Guest Post – ODI Survey on AI and Web-Scale Discovery
NISO’s Open Discovery Initiative (ODI) survey reflects the positive and negative expectations of generative AI in web-scale discovery tools.
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.
Today’s guest authors offer practical tips for publishing high-quality image descriptions, a key step toward ensuring genuine accessibility in scholarly communications.
In an era of information abundance and epistemic chaos, libraries serve as crucial sites for democratic knowledge practices — protecting them is critical to preserving the infrastructure of informed citizenship itself.
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.
What’s the magic word? Is it “please”? “Abracadabra”? Wingardium leviosa”? Why are humans drawn to incantations and affirmations?
As AI becomes a major consumer of research, scholarly publishing must evolve: from PDFs for people to structured, high-quality data for machines.
Catching up with the ongoing consolidation of the journals market — what has happened in the two years since this was last examined? And how does the market look if you add in a large number of relatively newly launched journals?
Open access has revolutionized how research reaches readers — yet, true accessibility is an ethical imperative for institutions, publishers, and service providers to create genuinely inclusive scholarly communication.
A scholarly communication ecosystem that relies on voluntary support rather than charging for access to content becomes radically less capable of keeping money in the system.
What happens when AI-infused information systems increasingly provide answers rather than directing people to sources?
The MIT Press surveyed book authors on attitudes towards LLM training practices. In Part 2 of this 2 part post, we discuss recommendations for stakeholders to avoid unintended harms and preserve core scientific and academic values.
The MIT Press surveyed book authors on attitudes towards LLM training practices. In Part 1 of this 2 part post, we discuss the results: authors are not opposed to generative AI per se, but they are strongly opposed to unregulated, extractive practices and worry about the long-term impacts of unbridled generative AI development on the scholarly and scientific enterprise.
FAIR represents the best opportunity of the models under consideration to ensure that research information services receive appropriate recognition and sustainable funding