A FAIR Data and Metadata Foundation for Reproducible Research

研发项目1：可重复研究的公平数据和元数据基础(DISCOVER) 摘要 我们的NCBIB资源，ReproNim：可复制神经成像计算中心，寻求 继续推动神经成像研究的执行和报告方式的转变，以改善 神经成像科学的重现性，并扩大我国在神经成像方面的投资价值 研究。在这个技术研发项目中，研发1-A公平的数据和元数据 作为可重现研究的基础，我们专注于必要的工具和最佳实践，以实现 科学数据的有效注释以及对该数据及其相关数据的有效搜索和发现 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和 用于发现的派生数据和相关工具。数据注记工具由基础架构提供支持 用于管理批注所需的必要术语。我们的工具和程序支持“博览会” 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界的价值 使得数据是可找到的(具有足够的显式元数据)、可访问的(对于人和机器)， 可互操作(使用标准定义和公共数据元素)和可重用(会议社区 标准，并有充分的文件)。美国国立卫生研究院数据科学办公室已经认可了这些原则和 NIH最近将它们纳入了他们最新的数据管理和共享政策 (非-OD-21-013)，这要求保存和共享来自所有研究、资助或 全部或部分由美国国立卫生研究院进行。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现的神经成像时，也在利用增加的 将数据作为此数据共享策略的一部分提供。对研究人员的支持将通过以下方式实现 两个具体目标：1)通过元数据注释和对齐产生公平的数据，从而允许 共享和发布这些数据；以及2)使研究人员能够发现数据和生成队列 能够有效地重复使用公平的数据进行重新分析或重新执行。这两个互补的目标将是 由侧重于支持和培训的第三个目标支持：3)扩展和强化现有的ReproNim软件 与社区协调，进行公平的数据发布和发现。这一目标将确保工具 我们的开发将对那些技术经验有限的人更容易获得，并将得到补充 通过培训模块以及对不同用户体验级别和用例的支持。这套工具是 更大的ReproNim工具集使研究人员能够在公平的数据生态系统中工作。我们将执行这项任务 与其他ReproNim技术研发项目和我们的 协作和服务项目。我们将共同帮助研究人员提高生产效率 和共享公平的数据，促进这些研究人员利用日益增长的油井集合的能力 描述数据，推进知识，探索科学主张的概括性。

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.