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：静电纺丝是一种通用的纳米制造技术，其最终产物源于溶液性质、工艺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身极具挑战性，往往需要大量试错实验，进而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对成功结果的偏好，这些知识仍处于碎片化状态且未得到充分利用，这限制了实验的可重复性并阻碍了数据驱动的研究。本文介绍了Electrospinning-Data.org，一个符合FAIR原则的数据聚合基础设施，它将分散的静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。该平台围绕统一的"工艺-结构-性能"数据模型构建，将实验输入、环境条件和纳米纤维形态关联起来，并通过受控词汇表在一致的、机器可读的模式中进行标注。一个结合自动验证与专家评审的两阶段审核流程，确保了数据质量与长期互操作性。由此产生的结构化、包含失败案例的语料库，为数据驱动的研究提供了框架，包括预测建模、目标形态逆向设计以及不稳定性模式的系统映射——这些工作原本需要大量的试错实验才能完成。