Improving Research Efficiency through Better Descriptors

TR&D项目2：通过更好的描述符提高研究效率 摘要：神经影像学研究的规模和复杂性在过去三年中呈指数级增长。 几十年来，它使人们对健康和疾病的认知有了新的认识，并使新的 成像硬件、处理和信息技术。随着新的信息扩散到 研究生态系统，需要整合来自出版物，数据源和分析的知识 工具.由于这些数字产出的描述协调有限，这种整合受到阻碍。 在本报告所述期间，技术研究和开发项目TR&D2解决了一些问题， 这些挑战。我们扩展了神经成像数据模型（NIDM）-一个建立在 万维网联盟的起源数据模型（W3C-PROV），并得到社区的支持- 发展本体论。使用这些标准，我们还在ReproNim项目中创建了一套技术 和合作伙伴，以实现可重复的分析，协调数据和结果，并收集标准化的 出处该提案旨在通过以下方式提高研究效率和对科学发现的整体信任度 更好地描述数字对象和更好的分析来源。为了实现这些总体目标， 我们将：1）形式化神经影像研究工作流程所有阶段的详细且结构化的描述符。 这对于解释和信任科学结果至关重要。2)开发资源，创建和传播 可查找、可扩展、可互操作、可重用（FAIR）和强大的科学工作流程。这将使用户能够 信任和重用现有的和经过良好测试的分析，以及传播自己的脚本时，这种分析 都没有。3)与社区协调，扩展和强化现有的ReproNim技术。 我们将通过其他工具的开发者整合我们的技术，从而使我们的技术更加 对于那些技术经验有限的人来说。这项工作将辅之以培训和 支持不同的用户体验级别和用例。我们将提供一套技术， 研究人员通过设计协调其输出，从评估和成像数据收集到最终结果。 这些技术还将支持现有工作流程的整合和重用， 只在必要时开发。最后，我们的工具将支持基于社区的生成，策展， 信息标准化管理。我们将与其他ReproNim合作开展这项工作 技术研发项目，以及我们的合作和服务项目。我们将共同 通过提高研究生命周期各个方面的效率，帮助研究人员提高工作效率。 TR&D2技术支持ReproNim的整体使命，以改善神经成像研究的方式。 执行和报告，以实现一套全面的数据管理、分析和利用 支持基础研究和临床活动的框架，并提高 神经影像科学和扩大我们的国家在神经影像研究投资的价值。

TR&D Project 2: Improving Research Efficiency through Better Descriptors (DESCRIBE) SUMMARY: The scale and complexity of neuroimaging research have grown exponentially over the last three decades and have enabled new insights into human cognition in health and disease and development of new imaging hardware, processing, and informatics technologies. As new information has proliferated into the research ecosystem, there is a need to integrate this knowledge from publications, data sources, and analysis tools. This integration has been hampered by limited harmonization of description across these digital outputs. During the current period, this Technology Research and Development Project, TR&D2, has addressed some of these challenges. We extended the Neuroimaging Data Model (NIDM) - a descriptor framework built on top of the World Wide Web Consortium's Provenance Data Model (W3C-PROV) and backed by community- developed ontologies. Using such standards we also created a set of technologies with our ReproNim projects and partners to enable reproducible analytics, to harmonize data and results, and to gather standardized provenance. This proposal aims to increase research efficiency and overall trust in scientific findings through better description of digital objects and better provenance of analytics. To accomplish these overarching goals, we will: 1) Formalize detailed and structured descriptors of all stages of a neuroimaging research workflow. This is critical for interpreting and trusting scientific results. 2) Develop a resource to create and disseminate Findable, Accessible, Interoperable, Reusable (FAIR) and robust scientific workflows. This will enable users to trust and reuse existing and well-tested analyses, as well as disseminate their own scripts when such analyses are not available. 3) Extend and harden existing ReproNim technologies in coordination with the community. We will integrate our technologies through developers of other tools, thus making our technologies more accessible to those who have limited technical experience. This effort will be complemented by training and support for different user experience levels and use cases. We will deliver a set of technologies that allows researchers to harmonize their output by design, from assessment and imaging data collection to final results. These technologies will also support consolidation and reuse of existing workflows, with new processes being developed only when necessary. Finally, our tools will support community-based generation, curation, and management of standardized information. 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 effective through increased efficiency in every facet of the research lifecycle. TR&D2 technologies support the overall mission of ReproNim to improve the way neuroimaging research is performed and reported, to enable a comprehensive set of data management, analysis and utilization frameworks in support of both basic research and clinical activities, and to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research.