<figure><img src="image_1.png" /></figure>

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# Caring about reproducibility in scientific research

Timothée Flutre

► To cite this version:

Timothée Flutre. Caring about reproducibility in scientific research. Master. EvoGEM, MNHN, Paris, France. 2025, pp.45. hal-05294281

HAL Id: hal-05294281

https://hal.inrae.fr/hal-05294281v1

Submitted on 2 Oct 2025

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# Caring about reproducibility in scientific research

Timothée Flutre

UMR Génétique Quantitative et Evolution
Université Paris-Saclay — INRAE — CNRS — AgroParisTech

02/10/2025
# Outline

## Global context

## Spectrum of solutions
## Global context

## Spectrum of solutions
Statistical Inference: the Big Picture (Kass, 2011)

<figure><img src="image_3.png" /><figcaption>FIG. 1. The big picture of statistical inference. Statistical procedures are abstractly defined in terms of mathematics but are used, in conjunction with scientific models and methods, to explain observable phenomena. This picture emphasizes the hypothetical link between variation in data and its description using statistical models.</figcaption></figure>
# Statistical Inference: the Big Picture (Kass, 2011)

Standard conception:

<figure><img src="image_4.png" /></figure>
Statistical Inference: the Big Picture (Kass, 2011)

REAL WORLD

<figure><img src="image_5.png" /></figure>

THEORETICAL WORLD

<figure><img src="image_6.png" /></figure>
# Why Most Published Research Findings Are False

John P. A. Ioannidis

Published: August 30, 2005 • DOI: 10.1371/journal.pmed.0020124
# Why Most Published Research Findings Are False

John P. A. Ioannidis

Published: August 30, 2005 • DOI: 10.1371/journal.pmed.0020124

► Did you hear about this paper?
# Why Most Published Research Findings Are False

John P. A. Ioannidis

Published: August 30, 2005 • DOI: 10.1371/journal.pmed.0020124

► Did you hear about this paper?

Total citations

Cited by 2352

<figure><img src="image_7.png" /></figure>
# Why Most Published Research Findings Are False

John P. A. Ioannidis

Published: August 30, 2005 • DOI: 10.1371/journal.pmed.0020124

► Did you hear about this paper?

<figure><img src="image_8.png" /></figure>
# Why Most Published Research Findings Are False

John P. A. Ioannidis

Published: August 30, 2005 • DOI: 10.1371/journal.pmed.0020124

► Did you hear about this paper?

<figure><img src="image_9.png" /></figure>

► Did you read it?
Statistical model

PPV: positive predictive value; P(true | finding)

► posterior probability that a given research finding is true
Statistical model

PPV: positive predictive value; P(true | finding)

► posterior probability that a given research finding is true

Depends on:

► the prior probability of it being true; R := P(true)

► the power of the study; 1 – β where β is the Type II error rate

► the probability of claiming a relationship when none truly exists; α (type I error rate)
PPV: positive predictive value; P(true | finding)

► posterior probability that a given research finding is true

Depends on:

► the prior probability of it being true; R := P(true)

► the power of the study; 1 - β where β is the Type II error rate

► the probability of claiming a relationship when none truly exists; α (type I error rate)

$$PPV = \frac{R(1 - \beta)}{R(1 - \beta) + (1 - R)\alpha}$$
PPV: positive predictive value; P(true | finding)

► posterior probability that a given research finding is true

Depends on:

► the prior probability of it being true; $R := P(true)$

► the power of the study; $1 - \beta$ where $\beta$ is the Type II error rate

► the probability of claiming a relationship when none truly exists; $\alpha$ (type I error rate)

But also on:

► bias (*u*): combination of various design, data, analysis, and presentation factors that tend to produce research findings when they should not be produced

► number of independent studies of equal power (*n*)
Example

<figure><img src="image_10.png" /></figure>

Ioannidis (2005)
Corollaries

1. The smaller the studies conducted in a scientific field, the less likely the research findings are to be true.
Corollaries

1. The smaller the studies conducted in a scientific field, the less likely the research findings are to be true.

2. The smaller the effect sizes in a scientific field, the less likely the research findings are to be true.
# Corollaries

1. The smaller the studies conducted in a scientific field, the less likely the research findings are to be true.

2. The smaller the effect sizes in a scientific field, the less likely the research findings are to be true.

3. The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true.
1. The smaller the studies conducted in a scientific field, the less likely the research findings are to be true.

