Abstract
As researchers are increasingly seeking tools and specialized support to perform research data management activities, the collaboration with data curators can be fruitful. Yet, establishing a timely collaboration between researchers and data curators, grounded in sound communication, is often demanding. In this paper we propose manual content analysis as an approach to streamline the data curator workflow. With content analysis curators can obtain domain-specific concepts used to describe experimental configurations in scientific publications, to make it easier for researchers to understand the notion of metadata and for the development of metadata tools. We present three case studies from experimental domains, one related to sustainable chemistry, one to photovoltaic generation and another to nanoparticle synthesis. The curator started by performing content analysis in research publications, proceeded to create a metadata template based on the extracted concepts, and then interacted with researchers. The approach was validated by the researchers with a high rate of accepted concepts, 84 per cent. Researchers also provide feedback on how to improve some proposed descriptors. Content analysis has the potential to be a practical, proactive task, which can be extended to multiple experimental domains and bridge the communication gap between curators and researchers. [This paper is a conference pre-print presented at IDCC 2020 after lightweight peer review.]

Abstract
Recent initiatives in data management recognize that involving the researchers is one of the more problematic issues and that taking into account the practices of each domain can ease this process. We describe here an experiment in the adoption of data description by researchers in the biomedical domain. We started with a generic lightweight ontology based on the Minimum Information for Biological and Biomedical Investigations (MIBBI) standard and presented it to researchers from the Institute of Innovation and Investigation in Health (I3S) in Porto. This resulted in seven interviews and four data description sessions using a RDM platform. The feedback from researchers shows that this intentionally restricted ontology favours an easy entry point into RDM but does not prevent them from identifying the limitations of the model and pinpointing their specific domain requirements. To complete the experiment, we collected the extra descriptors suggested by the researchers and compared them to the full MIBBI. Part of these new descriptors can be obtained from the standard, reinforcing the importance of common metadata models for broad domains such as biomedical research. © 2019, Springer Nature Switzerland AG.

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Researchers have to ensure that their projects comply with Research Data Management (RDM) requirements. Consequently, the main funding agencies require Data Management Plans (DMPs) for grant applications. So, institutions are investing in RDM tools and implementing RDM workflows in order to support their researchers. In this context, we propose a collaborative DMP-building method that involves researchers, data stewards and other parties if required. This method was applied as part of an RDM workflow in research groups across several scientific domains. We describe it as a systematic approach and illustrate it through a set of case studies. We also address the DMP monitoring process during the life cycle of projects. The feedback from the researchers highlighted the advantages of creating DMPs and their growing need. So, there is motivation to improve the DMP support process according to the machine-actionable DMPs concept and to the best practices in each scientific community. © 2021 Inderscience Enterprises Ltd.. All rights reserved.

Abstract
[No abstract available]