Interseções entre Planos de Gestão de Dados acionáveis por máquina e métricas para os Princípios FAIR

Laura Vilela Rodrigues Rezende; Sandra de Albuquerque Siebra; Fabiano Couto Corrêa da Silva; Denise Oliveira de Araújo; Alexandre Faria de Oliveira

doi:10.5433/1981-8920.2024v29n4p147

Authors

Laura Vilela Rodrigues Rezende Universidade Federal de Goiás - UFG
Sandra de Albuquerque Siebra Universidade Federal de Pernambuco - UFPB https://orcid.org/0000-0002-0078-6918
Fabiano Couto Corrêa da Silva Universidade Federal do Rio Grande do Sul - UFRGS https://orcid.org/0000-0001-5014-8853
Denise Oliveira de Araújo Universidade de Brasília - UnB https://orcid.org/0000-0003-4033-7882
Alexandre Faria de Oliveira Instituto Brasileiro de Informação em Ciência e Tecnologia - IBICT https://orcid.org/0000-0003-0470-4972

DOI:

https://doi.org/10.5433/1981-8920.2024v29n4p147

Keywords:

Data Management, FAIR Principles, maDMPs, Open Science

Abstract

Objective: This study seeks to analyze intersections between Machine Actionable Data Management Plans (maDMPs) and the FAIR Principles, investigating how these two approaches can be applied to improve the management and reuse of scientific data. Methodology: The research performed a relational analysis between the ten principles of maDMPs, proposed by Miksa et al. (2019), and the metrics of the FAIRsFAIR Data Object Assessment Metrics project (v.0.4), focusing on how the FAIR principles can be implemented in data management workflows that use machine actionable management plans. Results: The study identified that all 10 maDMPs principles analyzed are aligned with the FAIR metrics studied. Conclusions: maDMPs have the potential to become a central tool in the scientific ecosystem, facilitating interoperability and the use of data in multiple contexts. The alignment between maDMP and FAIR principles can increase efficiency in data management, promote automated information sharing between systems and reduce the bureaucratic burden for researchers, while improving the quality of the data generated. However, their widespread adoption, in addition to the technological and technical requirements needed by all those involved, requires institutional, regulatory and cultural change incentives.

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Author Biographies

Laura Vilela Rodrigues Rezende, Universidade Federal de Goiás - UFG

PhD in Information Science from the Universidade de Brasília (UnB). Professor at the Universidade Federal de Goiás (UFG), Goiania, Brasil.

Sandra de Albuquerque Siebra, Universidade Federal de Pernambuco - UFPB

PhD in Computer Science from the Universidade Federal de Pernambuco (UFPE). Professor at the Universidade Federal de Pernambuco (UFPE), Recife, Brasil.

Fabiano Couto Corrêa da Silva, Universidade Federal do Rio Grande do Sul - UFRGS

PhD in Information and Documentation Sociedad Conocimiento from the Universitat de Barcelona (UB). Professor at the Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brasil.

Denise Oliveira de Araújo, Universidade de Brasília - UnB

Master in Information Science from the Universidade de Brasília (UnB), Brasília, Brasil.

Alexandre Faria de Oliveira, Instituto Brasileiro de Informação em Ciência e Tecnologia - IBICT

Master in Strategic Management of Organizations from the Instituto de Educação Superior de Brasília (IESB). Coordinator of Governance in Information and Communication Technology at the Instituto Brasileiro de Informação em Ciência e Tecnologia (IBICT), Brasília, Brasil.

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