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https://doi-org.turing.library.northwestern.edu/10.13003/axeer1ee

In our previous entry, we explained that thorough evaluation is key to understanding a matching strategy’s performance. While evaluation is what allows us to assess the correctness of matching, choosing the best matching strategy is, unfortunately, not as simple as selecting the one that yields the best matches. Instead, these decisions usually depend on weighing multiple factors based on your particular circumstances. This is true not only for metadata matching, but for many technical choices that require navigating trade-offs. In this blog post, the last one in the metadata matching series, we outline a subjective set of criteria we would recommend you consider when making decisions about matching.

https://doi-org.turing.library.northwestern.edu/10.13003/ief7aibi

In our previous blog post in this series, we explained why no metadata matching strategy can return perfect results. Thankfully, however, this does not mean that it’s impossible to know anything about the quality of matching. Indeed, we can (and should!) measure how close (or far) we are from achieving perfection with our matching. Read on to learn how this can be done!

How about we start with a quiz? Imagine a database of scholarly metadata that needs to be enriched with identifiers, such as ORCIDs or ROR IDs. Hopefully, by this point in our series this is recognizable as a classic matching problem. In searching for a solution, you identify an externally-developed matching tool that makes one of the below claims. Which of the following would demonstrate satisfactory performance?

https://doi-org.turing.library.northwestern.edu/10.13003/pied3tho

In our previous instalments of the blog series about matching (see part 1 and part 2), we explained what metadata matching is, why it is important and described its basic terminology. In this entry, we will discuss a few common beliefs about metadata matching that are often encountered when interacting with users, developers, integrators, and other stakeholders. Spoiler alert: we are calling them myths because these beliefs are not true! Read on to learn why.

https://doi-org.turing.library.northwestern.edu/10.13003/zie7reeg

In our previous blog post about metadata matching, we discussed what it is and why we need it (tl;dr: to discover more relationships within the scholarly record). Here, we will describe some basic matching-related terminology and the components of a matching process. We will also pose some typical product questions to consider when developing or integrating matching solutions.

Basic terminology

Metadata matching is a high-level concept, with many different problems falling into this category. Indeed, no matter how much we like to focus on the similarities between different forms of matching, matching affiliation strings to ROR IDs or matching preprints to journal papers are still different in several important ways. At Crossref and ROR, we call these problems matching tasks.

https://doi-org.turing.library.northwestern.edu/10.13003/aewi1cai

At Crossref and ROR, we develop and run processes that match metadata at scale, creating relationships between millions of entities in the scholarly record. Over the last few years, we’ve spent a lot of time diving into details about metadata matching strategies, evaluation, and integration. It is quite possibly our favourite thing to talk and write about! But sometimes it is good to step back and look at the problem from a wider perspective. In this blog, the first one in a series about metadata matching, we will cover the very basics of matching: what it is, how we do it, and why we devote so much effort to this problem.

In the scholarly communications environment, the evolution of a journal article can be traced by the relationships it has with its preprints. Those preprint–journal article relationships are an important component of the research nexus. Some of those relationships are provided by Crossref members (including publishers, universities, research groups, funders, etc.) when they deposit metadata with Crossref, but we know that a significant number of them are missing. To fill this gap, we developed a new automated strategy for discovering relationships between preprints and journal articles and applied it to all the preprints in the Crossref database. We made the resulting dataset, containing both publisher-asserted and automatically discovered relationships, publicly available for anyone to analyse.

One of the main motivators for funders registering grants with Crossref is to simplify the process of research reporting with more automatic matching of research outputs to specific awards. In March 2022, we developed a simple approach for linking grants to research outputs and analysed how many such relationships could be established. In January 2023, we repeated this analysis to see how the situation changed within ten months. Interested? Read on!

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?

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!