Many additional references

module4/ressources/resources.org

@@ -6,7 +6,126 @@
 #+OPTIONS: num:nil toc:t
 #+PROPERTY: header-args :eval never-export
 
-* Getting information about your Git repository
+* Additional references
+** "Thoughts" on language/software stability
+As we explained, the programming language used in an analysis has a
+clear influence on the reproducibility of your analysis. It is not a
+characteristic of the language itself but rather a consequence of the
+development philosophy of the underlying community. For example C is a
+very stable language with a [[https://en.wikipedia.org/wiki/C_(programming_language)#ANSI_C_and_ISO_C][very clear specification designed by a
+committee]] (even though some compilers may not respect this norm).
+
+On the other end of the spectrum, [[https://en.wikipedia.org/wiki/Python_(programming_language)][Python]] had a much more organic
+development based on a readability philosophy and has evolved with
+time. Furthermore, python is commonly used as a wrapping language
+(e.g., to easily use C or FORTRAN libraries) and has its own packaging
+system to make everyone's life easier. All these design choices tend
+to make reproducibility often a bit painful with python, even though
+the community is slowly taking this into account.
+
+[[https://en.wikipedia.org/wiki/R_(programming_language)][R]], in comparison is much closer (in terms of developer community) to
+languages like [[https://en.wikipedia.org/wiki/SAS_(software)][SAS]], which is heavily used in the pharmaceutical
+industry where statistical procedures need to be standardized and rock
+solid/stable. R is obviously not immune to evolutions that break old
+versions and hinder reproducibility/backward compatibility. Here is a
+relatively recent [[http://members.cbio.mines-paristech.fr/~thocking/HOCKING-reproducible-research-with-R.html][true story about this]] and some colleagues who worked
+on the [[https://www.fun-mooc.fr/courses/UPSUD/42001S06/session06/about][statistics introductory course with R on FUN]] reported us
+several issues with functions from a few functions (=plotmeans= from
+=gplots=, =survfit= from =survival=, or =hclust=) whose default
+parameters had changed over the years. It is thus probably a good
+practice to explicitly indicate in your code default values (, which
+can be cumbersome) and to restrict your dependencies as much as
+possible.
+
+This being said, the R development community is generally quite
+careful about stability. We (the authors of this MOOC) think open
+source (, which allows to inspect how computation is done and to
+identify both mistakes and sources of non reproducibility) is more
+important than the rock solid stability of SAS, which is a proprietary
+software. Yet, if you really need to stay with SAS (similar solutions
+probably exist for other languages as well), you should know that SAS
+can be used within Jupyter using either the [[https://sassoftware.github.io/sas_kernel/][Python SASKernel]] or the
+[[https://sassoftware.github.io/saspy/][Python SASPy]] package (step by step explanations about this are given
+[[https://app-learninglab.inria.fr/gitlab/85bc36e0a8096c618fbd5993d1cca191/mooc-rr/blob/master/documents/tuto_jupyter_windows/tuto_jupyter_windows.md][here]]). Using such literate programming approach allied with systematic
+control version and environment control will help anyway.
+** Controlling your software environment
+As we mentioned in the video sequences, there are several solutions to
+control your environment:
+- The easy (preserve the mess) ones: [[http://www.pgbovine.net/cde.html][CDE]] or [[https://vida-nyu.github.io/reprozip/][ReproZip]]
+- The more demanding (encourage cleanliness) where you start with a
+  clean environment and install only what's strictly necessary (and document it):
+  - The very well known [[https://www.docker.io/][Docker]]
+  - [[https://singularity.lbl.gov/][Singularity]] or [[https://spack.io/][Spack]], which are more targeted toward high
+    performance computing users that have specific needs
+  - [[https://www.gnu.org/software/guix/][Guix]], [[https://nixos.org/][Nix]] that are very clean (perfect?) solutions to this
+    dependency hell and which we recommend
+
+It may be hard to understand the difference between these different
+approaches and decide which one is better in your context.
+
+Here is a webinar where some of these tools are demoed in a
+reproducible research context: [[https://github.com/alegrand/RR_webinars/blob/master/2_controling_your_environment/index.org][Controling your environment (by Michael
+Mercier and Cristian Ruiz)]]
+
+You may also want to have a look at [[http://falsifiable.us/][the Popper conventions]] ([[https://github.com/alegrand/RR_webinars/blob/master/11_popper/index.org][webinar by
+Ivo Gimenez through google hangout]]) or at the [[https://github.com/alegrand/RR_webinars/blob/master/7_publications/index.org][presentation of Konrad
+Hinsen on Active Papers]] (http://www.activepapers.org/).
+** Preservation/Archiving
+Ensuring software is properly archived, i.e, is safely stored so that
+it can be accessed in a perennial way, can be quite tricky. If you
+have never seen [[https://github.com/alegrand/RR_webinars/blob/master/5_archiving_software_and_data/index.org][Roberto Di Cosmo presenting the Software Heritage
