FORTH organized a Workshop on modeling scientific data in cooperation with the ARIADNE Scientific Data special interest group at Plakias on Crete on 21-24 July this year. The workshop focused on scientific investigation methods for archaeology, but took also into account scientific investigation in biodiversity and geology.
For the first time, scientists of 6 different disciplines were brought together to directly compare the detailed protocols of their methods and to identify which metadata representation would be adequate so that later research could re-estimate precision and reevaluate results based on old and new evidence.
Six different methods were presented, analyzed, understood, and compared:
- Geophysical Survey Workflow
- DNA analysis
- Isotope analysis
- TL/OSL ceramics analysis
- Elemental Analysis of Archaeological Objects
The presentations focused on the method used, potential problems, reasoning on quality and accuracy, and calibration.
The workshop also looked at the creation and maintenance of reference data collection that allow for inferring from measured properties propositions about provenance, kind, identity or events in the past of the analyzed objects.
During the workshop, the ontologies CIDOC-CRM, CRMgeo, CRMdig, CRMsci and CRMarchaeo (all part of the ARIADNE Global Model) were presented and some examples of how they could be used to model scientific data were shown. It became apparent that these models need to be further extended. We employ a method of “deep” knowledge engineering to analyze informal descriptions and formal metadata about the processes of various scientific investigation methods, in order to produce an integrated conceptual model or ontology for information integration in different descriptive sciences.
In conclusion, following the analysis of the workshop, ARIADNE aims to define:
- a general ontology of scientific observation and reference data building that will be part of the ARIADNE Global Model.
- a methodology for designing effective generic metadata schemata and their discipline-specific specialization, with objective relevance criteria.
The expected findings can inform new methodologies and guidelines of effective metadata generation, and contribute to a deeper understanding of the requirements of research infrastructures to support an actual knowledge ecosystems of scientific research interaction and to implementing more adequate IT services for such ecosystems.