Abstract
This paper presents work-in-progress of the POSTDATA project. This project aims to provide means to solve the interoperability issues that exist among the digital poetry repertoires. These repertoires hold data of poetry metrics that is locked in their own databases and it is not freely available to be compared and to be used by intelligent machines that could infer over the data. The POSTDATA project will use Linked Open Data (LOD) technologies to overcome the interoperability problems. POSTDATA is developing a metadata application profile (MAP) for the digital poetry repertoires, a construct that enhances interoperability. This development follows the method for the development of MAP (Me4MAP). A MAP for the digital poetry repertoires will open doors for these repertoires to be able to structure the data with a common model in order to publish it as Linked Open Data. This paper presents how this MAP is being developed so far.

Abstract
The effort of generating large city scenes is greatly reduced when using procedural modelling techniques. Nonetheless. users are limited to defining rules and tuning parameters in a define-generation reanalyse cycle, reducing direct control. Describing buildings with split-based operations may disrupt the creative process by forcing the user to focus on how to describe a building instead of how a building should look. We use planar shapes and two-dimensional boo lean operations allowing procedural descriptions that are More aligned with humans' perceptions of buildings while letting users directly manipulate shapes (e.g., by dragging) in an interactive application.

Abstract
In many institutions relational databases are used as a tool for managing information related to day to day activities. Institutions may be required to keep the information stored in relational databases accessible because of many reasons including legal requirements and institutional policies. However, the evolution in technology and change in users with the passage of time put the information stored in relational databases in danger. In the long term the information may become inaccessible when the operating system, database management system or the application software is not available any more or the contextual information not stored in the database may be lost thus affecting the authenticity and understandability of the information. This paper presents an approach for preserving relational databases for the long-term. The proposal involves migrating a relational database to a dimensional model which is simple to understand and easy to write queries against. Practical transformation rules are developed by carrying out multiple case studies. One of the case studies is presented as a running example in the paper. Systematic implementation of the rules ensures no loss of information in the process except for the unwanted details. The database preserved using the approach is converted to an open format but may be reloaded to a database management system in the long-term.

Abstract
Research data management is acknowledged as an important concern for institutions and several platforms to support data deposits have emerged. In this paper we start by overviewing the current practices in the data management workflow and identifying the stakeholders in this process. We then compare four recently proposed data repository platforms-DSpace, CKAN, Zenodo and Figshare-considering their architecture, support for metadata, API completeness, as well as their search mechanisms and community acceptance. To evaluate these features, we take into consideration the identified stakeholders' requirements. In the end, we argue that, depending on local requirements, different data repositories can meet some of the stakeholders requirements. Nevertheless, there is still room for improvements, mainly regarding the compatibility with the description of data from different research domains, to further improve data reuse.

Abstract
Prior studies have shown that terminology support can improve health information retrieval but have not taken into account the characteristics of the user performing the search. In this chapter, the impact of translating queries' terms between lay and medico-scientific terminology, in users with different levels of health literacy and topic familiarity, is evaluated. Findings demonstrate that medico-scientific queries demand more from the users and are mostly aimed at health professionals. In addition, these queries retrieve documents that are less readable and less well understood by users. Despite this, medico-scientific queries are associated with higher precision in the top-10 retrieved documents results and tend slightly to generate knowledge with less incorrect contents, the researchers concluded that search engines should provide query suggestions with medico-scientific terminology, whenever the user is able to digest it, that is, in users above the lowest levels of health literacy and topic familiarity. On the other hand, retrieval systems should provide lay alternative queries in users with inadequate health literacy or in those unfamiliar with a topic. In fact, the quantity of incorrect contents in the knowledge that emerges from a medico-scientific session tends to decrease with topic familiarity and health literacy. In terms of topic familiarity, the opposite happens with Graded Average Precision. Moreover, users most familiar with a topic tend to have higher motivational relevance with medico-scientific queries than with lay queries. This work is the first to consider user context features while studying the impact of a query processing technique in several aspects of the retrieval process, including the medical accuracy of the acquired knowledge. © 2015 by Emerald Group Publishing Limited.

Abstract
In the recent trend of data-intensive science, data publication is essential and institutions have to promote it with the researchers. For the past decade, institutional repositories have been widely established for publications, and the motivations for deposit are well established. The situation is quite different for data, as we argue on the basis of a 5-year experience with research data management at the University of Porto. We address research data management from a disciplined yet flexible point of view, focusing on domain-specific metadata models embedded in intuitive tools, to make it easier for researchers to publish their datasets. We use preliminary data from a recent experiment in data publishing to identify motivators and deterrents for data publishing.