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Suggested Citation:"Appendix A: References." Institute of Medicine. 2015. Developing a 21st Century Neuroscience Workforce: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/21697.
Page 69
Suggested Citation:"Appendix A: References." Institute of Medicine. 2015. Developing a 21st Century Neuroscience Workforce: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/21697.
Page 70
Suggested Citation:"Appendix A: References." Institute of Medicine. 2015. Developing a 21st Century Neuroscience Workforce: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/21697.
Page 71
Suggested Citation:"Appendix A: References." Institute of Medicine. 2015. Developing a 21st Century Neuroscience Workforce: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/21697.
Page 72

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A References Aarts, E., M. Verhage, J. V. Veenvliet, C. V. Dolan, and S. van der Sluis. 2014. A solution to dependency: Using multilevel analysis to accommodate nested data. Nature Neuroscience 17(4):491–496. Abbott, A. 2011. Novartis to shut brain research facility. Nature 480(7376):161– 162. Alberts, B., M. W. Kirschner, S. Tilghman, and H. Varmus. 2014. Rescuing U.S. biomedical research from its systemic flaws. Proceedings of the National Academy of Sciences of the United States of America 111(16):5773–5777. American Statistical Association. 2014. Statistical research and training under the BRAIN initiative. American Statistical Association. http://www.amstat. org/policy/pdfs/StatisticsBRAIN_April2014.pdf (accessed March 18, 2015). Begley, C. G., and L. M. Ellis. 2012. Drug development: Raise standards for preclinical cancer research. Nature 483(7391):531–533. Button, K. S., J. P. Ioannidis, C. Mokrysz, B. A. Nosek, J. Flint, E. S. Robinson, and M. R. Munafò. 2013. Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience 14(5):365–376. Chatterjee, R. 2007. Cases of mistaken identity. Science 315(5814):928–931. Choudhury, S., J. R. Fishman, M. L. McGowan, and E. T. Juengst. 2014. Big data, open science and the brain: Lessons learned from genomics. Frontiers in Human Neuroscience 8(239):1–10. doi:10.3389/fnhum.2014.00239. Cook, D., D. Brown, R. Alexander, R. March, P. Morgan, G. Satterthwaite, and M. N. Pangalos. 2014. Lessons learned from the fate of AstraZeneca’s drug pipeline: A five-dimensional framework. Nature Reviews Drug Discovery 13(6):419–431. Cyranoski, D., N. Gilbert, H. Ledford, A. Nayar, and M. Yahia. 2011. Education: The PhD factory. Nature 72:276–279. The Economist. 2013. Unreliable research: Trouble at the lab. The Economist, self-correcting-alarming-degree-it-not-trouble (accessed March 18, 2015). 69

