Shared High Value Research Resources: The CamCAN Human Lifespan Neuroimaging Dataset Processed on the Open Science Grid
The CamCAN Lifespan Neuroimaging Dataset, Cambridge (UK) Centre for Ageing and Neuroscience, was acquired and processed beginning in December, 2016. The referee consensus solver deployed to the Open Science Grid was used for this task. The dataset includes demographic and screening measures, a high-...
Gespeichert in:
Hauptverfasser: | , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | The CamCAN Lifespan Neuroimaging Dataset, Cambridge (UK) Centre for Ageing
and Neuroscience, was acquired and processed beginning in December, 2016. The
referee consensus solver deployed to the Open Science Grid was used for this
task. The dataset includes demographic and screening measures, a
high-resolution MRI scan of the brain, and whole-head magnetoencephalographic
(MEG) recordings during eyes closed rest (560 sec), a simple task (540 sec),
and passive listening/viewing (140 sec). The data were collected from 619
neurologically normal individuals, ages 18-87. The processed results from the
resting recordings are completed and available online. These constitute 1.7
TBytes of data including the location within the brain (1 mm resolution), time
stamp (1 msec resolution), and 80 msec time course for each of 3.7 billion
validated neuroelectric events, i.e. mean 6.1 million events for each of the
619 participants.
The referee consensus solver provides high yield (mean 11,000 neuroelectric
currents/sec; standard deviation (sd): 3500/sec) high confidence (p < 10-12 for
each identified current) measures of the neuroelectric currents whose magnetic
fields are detected in the MEG recordings. We describe the solver, the
implementation of the solver deployed on the Open Science Grid, the workflow
management system, the opportunistic use of high performance computing (HPC)
resources to add computing capacity to the Open Science Grid reserved for this
project, and our initial findings from the recently completed processing of the
resting recordings. This required 14 million core hours, i.e. 40 core hours per
second of data. |
---|---|
DOI: | 10.48550/arxiv.1710.05246 |