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.

Abstract
Distributed and heterogeneous systems (DHS), running over interconnected mobile and cloud-based platforms, are used in a growing number of domains for provisioning end-to-end services to users. Testing DHS is particularly important and challenging, with little support being provided by current tools. In order to assess the current state of the practice regarding the testing of DHS and identify opportunities and priorities for research and innovation initiatives, we conducted an exploratory survey that was responded by 147 software testing professionals that attended industry-oriented software testing conferences. The survey allowed us to assess the relevance of DHS in software testing practice, the most important features to be tested in DHS, the current status of test automation and tool sourcing for testing DHS, and the most desired features in test automation solutions for DHS. Some follow up interviews allowed us to further investigate drivers and barriers for DHS test automation. We expect that the results presented in the paper are of interest to researchers, tool vendors and service providers in this field.

Abstract
In a growing number of domains, the provisioning of end-to-end services to the users depends on the proper interoperation of multiple products, forming a new distributed system. To ensure interoperability and the integrity of this new distributed system, it is important to conduct integration tests that verify not only the interactions with the environment but also the interactions between the system components. Integration test scenarios for that purpose may be conveniently specified by means of UML sequence diagrams, possibly allowing multiple execution paths. The automation of such integration tests requires that test components are also distributed, with a local tester deployed close to each system component, and a central tester coordinating the local testers. In such a test architecture, it is important to minimize the communication overhead during test execution. Hence, in this paper we investigate conditions upon which conformance errors can be detected locally (local observability) and test inputs can be decided locally (local controllability) by the local testers, without the need for exchanging coordination messages between the test components during test execution. The conditions are specified in a formal specification language that allows executing and validating the specification. Examples of test scenarios are also presented, illustrating local observability and controllability problems associated with optional messages without corresponding acknowledgment messages, races and non-local choices. © 2017 IEEE.