2. The smaller the effect sizes in a scientific field, the less likely the research findings are to be true.

3. The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true.

4. The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true.
# Corollaries

1. The smaller the studies conducted in a scientific field, the less likely the research findings are to be true.

2. The smaller the effect sizes in a scientific field, the less likely the research findings are to be true.

3. The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true.

4. The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true.

5. The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true.
# Corollaries

1. The smaller the studies conducted in a scientific field, the less likely the research findings are to be true.

2. The smaller the effect sizes in a scientific field, the less likely the research findings are to be true.

3. The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true.

4. The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true.

5. The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true.

6. The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true.
First, is it really that bad?

<figure><img src="image_11.png" /></figure>

Jager & Leek (2014)
DERIVING CHEMOSENSITIVITY FROM CELL LINES:
FORENSIC BIOINFORMATICS AND REPRODUCIBLE
RESEARCH IN HIGH-THROUGHPUT BIOLOGY

BY KEITH A. BAGGERLY¹ AND KEVIN R. COOMBES²

University of Texas

High-throughput biological assays such as microarrays let us ask very detailed questions about how diseases operate, and promise to let us personaliize therapy. Data processing, however, is often not described well enough to allow for exact reproduction of the results, leading to exercises in "forensic bioinformatics" where aspects of raw data and reported results are used to infer what methods must have been employed. Unfortunately, poor documentation can shift from an inconvenience to an active danger when it obscures not just methods but errors. In this report we examine several related papers purporting to use microarray-based signatures of drug sensitivity derived from cell lines to predict patient response. Patients in clinical trials are currently being allocated to treatment arms on the basis of these results. However, we show in five case studies that the results incorporate several simple errors that may be putting patients at risk. One theme that emerges is that the most common errors are simple (e.g., row or column offsets); conversely, it is our experience that the most simple errors are common. We then discuss steps we are taking to avoid such errors in our own investigations.
# How to Make More Published Research True

John P. A. Ioannidis

Published: October 21, 2014 • DOI: 10.1371/journal.pmed.1001747

Read it here!

> To make more published research true, practices that have improved credibility and efficiency in specific fields may be transplanted to others which would benefit from them—possibilities include the adoption of large-scale collaborative research; replication culture; registration; sharing; reproducibility practices; better statistical methods; standardization of definitions and analyses; more appropriate (usually more stringent) statistical thresholds; and improvement in study design standards, peer review, reporting and dissemination of research, and training of the scientific workforce.
Some terminology (1/2)

From a U.S. NSF subcommittee:

> Reproducibility refers to the ability of a researcher to duplicate the results of a prior study using the same materials as were used by the original investigator. That is, a second researcher might use the same raw data to build the same analysis files and implement the same statistical analysis in an attempt to yield the same results. Reproducibility is a minimum necessary condition for a finding to be believable and informative.
Some terminology (2/2)

Goodman et al (2016):

► methods reproducibility: ability to implement, as exactly as possible, the experimental and computational procedures, with the same data and tools, to obtain the same results

► capture the original meaning of "reproducibility"

► here, I focus on this notion

► results reproducibility: production of corroborating results in a new study, having followed the same experimental methods

► refers to what was previously described as "replication"
Reprod. Research in Computational Science (Peng, 2011)

<figure><img src="image_12.png" /></figure>
Reprod. Research in Computational Science (Peng, 2011)

<figure><img src="image_13.png" /></figure>

► Would you define yourself as a “computational scientist”?
Reprod. Research in Computational Science (Peng, 2011)

<figure><img src="image_14.png" /></figure>

► Would you define yourself as a “computational scientist”?

► Do you use Excel?
<figure><img src="image_15.png" /></figure>

► Would you define yourself as a “computational scientist”?

► Do you use Excel? R and/or Python?
<figure><img src="image_16.png" /></figure>

► Would you define yourself as a “computational scientist”?

► Do you use Excel? R and/or Python? Julia?
Reprod. Research in Computational Science (Peng, 2011)

<figure><img src="image_17.png" /></figure>

► Would you define yourself as a “computational scientist”?

