dkNET Coordinating Unit: An information network for FAIR resources and data

dkNET 协调单元：FAIR 资源和数据的信息网络

• 批准号: 10004368
• 负责人: Jeffrey S. Grethe
• 金额: $ 100万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-12-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10004368
• 关键词: Address; Adoption; Atlases; Awareness; Communities; Data; Data Aggregation; Data Analytics; Data Science; Data Set; Databases; Ensure; Enzymes; Europe; Evaluation; Event; FAIR principles; Feedback; Fostering; Foundations; Funding; Goals; Guidelines; Individual; Information Networks; Infrastructure; Journals; Libraries; Literature; Maintenance; Meta-Analysis; Metabolic; Metabolic Diseases; Metadata; Mining; Modeling; National Institute of Diabetes and Digestive and Kidney Diseases; Nuclear Receptors; Organ; Organism; Performance; Phase; Pilot Projects; Process; Publishing; Reagent; Receptor Signaling; Recommendation; Reproducibility; Research; Research Personnel; Resources; Services; Signal Pathway; Signal Transduction; System; United States National Institutes of Health; Universities; Work; analytical tool; body system; cloud based; dashboard; data format; data management; data resource; data sharing; indexing; interoperability; next generation; novel strategies; outreach; receptor; research and development; response; success; symposium; text searching; tool; transcription factor; user centered design; user-friendly

Project Summary We outline plans for the next generation NIDDK Information Network (dkNET), a centralized portal for discovery and information about research resources-data, reagents, organisms, tools available to researchers through NIDDK-supported centers and other relevant projects. In this phase, we will continue to support and maintain aggregated data from biomedical databases. But since the launch of dkNET in 2012, the data and resource landscape has changed, there are new concerns about rigor and reproducibility, and we are seeing data mandates and increased demand for data and data services. We will therefore capitalize upon our considerable success in developing and deploying Research Resource Identifiers (RRIDs), a system for identifying, tracking and aggregating data about research resources in the literature currently used in over 350 journals. RRIDs are unique identifiers assigned to individual research resources and form the basis of a citation and tracking system for use within the scientific literature. Through RRIDs, we will further develop our Resource Information Network, including analytics tools, to help researchers not only to identify appropriate resources, but to provide them with up to date information about the use and performance of these resources. We will also develop services in support of NIH's new rigor and reproducibility guidelines to help researchers develop plans to identify and validate research resources and help support centers in tracking use of their products. In response to user feedback and new NIH mandates and recommendations for data management and sharing, we will also add new services and tools in support of data science. During this past phase, the PI's helped develop the FAIR principles, recommendations for making data Findable, Accessible, Interoperable and Reusable. Major data initiatives in both the US and Europe are now supporting FAIR. Full implementation of FAIR requires that the community have a way to develop and disseminate best practices and standards for metadata, data formats, etc. NIDDK is uniquely poised to help coordinate and accelerate this process for NIDDK supported researchers. We believe to gain support for proper data management and data publishing, it is important not just to provide help in compliance, but to provide tools and services for integrating and analyzing FAIR data to address critical questions. In conjunction with the Signaling Pathways Project (SPP, formerly the Nuclear Receptor Signaling Atlas, or NURSA), we will develop a new meta-analysis platform for FAIR `omics data. Through the application of biocuration and consensomic analysis of NIDDK-funded and relevant `omics data assets, dkNET users will have access to a user-friendly but powerful platform for modeling signaling events in metabolic organs and intersections between cellular signaling pathways and metabolic disease. These capabilities will be accompanied by an aggressive and extensive outreach and dissemination plan to help broaden awareness of and use of dkNET and NIDDK-supported centers.

项目总结