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.

翻译：电纺丝是一种多功能的纳米制备技术，其成果源于溶液性质、加工参数和环境条件之间复杂的高维相互作用。针对目标纤维形态优化这一参数空间本质上极具挑战性，往往需要大量试错实验，并在全球实验室中产生海量实验数据。然而，由于报告标准不统一且普遍存在偏向成功结果的现象，这些知识仍处于碎片化且未被充分利用的状态，限制了研究的可重复性和数据驱动型研究的发展。本文介绍电纺丝数据平台.org，这是一个符合FAIR原则的数据聚合基础设施，可将分散的电纺丝实验组织成结构化、可复用且包含失败案例的科学记录。该平台基于统一的过程-结构-性能数据模型构建，通过受控词汇表在一致的机器可读模式中关联实验输入、环境条件和纳米纤维形态。结合自动验证与专家评审的两阶段审核流程，可保障数据质量与长期互操作性。最终形成的结构化、包含失败案例的语料库为数据驱动型研究提供了基础框架，涵盖预测建模、目标形态逆向设计以及失稳区间的系统映射——这些研究原本需要大量试错实验才能实现。