When you thought about your resolutions for 2019, Crossref probably didn’t cross your mind—but, maybe it should have…
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!
As the end of the year approaches it’s useful to look back and reflect on what we’ve achieved over the last 12 months—a lot! To be honest, there were some things we didn’t get done—or didn’t make as much progress with as we hoped—but that happens when you have an ambitious agenda. However, we also got some things done that we didn’t expect to or that weren’t even on our radar at the end of 2017—this is inevitable as the research and scholarly communications landscape is rapidly changing.
In its July 2018 meeting, the Crossref Board voted unanimously to approve and introduce a new set of membership terms. At the same meeting, the board also voted to change the description of membership eligibility in our Bylaws, officially broadening our remit beyond publishers, in line with current practice and positioning us for future growth.
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. The Committee has met regularly this year and the following questions are under deliberation regarding term limits, role of the Nominating Committee, implications of contested elections, and more.
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?
2019 October 04
Request for feedback: Conference ID implementation
2019 September 13
Speaking, Traveling, Listening, Learning
2019 August 29
