Hardware-accelerated interactive data visualization for neuroscience in Python.

Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization

Developing a curriculum of open educational resources for Linked Data

The EUCLID project is developing an educational curriculum about Linked Data, supported by multimodal Open Educational Resources (OERs) tailored to the real needs of data practitioners. The EUCLID OERs facilitate professional training for data practitioners, who aim to use Linked Data in their daily work. The EUCLID OERs are implemented as a combination of living learning materials and activities (eBook, online courses, webinars, face-to-face training), produced via a rigorous process and validated by the user community through continuous feedback

Parametric Surfaces for Augmented Architecture representation

Augmented Reality (AR) represents a growing communication channel, responding to the need to expand reality with additional information, offering easy and engaging access to digital data. AR for architectural representation allows a simple interaction with 3D models, facilitating spatial understanding of complex volumes and topological relationships between parts, overcoming some limitations related to Virtual Reality. In the last decade different developments in the pipeline process have seen a significant advancement in technological and algorithmic aspects, paying less attention to 3D modeling generation. For this, the article explores the construction of basic geometries for 3D model’s generation, highlighting the relationship between geometry and topology, basic for a consistent normal distribution. Moreover, a critical evaluation about corrective paths of existing 3D models is presented, analysing a complex architectural case study, the virtual model of Villa del Verginese, an emblematic example for topological emerged problems. The final aim of the paper is to refocus attention on 3D model construction, suggesting some "good practices" useful for preventing, minimizing or correcting topological problems, extending the accessibility of AR to people engaged in architectural representation

Collaborative geographic visualization

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas AmbientaisThe present document is a revision of essential references to take into account when developing ubiquitous Geographical Information Systems (GIS) with collaborative visualization purposes. Its chapters focus, respectively, on general principles of GIS, its multimedia components and ubiquitous practices; geo-referenced information visualization and its graphical components of virtual and augmented reality; collaborative environments, its technological requirements, architectural specificities, and models for collective information management; and some final considerations about the future and challenges of collaborative visualization of GIS in ubiquitous environment

Competency level of technological pedagogical contents knowledge (TPCK) framework amongst graduate teachers

This article propose a framework for educational technology based on Shulman’s formulation of ‘‘pedagogical content knowledge’’ and extend it to the integration of technology into it. It attempts to capture some of the essential qualities of teacher knowledge required for technology integration in teaching. Briefly, that thoughtful pedagogical uses of technology require the development of a complex, situated form of knowledge that we call Technological Pedagogical Content Knowledge (TPCK). The TPCK framework has much to offer to discussions of technology integration at multiple levels: theoretical, pedagogical, and methodological as well as the complex roles of, and interplay among, three main components of learning environments: content, pedagogy, and technology