► Do you use Excel? R and/or Python? Julia? C/C++?
Reprod. Research in Computational Science (Peng, 2011)

<figure><img src="image_18.png" /></figure>

► Would you define yourself as a “computational scientist”?

► Do you use Excel? R and/or Python? Julia? C/C++?

► Where are you along the spectrum?
Claerbout & Karrenbach (1992):

An article about a computational result is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result.
Claerbout & Karrenbach (1992):

An article about a computational result is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result.

Buckheit & Donohoe (1995):

Publishing figures or results without the complete software environment could be compared to a mathematician publishing an announcement of a mathematical theorem without giving the proof.
# Where Have All the Crop Phenotypes Gone?

Dani Zamir

Published: June 25, 2013 • DOI: 10.1371/journal.pbio.1001595
# Where Have All the Crop Phenotypes Gone?

Dani Zamir

Published: June 25, 2013 • DOI: 10.1371/journal.pbio.1001595

> Currently, virtually none of the data generated from the hundreds of phenotypic studies conducted each year are being made publically available as raw data; thus there is little we can learn from past experience when making decisions about how to breed better crops for the future.
# Where Have All the Crop Phenotypes Gone?

Dani Zamir

Published: June 25, 2013 • DOI: 10.1371/journal.pbio.1001595

> Currently, virtually none of the data generated from the hundreds of phenotypic studies conducted each year are being made publically available as raw data; thus there is little we can learn from past experience when making decisions about how to breed better crops for the future.

Zamir (Science, 2014):

without the corresponding potentially commercially valuable phenotypic data. For example, in rice (Oryza sativa), which feeds roughly half the world population, 3000 variants from 89 countries were sequenced revealing 18.9 million single-nucleotide polymorphisms (SNPs) (4). But what good are 3000 genomes if the associated phenotypic data, and sometimes seed stocks, are kept proprietary? A wake-up call is needed for scientists, granting agencies, journal editors, and referees: What we eat are phenotypes, and seriously addressing global food security demands that, at least in the domain of crop plants, phenotypic data should be shared between scientists in the same manner as for sequences (3).
<figure><img src="image_19.png" /></figure>

The NEW ENGLAND JOURNAL of MEDICINE

HOME ARTICLES & MULTIMEDIA ▼ ISSUES ▼ SPECIALTIES & TOPICS ▼ FOR AUTHORS

EDITORIAL

Data Sharing

Dan L. Longo, M.D., and Jeffrey M. Drazen, M.D.

N Engl J Med 2016; 374:276-277 | January 21, 2016 | DOI: 10.1056/NEJMe1516564

"research parasites" [...] people who had nothing to do with the design and execution of the study but use another group's data for their own ends, possibly stealing from the research productivity planned by the data gatherers, or even use the data to try to disprove what the original investigators had posited
All reporting errors:

<figure><img src="image_20.png" /></figure>

Large reporting errors
(2<sup>nd</sup> decimal):

<figure><img src="image_21.png" /></figure>

Reporting errors
concerned with p < .05:

<figure><img src="image_22.png" /></figure>

Wicherts et al (2011):

We found the reluctance to share data to be associated with weaker evidence (against the null hypothesis of no effect) and a higher prevalence of apparent errors in the reporting of statistical results.
<figure><img src="image_23.png" /></figure>

Code share

Papers in Nature journals should make computer code accessible where possible.

29 October 2014

Publication is conditional upon the agreement of the authors to make freely available any materials and information described in their publication that may be reasonably requested by others.

Data Availability

PLOS journals require authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception¹.
Beyond data: analytical decisions

<figure><img src="image_24.png" /></figure>
Blocker & Meng (Bernoulli, 2013):

Decisions made in preprocessing constrain all later analyses and are typically irreversible. Hence, data analysis becomes a collaborative endeavor by all parties involved in data collection, preprocessing and curation, and downstream inference.

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>
Devezer et al (RSOS, 2021):

**Formal approach to solving methodological problems.**

0. **Conception.** An informal problem statement and a proposed solution to that problem, often expressed non-technically.

