The open data movement represents a major advancement for informed water management. Data that are findable, accessible, interoperable, and reusable—or FAIR—are now prerequisite to responsible data stewardship. In contrast to FAIR, accessibility and usability case studies and guidelines designed around human access and understanding are lacking in the literature, especially for water resources. Such decision support guidelines are critical because (i) inherent visual design trade-offs are not best made using intuition or feedback (perceived preference), and (ii) choosing designs requires a nuanced understanding of why and how the design works (revealed effectiveness). Thus, the goal of this commentary is to highlight knowledge gaps and discuss a general usability testing method which can be applied to any water resources decision support product. The user-testing approach includes (i) interviews about visualization goals, audiences, and the uses and decisions made with the data products, (ii) diagnosis of usability challenges, and (iii) redesign of decision support products given best practices and control versus treatment with intended end-user audiences. We illustrate the method using high-profile U.S. Geological Survey water science products. In sum, optimizing and testing for usability and understandability are as central to stakeholder use as FAIR standards are, and warrant being part of the development of data products and geovisualizations.

中文翻译：

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水资源开放数据和可操作的科学举措的可用性差距

开放数据运动代表了知情水管理的重大进步。可查找、可访问、可互操作和可重用（或公平）的数据现在是负责任的数据管理的先决条件。与 FAIR 相比，文献中缺乏围绕人类获取和理解而设计的可及性和可用性案例研究和指南，尤其是水资源方面。此类决策支持指南至关重要，因为 (i) 固有的视觉设计权衡不能最好地利用直觉或反馈（感知偏好）进行，并且 (ii) 选择设计需要对设计为何以及如何发挥作用有细致的了解（显示的有效性） 。因此，本评论的目的是强调知识差距并讨论可应用于任何水资源决策支持产品的通用可用性测试方法。用户测试方法包括（i）有关可视化目标、受众以及数据产品的使用和决策的访谈，（ii）可用性挑战的诊断，以及（iii）根据最佳实践和控制对决策支持产品进行重新设计与目标最终用户受众的治疗。我们使用备受瞩目的美国地质调查局水科学产品来说明该方法。总之，可用性和可理解性的优化和测试与 FAIR 标准一样对于利益相关者的使用至关重要，并且有必要成为数据产品和地理可视化开发的一部分。