Linked Art - Transformation Exemplar

Introduction

Linked Art is a community working together to create a shared Model based on Linked Open Data to describe Art.

A number of exemplars will be published to demonstrate the processes involved in producing Linked Art JSON-LD, and also the potential applications of Linked Art, on the theme of:

Transformation - Documented transformation process - using code, documentation and possibly visualisation
Reconciliation - Documented reconciliation process - matching data with an external identifier source
Visualisation - Documented transformation of Linked Art JSON-LD to data visualisation

Linked Art exemplars

This exemplar is concerned with Transformation - the transformation process, from collections data to Linked Art JSON-LD.

Transformation Exemplar - From Collections Data to Linked Art JSON-LD

This repository contains a worked example of the transformation process for Linked Art, transforming collections data into Linked Art JSON-LD.

Aim

The aim is to demonstrate how easy it is to transform collections data to Linked Art JSON-LD.

How

The Transformation exemplar is in the form of collection of interactive documented code, as Jupyter notebooks.

How to view Jupyter Notebooks

The following options are available to view the Jupyter notebooks. Installation of Anaconda with a local download of the Jupyter Notebooks is recommended to provide the interactive, documented code features of the notebooks.

Anaconda

Anaconda is a data science workbench that can be installed locally, that included Jupyter Notebook. To view all features of the notebook, it’s recommended that you use Anaconda. Anaconda is available to download.

Binder

Binder is an online services that allows interactive Jupyter Notebooks to be viewed online. The following link will allows you to view the Jupyter Notebooks in this repository using Binder.

nbviewer

nbviewer is an online service that will render a static view of a Jupyter notebook using a URL.

GitHub

Github offers a static view of a Jupyter Notebook. Viewing the file in GitHub is simply a matter of selecting the relevant *.iynb file in the jupyter_notebooks folder

Documented Interactive Code Jupyter Notebooks

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

Notebook type	Notebook	Download	nbviewer
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	Rijksmuseum	download	nbviewer
Transform	Ashmolean 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

Availability

All of the Jupyter notebooks can be downloaded as a zip file or checked out of the Github repository:

https
GitHub client gh repo clone tgra/Linked-Art

Alternatively, they can be viewed directly on GitHub.

Community Feedback

We welcome feedback on the notebooks provided here for the Transformation exemplar. If you have a moment, please consider the following questions and send your comments to Tanya Gray at tanya.gray@humanities.ox.ac.uk. Thank you.

Thinking about your colleagues who are less familiar with Linked Art, can we do anything with this tool to make it more useful?

Does the tool make sense?

Are the workbook steps too large or too small? Are the steps of the correct granularity?

Transformation of Other Collections Data

This workbook works with EMu export data in XML - can you offer data in this format to transform?

We would also welcome collections data from other collections in other formats to transform to Linked Art JSON-LD.

Please contact Tanya Gray at tanya.gray@humanities.ox.ac.uk to discuss.

What’s next?

Further exemplars for Linked Art will be published on the themes of Reconciliation and Visualisation

Acknowledgements

This work was undertaken by the Linked Art II project at the University of Oxford (Principal Investigator: Dr. Kevin Page, Oxford e-Research Centre) funded by the UK Arts and Humanities Research Council (AHRC project reference AH/T013117/1). The project’s Research Software Engineer was Tanya Gray. We gratefully acknowledge the participation and contributions of our project partners and the wider Linked Art community.