Over the past few years, I’ve been really interested in seeing the breadth of uses that the research community is finding for the Crossref REST API. When we ran Crossref LIVE Kyiv in March 2019, Serhii Nazarovets joined us to present his plans for the Open Ukrainian Citation Index, an initiative he explains below.
But first an introduction to Serhii and his colleague Tetiana Borysova.
Serhii Nazarovets is a Deputy Director for Research at the State Scientific and Technical Library of Ukraine. Serhii has a Ph.D. in Social Communication Science. His research interests lie in the area of scientometrics and library science. Serhii is the Associate Editor for DOAJ (www.doaj.org) and the Regional Editor for E-LIS (Eprints in Library and Information Science). Serhii has worked in different scientific libraries of Ukraine for more than 10 years. Tetiana Borysova is a Senior Researcher at the State Scientific and Technical Library of Ukraine. Her research interests are focused on topics such as research data management, journal management and scientometrics.
It’s been a year since Metadata Manager was first launched in Beta. We’ve received a lot of helpful feedback from many Crossref members who made the switch from Web Deposit Form to Metadata Manager for their journal article registrations.
The most common use for Metadata Manager is to register new DOIs for newly published articles. For the most part, this is a one-time process. You enter the metadata, register your DOI, and success!
Bibliographic references in scientific papers are the end result of a process typically composed of: finding the right document to cite, obtaining its metadata, and formatting the metadata using a specific citation style. This end result, however, does not preserve the information about the citation style used to generate it. Can the citation style be somehow guessed from the reference string only?
Threadgill-Sowder, J. (1983). Question Placement in Mathematical Word Problems. School Science and Mathematics, 83(2), 107-111
This reference is the end result of a process that typically includes: finding the right document, obtaining its metadata, and formatting the metadata using a specific citation style. Sadly, the intermediate reference forms or the details of this process are not preserved in the end result. In general, just by looking at the reference string we cannot be sure which document it originates from, what its metadata is, or which citation style was used.
Last year I spent several weeks studying how to automatically match unstructured references to DOIs (you can read about these experiments in my previous blog posts). But what about references that are not in the form of an unstructured string, but rather a structured collection of metadata fields? Are we matching them, and how? Let’s find out.
About 11% of available references in records in our OAI-PMH & REST API don’t have DOIs when they should. We have deployed a fix, but it is running on billions of records, and so we don’t expect it to be complete until mid-April.
Note that the Cited-by API that our members use appears to be unaffected by this problem.
Hi, Crossref blog-readers. I’m Shayn, from Crossref’s support team. I’ve been fielding member questions about how to effectively deposit metadata and register content (among other things) for the past three years. In this post, I’ll take you through some of the improvements that Metadata Manager provides to those who currently use the Web Deposit form.
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.
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?
