João Aguiar Castro

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Research datasets in the so-called " long-tail of science " are easily lost after their primary use. Support for preservation, if available, is hard to fit in the research agenda. Our previous work has provided evidence that dataset creators are motivated to spend time on data description , especially if this also facilitates data exchange within a group or(More)
It has been shown that data management should start as early as possible in the research workflow to minimize the risks of data loss. Given the large numbers of datasets produced every day, curators may be unable to describe them all, so researchers should take an active part in the process. However, since they are not data management experts, they must be(More)
The value of research data is recognized, and so is the importance of the associated metadata to contextualize, describe and ultimately render them understandable in the long term. Laboratory notebooks are an excellent source of domain-specific metadata, but this paper-based approach can pose risks of data loss, while limiting the possibilities of(More)
Metadata production for research datasets is not a trivial problem. Standardized descriptors are convenient for interoperability, but each area requires specific descriptors in order to guarantee metadata comprehensiveness and accuracy. In this paper, we report on an ongoing research data management experience at the University of Porto (U. Porto), which(More)
Research data management is rapidly becoming a regular concern for researchers, and institutions need to provide them with platforms to support data organization and preparation for publication. Some institutions have adopted institutional repositories as the basis for data deposit, whereas others are experimenting with richer environments for data(More)
The description of data is a central task in research data management. Describing datasets requires deep knowledge of both the data and the data creation process to ensure adequate capture of their meaning and context. Metadata schemas are usually followed in resource description to enforce comprehensiveness and interoperability, but they can be hard to(More)
Managing research data often requires the creation or reuse of specialised metadata schemas to satisfy the metadata requirements of each research group. Ontologies present several advantages over meta-data schemas. In particular, they can be shared and improved upon more easily, providing the flexibility required to establish relationships between datasets(More)