1. **Definitions.** Identification of variables, population parameters, and constants involved in the problem, and statistical model building using these quantities, with explicitly stated model assumptions.

2. **Formal problem statement.** Mathematical propositions or algorithms positing methodological claims.

3. **Formal result.** Mathematical or simulation-based proofs that interrogate the validity of the statements in step 2.

4a. **Demonstrations.** If the statements are valid, examples showing their relevance in application.
After all, why is reproducibility important

Which reason(s) would you choose?
After all, why is reproducibility important

Which reason(s) would you choose?

* reproducing work is also the first step to extending it

* we are forgetful, error-prone (or dishonest)

* helps communication with your collaborators

* bigger visibility in the community

* we are mostly funded by public money

* ...
# Artificial intelligence faces reproducibility crisis

Unpublished code and sensitivity to training conditions make many claims hard to verify.

<footnote>MATTHEW HUTSON Authors Info & Affiliations</footnote>

<footnote>SCIENCE · 16 Feb 2018 · Vol 359, Issue 6377 · pp. 725-726 · DOI: 10.1126/science.359.6377.725</footnote>
Is it going to be worse?

NEWS

26 July 2022

# Could machine learning fuel a reproducibility crisis in science?

> 'Data leakage' threatens the reliability of machine-learning use across disciplines, researchers warn.
Is it going to be worse?

**TECHNOLOGY FEATURE**

09 January 2023

Correction **12 January 2023**

# The reproducibility issues that haunt health-care AI

Health-care systems are rolling out artificial-intelligence tools for diagnosis and monitoring. But how reliable are the models?
Not only a technical issue

Han (2025):

Researchers often face a game-theoretical dilemma when navigating the landscape of AI reproducibility. [...] Rigorous adherence to reproducibility practices can conflict with personal research objectives, such as maximizing model performance, accelerating project timelines, or achieving groundbreaking innovation. These competing priorities often lead to tension between individual ambitions and the communal goal of reliable scientific progress.
# Outline

## Global context

## Spectrum of solutions
► question: mono- or inter-disciplinary; closer to applied or to basic research

► won't be discussed here, but obviously crucial...
* question: mono- or inter-disciplinary; closer to applied or to basic research

* won't be discussed here, but obviously crucial...

* data: few small files or many large files; text or binary; custom or standardized formats; databases
* **question**: mono- or inter-disciplinary; closer to applied or to basic research

* won't be discussed here, but obviously crucial...

* **data**: few small files or many large files; text or binary; custom or standardized formats; databases

* **code**: in interpreted languages (e.g. R, Python, Julia, Perl, Bash) or compiled languages (e.g. C, C++, Fortran)
* **question**: mono- or inter-disciplinary; closer to applied or to basic research

* won't be discussed here, but obviously crucial...

* **data**: few small files or many large files; text or binary; custom or standardized formats; databases

* **code**: in interpreted languages (e.g. R, Python, Julia, Perl, Bash) or compiled languages (e.g. C, C++, Fortran)

* **model**: informal (any word processor is enough, e.g. Writer, Word) or formal (many equations, easier in LaTeX or Typst)
* **question**: mono- or inter-disciplinary; closer to applied or to basic research

* won't be discussed here, but obviously crucial...

* **data**: few small files or many large files; text or binary; custom or standardized formats; databases

* **code**: in interpreted languages (e.g. R, Python, Julia, Perl, Bash) or compiled languages (e.g. C, C++, Fortran)

* **model**: informal (any word processor is enough, e.g. Writer, Word) or formal (many equations, easier in LaTeX or Typst)

* **collaborators**: professional scientist or anyone else; beginner or experienced; "open curious" or already fully proficient (N.B.: to specify author contributions, take a look at CRediT)
Initial steps toward reproducible research

Karl Broman's tutorial: http://kbroman.org/steps2rr/

1. Everything with a script

2. Organize your data and code

3. Automate the process

4. Turn scripts into reproducible reports

5. Turn repeated code into functions

6. Package functions for reuse

7. Use version control
Initial steps toward reproducible research

Karl Broman's tutorial: http://kbroman.org/steps2rr/

1. Everything with a script

2. Organize your data and code

3. Automate the process

4. Turn scripts into reproducible reports

5. Turn repeated code into functions

6. Package functions for reuse

7. Use version control

Teach yourself: Software Carpentry, MOOC RR, forum RR
► word processors: LibreOffice Writer, Microsoft Word, etc

