Arquitetura de Curadoria e Publicação de Dados Científicos Amazônicos: o Caso do DataMap/Amazon
Resumo
Este artigo descreve o modelo de curadoria do DataMap/Amazon, desenvolvido para mitigar a dispersão e heterogeneidade de dados ambientais amazônicos. A solução integra quatro componentes funcionais (Dicionário, Arquivos, Datasets e Produtos) a um pipeline de qualificação progressiva (camadas Bronze, Prata e Ouro). Aplicada a quatro iniciativas de pesquisa (LBA, AmazonFACE, ATTO-Campina e CHUVA), a plataforma transformou acervos brutos em ativos científicos FAIR. Os resultados incluem a estruturação de 183 datasets, 1,58 TB de arquivos organizados e 165 DOIs gerados, fortalecendo a reprodutibilidade e o acesso aberto à ciência na região.
Palavras-chave:
Ciência Aberta, Curadoria de Dados, Dados Científicos, Amazônia, Infraestrutura de Dados, Princípios FAIR, Data Lakehouse
Referências
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Brickley, D., Burgess, M., and Noy, N. F. (2019). Google Dataset Search: Building a search engine for datasets in an open Web ecosystem. In Proceedings of The World Wide Web Conference (WWW 2019), pages 1365–1375, New York. ACM. DOI: 10.1145/3308558.3313685.
CERN e OpenAIRE (2013). Zenodo: Research. shared. CERN, Geneva. [link].
Data Citation Synthesis Group (2014). Joint declaration of data citation principles. FORCE11, San Diego, CA. [link].
DataCite Metadata Working Group (2021). DataCite metadata schema documentation for the publication and citation of research data and other research outputs. version 4.4. DataCite. DOI: 10.14454/3w3z-sa82.
Davidson, E. A. et al. (2012). The Amazon basin in transition. Nature, 481:321–328.
Fenner, M. et al. (2019). A data citation roadmap for scholarly data repositories. Scientific Data, 6:28.
Groth, P. and Moreau, L. (2013). PROV-overview: An overview of the PROV family of documents. W3C Working Group Note, 30 April 2013. [link].
Hey, T., Tansley, S., and Tolle, K. (2009). The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, Redmond, WA.
Higgins, S. (2008). The DCC curation lifecycle model. International Journal of Digital Curation, 3(1):134–140.
King, G. (2007). An introduction to the Dataverse Network as an infrastructure for data sharing. Sociological Methods & Research, 36(2):173–199.
Michener, W. K. et al. (2012). Participatory design of DataONE – enabling cyberinfrastructure for the biological and environmental sciences. Ecological Informatics, 11:5–15.
Thelwall, M. and Kousha, K. (2016). Figshare: a universal repository for academic resource sharing? Online Information Review, 40(3):333–346. DOI: 10.1108/OIR-06-2015-0190.
tus.io (2023). Resumable uploads protocol tus: open protocol for resumable file uploads. [link].
UNESCO (2021). Recommendation on Open Science. Adopted by the General Conference of UNESCO at its 41st session, 23 November 2021. Paris: UNESCO. [link].
Wieringa, R. J. (2014). Design Science Methodology for Information Systems and Software Engineering. Springer, Berlin.
Wilkinson, M. D. et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3:160018.
Publicado
08/09/2026
Como Citar
MONTEIRO, Danielle S.; TEIXEIRA, Marcio; ALMEIDA, Adriano; CALHEIROS, Alan James; MACHICAO, Janete; CORRÊA, Pedro Pizzigatti.
Arquitetura de Curadoria e Publicação de Dados Científicos Amazônicos: o Caso do DataMap/Amazon. In: DATA SCIENCE FOR SOCIAL GOOD BRAZILIAN WORKSHOP (DS4SG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 473-482.
DOI: https://doi.org/10.5753/sbbd_estendido.2026.249676.
