The Consensus Trap: Dissecting Subjectivity and the “Ground Truth” Illusion in Data Annotation

The Application and Its Consequences for Non-Standard Knowledge Work

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As we may think

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Rethinking the human-readability infrastructure

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From novices to co-pilots: Fixing the limits on scientific knowledge production by accessing or building expertise

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AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances

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The open authorial principle: supporting networks of authors in creating externalisable designs

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Sensibilities as Knowledge in Design Research

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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.

Local-first software: you own your data, in spite of the cloud

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Reflective design

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User-tailorable systems: pressing the issues with buttons

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Coherent reaction

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The operating system: should there be one?

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The world is not a desktop | Interactions

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Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.