[R-meta] Open Data: preferred way of publishing

Emerson M. Del Ponte delponte at ufv.br
Sat Jan 27 22:53:47 CET 2018

Dear Moritz,

It is very nice that you are considering to make your meta-analytic
research more open by sharing data, codes, post pre-prints, etc.

I am sending below two examples of reproducible reports for meta-analysis
manuscripts I have published recently using R Markdown (Rmd)  (literate

Here are the usual steps I follow:

1) Use excel or alike for preparing the data but export and work with text
files such as csv
2) Create an Rproj. and perform all data munging, visualisation, analysis,
etc in Rmd files (text + codes)
3) Produce a webpage (single) or website (multiple html and a nav bar) as
output using knitr
4) Host all files on a GitHub repository
5) Store data (csv) and codes in a permanent repo such as http://www.osf.io
to get the DOI and a citation
6) Post a pre-print of the manuscript

Example 1
My first one, uses knitr to produce a single page.

webpage: https://emdelponte.github.io/paper-white-mold-meta-analysis/
repo: https://github.com/emdelponte/paper-white-mold-meta-analysis

Example 2
Most recent one is a website + nav bar with four main Rmd files for intro,
data, code and manuscript:

website: https://emdelponte.github.io/paper-FHB-Brazil-meta-analysis/
repo: https://github.com/emdelponte/paper-FHB-Brazil-meta-analysis

this is not a MA paper, but it follows the same structure

I prepared a template for a research compendium (data + code + manuscript +
figures) for this last example
website: https://emdelponte.github.io/research-compendium-website/
report: https://github.com/emdelponte/research-compendium-website

Finally, I post the manuscript (bioRxiv or PeerJ) with the following

"Data processing and analyses.
All data processing and analyses, as well as graphical work, were performed
running R version 3.4.3 (R Core Team, 2017). Texts and scripts were
prepared as R Markdown documents. A collection of these latter files were
rendered as a website, using the render_site function of the R package
rmarkdown (Allaire et al. 2017), where all analysis are documented,
reproducible and openly available at
https://github.com/emdelponte/paper-FGSC-fitness. The data in text format
are deposited at the Open Science Framework data repository and available
at https://osf.io/c2mbr/."

One of the most important aspects to ensure reproducibility of methods is a
clear documentation, besides access to all files. I found that this is not
only good for others but for myself  when I need to go back to previous
analysis and understand what and why I did something!

Hope these are useful. Any question, let me know.

Best wishes,


Prof. Emerson M. Del Ponte
Departamento de Fitopatologia
Universidade Federal de Viçosa
Viçosa, MG - Brasil
+55 (31) 3899-1103
Twitter: @edelponte

2018-01-27 13:19 GMT-02:00 Moritz Tobiasch <moritztobiasch at gmail.com>:

> Dear colleagues,
> I have not a primary technical question, but I think it is worth asking
> the experts:
> I am currently finishing on a meta-analysis, preparing for publication. I
> intend to publish my set of primary data (it’s been quite some work, and I
> guess it could be helpful to review and discuss on the topic). Primary data
> were collected in a (I know, I apologize for it …) Excel spreadsheet before
> being imported to R, and analysis was run in Rstudio in a Markup file.
> My question is now: based on your experience and preferences, what would
> be your ideal way to make the primary dataset and the calculations
> accessible for review and further research? Add it to the article as
> supplemental material, upload it to arxiv, GitHub, or just on my website?
> Any suggestions are welcome!
> Sincerely yours
> M. Tobiasch
> --
> Dr. med. Moritz Tobiasch
> Staff Physician
> Universitätskliniken LKH Innsbruck
> Dept. of Medicine
> Division of Gastroenterology and Hepatology
> Anichstr. 35
> A-6020 Innsbruck
> Austria
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