The proposal for this tutorial is a participatory demonstration of data visualisation, focusing on content exploration and analysis using interactive inputs. The term GLAM+ data leans toward a more inclusive definition of cultural heritage or GLAM data. Smaller or unconventional non-profit cultural organisations, local councils and educational institutions are also engaged in preserving, documenting, and mediating art and culture, as well as in creating and disseminating datasets.

Such GLAM+ data typically documents a series of objects, such as artworks or archival documents. As they are all described with the same properties, one can visualise them in timelines, categorical charts, etc. Participants with coding experience may bring their own dataset or sample in JSON or CSV (if you haven’t collected your data yet, you can create a demo sample to use during the course, and update your dataset source once it is ready). Participants with little or no coding experience are welcome to the tutorial as a way to read through the code and understand the process behind it. Peer-coding or partnering up to share knowledge is recommended, and there will be time to form small groups at the beginning of the tutorial.

The demonstration will begin with an introduction to Notebook environments on the Observable platform. Observable can be used as a web-hosted sandbox for playing with data and visualising it. It enables collaborative exploration, analysis, visualisation, and communication of data. Public individual and collective workspaces are free, favouring an open-source policy and generating a trove of examples to learn from or to fork. Coding itself is in Javascript, using the librairies Plot and D3.js.

The workshop will focus on a single notebook (provided) allowing all to follow step by step, with the possibility of forking it to add comments or to make some twists, such as using your own data. After a brief overview of the goal of the visualisation, we will examine each step required to produce it, including:

• getting data in JSON or CSV from an API, file link or simple upload
• exploring the content of the dataset
• cleaning or preparing it for the visualisation
• choosing the inputs and filters
• updating the output data based on the selection
• visualising it and thinking about scales, colours and interactions such as hover and clicks

We will also look at other examples on Observable, thus seeing a wide array of code and visualisations whilst exploring one in depth and detail.

Learning outcomes

• discover the Observable platform and its notebooks
• understand the main steps of interactive data visualisation
• handle typical GLAM+ data challenges
• learn to use the Plot javascript library
• take in some d3.js code examples

Target audience: all welcome. Computer scientists, digital humanists and cultural heritage workers alike, the tutorial is organised for a collective experience and sharing knowledge.

Anticipated number of participants: max ~ 15 to 20 participants

Ideal length: 3 hours

Technical requirements: participants should bring their own laptop and have WIFI access. They can create an Observable account or use their Github identifiers to log into the platform.

Instructor: Lena MK is an art historian and computer scientist, currently working as CTO of Maison MONA and as lab manager at Ouvroir Laboratory of Digital Art History and Museology, University of Montreal (UdeM). Specialising in data visualisation for cultural data, she is also a PhD candidate in art history and research-creation at UdeM and teaches data visualisation (HNU3056-6056) in the digital humanities program there.