11,877 research outputs found

    NASA GIBS and Worldview: Visualizing NASA's Earth Science Data for All to Explore

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    For more than 20 years, the NASA Earth Observing System (EOS) has operated dozens of remote sensing satellites collecting nearly 15 Petabytes of data that span thousands of science parameters. Within these observations are keys the Earth Scientists have used to unlock many discoveries that we now understand about our planet. Also contained within these observations are a myriad of opportunities for learning and education. The challenge is making them accessible to educators and students in intuitive and simple ways so that effort can be spent on lesson enrichment and not overcoming technical hurdles.The NASA Global Imagery Browse Services (GIBS) system and NASA Worldview interactive mapping site provide a unique view into EOS data through daily full resolution visualizations of hundreds of Earth science parameters. For many of these parameters, visualizations are available within hours of acquisition from the satellite. For others, visualizations are available for the entire mission of the satellite. Accompanying the visualizations are visual aids such as color legends, place names, and orbit tracks. By using these visualizations, educators and students can observe natural phenomena that enrich a scientific education.This presentation will provide an overview of the visualizations available in NASA GIBS and Worldview and how they are accessed. Specific attention will be given to the newer capabilities and accomplishments, including: Support for geostationary sub-daily visualizations, Enhanced support for vector-based visualizations, Improved Worldview tour and snapshot capabilities, New imagery products across a growing set of scientific areas

    Leading Undergraduate Students to Big Data Generation

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    People are facing a flood of data today. Data are being collected at unprecedented scale in many areas, such as networking, image processing, virtualization, scientific computation, and algorithms. The huge data nowadays are called Big Data. Big data is an all encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. In this article, the authors present a unique way which uses network simulator and tools of image processing to train students abilities to learn, analyze, manipulate, and apply Big Data. Thus they develop students handson abilities on Big Data and their critical thinking abilities. The authors used novel image based rendering algorithm with user intervention to generate realistic 3D virtual world. The learning outcomes are significant

    Proceedings of the Second Program Visualization Workshop, 2002

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    The Program Visualization Workshops aim to bring together researchers who design and construct program visualizations and, above all, educators who use and evaluate visualizations in their teaching. The first workshop took place in July 2000 at Porvoo, Finland. The second workshop was held in cooperation with ACM SIGCSE and took place at HornstrupCentret, Denmark in June 2002, immediately following the ITiCSE 2002 Conference in Aarhus, Denmark

    Visualyzart Project – The role in education

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    The VisualYzARt project intends to develop research on mobile platforms, web and social scenarios in order to bring augmented reality and natural interaction for the general public, aiming to study and validate the adequacy of YVision platform in various fields of activity such as digital arts, design, education, culture and leisure. The VisualYzARt project members analysed the components available in YVision platform and are defining new ones that allow the creation of applications to a chosen activity, effectively adding a new language to the domain YVision. In this paper we will present the role of the InstitutoPolitĂ©cnico de SantarĂ©m which falls into the field of education.VisualYzART is funded by QREN – Sistema de Incentivos Ă  Investigação e Desenvolvimento TecnolĂłgico (SI I&DT), Project n. Âș 23201 - VisualYzARt (from January 2013 to December 2014). Partners: YDreams Portugal; Instituto PolitĂ©cnico de SantarĂ©m - Gabinete de e-Learning; Universidade de Coimbra - Centro de InformĂĄtica e Sistemas; Instituto PolitĂ©cnico de Leiria - Centro de Investigação em InformĂĄtica e ComunicaçÔes; Universidade CatĂłlica do Porto - Centro de Investigação em CiĂȘncia e Tecnologia das Artes.info:eu-repo/semantics/publishedVersio

    Teaching data structures through group based collaborative peer interactions

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    Management and display of four-dimensional environmental data sets using McIDAS

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    Over the past four years, great strides have been made in the areas of data management and display of 4-D meteorological data sets. A survey was conducted of available and planned 4-D meteorological data sources. The data types were evaluated for their impact on the data management and display system. The requirements were analyzed for data base management generated by the 4-D data display system. The suitability of the existing data base management procedures and file structure were evaluated in light of the new requirements. Where needed, new data base management tools and file procedures were designed and implemented. The quality of the basic 4-D data sets was assured. The interpolation and extrapolation techniques of the 4-D data were investigated. The 4-D data from various sources were combined to make a uniform and consistent data set for display purposes. Data display software was designed to create abstract line graphic 3-D displays. Realistic shaded 3-D displays were created. Animation routines for these displays were developed in order to produce a dynamic 4-D presentation. A prototype dynamic color stereo workstation was implemented. A computer functional design specification was produced based on interactive studies and user feedback

    A New Approach to Developing Interactive Software Modules Through Graduate Education

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    Educational technology has attained significant importance as a mechanism for supporting experiential learning of science concepts. However, the growth of this mechanism is limited by the significant time and technical expertise needed to develop such products, particularly in specialized fields of science. We sought to test whether interactive, educational, online software modules can be developed effectively by students as a curriculum component of an advanced science course. We discuss a set of 15 such modules developed by Harvard University graduate students to demonstrate various concepts related to astronomy and physics. Their successful development of these modules demonstrates that online software tools for education and outreach on specialized topics can be produced while simultaneously fulfilling project-based learning objectives. We describe a set of technologies suitable for module development and present in detail four examples of modules developed by the students. We offer recommendations for incorporating educational software development within a graduate curriculum and conclude by discussing the relevance of this novel approach to new online learning environments like edX.Astronom

    Prospects for computational steering of evolutionary computation

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    Currently, evolutionary computation (EC) typically takes place in batch mode: algorithms are run autonomously, with the user providing little or no intervention or guidance. Although it is rarely possible to specify in advance, on the basis of EC theory, the optimal evolutionary algorithm for a particular problem, it seems likely that experienced EC practitioners possess considerable tacit knowledge of how evolutionary algorithms work. In situations such as this, computational steering (ongoing, informed user intervention in the execution of an otherwise autonomous computational process) has been profitably exploited to improve performance and generate insights into computational processes. In this short paper, prospects for the computational steering of evolutionary computation are assessed, and a prototype example of computational steering applied to a coevolutionary algorithm is presented
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