Repositório Polen Fundação para a Ciência e a Tecnologia
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Neural correlates of emotional responses to self-selected music: Evidence from multivariate pattern analysis
No full textMusic is a uniquely powerful stimulus for evoking complex and deeply felt emotions. While previous research has identified neural correlates of music-evoked emotional responses, less is known about how these felt emotions are represented in the brain, particularly when elicited by familiar, personally meaningful music. Here, we used a personalized fMRI paradigm in which participants (N = 20) each selected musical excerpts corresponding to the nine emotion categories defined by the Geneva Emotional Music Scale. These self-selected excerpts were presented during functional MRI scanning. We first examined the neural correlates of music-evoked emotion by comparing brain activity during music listening to that during exposure to white noise. The maps were consistent with previous research, highlighting clusters in sensory and limbic regions. We then used multivoxel pattern analysis to decode emotion categories from whole-brain activation patterns. The results revealed that music-evoked emotions could be reliably discriminated based on distributed neural activity, with consistent involvement of the superior temporal gyrus, supplementary motor area, amygdala, and cerebellum, among other auditory, motor, and interoceptive regions. These findings provide new insight into the neural encoding of musical emotions and highlight the value of personalized, music-based paradigms for research in auditory and affective neuroscience
Portuguese Organizations in ROR
No full textThe files in this dataset contain detailed information on the presence of Portuguese organizations in the Research Organization Registry (ROR), based on matching with ISNI (International Standard Name Identifier) records. Each file includes the following columns:
ISNI – Unique identifier assigned to the organization by the ISNI system.
Organization Name – Official name of the entity associated with the ISNI.
ROR ID – Unique identifier assigned by ROR, when applicable.
A new file is generated with each ROR data dump, allowing for continuous updates and monitoring of the representation of Portuguese organizations over time
Nubian points of Nazlet Khater
No full text3D models and landmarks from Nubian stone artifacts recovered from the site of Nazlet Khate
UBIRIS.v1
No full textUBIRIS.v1 database is composed of 1877 images collected from 241 persons during September, 2004 in two distinct sessions. Its most relevant characteristic is to incorporate images with several noise factors, simulating less constrained image acquisition environments. This enables the evaluation of the robustness of iris recognition methods.
For the first image capture session, the enrollment one, we tried to minimize noise factors, specially those relative to reflections, luminosity and contrast, having installed image capture framework inside a dark room.
In the second session we changed the capture place in order to introduce natural luminosity factor. This propitiates the appearance of heterogeneous images with respect to reflections, contrast, luminosity and focus problems. Images collected at this stage simulate the ones captured by a vision system without or with minimal active participation from the subjects, adding several noise problems. These images will be on the recognition stage compared to the ones collected during first session
Consumers financial health
No full textThe dataset comprises a broad range of variables to understand the full picture of consumers’ financial health: family socio-demographics, total income, total expenses, employment information, as well as all credit details. The features considered for the analyses were: socio-demographic characterization (marital status, level of education completed, number of people in the household), the perceived causes for over-indebtedness (from a predetermined pool of causes), and data concerning their economic situation, including the total income and expenses of the household as well as data concerning their credits and debts (amount of the monthly installments for credit cards, housing credit, car credit, personal credit and other types of credit or debts; total monthly installment concerning all credits). Each household is represented by one record (one observation) of the dataset with many features to describe their characteristics and behavio