# Cogni-e-SpinDB 1.0: Open Dataset of Electrospinning Parameter Configurations and Resultant Nanofiber Morphologies

## TL;DR

Cogni-e-SpinDB 1.0 is a new dataset of electrospinning parameters and nanofiber outcomes to support research and machine learning.

## Contribution

The first large-scale, diverse, and machine-learning-ready electrospinning dataset with 809 experimental records.

## Key findings

- The dataset enables comprehensive process-structure-property analyses in electrospinning.
- Machine learning models were trained as a proof of concept using the dataset.
- A companion web platform supports live contributions and interactive exploration.

## Abstract

Electrospinning is a versatile technique for producing nanofibers by elongating and depositing a polymer solution in an electrostatic field. Nanofiber quality is governed by process, environmental, and solution parameters, requiring extensive fine-tuning. Optimization is largely driven by independent experimental data, yet few openly available datasets exist to support modeling or provide reference parameters for stable nanofiber formation. We present Cogni-e-Spin DB 1.0, a dataset containing 809 experimental records of electrospinning parameters and corresponding nanofiber morphologies. This is the first large-scale, diverse, and machine-learning-ready dataset designed to address data scarcity. Its scale and diversity enable comprehensive process-structure-property analyses and support machine learning methods to gain insights into the complex electrospinning process. To assess the dataset’s reliability and utility, we verified data integrity against source publications and trained machine learning models as a proof of concept. These data are intended to accelerate fundamental research, model training, and process optimization across various electrospinning applications. We also developed a companion web platform to enable live dataset contributions and interactive exploration, fostering continuous community-driven updates.