► text editors: Emacs, Vim, Notepad++, Gedit, Geany, etc

► also Jupyter, RStudio, Positron, Atom, Sublime, Visual Studio
# A text editor, not a word processor

* word processors: LibreOffice Writer, Microsoft Word, etc

* text editors: Emacs, Vim, Notepad++, Gedit, Geany, etc

* also Jupyter, RStudio, Positron, Atom, Sublime, Visual Studio

## Why knowing/mastering a good text editor is crucial?

* essential tool of any data analysis (source code and scripts)

* light-weight markup languages convert plain text to PDF/HTML/odt/docx/etc, check out pandoc

* delivers full power of text manipulation tools, e.g. `diff` to compare, `grep` to search, `awk` to extract, ...

* allows efficient use of version control systems such as `git`
Reproducible reports

Literate programming (Knuth, 1984): a computer program is given as an explanation of how it works in a natural language, interspersed (embedded) with traditional source code, from which compilable source code can be generated.
Literate programming (Knuth, 1984): a computer program is given as an explanation of how it works in a natural language, interspersed (embedded) with traditional source code, from which compilable source code can be generated.

1. ...
2. title: 'Test'
3. author: 'Timothée Flutre'
4. date: '2023-11-08'
5. output: html_document
6. ...
7. ...
8. ---{r setup, include=FALSE}
9. knitr::opts_chunk::set(echo = TRUE)
10. ...
11
12. Draw from a Normal distribution:
13. ---{r}
14. x = rnorm(n=100, mean=0, sd=1)
15. ...
16
17. Plot a histogram:
18. ---{r}
19. hist(x)
20. =

Test

Timothée Flutre

2023-11-08

Draw from a Normal distribution

{r = rnorm(100, mean = 0, sd = 1)}

Plot a histogram

hist(x)

<figure><img src="image_27.png" /><figcaption>Histogram of x</figcaption></figure>
https://posit.co/products/open-source/rstudio/

<figure><img src="image_28.png" /></figure>

https://jupyter.org/

<figure><img src="image_29.png" /></figure>
Wrap your code into functions, and use unit tests

Start by writing a test (e.g., setting 2 to 0), here in R:

1
input <- c(1, 2, 3, 4)
2
expected <- c(1, 0, 3, 4)
## Wrap your code into functions, and use unit tests

Start by writing a test (e.g., setting 2 to 0), here in R:

```R
input <- c(1, 2, 3, 4)
expected <- c(1, 0, 3, 4)
```

Then implement the function:

```R
setTwoToZero <- function(input){
  output <- input
  output(input == 2) <- 0
  return(output)
}
```
Start by writing a test (e.g., setting 2 to 0), here in R:

```R
input <- c(1, 2, 3, 4)
expected <- c(1, 0, 3, 4)
```

Then implement the function:

```R
setTwoToZero <- function(input) {
  output <- input
  output(input == 2) <- 0
  return(output)
}
```

Check that the test passes; until then, fix the implementation:

```R
observed <- setTwoToZero(input)
library(testthat)
testthat::expect_equal(observed, expected)
```
Gather all your R functions into a package

Research funding being more and more project-based, it is frequent
to need the same kind of analysis in different projects. But don't
copy-paste your code, gather it into an R package instead.

► read H. Wickham's great book (freely available online)

Then, version it with git, host it somewhere (e.g., locally with
GitLab, online with SourceSup, GitHub, etc), and use the R
package devtools to install it easily on any computer.
Make a R package in 1 min

1 library usethis
2 library(devtools)

1 pkg_name <- "mypkg"
2 pkg_dir <- path.expand("/Documents")
3 path_to_pkgs <- file.path.pkg_dir, pkg_name)
4 pkg_fields <- list()
5 pkg_fields[["Package"]] <- pkg_name
6 pkg_fields[["Version"]] <- "0.1.0"
7 pkg_fields[["Title"]] <- "My First package"
8 pkg_fields[["Description"]] <- "This package is my first one."
9 pkg_fields[["Authors@R"]] <- "person('Timothée', 'Flutre', '','timothée. flutre@inrae.fr', c('aut', 'cre'))"
10 pkg_fields[["License"]] <- "AGPL-3"
11 usethis::create_package(path=path_to_pkgs, fields=pkg_fields)

