File Format

Suhas Somnath



No one really wants yet another file format in their lives. We wanted to adopt a file format that satisfies some basic requirements:

  • already widely accepted in scientific research

  • support parallel read and write capabilities.

  • store multiple datasets of different shapes, dimensionalities, precision and sizes.

  • scale very efficiently from few kilobytes to several terabytes

  • can be (readily) read and modified using any language including Python, R, Matlab, C/C++, Java, Fortran, Igor Pro, etc. without requiring installation of modules that are hard to install

  • store and organize data in a intuitive and familiar hierarchical / tree-like structure that is similar to files and folders in personal computers.

  • facilitates storage of any number of experimental or analysis parameters in addition to regular data.

  • highly flexible and poses minimal restrictions on how the data can and should be stored.

  • readily compatible with high-performance computing (HPC) and (soon) cloud-computing.


  • We found that existing file formats in science such as the XDMF, and NetCDF:

    • were designed for specific / narrow scientific domains only and we did not want to shoehorn our data structure into those formats.

    • Furthermore, despite being some of the more popular scientific data formats, it is not immediately straightforward to read those files on every computer using any programming language. For example - the Anaconda python distribution does not come with any packages for reading these file formats.

  • Adios is perhaps the ultimate file format for storing petabyte sized data on supercomputers but it was specifically designed for simulations, check-pointing, and it trades flexibility, and ease-of-use for performance.

  • The hierarchical data format (HDF5) is the implicitly or explicitly the de-facto standard in scientific research. In fact, Nexus, NetCDF, and even Matlab’s .mat files are actually (now) just custom flavors of HDF5 thereby validating the statement that HDF5 is the unanimous the file format of choice

We acknowledge that it is nearly impossible to find the perfect file format and HDF5 too has its fair share of drawbacks. For cloud-based environments it is beneficial to in fact break up the data into small chunks that can be individually addressed and used. We think Zarr and N5 would be good alternatives; however, most of these file formats are very much in their infancy and have not proven themselves like HDF5 has.

Quick basics of HDF5

Information can be stored in HDF5 files in several ways:

  • Datasets allow the storage of data matrices and these are the vessels used for storing the main, ancillary, and any extra data matrices

  • Groups are similar to folders in conventional file systems and can be used to store any number of datasets or groups themselves

  • Attributes are small pieces of information, such as experimental or analytical parameters, that are stored in key-value pairs in the same way as dictionaries in python. Both groups and datasets can store attributes.

  • While they are not means to store data, Links or references can be used to provide shortcuts and aliases to datasets and groups. This feature is especially useful for avoiding duplication of datasets when two main datasets use the same ancillary datasets.