SI2-SSI: CRESCAT, A Computational Research Ecosystem for Scientific Collaboration on Ancient Topics, Spanning the Full Data Life Cycle

该项目集成、测试和记录了一套可互操作的软件工具，以支持协作研究。这些工具被统称为CRESCAT（古代主题科学合作计算研究生态系统）。最初的重点是学科，处理动态相互作用和结构变化的空间定位人口在过去很长一段时间内，例如，古生物学考古学和经济史尽管存在差异，但这些学科在建模和分析数据方面具有相似的计算需求。此外，相同的软件可以用于许多其他学科，通过构建和维护一套通用的可互操作工具来为广泛的研究人员提供服务，同时跨越整个研究数据生命周期，包括（1）采集，（2）集成，（3）分析，（4）出版和（5）数据存档，从而实现规模经济。为最终用户研究人员提供了直观的图形用户界面，以便在生命周期的所有阶段使用其数据，而无需繁琐的手动数据传输和转换。该项目将解决一个影响许多科学学科的主要计算问题，因为基于异构空间，时间和分类本体的不同来源的数据的整合和分析面临挑战。因此，它将在科学和其他领域产生广泛的影响，因为它展示了如何明确地表示个体判断的全部可变性以及表达这些判断的不同概念化和术语，并将每个观察，解释和概念本体明确地归属于特定命名的人或群体。与许多科学研究的计算工具不同，CRESCAT假设了某种程度上不存在的本体论共识，符合实际的研究实践。它不强加一个标准化的本体论，从而忽略或压制研究人员之间不可避免的分歧和相互冲突的解释。相反，它代表本体论的多样性，观察的不确定性和解释的分歧，明确在一个更大的共同框架，最终用户可以查询，分析和比较全方位的观察，解释和术语，以告知自己的判断证据。CRESCAT的目的是允许科学分歧和观察和解释的不确定性以数字方式表示，将这些差异本身作为数据进行分析和辩论。因此，除了为高级研究建立一个更有效的共享框架的实际目标外，拟议的工作将引发关于计算工具应如何与科学实践相关联的理论思考。CRESCAT项目是计算机科学家，古生物学家，地球科学家，考古学家，经济历史学家和其他社会科学家之间的跨学科合作。我们的目标是展示一个综合软件生态系统的价值，该生态系统跨越社会科学和自然科学，可以促进任何以时空关系重叠模型或相互冲突的术语和分类为特征的研究。CRESCAT对科学知识的表述避免了强制标准化，由于缺乏执行机制，强制标准化在许多情况下是不切实际的，而且原则上也是有问题的，因为不同的本体往往合理地反映了不同的理论假设和研究议程。CRESCAT工具套件的核心是一个创新的数据集成系统，它明确地表示研究数据和数据中固有的本体。本体在这里被定义为给定知识领域中的实体及其之间关系的概念模型，与模式相反，模式是本体在工作系统内的逻辑数据结构中的实现。CRESCAT的数据集成系统在一个足以提供一个可预测的和有效的查询数据库结构的抽象层次上运作的一个抽象的全球模式的基础上，这反过来又是基于一个上层本体论的基本概念和适用于所有科学和学术学科的关系方面指定。数据集成系统是在企业级的XML/XQuery DBMS中实现的，该DBMS用作数据仓库（使用非关系图数据模型），其中存储了来自代表许多学科的广泛研究项目的各种数据。每个研究项目的术语和概念区别都得到充分保留。CRESCAT项目对研究数据采取的方法是：（1）连贯一致，将软件工具和数据格式紧密结合在一个单一的分析框架内;（2）开放式，将现有工具相互连接，同时允许今后增加新工具;（3）非排他性，绝不阻止其组成工具参与其他软件生态系统;（4）可扩展性，旨在处理大规模数据管理，分析和可视化;（5）可持续性，维护共享资源以满足软件和技术支持的共同需求，从而实现可观的规模经济。

This project integrates, tests, and documents a suite of interoperable software tools to support collaborative research. The tools are collectively called CRESCAT (Computational Research Ecosystem for Scientific Collaboration on Ancient Topics). The initial focus is on disciplines that deal with dynamic interactions and structural changes within spatially situated populations over long time spans in the past, e.g., paleobiology, archaeology, and economic history. Despite their differences, these disciplines have similar computational needs for modeling and analyzing data. Moreover, the same software can be used in many other disciplines, enabling economies of scale by building and maintaining a common set of interoperable tools to serve a wide range of researchers, while spanning the full research data life cycle, consisting of (1) acquisition, (2) integration, (3) analysis, (4) publication, and (5) archiving of data. An intuitive graphical user interface is provided for end-user researchers to work with their data in all stages of the life cycle without cumbersome manual data transfers and transformations. The project will address a major computational problem that affects many scientific disciplines due to the challenge of integrating and analyzing data of diverse origins based on heterogeneous spatial, temporal, and taxonomic ontologies. Thus it will have a broad impact in the sciences and beyond by showing how to represent explicitly the full variability of individual judgments and the divergent conceptualizations and terminologies through which those judgments are expressed, with explicit attribution of each observation, interpretation, and conceptual ontology to a particular named person or group. Unlike many computational tools for scientific research, which assume a degree of ontological consensus that does not