HumanISE - Indexed Articles in Journals

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Browsing HumanISE - Indexed Articles in Journals by Author "215"
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    Can user and task characteristics be used as predictors of success in health information retrieval sessions?
    ( 2018) Carla Lopes ; Sérgio Nunes ; Oroszlanyova,M ; Cristina Ribeiro ; 215 ; 5448 ; 6205
    Introduction. The concept and study of relevance has been a central subject in information science. Although research in information retrieval has been focused on topical relevance, other kinds of relevance are also important and justify further study. Motivational relevance is typically inferred by criteria such as user satisfaction and success. Method. Using an existing dataset composed by an annotated set of health Web documents assessed for relevance and comprehension by a group of users, we build a multivariate prediction model for the motivational relevance of search sessions. Analysis. The analysis was based on lasso variable selection, followed by model selection using multiple logistic regression. Results. We have built two regression models; the full model, which considers all variables of the dataset, has a lower estimated prediction error than the reduced model, which contains the statistically-significant variables from the full model. The higher values of evaluation metrics, including accuracy, specificity and sensitivity in the full model support this finding. The full model has an accuracy of 91.94%, and is better at predicting motivational relevance. Conclusions. Our findings suggest features that can be considered by search engines to estimate motivational relevance, to be used in addition to topical relevance. Among these features, a high level of success in Web search and in health information search on social networks and chats are some of the most influencing user features. This shows that users with higher computer literacy might feel more satisfied and successful after completing the search tasks. In terms of task features, the results suggest that users with clearer goals feel more successful. Moreover, results show that users would benefit from the help of the system in clarifying the retrieved documents.
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    A comparison of research data management platforms: architecture, flexible metadata and interoperability
    ( 2017) Ricardo Carvalho Amorim ; João Aguiar Castro ; João Rocha Silva ; Cristina Ribeiro ; 215 ; 5820 ; 5961 ; 6528
    Research data management is rapidly becoming a regular concern for researchers, and institutions need to provide them with platforms to support data organization and preparation for publication. Some institutions have adopted institutional repositories as the basis for data deposit, whereas others are experimenting with richer environments for data description, in spite of the diversity of existing workflows. This paper is a synthetic overview of current platforms that can be used for data management purposes. Adopting a pragmatic view on data management, the paper focuses on solutions that can be adopted in the long tail of science, where investments in tools and manpower are modest. First, a broad set of data management platforms is presented-some designed for institutional repositories and digital libraries-to select a short list of the more promising ones for data management. These platforms are compared considering their architecture, support for metadata, existing programming interfaces, as well as their search mechanisms and community acceptance. In this process, the stakeholders' requirements are also taken into account. The results show that there is still plenty of room for improvement, mainly regarding the specificity of data description in different domains, as well as the potential for integration of the data management platforms with existing research management tools. Nevertheless, depending on the context, some platforms can meet all or part of the stakeholders' requirements.
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    Research Data Management Tools and Workflows: Experimental Work at the University of Porto
    ( 2018) Cristina Ribeiro ; Rocha da Silva,J ; João Aguiar Castro ; Carvalho Amorim,R ; João Correia Lopes ; Gabriel David ; 212 ; 215 ; 250 ; 5961
    Research datasets include all kinds of objects, from web pages to sensor data, and originate in every domain. Concerns with data generated in large projects and well-funded research areas are centered on their exploration and analysis. For data in the long tail, the main issues are still how to get data visible, satisfactorily described, preserved, and searchable. Our work aims to promote data publication in research institutions, considering that researchers are the core stakeholders and need straightforward workflows, and that multi-disciplinary tools can be designed and adapted to specific areas with a reasonable effort. For small groups with interesting datasets but not much time or funding for data curation, we have to focus on engaging researchers in the process of preparing data for publication, while providing them with measurable outputs. In larger groups, solutions have to be customized to satisfy the requirements of more specific research contexts. We describe our experience at the University of Porto in two lines of enquiry. For the work with long-tail groups we propose general-purpose tools for data description and the interface to multi-disciplinary data repositories. For areas with larger projects and more specific requirements, namely wind infrastructure, sensor data from concrete structures and marine data, we define specialized workflows. In both cases, we present a preliminary evaluation of results and an estimate of the kind of effort required to keep the proposed infrastructures running.  The tools available to researchers can be decisive for their commitment. We focus on data preparation, namely on dataset organization and metadata creation. For groups in the long tail, we propose Dendro, an open-source research data management platform, and explore automatic metadata creation with LabTablet, an electronic laboratory notebook. For groups demanding a domain-specific approach, our analysis has resulted in the development of models and applications to organize the data and support some of their use cases. Overall, we have adopted ontologies for metadata modeling, keeping in sight metadata dissemination as Linked Open Data.
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    Role of Content Analysis in Improving the Curation of Experimental Data
    ( 2020) João Aguiar Castro ; Landeira,C ; da Silva,JR ; Cristina Ribeiro ; 215 ; 5961
    As researchers are increasingly seeking tools and specialized support to perform research data management activities, the collaboration with data curators can be fruitful. Yet, establishing a timely collaboration between researchers and data curators, grounded in sound communication, is often demanding. In this paper we propose manual content analysis as an approach to streamline the data curator workflow. With content analysis curators can obtain domain-specific concepts used to describe experimental configurations in scientific publications, to make it easier for researchers to understand the notion of metadata and for the development of metadata tools. We present three case studies from experimental domains, one related to sustainable chemistry, one to photovoltaic generation and another to nanoparticle synthesis. The curator started by performing content analysis in research publications, proceeded to create a metadata template based on the extracted concepts, and then interacted with researchers. The approach was validated by the researchers with a high rate of accepted concepts, 84 per cent. Researchers also provide feedback on how to improve some proposed descriptors. Content analysis has the potential to be a practical, proactive task, which can be extended to multiple experimental domains and bridge the communication gap between curators and researchers.