分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-18 合作期刊: 《数据智能（英文）》
Welten, Sascha
Neumann, Laurenz
Yediel, Yeliz Ucer
da Silva Santos, Luiz Olavo Bonino
Decker, Stefan
Beyan, Oya
摘要： In recent years, implementations enabling Distributed Analytics (DA) have gained considerable attention due to their ability to perform complex analysis tasks on decentralised data by bringing the analysis to the data. These concepts propose privacy-enhancing alternatives to data centralisation approaches, which have restricted applicability in case of sensitive data due to ethical, legal or social aspects. Nevertheless, the immanent problem of DA-enabling architectures is the black-box-alike behaviour of the highly distributed components originating from the lack of semantically enriched descriptions, particularly the absence of basic metadata for data sets or analysis tasks. To approach the mentioned problems, we propose a metadata schema for DA infrastructures, which provides a vocabulary to enrich the involved entities with descriptive semantics. We initially perform a requirement analysis with domain experts to reveal necessary metadata items, which represents the foundation of our schema. Afterwards, we transform the obtained domain expert knowledge into user stories and derive the most significant semantic content. In the final step, we enable machine-readability via RDF(S) and SHACL serialisations. We deploy our schema in a proof-of-concept monitoring dashboard to validate its contribution to the transparency of DA architectures. Additionally, we evaluate the schemas compliance with the FAIR principles. The evaluation shows that the schema succeeds in increasing transparency while being compliant with most of the FAIR principles. Because a common metadata model is critical for enhancing the compatibility between multiple DA infrastructures, our work lowers data access and analysis barriers. It represents an initial and infrastructure-independent foundation for the FAIRification of DA and the underlying scientific data management.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-18 合作期刊: 《数据智能（英文）》
Sales, Luana
Henning, Patricia
Veiga, Viviane
Costa, Maira Murrieta
Sayao, Luis Fernando
da Silva Santos, Luiz Olavo Bonino
Pires, Luis Ferreira
摘要： The FAIR principles, an acronym for Findable, Accessible, Interoperable and Reusable, are recognised worldwide as key elements for good practice in all data management processes. To understand how the Brazilian scientific community is adhering to these principles, this article reports Brazilian adherence to the GO FAIR initiative through the creation of the GO FAIR Brazil Office and the manner in which they create their implementation networks. To contextualise this understanding, we provide a brief presentation of open data policies in Brazilian research and government, and finally, we describe a model that has been adopted for the GO FAIR Brazil implementation networks. The Brazilian Institute of Information in Science and Technology is responsible for the GO FAIR Brazil Office, which operates in all fields of knowledge and supports thematic implementation networks. Today, GO FAIR Brazil-Health is the first active implementation network in operation, which works in all health domains, serving as a model for other fields like agriculture, nuclear energy, and digital humanities, which are in the process of adherence negotiation. This report demonstrates the strong interest and effort from the Brazilian scientific communities in implementing the FAIR principles in their research data management practices.
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