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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.

We’re in year two of the Resourcing Crossref for Future Sustainability (RCFS) research. This report provides an update on progress to date, specifically on research we’ve conducted to better understand the impact of our fees and possible changes. Crossref is in a good financial position with our current fees, which haven’t increased in 20 years. This project is seeking to future-proof our fees by: Making fees more equitable Simplifying our complex fee schedule Rebalancing revenue sources In order to review all aspects of our fees, we’ve planned five projects to look into specific aspects of our current fees that may need to change to achieve the goals above.

On behalf of the Nominating Committee, I’m pleased to share the slate of candidates for the 2024 board election. Each year we do an open call for board interest. This year, the Nominating Committee received 53 submissions from members worldwide to fill four open board seats. We maintain a balanced board of 8 large member seats and 8 small member seats. Size is determined based on the organization’s membership tier (small members fall in the $0-$1,650 tiers and large members in the $3,900 - $50,000 tiers).

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.

In the first half of this year we’ve been talking to our community about post-publication changes and Crossmark. When a piece of research is published it isn’t the end of the journey—it is read, reused, and sometimes modified. That’s why we run Crossmark, as a way to provide notifications of important changes to research made after publication. Readers can see if the research they are looking at has updates by clicking the Crossmark logo.

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.

Update 2024-07-01: This post is based on an interview with Euan Adie, founder and director of Overton._ What is Overton? Overton is a big database of government policy documents, also including sources like intergovernmental organizations, think tanks, and big NGOs and in general anyone who’s trying to influence a government policy maker. What we’re interested in is basically, taking all the good parts of the scholarly record and applying some of that to the policy world.