Warning

NeuroLibre is at an alpha stage of development, and is not currently open for submissions.

🗂 Preprint repository structure

We expect to find all the submission material in a public GitHub repository that has the following structure:

.

├── binder

│ ├── requirements.txt

│ └── (data_requirement.json)

│

├── content

│ ├── _toc.yml

│ ├── _config.yml

│ ├── 01-simple_notebook.ipynb

│ └── (…)

├── paper.md

└── paper.bib

Our meta anlaysis on myelin submission which is publically available in this github repository should help you understand the layout.

Warning

If RoboNeuro does not see this file layout, it will fail to build the jupyter book build (but may be able to build the computing environment). Make sure that your file layout never change during runtime (especially if using a Dockerfile).

⏩ Quickstart: Preprint templates

To give you a head-start, we created preprint template repositories:

Preprint	Programming language	GitHub repository
Link	Python	neurolibre/template
Link	C++	neurolibre/cpp

To use them, make sure to read the following steps:

1. Choose your template from the list below and create a new repository into your (or to an organization) account.
2. Follow the instructions in the README file.

The following section provides further detail about the structure of a NeuroLibre preprint repository.

1. 📁 The binder folder

1.1 ⚙️ Runtime

1.1.1 Preprint-specific runtime dependencies

The execution runtime can be based on any of the (non-proprietary) programming languages supported by Jupyter. NeuroLibre looks at the binder folder to find some configuration files such as a requirements.txt (Python), R.install (R), Project.toml (Julia) or a Dockerfile.

See also

The full list of supported configuration files is available here.

1.1.2 Environment configuration for NeuroLibre

You should try to make your environment clean and concize, that is why the prefered configuration file for NeuroLibre are the requirements.txt.

It should be small (to keep environment building and loading as short as possible), and versionnized (so your environment is fully reproducible, and cache-able).

For example this requirement is bad because it has lot of unnecessary dependencies:

numpy
scipy
jupyter
matplotlib
Pillow
scikit-learn
tensorflow

On the other hand, this one is concise, reproducible and will take much less time to build:

scikit-learn==0.16.1
tensorflow==2.4.0

Warning

Starting from pip 20.3, the package resolver changed its behaviour to reduce inconsistencies in software versions. As a consequence and if your submission has lot of interdependent dependencies, your build may take a while. This is typically the case if you see messages like this during the build:

INFO: pip is looking at multiple versions of linkify-it-py to determine which version is compatible with other requirements. This could take a while.

Warning

Make sure that your whole environment is not too big (<1GB of installed dependencies), and installation is fast (<10min). Large environments increase the binder spawn time, impact your computing performance, and takes a lot of space on our servers.

Tip

If your binder build fails with timeout errors, this is because your environment is too complex and slow to build. But thanks to Docker internal caching mechanism, you can still re-try to submit the same repository so it catch-up the build.

Best practices when using Dockerfiles

While Neurolibre can build a Dockerfile environment, we don’t recommend it as this can be a source of lot of erros during build. If you don’t have choice, please make sure to follow these specific instructions:

1. We recommend that you use our base image to help you build your Dockerfile for Neurolibre:

FROM neurolibre/book:latest
...

2. Using a Dockerfile will tend to increase the size and complexity of your environment. Make sure to have layers (RUN command) that do not exceed 1GB to help the build and push process.
3. Keep the directory layout the same as your github repository. Modifying this layout in the Dockerfile is a high source of RoboNeuro build errors. For example, you should not:

RUN git clone bad_layout && cd bad_layout
WORKDIR bad_layout

4. DO NOT install and download data into the docker image, check the data section for that.

See also

Read the Dockerfile instructions for binderhub for more information.

1.1.3 NeuroLibre dependencies

Our test server creates a virtual environment in which your content is re-executed to build a Jupyter Book. To enable this, we need some Python packages.

If you are using configuration files, we need latest version of jupyter-book in a requirements.txt file:

jupyter-book
jupytext
(if applicable) repo2data

1.2 💽 Data

NeuroLibre offers generous data storage and caching to supercharge your preprint. If your executable content consumes input data, you need to read this section carefully. Indeed, we don’t allow data download other than through our method.

To download data, NeuroLibre looks for a repo2data configuration file: data_requirement.json. This file must point to a publicly available dataset, so it can be available during preprint runtime.

See also

Repo2data can download data from several resources including OSF, datalad, zenodo or aws. For details, please visit the documentation.

Example preprint templates using repo2data for caching data on NeuroLibre servers:

Download Resource	GitHub repository
Nilearn	neurolibre/repo2data-nilearn
OSF	neurolibre/repo2data-osf

Warning

RoboNeuro may fail downloading relatively large datasets (exceeding 5GB) or if the data server is to slow. This is because of some limitations, independent from us, in our software stack. If you face some problems when downloading your data, please create an issue in your github repository so a Neurolibre admin can check it.

