Share your Binder!


#1

Do you have an awesome Binder that you want to share with the community? Paste a brief description and a link below so that we can check it out! In particular, we’d love to see Binders that cover things like:

  • Educational uses
  • Reproducible science
  • Data journalism
  • Data exploration

#2

I have some stalled WIP repos where I started building up environments and demos relevant to particular subject areas here: https://github.com/psychemedia/showntell/

I’m hoping to get back on the case but can’t guarantee when…

–tony


#3

ah that’d be really cool! we’d love to set up a “highlighted binders” page so that other people can discover particularly interesting ones


#4

https://github.com/minrk/ligo-binder showcasing Gravitational Waves is (to me) the original ‘cool binder’.


#5

OpenDreamKit is collecting some examples of reproducible science here: https://opendreamkit.org/2017/11/02/use-case-publishing-reproducible-notebooks/. ATM, we have links to only two repos (both based on SageMath):

edit: this is chris silently editing this post to see if it makes it possible to remove spam listings


Spam filters too aggressive?
Spam filters too aggressive?
#6

here’s @defeo’s post from above:


#7

This is one of my first attempts at giving a workshop at work. Most of us use Stata at work and some know SAS or R too so I wanted to give a small intro to data analysis with Python and pandas. it was sooooo much easier to just provide a mybinder.org link than having everyone download python, the materials, and install the dependencies. especially for those who were only slightly interested in using python at work.

here’s the link: https://mybinder.org/v2/gh/chekos/RATS-pandas-python/master?filepath=notebooks%2F00_Intro.ipynb


#8

Here are several I have done:

  • patmatch-binder - launchable Jupyter sessions for running command line-based PatMatch in Jupyter environment provided via Binder (Perl and Python-based).

  • blast-binder - launchable Jupyter sessions for running command line-based BLAST+ in Jupyter environment provided via Binder.

  • InterMine-binder -
    Intermine Web Services available in a Jupyter environment running via the Binder service. (See the guide to getting started with using Intermine sites and Jupyter using MyBinder-served Jupyter notebooks.)

  • mcscan-binder -
    MCscan software available in a launchable Jupyter environment running via the Binder service (Python 2-based), with an example workflow and some other use examples.

  • synchro-binder - SynChro software available in a launchable Jupyter environment running via the Binder service with Quick start and some other illustrations of its use.

  • clausen_ribonucleotides binder - Analyze ribonucleotide incorporation data from Clausen et al. 2015 data using script plot_5prime_end_from_bedgraph.py.

  • bio3d-binder - launchable, working Jupyter-based environment with the Bio3D package for Macromolecular Structure Analysis running in R+Jupyter (RStudio is an option there, too) with some examples (R-based).

  • cl structurework demo-binder - launchable, working Jupyter-based environment that has a collection of demonstrations of useful resources on command line for manipulating structure files

  • circos-binder - Circos software available in a launchable Jupyter environment running via the Binder service with tutorials illustrating use (WIP <----Possible Binderizing sprint material?)(Perl and Python-based).


#9

This is a Binder that uses nbsessionproxy to give people access to a web server that is running inside the binder.

Launch: https://mybinder.org/v2/gh/betatim/openrefineder/master?urlpath=%2Fopenrefine
Repository: https://github.com/betatim/openrefineder/

OpenRefine is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data. I learnt about it in the context of Data Carpentry.


#10

We hosted a workshop: “Structural Bioinformatics Training Workshop & Hackathon 2018 - Application of Big Data Technology and 3D Visualization” at UC San Diego. All notebooks are runnable through binder.

The notebooks covers:

  • 3D Visualization of protein structures
  • Introduction to PySpark
  • Dataming 3D protein structures with PySpark
  • Application of machine learning to protein structures

#11

This repository is a supplement to the “Ten Simple Rules for Reproducible Research in Jupyter Notebook” paper.

The example notebooks can be launched in Jupyter Notebook and Jupyter Lab using binder and demonstrate reproducible workflows: