Electrospinning-Data.org: A FAIR, Structured Knowledge Resource for Nanofiber Fabrication

TL;DR

Electrospinning-Data.org is a structured, FAIR-compliant data platform that consolidates electrospinning experiments to facilitate data-driven research and improve reproducibility in nanofiber fabrication.

Contribution

It introduces a comprehensive, structured database with a unified data model and moderation pipeline, enabling systematic analysis and predictive modeling of electrospinning outcomes.

Findings

Creates a reusable, failure-aware electrospinning data corpus

Supports predictive modeling and inverse design of nanofibers

Enhances reproducibility and data sharing in nanofiber research

Abstract

Electrospinning is a versatile nanofabrication technique whose outcomes emerge from a complex, high-dimensional interplay between solution properties, processing parameters, and environmental conditions. Optimizing this parameter space for targeted fiber morphology is inherently challenging, often driving extensive trial-and-error experimentation and generating vast experimental data across laboratories worldwide. Yet this knowledge remains fragmented and underutilized due to inconsistent reporting and a pervasive bias toward successful outcomes, limiting reproducibility and hindering data-driven research. Here we introduce Electrospinning-Data.org, a FAIR-aligned data aggregation infrastructure that organizes dispersed electrospinning experiments into structured, reusable, and failure-aware scientific records. The platform is built around a unified process-structure-property data model linking experimental inputs, environmental conditions, and nanofiber morphology, annotated through a controlled vocabulary, within a consistent, machine-readable schema. A two-stage moderation pipeline combining automated validation with expert review supports data quality and long-term interoperability. The resulting structured, failure-inclusive corpus provides a framework for data-driven research, including predictive modelling, inverse design of target morphologies, and systematic mapping of instability regimes that would otherwise require extensive trial-and-error experimentation.