Abstract
Managing research data often requires the creation or reuse of specialised metadata schemas to satisfy the metadata requirements of each research group. Ontologies present several advantages over metadata schemas. In particular, they can be shared and improved upon more easily, providing the flexibility required to establish relationships between datasets and concepts from distinct domains. In this paper, we present a preliminary experiment on the use of ontologies for the description of biodiversity datasets. With a strong focus on the dynamics of individual species, species diversity, biological communities and ecosystems, the Predictive Ecology research group of CIBIO has adopted the INSPIRE European recommendation as the primary tool for metadata compliance across its research data description. We build upon this experience to model the BIOME ontology for the biodiversity domain. The ontology combines concepts from INSPIRE, matching them against the ones defined in the Dublin Core, FOAF and CERIF ontologies. Dendro, a prototype for collaborative data description, uses the ontology to provide an environment where biodiversity metadata records are available as Linked Open Data.

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
The Wind Scanner e-Science platform architecture and the underlying premises are discussed. It is a collaborative platform that will provide a repository for experimental data and metadata. Additional data processing capabilities will be incorporated thus enabling in-situ data processing. Every resource in the platform is identified by a Uniform Resource Identifier (URI), enabling an unequivocally identification of the field(s) campaign(s) data sets and metadata associated with the data set or experience. This feature will allow the validation of field experiment results and conclusions as all managed resources will be linked. A centralised node (Hub) will aggregate the contributions of 6 to 8 local nodes from EC countries and will manage the access of 3 types of users: data-curator, data provider and researcher. This architecture was designed to ensure consistent and efficient research data access and preservation, and exploitation of new research opportunities provided by having this "Collaborative Data Infrastructure". The prototype platform-WindS@UP-enables the usage of the platform by humans via a Web interface or by machines using an internal API (Application Programming Interface). Future work will improve the vocabulary ("application profile") used to describe the resources managed by the platform.

Abstract
High-maturity software development processes, making intensive use of metrics and quantitative methods, such as the Team Software Process (TSP) and the accompanying Personal Software Process (PSP), can generate a significant amount of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions. However, there is a lack of tool support for automating the data analysis and the recommendation of improvement actions, and hence diminish the manual effort and expert knowledge required. So, we propose in this paper a comprehensive performance model, addressing time estimation accuracy, quality and productivity, to enable the automated (tool based) analysis of performance data produced in the context of the PSP, namely, identify performance problems and their root causes, and subsequently recommend improvement actions. Performance ranges and dependencies in the model were calibrated and validated, respectively, based on a large PSP data set referring to more than 30,000 finished projects. © 2014 ACM.

Abstract
Understanding the factors that affect the productivity of software developers and may cause productivity variations among individuals and projects is important for anyone interested in improving software engineering performance and estimates, and in particular for users of high-maturity processes, such as the Personal Software Process (PSP) and the Team Software Process (TSP). In order to contribute to the understanding of the personal and non-personal factors that affect productivity, we analyzed the data from more than 3000 developers that concluded successfully the 10 projects of the PSP for Engineers I/II training course. Regarding non-personal factors, by conducting a detailed per-phase analysis, we found significant variations of productivity among projects that can be partially explained by process changes. Regarding personal factors, we found significant variations among individuals that can be partially explained by personal experience.

Abstract
This paper presents an approach for testing mobile applications using reverse engineering and behavioural patterns. The goal of this research work is to ease the testing of mobile applications by automatically identifying and testing behaviour that is common in this type of applications, i.e., behaviour patterns. The approach includes a tool to automatically explore an Android application. This tool also identifies patterns in the behaviour of the application and apply tests previously associated with those patterns. The final results of this research work will be a catalogue of behavioural patterns and the tool which will output a report on the matched patterns and another one on the testing of those patterns.