Decomposing Observable Property Descriptions into Machine-Readable Components

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28 Jul 2021 UTC

Decomposing Observable Property Descriptions into Machine-Readable Components

28 Jul 2021 - 15:00 to 16:00 UTC


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The RDA I-Adopt Working Group presents the "Decomposing Observable Property Descriptions into Machine-Readable Components to Increase Interoperability Across Data Standards" webinar.  This webinar introduces using the Interoperable Descriptions of Observable Property Terminology (I-ADOPT) framework for representing components of observable properties, also called variables, in a machine-readable format to allow for easier mapping between dedicated terminologies.



The I-ADOPT framework was developed through community input. The process involved gathering a series of variable use cases from the environmental science community, compiling and analyzing a catalogue of existing vocabularies and conceptual models, and determining a minimal viable set of components and relationships for describing these variables. Annotation of current observable property standards with components of the I-ADOPT framework would aid in increasing interoperability across the wide variety of data standards used within the environmental science domain.


Attendees will learn how to first analyze their variable descriptions to determine critical components and then use the I-ADOPT framework to decompose and express these variables in a machine-readable form. 


This webinar will be of interest to researchers and research data practitioners who have a need to annotate electronic resources with information about observable properties.




Maria Stoica, Institute of Arctic and Alpine Research, University of Colorado  United States

ORCID: 0000-0002-6612-3439


Maria is a research scientist specializing in ontology development and knowledge reasoning and representation applied to the physical sciences. For the past five years, she has been developing the Scientific Variables Ontology, a framework and set of computation tools for constructing principled, machine-readable representations of scientific variable descriptions.


Barbara Magagna, Umweltbundesamt GmbH (Environment Agency Austria) Austria

ORCID: 0000-0003-2195-3997


Barbara is a landscape ecologist with 25 years of experience working in the field of GIS and database management for international projects. In the last 15 years, her interests and formation also moved towards ontology engineering and process facilitation for the creation and governance of terminologies, the FAIR assessment of Research infrastructures and the development of best practices for provenance tracking.


Alison Pamment, NCAS/Centre for Environmental Data Analysis, STFC Rutherford Appleton Laboratory United Kingdom

ORCID: 0000-0001-5040-4626


Alison is an environmental data scientist at the Centre for Environmental Data Analysis, part of the UK National Centre for Atmospheric Science. She works on the CF (Climate-Forecast) metadata conventions which are widely used for data sharing in atmospheric and climate science.


Sirko Schindler, Institute for Data Science, German Aerospace Center (DLR) Germany

ORCID 0000-0002-0964-4457


Sirko is working as a Data Scientist. His focus is to make data understandable and enable (re)use across institutional and domain borders. He participated in projects in domains ranging from Biodiversity and Mobility to Earth Observation and Radio-Astronomy.



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