A FAIR Data and Metadata Foundation for Reproducible Research

TR&D项目1：可重复研究的公平数据和元数据基础（DISCOVER）

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.