Trove API introduction

With the cancellation of my Trove API keys by the National Library of Australia, I've made the difficult decision to stop work on Trove and archive all related code repositories.

The Trove sections of the GLAM Workbench will remain online, but they won't be updated. Everything here is openly-licensed, so feel free to take what’s useful and develop it further yourself.

Given the fact that the NLA is willing to change the API terms of use to restrict access without any consultation, provides no transparency around acceptable use of full text content, and is willing to cancel API keys without warning, I can no longer recommend Trove as a reliable source for digital research.

Trove provides access to much of its data through APIs (Application Programming Interfaces). These include:

the main Trove API delivers data from aggregated collections, digitised resources (including newspapers), and user activity (such as lists and tags)
OAI-PMH and SRU APIs deliver data from the Trove People and organisations zone
Memento and CDX APIs deliver data from the Australian Web Archive

The notebooks in this section provide examples of using the main Trove API to harvest data and analyse the contents of Trove resources. You'll find more examples throughout the Trove sections of the GLAM Workbench. For help building your own Trove API requests, try the Trove API Console.

Before you can use the API you need to obtain a key — it's free and quick. Just follow these instructions.

What's an API?¶

An API is an Application Programming Interface. It's a set of predefined requests and responses that enables computer programs talk to each other.

Web APIs are generally used to deliver data. While humans can easily interpret information on a web page, computers need more help. APIs provide data in a form that computers can understand and use (we call this machine-readable data).

The Trove API works much like the Trove website. You make queries and you get back results. But instead of going through a nicely-designed web interface, requests to the API are just URLs, and the results are just structured data.

While you can just type an API request into the location box of your web browser, most of the time requests and responses will be handled by a computer script or program. APIs don't care what programming language you use as long as you structure requests in the way they expect.

In these notebooks we'll be using the programming language Python. No prior knowledge of Python is expected or required – just follow along! The examples and approaches used could be easily translated into any another programming language.

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

Tools, tips, examples¶

Your first Trove API request ¶

In this notebook we're going to learn how to send a request for information to the Trove API. API requests are just like normal urls. However, instead of sending us back a web page, they deliver data in a form that computers can understand. We can then use that data in our own programs.

Working with Trove zones ¶

Trove's zones are important in constructing API requests and interpreting the results. So let's explore them a bit.

Facets aggregate collection data in interesting and useful ways, allowing us to build pictures of the collection. This notebook shows you how to get facet data from Trove.

Useful links¶

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¶

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¶

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¶

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-api-intro 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-api-intro
Activate the virtual environment: pyenv local trove-api-intro
Use git clone to create a local version of the GLAM Workbench repository: git clone https://github.com/GLAM-Workbench/trove-api-intro.git
Use cd to move into the newly-cloned folder: cd trove-api-intro
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¶

Tim Sherratt

Cite as¶

Sherratt, Tim. (2023). trove-api-intro (version v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7545885