A FAIR Data and Metadata Foundation for Reproducible Research

TR&D项目1：可复制研究的公平数据和元数据基础（DISCOVER） 总结 我们的NCBIB资源，ReproNim：可再现神经成像计算中心，旨在 继续推动神经影像学研究的方式转变，并报告以改善 神经影像学科学的可重复性，并扩大我们国家在神经影像学方面的投资价值 research.在本技术研发项目中，TR&D 1 - A FAIR数据和元数据 基金会的可再生研究，我们专注于必要的工具和最佳实践，使 科学数据的有效注释以及有效搜索和发现这些数据及其相关的 工作流程和软件。在本期间，我们开发了强大的原始数据注释工具， 用于发现的衍生数据和相关工具。数据注释工具由基础设施支持 用于管理注释所需的必要术语。我们的工具和程序支持“公平 数据原则”描述了一组关键原则，这些原则将确保数据对研究社区的价值 使得数据是可查找的（具有足够的显式元数据）、可解释的（对于人类和机器）， 可互操作（使用标准定义和通用数据元素）和可重用（满足社区需求 标准，并充分记录）。NIH的数据科学办公室已经认可了这些原则， NIH最近将其纳入了最新的数据管理和共享政策 （NOT-OD-21-013），要求保存和共享所有研究、资助或 全部或部分由NIH进行。因此，TR&D1提供的工具和服务不仅有助于 研究人员在进行可重复的神经成像，而且在利用增加的数量， 数据作为此数据共享政策的一部分提供。对研究人员的支持将通过 两个具体目标：1）通过元数据注释和调整制作FAIR数据， 这些数据的共享和发布;以及2）使研究人员能够进行数据发现和队列生成， 能够有效地重新使用FAIR数据进行重新分析或重新执行。这两个相辅相成的目标将是 第三个目标是支持和培训：3）扩展和强化现有的ReproNim软件 与社区协调发布和发现FAIR数据。这一目标将确保这些工具 我们开发的将更容易为那些谁拥有有限的技术经验，并将补充 通过培训模块和对不同用户体验级别和用例的支持。这套工具， 更大的ReproNim工具集使研究人员能够在FAIR数据生态系统中工作。我们会执行这个 与其他ReproNim技术研发项目和我们的 合作和服务项目。我们将共同帮助研究人员提高生产效率 和FAIR数据的共享，促进这些研究人员利用越来越多的井 描述的数据和推进知识和探索科学主张的普遍性。

TR&D Project 1: A FAIR Data and Metadata Foundation for Reproducible Research (DISCOVER) SUMMARY Our NCBIB resource, ReproNim: A Center for Reproducible Neuroimaging Computation, seeks to continue to drive a shift in the way neuroimaging research is performed and reported to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research. In this Technology Research and Development Project, TR&D 1 - A FAIR Data and Metadata Foundation for Reproducible Research, we focus on the necessary tools and best practices to enable the efficient annotation of scientific data and the effective search for and discovery of this data and its associated workflows and software. During the current period, we have developed robust data annotation tools for raw and derived data and associated tools for discovery. The data annotation tools are supported by an infrastructure for managing the necessary terminologies required for annotation. Our tools and procedures support the “FAIR Data Principles” which describe a set of key principles that will ensure data’s value to the research community such that the data are Findable (with sufficient explicit metadata), Accessible (for humans and machines), Interoperable (using standard definitions and Common Data Elements), and Reusable (meeting community standards, and sufficiently documented). The Office of Data Science at NIH has endorsed these principles and NIH has recently incorporated them in their most recent policy for data management and sharing (NOT-OD-21-013) that requires the preservation and sharing of scientific data from all research, funded or conducted in whole or in part by NIH. The tools and services provided by TR&D1 will therefore not only assist researchers in performing reproducible neuroimaging, but also in the utilization of the increased amounts of data being made available as part of this data sharing policy. Support for researchers will be accomplished via two specific aims: 1) Production of FAIR data through metadata annotation and alignment allowing for the sharing and publication of these data; and 2) Enabling data discovery and cohort generation for researchers to be able to effectively re-use FAIR data for re-analysis or re-execution. These two complementary aims will be supported by a third aim focused on support and training: 3) Extend and harden existing ReproNim software for FAIR data publication and discovery in coordination with the community. This aim will ensure that the tools we develop will be more accessible to those who have limited technical experience and will be complemented by training modules and support for different user experience levels and use-cases. This suite of tools, part of the larger ReproNim toolset, enables researchers to work within a FAIR data ecosystem. We will carry out this work in collaboration with the other ReproNim technology research and development projects and our Collaborative and Service projects. Together, we will help researchers become more efficient in the production and sharing of FAIR data, promoting the ability of these researchers to utilize a growing collection of well described data and to advance knowledge and explore the generalizability of scientific claims.