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Front Neuroinform. 2016 Jul 18;10:28. doi: 10.3389/fninf.2016.00028. eCollection 2016.

Proposed Training to Meet Challenges of Large-Scale Data in Neuroscience.

Author information

1
Department of Psychology, University of California Los Angeles, CA, USA.
2
Biology Department, Davidson College Davidson, NC, USA.
3
International Neuroinformatics Coordinating Facility Stockholm, Sweden.
4
Department of Biomedical Sciences, College of Osteopathic Medicine, New York Institute of Technology Old Westbury, NY, USA.

Abstract

The scale of data being produced in neuroscience at present and in the future creates new and unheralded challenges, outstripping conventional ways of handling, considering, and analyzing data. As neuroinformatics enters into this big data era, a need for a highly trained and perhaps unique workforce is emerging. To determine the staffing needs created by the impending era of big data, a workshop (iNeuro Project) was convened November 13-14, 2014. Participants included data resource providers, bioinformatics/analytics trainers, computer scientists, library scientists, and neuroscience educators. These individuals provided perspectives on the challenges of big data, the preparation of a workforce to meet these challenges, and the present state of training programs. Participants discussed whether suitable training programs will need to be constructed from scratch or if existing programs can serve as models. Currently, most programs at the undergraduate and graduate levels are located in Europe-participants knew of none in the United States. The skill sets that training programs would need to provide as well as the curriculum necessary to teach them were also discussed. Consistent with Vision and Change in Undergraduate Biology Education: A Call to Action, proposed curricula included authentic, hands-on research experiences. Further discussions revolved around the logistics and barriers to creating such programs. The full white paper, iNeuro Project Workshop Report, is available from iNeuro Project.

KEYWORDS:

analyses skill sets; big data; pedagogy; teaching; training programs; workforce preparation

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