Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result’s metadata. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.

计算分析的结果需要透明地公开其支持资源，而分析本身通常规模可能非常大，并且涉及及时分离的多个处理步骤。任何分析正确性的证据不仅应包括文本描述，还应包括产生结果的计算的正式记录，包括可访问的数据和软件以及运行时参数、环境和相关人员。本文介绍了 FAIRSCAPE，这是一个可重用的计算框架，可以简化对现代可扩展的基于云的组件的访问。FAIRSCAPE 完全实现了公平数据原则，并将其扩展为提供完全公平的证据，包括机器可解释的数据集、软件和计算的来源，作为所有计算结果的元数据。FAIRSCAPE 微服务框架为每个计算结果创建一个完整的证据图，包括带有元数据的持久标识符，可解析为软件、计算和计算中使用的数据集；并将 URI 存储到结果元数据中图的根。证据图本体 EVI (https://w3id.org/EVI) 支持对证据进行推理。FAIRSCAPE 可以运行嵌套或不相交的工作流程，并保留它们之间的来源。它可以运行 Apache Spark 作业、脚本、工作流或用户提供的容器。所有对象都分配有持久 ID，包括软件。所有结果均使用证据图模型通过 FAIR 元数据进行注释，以实现存档数据和软件的访问、验证、再现性和重用。