Experimental Data Analysis Introduction Series v1.0
Analyzing Materials Characterization Data with Python
Series Overview
This series is a comprehensive 4-chapter educational resource designed for progressive learning, suitable for those new to experimental data analysis in materials science and those seeking to acquire data-driven experimental analysis skills.
In materials science research, data is acquired from diverse characterization techniques including XRD, XPS, SEM/TEM, and various spectroscopic measurements. However, traditional manual analysis cannot keep pace with increasing data volumes, resulting in issues of analyst-dependent bias and reproducibility problems.
Why This Series is Needed
Background and Challenges: With the proliferation of high-throughput experiments and automated measurement equipment, hundreds to thousands of spectra and image data are now generated daily. Traditional manual peak identification and visual image analysis face limitations: (1) excessively time-consuming, (2) results vary between analysts, and (3) inability to systematically handle large datasets.
What You'll Learn in This Series: This series provides hands-on learning from experimental data preprocessing, noise removal, feature extraction, statistical analysis, to machine learning integration using Python. Leveraging libraries such as scipy, scikit-image, and OpenCV, it covers XRD pattern analysis, SEM/TEM image processing, spectral data analysis, and time-series sensor data analysis.
Features:
- ✅ Progressive Structure: Each chapter can be read independently, with comprehensive coverage across all 4 chapters
- ✅ Practice-Oriented: 37 executable code examples, hands-on exercises using experimental data
- ✅ Materials Science Focus: Specialized focus on materials characterization techniques like XRD, SEM/TEM, IR/Raman
- ✅ Automation-Centric: Practical implementation of batch processing, pipeline construction, and reproducibility assurance
- ✅ Machine Learning Integration: Advanced analysis combining traditional methods with deep learning
Total Learning Time: 100-120 minutes (including code execution and exercises)
Target Audience:
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How to Learn
Recommended Learning Sequence
For Beginners (New to Experimental Data Analysis):
For Intermediate Learners (Experience with Python and Basic Data Analysis):
Technology-Specific Focus (Spectral Analysis Only, or Image Analysis Only):
Learning Flowchart
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Chapter Details
Chapter 1: Fundamentals of Experimental Data Analysis
Difficulty: Beginner Reading Time: 20-25 minutes Code Examples: 8
Learning Content
1. Importance and Workflow of Experimental Data Analysis - Why data-driven analysis is necessary - Materials characterization technology overview - Typical analysis workflow (5 steps)
2. Data Preprocessing Basics - Data loading (CSV, text, binary) - Understanding and formatting data structures (pandas) - Missing value and anomaly detection and handling
3. Noise Removal Techniques - Moving average filter - Savitzky-Golay filter - Gaussian filter - Selecting appropriate filters
4. Outlier Detection - Z-score method - IQR (Interquartile Range) method - DBSCAN clustering - Physical validity checking
5. Standardization and Normalization - Min-Max scaling - Z-score standardization - Baseline correction - Choosing between normalization methods
6. Exercise Project - Building XRD pattern preprocessing pipeline
Learning Objectives
By reading this chapter, you will master:
Major Concepts Covered in This Chapter
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Chapter 2: Spectral Data Analysis
Difficulty: Beginner to Intermediate Reading Time: 25-30 minutes Code Examples: 11
Learning Content
1. Overview of Spectroscopic Measurement Techniques - XRD (X-ray Diffraction) - XPS (X-ray Photoelectron Spectroscopy) - IR (Infrared Spectroscopy) - Raman (Raman Spectroscopy) - Characteristics and selection criteria for each technique
2. Peak Detection Algorithms
- Using scipy.signal.find_peaks
- Peak detection parameters (height, distance, prominence)
- Peak detection in complex spectra
- Automated peak identification
3. Background Removal - Polynomial fitting - Rolling Ball algorithm - SNIP (Statistics-sensitive Non-linear Iterative Peak-clipping) - Comparison of baseline correction methods
4. Peak Separation and Deconvolution - Gaussian fitting - Lorentzian fitting - Voigt profile - Simultaneous fitting of multiple peaks
5. Quantitative Analysis - Calculating peak area - Creating calibration curves using standard samples - Relative and absolute quantification - XRD phase fraction analysis
