Essential Resources¶
Annotated, Essential Resources¶
Nature’s special collection: Challenges in Irreproducible Research
Interactive notebooks: Sharing the code by Helen Shen. Nature. 2014 Nov 6;515(7525):151-2. doi: 10.1038/515151a. PMID: 25373681. This article is a couple of years old now but gives a good background on the issues and impetus for now what are Jupyter Notebooks.
How Jupyter Notebooks Will Improve Your Computational Life - a nice illustrated introduction to Jupyter Notebooks
IPython Notebook/Project Jupyter Half day Workshop
“Project Jupyter is a literate data analysis environment (similar to knitr and Shiny) that supports over 40 different programming languages, including R and Python both. It can be used to build reproducible analyses for publication, collaborate over distances on data analysis, and build interactive tutorials and homeworks around data analysis.”
A gallery of interesting IPython Notebooks, in particular see Reproducible academic publications section . Some other notebooks are highlighted here.
Installation, Configuration, and Usage - start here if you need a solution not provided by tmpnb.org or the Github/Binder combination; the site will suggest the Anaconda distribution of Python associated environment as the easiest route for installing Jupyter Notebooks. Alternatively, if you rather easily work in the cloud and not be linked to one machine, I suggest a SageMathCloud account as it is the only option I can confirm works on Upstate’s network due to campus firewall. Domino Data Lab and Wakari.io are other places that offer a place to develop Jupyter Notebooks in the cloud but last time I checked their implementation was incompatible with Upstate’s network.
Introduction to Applied Bioinformatics (or IAB) is a free, open source interactive text that introduces readers to core concepts of bioinformatics in the context of their implementation and application. - uses Jupyter notebooks and mybinder.org
Exploratory Computing with Python - a collection of notebooks helpful for learning Python and illustrative of the exploratory computing aproach
“Lots of books are written on scientific computing, but very few deal with the much more common exploratory computing (a term coined by Fernando Perez), which represents daily tasks of many scientists and engineers that try to solve problems but are not computer scientists. This set of Notebooks is written for scientists and engineers who want to use Python programming for exploratory computing, scripting, data analysis, and visualization. Python makes many of these programming tasks quick and easy and, probably most importantly, fun.”“
Freeman Lab’s MyBinder.org site - where you’ll go to point their system at your Github repository with a Jupyer Notebook to make active notebooks available online
Is mybinder 95% of the way to next-gen computational science publishing, or only 90%?
“The split that my lab has made here is to use a workflow engine (e.g. make, pydoit, or snakemake) for the compute & data intensive stuff, and then feed those intermediate results (assembly and mapping stats, quantification, etc.) into analysis notebooks. For mybinder purposes, there should be no problem saving those intermediate results into a github repo for us and everyone else to analyze and reanalyze.”
tmpnb.org or try.jupyter.org - launch active, transient Jupyter Notebooks in the cloud for basic development, see Instant Temporary IPython Notebooks