This page provides additional background material for the Linked Art questionnaire.
Code Notebooks webinar presentations
The AHRC-funded Linked Art II project and Digital Scholarship at Oxford held a webinar on Tuesday 3 May 2022. In this webinar, Research Software Engineer Tanya Gray guided attendees through a practical exploration of transforming, reconciling, and visualising Linked Art, using real-world data from museums and galleries worldwide. These were be demonstrated using ‘code notebooks’ developed during the Linked Art II project and implemented in Jupyter. The notebooks provide step-by-step illustration and explanation, and can provide a foundation for further customisation.
The slides from the webinar are available:
Linked Art workshop
Linked Art ran a 2 hour workshop at the CIDOC 2020 conference providing a wealth of background motivation and detail, a recording of which can be found below.
The workshop included: - An overview of the Linked Art initiative and community (00:00) - A technical introduction to the Linked Art profile and overview of the core principles (from 00:14) (also available separately in the next section) - The Van Gogh Worldwide project and its use of the Linked Art model (from 00:34) Followed by audience Q&A - A live encoding of data from the Rijksmuseum into Linked Art (from 01:35)
Video length is 1 hour 53 minutes.
Linked Art Profile - overview
A short (20 minute) technical introduction to the Linked Art profile:
Video length is 19 minutes.
Linked Art is a targeted and focussed realisation of Linked Data. This video gives a quick 12 minute introduction to what Linked Data is.
Video length is 12 minutes.
JSON-LD is serialisation format for Linked Data that is used to represent descriptions of artworks as Linked Art. This video gives a quick 13 minute introduction to JSON-LD:
Video length is 13 minutes.
For those new to Jupyter Notebooks, we recommend you watch this short 7 minute introduction:
Video length is 7 minutes.
Linked Art Jupyter Code Notebooks
For convenience, here is an annotated list of all the code notebooks from Section 2 of the questionnaire.
The different types of Jupyter notebooks: - Transform - transformations using real-world collections data - Reconcile - reconciliation of collections data with authoritative data on geographical place names - Visualise - visualisation using Linked Art JSON-LD
|Transform||Indianapolis Museum of Art||download||nbviewer|
|Transform||Philadelphia Museum of Art||download||nbviewer|
|Transform||Cleveland Museum of Art||download||nbviewer|
|Transform||Cleveland Museum of Art - simplified||download||nbviewer|
|Transform||National Gallery of Art||download||nbviewer|
|Transform||Harvard Art Museum||download||nbviewer|
|Transform||John Ruskin artworks - Transform Data||download||nbviewer|
|Reconcile||John Ruskin artworks - Reconcile place names||download||nbviewer|
|Visualise||John Ruskin artworks - Timeline||download||nbviewer|
|Visualise||John Ruskin artworks - StoryMap||download||nbviewer|