6. Materials Identification Using Machine Learning - Spectral feature extraction - Phase classification using Random Forest - Identification of unknown samples
Learning Objectives
By reading this chapter, you will master:
Mathematical Equations and Theory
This chapter covers the following equations:
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Chapter 3: Image Data Analysis
Difficulty: Intermediate Reading Time: 30-35 minutes Code Examples: 13 (All executable)
Learning Content
1. Fundamentals of Microscopy Images - SEM (Scanning Electron Microscopy) images - TEM (Transmission Electron Microscopy) images - Optical microscopy images - Image formats and resolution
2. Image Preprocessing - Image Loading: OpenCV, PIL - Noise Removal: Median filter, Gaussian filter, Non-local Means - Contrast Adjustment: Histogram equalization, CLAHE - Binarization: Otsu's method, adaptive thresholding
3. Particle Detection and Segmentation
- Watershed Algorithm: Separation of touching particles
- Contour Detection: cv2.findContours
- Shape Feature Extraction: Area, circularity, aspect ratio
- Particle Counting: Automated counting
4. Particle Size Distribution Analysis - Calculating equivalent circular diameter - Histograms and cumulative distributions - Mean particle size, median, standard deviation - Log-normal distribution fitting
5. Deep Learning for Image Classification - Convolutional Neural Networks (CNN): Structure and principles - Transfer Learning: Materials image classification using ResNet, VGG - Data Augmentation: Rotation, flipping, brightness adjustment - Model Evaluation: Confusion matrix, F1 score
6. Semantic Segmentation Using U-Net - U-Net architecture - Training data creation (annotation) - Segmentation accuracy evaluation (IoU, Dice coefficient) - Automated detection of nanostructures
7. Exercise Projects - Particle size distribution analysis from SEM images of nanoparticles - Deep learning for materials microstructure classification (ferrite, pearlite, martensite)
Learning Objectives
By reading this chapter, you will master:
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Chapter 4: Time-Series Data and Integrated Analysis
Difficulty: Intermediate Reading Time: 20-25 minutes Code Examples: 5
Learning Content
1. Time-Series Sensor Data Analysis - Temperature, pressure, flow rate sensor data - Loading and visualizing time-series data - Trend analysis and seasonal decomposition - Anomaly detection (change point detection)
2. Real-Time Data Analysis - Streaming data processing - Online machine learning (Incremental Learning) - Real-time alert systems - Data buffering and window processing
3. Multivariate Analysis - Principal Component Analysis (PCA): Dimensionality reduction and visualization - Correlation Analysis: Pearson correlation, Spearman correlation - Clustering: K-Means, hierarchical clustering - Process variable relationship analysis
4. Integration of Experimental and Computational Data - Comparison of XRD experiments and DFT calculations - Mapping spectra to electronic structure - Multimodal learning (integrating multiple data sources) - Experiment-computation closed loop
5. Building Automated Pipelines - Automation of data acquisition → preprocessing → analysis → visualization - Error handling and logging - Ensuring reproducibility (version control, Docker containers) - Cloud integration (data storage, API)
Learning Objectives
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Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
Practical Skills (Doing)
Application Ability (Applying)
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Recommended Learning Patterns
Pattern 1: Complete Mastery (For Beginners)
Target: Those learning experimental data analysis for the first time, seeking systematic understanding Period: 2 weeks Approach:
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Week 1:
Week 2:
Deliverables:
Pattern 2: Fast Track (For Experienced)
Target: Those with Python and data analysis foundations Period: 3-4 days Approach:
`
Day 1: Chapter 2 (Complete spectral analysis)
Day 2: Chapter 3 (Image analysis, excluding machine learning)
Day 3: Chapter 3 (Deep learning section)
Day 4: Chapter 4 (Integrated analysis, pipeline construction)
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Deliverables:
Pattern 3: Targeted Learning
Target: Those seeking to strengthen specific skills Period: Flexible Selection Examples:
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FAQ (Frequently Asked Questions)
Q1: Can beginners in programming understand this?
