Team

Editorial board

IT support

Communication

Julien Chiquet (chief editor)

Stat. learning, DR INRAE
Paris-Saclay University

Pierre Neuvial

Statistique, DR CNRS
IMT Toulouse

Fra.-Dav. Collin

CS/Stats/ML, IR CNRS
IMAG, Montpellier University

Chloé Azencott

Machine Learning
CR MinesParisTech

Nelle Varoquaux

Machine learning, CR CNRS
Grenoble Alpes University

Marie-Pierre Étienne

Statistics, MCF
Institut Agro Rennes-Angers

Aymeric Stamm

Stats/dev, IR
Jean Leray, Univ. Nantes

Journal setup

Official launch at the end of 2021

“Economical” model

  • A few tenacious people…
  • Free/Open-source community tools (Pandoc, Quarto, Git forge)
  • Institutional support (AgroParisTech, CNRS, INRAE, INRIA, SFdS)

AAP Science Ouverte 🤩

Motivations


Origin (~ 2020s)

Mission carried out at the French statistical society (SFdS)


Assessment

😔 Multiplication of “traditional” journals, often predatory journal…

😱 ↘ of publication quality and time dedicated to each article (on author or reviewer sides) [1]

😔 Not enough valorization of “negative” results or source codes/case studies

😱 Issue with scientific reproducibility (analyses, experiments) [2–7]


Point of view

  • Need for renewal regarding scientific research implementation
  • Need for higher standard regarding result publications

Philosophy


Scientific perimeter

Promote contributions in stat/ML that provide insight into which models/methods are appropriate to address a scientific question


Open access

  • “Diamond” open access (free to publish and free to read, possible to reuse)
  • Content published under CC-BY license (attribution, share, adapt)
  • Reviews and discussions available after acceptance for publication (anonymous reviews)


Reproducible

Numerical reproducibility is a necessary condition (Source code and data should be available)

What is reproducible research?

Fundamentally, it provides three things:

Tools to reproduce the results (that’s like cooking)

A “recipe” to reproduce the results (still like cooking)

A path to understanding the results and the process that led to them (unlike cooking…1)

Why reproducible research? [8–10]


Why reproducing scientific results?

  • To strengthen their credibility
  • To check for errors (everyone makes error at some point!!!)
  • To build new research upon them (science is incremental)

Issues?

  • Reproduce numerical scientific results is often difficult (technology/environment evolution, source code/environment configuration/software partially available or not available)
  • Waste of time and resources to reproduce existing non-reproducible results

Reproducible research?

  • For others but also for your future self
  • Improve result credibility
  • Facilitate future research works

Functioning of reproducibility at Computo

Writing system

Notebook and literate programming
text (markdown) + math (\(\LaTeX\)) + code (Python/R/Julia), references (bib\(\TeX\))

Publication system

Environment management, Compilation, Multi-format publication (pdf, html)
Continuous integration/Continuous deployment (CI/CD)

Reviewing system

  • Anonymous exchange published after acceptance
  • Reviewer pool (you can join)
  • [Ongoing switch from Open review to PCI]

Solutions/Prototype

Reproducible article and computations

Automatic editorial reproducibility

Scientific validation

Tools for authors

Document model

Document template

Git template repository

with template notebook document + doc + pre-configured compilation and publication setup

Locally

  • Text editor/IDE (VS Code, Rstudio, NeoVim, etc.)
  • Quarto (compilation)
  • Julia / R / Python code + computations
  • git versioning system

Author point of view (1/3)


Step 0: setup a git repository for your article

Startup from a template repository (R, Python, Julia)

Tip

You can host your git repository on github and soon an any gitlab forge1.


Step 1: write your article

Let’s go, locally (same spirit as Jupyter/Rmarkdown notebooks)

Author point of view (2/3)

Step 2: configure the environment (dependencies management)

venv: use a virtual environment and generate the requirements.txt file

# requirements.txt
jupyter
matplotlib
numpy

renv: generate the renv.lock file

renv::init()
renv::install("ggplot2")
# or equivalently install.packages("ggplot2")
renv::snapshot()

Pkg: native Julia package manager (with generated Project.toml et Manifest.toml files)

add Plots
add IJulia

Configuration file versionned and used during CI compilation/publication action

Step 3: (re)production

A git push command will trigger your article compilation (including computations) and publication as a github page1

See the preconfigured .github/workflows/build.yml file for the github action configuration2

Author point of view (3/3)


Step 4: submit your article

If the CI process succeeds, both HTML and PDF versions are published on the github-page associated to the repository



Open review

https://openreview.net/group?id=Computo

Submit:

  • your article PDF (scientific content review)
  • your git repository (source code and reproducibility review)

Editor point of view

After a “traditionnal” review process, a 3 step procedure:

  1. Acceptance
  2. Pre-production
  3. Publication in Computo (with a DOI)

including

  • Copy of the author git repository to https://github.com/computorg/
  • Final version formatting
  • Review report publication
  • Registration in the journal bibliographic data base
  • Copy of the repository to Software Heritage for archiving
  • Publication of the article on the journal website

Task-list = github issue