| Summary: | The current context of research data production leads to the development of tools to face the challenges of the management and the description of data that result from the research processes. Metadata support data sharing, widely advocated by funding agencies. However, the development of metadata is not always an easy task, since it is necessary to promote the collaboration of the data curator and the researcher. They have complementary knowledge on domains and data management, making communication a demanding process. The objective of this work is to understand if the curator, without specific knowledge about the domain, can define a preliminary data model, using content analysis of papers by the researchers. This approach is expected to optimize the workflow of the curator and to test the hypothesis that it is possible to define requirements that are related to the typology of data, in this specific case experimental data. In order to examine this approach, content analyses were carried out based on scientific publications for five use cases, for which metadata models are produced and evaluated with domain researchers. Evaluation with the researchers has provided evidence to support the approach. The results are promising with a high rate of accepted concepts. In the data curator's perspective, content analysis has potential as a proactive task that can be applied in several experimental domains and enables a more efficient communication between the curator and the researchers. However, the approach also has some limitations that relate to the curator's lack of knowledge about the specificities of the domains, so that the final interaction between curators and researchers is essential to bridge some of the gaps in the model.
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