Introduction

The Analytics Lab is technology that runs a computational environment in the cloud.  You can access it through your web browser and run code in our environment, rather than on your local machine.  The technology we are using at the moment is BinderHub running Jupyter Notebooks (review a list of some of the technology behind Constellate).   You can get into our Analytics Lab by selecting “Analyze” from any of the datasets you create or from any of our text analysis lessons.  It will take between 30 seconds and 2 minutes to spin up your personal environment - be patient when your screen looks like this, it's coming!

Please note that this space is temporary and ephemeral.  If you do not use it for 10 minutes, it will be shut down (just start another one up if this happens to you, by clicking “Analyze” again or accessing through our lessons).  If you edit the notebooks and want to keep your changes, be sure to export the files to your local computer. If you want to substantively edit the Notebooks we provide, we recommend you fork our Git repository.

Jupyter Notebooks

We have built our tutorials and analysis code using Python in Jupyter Notebooks.  Jupyter Notebooks are each an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text.   If you are brand new to interacting with Jupyter Notebooks, we recommend you work through our Getting Started with Jupyter Notebooks lesson.

Python

Python is the highly-flexible, easy-to learn programming language that we use in our tutorial and analysis Jupyter Notebook. It is widely-used in the digital humanities and data science.  Trust us, it is very easy to learn -- and if you are new to it, after taking the Getting Started with Jupyter Notebooks lesson, go ahead and take our Python Basics I lesson.

R

R is another programming language used for statistical computing and graphics.  It is possible to run R in our Analytics Lab and if you have R skills and would like to work with us on developing some tutorials and analytics scripts in R, please contact us at tdm@ithaka.org.

Running My Own Git Repository

If your institution is a participant in our text analytics service, you can load and run your Git repository in our Analytics Lab.  Open our Binder, put your GitHub information into the form and launch.

Saving Notebooks

Each Analytics Lab we spin up is temporary and ephemeral.  If you stop working in it for 10 minutes, it will shut down.  This lets us not require users to log-in and helps us contain costs.  It does mean that if you edit any of the Notebooks, you need to export them to your local computer to save them.

If you want to substantively edit the Notebooks we provide, we recommend you fork our Git repository.

Working with Your Own Content in the Analytics Lab

If you have content that you would like to interact with in our Analytics Lab -- perhaps you want to combine existing data with one of our datasets, you are more than welcome to upload it.  Please remember that each instance of the Lab is temporary and ephemeral.