Speaker
Mr
Pierre-Yves Barriat
(UCL/ELIC)
Description
NetCDF and HDF5 are among the most widely used data formats in scientific computing. They provide efficient, portable, and self-describing ways to store large and complex datasets while supporting high-performance I/O on modern HPC systems. This session introduces both formats, explains their strengths and differences, and provides hands-on experience using Python, Fortran, and C to create, manipulate, and visualize scientific data.
| Contents | Information |
|---|---|
| • Overview of data formats in scientific computing • Comparing NetCDF and HDF5: strengths, weaknesses, and use cases • Data models, metadata, dimensions, and attributes • Tools and libraries for working with NetCDF and HDF5 • Reading and writing data with Python, Fortran, and C • Efficient I/O practices for large datasets • Use cases in scientific research and HPC • Hands-on exercises in creating, reading, and visualizing data • Best practices for managing structured scientific datasets |
Prerequisite: • Being able to connect to the clusters • Basic programming skills (Python, Fortran, or C) • Familiarity with Linux (navigation, file creation, and editing) Type: Hands-on Target audience: Researchers and data scientists using HPC systems Must: Essential for geoscientists, climate scientists, and anyone needing efficient and structured scientific data formats. |