From cosmos to connectomes: the evolution of data-intensive science

Randal Burns; Joshua T Vogelstein; Alexander S Szalay

doi:10.1016/j.neuron.2014.08.045

From cosmos to connectomes: the evolution of data-intensive science

Neuron. 2014 Sep 17;83(6):1249-52. doi: 10.1016/j.neuron.2014.08.045.

Authors

Randal Burns¹, Joshua T Vogelstein², Alexander S Szalay³

Affiliations

¹ Institute for Data-Intensive Science and Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA. Electronic address: randal@cs.jhu.edu.
² Institute for Data-Intensive Science and Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA.
³ Institute for Data-Intensive Science and Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD 21218, USA.

PMID: 25233306
DOI: 10.1016/j.neuron.2014.08.045

Abstract

The analysis of data requires computation: originally by hand and more recently by computers. Different models of computing are designed and optimized for different kinds of data. In data-intensive science, the scale and complexity of data exceeds the comfort zone of local data stores on scientific workstations. Thus, cloud computing emerges as the preeminent model, utilizing data centers and high-performance clusters, enabling remote users to access and query subsets of the data efficiently. We examine how data-intensive computational systems originally built for cosmology, the Sloan Digital Sky Survey (SDSS), are now being used in connectomics, at the Open Connectome Project. We list lessons learned and outline the top challenges we expect to face. Success in computational connectomics would drastically reduce the time between idea and discovery, as SDSS did in cosmology.

Publication types

Review

MeSH terms

Animals
Computational Biology / methods
Computers*
Connectome / methods*
Humans
Information Systems*
Software*
Statistics as Topic / methods*

Grants and funding

R01 NS092474/NS/NINDS NIH HHS/United States