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.
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).
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Labeled dataset | Raw dataset (2263MB each)
Two-photon lithography (TPL) is a widely used 3D nanoprinting technique that uses laser light to create objects. Challenges to large-scale adoption of this additive manufacturing method include identifying light dosage parameters and monitoring during fabrication. A research team from LLNL, Iowa State University, and Georgia Tech is applying machine learning models to tackle these challenges—i.e., accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. Funded by LLNL’s Laboratory Directed Research and Development Program, the project team has curated a video dataset of TPL processes for parameters such as light dosages, photo-curable resins, and structures. Both raw and labeled versions of the datasets are available via the links above.
Learn more:
- “Machine learning model may perfect 3D nanoprinting,” LLNL News, July 29, 2020.
- X.Y. Lee, S.K. Saha, S. Sarkar, B. Giera. “Automated detection of part quality during two-photon lithography via deep learning,” Additive Manufacturing 36, December 2020. doi.org/10.1016/j.addma.2020.101444
- X.Y. Lee, S.K. Saha, S. Sarkar, B. Giera. “Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures,” Data in Brief 32, October 2020. doi.org/10.1016/j.dib.2020.106119
Data portal | Project information
A public data portal provides a wealth of data LLNL scientists have gathered from their ongoing COVID-19 molecular design projects, particularly the computer-based “virtual” screening of small molecules and designed antibodies for interactions with the SARS-CoV-2 virus for drug design purposes. The data is queryable by criteria such as chemical structure and binding probability scores, so outside researchers can easily locate relevant data for their own work. The portal is regularly updated and will eventually provide the results of experiments performed at the Laboratory on the effectiveness of small molecules and antibodies against SARS-CoV-2.
CORD-19 dataset | Visualization dashboard
An LLNL research team has extracted drug compounds and therapeutic agents mentioned in the COVID-19 Open Research Dataset (CORD-19 as of June 22, 2020) using natural language processing. The extracted information is provided in an interactive data visualization dashboard (via Tableau) for researchers to explore which drug compounds have been used to treat and/or study coronavirus/COVID-19. With this interactive tool, researchers studying treatments for COVID-19 can quickly visualize the drug compounds that have been studied, the combination of drugs that have been explored, and the classes of drugs that are being mentioned in scientific literature.
Learn more:
- COVID-19 Open Research Dataset informational page on main LLNL website
- Instructions for using the Tableau visualization dashboard
- ChEMBL database for individual compounds
- Anatomical therapeutic codes used by the World Health Organization
This dataset contains protein-ligand complexes in a 3D representation for anti-viral drug screening against SARS-CoV-2. This is a part of the LLNL COVID-19 database but is specifically designed to facilitate machine learning and other data science tasks with regard to both efficacy (protein-ligand binding affinity) and safety. This complex dataset is called ml-hdf, comprised of ligands and four potential binding pockets of the SARS-CoV-2 protein targets in a 3D atomic representation. The ligands in this dataset includes U.S. Federal Drug Administration (FDA) approved drugs and "Other-world-approved" drugs that have been approved for use by the European Union, Canada, and Japan. The compounds were docked against two binding pockets from the Spike protein (spike, spike1) and two conformations of the main protease (protease, protease2).
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The dataset comprises trace files from high-performance computing (HPC) simulations. The trace files contain records of every I/O operation executed by a simulation application run, including I/O operations from HDF5, MPI-IO, and POSIX and all of the parameters supplied to those operations (e.g., file name, offset, and flags). The traces are generated by executing a simulation application that is linked with the Recorder tracing tool. The Recorder tracing tool intercepts the I/O calls made by the application, records the I/O trace record, and then calls the intended I/O call so that the operation executes.
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This dataset includes 20-year-long records from 1996 to 2015 of the Community Atmospheric Model v5 (CAM5) dataset. It contains snapshots of the global atmospheric states for every 3 hours (1 timestep = 3 hours). Each snapshot contains multiple physical variables among which we use the six most important climate variables to define hurricane from scientific literature:
- PSL (Sea level pressure)
- U850 (Zonal wind)
- V850 (Meridional wind)
- PRECT (Precipitation)
- TS (Surface temperature)
- QREFHT (Reference high humidity)
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The Cars Overhead With Context (COWC) dataset is a large set of annotated cars from overhead. It is useful for training a device such as a deep neural network to learn to detect and/or count cars. More information is available via the researchers’ paper and poster.
The data includes wide area imagery with annotations as well as precompiled image sets for training/validation of classification and counting. The dataset and research to create this data was done by members of the Computer Vision group within LLNL’s Computation Engineering Division under grant from NA-22 in the Global Security Directorate.
The dataset has the following attributes:
- Data from overhead at 15 cm per pixel resolution at ground.
- Data from 6 distinct locations: Toronto, Canada; Selwyn, New Zealand; Potsdam and Vaihingen, Germany; Columbus, Ohio, USA; and Utah, USA.
- 32,716 unique annotated cars. 58,247 unique negative examples.
- Intentional selection of hard negative examples.
- Established baseline for detection and counting tasks.
- Extra testing scenes for use after validation.
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The JAG model has been designed to give a rapid description of the observables from inertial confinement fusion (ICF) experiments, which are all generated very late in the implosion. This code contains pre-trained ML models, architectures and implementations for building surrogate models in scientific ML. The provided dataset is intended for testing/training the models. It is a tarball inside 'data/', which contains .npy files for images, scalars, and the corresponding input parameters. This 10K dataset represents a larger 100M dataset and is beginning to push the 1B threshold with more to come.
View more datasets in the UCSD LLNL collection.
