Award Abstract #1429999
CRCNS US-German Data Sharing: DataGit - converging catalogues, warehouses, and deployment logistics into a federated 'data distribution'
Dartmouth College
OFFICE OF SPONSORED PROJECTS
HANOVER, NH
03755-1404
(603)646-3007
NSF Program(s):
ADVANCES IN BIO INFORMATICS,
DECISION RISK & MANAGEMENT SCI,
CRCNS
Program Reference Code(s):
7327, 8089, 8091, 9150
Program Element Code(s):
1165, 1321, 7327
ABSTRACT
Contemporary neuroscience is heavily data-driven, but today's data management technologies and sharing practices fall at least a decade behind software ecosystem counterparts. Distributed version control systems, such as Git, facilitate collaborative software development, and turnkey distributions, like NeuroDebian, free researchers from tedious and unreliable maintenance tasks. Likewise, neuroscientists will need to incorporate recent technological developments to access, manage, and contribute back to the ever growing array of scientific data more efficiently. Making a rich collection of disjoint datasets available through a simple unified interface can transcend limitations of individual studies and revolutionize how scientific data are managed, distributed, and shared across all fields of science. With support from the National Science Foundation, Dr. Yaroslav O. Halchenko of Dartmouth College, along with Dr. Michael Hanke of the University of Magdeburg (Germany), will develop DataGit, a suite of data distribution tools. DataGit will employ software for data tracking and deployment logistics to unify access to many existing neuroimaging data hosting portals, such as crcns.org, openfmri.org and humanconnectome.org. DataGit will make it easy to access existing data and to share new or derived data with full support for distributed version control, data integrity protection and authenticated access to original data hosting.
Making data management as easy and as versatile as source code management will further the efforts toward open and fully reproducible science. Uniform access to federated collections of data will promote the visibility and accessibility of neuroscientific data inside and outside the field, far beyond the scope of any individual data-sharing effort. The benefits from the proposed developments will translate directly to educators' aims in the classroom. Through integration with software distributions, uniform access to software elements and datasets for online training materials will enable educators to teach not only from textbooks but also through hands-on replication of state-of-the-art original publications. In addition, giving any researcher the ability to easily deploy complex heterogeneous analysis pipelines will be instrumental in translating the achievements of flagship efforts, such as the Human Connectome Project, into accessible tools for clinical applications. Consequently, even more researchers will be able to tackle even larger challenges to benefit society by improving our understanding of the human brain.
A companion project is being funded by the German Ministry of Education and Research (BMBF).
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Gorgolewski, Krzysztof J. and Auer, Tibor and Calhoun, Vince D. and Craddock, R. Cameron and Das, Samir and Duff, Eugene P. and Flandin, Guillaume and Ghosh, Satrajit S. and Glatard, Tristan and Halchenko, Yaroslav O. and Handwerker, Daniel A. and Hanke,. "The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments," Scientific Data, v.3, 2016, p. 160044.
Samuel A. Nastase, Andrew C. Connolly, Nikolaas N. Oosterhof, Yaroslav O. Halchenko, J. Swaroop Guntupalli, Matteo Visconti di Oleggio Castello, Jason Gors, M. Ida Gobbini, James V. Haxby. "Attention Selectively Reshapes the Geometry of Distributed Semantic Representation (dataset was shared via DataLad from http://datasets.datalad.org/?dir=/labs/haxby/attention)," Cerebral Cortex, v.27, 2017, p. 4277.
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