This is a collection of Jupyter notebooks to help you explore and use data from GLAM institutions. It's aimed at researchers in the humanities, but will include examples and tutorials of more general interest.
Over the past decade I've created and shared a wide variety of digital tools, examples, tutorials, and datasets. Some like QueryPic and TroveHarvester are fairly polished and well documented. Others are just fragments of code. All of them are intended to support research into our rich cultural collections.
But even though something like the TroveHarvester is pretty easy to use, it does require a bit of set-up, and I've been very aware that this can be a barrier to people starting their explorations. I created the dhistory site many years ago to provide the foundation for a digital workbench, but I couldn't quite achieve what I wanted — tools that were easy to use and required minimal setup, but also tools that exposed their own workings, that inspired novice users to question and to tinker.
So here we are. My plan is to use Jupyter, GitHub, and Binder to bring together all those tools, examples, tutorials, and datasets in a way that supports people's explorations through digital GLAM collections. I'm really excited, for example, that I can create a notebook that provides a deconstructed (or perhaps see-through) version of QueryPic — that enables you to build, step by step, the same sort of visualisations, while learning about how it works. And at the end you can download the results as a CSV for further analysis. I love the way that Jupyter notebooks combine learning with real, live, digital tools and methods. You don't have to read a tutorial then go away and try to follow the instructions on your own. It's all together. Live code. Real research. Active learning.
Like most of my projects this is in itself an experiment. I'm still learning what's possible and what works. But I'm hopeful.
If you think this project is worthwhile, you might like to support me on Patreon.