Research data management platforms: expanding the concept of data repositories

Abstract

In the contemporary research environment, well-managed research data is recognized as an essential factor for high-quality research. This is because good management makes the datasets easier to reuse, which is translated into a higher coefficient of collaboration between scientists, maximizing the return on investment of research funding agencies, increasing transparency in methods and workflows, enabling, in this way, a greater coefficient of reproducibility of scientific experiments. However, data management is a multifaceted problem that demands technologies, organizational structures, human knowledge and skills to combine, in a complementary way, a wide spectrum of variables, thus characterizing them as a complex solution equation. Faced with this challenge, the present research starts from the following question: are the repositories enough to solve all the challenges presented by research data management? To answer this question, a theoretical and exploratory research was developed, based on literature analysis and observation of repositories and data management service platforms available on the web. As a result, the concept of disciplinary platform for research data management is presented as a possible alternative for solving several challenges encountered by researchers and academics, who aim to find, access, share and reuse data as inputs for new research. It is concluded that the offer of new data management services must be supported by the available computational and informational infrastructures, the analysis methodologies and workflows inherent to the disciplinary research processes and incorporate expertise that is capable of dealing with the environments and technologically sophisticated processes of current research. It is concluded that data management should be guided by the provision of a set of data services that can be classified as scientific, computational, informational and administrative. Those services must closely support disciplinary workflows, processing and analysis methodologies through specific computational and informational infrastructures and incorporate multidisciplinary expertise that can deal with the technologically sophisticated environments and processes of current research.