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) 数据集变得公平只是实现其潜力的第一步 在“大数据”革命中，科学通过知识的积累而进步， 只有当不同领域的研究人员都能接触到它时，它才能实现广泛的影响。 计算机科学家在“知识”内的知识建模方面取得了实质性进展 图”（KG），非计算科学家可能会发现很难解释基于图的 知识图谱附带的推理工具和可视化工具，因为这些工具使用逻辑 推理不考虑科学背景或不确定性，并且可能产生过多的结果 科学上无效的推论。 我们的 CFDE KC 旨在展示 CF 项目产生的科学有效的知识。 将这些知识表示为 KG，符合现有的 CFDE 和外部知识 但我们将通过（a）仔细的知识来关注科学有效性。 提取，通过确保 KG 中的每条边要么是主要实验发现，要么是 专家应用分析的结果，以及 (b) 通过建立门户仔细介绍知识 这不再强调通用图形遍历，而是支持单一目的可视化。 为了实施这个 KC，我们将借鉴管理 NIH 资助的四个大型项目的经验 首先，我们在 Terra 上的工作提供了在相关环境中面临类似挑战的项目。 安全存储生物医学数据并通过基于云的方式提供这些数据的基础 其次，我们在常见代谢疾病知识门户上的工作提供了。 一种通过专家设计的分析将数据提炼为知识的方法，产生“摘要” 表示”，然后通过简单的可视化或多步骤呈现 第三，我们在 A2FKP 上的工作提供了经验定制知识。 提取并呈现给具有不同文化和偏好的各种社区。 最后，我们在生物医学翻译器方面的工作提供了开发和合规的经验 具有知识表示和交换的标准。 在具体目标 1 中，我们将协调 CFDE 工作组和外部调查人员，以 审查 CF 项目中的知识，并提出如何在 CF 项目中提取和表示这些知识 KC。在具体目标 2 中，我们将与 CF DCC 合作定义其概要表示。 数据，向他们提供软件，以便我们可以使用这些摘要表示，以及 定期“拉取”这些摘要并将其集成到符合 Translator 标准的 KG 中。 在具体目标 3 中，我们将使用为以下目标开发的软件 UI/UX 和搜索基础设施： CMDKP 和 A2FKP 旨在构建一个知识门户，使不同领域的科学家能够 在具体目标 4 中，我们将结合我们和 CF DCC 的先验知识来可视化和搜索 CF 数据。 最后，宣传门户网站并教育人们使用门户网站的教育和推广策略。 在具体目标 5 中，我们将与其他 CFDE 中心对接，建立一个综合资源门户 并与外部资源建立合作伙伴关系，以扩大我们知识库的影响范围。 这些目标共同将产生 CFDE KC，从而释放 CF 资源的全部潜力 通过强调科学有效性，使各个专业水平的科学家能够 理解、信任并以此为基础。