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
Relevance is usually estimated by search engines using document content, disregarding the user behind the search and the characteristics of the task. In this work, we look at relevance as framed in a situational context, calling it situational relevance, and analyze if it is possible to predict it using documents, users and tasks characteristics. Using an existing dataset composed of health web documents, relevance judgments for information needs, user and task characteristics, we build a multivariate prediction model for situational relevance. Our model has an accuracy of 77.17%. Our findings provide insights into features that could improve the estimation of relevance by search engines, helping to conciliate the systemic and situational views of relevance. In a near future we will work on the automatic assessment of document, user and task characteristics.

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.