Data Science Core

数据科学核心

• 批准号: 10595073
• 负责人: Shuyi Ma
• 金额: $ 14.65万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-21 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10595073
• 关键词: Agreement; Algorithms; Applications Grants; Biological; Biology; Biometry; Clinical; Clinical Research; Cloud Computing; Collaborations; Communication; Communities; Computer Analysis; Computers; Consultations; Data; Data Analyses; Data Science; Data Science Core; Data Scientist; Data Set; Development; Environment; Epidemiologist; Epidemiology; Event; Faculty; Fostering; Genes; Goals; Grant; Hour; Image Analysis; Institution; Laboratories; Laboratory Research; Laboratory Scientists; Learning; Modeling; Monitor; Multiomic Data; Outcome; Output; Pathway Analysis; Pathway interactions; Peer Review; Phase; Play; Process; Productivity; Reproducibility; Research; Research Activity; Research Design; Research Personnel; Research Support; Role; Science; Scientist; Services; Statistical Data Interpretation; Statistical Methods; Structure; Systems Biology; Training; Tuberculosis; Vaccines; Visualization; Vocabulary; Wages; Work; assay development; bioinformatics tool; community building; complex data; computerized data processing; computing resources; cost; data integration; data management; data tools; data visualization; experience; improved; industry partner; literacy; mathematical model; member; multidimensional data; multidisciplinary; novel; pandemic disease; power analysis; single cell analysis; software development; structural biology; success; symposium; tool; tuberculosis diagnostics; virtual

PROJECT SUMMARY (DATA SCIENCE CORE) The Data Science Core (DSC) aims to support research efforts to control the TB pandemic through expansion and enhancement of TB-focused data science. Data science plays an important role in a broad spectrum of activities throughout the iterative cycles of productive research, including study design, assay development, data processing, statistical analysis, data visualization, mathematical modeling, and communication of findings. The DSC aims to enhance data science for TB by building initiatives to improve data science literacy and catalyze cross-disciplinary collaborations between TRAC researchers and New-to-TB data scientists (Aim 1), as well as to directly enable increased utilization of data science approaches in TB research (Aim 2). Aim 1 objectives will be achieved through virtual trainings paired with office hour consultations, as well as the organization of annual semi-structured, TB-focused Hackweeks and meet-up events. The DSC will accomplish Aim 2 objectives by providing hands-on data science assistance to TRAC researchers in the form of targeted consultations, limited-scope catalytic analyses, and broader collaborative efforts. We will support these activities by directly supporting partial salary of data scientists at FHCRC and SCRI in addition to the co-directors. New-to-TB data science collaboration will also be encouraged by a diverse group of DSC Faculty Partners— data science experts who have extensive experience studying TB. Faculty Partners have agreed to provide their analytical expertise for collaborations and consultations in subjects that include stochastic modeling, software development, biostatistics, image analysis, lab data management, structural biology, systems biology, and epidemiology. An additional Industry Partner will help subsidize cloud-computing costs for this work and help train TRAC researchers on how and when to utilize cloud-computing resources. Together, the combination of training, community building, and hands-on data science activities that the DSC will establish will work in tandem with the other TRAC Cores to directly amplify TB science, catalyze new collaboration on TB-related questions, and expand the number of TB scientists in our community.

项目总结（数据科学核心）