Data Reuse is the Sincerest Form of Flattery
A powerful way to quantify article quality has been hiding in plain sight. It’s time to bring data citations into the limelight.
A powerful way to quantify article quality has been hiding in plain sight. It’s time to bring data citations into the limelight.
Today’s post shares the results of an initiative designed to answer the question: what would it actually take to build a publishing model fit for the research ecosystem we have now, rather than the one we inherited?
With CC Signals, Creative Commons wants to help authors put rules on use of their licensed content for AI training. The problem is, one of the licenses already permits free and unlimited reuse of that content, for any and all purposes. And the licenses are irrevocable.
Today’s guest post explains the new data space pilot, which will be the focus of the upcoming BISG/SSP webinar on May 12, 2026.
There is more and more skepticism toward the role of publishers, a steady commoditization of publishing services, and growing fragmentation across the research ecosystem. If that is the case, the question is no longer what publishers do, but how that value is understood and extended.
Today’s guest blogger calls for adding “understandable” to the FAIR data principles, to ensure we do not surrender human knowledge in our rush for automation.
In this interview with Alice Meadows, Sami Benchekroun (Morressier/Molecular Connections) and Rod Cookson (The Royal Society) share their thoughts about how and why scholarly publishing needs to move away from being article-based.
Is open scholarship an honest signal of researcher integrity? We present preliminary evidence that data and code sharing, preprinting, and other open behaviors are indeed less common in papermill articles.
At the STM innovation and Integrity days in London last week, it’s clear that research integrity has become an increasingly pressing issue. Many publishers are reporting significant increases in submissions of questionable legitimacy. perhaps now is the time for a new alliance between publishers, funders, institutions and researchers to protect the integrity of the scholarly record, before it’s too late.
Between a political policy environment focused on defunding and deleting data collections – an environment in which little can be trusted – and an onslaught of new AI tools that feed indiscriminately on data, bits of information at the intersection of rows and columns are appearing in headlines more than ever before. To avoid cultural memory loss, we must build systems that save what humanity needs across disciplinary silos rather than saving some archives and losing others through an accident of history.
If science is to be both honest and healthy, we must accept that statistically non-significant results are part of reality. The SAMPL guidelines, if adopted widely by scholarly publishers and journal editors, hold a solution for authors who worry their results are not “significant.”
As AI becomes a major consumer of research, scholarly publishing must evolve: from PDFs for people to structured, high-quality data for machines.
Data sonification is the process of translating data into sound. Here, Lutz Bornmann and Christian Leibel present the sonified results of a recent analysis of the impact of scientific team size on innovation.
An AAAS survey reveals authors’ concerns and confusion regarding open licensing of their work.
Nicola Davies from IOPP details the publisher’s new data sharing requirements for authors.