Planes de gestión de datos brasileños en DMPTool: caracterización y diversidad de datos científicos

• Autor(a) principal: Stueber, Ketlen
• Data de Publicação: 2023
• Outros Autores: Monteiro, Elizabete Cristina de Souza de Aguiar, Silva, Fabiano Couto Corrêa da, Alves, Romulo Arantes, Oliveira, Alexandre Faria de
• Tipo de documento: Artigo
• Idioma: spa
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/268325
• topic: Ciência da informação; Open Science; Scientific data; Information management; Digital preservation; Ciencia abierta; Datos científicos; Gestión de la información; Preservación digital

The study presents an analysis of the public data management plans developed in the DMPTool, focusing on the plans developed by Brazilian researchers. The objective is to verify the area of knowledge, research institutions, funding agencies, and the types of data generated. A qualitative-quantitative research approach uses content analysis techniques for data collection, organization, and interpretation. The study presents reflections for future research in this area and highlights the need to increase the training process and support for researchers so that they can understand the dynamics of planning and management of scientific data so that over time it becomes a practice incorporated into scientific work in general. The main results indicate that the area of Health Sciences stands out with 166 registered plans, forming the majority of the total. In terms of institutions, most plans were associated with universities and research institutes in the Southeast region of Brazil. The typological analysis regarding the Origin of the data forms the majority, 42.2% are derivatives, while the typological analysis regarding the Nature results that 67.8% of the data collected and generated are from texts and images. It is concluded that DMPTool is a resource that favors the insertion of the Brazilian scientific ecosystem in the list of best practices in Open Science, especially the management of scientific data.