The Common Fund Knowledge Center (CFKC): providing scientifically valid knowledge from the Common Fund Data Ecosystem to a diverse biomedical research community.

共同基金知识中心（CFKC）：从共同基金数据生态系统向多元化的生物医学研究社区提供科学有效的知识。

• 批准号: 10851461
• 负责人: Noel P Burtt
• 金额: $ 147.34万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2028-09-17
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10851461
• 关键词: Address; Big Data; Biological; Biological Models; Biology; Biomedical Research; Communities; Computer software; Computers; Coupled; Data; Data Provenance; Data Set; Data Sources; Disease; Education and Outreach; Educational process of instructing; Engineering; Ensure; Foundations; Funding; Generations; Genes; Genetic; Graph; Infrastructure; Knowledge; Knowledge Extraction; Knowledge Portal; Libraries; Manuals; Maps; Metabolic Diseases; Methods; Modeling; National Center for Advancing Translational Sciences; National Human Genome Research Institute; National Institute of Diabetes and Digestive and Kidney Diseases; Persons; Procedures; Property; Provider; Regulatory Element; Research Personnel; Resources; Science; Scientist; Secure; Services; Statistical Computing; Testing; Thinking; Training; Trust; Uncertainty; United States National Institutes of Health; Variant; Visualization; Work; bioinformatics pipeline; cell type; cloud based; data ecosystem; data quality; data resource; data to knowledge; design; diverse data; experience; genome resource; genomic data; information organization; knowledge curation; knowledge graph; knowledge of results; novel; outreach; preference; tool; user-friendly; web portal; working group

Abstract Making NIH Common Fund (CF) datasets FAIR is but the first step in realizing their potential within the “big data” revolution. Science progresses through the accumulation of knowledge, which achieves a wide reach only if it is accessible to a diverse spectrum of researchers. While computer scientists have made substantial strides in modeling knowledge within “knowledge graphs” (KGs), non-computational scientists can find it hard to interpret the graph-based reasoning tools and visualizations that accompany KGs because such tools use logical reasoning that does not account for scientific context or uncertainty and can produce a plethora of scientifically invalid inferences. Our CFDE KC will aim to present scientifically valid knowledge produced by CF projects. We will represent this knowledge as a KG, compliant with existing CFDE and external knowledge curation efforts. But we will focus on scientific validity through both (a) careful knowledge extraction, by ensuring that each edge in the KG is either a primary experimental finding or the result of an expert-applied analysis, and (b) careful knowledge presentation, by building a portal that de-emphasizes general-purpose graph traversal in favor of single-purpose visualizations. To implement this KC, we will draw from our experience managing four large-scale NIH-funded projects that have faced similar challenges in related settings. First, our work on Terra provides a foundation for securely storing biomedical data and making it available through cloud-based workspaces. Second, our work on the Common Metabolic Diseases Knowledge Portal provides a means to distill data into knowledge through expert-designed analyses that produce “summary representations”, which are then presented through simple visualizations or multi-step prescriptive workflows. Third, our work on the A2FKP provides experience tailoring knowledge extraction and presentation to a variety of communities with different cultures and preferences. Finally, our work on the Biomedical Translator provides experience developing and complying with standards for knowledge representation and exchange. In specific aim 1, we will coordinate working groups of CFDE and external investigators to review the knowledge across CF projects and propose how to extract and represent it within the KC. In specific aim 2, we will work with CF DCCs to define summary representations of their data, provide them with software to make these summary representations available to us, and regularly “pull” and integrate these summaries within a KG compliant with Translator standards. In specific aim 3, we will use the software UI/UX and search infrastructure developed for the CMDKP and A2FKP to build a knowledge portal that enables a diverse spectrum of scientists to visualize and search CF data. In specific aim 4, we will combine our and the CF DCC’s prior education and outreach strategies to publicize the portal and educate people in its use. Finally, in specific aim 5, we will interface with other CFDE centers to build a combined Resource Portal and form partnerships with external resources to amplify the reach of our KC. Together, these aims will produce a CFDE KC that will unlock the full potential of CF resources through an emphasis on scientific validity, enabling scientists of all levels of expertise to understand, trust, and build upon them.

抽象的 使NIH共同基金（CF）数据集公平是实现潜力的第一步 在“大数据”革命中。科学通过知识的积累而发展， 只有当研究人员可以使用它的情况下，这才能达到广泛的范围。尽管 计算机科学家在建模知识中取得了长足的进步。 图（kgs），非计算科学家可以发现很难解释基于图的 适应公斤的推理工具和可视化，因为这样的工具使用逻辑 不考虑科学环境或不确定性的推理，可以产生大量的推理 科学无效的推论。 我们的CFDE KC将旨在介绍CF项目产生的科学有效知识。我们将 将此知识表示为公斤，符合现有的CFDE和外部知识 策划工作。但是我们将通过两者的仔细知识来关注科学有效性 提取，确保kg中的每个边缘是主要的实验发现，或者 专家应用分析的结果，以及（b）通过构建门户网站来仔细的知识呈现 这取消强调通用图遍历，而有利于单一用途的可视化。 为了实施此KC，我们将从管理四个大规模NIH资助的经验中汲取 在相关环境中面临类似挑战的项目。首先，我们在Terra上的工作提供 一个牢固存储生物医学数据并通过基于云的基础 工作区。第二，我们在常见代谢疾病知识门户提供的工作提供的工作 通过专家设计的分析将数据提炼成知识的一种手段，该分析产生“摘要 表示”，然后通过简单的可视化或多步骤呈现它们 规定工作流程。第三，我们在A2FKP上的工作提供了经验裁缝知识 提取和向具有不同文化和偏好的各个社区提出。 最后，我们在生物医学翻译方面的工作提供了发展和遵守的经验 具有知识代表和交流的标准。 在特定目标1中，我们将协调CFDE和外部研究人员的工作组 查看跨CF项目的知识和建议如何在 KC。在特定目标2中，我们将与CF DCC合作，以定义其摘要表示 数据，向他们提供软件，以使我们可以使用这些摘要表示形式，并且 通常，“拉”并将这些摘要集成到符合翻译标准的kg中。 在特定目标3中，我们将使用软件UI/UX和为该软件开发的搜索基础架构 CMDKP和A2FKP建立一个知识门户，使科学家能够多样化 可视化和搜索CF数据。在特定目标4中，我们将结合我们和CF DCC的先验 教育和外展策略以宣传门户和教育人员的使用。最后， 在特定的目标5中，我们将与其他CFDE中心交互以构建一个组合的资源门户网站 并与外部资源建立合作伙伴关系，以扩大我们的KC覆盖范围。 这些目标在一起将产生CFDE KC，该KC将释放CF资源的全部潜力 通过强调科学有效性，使各个级别的专业知识的科学家能够 理解，信任和建立在他们的基础上。