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Today, we’re delighted to let you know that Crossref members can now use ROR IDs to identify funders in any place where you currently use Funder IDs in your metadata. Funder IDs remain available, but this change allows publishers, service providers, and funders to streamline workflows and introduce efficiencies by using a single open identifier for both researcher affiliations and funding organisations.

https://doi-org.ezproxy.csu.edu.au/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.

Metadata is one of the most important tools needed to communicate with each other about science and scholarship. It tells the story of research that travels throughout systems and subjects and even to future generations. We have metadata for organising and describing content, metadata for provenance and ownership information, and metadata is increasingly used as signals of trust.

Following our panel discussion on the same subject at the ALPSP University Press Redux conference in May 2024, in this post we explore the idea that metadata, once considered important mostly for discoverability, is now a vital element used for evidence and the integrity of the scholarly record. We share our experiences and views on the metadata significance and workflows from the perspective of academic and university presses – thus we primarily concentrate on the context of books and journal articles.

https://doi-org.ezproxy.csu.edu.au/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.ezproxy.csu.edu.au/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.

We’ve just released an update to our participation report, which provides a view for our members into how they are each working towards best practices in open metadata. Prompted by some of the signatories and organizers of the Barcelona Declaration, which Crossref supports, and with the help of our friends at CWTS Leiden, we have fast-tracked the work to include an updated set of metadata best practices in participation reports for our members. The reports now give a more complete picture of each member’s activity.

It’s been a while, here’s a metadata update and request for feedback

In Spring 2023 we sent out a survey to our community with a goal of assessing what our priorities for metadata development should be - what projects are our community ready to support? Where is the greatest need? What are the roadblocks?

The intention was to help prioritize our metadata development work. There’s a lot we want to do, a lot our community needs from us, but we really want to make sure we’re focusing on the projects that will have the most immediate impact for now.

We’re happy to note that this month, we are marking five years since Crossref launched its Grant Linking System. The Grant Linking System (GLS) started life as a joint community effort to create ‘grant identifiers’ and support the needs of funders in the scholarly communications infrastructure.

Some organisations at the forefront of adopting Crossref’s Grant Linking System presented their challenges and how they overcame them, shared the benefits they are reaping from participating, and provided some tips about their processes and workflows.

https://doi-org.ezproxy.csu.edu.au/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.ezproxy.csu.edu.au/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.