A: Chapter 1 and the first half of Chapter 2 assume understanding of Python basics (variables, functions, lists, NumPy/pandas fundamentals). Code examples are detailed with comments, so if you understand basic syntax, you can learn progressively. The deep learning section in Chapter 3 is intermediate level, but covers TensorFlow/PyTorch basic usage from scratch.
Q2: Which chapter should I read first?
A: First-time learners are recommended to read from Chapter 1 in sequence. While each chapter is independent, preprocessing concepts are common, so establishing foundations in Chapter 1 accelerates understanding. For learning specific measurement techniques only, proceed with Chapter 1 → relevant chapter (Chapter 2 or Chapter 3).
Q3: Do I need to actually run the code?
A: Actually running code is strongly recommended. Since experimental data varies by material, understanding deepens through the process of confirming operation with sample data and applying to your own data. If environment setup is difficult, start with Google Colab (free, no installation required).
Q4: How long does it take to master?
A: Depends on learning time and goals:
Q5: Can I become an expert in experimental analysis with just this series?
A: This series targets "fundamentals to practice." To reach expert level: 1. Establish foundations with this series (2-3 weeks) 2. Continuous practice with actual measurement data (3-6 months) 3. Learn advanced machine learning and deep learning content (3-6 months) 4. Implementation experience through publications or work (1+ years)
Q6: What's the difference between OpenCV and scikit-image?
A:
Usage: Basic image processing (filters, binarization) is possible with either. OpenCV excels in real-time processing and complex object detection, while scikit-image has abundant algorithms for scientific research. This series covers both and uses them appropriately.
Q7: Is GPU required for deep learning sections?
A: The deep learning section in Chapter 3 can be executed even without a local GPU by using Google Colab's free GPU. Training time differences are approximately: CPU (several hours), GPU (tens of minutes). Inference (prediction) is sufficiently fast on CPU.
Q8: Can I learn how to acquire data directly from measurement equipment?
A: Chapter 4 covers real-time sensor data acquisition, but please refer to each manufacturer's documentation for device-specific APIs and SDKs. Methods for loading common data formats (CSV, text, binary) are explained in detail across all chapters.
Q9: Are there communities for questions and discussion?
A: You can ask questions and discuss in the following communities:
, image-processing, scipy tags---
Prerequisites and Related Series
Prerequisites
Required:
Recommended:
Prerequisite Series
None (Can be learned independently)
Related Series
1. NM Introduction (Nanomaterials Introduction) (Beginner) - Relevance: Combining nanomaterials characterization techniques with analysis methods from this series - Link: ../nm-introduction/index.html
2. MI Introduction (Materials Informatics Introduction) (Beginner) - Relevance: Integrating experimental data into machine learning pipelines - Link:
Overall Learning Path Diagram
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Tools and Resources
Major Tools
| Tool Name | Purpose | License | Installation |
|---|---|---|---|
| scipy | Spectral analysis, signal processing | BSD | pip install scipy |
| scikit-image | Scientific image processing | BSD | pip install scikit-image |
| OpenCV | Computer vision | BSD | pip install opencv-python |
| pandas | Data manipulation | BSD | pip install pandas |
| matplotlib | Data visualization | PSF | pip install matplotlib |
| TensorFlow | Deep learning | Apache 2.0 | pip install tensorflow |
| PyTorch | Deep learning | BSD | pip install torch |
Datasets
| Dataset Name | Description | Data Count | Access |
|---|---|---|---|
| RRUFF Database | Raman spectra of minerals | 14,000+ | https://rruff.info/ |
