Archives Unleashed Cloud: Jupyter Notebooks

Jupyter notebooks to assist in creating additional analysis and visualizations of Archives Unleashed Cloud derivatives.

Requirements

Anaconda Distribution is very helpful here.

• Python 3.7+
• Jupyter Notebook (1.0.0)
• matplotlib (3.0.2)
• numpy (1.15.1)
• pandas (0.23.4)
• networkx (2.2)
• nltk (3.4)

Usage

We suggest using Docker, or Anaconda Distribution.

Docker Hub

docker run --rm -it -p 8888:8888 archivesunleashed/auk-notebooks

Docker Locally

git clone https://github.com/archivesunleashed/auk-notebooks.git
cd auk-notebooks
docker build -t auk-notebook .
docker run --rm -it -p 8888:8888 auk-notebook

This repository comes with sample data, you can swap out the sample data with your own Archives Unleashed Cloud data.

docker run --rm -it -p 8888:8888 -v "/path/to/own/data:/home/jovyan/data" auk-notebook

You must grant the within-container notebook user or group (NB_UID or NB_GID) write access to the host directory (e.g., sudo chown 1000 /some/host/folder/for/work).

This repository also uses the Jupyter Docker Stacks, which provide a lot of helpful options to take advantage of.

Local (Anaconda)

git clone https://github.com/archivesunleashed/auk-notebooks.git
cd auk-notebooks
jupyter notebook

License

This application is available as open source under the terms of the Apache License, Version 2.0.

Resources

The example dataset in the data directory was created with the Archives Unleashed Cloud, and is drawn from the B.C. Teachers' Labour Dispute (2014), collected by the University of Victoria Libraries. We are grateful that they've allowed us to use this material. The full-text derivative file is a random sample (37,000 lines) of the complete file because of GitHub file size limitations.

If you use this material, please cite it along the following lines:

• Archives Unleashed Project. (2018). Archives Unleashed Toolkit (Version 0.17.0). Apache License, Version 2.0.
• University of Victoria Libraries, B.C. Teachers' Labour Dispute (2014), Archive-It Collection 4867, https://archive-it.org/collections/4867.

Acknowledgments

This work is primarily supported by the Andrew W. Mellon Foundation. Any opinions, findings, and conclusions or recommendations expressed are those of the researchers and do not necessarily reflect the views of the sponsors.