SearcharxivSearch

arXiv · 2606.23660

The Dataset Friction Framework: measuring user-facing friction as a complement to FAIR

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.

Explore related subjects

Keep this discovery

BibTeXRIS

Emma Pidduck, Umberto Modigliani. 2026-06-22. The Dataset Friction Framework: measuring user-facing friction as a complement to FAIR. https://arxiv.org/abs/2606.23660

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph