In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of… Continue reading →
FAIRdata.ai — FAIR Data Assessment
Assess your research data's FAIRness. Automated pipeline using F-UJI + Claude AI. Free to use.
GO FAIR initiative: Make your data & services FAIR
A bottom-up international approach for the practical implementation of the European Open Science Cloud (EOSC) as part of a global Internet of FAIR Data & Services
A Data Utopia for Science-of-Science
Here I want to briefly sketch out a vision for how to solve a key set of problems facing science-of-science researchers, using the relatively new idea of a ‘data trust.’ In my ideal wor…
Perspective Chapter: Fit for Purpose? Creative Commons Licensing for Research Data in the Age of Artificial Intelligence
Licensing is an important component of the re-usability of research data, itself part of the FAIR principles: without clear, machine-readable licensing, datasets risk becoming technically...
From Albums to Streams: How Modularity Changes Systems
Scientific publishing is breaking under document-centric formats designed for a physical world. Borrowing from music’s shift from albums to streaming, we make the case that open access alone cannot deliver reuse, trust, or scale. The future of science depends on modular, interoperable research components that move across tools, enabling new workflows, tools, and ecosystems.
Science Live - The Platform for FAIR Research
Transform research into FAIR nanopublications. Create, discover, cite, and embed structured knowledge bricks.
science.latha.org
A permissioned appview for scientific documents on AT Protocol. True data ownership with optional monetization.
Data Feminism
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.Today, data science is a form of power. I
Scientific Content Management System | Curvenote
Create, manage, and publish interactive research with connected workflows, modular content, and built-in provenance. Book a demo
What AI Asks of Open Access - The Scholarly Kitchen
As AI systems increasingly reason from the scientific literature, the integrity signals that make research trustworthy - open data, structured metadata, robust retraction processes - matter more than ever. PLOS CEO Alison Mudditt on why open access publishers have a different set of obligations in an AI world.
Fair Moderation, Equitable Access, and AI: arXiv’s Updated Rate Limit Policy
arXiv, and the scientific community at large, are facing a watershed moment. Scholarly publishing is currently changing at a rapid pace, and we are seeing a…
FAIR Metadata Check (Extended): the FAIR rubric for any repository
Data Feminism
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.
Governing by dismantling: tech oligarchy and the stifling of public data infrastructure
Published in Science as Culture (Ahead of Print, 2026)
RDM for ATProto Open Science
Connecting the dots on how to implement FAIR compliant Research Data Management features to the emerging Open Science infrastructure on AT Protocol.
| Eden-Fidelis
About RO-Crate | Research Object Crate (RO-Crate)
Lea: A Social App for Researchers - ATmosphereConf 2026
Chive | Decentralized Eprint Service
What It Means for Universities to Run Their Own PDS: The Potential of DID as Academic Infrastructure - Nightflight
Standard.site - One schema. Every platform.
