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
The paper analises the use of recommender systems in academic libraries, examining the use of the " Related books in Aleph OPAC" recommendation system for academic libraries' online catalogues. A quantitative approach and descriptive methodology is used to collect, process and analyse the data from a usage log provided by the University of Dundee. The analysis of 13,654 posts and 6,347 sessions provided the following observations: the recommendation was used in 11% of the sessions, and 43.9% of the recorded document views on those sessions where generated by recommendation. 9.6% of the records of document views, were derived from recommendation. Sessions using recommendations were on average 1 minute 18 seconds shorter than the sessions without recommendations. In sessions with recommendation 4.30 records were viewed on average while in sessions without recommendation the average is 1.88. Using more than one type of recommendation is not common, as 82% of the sessions with recommendation have recorded the use of only one kind of recommendation. The analysis of recommendations by kind provided two results: "Related works include" appears in more sessions (348), while " People who borrowed this work also borrowed" has the highest number of posts (584).

Abstract
Research data management has become an integral part of the research workflow. Currently, concern with data appears mainly at the very last stages of projects, rather than being present from the moment of data creation. The goal of this work is to make data easier to find, share and reuse through early metadata production and in-group review. The approach proposed in this paper, Social Dendro, introduces social network concepts such as posts, shares and comments, in Dendro, our research data management platform. The implementation follows the ontology-based architecture of the platform. Results of a preliminary user test have provided insights for future improvements.

Abstract
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 description, in spite of the diversity of existing workflows. This paper is a synthetic overview of current platforms that can be used for data management purposes. Adopting a pragmatic view on data management, the paper focuses on solutions that can be adopted in the long tail of science, where investments in tools and manpower are modest. First, a broad set of data management platforms is presented-some designed for institutional repositories and digital libraries-to select a short list of the more promising ones for data management. These platforms are compared considering their architecture, support for metadata, existing programming interfaces, as well as their search mechanisms and community acceptance. In this process, the stakeholders' requirements are also taken into account. The results show that there is still plenty of room for improvement, mainly regarding the specificity of data description in different domains, as well as the potential for integration of the data management platforms with existing research management tools. Nevertheless, depending on the context, some platforms can meet all or part of the stakeholders' requirements.

Abstract
Metadata puts research data in their context, making data intelligible and apt to sustain technology evolution and to be reused, in compliance with the FAIR principles. The workflow proposed in this work includes metadata generation in the context of research projects, created with the Dendro platform, and metadata originated in the interaction of people with the deposited data, created with the B2NOTE service from EUDAT. In our experiments, datasets are prepared with Dendro, taking into consideration general-purpose descriptors and domain-specific ones, then transparently deposited in B2SHARE. After publication, B2NOTE provides an environment where authors, other researchers, and any interested party can enrich the description with less formal comments, tags or keywords. This work contributes with (a) a set of use cases in several domains, (b) details on the descriptors used by authors in each case, and (c) reflections on the use of data after publication, using the B2NOTE contributions. © Copyright 2017 Inderscience Enterprises Ltd.

Abstract
Current mobile devices allow the execution of sophisticated applications with the capacity for identifying the user situation, which can be helpful in treatments of mental disorders. In this paper, we present SituMan, a solution that provides situation awareness to MoodBuster, an ecological momentary assessment and intervention mobile application used to request self-assessments from patients in depression treatments. SituMan has a fuzzy inference engine to identify patient situations using context data gathered from the sensors embedded in mobile devices. Situations are specified jointly by the patient and mental health professional, and they can represent the patient's daily routine (e.g., "studying", "at work", "working out"). MoodBuster requests mental status self-assessments from patients at adequate moments using situation awareness. In addition, SituMan saves and displays patient situations in a summary, delivering them for consultation by mental health professionals. A first experimental evaluation was performed to assess the user satisfaction with the approaches to define and identify situations. This experiment showed that SituMan was well evaluated in both criteria. A second experiment was performed to assess the accuracy of the fuzzy engine to infer situations. Results from the second experiment showed that the fuzzy inference engine has a good accuracy to identify situations.