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.

摘要 使NIH共同基金（CF）数据集公平只是实现其潜力的第一步 在“大数据”革命中。科学是通过知识的积累而进步的， 只有当它能被各种各样的研究人员所使用时，它才能达到广泛的范围。而 计算机科学家在“知识内”知识建模方面取得了长足的进步 图”（KG），非计算科学家可能会发现很难解释基于图的 推理工具和可视化，因为这些工具使用逻辑 不考虑科学背景或不确定性的推理，可能产生过多的 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。我们将 将此知识表示为KG，符合现有CFDE和外部知识 策展工作。但我们将通过以下两个方面来关注科学有效性：（a）仔细了解 提取，通过确保KG中的每个边缘是主要的实验发现或 专家应用分析的结果，以及（B）通过建立门户网站， 它不强调通用的图遍历，而支持单一目的的可视化。 为了实施这一知识中心，我们将借鉴我们管理四个大型NIH资助的 在相关环境中面临类似挑战的项目。首先，我们在Terra上的工作提供了 安全存储生物医学数据并通过基于云的 - 是的其次，我们在常见代谢疾病知识门户网站上的工作提供了 一种通过专家设计的分析将数据提炼成知识的方法， 表示”，然后通过简单的可视化或多步骤呈现 规范的工作流程。第三，我们在A2 FKP方面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上的工作提供了开发和遵守的经验 知识表示和交换的标准。 在具体目标1中，我们将协调CFDE和外部调查人员的工作组， 审查CF项目中的知识，并提出如何在 KC公司。在具体目标2中，我们将与CF DCC合作，定义其 数据，为他们提供软件，使我们可以使用这些摘要表示，以及 定期“拉”并将这些摘要整合到符合Translator标准的KG中。 在具体的目标3中，我们将使用软件UI/UX和搜索基础设施， CMDKP和A2 FKP将建立一个知识门户，使各种科学家能够 可视化和搜索CF数据。在具体目标4中，我们将结合联合收割机和CF DCC的优先级 教育和外联战略，以宣传门户网站并教育人们使用门户网站。最后， 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网站 并与外部资源建立合作伙伴关系，以扩大我们的KC的覆盖范围。 这些目标将共同产生一个CFDE KC，释放CF资源的全部潜力 通过强调科学有效性，使各级专业知识的科学家能够 理解，信任，并在此基础上建立。