70 DEVELOPING A 21st CENTURY NEUROSCIENCE WORKFORCE Gomez-Marin, A., J. J. Paton, A. R. Kampff, R. M. Costa, and Z. F. Mainen. 2014. Big behavioral data: Psychology, ethology and the foundations of neuroscience. Nature 17(11):1455–1462. Hyman, S. 2012. Revolution stalled. Science Translational Medicine 4(155):155cm11. IOM (Institute of Medicine). 2015. Sharing clinical trial data: Maximizing benefits, minimizing risk. Washington, DC: The National Academies Press. Karayiorgou, M., J. Flint, J. A. Gogos, R. C. Malenka, and the Genetic and Neural Complexity in Psychiatry 2011 Working Group. 2012. The best of times, the worst of times for psychiatric disease. Nature Neuroscience 15(6):811–812. Koroshetz, W. J., and S. Landis. 2014. Neurology’s stake in foundational neuroscience research. Annals of Neurology 83(8):670–671. Landis, S. 2014. Back to basics: A call for fundamental neuroscience research. NINDS blogs. March 27. (accessed May 5, 2014). Landis, S. C., et al. 2012. A call for transparent reporting to optimize the predictive value of preclinical research. Nature 490:187–191. Macilwain, C. 2013. Biology boom goes bust. Cell 154(1):16–19. Miller, G. 2010. Is pharma running out of brainy ideas? Science 329(5991):502– 504. Nielsen, M. 2012. Reinventing discovery: The new era of networked science. Princeton, NJ: Princeton University Press. NIH (National Institutes of Health). 2014. BRAIN 2025: A scientific vision. Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Working Group Report to the Advisory Committee to the Director, NIH. (accessed February 2, 2015). Perrin, S. 2014. Preclinical research: Make mouse models work. Nature 507(7493):423–425. Prinz, F., T. Schlange, and K. Asadullah. 2011. Believe it or not: How much can we rely on published data on potential drug targets? Nature Reviews Drug Discovery 10:712. Rockey, S., and F. Collins. 2013. One nation in support of biomedical research? NIH Rock Talk blog. September 24. one-nation-in-support-of-biomedical-research (accessed February 20, 2015). Sauermann, H., and M. Roach. 2012. Science PhD career preferences: Levels, changes, and advisor encouragement. PLoS ONE 7(5):e36307. Scott, S., J. E. Kranz, J. Cole, J. M. Lincecum, K. Thompson, N. Kelly, A. Bostrom, J. Theodoss, B. M. Al-Nakhala, F. G. Vieira, J. Ramasubbu, and J. A. Heywood. 2008. Design, power, and interpretation of studies in the standard murine model of ALS. Amyotrophic Lateral Sclerosis 9(1):4–15. Silber, B. M. 2010. Driving drug discovery: The fundamental role of academic labs. Science Translational Medicine 2(30):30cm16.

APPENDIX A 71 Soranno, P. A., K. S. Cheruvelil, K. C. Elliott, and G. M. Montgomery. 2014. It’s good to share: Why environmental scientists’ ethics are out of date. BioScience 65(1):69–73. doi:10.1093/biosci/biu169. Steward, O., and R. Balice-Gordon. 2014. Rigor or mortis: Best practices for preclinical research in neuroscience. Neuron 84(3):572–581. Steward, O., P. G. Popovich, W. D. Dietrich, and N. Kleitman. 2012. Replication and reproducibility in spinal cord injury research. Experimental Neurology 233:597–605. Tomer, R., L. Ye, B. Hsueh, and K. Deisseroth. 2014. Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nature Protocols 9(7):1682–1697. Wadman, M. 2013. Science agencies prepare for cuts. Nature News. February 12. (accessed February 20, 2015). Yamaner, M. 2014. Federal funding for basic research at universities and colleges essentially unchanged in FY 2012. National Center for Science and Engineering Statistics Info Brief. September. infbrief/nsf14318 (accessed February 20, 2015).

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From its very beginning, neuroscience has been fundamentally interdisciplinary. As a result of rapid technological advances and the advent of large collaborative projects, however, neuroscience is expanding well beyond traditional subdisciplines and intellectual boundaries to rely on expertise from many other fields, such as engineering, computer science, and applied mathematics. This raises important questions about to how to develop and train the next generation of neuroscientists to ensure innovation in research and technology in the neurosciences. In addition, the advent of new types of data and the growing importance of large datasets raise additional questions about how to train students in approaches to data analysis and sharing. These concerns dovetail with the need to teach improved scientific practices ranging from experimental design (e.g., powering of studies and appropriate blinding) to improved sophistication in statistics. Of equal importance is the increasing need not only for basic researchers and teams that will develop the next generation of tools, but also for investigators who are able to bridge the translational gap between basic and clinical neuroscience.

Developing a 21st Century Neuroscience Workforce is the summary of a workshop convened by the Institute of Medicine's Forum on Neuroscience and Nervous System Disorders on October 28 and 29,2014, in Washington, DC, to explore future workforce needs and how these needs should inform training programs. Workshop participants considered what new subdisciplines and collaborations might be needed, including an examination of opportunities for cross-training of neuroscience research programs with other areas. In addition, current and new components of training programs were discussed to identify methods for enhancing data handling and analysis capabilities, increasing scientific accuracy, and improving research practices. This report highlights the presentation and discussion of the workshop.

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