I am excited to announce the availability of Cloud Computing for Science and Engineering, a new book written by Dennis Gannon and myself and published by MIT Press. The full text is also available online at https://cloud4scieng.org, along with associated Jupyter notebooks and other supporting material.
Clouds operated by Amazon, Microsoft, Google, and others provide convenient on-demand access to storage and computing. They also provide powerful services for organizing data, processing data streams, machine learning, and many other tasks. Every scientist and engineer needs to understand what these services can and cannot do,
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We see much discussion of late about how the importance of not only preserving scientific data but making data freely accessible. It is surely the case that much valuable data, produced at great cost, is subsequently discarded. But making data accessible incurs a cost (in some cases, a substantial cost). Thus, any systematic program aimed at preserving more data requires some systematic process for deciding what data is worth keeping.
I suggest that we can usefully approach the question of data preservation from an economic perspective. Data costs money to collect or create, a cost that may depend on both the data d involved and the time t at which it is created: thus,
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