With trust in research a critical issue, our team takes a detailed look at a key 'trust marker' in research publications on vaccine hesitancy.

In the second part of this three-part series on artificial intelligence, machine learning and open research, Mark Hahnel takes a look into how this technology can actually result in better, more diverse, more inclusive and ultimately FAIR-er data for the whole research community.

Our popular #FuturePub event was a big success in beautiful San Francisco! See details of the event's lightning talks, good times, and a photo gallery from the evening.

Could Generative AI provide a solution to the problem of predatory journals? We share the good and the bad news.

Who benefits from Findable, Accessible, Interoperable and Reusable (FAIR) data? We explore how researchers are the primary beneficiaries, despite shouldering the FAIR data responsibility.

Trust. Five letters, multiple meanings, immense power. Trust arrives on foot and leaves on horseback. Trust is the basis for society, but foundations are fracturing in a world of growing divides. Trust in research has never been more important in our lifetimes. In the vast subway system of the scientific world, we must navigate through research integrity. All who create or consume science are on this journey. How do we safely traverse information and hold on to the sanctity of science. How do we mind the trust gap?

TL;DR goes on tour with "What does a university look like?" at ICSSI conference in Chicago. We exploredthe shapes of Harvard, MIT, Oxford, Peking, ETH Zurich, Chicago, and Northwestern

This is the story of how a school science fair inspired a passion for science communication, a PhD in microbiology, and a valuable perspective on the current AI debate. Dr Jessica Miles recently participated in the SSP2023 panel on AI and the Integrity of Scholarly Publishing, the writeup from which has just been published on the Scholarly Kitchen. I caught up with Jessica to chat about how she came to be on the panel, her background in microbiology, and her thoughts on the future of scholarly publishing in an AI world. I got a Barack Obama impression for free 😊

The effects of new technology are seldom either solely positive or solely negative and, as such, there is a responsibility that sits with those that develop technology to consider how it will be used.  Underlying that responsibility sits a need to deeply understand technology.  The recent rise of Large Language Models (LLMs) and its rapid adoption begs many questions about whether we understand the technology and whether we understand how it will impact us at a cultural, societal, or economic level. When I have written before on this topic I have noted that it is not the large biases that are concerning to me, rather it is the subtle biases that are difficult to detect that I think should be of more concern. I found recently what I regard to be an excellent illustration of the phrase that I have chosen to describe the output of AI. I call this the ‘Lone Banana Problem’.

Last week I was in London for CERN FCC Week 2023, an event which brings together the people – scientists, engineers, researchers and more – working on CERN’s Future Circular Collider (FCC) project to present and discuss the latest details from the ongoing feasibility studies and planning assessments.  CERN is an inspiration to people worldwide, and I'm always excited to hear the latest on their plans for the future. Here's my take on CERN FCC Week 2023.