Open Science 2.0 must focus not only on access, but also on trust, interpretation, learning, and effective communication. The challenge facing the scholarly publishing ecosystem is ensuring that what is open is also trustworthy, understandable, and genuinely useful.
Today’s post features conversations with society publishing leaders resisting the proposed OMB rule change for federal research funding.
Today, we ask the Chefs how they (or their organizations) are responding to the proposed changes to how US federal research grant funds can be used.
Today, we take an emotional journey through scholarly publishing and aim to understand how emotions impact authors’, reviewers’, and editors’ experiences in the scholarly communications lifecycle.
SSP’s latest Pulse Check survey offers a community-wide snapshot of social media in scholarly publishing as it stands today.
Today’s guest post introduces the YCR-index as an alternative to measuring value with raw citation counts.
Today, we reprise the talk by outgoing SSP President Rebecca McLeod at last month’s SSP Annual Meeting.
Today’s post explores what happens to the scholarly content platform when AI agents become the users.
Today, we offer reflections on the SSP Annual Meeting from our 2026 Fellowship cohort.
Building robust citation and attribution into generative AI systems are foundational to usage, credit and trust. We need to expect more from AI.
A German court ruled against a mandatory article deposit requirement under Germany’s “secondary publication right” (SPR). Whatever the intentions, SPR is mainly going to contribute to the degradation of the record of science.
We asked some of the attendees of the recent SSP Annual Meeting in Chula Vista, CA, to answer the question: “What are some takeaways from your experience at SSP 2026?”
Today’s guest post asserts that AI infrastructure will let publishers truly leverage machines, while brand and community are what will keep them meaningful to humans.
The Chefs offer their reflections on last week’s SSP Annual Meeting.
AI scholarly search tools often miss important literature due to incomplete metadata. Better full-text-derived metadata could significantly improve discovery.