D., Missier, P., Ainsworth, J., Bhagat, J., Couch, P., et al. (2013). Why linked data is not enough for scientists. Future Generation Computer Systems, 29(2), 599–611. doi:http://dx.doi.org/10.1016/j.future.2011.08.004 Bernal, J. D. (1960). Scientific information and its users. In Aslib proceedings (Vol. 12, pp. 432–438). MCB UP Ltd. Donoho, D. L. (2010). An invitation to reproducible computational research. Biostatistics, 11(3), 385–388. Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., & Ghosh, S. S. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in neuroinformatics, 5. Kronick, D. A. (1990). Peer review in 18th-century scientific journalism. JAMA: the journal of the American Medical Association, 263(10), 1321–1322. Poline, J.-B., Breeze, J. L., Ghosh, S., Gorgolewski, K., Halchenko, Y. O., Hanke, M., Haselgrove, C., et al. (2012). Data sharing in neuroimaging research. Frontiers in neuroinformatics, 6. Ragan-Kelley, B., Walters, W. A., McDonald, D., Riley, J., Granger, B. E., Gonzalez, A., Knight, R., et al. (2012). Collaborative cloud-enabled tools allow rapid, reproducible biological insights. The ISME journal. Satrajit S. Ghosh -
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