📡 Acquiring & Visualising
Academic Research Trends
A Python Tutorial with ICASSP Data from IEEE Xplore
What you will learn in this notebook:
- How to install Python libraries and understand what each one does
- How to acquire structured data from a real academic database (IEEE Xplore) via its REST API
- How to clean and organise that data using pandas
- How to build a publication-quality donut chart using matplotlib
- How to animate your chart into a GIF across multiple years
- How to build an interactive HTML visualisation without any web framework
- How to analyse and plot research trajectories over time
- How to interpret research output trends, including China's remarkable growth over the last decade
No prior Python experience is required. Every concept is explained before the code that uses it.
🎓 About the dataset
ICASSP stands for the IEEE International Conference on Acoustics, Speech and Signal Processing. It is one of the most prestigious and high-volume annual conferences in the world for signal processing, audio, speech technology, and machine learning research. Each year, thousands of papers are submitted and the accepted ones are published through IEEE Xplore — the IEEE's official digital library, found at https://ieeexplore.ieee.org.
IEEE Xplore stores rich metadata for every paper, including titles, authors, publication details, and institutional affiliation information. By analysing those affiliations, we can determine which countries contributed to a paper and build country-level statistics across conference editions.
This makes it possible to answer questions like:
How many ICASSP papers in 2022 included at least one author affiliated with an institution in China? How does that compare to the USA? How has it changed over ten years?
That is exactly what this notebook does. We will acquire ICASSP metadata from IEEE Xplore for editions between 2016 and 2026, derive country participation from author affiliations, and visualise the results in several ways.
🗺️ How to use this notebook
- Read the explanation text (cells like this one) before running any code
- Run cells in order from top to bottom — later cells depend on earlier ones
- Cells marked 🏋️ Challenge are optional exercises to test your understanding