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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
The Biblioteca Digital de Arte (BDArt) Digital Library hosted by the Thematic Repository at the University of Porto (Repositório Tem ático da U.Porto) aggregates documents from the library and the archive collections belonging to the Fine Arts School of the University of Porto (Faculdade de Belas Artes da U.Porto). This school has a museum collection containing a significant set of world-class ob- jects managed with distinct processes and tools from those currently used in libraries and archives elsewhere. Interoperability between the collections of the archive, the library, and the museum is necessary because many works allocated to different collections are closely related and can only be seen as a whole by cross-collection search functionalities. The goal of this work, the first of its kind to be developed at the University of Porto (U. Porto), is to integrate the museum collection with archives and library collections in the repository and to use an open-source technology (DSpace). Our experiment involved the selection of appropriate representations of the objects and the definition of a metadata crosswalk between the original metadata standards and qualified Dublin Core. As a result, we created the BDA Museum Collection as a BDArt subcom- munity using an XML export procedure that we expect to be helpful in future developments of other museum collections in the Thematic Repository at U.Porto. © Isabel Barroso, Nadia Hartmann, and Cristina Ribeiro.

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
Identifying the user's intent behind a query is a key challenge in Information Retrieval. This information may be used to contextualize the search and provide better search results to the user. The automatic identification of queries targeting a search for health information allows the implementation of retrieval strategies specifically focused on the health domain. In this paper, two kinds of automatic methods to identify and classify health queries based on domain-specific terminology are proposed. Besides evaluating these methods, we compare them with a method that is based on co-occurrence statistics of query terms with the word "health". Although the best overall result was achieved with a variant of the co-occurrence method, the method based on domain-specific frequencies that generates a continuous output outperformed most of the other methods. Moreover, this method also allows the association of queries to the semantic tree of the Unified Medical Language System and thereafter their classification into appropriate subcategories. Copyright © 2014, IGI Global.

Abstract