Guest Post — From Cloud to Carbon: Exploring the Digital Carbon Footprint of Knowledge
Today’s guest post summarizes the discussion in the recent EASE / STM / webinar, exploring the digital carbon footprint of scholarly publishing.
Today’s guest post summarizes the discussion in the recent EASE / STM / webinar, exploring the digital carbon footprint of scholarly publishing.
After five years of GetFTR, four librarians discuss how it is working in practice, its value to libraries and researchers, and what opportunities lie ahead.
Rather than just bolting on AI to existing publication workflows,there is a real opportunity to rethink and redesign them for human–AI collaboration. Some thoughts on what that looks like in practice.
We talk a lot about AI in scholarly communications and publishing, but today, we ask the Chefs: What’s your favorite AI hack?
Nearly three years after ChatGPT’s debut, generative AI continues to reshape scholarly publishing. The sector has moved from experimentation toward integration, with advances in ethical writing tools, AI-driven discovery, summarization, and automated peer review. While workflows are becoming more efficient, the long-term impact on research creation and evaluation remains uncertain.
In the fast-moving world of AI research tools, there are many community-focused concerns that vendors should have strong opinions on and plans for, from privacy and security to sustainability and copyright. But the most misunderstood issue, in my view, is the one at the heart of it all — how AI will reshape the economics of academic research.
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.
AI web harvesting bots are different from traditional web crawlers and violate many of the established rules and practices in place. Their rapidly expanding use is emerging as a significant IT management problem for content-rich websites across numerous industries.
NISO’s Open Discovery Initiative (ODI) survey reflects the positive and negative expectations of generative AI in web-scale discovery tools.
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.
What happens when AI-infused information systems increasingly provide answers rather than directing people to sources?
If LLMs are the future of information discovery, valuable scholarly content risks being left behind — unless we build a bridge with better licensing.
Scholarly communications leaders have the opportunity to turn AI uncertainty into discovery.
Industry pros offer a marketing manifesto of sorts, to help our non-marketing colleagues see behind the curtain and understand how to best leverage these critical team members.
We asked the Chefs for their thoughts on two important court decisions on the legality of using copyrighted materials for AI training.