The Dataset Friction Framework: measuring user-facing friction as a complement to FAIR
2026-06-22 • Computers and Society
Computers and Society
AI summaryⓘ
The authors explain that while FAIR principles focus on how well data is organized and shared, they don’t measure how easy or hard it is for users to actually work with the data. To fix this, they created the Dataset Friction Framework (DFF), which looks at the user experience by spotting different types of hurdles, like intentional design choices or accidental problems. They tested DFF using thousands of support requests from a large weather data center and found that FAIR scores and DFF friction don’t always match. This means that both measures are important to understand data usability better.
FAIR principlesDataset Friction Frameworkdata usabilityopen research datadata stewardshipuser frictionsupport tickets analysisdata accessdata interoperabilityEuropean Centre for Medium-Range Weather Forecasts
Authors
Emma Pidduck, Umberto Modigliani
Abstract
Open research data services have matured to the point where the cost of sustaining them at scale has become a primary design constraint, driving providers to make deliberate choices that may reduce user convenience to keep the service viable. The FAIR (Findable, Accessible, Interoperable, Reuseable) principles describe whether a dataset is well stewarded, and FAIR compliance is often treated as a proxy for usability. FAIR does not capture the cost to a user of finding, accessing, interpreting, and applying a dataset. We introduce the Dataset Friction Framework (DFF) as a complement to FAIR, directly addressing usability. DFF measures user-facing friction across six dimensions, distinguishing engineered friction (deliberate data provider design choices that sustain a service) from accidental friction (defects that require remediation). The framework is validated against 18,556 support tickets from the European Centre for Medium-Range Weather Forecasts (January 2024 to May 2026), which serves 280,000 registered users. Restricting the analysis to tickets raised by external reporters reduces the corpus by 12.3%, but every dimension's internal-staff share falls below this baseline -- confirming that the reported friction signals are genuinely user-facing. We then assess three real datasets across three providers and show that FAIR compliance and DFF friction can disagree in both directions: a 92% FAIR-compliant dataset can still carry substantial friction, and a 42% FAIR score can be an artefact of anti-scraping policy rather than poor stewardship. The two measures are non-redundant and jointly informative: FAIR compliance does not predict DFF friction in either direction. This constitutes the first large-scale empirical application of the framework; cross-institutional validation is identified as the immediate next step.