Published July 12, 2023 | Version 1.2
Poster Open

Enabling Participatory Data Perspectives for Image Archives through a Linked Art Workflow (Poster)

  • 1. Digital Humanities Lab, University of Basel
  • 2. Centre for Digital Scholarship, University of Oxford
  • 3. Oxford e-Research Centre, University of Oxford



A collaboration between the Linked Art II project and Participatory Knowledge Practices in Analogue and Digital Image Archives (PIA) has led to a reconfigurable Python-based workflow to transform cultural heritage data, initially photographic collections, into Linked Open Usable Data (LOUD), as a foundation for varied participatory interfaces supporting scholarship and beyond.


PIA, led by the University of Basel and the Bern Academy of the Arts, aims to encourage participation from scholars and the wider public through three collections from the photographic archives of the Swiss Society for Folklore Studies (SSFS): The Atlas of Swiss Folklore, Ernst Brunner, and Kreis Family. PIA aims to create multiple interfaces reflecting diverse perspectives by deploying community-developed LOUD specifications such as the International Image Interoperability Framework (IIIF) and Linked Art, an RDF application profile (JSON-LD) based on CIDOC-CRM to describe object-based cultural heritage.

In collaboration with the University of Oxford via the Linked Art II project, PIA has transformed their cultural heritage collection data into Linked Art using templates, which encapsulate the data characteristics and cataloguing practices. 

A Linked Art API will provide an additional entry point, as a means of conveying semantically enriched events and as a benchmark against other collections leveraging this model.

Python-based Workflow

To generate Linked Art files for the combined PIA collection a data transformation workflow has been created through which PIA templates were encoded in Python for a given Linked Art API entity endpoint, currently: DigitalObject HumanMadeObject and Set. The workflow, described in a use case example with a photograph from the Ernst Brunner collection, provides a three-step software process for transforming data into Linked Art.

  1. Query: The first Python script extracts collection data from the (legacy) PIA JSON API. YAML front matters are used for script variables. The filepath for the relevant .yaml file is specified as a script argument throughout the workflow.

    For our example, the script queried data for all images looking for DigitalObject entities (one of the variables) and the object's metadata that are stored come specifically from}

  2. Map: Templates are used to map collection data to an intermediate JSON data format. The intermediate JSON data format means that the later transformation script that creates Linked Art JSON-LD does not necessarily need to be modified if a new data source is introduced.
  3. Transform: The intermediate JSON data format is transformed to Linked Art with Python functions that define ‘patterns’ (for example classified_as) for representing different aspects of photographic collection data as Linked Art.

Future Work

The SSFS will migrate its database into the DaSCH Service Platform (DSP) and amend their data model which will affect PIA, requiring an upgrade of its infrastructure and APIs. The workflow for creating Linked Art representations will have to be reconfigured and repurposed with different data sources.

After the migration of the SSFS database, the PIA team will investigate remaining issues in the GitHub repository regarding the correct mapping of Linked Art entities and attribution of IDs. The team will work to ensure that the appropriate Linked Art modelling is achieved through the workflow.


The reconfigurable Python-based workflow is able to transform cultural heritage data, initially photographic collections, into LOUD, as a foundation for varied participatory interfaces supporting scholarship and beyond. The adaptability and extensibility of the workflow allows for potential future transformations of data from other collections to Linked Art.


This work has been supported by the PIA research project which is funded by the Swiss National Science Foundation (SNSF) as part of the Sinergia funding scheme (contract no. CRSII5_193788) and by the Linked Art II project at the University of Oxford (Principal Investigator: Dr. Kevin R. Page, Oxford e-Research Centre) funded by the UK Arts and Humanities Research Council (AHRC project reference AH/T013117/1). 


This poster is related to the extended abstract that is published as part of the DH2023 Book of Abstracts. All versions are identical, only the malfunctioning QR code was replaced.



Additional details

Related works

Is source of
Book: 10.5281/zenodo.7961822 (DOI)
Is supplemented by
Conference paper: 10.5451/unibas-ep95099 (DOI)
Conference paper: 10.5281/zenodo.8107399 (DOI)


Linked Art II: Developing Community, Practice, and Scholarship AH/T013117/1
UK Research and Innovation
Participatory Knowledge Practices in Analogue and Digital Image Archives CRSII5_193788
Swiss National Science Foundation


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