Abstract
Documenting datasets in an actionable way is an essential approach to ensure data interoperability. Guidelines like FAIR (Findability, Accessibility, Interoperability, and Reusability) ensures better use-cases for the data. Proposals like metadata applications profiles provide mechanisms to express constraints and metadata schema of the datasets. In order to provide Ethical, Legal, and Social Aspects/Implications (ELSA/ELSI), datasets require more than the application profiles. Along with the schema, expressing privacy aspects of the data and constraints on rights and licenses also ensures proper ELSI. A good dataset profile needs validation rules provided in actionable formats and with human-readable documentation. A sample data will help the consumers to streamline the process of adapting the datasets. Different solutions exist to express these various components required to represent the datasets, such as DCAT to express the datasets, ShEx, and SHACL to provide validation for datasets, Datapackage for providing the schema for tabular data, DCAP for creating metadata application profiles, vocabularies like DPV to provide privacy constraints and ORDL to express rights of datasets. However, there is no simplified mechanism to interlink and distinguish these various elements in an actionable format. This research is intended to devise a mechanism to express a complete profile package for datasets, as ‘MetaProfile.’ MetaProfile is intended to cover a dataset’s profile with privacy, rights, and other essential components to ensure ELSI and interoperability of datasets. This research’s expected outcome is to provide a format and vocabulary to fill in the gaps of existing solutions for interlinking and notating different components of a profile.
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Thalhath, N., Nagamori, M., Sakaguchi, T. (2020). MetaProfiles - A Mechanism to Express Metadata Schema, Privacy, Rights and Provenance for Data Interoperability. In: Ishita, E., Pang, N.L.S., Zhou, L. (eds) Digital Libraries at Times of Massive Societal Transition. ICADL 2020. Lecture Notes in Computer Science(), vol 12504. Springer, Cham. https://doi.org/10.1007/978-3-030-64452-9_34
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DOI: https://doi.org/10.1007/978-3-030-64452-9_34
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