HEFTIE stands for "Handling Enormous Files from Tomography Imaging Experiments". We were an EU funded project in 2024/2025, which aimed to:
develop a comprehensive digital textbook and new software tools for working with chunked 3D imaging datasets
For a high level, public facing view of the project, see our project page on the funder website. This organisation README gives some more technical overview of what we are doing, targeted at existing chunked data communities so you can understand what we're up to.
- @dstansby is the project lead, and led writing of the digital texbook.
- @K-Meech and @ruaridhg from UCL's Centre for Advanced Research Computing. worked on Python tools and benchmarking.
- @scalableminds worked on visualisation improvements, led by @normanrz.
Our work was split into three work packages:
The goal of this work package was to write a digital textbook for working with huge 3D imaging datasets. This was developed at HEFTIEProject/heftie-book, with the final textbook available at https://heftie-textbook.readthedocs.io.
The goals of this work package were to:
- Develop a set of benchmarks to understand the best parameters for creating OME-Zarr datasets.
- Work on Python tools for working with OME-zarr datasets.
For a summary of what we worked on, check out the final project report.
In this work package we improved the existing open source webknossos tool to add some more features for 3D datasets.
For a summary of what we worked on, again check out the final project report.
This project is funded by the OSCARS project, which has received funding from the European Commission’s Horizon Europe Research and Innovation programme under grant agreement No. 101129751.
