We have relevant datasets, repositories, frameworks and tools of relevance for research and technology transfer initiatives related to knowledge extraction. This section provides an overview on a selection of them and links to download or contact details.

The MdM Strategic Research Program has its own community in Zenodo for material available in this repository  as well as at the UPF e-repository  . Below a non-exhaustive list of datasets representative of the research in the Department.

As part of the promotion of the availability of resources, the creation of specific communities in Zenodo has also been promoted, at level of research communities (for instance, MIR and Educational Data Analytics) or MSc programs (for instance, the Master in Sound and Music Computing)

 

 

Back Mangado N., Piella G., Noailly J., Pons-Prats J., González Ballester M.A. Analysis of uncertainty and variability in finite element computational models for biomedical engineering: characterization and propagation. Frontiers in Bioengineering and Biotechnology.

Mangado N., Piella G., Noailly J., Pons-Prats J., González Ballester M.A. Analysis of uncertainty and variability in finite element computational models for biomedical engineering: characterization and propagation. Frontiers in Bioengineering and Biotechnology, vol. 4(85), 2016. (doi:10.3389/fbioe.2016.00085)

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

Keywords: uncertainty quantification, finite element models, random variables, intrusive and non-intrusive methods, sampling techniques, computational modeling