Project Context / Objective:
Heritage science laboratories often face significant challenges in managing, sharing, and preserving their research data, which is frequently stored in disparate systems with little standardisation. To address this, Arches for Science (AfS) was developed as an open-source, Arches-based application for the management of heritage science data. The platform supports technical examinations of art, including material sampling, image annotation, instrument data visualisation, and comparison workflows — building on the robust, standards-based capabilities of the Arches platform to provide heritage scientists with a flexible, interoperable solution for data management and collaboration.
Takin.Solutions’ Role / Contributions:
Takin.Solutions contributed to Arches for Science by providing semantic data modelling and documentation expertise, ensuring that AfS resource models accurately represented heritage science workflows, data types, and research practices — and that they were structured for interoperability, usability, and future extension.
Key Activities:
- Developed and refined semantic resource models representing Physical Thing, Project, Collection or Set, Digital Resources, Person, Group, Instrument, Observation, Modification, Sampling Activity, Textual Work, and Place.
- Aligned all data models with Linked.Art and CIDOC CRM to ensure semantic consistency across heritage and scientific data domains.
- Produced comprehensive documentation to support implementation, maintenance, and future expansion of the models.
- Provided guidance on best practices for structuring workflows, annotations, and instrument data representations within the Arches platform.
Deliverables / Outputs:
- Fully documented semantic resource models tailored for heritage science.
- Guidance materials for extending and applying models to new workflows and research scenarios.
- Integration-ready data structures for immediate deployment within AfS.
Outcome / Impact:
The work strengthened the semantic foundation of Arches for Science, ensuring that heritage science data could be captured, visualised, and shared with consistency and precision. The project enabled research laboratories to adopt a standards-based, open-source solution for managing complex scientific datasets, improving accessibility, interoperability, and long-term preservation of valuable heritage science information.
