CDI-Type I: CiiWork: an interactive workbench for integration, exploration, and analysis of chronological information

跨越大时间段的时间顺序数据集是几个不同学科的研究重点：气候学、地质学、生物学、历史学和人类学，仅举几例。虽然这些数据集与时间尺度有很强的联系，但它们在大多数其他方面表现出很大的多样性。例如，一些数据集来自冰芯，而另一些数据集来自考古文物、仪器记录和数值模拟。在每个学科中，研究人员都花费了相当大的精力来仔细地为每个这样的数据集开发一个时间尺度，并解释由此产生的时间数据，从而获得科学见解。这类按时间顺序排列的数据集在规模和多样性上都在迅速增长，这不仅是由于全世界科学家活动的增加，也是由于技术的改进。对这些庞大而多样的数据集的整合和分析有望产生重大的科学突破。然而，这种整合和分析目前需要大量的人力，这严重限制了我们实现其潜在价值的能力。过去开发的大多数分析技术都不能适应我们现在必须分析的数据的规模和多样性。该项目的目标是通过开发解决数据多样性和数量带来的问题的方法，并在交互式工作台中实现、测试和应用这些方法，从而打破科学突破的这一障碍。开发上述工作台需要在多个网络基础设施研究课题中进行工作，这些课题本身已经受到了相当大的研究关注。然而，就其本质而言，这些主题不承认许多通用的解决方案，并且目前在信息学文献中可用的广义结果与其在领域科学家准备使用的操作系统中的实现之间存在很大差距。我们的努力将集中在通过调整和专业化已知方法来弥合这一差距，并在需要时根据感兴趣的时序数据的特征开发新的方法。这项工作并不局限于基于已知方法实现所描述领域的网络基础设施的任务。相反，这项工作需要研究努力将计算机科学的概念和技术映射到各个科学领域，反之亦然，仔细地调整先前的结果并开发新的结果。这项工作不仅需要网络基础设施和领域科学方面的专业知识，还需要能够克服传统领域间障碍的紧密合作。通过有效利用大量不同的按时间顺序收集的数据，我们的工作台为多个学科的科学突破打开了大门。作为一个具体的例子，我们的工作台将提高我们对最重要的科学和社会问题之一的理解：全球气候系统的运行，过去，现在和未来。除了提高每个学科的科学家的效率外，我们的工作还将使其他学科的研究人员和兴趣更随意的人能够轻松访问，更重要的是，处理和解释来自关键研究结果和预测的相同数据。这方面是特别重要的，因为它揭开了相关研究的神秘面纱，否则可能会显得武断或固执己见。共享的示例可以被其他人一直追溯到原始数据。我们的成果不仅将通过传统的专业渠道传播，还将通过向更广泛的社区外展来传播。

Chronological datasets that span large periods of time are the focusof study in several diverse disciplines: climatology, geology,biology, history, and anthropology, to name just a few. While thesedatasets share strong links to time scales, they exhibit a greatdiversity in most other aspects. For example, some datasets arederived from ice cores, while others arise from archaeologicalartifacts, instrumental records, and numerical modeling. In eachdiscipline, researchers have spent considerable effort to carefullydeveloping a time scale for each such dataset, and to interpreting theresulting temporal data, in turn leading to scientific insights. Suchchronological datasets are growing rapidly in both size and diversity,due to not only increasing activity by scientists worldwide but alsoimproving technology. The integration and analysis of this large anddiverse collection of datasets promises to yield significantscientific breakthroughs. However, such integration and analysiscurrently requires a very significant human effort, which severelyrestricts our ability to realize its potential value. Most of theanalysis techniques developed in the past do not scale up to the sizeand diversity of data we must now analyze. The goal of this project isto break this barrier to scientific breakthroughs by developingmethods to solve the problems arising from the diversity and volume ofdata, and to implement, test, and apply these methods in aninteractive workbench.Developing the above workbench requires work in multiplecyberinfrastructure research topics, which by themselves have receivedconsiderable research attention. By their nature, however, thesetopics do not admit many universal solutions and there is currently alarge gap separating the generalized results available in theinformatics literature and their realization in operational systemsthat are ready for use by domain scientists. Our effort will focus onbridging this gap by adapting and specializing known methods, anddeveloping new ones when needed, to the characteristics of thechronological data of interest. This work is not limited to the taskof implementing cyberinfrastructure for the described domains based onknown methods. Rather, the work requires research effort to mapconcepts and techniques from computer science to various scientificdomains, and vice versa, carefully adapting prior results anddeveloping new ones. This work requires not only expertise on both thecyberinfrastructure and domain sciences sides of the gap, but also aclose- knit collaboration that can overcome traditional inter-domainbarriers.By enabling effective use of the large and diverse collection ofchronological data, our workbench opens the door to scientificbreakthroughs in multiple disciplines. As just one specific example,our workbench will improve our understanding of one of the mostimportant scientific and societal problems: the operation of theglobal climate system, past, present, and future. In addition toenhancing the effectiveness of the scientists specializing in eachdiscipline, our work will allow researchers from other disciplines,and people with a more casual interest, to easily access and,importantly, process and interpret the same data from which keyresearch results and forecasts emerge. This aspect is particularlyimportant as it demystifies the related research that may otherwiseappear arbitrary or opinionated. Shared examples will be traceable byothers all the way back to raw data. Our results will be disseminatednot only through the traditional professional channels but alsothrough outreach to the broader community.