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

Mehrab Mahdian, Ferenc Ender, Tamas Pardy

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.

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

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.

Paper Structure

This paper contains 17 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Data Representation and Schema
  3. Database Architecture
  4. Required and Optional Fields
  5. Controlled Morphology Vocabulary
  6. Data Ingestion Pipeline
  7. Data Sources
  8. Submission
  9. Validation
  10. Validation and Moderation Workflow
  11. Initial Dataset Release
  12. Data Access, Versioning, and Provenance
  13. Illustrative Use Case: Data-Driven Experimental Query
  14. Discussion
  15. Methods
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Conceptual schema for electrospinning experimental knowledge. Electrospinning experiments are represented as structured records linking experimental inputs, observed structure, and derived properties within a process–structure–property perspective.
  • Figure 2: Two-stage data moderation workflow comprising automated validation and expert review, ensuring data quality and long-term reliability.
  • Figure 3: Fiber diameter distribution of the filtered PVA subset (n = 108), illustrating the constrained morphological range defined by the query.
  • Figure 4: Distributions of core process parameters for the filtered PVA subset (n = 108). Boxplots summarize applied voltage, flow rate, solution concentration, and tip-to-collector distance associated with the selected morphological range.
  • Figure 5: Schematic overview of the Electrospinning-Data.org platform, illustrating the three-layer architecture comprising the data storage, backend application, and user interface layers.