Jupyter and Stencila Pawlik Aleksandra

An example of a Jupyter notebook converted into a JATS document for editing in Stencila.

 Introduction

Jupyter notebooks (1–3) are one of the most popular platforms for doing reproducible research. Stencila supports importing of Jupyter Notebook .ipynb files. This allows you to work with collegues to refine a document for final publication while still retaining the code cells, and thus reprodubility of your the work. In the future we also plan to support exporting to .ipynb files.

 Markdown cells

Most standard Markdown should be supported by the importer including inline code, headings etc (although the Stencila user interface do not currently support rendering of some elements e.g. math and lists).

 Code cells

Code cells in notebooks are imported without loss. Stencila’s user interface currently differs from Jupyter in that code cells are executed on update while you are typing. This produces a very reactive user experience but is inappropriate for more compute intensive, longer running code cells. We are currently working on improving this to allowing users to decide to execute cells explicitly (e.g. using Ctrl+Enter).

import sys import time 'Hello this is Python %s.%s and it is %s' % (sys.version_info[0], sys.version_info[1], time.strftime('%c')) {}

Stencila also support Jupyter code cells that produce plots. The cell below produces a simple plot based on the example from the Matplotlib website. Try changing the code below (for example, the variable N).

import numpy as np import matplotlib.pyplot as plt N = 50 N = min(N, 1000) # Prevent generation of too many numbers :) x = np.random.rand(N) y = np.random.rand(N) colors = np.random.rand(N) area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii plt.scatter(x, y, s=area, c=colors, alpha=0.5) plt.show() {}

We are currently working on supporting Jupyter’s magic commands in Stencila via a bridge to Jupyter kernels.

 Metadata

To add some metadata about the document (such as authors, title, abstract and so on), In Jupyter, select Edit -> Edit Notebook metadata from the top menu. Add the title and abstract as JSON strings and authors and organisations metadata as JSON arrays. Author affiliation identifiers (like university-of-earth below) must be unique and preferably use only lowercase characters and no spaces.

For example,

"authors": [ { "given-names": "Your first name goes here", "surname": "Your last name goes here", "email": "your.email@your-organisation", "corresponding": "yes / no", "affiliation": "university-of-earth" } ], "organisations": [ { "university-of-earth": { "institution": "Your organisation name", "city": "Your city", "country": "Your country" } ], "title": "Your title goes here", "abstract": "This is a paper about lots of different interesting things", Citations and references

Stencila supports Pandoc style citations and reference lists within Jupyter notebook Markdown cells. Add a bibliography entry to the notebook’s metadata which points to a file containing your list of references e.g.

"bibliography": "my-bibliography.bibtex"

Then, within Markdown cells, you can insert citations inside square brackets and separated by semicolons. Each citation is represented using the @ symbol followed by the citation identifier from the bibliography database e.g.

[@perez2015project; @kluyver2016jupyter]

The cite2c Jupyter extension allows for easier, “cite-while-you-write” insertion of citations from a Zotero library. We’re hoping to support conversion of cite2cstyle citations/references in the future.

 Perez Fernando Granger Brian E Project jupyter: Computational narratives as the engine of collaborative data science Retrieved September 2015 11 207 Kluyver Thomas Ragan-Kelley Benjamin Pérez Fernando Granger Brian E Bussonnier Matthias Frederic Jonathan Kelley Kyle Hamrick Jessica B Grout Jason Corlay Sylvain Others Jupyter notebooks-a publishing format for reproducible computational workflows. ELPUB 2016 87 Ragan-Kelley M Perez F Granger B Kluyver T Ivanov P Frederic J Bussonnier M The jupyter/ipython architecture: A unified view of computational research, from interactive exploration to communication and publication. AGU Fall Meeting Abstracts 2014