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Trove lists and tags

Current version: v1.1.0

Trove lists and tags are created by Trove users to organise and describe resources. The details of public lists and tags are available through the Trove API. The notebooks in this repository demonstrate how to harvest and analyse list and tag data.

See below for information on running these notebooks in a live computing environment. Or just take them for a spin using Binder.

ARDC Binder Binder

Notebooks

Lists

Convert a Trove list into a CollectionBuilder exhibition

This notebook converts Trove lists into a series of files that can be uploaded to a CollectionBuilder-GH repository to create an instant exhibition. See the CollectionBuilder site for more information on how CollectionBuilder works and what it can do.

Convert a Trove list into a CSV file

This notebook converts Trove lists into CSV files (spreadsheets). Separate CSV files are created for newspaper articles and works from Trove's other zones. You can also save the OCRd text, a PDF, and an image of each newspaper article.

Harvest summary data from Trove lists

Use the Trove API to harvest data about all public lists, then extract some summary data and explore a few different techniques to analyse the complete dataset.

Tags

Harvest public tags from Trove zones

This notebook harvests all the public tags that users have added to records in Trove. However, tags are being added all the time, so by the time you've finished harvesting, the dataset will probably be out of date.

Analyse public tags added to Trove

This notebook loads the complete dataset of Trove tags and explores some ways of analysing and visualising the tag data.

Datasets

Trove lists metadata

Harvested: 29 May 2024. CSV formatted file containing a complete harvest of metadata describing user-created Trove lists.

Trove public tags

Harvested: 6 June 2024. CSV formatted file containing a complete harvest of public tags added to Trove resources.

Trove tag counts

Harvested: 6 June 2024. CSV formatted file containing the total number of times each tag in Trove has been applied to resources.

Run these notebooks

There are a number of different ways to use these notebooks. Binder is quickest and easiest, but it doesn't save your data. I've listed the options below from easiest to most complicated (requiring more technical knowledge).

Using ARDC Binder

Launch on ARDC Binder

Click on the button above to launch the notebooks in this repository using the ARDC Binder service. This is a free service available to researchers in Australian universities. You'll be asked to log in with your university credentials. Note that sessions will close if you stop using the notebooks, and no data will be preserved. Make sure you download any changed notebooks or harvested data that you want to save.

See Using ARDC Binder for more details.

Using Binder

Launch on Binder

Click on the button above to launch the notebooks in this repository using the Binder service (it might take a little while to load). This is a free service, but note that sessions will close if you stop using the notebooks, and no data will be saved. Make sure you download any changed notebooks or harvested data that you want to save.

See Using Binder for more details.

Using Reclaim Cloud

Launch on Reclaim Cloud

Reclaim Cloud is a paid hosting service, aimed particularly at supported digital scholarship in hte humanities. Unlike Binder, the environments you create on Reclaim Cloud will save your data – even if you switch them off! To run this repository on Reclaim Cloud for the first time:

  • Create a Reclaim Cloud account and log in.
  • Click on the button above to start the installation process.
  • A dialogue box will ask you to set a password, this is used to limit access to your Jupyter installation.
  • Sit back and wait for the installation to complete!
  • Once the installation is finished click on the 'Open in Browser' button of your newly created environment (note that you might need to wait a few minutes before everything is ready).

See Using Reclaim Cloud for more details.

Running in a container on your own computer

GLAM Workbench repositories are stored as pre-built container images on quay.io. You can run these containers on your own computer to set up a virtual machine with everything you need to use the notebooks. This is free, but requires more technical knowledge – you'll have to install Podman on your computer, and be able to use the command line.

  • Install Podman.
  • In a terminal, run the following command:
    podman run --rm -p 8888:8888 quay.io/glamworkbench/trove-lists jupyter lab --ip=0.0.0.0 --port=8888 --ServerApp.token="" --LabApp.default_url="/lab/tree/index.ipynb"
  • It will take a while to download and configure the container image. Once it's ready you'll see a message saying that Jupyter Notebook is running.
  • Point your web browser to http://127.0.0.1:8888
  • When you've finished, download any files or data you want to keep from Jupyter Lab, and enter Ctrl+C int the terminal.

See Running in a container on your own computer for more details.

Setting up on your own computer

If you know your way around the command line and are comfortable installing software, you might want to set up your own computer to run these notebooks. You'll need to have recent versions of Python and Git installed. I use pyenv, pyenv-virtualenv, and pip-tools to create and manage Python versions and environments.

In a terminal:

  • Create a Python virtual environment (Python >= 3.10 should be ok): pyenv virtualenv 3.10.12 trove-lists
  • Activate the virtual environment: pyenv local trove-lists
  • Use git clone to create a local version of the GLAM Workbench repository: git clone https://github.com/GLAM-Workbench/trove-lists.git
  • Use cd to move into the newly-cloned folder: cd trove-lists
  • Run pip install pip-tools to install pip-tools.
  • Run pip-sync requirements.txt dev-requirements.txt to install the required Python packages.
  • Start Jupyter with jupyter lab – a browser window should open automatically. If not, copy and paste the url from the command line to your web browser.
  • To shut down your Jupyter Lab session enter Ctrl+C in the terminal.

See Using Python on your own computer for more details.

Contributors

Cite as

Sherratt, Tim. (2024). GLAM-Workbench/trove-lists (version v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.11504218