Data Reproducibility Workshop: Course Materials & Resources

Resources from session 1 - Electronic protocols and lab books:

• Electronic protocols repository - http://protocols.io
• Guidelines for writing reproducible protocols - https://www.aje.com/en/arc/how-to-write-an-easily-reproducible-protocol/
• Example electronic notebook tool - https://www.labarchives.com/

Resources from session 2 - Power analysis and experimental design

• G-power >> Click and play software for calculating the effect size/power: Mac download link, Windows download link
• Paper with a simple description of power/effect size - Krzywinski & Altmann 2013 
• Interesting series of papers on using stats - Nature Points of Significance Series

Resources from session 3 - Data management & Open Data

• Guidelines for best practices in data management - https://www.dataone.org/best-practices
• Tool for creating a data management plan - https://dmptool.org/
• Guide for choosing data license - http://www.dcc.ac.uk/resources/how-guides/license-research-data
• Guide for choosing code license - http://kbroman.org/steps2rr/pages/licenses.html and https://opensource.org/licenses
• Find and compare disciplinary research repositories - https://www.re3data.org/

Other data repositories: 

• DataDryad - curated repository, free to access, 120 USD to publish dataset up to 20GB 
• Zenodo - free digital repository, 50GB per dataset limit 
• Figshare - free digital repository, 5GB per file limit

Resources from session 4 - Reproducible analysis pipelines

• Pre-registration of the data using https://osf.io/
• CodeOcean - https://codeocean.com/
• Introductory course for using CONDA - reproducible python analysis pipeline -    SWCarpentry

Resources from session 5 - Version control using Git & GitHub 

• GitHub - http://github.com
• KAUST owned GitLab - https://gitlab.kaust.edu.sa/users/sign_in
• Some additional guidelines for using GitHub - https://guides.github.com/introduction/flow/
• Git course from Software Carpentry - https://swcarpentry.github.io/git-novice/
• Material from KAUST course on git & GitHub

Resources from session 6 - Statistics refresher

• P-value app - http://rpsychologist.com/d3/pdist
• Confidence Interval app - http://rpsychologist.com/d3/CI/
• Effect size app - https://rpsychologist.com/d3/cohend/
• Null hypothesis testing app - https://rpsychologist.com/d3/NHST/ 
• p-value hacker app - http://shinyapps.org/apps/p-hacker/
• Calculate your own p-curve - http://www.p-curve.com/
• Ioannidis (2005) - Why most published research findings are false. 
• Positive predictive value app - http://shinyapps.org/apps/PPV/
• Another tool for visualizing statistics concepts - seeing-theory developed by Brown university

Resources from session 7 - Data visualization

• A Brief Guide to Designing Effective Figures for the Scientific Paper - Rolandi et al., 2011
• Ten simple rules for better figures - Rougier et al., 2014
• Data visualization: A view of every Points of View column - Nature Methods series
• A very accessible introduction to the grammar of graphics by Sarkar - includes some example python code
• The basic concepts of the grammar of graphics by Wickham
• Visualizing grammar of graphics concepts using GGPlot flip-book - developed by R-ladies
• Telling stories with the grammar of graphics by Sander 
• Use of color in scientific graphs
• A handy tool for choosing the best colors for your graphs - colorbrewer2.org
• Tool optimized for tweaking colors for JavaScript - VizPalette by Suzy Lu

Resources from session 8 - Peer review, preprints & publication

• Register and get credit for your anonymous reviewing efforts - publons.com
• Nature Master class on peer review
• Rate trail of open peer review allays common concerns - Nature 2019
• The effect of publishing peer review reports on referee behavior in five scholarly journals - Bravo et al., 2019
• On the value of preprints: An Early Career Researcher perspective - Sarabipour et al., 2019
• What bioRxiv's first 30.000 preprints reveal about biologists - Nature 2019
• Tracking the popularity and outcomes of all bioRxiv preprints - Abdil & Blekham, 2019
• Check your funder policy concerning Open Access publication - ASAPbio.org
• List of academic journals by preprint policy - http://en.wikipedia.org/wiki/List_of_academic_journals_by_preprint_policy
• Search publisher copyrights policies & self-archiving - http://sherpa.ac.uk/romeo
• Resources for reviewing pre-prints - http://asapbio.org/preprint-journal-clubs
• Research impact of   paywalled versus open access papers - Archambault et al. 2016
• KAUST Open Access policy - http://libguides.kaust.edu.sa/openaccesspolicy
• KAUST repository - https://repository.kaust.edu.sa/
• Deposit new items into KAUST repository - https://repository.kaust.edu.sa/submit
• Register your ORCID with KAUST - https://orcid.kaust.edu.sa/ 

The video recordings from individual sessions are available here

• Session 7 - Data Visualization - presentation slides available for download on Figshare

These materials are also archived in the KAUST repository at: https://repository.kaust.edu.sa/handle/10754/631812

Session 4 - Reproducible analysis pipelines

• Link to the exercise code used in the workshop - https://osf.io/8qprv/

Session 5:

- the GitHub repository with the code to share - https://github.com/BenjaminSchwessinger/20190325_KAUST

The material used in Session 5 was based on the Software Carpentry course - https://swcarpentry.github.io/git-novice/