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<p>Below are some links to useful resources for transitioning to open science practices in individual labs. A few papers have been added to the "files" section of the page, and one document that contains the same information presented in this wiki. </p> <ol> <li> <p>Information on using the OSF for <a href="" rel="nofollow">pre-registration</a>, including templates</p> </li> <li> <p>One example of using the OSF for organization of <a href="" rel="nofollow">lab research</a>, including templates</p> </li> <li> <p>Useful resources for <a href="" rel="nofollow">using R and the OSF</a> for lab organization and pre-registration</p> </li> <li> <p>Using the "<a href="" rel="nofollow">as predicted</a>" webiste for pre-registration</p> </li> <li> <p>Resources for many lab based activities from the <a href="" rel="nofollow">Berkeley Initiative for Transparency in the Social Science (BITTS)</a></p> </li> <li> <p>Blog post on <a href="" rel="nofollow">organized open science</a></p> </li> <li> <p>Blog posts on using github and RMarkdown (<a href="" rel="nofollow">1</a>, <a href="" rel="nofollow">2</a>) for reproducible research within a lab. </p> </li> <li> <p><a href="" rel="nofollow">Course syllabus</a> from Karl Broman on the "techie workflow" for reproducible research with github, R, unit testing, etc.</p> </li> <li> <p><a href="" rel="nofollow">Course syllabus</a> for Mike's graduate methods course, including large set of resources for learning reproducible resources. </p> </li> <li> <p><a href="" rel="nofollow">Tutorial list</a> for technical skills involved in research with R, github, mturk, etc.</p> </li> <li> <p>"<a href="" rel="nofollow">101 Innovations in Scholary Communication</a>"</p> </li> </ol> <p><strong><em>*</em></strong><strong><em>discussion notes</em></strong><strong><em>*</em></strong>** as of 4:05pm (in development)</p> <p>• Overarching goal—cumulative science; test/build theory</p> <p>• Aspirational goal (sub-overarching goal)—transparency in the research process</p> <p>o The “who, what, why, where and how” questions: what you did, how you did it, who you did it with, where you did it, and why you did it? ♣ Discussion of degree of consensus within the lab ♣ Power analysis or discussion of sample size and composition</p> <p>o Archiving: data, materials, backed-up/accessible, intermediate steps, code</p> <p>o Reporting null results</p> <p>o Analysis: contingencies; deviations clearly noted</p>
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