1 devtools::document.pkg=path_to_pkgs
2 built_pkgs <- devtools::build.pkg=path_to_pkgs
3 devtools::check.pkg=path_to_pkgs
4 devtools::install_local(path=built_pkgs)

And now add your functions in a R subdirectory, along with
roxygen2 documentation.
Project organization

Follow the spirit of Noble (2009):

* someone unfamiliar with your project should be able to look at your computer files and understand in detail what you did and why;

* everything you do, you will probably have to do it over again.
# Project organization

Follow the spirit of Noble (2009):

* someone unfamiliar with your project should be able to look at your computer files and understand in detail what you did and why;

* everything you do, you will probably have to do it over again.

1. Choose a short project name and create a directory
Project organization

Follow the spirit of Noble (2009):

* someone unfamiliar with your project should be able to look at your computer files and understand in detail what you did and why;

* everything you do, you will probably have to do it over again.

1. Choose a short project name and create a directory

2. Give brief explanations in a text file named README

* including sharing/modifying rights

* list authors/collaborators
# Project organization

Follow the spirit of Noble (2009):

* someone unfamiliar with your project should be able to look at your computer files and understand in detail what you did and why;

* everything you do, you will probably have to do it over again.

1. Choose a short project name and create a directory

2. Give brief explanations in a text file named README

* including sharing/modifying rights

* list authors/collaborators

3. Create subdirectories doc/, data/, src/ and results/
Karl Broman on version control systems (VCS):

* not strictly necessary for *reproducibility*, but can be hugely useful for *sanity*

* requires a big initial investment in time and effort, but become a natural part of your daily workflow after a month or so

* huge *short-term* advantages in collaborative projects (keeping in sync, merging simultaneous changes)
Karl Broman on version control systems (VCS):

* not strictly necessary for *reproducibility*, but can be hugely useful for *sanity*

* requires a big initial investment in time and effort, but become a natural part of your daily workflow after a month or so

* huge short-term advantages in collaborative projects (keeping in sync, merging simultaneous changes)

My advice: use **git** (free software; multi-platform; used all over the globe by programmers, scientists, journalists, etc)

* follow a *tutorial* online

* download git and install it

* read chapters 1 to 3 of the official book
# Git history

<figure><img src="image_30.png" /></figure>

Microsoft (2023)
# Git history

<figure><img src="image_31.png" /></figure>

Microsoft (2023)
# Git history

<figure><img src="image_32.png" /></figure>

Microsoft (2023)
# Git history

<figure><img src="image_33.png" /></figure>

Microsoft (2023)
# Git history

<figure><img src="image_34.png" /></figure>

Microsoft (2023)
# Git workflow

* local repositories: your laptop, your supervisor's laptop

* remote repository: hosted on a forge "in the cloud"

<figure><img src="image_35.png" /></figure>

DevDojo (2021)
# Git workflow

## Local

<figure><img src="image_36.png" /></figure>

## Remote

<figure><img src="image_37.png" /></figure>
# Git workflow

<figure><img src="image_38.png" /><figcaption>Diego C. Martin</figcaption></figure>
Two case studies

1. **project "light"**: a few small data files (most in plain text);
laptop; classical models; available implementations for
interpreted languages

► ok to re-run the whole analysis from time to time

► write notebooks in text file in Rmd format and use RStudio

► example: github.com/timflutre/tuto-reproducible-research
Two case studies

1. **project "light"**: a few small data files (most in plain text);
laptop; classical models; available implementations for
interpreted languages

► ok to re-run the whole analysis from time to time

► write notebooks in text file in Rmd format and use RStudio

► example: github.com/timflutre/tuto-reproducible-research

2. **project "heavy"**: many large files (notably in binary formats);
cluster; possibly new models and implementations

► can't re-run the whole analysis because of intensive computations

► write notebooks as above, but develop also “real” software
Example of a "heavy" project

