Descrição de dados de investigação: requisitos de investigadores para modelos de metadados na Psicologia e Ciências da Educação

• Autor(a) principal: Laura Mafalda Carvalho Lopes
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/122418
• topic: Ciências da comunicação; Media and communications

Data management has become increasingly relevant to the scientific community and tools are emerging that facilitate the process of processing and storing research data. With a view to data management at the Faculty of Psychology and Educational Sciences, data sets were analysed that correspond to research projects that have already been completed, the so-called legacy data. A census of the documentation was carried out as a way of analysing the possibilities of treatment, description, evaluation, conservation and preservation. Twenty interviews were conducted with researchers for the domains of Psychology and Education Sciences and we presented results that provide us with information about their work processes, identifying needs regarding the procedures for access, storage, conservation and preservation of research data for these domains. We studied models and workflows for the management of data sets of completed projects and the results obtained were used to support decision making regarding the elimination of data collection documentation. Through interviews, we were able to identify the researchers' needs and expectations. We were able to support decision making regarding the elimination of documents from research data collection. Around 200 kg of paper have already been eliminated and 3 datasets of completed projects have been described. As a result of our work, we identified needs for the description of legacy data and proposed the use of Nesstar software for internal use, preparing the information with a suitable structure for repository storage. The description of data according to the DDI model proved to be appropriate to the researchers' needs; through the interviews we identified a proximity to the relevant descriptors with emphasis on the description of the variables.