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Connecting European Archaeology datasets: prospects and challenges

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Presentation given by Kate Fernie at the Big Data in Archaeology conference in March 2019. The presentation covers the background to European initiatives to connect monument and building inventories with museum collection databases, introduces CARARE and its work to aggregate a diverse range of archaeological datasets for Europeana, the development of the CARARE metadata schema, the process of metadata mapping, the challenges and opportunities for normalising and enriching the provided metadata to increase its discoverability in the multilingual context of Europeana.

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Connecting European Archaeology datasets: prospects and challenges

  1. 1. Connecting European archaeology datasets: prospects and challenges Kate Fernie, 2Culture Associates Big Data in Archaeology: Practicalities and Possibilities 27-28 March 2019
  2. 2. • CARARE • A brief history • Datasets and their diversity • Metadata and schemas • Challenges • Possibilities Introduction
  3. 3. CARARE Connecting Archaeology and Architecture in Europe • Began as an EU-funded best practice network in 2010 • Established as a membership association in 2016 • Objective: Advancing professional practice and fostering appreciation of the digital archaeological and architectural heritage • Areas: • Good practices, advice and guidance • Services to enable data sharing • CARARE metadata schema • Promoting re-use http://www.carare.eu/
  4. 4. Steps on the way to CARARE • A shared vision • International collaborations on heritage data (CIDOC, Arena, Acquarelle, DARIAH, INSPIRE, Europeana, etc.) • Digitisation and use of digital technologies • GIS • Technical infrastructures A brief history
  5. 5. Who is collecting archaeological and architectural heritage data? • State agencies • inventories of protected sites, monuments and buildings • conservation records, field investigations, surveys • Museums – finds and excavation archives • Research Institutions & researchers • Libraries Datasets Image: Swedish National Heritage Board
  6. 6. CARARE and related projects have aggregated over 6 million digital objects from 20+ countries for Europeana.eu Many different types of object • Inventory records, reports, photographs, drawings, books, videos, objects, aerial photos, GIS datasets, 3D datasets, models, reconstructions, and more Many different ways of recording objects • Heritage agencies, museums, archives, libraries, researchers all have different ways of describing objects Many different languages, vocabularies, time periods and map systems Rather diverse
  7. 7. Tournoi royal de motos à Londres changement d'une roue de side-car en marche, 1932 Agence de presse Mondial Photo-Presse. We work with the metadata that’s provided
  8. 8. CARARE defined a metadata model for metadata aggregation • Standards based: CIDOC core standards, MIDAS Heritage, LIDO and EDM • Distinguishes between “heritage assets” (monument, building, painting, book, image, film, 3D) and digital representations found online • Allows for events (field activities, lab work) and collections • Supports objects that are composed of other objects (complexes and hierarchies) • Is rich where the domain calls for it (e.g. time, space, monument character) The schema meets a need to mediate between native data (exports) and enable their transformation into a common format Combining datasets
  9. 9. Let’s see an example MINT • Metadata mapping (from native to target schema) • Preview • Statistics • Transformation (to target schema(s)) Rijksdienst voor het Cultureel Erfgoed: Rijsmonmumenten
  10. 10. Making connections Heritage asset Has representation Images: Instituto Universitario de Investigación en Arqueología Ibérica “Hornos de Peal, Jaén” Has representation is related Relationships between the main CARARE classes: • Heritage asset, digital resources and events Has Met
  11. 11. Enriching metadata during mapping Heritage asset Images: Instituto Universitario de Investigación en Arqueología Ibérica “Hornos de Peal, Jaén” <car:heritageAssetType>http://vocab.getty.edu/aat/300054328</car:heritageAssetType> <car:heritageAssetType>http://vocab.getty.edu/aat/300000810</car:heritageAssetType> <car:heritageAssetType>http://vocab.getty.edu/aat/300305500</car:heritageAssetType> Adding constants: LOD AAT concepts <car:heritageAssetType lang="es">Necrópolis</car:heritageAssetType> Languages identification Mapping the metadata gives an opportunity to make some simple enrichments, by adding: • Language of the metadata • Name of the provider • Country of provider
  12. 12. There’s a difference between doing a schema mapping and a mapping to transform real data. Data issues can include: • Data that doesn’t conform entirely to the scope of an element • Multiple values within a single element (separators) • Data inserted in mandatory elements (n/a) • Lack of unique values A good mapping can address some of these issues, e.g. by splitting multiple subject concepts into separate elements. (issues can be fixed at source, but this can be time consuming with datasets that include hundreds of thousands of records). Quality issues
  13. 13. Transformation: some semantic gains Through transformation to a common schema, we achieve interoperability between disparate datasets  Enabling cross searches (what, when, where, who)  Open licencing of the metadata and APIs enables reuse in various applications http://eculturemap.eculturelab.eu/eCulture14m/Map.html?
  14. 14. • Metadata mapping is rarely easy • Metadata models are complex with subtle difference in world view • Statistical metrics can show that recording practices diverge and other quality issues • Native metadata is designed to serve specific purposes • Local context, audiences and questions • Merging metadata from various organisations in different countries/languages poses special challenges Some challenges
  15. 15. Aggregators like CARARE enable transformation of metadata into a common model and have some services to enable further work • Language labelling • Adding Linked Open Data • Automatic enrichment • Crowdsourcing Aggregating and enriching MORe
  16. 16. One of the big challenges in searching across datasets in Europe is dealing with data in different languages Linguistic resources and translation tools are increasingly available, but to work they need first to identify which language is involved  Language labels are often missing  Language identification and labelling microservices Interfaces, displays and search services can adapt to users’ preferred language and in this way return results which are relevant but which have been catalogued in unfamiliar languages. Why add language information to data?
  17. 17. CARARE microservices include: • Natural language processing techniques to enable subject concepts and names to be extracted from text • Geocoding services to add coordinates for named places • Vocabulary matching services • Geo conversion, inversion and normalization services Automated enrichment
  18. 18. Location case study • Location is important for archaeology but place information is often missing, especially for content from library, archive and museum collections • Automated extraction techniques can identify place names in data, but place names are not unique • The process requires quality control • Crowd sourcing is one way of harnessing the knowledge of individuals to check the results of automated enrichment and place objects correctly on the map • One such service was developed by the LoCloud project Crowd sourcing
  19. 19. Map tools
  20. 20. The content aggregated by CARARE is in Europeana Take a look: www.europeana.eu
  21. 21. Is it big data? • Volume – 2-4 million assets aggregated by CARARE • Includes the national heritage inventories for several countries, which are individually quite large datasets • Europeana includes another 1 million+ assets relevant for archaeology aggregated by other projects • Includes museum and library collections, film archives, newspaper reports • Quite big? • New research would be great!
  22. 22. kfernie27@gmail.com Any questions? www.carare.eu

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