Abstract
Searching for health information is one of the most popular activities on the Web. In this domain, users frequently encounter difficulties in query formulation, either because they lack knowledge of the proper medical terms or because they misspell them. To overcome these difficulties and attempt to retrieve higher-quality content, we developed a query suggestion system that provides alternative queries combining the users' native language and English language with lay and medico-scientific terminology. To assess how the language and terminology impact the use of suggestions, we conducted a user study with 40 subjects considering their English proficiency, health literacy and topic familiarity. Results show that suggestions are used most often at the beginning of search sessions. English suggestions tend to be preferred to the ones formulated in the users' native language, at all levels of English proficiency. Medico-scientific suggestions tend to be preferred to lay suggestions at higher levels of health literacy.

Abstract
Depositing and sharing research data is at the core of open science practices. However, institutions in the long tail of science are struggling to properly manage large amounts of data. Support for research data management is still fragile, and most existing solutions adopt generic metadata schemas for data description. These might be unable to capture the production contexts of many datasets, making them harder to interpret. EUDAT is a large ongoing EU-funded project that aims to provide a platform to help researchers manage their datasets and share them when they are ready to be published. Data-Publication@U. Porto is an EUDAT Data Pilot proposing the integration between Dendro, a prototype research data management platform, and the EUDAT B2Share module. The goal is to offer researchers a streamlined workflow: they organize and describe their data in Dendro as soon as they are available, and decide when to deposit in a data repository. Dendro integrates with the API of B2Share, automatically filling the standard metadata descriptors and complementing the data package with additional files for domain-specific descriptors. Our integration offers researchers a simple but complete workflow, from data preparation and description to data deposit.

Abstract
The Web is frequently used as a way to access health information. In the health domain, the terminology can be very specific, frequently assuming a medico-scientific character. This can be a barrier to users who may be unable to understand the retrieved documents. Therefore, it would be useful to automatically assess how well a certain document will be understood by a certain user. In the present work, we analyse whether it is possible to predict the comprehension of documents using document features together with user features, and how well this can be achieved. We use an existing dataset, composed by health documents on the Web and their assessment in terms of comprehension by users, to build two multivariate prediction models for comprehension. Our best model showed very good results, with 96.51% accuracy. Our findings suggest features that can be considered by search engines to estimate comprehension. We found that user characteristics related to web and health search habits, such as the success of the users with Web search and the frequency of the users' health search, are some of the most influential user variables. The promising results obtained with this dataset with manual comprehension assessment will lead us to explore the automatic assessment of document and user characteristics. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
Research institutions are considering data repositories to manage their outputs and ensure their visibility. In many domains, purpose-built tools can help collect data and metadata as they are created. LabTablet is such a tool, designed to provide the functions of a laboratory notebook, and being able to accompany users in either experimental sessions or field trips. In these contexts, the interaction with the device can be problematic, so we experimented with a speech recognition extension for two purposes: to provide commands, such as requesting readings from the built-in sensors, and to record observations such as a dictated note in a field trip. Copyright 2016 ACM.

Abstract
Weather and sea-related forecasts provide crucial insights for the practice of nautical sports such as surf and kite surf, and mobile devices are appropriate interfaces for the visualization of meteorology and operational oceanography data. Data are collected and processed by several agencies and are often obtained from forecast models. Their use requires adaptation and refinement prior to visualisation. We describe a set of semantic data services using standard common vocabularies and interoperable interfaces following the recommendations of the INSPIRE directive. NautiCast, a mobile application for forecast delivery illustrates the adaptation of data at two levels: 1) semantic, with the integration of data from different sources via standard vocabularies, and 2) syntactic, with the manipulation of the spacial and temporal resolution of data to get effective mobile communication. Copyright 2016 ACM.

Abstract
Dublin Core schemas are the core metadata models of most repositories, and this includes recent repositories dedicated to datasets. DC descriptors are generic and are being adapted to the needs of different communities with the so-called Dublin Core Application Profiles. DCAPs rely on the agreement within user communities, in a process mainly driven by their evolving needs. In this paper, we propose a complementary automated process, designed to help curators and users discover the descriptors that better suit the needs of a specific research group. We target the description of datasets, and test our approach using Dendro, a prototype research data management platform, where an experimental method is used to rank and present DC Terms descriptors to the users based on their usage patterns. In a controlled experiment, we gathered the interactions of two groups as they used Dendro to describe datasets from selected sources. One of the groups had descriptor ranking on, while the other had the same list of descriptors throughout the whole experiment. Preliminary results show that 1. some DC Terms are filled in more often than others, with different distribution in the two groups, 2. selected descriptors were increasingly accepted by users in detriment of manual selection and 3. users were satisfied with the performance of the platform, as demonstrated by a post-study survey.