Guest Post — Building Sustainable Infrastructure for OA Book Metrics
Today’s guest author offers a progress report on recent efforts to build open-source technology for open access book metrics.
Today’s guest author offers a progress report on recent efforts to build open-source technology for open access book metrics.
Today, we talk to thought leaders Helen King and Chris Leonard, who offer a nuanced look at how peer review might adapt, fracture, or reinvent itself in the AI era.
The future of peer review isn’t about choosing between humans and AI, or between speed and quality, but about combining the strengths of both to enable speed with quality, to ensure quality, ethics, and trust in the scholarly record.
Peer Review Quality Ratings could offer a powerful step toward restoring faith in the scholarly research system, highlight exemplary practices, and ensure that robust, verified science continues to illuminate the path forward for humanity.
To kick off Peer Review Week, we asked the Chefs, What’s a bold experiment with AI in peer review you’d like to see tested?
What can you expect from this fall’s New Directions in Scholarly Publishing Seminar in Washington, DC?
Today’s post discusses research metrics and their relationship to research integrity, inclusivity, and long-term impact.
NISO’s Open Discovery Initiative (ODI) survey reflects the positive and negative expectations of generative AI in web-scale discovery tools.
During the first Trump administration, Alice Meadows interviewed three women of color who are leaders in their fields about their experiences. In this post, they revisit the topic in the light of their new positions and today’s political environment.
This post explores author, reviewer, and publisher ethics and responsibilities related to the use of AI in coding and publishing research software.
Summing up the Committee on Publication Ethics (COPE) Forum discussion on Emerging AI Dilemmas in Scholarly Publishing, which explored the many challenges AI presents for the scholarly community.
FAIR represents the best opportunity of the models under consideration to ensure that research information services receive appropriate recognition and sustainable funding
A scholarly disinformation taxonomy could help prevent scholarly communications from being gamed by fraudulent actors.
Wait, Homer Simpson doesn’t say “D’oh!” in different countries?
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.