Dominika Tkaczyk – 2018 December 18
In my previous blog post, Matchmaker, matchmaker, make me a match, I compared four approaches for reference matching. The comparison was done using a dataset composed of automatically-generated reference strings. Now it’s time for the matching algorithms to face the real enemy: the unstructured reference strings deposited with Crossref by some members. Are the matching algorithms ready for this challenge? Which algorithm will prove worthy of becoming the guardian of the mighty citation network? Buckle up and enjoy our second matching battle!
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.
Dominika Tkaczyk – 2018 November 12
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?
Dominika Tkaczyk – 2018 November 09
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?
Christine Buske – 2018 September 12
Jennifer Lin – 2018 May 31
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.
Anna Tolwinska – 2018 May 30
Christine Buske – 2018 March 29
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.
2019 January 17
2019 January 03
2018 December 18