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 resesarch 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.
We test a broad sample of DOIs to ensure resolution. For each journal crawled, a sample of DOIs that equals 5% of the total DOIs for the journal up to a maximum of 50 DOIs is selected. The selected DOIs span prefixes and issues.
The results are recorded in crawler reports, which you can access from the depositor report expanded view. If a title has been crawled, the last crawl date is shown in the appropriate column. Crawled DOIs that generate errors will appear as a bold link:
Click Last Crawl Date to view a crawler status report for a title:
The crawler status report lists the following:
Total DOIs: Total number of DOI names for the title in system on last crawl date
Checked: number of DOIs crawled
Confirmed: crawler found both DOI and article title on landing page
Semi-confirmed: crawler found either the DOI or the article title on the landing page
Not Confirmed: crawler did not find DOI nor article title on landing page
Bad: page contains known phrases indicating article is not available (for example, article not found, no longer available)
Login Page: crawler is prompted to log in, no article title or DOI
Exception: indicates error in crawler code
httpCode: resolution attempt results in error (such as 400, 403, 404, 500)
httpFailure: http server connection failed
Select each number to view details. Select re-crawl and enter an email address to crawl again.
Page owner: Isaac Farley | Last updated 2020-April-08