Open Data Initiative

The DSI’s Open Data Initiative (ODI) enables us to share LLNL’s rich, challenging, and unique datasets with the larger data science community. Our goal is for these datasets to help support curriculum development, raise awareness around LLNL’s data science efforts, foster new collaborations, and be leveraged across other learning opportunities.

As we develop this catalog over time, the data will represent a wide variety of key LLNL mission areas and may include subsets of some of the world’s largest datasets. We plan to provide data ranging in complexity from dense, featureful, labeled datasets with well understood solutions to those that are sparse, noisy, and largely unexplored. These datasets can also be used to test novel hardware solutions for scalable machine learning platforms.



ten frames of sample volume sequences in D4DCT-DFM given a single MPM deformation data

Dynamic computed tomography (DCT) refers to reconstruction of moving or non-rigid objects over time while x-ray projections are acquired over a range of angles. The measured x-ray sinogram data represents a time-varying sequence of dynamic scenes, where a small angular range of the sinogram will correspond to a static or quasi-static scene, depending on the amount of motion or deformation as well as the system setup. The reconstruction of DCT is widely applicable to the study of object deformation and dynamics in a number of industrial and clinical applications (e.g., heart CT). In material science and additive manufacturing applications, the DCT capabilities aid in the study of damage evolution due to dynamic thermal loads and mechanical stresses over time which provides crucial information about their overall performance and safety.

We provide two dynamic CT datasets (D4DCT-DFM, D4DCT-AFN) where the sinogram data represent a time-varying object deformation to demonstrate damage evolution due to several mechanical stresses (compression). The provided datasets enable training and evaluation of the data driven machine learning methods for DCT reconstruction. To build the datasets, we used Material Point Method (MPM)-based methods to simulate deformation of objects under mechanical loading, and then simulated CT sinogram data using Livermore Tomography Tools (LTT).

View more datasets in the UCSD LLNL collection.