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Suggested Citation:"Appendix C: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2016. Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/21915.
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C

Acronyms

ACI Advanced Cyberinfrastructure
ACM Association for Computing Machinery
BD2K Big Data to Knowledge (project)
CATS Committee on Applied and Theoretical Statistics
DARPA Defense Advanced Research Projects Agency
DMS Division of Mathematical Sciences
DoD Department of Defense
DOI digital object identifier
GxL genotype x laboratory
IACUC Institutional Animal Care and Use Committee
ICERM Institute for Computational and Experimental Research in Mathematics
IEEE Institute of Electrical and Electronics Engineers
ILAR Institute for Laboratory Animal Research
IRB Internal Review Board
NASEM National Academies of Sciences, Engineering, and Medicine
NIH National Institutes of Health
NIST National Institute of Standards and Technology
Suggested Citation:"Appendix C: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2016. Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/21915.
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NRC National Research Council
NSF National Science Foundation
OMOP Observational Medical Outcomes Partnership
SIAM Society for Industrial and Applied Mathematics
SIGMOD Special Interest Group on the Management of Data
SPS Signal Processing Society
TOMS Transactions on Mathematical Software
TSCS time series cross section
UMPBT uniformly most powerful Bayesian test
XSEDE Extreme Science and Engineering Discovery Environment
Suggested Citation:"Appendix C: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2016. Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/21915.
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Suggested Citation:"Appendix C: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2016. Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/21915.
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Page 118
Suggested Citation:"Appendix C: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2016. Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/21915.
×
Page 119
Suggested Citation:"Appendix C: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2016. Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/21915.
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Questions about the reproducibility of scientific research have been raised in numerous settings and have gained visibility through several high-profile journal and popular press articles. Quantitative issues contributing to reproducibility challenges have been considered (including improper data measurement and analysis, inadequate statistical expertise, and incomplete data, among others), but there is no clear consensus on how best to approach or to minimize these problems.

A lack of reproducibility of scientific results has created some distrust in scientific findings among the general public, scientists, funding agencies, and industries. While studies fail for a variety of reasons, many factors contribute to the lack of perfect reproducibility, including insufficient training in experimental design, misaligned incentives for publication and the implications for university tenure, intentional manipulation, poor data management and analysis, and inadequate instances of statistical inference.

The workshop summarized in this report was designed not to address the social and experimental challenges but instead to focus on the latter issues of improper data management and analysis, inadequate statistical expertise, incomplete data, and difficulties applying sound statistic inference to the available data. Many efforts have emerged over recent years to draw attention to and improve reproducibility of scientific work. This report uniquely focuses on the statistical perspective of three issues: the extent of reproducibility, the causes of reproducibility failures, and the potential remedies for these failures.

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