Information literacy for data reuse in the Social Sciences: requirements and competences

• Autor(a) principal: Estevão, Janete Saldanha Bach
• Data de Publicação: 2020
• Outros Autores: Strauhs, Faimara do Rocio
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Informação & Informação
• Texto Completo: https://ojs.uel.br/revistas/uel/index.php/informacao/article/view/38995
• topic: Open Science; Research Data – reuse; Data Literacy; Ciencia Abierta; Datos de Investigación - Reutilización; Alfabetización en Datos; Ciência Aberta; Dados de Pesquisa – reúso; Letramento em dados

Introduction: The scientific research environment has been impacted by the increase availability of research data in open access, however, researchers face difficulties in managing their own data as well as third parties’; especially in data reuse related matters. The relation of availability versus data management is more complex in the Social Sciences, whose data are heterogeneous and variable. Objective: Propose the necessary requirements and minimum data literacy skills for research data reuse. Methodology: Based on a bibliographical background, it uses the NVivo as an analysis tool to support the content analysis techniques into three research instances. It compares (1) the selected conceptual frameworks of data literacy, it reuses (2) secondary datasets about researchers' behavior in reusing data, it identifies (3) in the Virtual Learning Environments what are the competencies for reuse purposes. Results: It presents the necessary requirements and competences set for research data reuse and test its validity by adherence checking with the best practices recommended by the World Wide Web Consortium (W3C). Conclusions: The proposal for 16 requirements for a virtual environment for literacy in the reuse of contextualized data in the area of Social Sciences and 37 competencies for researchers is consolidated. The main requirements are access, license and use, and essential competencies are the access rules, licenses types and how to use the available datasets, especially considering provenance, quality and reliability criteria.