Aligning DMBOK and open government with the FAIR data principles

Glaucia Botelho de Figueiredo*, João Luiz Rebelo Moreira, Kelli de Faria Cordeiro, Maria Luiza Machado Campos

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

In enterprise organizations, the value of data has been considered on strategic level for a long time. As valuable assets, data need to be managed from source to disposal, considering their whole life cycle. To guide the data managing needs of enterprise organizations, the non-profit organization DAMA promotes the development and practice of data management as key enterprise assets. In 2017, DAMA has published the second edition of the DAMA International Guide to Data Management Body of Knowledge (DAMA DMBOK®). While the DAMA DMBOK focuses on corporate data, the FAIR data principles target research data management involving researchers and publishers in Academia. Data management is also a core issue in the Government sector, which has a great relevance in the open government initiatives, supporting the civil society to follow the actions of government bodies. This article makes a systematic analysis of these three data natures – research data, corporate data and government data – and the respective sets of principles that act as a basis for their management. These principles are correlated to identify similarities and possible complementarities focusing on the improvement of research data management, represented by the FAIR initiative, proposing an initial framework to support it.

Original languageEnglish
Title of host publicationAdvances in Conceptual Modeling
Subtitle of host publicationER 2019 Workshops FAIR, MREBA, EmpER, MoBiD, OntoCom, and ER Doctoral Symposium Papers, Salvador, Brazil, November 4–7, 2019, Proceedings
EditorsGiancarlo Guizzardi, Frederik Gailly, Rita Suzana Pitangueira Maciel
PublisherSpringer
Pages13-22
Number of pages10
ISBN (Electronic)9783030341466
ISBN (Print)9783030341459
DOIs
Publication statusPublished - 2019
EventWorkshop on Conceptual Modeling, Ontologies and Metadata Management for FAIR Data, FAIR 2019, 6th Workshop on Conceptual Modeling in Requirements Engineering and Business Analysis, MREBA 2019, 2nd International Workshop on Empirical Methods in Conceptual Modeling, EmpER 2019, 8th International Workshop on Modeling and Management of Big Data, MoBiD19 2019 and 7th International Workshop on Ontologies andConceptual Modelling, OntoCom 2019 held at the 38th International Conference on Conceptual Modeling, ER 2019 - Salvador, Brazil
Duration: 4 Nov 20197 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11787 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceWorkshop on Conceptual Modeling, Ontologies and Metadata Management for FAIR Data, FAIR 2019, 6th Workshop on Conceptual Modeling in Requirements Engineering and Business Analysis, MREBA 2019, 2nd International Workshop on Empirical Methods in Conceptual Modeling, EmpER 2019, 8th International Workshop on Modeling and Management of Big Data, MoBiD19 2019 and 7th International Workshop on Ontologies andConceptual Modelling, OntoCom 2019 held at the 38th International Conference on Conceptual Modeling, ER 2019
CountryBrazil
CitySalvador
Period4/11/197/11/19

Keywords

  • Corporate data
  • Data management
  • DMBOK
  • FAIR
  • FAIR principles
  • Government data
  • Open government data
  • Research data

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  • Cite this

    de Figueiredo, G. B., Moreira, J. L. R., de Faria Cordeiro, K., & Campos, M. L. M. (2019). Aligning DMBOK and open government with the FAIR data principles. In G. Guizzardi, F. Gailly, & R. Suzana Pitangueira Maciel (Eds.), Advances in Conceptual Modeling: ER 2019 Workshops FAIR, MREBA, EmpER, MoBiD, OntoCom, and ER Doctoral Symposium Papers, Salvador, Brazil, November 4–7, 2019, Proceedings (pp. 13-22). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11787 LNCS). Springer. https://doi.org/10.1007/978-3-030-34146-6_2