CC*DNI DIBBS: Merging Science and Cyberinfrastructure Pathways: The Whole Tale

尽管越来越多的人认识到需要分享研究过程的各个方面，但今天的学术出版物仍然大多与用于产生已发表的结果和发现的基础数据和代码脱节。随着数据变得更加开放和可传输，研究产出的第二层已经出现，将研究出版物与相关数据联系起来，可能还有其来源。这一趋势很快被新的第三层所追随：通过共享完整的计算叙述（将方法描述与可执行代码和数据联系起来）来交流探究过程本身，从而引入可重复科学和加速知识发现的新时代。在整个故事（WT）项目中，所有这些组件都是链接的，并且可以从学术出版物中访问。第三层是广泛的，通过科学途径（例如，天文学、生命与地球科学、材料科学、社会科学）涵盖了众多的研究社区，并深入到使用相互关联的网络基础设施途径和共享技术。该项目的目标是加强第二层的研究产出，并建立一个强大的第三层，整合故事的所有部分，通过(1)通过流行的前端，例如数字笔记本（IPython, Jupyter），传统脚本环境和工作流系统，暴露现有的网络基础设施，传达可重复的科学探究的整体体验；(2)开发必要的“软件粘合剂”，以无缝访问不同的后端功能，包括DataNet联盟和数据基础设施构建块（DIBBs）项目；(3)通过授权科学家在其通常的编程环境中创建计算叙述，增强了完整的数据到发布生命周期，并通过底层网络基础设施（例如，身份管理，高级数据访问和来源api，以及基于数字对象标识符的数据发布）的新功能进行了增强。这些技术和接口将使用不同的数据类型、技术框架和一系列科学领域的早期采用者进行开发和压力测试。

Scholarly publications today are still mostly disconnected from the underlying data and code used to produce the published results and findings, despite an increasing recognition of the need to share all aspects of the research process. As data become more open and transportable, a second layer of research output has emerged, linking research publications to the associated data, possibly along with its provenance. This trend is rapidly followed by a new third layer: communicating the process of inquiry itself by sharing a complete computational narrative that links method descriptions with executable code and data, thereby introducing a new era of reproducible science and accelerated knowledge discovery. In the Whole Tale (WT) project, all of these components are linked and accessible from scholarly publications. The third layer is broad, encompassing numerous research communities through science pathways (e.g., in astronomy, life and earth sciences, materials science, social science), and deep, using interconnected cyberinfrastructure pathways and shared technologies. The goal of this project is to strengthen the second layer of research output, and to build a robust third layer that integrates all parts of the story, conveying the holistic experience of reproducible scientific inquiry by (1) exposing existing cyberinfrastructure through popular frontends, e.g., digital notebooks (IPython, Jupyter), traditional scripting environments, and workflow systems; (2) developing the necessary 'software glue' for seamless access to different backend capabilities, including from DataNet federations and Data Infrastructure Building Blocks (DIBBs) projects; and (3) enhancing the complete data-to-publication lifecycle by empowering scientists to create computational narratives in their usual programming environments, enhanced with new capabilities from the underlying cyberinfrastructure (e.g., identity management, advanced data access and provenance APIs, and Digital Object Identifier-based data publications). The technologies and interfaces will be developed and stress-tested using a diverse set of data types, technical frameworks, and early adopters across a range of science domains.