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原文摘要

Electrospinning produces nanofibers by depositing a polymer solution in an electrostatic field. Fiber morphology is governed by interacting material, solution, process, hardware, and environmental conditions, requiring extensive tuning. Yet observations remain dispersed across the literature in heterogeneous forms that hinder aggregation and reuse. We present Electrospinning-Data.org, the first open, community-oriented research infrastructure dedicated to electrospinning experimental data. Its domain-specific data model links materials, solution properties, process and hardware configurations, environmental conditions, and fiber outcomes, including unstable and unsuccessful experiments. Numeric quantities are harmonized to canonical units and morphology is represented through controlled descriptors. The platform supports guided web, batch, and programmatic contributions, governed by automated structural validation and expert moderation. Its continuously evolving data are complemented by immutable DOI-bearing releases that support reproducible analyses. Public web interfaces, a REST API, and a Python client enable interactive and programmatic access. The integrated Virtual Lab supports exploratory analysis, machine-learning modeling, model interpretation, and evidence-linked parameter exploration. Together, these components establish a failure-aware foundation for reproducible, data-centric electrospinning research.