Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userShared High Value Research Resources: The CamCAN Human Lifespan Neuroimaging Dataset Processed on the Open Science Grid
332
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
We describe the technical effort used to process a voluminous high value human neuroimaging dataset on the Open Science Grid with opportunistic use of idle HPC resources to boost computing capacity more than 5-fold. With minimal software development effort and no discernable competitive interference with other HPC users, this effort delivered 15,000,000 core hours over 7 months.
The Open Science Grid (OSG) includes work to enable new science, new scientists, and new modalities in support of computationally based research. There are frequently significant sociological and organizational changes required in transformation from the existing to the new. OSG leverages its deliverables to the large scale physics experiment member communities to benefit new communities at all scales through activities in education, engagement and the distributed facility. As a partner to the poster and tutorial at SciDAC 2008, this paper gives both a brief general description and some specific examples of new science enabled on the OSG. More information is available at the OSG web site: (http://www.opensciencegrid.org).
Snowmass is a US long-term planning study for the high-energy community by the American Physical Societys Division of Particles and Fields. For its simulation studies, opportunistic resources are harnessed using the Open Science Grid infrastructure. Late binding grid technology, GlideinWMS, was used for distributed scheduling of the simulation jobs across many sites mainly in the US. The pilot infrastructure also uses the Parrot mechanism to dynamically access CvmFS in order to ascertain a homogeneous environment across the nodes. This report presents the resource usage and the storage model used for simulating large statistics Standard Model backgrounds needed for Snowmass Energy Frontier studies.
The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to $simeq 10^6$ nodes and $simeq 10^8$ edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension $D$ and the small-world coefficient $sigma$ of these networks. While $sigma$ suggests a small-world topology, we found that $D < 4$ showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.
Human Augmentation (HA) spans several technical fields and methodological approaches, including Experimental Psychology, Human-Computer Interaction, Psychophysiology, and Artificial Intelligence. Augmentation involves various strategies for optimizing and controlling cognitive states, which requires an understanding of biological plasticity, dynamic cognitive processes, and models of adaptive systems. As an instructive lesson, we will explore a few HA-related concepts and outstanding issues. Next, we focus on inducing and controlling HA using experimental methods by introducing three techniques for HA implementation: learning augmentation, augmentation using physical media, and extended phenotype modeling. To conclude, we will review integrative approaches to augmentation, which transcend specific functions.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
