Abstract
Research data are the cornerstone of science and their current fast rate of production is disquieting researchers. Adequate research data management strongly depends on accurate metadata records that capture the production context of the datasets, thus enabling data interpretation and reuse. This chapter reports on the authors' experience in the development of the metadata models, formalized as ontologies, for several research domains, involving members from small research teams in the overall process. This process is instantiated with four case studies: vehicle simulation; hydrogen production; biological oceanography and social sciences. The authors also present a data description workflow that includes a research data management platform, named Dendro, where researchers can prepare their datasets for further deposit in external data repositories. © 2017, IGI Global.

Abstract
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 collaborative metadata production. The paper discusses the advantages of tools to complement paper-based laboratory notebooks in capturing metadata, regardless of the research domain. We propose LabTablet, an electronic laboratory book aimed at the collection of metadata from the early stages of the research workflow. To evaluate the use of LabTablet and the proposed workflow, researchers in two domains were asked to perform a set of tasks and provided insights about their experience. By rethinking the workflow and helping researchers to actively contribute to data description, the research outputs can be described with generic and domain-dependent metadata, thus improving their chances of being deposited, reused and preserved.

Abstract
Research data management is promoted at different levels with awareness actions carried out to encourage cooperation between researchers. However, data management requires tools to set the scene for researchers and institutions to disseminate the research data they produce. In this context good quality metadata play an important role by enabling data reuse. EUDAT is an European common data infrastructure, with integrated services for data preservation and dissemination. The TAIL project, at the University of Porto, proposes workflows based on Dendro, a collaborative environment that helps researchers prepare well described datasets and deposit them in a data repository. We propose a data deposit workflow use case for a small research project with emphasis in data annotation. Data is organized and described in Dendro; deposited in B2SHARE; and semantic annotation is performed with the new B2NOTE service from EUDAT. © Springer International Publishing AG 2017.

Abstract
Dealing with research data management can be a complex task, and recent guidelines prompt researchers to actively participate in this activity. Emergent research data platforms are proposing workflows to motivate researchers to take an active role in the management of their data. Other tools, such as electronic laboratory notebooks, can be embedded in the laboratory environment to ease the collection of valuable data and metadata as soon as it is available. This paper reports an extension of the previously developed LabTablet application to gather data and metadata for different research domains. Along with this extension, we present a case study from the social sciences, concerning the identification of the data description requirements for one of its domains. We argue that the LabTablet can be crucial to engage researchers in data organization and description. After starting the process, researchers can then manage their data in Dendro, a staging platform with stronger, collaborative management capabilities, which allows them to export their annotated datasets to selected research data repositories.

Abstract
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 metadata schemas. In particular, they can be shared and improved upon more easily, providing the flexibility required to establish relationships between datasets and concepts from distinct domains. In this paper, we present a preliminary experiment on the use of ontologies for the description of biodiversity datasets. With a strong focus on the dynamics of individual species, species diversity, biological communities and ecosystems, the Predictive Ecology research group of CIBIO has adopted the INSPIRE European recommendation as the primary tool for metadata compliance across its research data description. We build upon this experience to model the BIOME ontology for the biodiversity domain. The ontology combines concepts from INSPIRE, matching them against the ones defined in the Dublin Core, FOAF and CERIF ontologies. Dendro, a prototype for collaborative data description, uses the ontology to provide an environment where biodiversity metadata records are available as Linked Open Data.

Abstract
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 led to the proposal of a domain-specific application profile. We presented two curation tools to a group of researchers from mechanical engineering, to help them manage and describe their datasets. After monitoring their interactions with the solutions and analyzing the needs of the group, we were able to select a subset of qualified Dublin Core (DC), as well as a set of complementary descriptors, to capture the main aspects of their experiments. The resulting application profile combines generic, standardized DC descriptors with descriptors from a different experimental standard, and introduces extra domain-specific ones. The profile has been validated by the researchers and is now being used in the description of their datasets. © DCMI 2013.