Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Research Data: Who will share what, with whom, when, and why?

The deluge of scientific research data has excited the general public, as well as the scientific community, with the possibilities for better understanding of scientific problems, from climate to culture. For data to be available, researchers must be willing and able to share them. The policies of governments, funding agencies, journals, and university tenure and promotion committees also influence how, when, and whether research data are shared. Data are complex objects. Their purposes and the methods by which they are produced vary widely across scientific fields, as do the criteria for sharing them. To address these challenges, it is necessary to examine the arguments for sharing data and how those arguments match the motivations and interests of the scientific community and the public. Four arguments are examined: to make the results of publicly funded data available to the public, to enable others to ask new questions of extant data, to advance the state of science, and to reproduce research. Libraries need to consider their role in the face of each of these arguments, and what expertise and systems they require for data curation.

Theory and Practice of Data Citation

Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive", where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated datasets. Yet, given a dataset, there is no quantitative, consistent and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded or what value it has. The development of a theory and practice of data citation is fundamental for considering data as first-class research objects with the same relevance and centrality of traditional scientific products. Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation. The current panorama is many-faceted and an overall view that brings together diverse aspects of this topic is still missing. Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical (the why and what) and the practical (the how) angle.Comment: 24 pages, 2 tables, pre-print accepted in Journal of the Association for Information Science and Technology (JASIST), 201

Leopoldo Lugones and Jorge Luis Borges on Science: The Garden of Forking Opinions

This paper attempts to show how the fantastic authors Leopoldo Lugones and Jorge Luis Borges expressed different viewpoints about science and technology through their short stories. These Argentine authors are among Latin America’s most famous authors in the genre of the fantastic. However, these two literary luminaries diverged greatly with regard to their opinion about the role of science in society. While Lugones considered scientific progress to a grave threat to the moral fabric and well-being of society, Borges believed that scientific theories underpin and intersect with a variety of different experiences and thus can serve as tools to explore human perception of reality. Textual analyses of two short stories clearly illustrate these stark differences. The opinion of Lugones is evident in the short story, “Viola acherontia” while that of Borges is well-defined in “El libro de arena.” In the end, Borges’ treatment of science proves quite versatile and in contrast to Lugones’ fears, has helped lead the way to solutions to problems facing modern society

Using Augmented Reality as a Medium to Assist Teaching in Higher Education

In this paper we describe the use of a high-level augmented reality (AR) interface for the construction of collaborative educational applications that can be used in practice to enhance current teaching methods. A combination of multimedia information including spatial three-dimensional models, images, textual information, video, animations and sound, can be superimposed in a student-friendly manner into the learning environment. In several case studies different learning scenarios have been carefully designed based on human-computer interaction principles so that meaningful virtual information is presented in an interactive and compelling way. Collaboration between the participants is achieved through use of a tangible AR interface that uses marker cards as well as an immersive AR environment which is based on software user interfaces (UIs) and hardware devices. The interactive AR interface has been piloted in the classroom at two UK universities in departments of Informatics and Information Science

Implement a laboratory workshop in physics and electrotechnical disciplines in the face of COVID-19 pandemic

Studying physics and many related disciplines at all education levels includes not only learning the theoretical material, but also the formation of skills and abilities to apply the knowledge gained in practice. This occurs mainly during laboratory work when students directly contact with specific laboratory equipment. With the forced transition to distance learning due to the development of the COVID-19 pandemic, the main difficulties in many universities arose precisely during the implementation of laboratory workshops. This study analyzed the possibilities of modern digital teaching aids for the effective remote implementation of a laboratory workshop in physics and related disciplines. The study was carried out on the basis of the Elabuga Institute of the Kazan Federal University. There were 79 students took part in the experiment (second and third-year students). The results showed that a well-arranged combination of various digital tools and technologies makes it possible to effectively implement laboratory works in physics and electrotechnical disciplines in a distance learning format. The research results are useful for university teachers using distance learning technologies in laboratory work in natural science disciplines

What Can Artificial Intelligence Do for Scientific Realism?

The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling