Who benefits when, from FAIR data? Part 3 - The Public

Part 3: the public fund research through taxes but cannot read most of it. What open academic data owes the people paying for it.

Who benefits when, from FAIR data? Part 2 – Machines

AI and machine learning can be used to create more detailed, FAIR-er datasets to be consumed by the machines.

All of the knowledge for all of the machines

Two slow revolutions in publishing, blurred peer review and universal Open Access, decide what machines can legally read and actually trust.

Who benefits when, from FAIR data? Part 1 – Researchers

Part 1: seven years on from the FAIR principles, what do researchers themselves actually get back from making their data reusable?

Academic data publishing is the biggest ROI in research today

A decade of running Figshare says the big win was simply getting data onto the internet. The next ten years are about making it usable.

Halfway to happiness — what the OSTP update means in the grand scheme

The OSTP memo on federally funded research is real progress built on decades of SPARC advocacy. It is also only half the journey.

Why fast but good publishing matters

If we are to enter the world of fast and good academic publishing, we need a dataset curation model that scales.

Academic Research Data. Is it being cited?

Daily citation updates across Figshare show which research outputs actually attract citations, and why datasets still trail papers badly.

Academic Data Curation: Who checks? Who Pays? How Much?

The research publishing system works. We get new drugs and new breakthrough discoveries every year. The goal of FAIR research data is to optimise this.