Seminar 1: Data Publication and Citation: How do I get credit for promotion?
Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., … Bourne, P. E. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18
Seminar 2: Making Reproducibility Practical: Using R and R Markdown
Seminar 3: Rigor and Reproducibility at eLife
Seminar 4: An Introduction to Open Science Principles and Tools
Seminar 5: Reproducing Reproducibility? Challenges in Reproducing Classifier and Predictive Models.
Presentation Date: 4/29/2021
Citations from Presentation:
Nagendran M, Chen Y, Lovejoy C, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 2020; 368:m689 doi: https://doi.org/10.1136/bmj.m689
You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place November 5, 2019. Janelle Shane
Vollmer et al. (2020) Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ 368:l6927 doi: https://doi.org/10.1136/bmj.l6927
Seminar 6: An Introduction to the New NIH Data Management and Sharing Policy
Seminar 7: REDCap at Emory: An Introduction to Best Practices for Research
Product Manager, Research Solutions, Office of Information Technology (OIT)
Monica Crubezy, PhD
Director of Research Solutions, Office of Information Technology (OIT)
Seminar 8: Why is it so hard to do good science?
Kahneman, Daniel, and Amos Tversky. “Prospect Theory: An Analysis of Decision under Risk.” Econometrica, vol. 47, no. 2, [Wiley, Econometric Society], 1979, pp. 263–91, https://doi.org/10.2307/1914185. Emory Access: Here
Kahneman, Daniel. Thinking, Fast and Slow. Farrar, Straus and Giroux, 2011. Emory Libraries Information
Seminar 9: Significance Testing: The Reason that Scientific Results Have Poor Reproducibility