Abstract
Teaching complex assembly and maintenance skills to human operators usually requires extensive reading and the help of tutors. In order to reduce the training period and avoid the need for human supervision, an immersive teaching system using spatial augmented reality was developed for guiding inexperienced operators. The system provides textual and video instructions for each task while also allowing the operator to navigate between the teaching steps and control the video playback using a bare hands natural interaction interface that is projected into the workspace. Moreover, for helping the operator during the final validation and inspection phase, the system projects the expected 3D outline of the final product. The proposed teaching system was tested with the assembly of a starter motor and proved to be more intuitive than reading the traditional user manuals. This proof of concept use case served to validate the fundamental technologies and approaches that were proposed to achieve an intuitive and accurate augmented reality teaching application. Among the main challenges were the proper modeling and calibration of the sensing and projection hardware along with the 6 DoF pose estimation of objects for achieving precise overlap between the 3D rendered content and the physical world. On the other hand, the conceptualization of the information flow and how it can be conveyed on-demand to the operator was also of critical importance for ensuring a smooth and intuitive experience for the operator.

Abstract
We face a technology growth period that promotes STEM (Science, Technology, Engineering and Mathematics) as basic set of competences in diversified professional backgrounds. However, the process of teaching this areas is complex and abstract. We propose using game-based learning (GBL) for increased engagement and efficacy. More specifically, we use pervasive games as the drive of the learning process, promoting challenges in the context of the learner. To develop pervasive games, we require mobile devices (for location-based games) and a set of functionalities regarding location, time, and context of the user. In this article, we present the design of a mobile app to implement pervasive games. The perks of the mobile devices will be used to get unique and engaging challenges, to stimulate learning. Therefore, the learning process will become more enjoyable, and it will accomplish the goal of learning "anytime anywhere". This work sets upon previous work developed in the H2020 BEACONING project and the mobile app presented will allow other developers to create pervasive games for distinct areas of learning in a more engaging and effective way.

Abstract
Augmented reality has seen an increased popularity among the last decades due to technological advances and, a consequent growth of the amount of augmented reality systems, became available. However, in order to diffuse this technology successfully, understand users' feelings when using augmented reality applications is considered a major issue. This study implemented this technology in a cultural heritage outdoor context and tested it in-situ to evaluate user's perspective regarding to personal satisfaction-including cultural enrichment acquired -, ease of use and their intention to use it. The results obtained, through questionnaires, presented the visitors acceptance regarding the usage of this type of solutions among a cultural heritage context, since it may become future visits more pleasant and desirable. The user's majority expressed the request to use this technology more often in cultural heritage spaces.

Abstract
A grand challenge from the wind energy industry is to provide reliable forecasts on mountain winds several hours in advance at microscale (similar to 100 m) resolution. This requires better microscale wind-energy physics included in forecasting tools, for which field observations are imperative. While mesoscale (similar to 1 km) measurements abound, microscale processes are not monitored in practice nor do plentiful measurements exist at this scale. After a decade of preparation, a group of European and U.S. collaborators conducted a field campaign during 1 May-15 June 2017 in Vale Cobrao in central Portugal to delve into microscale processes in complex terrain. This valley is nestled within a parallel double ridge near the town of Perdigao with dominant wind climatology normal to the ridges, offering a nominally simple yet natural setting for fundamental studies. The dense instrument ensemble deployed covered a similar to 4 km x 4 km swath horizontally and similar to 10 km vertically, with measurement resolutions of tens of meters and seconds. Meteorological data were collected continuously, capturing multiscale flow interactions from synoptic to microscales, diurnal variability, thermal circulation, turbine wake and acoustics, waves, and turbulence. Particularly noteworthy are the extensiveness of the instrument array, space-time scales covered, use of leading-edge multiple-lidar technology alongside conventional tower and remote sensors, fruitful cross-Atlantic partnership, and adaptive management of the campaign. Preliminary data analysis uncovered interesting new phenomena. All data are being archived for public use.

Abstract
Dublin Core descriptors capture metadata in most repositories, and this includes recent repositories dedicated to datasets. DC descriptors are generic and are being adapted to the requirements of different communities with the so-called Dublin Core Application Profiles that rely on the agreement within user communities, taking into account their evolving needs. In this paper, we propose an automated process to help curators and users discover the descriptors that best suit the needs of a specific research group in the task of describing and depositing datasets. Our approach is supported on 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. User interaction is recorded and used to score descriptors. 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 viewed descriptors according to the ranking, while the other had the same list of descriptors throughout the experiment. Preliminary results show that (1) some DC Terms are filled in more often than others, with different distribution in the two groups, (2) descriptors in higher ranks were increasingly accepted by users in detriment of manual selection, (3) users were satisfied with the performance of the platform, and (4) the quality of description was not hindered by descriptor ranking.

Abstract
Novel data collection tools, methods and new techniques in biotechnology can facilitate improved health strategies that are customised to each individual. One key challenge to achieve this is to take advantage of the massive volumes of personal anonymous data, relating each profile to health and disease, while accounting for high diversity in individuals, populations and environments. These data must be analysed in unison to achieve statistical power, but presently cohort data repositories are scattered, hard to search and integrate, and data protection and governance rules discourage central pooling.