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About

I am an Associate Professor with the Department of Informatics Engineering of FEUP, University of Porto, and a senior researcher at INESC TEC. I have collaborated on and been in charge of projects in the areas of cultural heritage, multimedia databases, information retrieval and combinatorial optimisation. I have supervised 9 PhD and 33 MSc dissertations. I was the technical leader of the SAPO/U.Porto extension laboratory for 5 years. My teaching activities include courses in the Informatics Engineering and Information Science programmes. Research Data Management is the core of my current research activity. I am the PI of TAIL (FCT/POCI), on research data management workflows for data publication, and I lead the DataPublication@U.Porto pilot in the EUDAT european initiative. I am a member of the Working Group for the National Policy on Open Science with SECTES. My research interests include information retrieval, digital preservation and knowledge representation.

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Details

Details

  • Name

    Cristina Ribeiro
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st April 1985
007
Publications

2021

Institutional Support for Data Management Plans: Five Case Studies

Authors
Karimova, Y; Ribeiro, C; David, G;

Publication
Metadata and Semantic Research - Communications in Computer and Information Science

Abstract

2020

ArchOnto, a CIDOC-CRM-Based Linked Data Model for the Portuguese Archives

Authors
Koch, I; Ribeiro, C; Lopes, CT;

Publication
Digital Libraries for Open Knowledge - Lecture Notes in Computer Science

Abstract

2020

Evaluating the Quality of an Online Course in Information Literacy Applied to Engineering Students

Authors
Ramos, TO; Morais, C; Ribeiro, C;

Publication
Handbook of Research on Determining the Reliability of Online Assessment and Distance Learning - Advances in Mobile and Distance Learning

Abstract
An academic library created an online course in information literacy skills in 2007 for engineering students. This chapter reports the evaluation of the course's effectiveness in developing those skills. In the academic year 2015/2016, a case study with a mixed-methods approach was applied to 5th-year students (N=91) enrolled in a course unit for Master Dissertation's preparation in the informatics and computing engineering programme. Students showed high confidence in their information literacy skills. Online assignments' performance was good, but activities revealed quality issues. Performance in the course unit's assignments reveals a poor application of acquired skills. But satisfaction is high: students value independent learning and online access to resources and content. Despite evidence of some positive impact, the course lacks effectiveness due to issues in the course unit's assignments. Needed improvements include a better realignment with students' needs and a redesign with an instructional model to assure the promotion of students' success.

2020

Role of Content Analysis in Improving the Curation of Experimental Data

Authors
Aguiar Castro, JD; Landeira, C; da Silva, JR; Ribeiro, C;

Publication
Int. J. Digit. Curation

Abstract
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. [This paper is a conference pre-print presented at IDCC 2020 after lightweight peer review.]

2019

Data Deposit in a CKAN Repository: A Dublin Core-Based Simplified Workflow

Authors
Karimova, Y; Castro, JA; Ribeiro, C;

Publication
Communications in Computer and Information Science - Digital Libraries: Supporting Open Science

Abstract

Supervised
thesis

2021

Research data description in multiple domains: supporting researchers with data management plans and flexible metadata models

Author
Yulia Karimova

Institution
UP-FEUP

2020

Engaging researchers in research data management: creating metadata models for multi-domain dataset description

Author
João Daniel Aguiar de Castro

Institution
UP-FEUP

2020

Aplicação das recomendações da Research Data Alliance em grupos de investigação portugueses

Author
Jéssica Alexandra Lopes Barbosa

Institution
UP-FEUP

2020

Plano de gestão de dados para a produção de dados FAIR: O caso de uso FRAILSURVEY

Author
André Filipe da Costa Maciel

Institution
UP-FEUP

2019

Application of the LabTablet app in a laboratory environment: Case study I3S

Author
Ana Luís da Costa Ferreira

Institution
UP-FEUP