+project]], this is a must see. https://www.softwareheritage.org/
+
+For regular data, we highly recommend using https://www.zenodo.org/
+whenever data is not sensitive.
+** Workflows
+In the video sequences, we mentioned workflows (original domain in parenthesis):
+- [[https://galaxyproject.org/][Galaxy]] (genomics), [[https://kepler-project.org/][Kepler]] (ecology), [[https://taverna.apache.org/][Taverna]] (bio-informatics), [[https://pegasus.isi.edu/][Pegasus]]
+  (astronomy), [[http://cknowledge.org/][Collective Knowledge]] (compiling optimization) ,
+  [[https://www.vistrails.org][VisTrails]] (image processing)
+- Light-weight: [[http://dask.pydata.org/][dask]] (python), [[https://ropensci.github.io/drake/][drake]] (R), [[http://swift-lang.org/][swift]] (molecular biology),
+  [[https://snakemake.readthedocs.io/][snakemake]] (like =make= but more expressive and in =python=) ...
+- Hybrids: [[https://vatlab.github.io/sos-docs/][SOS-notebook]], ...
+
+You may want to have a look at this webinar: [[https://github.com/alegrand/RR_webinars/blob/master/6_reproducibility_bioinformatics/index.org][Reproducible Science in
+Bio-informatics: Current Status, Solutions and Research Opportunities
+(by Sarah Cohen Boulakia, Yvan Le Bras and Jérôme Chopard).]]
+
+** Numerical and statistical issues
+These topics could only be mentioned in our MOOC but could by no way
+be properly covered. We only suggest here a few interesting talks
+about this.
+- [[https://github.com/alegrand/RR_webinars/blob/master/10_statistics_and_replication_in_HCI/index.org][In this talk, Pierre Dragicevic provides a nice illustration of the
+  consequences of statistical uncertainty and of how some concepts
+  (e.G. p-values) are commonly badly understood.]]
+- [[https://github.com/alegrand/RR_webinars/blob/master/3_numerical_reproducibility/index.org][Nathalie Revol, Philippe Langlois and Stef Graillat present the main
+  challenges encountered when trying to achieve numerical
+  reproducibility and present recent research work on this topic.]]
+** Publication practices
+You may want to have a look at the following two webinars:
+- [[https://github.com/alegrand/RR_webinars/blob/master/8_artifact_evaluation/index.org][Enabling open and reproducible research at computer systems’
+  conferences (by Grigori Fursin)]]. In particular, this talk discusses
+  /artifact evaluation/ that are becoming more and more popular.
+- [[https://github.com/alegrand/RR_webinars/blob/master/7_publications/index.org][Publication Modes Favoring Reproducible Research (by Konrad Hinsen
+  and Nicolas Rougier)]]. In this talk, the motivation for the [[http://rescience.github.io/][ReScience
+  journal]] initiative are presented.
+- [[https://www.youtube.com/watch?v=HuJ2G8rXHMs][Simine Vazire - When Should We be Skeptical of Scientific Claims?]],
+  which is discussing publication practices in social sciences and in
+  particular HARKing (Hypothesizing After the Results are Known),
+  p-hacking, etc.
+** Experimentation
+Experimentation was not covered in this MOOC whereas it is an
+essential part of science. The main reason is that practices and
+constraints can vary so wildly from a domain to an other that it could
+not be properly covered in a first edition. We would be happy to
+gather references you consider as interesting in your domain so do not
+hesitate to provide us with such references by using the forum and we
+will update this page.
+
+- [[https://github.com/alegrand/RR_webinars/blob/master/9_experimental_testbeds/index.org][A recent talk by Lucas Nussbaum on Experimental Testbeds in Computer
+  Science]].
+* Tracking environment information
+** Getting information about your Git repository
 When taking notes, it may be difficult to remember which version of
 the code or of a file was used. This is what version control is useful
 for. Here are a few useful commands that we typically insert at the
@@ -84,8 +203,8 @@ Obviously, in this case you need to save the notebook before running
 this cell, hence the output of this final command (with the new git
 hash) will not be stored in the cell. This is not really a problem and
 is the price to pay for running git from within the notebook itself.
-* Getting information about Python(3) libraries 
-** Getting the list of installed packages and their version
+** Getting information about Python(3) libraries 
+*** Getting the list of installed packages and their version
 This topic is discussed on [[https://stackoverflow.com/questions/20180543/how-to-check-version-of-python-modules][StackOverflow]]. When using =pip= (the Python
 package installer) within a shell command, it is easy to query the
 version of all installed packages (note that on your system, you may
@@ -181,7 +300,7 @@ License: BSD License
 Location: /home/alegrand/.local/lib/python3.6/site-packages
 Requires: patsy, pandas
 #+end_example
-** How to list imported modules?
+*** How to list imported modules?
 Without resorting to pip (that will list all available packages), you
 may want to know which modules are loaded in a Python session as well
 as their version.  Inspiring from [[https://stackoverflow.com/questions/4858100/how-to-list-imported-modules][StackOverflow]], here is a simple
@@ -238,7 +357,7 @@ urllib.request 3.6
 zlib 1.0
 #+end_example
 
