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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.

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.

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. The system includes a funder-designed metadata schema and a unique link for each award which enables connections with millions of research outputs, better reporting on the research and outcomes of funding, and a contribution to open science infrastructure.

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.

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.

This year’s public data file is now available, featuring over 156 million metadata records deposited with Crossref through the end of April 2024 from over 19,000 members. A full breakdown of Crossref metadata statistics is available here. Like last year, you can download all of these records in one go via Academic Torrents or directly from Amazon S3 via the “requester pays” method. Download the file: The torrent download can be initiated here.

This past year has been a captivating journey of immersion within the Crossref community, a mix of online interactions and meaningful in-person experiences. From the engaging Sustainability Research and Innovation Conference in Port Elizabeth, South Africa, to the impactful webinars conducted globally, this has been more than just a professional endeavour; it has been a personal exploration of collaboration, insights, and a shared commitment to pushing the boundaries of scholarly communication.

Subject classifications have been available via the REST API for many years but have not been complete or reliable from the start and will soon be deprecated. dfdfd The subject metadata element was born out of a Labs experiment intended to enrich the metadata returned via Crossref Metadata Search with All Subject Journal Classification codes from Scopus. This feature was developed when the REST API was still fairly new, and we now recognize that the initial implementation worked its way into the service prematurely.