Help RoboNeuro find your data during book build

Repo2Data downloads your data to a folder named data, which is created at the base of your repository.

Note

We suggest using repo2data locally before you request a RoboNeuro preview service. Matching this data loading convention will increase your chances of having a successful NeuroLibre preprint build, and will make your data dependency agnostic to computer.

Assuming you are running a notebook on NeuroLibre and have a requirement file as:

{ "src": "download_my_brain(data_dir=_dst);",
"dataLayout": "neurolibre",
"projectName": "PROJECT_NAME"}

• A code cell in a content/my_notebook.ipynb would access data by:

  import nibabel as nib
  import os
  img = nib.load(os.path.join('..', 'data', 'PROJECT_NAME', 'my_brain.nii.gz'))
  

• A code cell in a content/01/my_01_notebook.ipynb would access data by:

  import nibabel as nib
  img = nib.load(os.path.join('..', '..', 'data', 'PROJECT_NAME', 'my_brain.nii.gz')) # In this case, 2 upper directories
  

If the data directories in your code cells are not following this convention, RoboNeuro will fail to re-execute your notebooks and interrupt the book build.

The best way to access data on Neurolibre servers is using the repo2data python api. This way all the data paths will be automatically recognized. For example if you have a notebook in content/my_notebook.ipynb:

from repo2data.repo2data import Repo2Data
# install the data if running locally, or points to cached data if running on neurolibre
data_req_path = os.path.join("..", "binder", "data_requirement.json")
# download data
repo2data = Repo2Data(data_req_path)
data_path = repo2data.install()

1. 📁 The content folder

2.1 Executable & narrative content

NeuroLibre accepts the following file types to create a preprint that is beyond PDF:

• ✅ Jupyter Notebooks,
• ✅ MyST formatted markdown.
• ✅ Plain text markdown files.
• ✅ A mixture of all above

Warning

❌ We don’t accept markdown files with narrative content only, that is not really beyond PDF :)

Note

✅ You can organize your content in sub-folders.

2.1.1 Writing narrative content

Jupyter Book provides you with an arsenal of authoring tools to include citations, equations, figures, special content blocks and more into your notebooks or markdown files.

See also

Please visit the corresponding Jupyter Book documentation page for guidelines.

2.1.2 Writing executable content

Based on the powerful Jupyter ecosystem, NeuroLibre preprints allow you to interleave computational material with your narrative. You can add some directives and metadata to your code cell blocks for Jupyter Book to determine the format and behavior of the outputs, such as interactive data visualization.

See also

Please visit the corresponding Jupyter Book documentation page for guidelines.

There are two mandatory files that we look for in the content folder: _config.yml and _toc.yml. These files help RoboNeuro structure your book and configure some settings.

2.2 ⑆ Table of contents

The _toc.yml file determines the structure of your NeuroLibre preprint. It is a simple configuration file specifying a table of content from all the executable & narrative content found in the content folder (and in subfolders).

See also

The complete reference for the _toc.yml can be found here.

2.3 ⚡︎ Book configuration

The _config.yml file governs all the configuration options for your Jupyter Book formatted preprint, such as adding a logo, enable/disable interactive buttons or control notebook execution and caching settings. Few important points:

• Please ensure that the title and the list of authors matches those specified in the paper.md.

title:  "NeuroLibre preprint template"  # Add your title
author: John Doe, Jane Doe  # Add author names

• Please ensure that the repository address is accurate.

repository:
  url: https://github.com/username/reponame  # The URL to your repository

• By default NeuroLibre force the notebook execution, still make sure you have it enabled.

  execute:
    execute_notebooks: force
  

See also

The complete reference for the _config.yml can be found here.

1. 📝 Static summary

The front matter of paper.md is used to collect meta-information about your preprint:

---
title: 'White matter integrity of developing brain in everlasting childhood'
tags:
  - Tag1
  - Tag2
authors:
  - name: Peter Pan
    orcid: 0000-0000-0000-0000
    affiliation: "1, 2"
  - name: Tinker Bell
    affiliation: 2
affiliations:
- name: Fairy dust research lab, Everyoung state university, Nevermind, Neverland
  index: 1
- name: Captain Hook's lantern, Pirate academy, Nevermind, Neverland
  index: 2
date: 08 September 1991
bibliography: paper.bib
---

The corpus of this static document is intended for a big picture summary of the preprint generated by the executable and narrative content you provided (in the content) folder. You can include citations to this document from an accompanying BibTex bibliography file paper.bib.

To check if your PDF compiles, visit RoboNeuro preprint preview page, select NeuroLibre PDF option and enter your repository address.

See also

For more information on how to format your paper, please take a look at JOSS documentation.