TY - JOUR
T1 - Progress toward openness, transparency, and reproducibility in cognitive neuroscience
AU - Gilmore, Rick O.
AU - Diaz, Michele T.
AU - Wyble, Brad A.
AU - Yarkoni, Tal
N1 - Funding Information:
R.O.G. acknowledges support from NSF BCS-1147440, NSF BCS-1238599, and NICHD U01-HD-076595. B.A.W. acknowledges support from NSF BCS-1331073.
Publisher Copyright:
© 2017 New York Academy of Sciences.
PY - 2017/7/3
Y1 - 2017/7/3
N2 - Accumulating evidence suggests that many findings in psychological science and cognitive neuroscience may prove difficult to reproduce; statistical power in brain imaging studies is low and has not improved recently; software errors in analysis tools are common and can go undetected for many years; and, a few large-scale studies notwithstanding, open sharing of data, code, and materials remain the rare exception. At the same time, there is a renewed focus on reproducibility, transparency, and openness as essential core values in cognitive neuroscience. The emergence and rapid growth of data archives, meta-analytic tools, software pipelines, and research groups devoted to improved methodology reflect this new sensibility. We review evidence that the field has begun to embrace new open research practices and illustrate how these can begin to address problems of reproducibility, statistical power, and transparency in ways that will ultimately accelerate discovery.
AB - Accumulating evidence suggests that many findings in psychological science and cognitive neuroscience may prove difficult to reproduce; statistical power in brain imaging studies is low and has not improved recently; software errors in analysis tools are common and can go undetected for many years; and, a few large-scale studies notwithstanding, open sharing of data, code, and materials remain the rare exception. At the same time, there is a renewed focus on reproducibility, transparency, and openness as essential core values in cognitive neuroscience. The emergence and rapid growth of data archives, meta-analytic tools, software pipelines, and research groups devoted to improved methodology reflect this new sensibility. We review evidence that the field has begun to embrace new open research practices and illustrate how these can begin to address problems of reproducibility, statistical power, and transparency in ways that will ultimately accelerate discovery.
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U2 - 10.1111/nyas.13325
DO - 10.1111/nyas.13325
M3 - Review article
C2 - 28464561
AN - SCOPUS:85018945021
SN - 0077-8923
VL - 1396
SP - 5
EP - 18
JO - Annals of the New York Academy of Sciences
JF - Annals of the New York Academy of Sciences
IS - 1
ER -