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The ecosystem of scholarly metadata is filled with relationships between items of various types: a person authored a paper, a paper cites a book, a funder funded research. Those relationships are absolutely essential: an item without them is missing the most basic context about its structure, origin, and impact. No wonder that finding and exposing such relationships is considered very important by virtually all parties involved. Probably the most famous instance of this problem is finding citation links between research outputs. Lately, another instance has been drawing more and more attention: linking research outputs with grants used as their funding source. How can this be done and how many such links can we observe?

Detective Matcher stopped abruptly behind the corner of a short building, praying that his loud heartbeat doesn’t give up his presence. This missing DOI case was unlike any other before, keeping him awake for many seconds already. It took a great effort and a good amount of help from his clever assistant Fuzzy Comparison to make sense of the sparse clues provided by Miss Unstructured Reference, an elegant young lady with a shy smile, who begged him to take up this case at any cost.

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? TL;DR I built an automatic citation style classifier. It classifies a given bibliographic reference string into one of 17 citation styles or “unknown”.

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.

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!

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?

As the linking hub for scholarly content, it’s our job to tame URLs and put in their place something better. Why? Most URLs suffer from link rot and can be created, deleted or changed at any time. And that’s a problem if you’re trying to cite them.

One of the cool things about working in Crossref Labs is that interesting experiments come up from time to time. One experiment, entitled “what happens if you plot DOI referral domains on a chart?” turned into the Chronograph project. In case you missed it, Chronograph analyses our DOI resolution logs and shows how many times each DOI link was resolved per month, and also how many times a given domain referred traffic to DOI links per day.

This is a joint blog post with Dario Taraborelli, coming from WikiCite 2016.

In 2014 we were taking our first steps along the path that would lead us to Crossref Event Data. At this time I started looking into the DOI resolution logs to see if we could get any interesting information out of them. This project, which became Chronograph, showed which domains were driving traffic to Crossref DOIs.

You can read about the latest results from this analysis in the “Where do DOI Clicks Come From” blog post.

Having this data tells us, amongst other things:

• where people are using DOIs in unexpected places
• where people are using DOIs in unexpected ways
• where we knew people were using DOIs but the links are more popular than we realised