| COD (Crystallography Open Database) | Crystal structures and XRD patterns | 500,000+ | http://www.crystallography.net/ |
| Materials Project | Computed XRD patterns | 140,000+ | https://materialsproject.org/ |
Learning Resources
Online Courses:
Books:
Papers and Reviews:
Communities:
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Next Steps
Recommended Actions After Series Completion
Immediate (Within 1-2 weeks): 1. ✅ Build analysis pipeline with your own measurement data 2. ✅ Publish code on GitHub (create portfolio) 3. ✅ Share analysis tools within your laboratory 4. ✅ Document analysis methods in lab notebooks or protocols
Short-term (1-3 months): 1. ✅ Automate batch processing of large datasets 2. ✅ Train deep learning models with your own data 3. ✅ High-quality visualization for conference presentations 4. ✅ Advance to NM Introduction or MI Introduction series 5. ✅ Document methods in paper Methods sections
Medium-term (3-6 months): 1. ✅ Integration of equipment and Python scripts (automated measurement → automated analysis) 2. ✅ Standardize analysis workflows across the laboratory 3. ✅ Publish machine learning models in papers 4. ✅ Contribute to open-source analysis tools
Long-term (1+ years): 1. ✅ Build high-throughput experimental systems 2. ✅ Autonomous experiments (loop of experiment proposal → execution → analysis → next experiment) 3. ✅ Publication and standardization of analysis methods 4. ✅ Create and share educational content
Recommended Learning Paths
Path A: Data-Driven Experimental Research
`
Complete Experimental Data Analysis Introduction
↓
Build automated analysis system for large datasets
↓
Integrate machine learning with MI Introduction
↓
Implement high-throughput experimentation
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Path B: Materials Data Scientist
`
Complete Experimental Data Analysis Introduction
↓
Strengthen domain knowledge with MI Introduction and NM Introduction
↓
Master cutting-edge AI technology with GNN Introduction
↓
R&D Data Scientist in industry
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Path C: Autonomous Experiment System Development
`
Complete Experimental Data Analysis Introduction
↓
Bayesian optimization and active learning introduction
↓
Robotics experiment automation introduction
↓
Build autonomous experiment platform
`
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Feedback and Support
About This Series
This series was created under Dr. Yusuke Hashimoto at Tohoku University as part of the AI Terakoya project.
Project: AI Terakoya Created: 2025-10-17 Version: 1.0 Language: Japanese
We Welcome Your Feedback
What to Report:
Contact Methods:
Contributions
1. Typo and Error Corrections: Pull Request 2. Additional Code Examples: New measurement techniques or algorithms 3. Translations: English version (future) 4. Dataset Provision: Educational sample data
See CONTRIBUTING.md for details
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License and Terms of Use
CC BY 4.0 (Creative Commons Attribution 4.0 International)
Permitted Uses
Conditions
Citation Method
`
Hashimoto, Y. (2025). Experimental Data Analysis Introduction Series v1.0.
AI Terakoya, Tohoku University.
Retrieved from [URL]
``
BibTeX:
@misc{hashimoto2025experimental_data_analysis,
author = {Hashimoto, Yusuke},
title = {Experimental Data Analysis Introduction Series},
year = {2025},
publisher = {AI Terakoya, Tohoku University},
url = {[URL]},
note = {Version 1.0}
}
Details: CC BY 4.0
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Let's Get Started!
Are you ready? Begin with Chapter 1 and start your journey into the world of experimental data analysis!
Chapter 1: Fundamentals of Experimental Data Analysis →
Or return to the top of this page to review the series overview.
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Version History
| Version | Date | Changes | Author |
|---|---|---|---|
| 1.0 | 2025-10-17 | Initial release | Dr. Yusuke Hashimoto |
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Your journey into experimental data analysis learning begins here!