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.


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multicolored grid showing hemispherical brain region activity with fMRI

The Human Connectome Project–Young Adult (HCP-YA) dataset includes multiple neuroimaging modalities from 1,200 healthy young adults. These modalities include functional magnetic resonance imaging (fMRI), which measures the blood oxygenation fluctuations that occur with brain activity.

The fMRI data were recorded in multiple sessions per subject: during rest and a set of tasks, designed to evoke specific brain activity. Each fMRI run is a sequence of 3D volumes, and processing these large collections of data is computationally expensive. LLNL researchers have processed these time-series and generated the hierarchically parcellated connectomes using high-performance computing resources. Using these data, LLNL and Purdue University researchers have assessed the “brain fingerprint” gradients in young adults by developing an extension of the differential identifiability framework. For more information, see the paper Functional Connectome Fingerprint Gradients in Young Adults.

This dataset contains timeseries parcellated using the Schaefer parcellations at resolutions ranging from 100 to 900 brain regions. The fMRI data contain resting state and task data for both left-to-right and right-to-left acquisition polarity. This release also contains outputs with and without Global Signal Regression (GSR). In addition to the timeseries data, correlation-based functional connectome (FC) matrices are also included for both GSR and non-GSR.

View more datasets in the UCSD LLNL collection.