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at the CS research community—including both researchers and funders. First, it discusses some of the fundamental aspects of CS research and computational thinking and how these aspects are also critical to the sustainability problem space. Then it explores the challenge of universality and emphasizes that a bottom-up approach is not only necessary in the sustainability space but also has precedent in many other areas of deep computer science. It describes the connection between universality, bottom-up approaches, and sustainability. It then offers suggestions on how to structure research to promote meaningful impact on sustainability. Finally, the chapter identifies methodological opportunities for optimizing research outcomes and impacts.
Chapter 2 highlighted the centrality of data and information to sustainability. Given this centrality, computer science and information technology (IT) are essential to meeting sustainability challenges. The challenge for IT experts and CS researchers is in ensuring that technologies and approaches represent usable, appropriate solutions; that they are highly effective; and that they take advantage of the deepest and most powerful insights that can be brought to bear. IT has been and continues to be a critical enabler of progress in vast arenas of society. Sustainability is no exception: IT offers a powerful tool to assist in addressing sustainability challenges.
Moreover, fundamentals of the computer science field itself offer unique and important contributions to sustainability. To name just a few such fundamentals, consider abstraction design, algorithms, operating systems and layering, real-time systems, machine learning, human computer interaction (HCI), and databases. For instance, the very notion of queryable structured data is at the heart of much of computer science; at the same time strides are being made to cope with the vast amounts of unstructured data now available. Given the scope and scale of sustainability challenges along with the vast amounts of relevant data, the structuring and understanding of these data present many challenges. The lens of computational thinking is essential to solving many complex problems,2 and there are key opportunities within computer science that are clearly applicable, even beyond those highlighted in Chapter 2. A sampling of these areas is outlined in Box 3.1.
2See National Research Council, Report of a Workshop on the Scope and Nature of Computational Thinking, Washington, D.C.: The National Academies Press (2010); and National Research Council, Report of a Workshop on the Pedagogical Aspects of Computational Thinking, Washington, D.C.: The National Academies Press (2011).