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Sobre
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Sobre

Sou Professora Associada no Departamento de Engenharia Informática da FEUP, Universidade do Porto, e investigadora senior do INESC TEC. Colaborei e fui responsável por projetos nas áreas do património cultural, bases de dados multimédia, recuperação de informação e otimização combinatória. Supervisionei 9 dissertações de doutoramento e 33 de mestrado. Fui responsável técnica do laboratório SAPO/U.Porto durante 5 anos. A minha atividade de ensino desenvolve-se na Engenharia Informática e na Ciência da Informação. A gestão de dados de investigação está no centro da minha atividade de investigação atual. Sou investigadora principal do projeto TAIL (FCT/POCI), que desenvolve ferramentas e métodos para a publicação de dados de investigação e responsável pelo piloto DataPublication@U.Porto da iniciativa europeia EUDAT. Sou membro do Grupo de Trabalho para a Política Nacional da Ciência Aberta da SECTES. Os meus interesses de investigação incluem a recuperação de informação, a preservação digital e a representação de conhecimento.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Cristina Ribeiro
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 abril 1985
007
Publicações

2021

Institutional Support for Data Management Plans: Five Case Studies

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

Publicação
Communications in Computer and Information Science

Abstract

2020

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

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

Publicação
Digital Libraries for Open Knowledge - 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, Lyon, France, August 25-27, 2020, Proceedings

Abstract

2020

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

Autores
Ramos, TO; Morais, C; Ribeiro, C;

Publicação
Handbook of Research on Determining the Reliability of Online Assessment 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

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

Publicação
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

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

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

Abstract

Teses
supervisionadas

2021

Analytical Tweezers for cell manipulation and diagnostic

Autor
Inês Alves Carvalho

Instituição
UP-FCUP

2021

Urban transport evaluation using knowledge extracted from social media

Autor
Francisco André Barreiros Murços

Instituição
UP-FEUP

2021

Os consumidores millennials de marcas de luxo: quais as suas especificidades e diferenças?

Autor
Alexandra Oliveira Frade

Instituição
UP-FEP

2021

Automation of Enterprise Architecture Discovery based on Event Mining from API Gateway logs

Autor
Carlos Pinheiro

Instituição
UTAD

2021

Análise de oferta de serviços na área de consultoria informática

Autor
Pedro Manuel Almeida Roseira

Instituição
UP-FEUP