-** Setting up an environment with pip
+*** Setting up an environment with pip
 The easiest way to go is as follows:
 #+begin_src shell :results output :exports both
 pip3 freeze > requirements.txt # to obtain the list of packages with their version
@@ -248,7 +367,7 @@ pip3 install -r requirements.txt # to install the previous list of packages, pos
 If you want to have several installed Python environments, you may
 want to use [[https://docs.pipenv.org/][Pipenv]]. I doubt it allows to track correctly FORTRAN or C
 dynamic libraries that are wrapped by Python though.
-** Installing a new package or a specific version
+*** Installing a new package or a specific version
 The Jupyter environment we deployed on our servers for the MOOC is
 based on the version 4.5.4 of Miniconda and Python 3.6. In this
 environment you should simply use the =pip= command (remember on your
@@ -300,8 +419,8 @@ It is even possible to install a specific (possibly much older) version, e.g.,:
 #+begin_src shell :results output :exports both
 pip install statsmodels==0.6.1
 #+end_src
-* Getting information about R libraries
-** Getting the list imported modules and their version
+** Getting information about R libraries
+*** Getting the list imported modules and their version
 The best way seems to be to rely on the =devtools= package (if this
 package is not installed, you should install it first by running in =R=
 the command =install.packages("devtools")=).
@@ -363,7 +482,7 @@ Packages ----------------------------------------------------------------------
 Some actually advocate that [[https://github.com/ropensci/rrrpkg][writing a reproducible research compendium
 can be done by writing an R package]]. Those of you willing to have a
 clean R dependency management should thus have a look at [[https://rstudio.github.io/packrat/][Packrat]].
-** Getting the list of installed packages and their version
+*** Getting the list of installed packages and their version
 Finally, it is good to know that there is a built-in R command
 (=installed.packages=) allowing to retrieve and list the details of all
 packages installed.
@@ -384,12 +503,12 @@ head(installed.packages())
 | StanHeaders                                                 | /home/alegrand/R/x86_64-pc-linux-gnu-library/3.5 |                                                  2.17.2 | nil         | nil                                                      | nil                                                            | nil       | RcppEigen, BH | nil                                   | BSD_3_clause + file LICENSE | nil           | nil                 | nil    | nil    | yes              | 3.5.1 |       |
 | acepack                                                     | /home/alegrand/R/x86_64-pc-linux-gnu-library/3.5 |                                                   1.4.1 | nil         | nil                                                      | nil                                                            | nil       | testthat      | nil                                   | MIT + file LICENSE        | nil           | nil                 | nil    | nil    | yes              | 3.5.1 |       |
 
-** Installing a new package or a specific version
+*** Installing a new package or a specific version
 This section is mostly a cut and paste from the [[https://support.rstudio.com/hc/en-us/articles/219949047-Installing-older-versions-of-packages][recent post by Ian
 Pylvainen]] on this topic. It comprises a very clear explanation on how
 to proceed.
 
-*** Installing a pre-compiled version
+**** Installing a pre-compiled version
 If you're on a Debian or a Ubuntu system, it may be difficult to
 access a specific version without breaking your system. So unless you
 are moving to the latest version available in your Linux distribution,
@@ -406,7 +525,7 @@ similar to the example below:
 packageurl <- "https://cran-archive.r-project.org/bin/windows/contrib/2.13/BBmisc_1.0-58.zip"
 install.packages(packageurl, repos=NULL, type="binary")
 #+end_src
-*** Using devtools
+**** Using devtools
 The simplest method to install the version you need is to use the
 =install_version()= function of the =devtools= package (obviously, you
 need to install =devtools= first, which can be done by running in =R= the
@@ -416,7 +535,7 @@ command =install.packages("devtools")=). For instance:
 require(devtools)
 install_version("ggplot2", version = "0.9.1", repos = "http://cran.us.r-project.org")
 #+end_src
-*** Alternatively, you may want to install an older package from source
+**** Alternatively, you may want to install an older package from source
 If you devtools fails or if you do not want to depend on it, you can
 install it from source via =install.packages()= directed to the right
 URL. This URL can be obtained by browsing the [[https://cran.r-project.org/src/contrib/Archive][CRAN Package Archive]].
@@ -434,7 +553,7 @@ line outside of R. For instance (in bash):
 wget http://cran.r-project.org/src/contrib/Archive/ggplot2/ggplot2_0.9.1.tar.gz
 R CMD INSTALL ggplot2_0.9.1.tar.gz
 #+end_src
-*** Potential issues
+**** Potential issues
 There are a few potential issues that may arise with installing older
 versions of packages: 
 - You may be losing functionality or bug fixes that are only present