Usually, (very) large data are kept on a computer cluster, in a
shared directory, e.g., /bigdata, itself organized in several
subdirectories:

► external_public/, external_private/, internal/
Example of a "heavy" project

Usually, (very) large data are kept on a computer cluster, in a
shared directory, e.g., /bigdata, itself organized in several
subdirectories:

► external_public/, external_private/, internal/

1. create a git repository as before

2. clone it into your $HOME on the cluster

3. do NOT copy the large data from /bigdata into
   `~/work/project-heavy/data/`

4. instead make a symbolic link

1. `ln -s /bigdata ~/work/project-heavy/data/`
Scientific workflows

Software supporting scientists in developing, running, and monitoring chains of data analysis programs (Cohen-Boulakia et al, 2017):

* open standard: https://www.commonwl.org/

* software:

* GNU: Make (example), GWL

* common in genomics: NextFlow, SnakeMake

* ex. from plant phenomics: OpenAlea
# State-of-the art for dealing with code dependencies

Guix: a *purely functional* package management tool

* support for transactional package upgrade and rollback, per-user installation, and garbage collection of packages

* maximize *build reproducibility* (bit-by-bit identical programs)

* linked to *Software Heritage*

For scientists:

* high-performance computing: GuixHPC

* mailing list: guix-science
# Are you teaching?

Example in **statistics** from Matthew Stephens: class notes
versioned with git and hosted on GitHub

<figure><img src="image_39.png" /></figure>

stephens999 / stat302

<figure><img src="image_40.png" /></figure>

Watch v 6

6

1

notes from my 302 class on Bayesian methods and principles of statistics

<figure><img src="image_41.png" /></figure>

37 commits

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1 branch

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0 releases

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1 contributor

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branch: master v

stat302 / +

<figure><img src="image_47.png" /></figure>

added notes8.pdf

<figure><img src="image_48.png" /></figure>

stephens999 authored on 2 Jun

latest commit 3dea4f8291

<figure><img src="image_49.png" /></figure>
# Are you teaching? Cont'd

Example in **population genetics** from Graham Coop: class notes versioned with git and hosted on GitHub

<figure><img src="image_50.png" /></figure>

cooplab / popgen-notes

<figure><img src="image_51.png" /></figure>

Watch

6

## Population genetics notes

<figure><img src="image_52.png" /></figure>

61 commits

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2 branches

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0 releases

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3 contributors

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branch: master

popgen-notes / +

<figure><img src="image_58.png" /></figure>

Vince pointed out should be /2

<figure><img src="image_59.png" /></figure>

cooplab authored 10 days ago

latest commit d3069b1179

<figure><img src="image_60.png" /></figure>
Are you writing an article?

Example in **plant biology** from Rubén Rellán Álvarez: article (text, figures) and software versioned with git and hosted on GitHub

<figure><img src="image_61.png" /></figure>

rr-lab / glo_root

<figure><img src="image_62.png" /></figure>

Watch

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3

Growth and Luminescence Observatory for Roots (GLO-Roots) http://www.rrlab.org/glo_root

<figure><img src="image_64.png" /></figure>

116 commits

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3 branches

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2 releases

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2 contributors

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branch: master

glo_root / +

bump ...

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reliant authored on 7 May

latest commit 1489af658

<figure><img src="image_70.png" /></figure>
# Are you writing a book?

Example in **mathematics** from the "Homotopy Type Theory" group which started at Princeton (IAS) and now writes a book versioned with git and hosted on GitHub

<figure><img src="image_71.png" /></figure>

HoTT / book

<figure><img src="image_72.png" /></figure>

Watch

102

<figure><img src="image_73.png" /></figure>

A textbook on informal homotopy type theory

<figure><img src="image_74.png" /></figure>

3,200 commits

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1 branch

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2 releases

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54 contributors

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branch: master

book / +

<figure><img src="image_80.png" /></figure>

Merge pull request #742 from mikeshulman/lax ...

<figure><img src="image_81.png" /></figure>

andreijbauer authored on 6 Nov

latest commit 1f660b8717
Whatever the exact tools you are using, it is the spirit that is
important!
Whatever the exact tools you are using, it is the spirit that is important!

* How well does your behavior favor others to understand and build on your work?

* When and how will you start?