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.
Many researchers want to carry out analysis and extraction of information from large sets of data, such as journal articles and other scholarly content. Methods such as screen-scraping are error-prone, place too much strain on content sites and may be unrepeatable or break if site layouts change. Providing researchers with automated access to the full-text content via DOIs and Crossref metadata reduces these problems, allowing for easy deduplication and reproducibility. Supporting text and data mining echoes our mission to make research outputs easy to find, cite, link, assess, and reuse.
In 2013 Crossref embarked on a project to better support Crossref members and researchers with Text and Data Mining requests and access. There were two main parts to the project:
To collect and make available full-text links and publisher TDM license links in the metadata.
To provide a service (TDM click-through service) for Crossref members to post their additional TDM terms and conditions and for researchers to access, review and accept these terms.
To date, 37.5 million works registered with Crossref have both full-text links and TDM license information. We continue to encourage all members to include full-text links and license information in the metadata they register to assist researchers with TDM. You can see how each member is doing via its Participation Report (e.g. Wiley’s).
Members are also making subscription content available for text mining (temporarily or otherwise) for specific purposes, such as to help the research community with its response to COVID-19. Back in April we highlighted how this can be achieved by including:
A “free to read” element in the access indicators section of publisher metadata indicating that the content is being made available free-of-charge (gratis)
An assertion element indicating that the content being made available is available free-of-charge.
To access Crossref’s click-through tool for text and data mining, users could log in via their ORCID iD. They could then review TDM license agreements posted by Crossref members and accept, reject or postpone their decisions until later. Having agreed to a publisher’s terms and conditions this action was logged against the user’s API token which they could use when requesting full-text from the publisher.
Since the pilot in 2014, only 2 publishers have continued with the tool and fewer than 300 API tokens have been issued.
Publishers have since developed their own mechanisms for managing TDM requests. The introduction of UK (2014) / EU (2019) copyright exceptions for TDM has significantly reduced the number of requests and at the same time, more and more content is published under an open access license.
Given the low take-up of the click-through by both publishers and researchers, its goals are no longer being met. Therefore we will retire the TDM click-through in December 2020. Until that date, it will still operate for the two publishers and various researchers who use it while they finish implementing their alternative plans.
Crossref will continue to collect member-supplied TDM licensing information in metadata for individual works, and researchers can continue to find this via the Crossref APIs.