Active History, Languages & Philosophy

Heritage Science Data Service (HSDS)

In plain English

AI plain-English summary

A single online portal will unify the UK’s scattered digital heritage science data—from 3D scans of medieval cathedrals to chemical analyses of Roman pigments—into one searchable, preservable national resource. Heritage science generates massive, born-digital datasets—gigabyte-scale files from X-ray fluorescence, LiDAR, and ancient DNA analysis—that are currently difficult to find and at constant risk of permanent loss. Without active curation, these irreplaceable records of the UK’s material past vanish. The Heritage Science Data Service (HSDS) solves this by providing a FAIR-compliant repository, a catalogue of facilities and expertise, and an aggregator that makes collections navigable. If successful, HSDS will transform how researchers, conservators, and engineers access and reuse heritage data. It will also serve as a digital skills incubator and the UK hub for the European Research Infrastructure for Heritage Science (E-RIHS). Beyond the heritage sector, the service’s interoperable structure and AI-ready datasets could accelerate advances in data science and machine learning applied to cultural materials—improving everything from conservation planning to the detection of forgeries.

View original technical description
The Heritage Science Data Service (HSDS) will provide key Digital Research Services enabling RICHeS to transform heritage science and conservation research (HSCR) and its capacity to advance understanding, preservation and management of UK heritage. It will offer a single discovery point to distributed facilities, cross-disciplinary expertise, and shared data as a research resource. This new co-ordinated approach brings considerable advantages, enabling international leadership and open innovation, including advances in AI and data science. Heritage science and conservation research has significant digital infrastructure requirements. It employs a broad range of technologies including: digital imaging (3D laser scanning, photogrammetry, X-ray, infrared, hyperspectral, XRF scanning etc), remote sensing (LiDAR, geophysics), 3D modelling, dating (dendrochronology, C14), many analytical approaches, non-invasive or on samples (stable isotopes, Ancient DNA, Zooms, SEM-EDX, FTIR, GC-MS, HPLC etc) and large-scale facilities (neutron, synchrotron). Many complementary methods are used in tandem, generating numerous datasets (routinely at gigabyte scale) requiring active data management to ensure long-term preservation and re-use. They are a primary resource, generally born-digital without paper surrogates. If lost they cannot be reacquired; it is essential they are managed and curated according to the FAIR principles. The HSDS will curate a substantial body of new data across RICHeS and be the national discovery tool for UK heritage science. Easier access to this difficult-to-find data through digital services unlocks its potential for research and wider impact in the heritage sector and beyond. It will be a digital skills development incubator, with an interoperable distributed structure on an unprecedented, transformative scale. It will widen access to advanced scientific research facilities for arts and humanities, heritage and archaeological professionals, researchers, engineers and scientists, through: (i) a catalogue of research facilities, reference collections and expertise; (ii) an aggregating function, making data collections navigable and searchable; (iii) a FAIR repository for research data, encouraging Access and Re-use. HSDS will be developed and managed by the Archaeology Data Service (ADS), an innovative CoreTrustSeal repository managing archaeological and heritage science data since 1996, in partnership with the STFC Hartree Centre, a high-performance computing, data analytics and AI research facility, formed in 2012. HSDS also brings together key UK heritage bodies covering England, Scotland and Wales. As initial data providers with associated digital expertise, they enable a 'design with data' approach, with sector coverage complementing ADS, and include the British Museum (BM), British Geological Survey (BGS), National Gallery (NG), The National Archives (TNA), Natural History Museum (NHM), Historic England (HE), Historic Environment Scotland (HES) and Museum Wales (NMW). Collaboration with the Museum Data Service (MDS) will ensure interoperability with metadata drawn from smaller museums. Cascading grants will provide crucial support for additional partners and wider coverage of user-needs, also fostering co-development of Virtual Research Environments for visualising and interrogating. Internationally, HSDS will provide the UK DIGILAB hub for E-RIHS (European Research Infrastructure for Heritage Science), and will work with the Getty Conservation Institute to integrate data from laboratories using their Arches for Science platform. NG and TNA will work with Kartography to integrate data through the ResearchSpace platform.

View the original record at the funder ↗

Researchers

Andrew Meek (Co-Investigator)Andrew Nicoll (Co-Investigator)Anthony Presland (Co-Investigator)Benjamin Edwards (Co-Investigator)Daniel Pett (Co-Investigator)David Meredith (Co-Investigator)Diane Gwilt (Co-Investigator)Edward Taylor (Co-Investigator)Jen Heathcote (Co-Investigator)Joseph Padfield (Co-Investigator)Julian Richards (Principal Investigator)Katrina Williams (Co-Investigator)Martin Nayembil (Co-Investigator)Michael Howe (Co-Investigator)Simon Harris (Co-Investigator)Sonja Schwoll (Co-Investigator)Tim Evans (Co-Investigator)Vincent Smith (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

Archaeology Data Service Infrastructure funding IDAH Years 5-7
Federated Data Science UK (FEDS UK)
Healthy and Sustainable Places Data Service (HASP)
Archaeology Data Service DRI Strand A 2021-22
Creating a national integrated mobile and fixed lab for collections, historic buildings and sites

Original classification

Research and Innovation

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.