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

This repository provides the search strategies, exploratory data analysis (EDA), and machine learning (ML) modeling used for a data descriptor paper for Cogni-e-SpinDB 1.0 data.

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📑 Table of Contents

1. Overview
2. Search Terms
3. Repository Structure
4. How to Use
5. Notes
6. Contact

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📖 Overview

Electrospinning is a versatile technique for producing nanofibers, and process control plays a crucial role in tuning fiber morphology and multi-layered architectures.
This repository documents the exact search strategies used to identify research publications related to:

• General electrospinning
• Process control in electrospinning
• Multi-layer process control
• Multi-parameter process control

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🔍 Search Terms

1. Electrospinning (Total)

TS=(electrospinning OR “electro-sprayed” OR “electro-fabrication” OR “electrostatic spinning”)

2. Electrospinning Process Control

TS=(electrospinning OR “electro-sprayed” OR “electro-fabrication” OR “electrostatic spinning”) AND TS=(control OR “feedback control” OR “closed-loop control” OR “process control” OR “parameter control” OR “parameter tuning” OR “process optimization”)

3. Multi-layer Process Control

TITLE-ABS-KEY (electrospinning OR “electro-sprayed” OR “electro-fabrication” OR “electrostatic spinning”) AND TITLE-ABS-KEY (control OR “feedback control” OR “closed-loop control” OR “process control” OR “parameter control” OR “parameter tuning” OR “process optimization”) AND TITLE-ABS-KEY (multilayer OR “Multilayered” OR “layers” OR “layered” OR “Sandwich” OR “multi-walled” OR “Cross layer” OR “Poly laminate” OR “multi-level”)

4. Multi-parameter Process Control

TITLE-ABS-KEY (electrospinning OR “electro-sprayed” OR “electro-fabrication” OR “electrostatic spinning”) AND TITLE-ABS-KEY (control OR “feedback control” OR “closed-loop control” OR “process control” OR “parameter control” OR “parameter tuning” OR “process optimization”) AND TITLE-ABS-KEY (Multiparameter OR “Multivariable” OR “multi-input” OR “multivariate” OR “multiplex” OR “multicomponent” OR “multifactorial” OR “Poly parameter” OR “multidimensional” OR “n-dimensional” OR “multicriteria” OR “multi-argument”)

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📂 Repository Structure

File	Description
requirements.txt	Python dependencies required to run the notebooks.
eda.ipynb	Exploratory Data Analysis notebook exploring distributions, correlations, and basic trends.
tail_analysis.ipynb	Tail analysis applied to average fiber diameter in the dataset.
pvdf_regression.ipynb	ML regression model trained on PVDF nanofiber data.
pva_regression.ipynb	ML regression model trained on PVA nanofiber data.

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⚙️ How to Use

1. Clone the repository:

git clone https://github.com/taltechloc/Cogni-e-SpinDB.git
cd cogni-e-spindb

Create a virtual environment and install dependencies:

pip install -r requirements.txt

Open Jupyter notebooks to explore the data and models:

• eda.ipynb
• tail_analysis.ipynb
• pvdf_regression.ipynb
• pva_regression.ipynb

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📝 Notes

• TS = Topic Search in Clarivate (Web of Science).
• TITLE-ABS-KEY = Title, Abstract, and Keyword search in Scopus.
• These queries were designed to capture the broadest relevant literature.

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📬 Contact

For questions or collaboration inquiries, please contact: 📧 mehrab.mahdian@taltech.ee