Abstract
Research datasets in the so-called "long-tail of science" are easily lost after their primary use. Support for preservation, if available, is hard to fit in the research agenda. Our previous work has provided evidence that dataset creators are motivated to spend time on data description, especially if this also facilitates data exchange within a group or a project. This activity should take place early in the data generation process, when it can be regarded as an actual part of data creation. We present the first prototype of the Dendro platform, designed to help researchers use concepts from domain-specific ontologies to collaboratively describe and share datasets within their groups. Unlike existing solutions, ontologies are used at the core of the data storage and querying layer, enabling users to establish meaningful domain-specific links between data, for any domain. The platform is currently being tested with research groups from the University of Porto.

Abstract
The value of research data is recognized, and so is the importance of the associated metadata to contextualize, describe and ultimately render them understandable in the long term. Laboratory notebooks are an excellent source of domain-specific metadata, but this paper-based approach can pose risks of data loss, while limiting the possibilities of collaborative metadata production. The paper discusses the advantages of tools to complement paper-based laboratory notebooks in capturing metadata, regardless of the research domain. We propose LabTablet, an electronic laboratory book aimed at the collection of metadata from the early stages of the research workflow. To evaluate the use of LabTablet and the proposed workflow, researchers in two domains were asked to perform a set of tasks and provided insights about their experience. By rethinking the workflow and helping researchers to actively contribute to data description, the research outputs can be described with generic and domain-dependent metadata, thus improving their chances of being deposited, reused and preserved.

Abstract
Most current research data management solutions rely on a fixed set of descriptors (e.g. Dublin Core Terms) for the description of the resources that they manage. These are easy to understand and use, but their semantics are limited to general concepts, leaving out domain-specific metadata. The textual values for descriptors are easily indexed through free-text indexes, but faceted search and dataset interlinking becomes limited. From the point of view of the relational database schema modeler, designing a more flexible metadata model represents a non-trivial challenge because it means representing entities with attributes unknown at the time of modeling and that can change in time. Those traits, combined with the presence of hierarchies among the entities, can make the relational schema quite complex. This work demonstrates the approaches followed by current opensource platforms and proposes a graph-based model for achieving modular, ontology-based metadata for interlinked data assets in the Semantic Web. The proposed model was implemented in a collaborative research data management platform currently under development at the University of Porto. © 2014 ACM.

Abstract

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.