The advancement of public research requires the participation of numerous laboratories around the world, each bringing its own perspectives and applications. It is through discussions and the emulation among these different laboratories that a field truly gains its significance. The academic environment has been built on the constant sharing of knowledge, allowing everyone to stand on the shoulders of others. This observation is the foundation of open science, which is strongly encouraged by national and supranational institutions such as the CNRS and the ERC.

In the context of open science, we have sought to share our knowledge through several initiatives. First, for five years, we introduced a course on ULM, attended by hundreds of academics at the IEEE conference. This course included a practical component focused on 2D ULM algorithms.

We also wanted the entire scientific community to have free access to reference algorithms on which they can build their own approaches. To enable them to compare their various improvements, we proposed evaluation metrics for ULM. Finally, to allow everyone to test their own algorithms, we made several datasets available on different preclinical situations, including cases involving two brains, a kidney, and a tumor. These open data, along with the codes and metrics, form the PALA corpus, published in Nature Biomedical Engineering and available on GitHub and Zenodo.

Furthermore, to enable physicians to reproduce our results on sULM with their own clinical ultrasound machines, we have also shared sensing-ULM algorithms on the Akebia platform.