https://github.com/dindagustiayu/data-processing
The digital text book to interpreting characterisation results.
https://github.com/dindagustiayu/data-processing
characterisation data-analysis gitbook latex-package myst qualitative-analysis quantitative-analysis
Last synced: 16 days ago
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The digital text book to interpreting characterisation results.
- Host: GitHub
- URL: https://github.com/dindagustiayu/data-processing
- Owner: dindagustiayu
- Created: 2026-05-25T05:29:58.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2026-06-01T08:37:02.000Z (23 days ago)
- Last Synced: 2026-06-01T10:21:58.408Z (23 days ago)
- Topics: characterisation, data-analysis, gitbook, latex-package, myst, qualitative-analysis, quantitative-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 4.81 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Innovative Characterisation of Materials
> ## NTU Open Research - Inspired
> As researchers, our goal is to communicate what we know and how we arrive at our conclusions. By sharing widely, we create transparent, replicable, and reproducible workflows. And by sharing our educational resources, we can help each other train generations to come.
>
> - Nanyang Asst. Prof. Suzy styles
> [!IMPORTANT]
> ## Academic Integrity
> Subject to the existing rights of third parties, **Data Processing** and/or **host institutions** are the copyright owner in this work, and no portion thereof is to be copied, reproduced, or disseminated, including for AI training, without the prior written consent of **Data Processing**.
>
>For more information, send email to [materials@dindagustiayu.com](mailto:materials@dindagustiayu.com)
In materials science, utilizing innovative characterization tools is essential for understanding physiochemical properties. The present of **Data Processing** focuses on the used computational and analytical steps for processing and interpreting data from several characterization tools:
- Chemical
- Stuctural mineralogical
- Morphological
- Optical
- Surface and pore size.
---
## Welcome
Welcome to **Data Processing!: A Digital Textbook and Practical Guide to Interpreting Materials Characterization Results**. I hope this textbook will be useful for your work, and I hope you enjoy learning with me!
And me?. Well, I'm __Dinda Gusti Ayu__ [](https://github.com/dindagustiayu). I'm interested in using Machine Learning to learn the rules of Quantum Mechanics and Simulate Crystalline and Amorphous Solid Systems. I earned an MS in chemistry physics at Universitas Sumatera Utara, Indonesia where I studied the fundamental optical properties and defects modelling in direct band-gap semiconductors for materials research.
### Goal
The goal is to provide a concise documentation of interpreting characterisation results on both practical and theoritical description of the techniques that are tied to work I have participated in.
### Contents
Contents will depend on the characterization tools and follow these components:
1. Pre-requisite knowledge: What we need to understand before analyse data;
2. Technique fundamentals: Reference on how is the working mechanism of the measurement technique;
3. Interpretation: Workflows to process data (with software or algorithm experimentations;
4. Case study: specific examples from published papers.
### Navigation
You can quickly navigate through the sections using the navigation bar on the left side, and the sub navigation bar on the right side. There is also a search box to help you find any content in this page swiftly.
### Contact
Please feel free to contact me through email at [materials@dindagustiayu.com](mailto:materials@dindagustiayu.com) if you have any questions or suggestions. I'm looking forward to hearing from you!