Large-scale science instruments, such as the distributed radio telescope LOFAR, show that we are in an era of data-intensive scientific discovery. Such instruments rely critically on significant computing resources, both hardware and software, to do science. Considering limited science budgets, and the small fraction of these that can be dedicated to compute hardware and software, there is a strong and obvious desire for low-cost computing. However, optimising for cost is only part of the equation; the value potential over the lifetime of the solution should also be taken into account. Using a tangible example, compute hardware, we introduce a conceptual model to approximate the lifetime relative science value of such a system. While the introduced model is not intended to result in a numeric value for merit, it does enumerate some components that define this metric. The intent of this paper is to show how compute system related design and procurement decisions in data-intensive science projects should be weighed and valued. By using both total cost and science value as a driver, the science output per invested Euro is maximised. With a number of case studies, focused on computing applications in radio astronomy past, present and future, we show that the hardware-based analysis can be, and has been, applied more broadly.

大型科学仪器，例如分布式射电望远镜LOFAR，表明我们正处于数据密集型科学发现的时代。这样的仪器严重依赖大量的硬件和软件计算资源来进行科学研究。考虑到有限的科学预算以及可以用于计算硬件和软件的一小部分，对低成本计算存在强烈而明显的需求。但是，优化成本只是其中的一部分。还应考虑解决方案生命周期内的潜在价值。使用一个有形的例子，计算硬件，我们引入一个概念模型来近似估计这种系统的生命周期相对科学价值。虽然引入的模型并非旨在产生价值的数值，它确实列举了一些定义此指标的组件。本文的目的是说明如何权衡和评估数据密集型科学项目中与计算系统相关的设计和采购决策。通过同时使用总成本和科学价值作为驱动因素，可以使每投资欧元的科学产出最大化。通过大量案例研究，重点关注过去，现在和将来在射电天文中的计算应用，我们表明基于硬件的分析可以并且已经得到更广泛的应用。