exist, CRESCAT conforms to actual research practices. It does not impose a standardized ontology, thereby ignoring or suppressing the inevitable disagreements and conflicting interpretations that arise among researchers. Instead, it represents ontological diversity, observational uncertainty, and interpretive disagreement explicitly within a larger common framework in which end users can query, analyze, and compare the full range of observations, interpretations, and terminologies to inform their own judgments about the evidence. CRESCAT is designed to allow scientific disagreements and observational and interpretive uncertainties to be represented digitally in a way that exposes these differences themselves as data for analysis and debate. Thus, in addition to the practical goal of building a more efficient shared framework for advanced research, the proposed work will provoke theoretical reflection about how computational tools should relate to scientific practice.The CRESCAT project is an interdisciplinary collaboration between computer scientists, paleobiologists, geoscientists, archaeologists, economic historians, and other social scientists. The goal is to demonstrate the value of an integrative software ecosystem that spans the social and natural sciences and can facilitate any research characterized by overlapping models of temporal and spatial relations or by conflicting terminologies and taxonomies. CRESCAT's representation of scientific knowledge eschews forced standardization, which is impractical in many cases due to lack of an enforcement mechanism and is also questionable in principle since divergent ontologies often legitimately reflect different theoretical assumptions and research agendas. Central to the CRESCAT suite of tools is an innovative data-integration system that represents explicitly both research data and the ontologies inherent in the data. An ontology is defined here as a conceptual model of entities and the relationships among them in a given domain of knowledge, in contrast to a schema,”which is the implementation of an ontology in logical data structures within a working system. CRESCAT's data-integration system operates at a level of abstraction sufficient to provide a predictable and efficiently queryable database structure based on an abstract global schema, which in turn is based on an upper ontology specified in terms of fundamental concepts and relationships applicable to all scientific and scholarly disciplines. The data-integration system is implemented in an enterprise-class XML/XQuery DBMS that serves as a data warehouse (using the non-relational graph data model), in which is stored diverse data from a wide range of research projects representing many disciplines. The terminology and conceptual distinctions of each research project are fully preserved. The approach to research data taken in the CRESCAT project is (1) coherent, tightly integrating software tools and data formats within a single analytical framework; (2) open-ended, interconnecting existing tools while allowing the addition of new tools in the future; (3) non-exclusive, in no way preventing its component tools from participating in other software ecosystems; (4) scalable, designed to handle large-scale data management, analysis, and visualization; and (5) sustainable, maintaining shared resources to meet common needs for software and technical support and thus enabling substantial economies of scale.