a bit about Europe PMC Europe PMC is a knowledgebase for life science research literature and a platform for innovation based on the content, such as text mining.