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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.

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. And now it is finally battle time: which of the two approaches is better?

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?

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?

Data citation is seen as one of the most important ways to establish data as a first-class scientific output. At Crossref and DataCite we are seeing growth in journal articles and other record types citing data, and datasets making the link the other way. Our organizations are committed to working together to help realize the data citation community’s ambition, so we’re embarking on a dedicated effort to get things moving.

We’ve been working on Event Data for some time now, and in the spirit of openness, much of that story has already been shared with the community. In fact, when I recently joined as Crossref’s Product Manager for Event Data, I jumped onto an already fast moving train—headed for a bright horizon.

The Crossref graph of the research enterprise is growing at an impressive rate of 2.5 million records a month - scholarly communications of all stripes and sizes. Preprints are one of the fastest growing types of content. While preprints may not be new, the growth may well be: ~30% for the past 2 years (compared to article growth of 2-3% for the same period). We began supporting preprints in November 2016 at the behest of our members. When members register them, we ensure that: links to these publications persist over time; they are connected to the full history of the shared research results; and the citation record is clear and up-to-date.

From time to time we get questions from members asking what the difference is between reference linking and registering references as part the Content Registration process. Here’s the distinction: Linking out to other articles from your reference lists is a key part of being a Crossref members - it’s an obligation in the membership agreement and it levels the playing field when all members link their references to one another.

I joined Crossref only a few weeks ago, and have happily thrown myself into the world of Event Data as the service’s new product manager. In my first week, a lot of time was spent discussing the ins and outs of Event Data. This learning process made me very much feel like you might when you’ve just bought a house, and you’re studying the blueprints while also planning the house-warming party.

The day I received my learner driver permit, I remember being handed three things: a plastic thermosealed reminder that age sixteen was not a good look on me; a yellow L-plate sign as flimsy as my driving ability; and a weighty ‘how to drive’ guide listing all the things that I absolutely must not, under any circumstances, even-if-it-seems-like-a-really-swell-idea-at-the-time, never, ever do.