Can we transform the Open Access Pathway? And who is already showing us how it could be done? Mark Hahnel explores the options - and consequences.

This week's #TLDRShorts episode features Dr Etosha Cave who shares her thoughts on climate change and one of the latest discoveries that could help us achieve a more sustainable future for our planet.

Transformation via and within research is a constant in our lives. But with AI, we now stand at a point where research (and many other aspects of our working life) will be transformed in a monumental way. As such, we are taking this moment to reflect on the activity of Research Transformation itself, and celebrating the art of change. Our campaign will show how research data can be transformed into actionable insights, how the changing role of research is affecting both those in academia and industry, and exploring innovative ways to make research more open, inclusive and collaborative, for all – especially for those beyond the walls of academia.

The landscape of open research data is ever-evolving, driven by the relentless advancement of technology and the burgeoning expectations of a data-driven society. This piece navigates research data publication fragmentation, elucidating how the principles of Findable, Accessible, Interoperable, and Re-usable (FAIR) data - are pivotal in this context. FAIR data was a term coined in 2016 that has gained global traction.

Digital Science's new VP Open Research, Mark Hahnel, explains why he's excited for the opportunities in the field - and what's next for open research at Digital Science.

In the final of his three-part series, Mark Hahnel looks at how FAIR data can benefit the public, whose taxes largely fund research.

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.

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

On April 13th 2023, Mark tweeted that Figshare had reached seven million outputs, a significant milestone in anyone’s book! He and I jumped on a quick call to discuss what that achievement means for Figshare, and before we knew it our chat had meandered onto what it was like in the very early days of Figshare and Overleaf, and how milestones and celebrations change as the numbers keep going up and to the right! :) We hope you enjoy where the conversation takes us.