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.

项目摘要（数据科学核心） 数据科学核心 (DSC) 旨在支持通过以下方式控制结核病大流行的研究工作： 扩大和加强以结核病为中心的数据科学 数据科学在广泛的领域发挥着重要作用。 贯穿生产性研究迭代周期的一系列活动，包括研究设计、分析 开发、数据处理、统计分析、数据可视化、数学建模等 DSC 旨在通过制定改进数据的举措来加强结核病数据科学。 科学素养并促进 TRAC 研究人员与新结核病数据之间的跨学科合作 科学家（目标 1），以及直接提高数据科学方法在结核病研究中的利用率 （目标 2）也将通过虚拟培训和办公时间咨询来实现。 DSC 将组织年度半结构化、以 TB 为重点的黑客周和聚会活动。 通过向 TRAC 研究人员提供实践数据科学帮助来实现目标 2 我们将支持有针对性的磋商、有限范围的催化分析和更广泛的合作努力。 除联合董事外，还直接支持 FHCRC 和 SCRI 的数据科学家的部分工资。 不同的 DSC 教员合作伙伴团体也将鼓励新的 TB 数据科学合作—— 具有丰富结核病研究经验的数据科学专家已同意提供他们的信息。 随机建模、软件等主题的合作和咨询的分析专业知识 开发、生物统计学、图像分析、实验室数据管理、结构生物学、系统生物学等 另一个行业合作伙伴将帮助补贴这项工作的云计算成本并提供帮助。 培训 TRAC 研究人员如何以及何时利用云计算资源。 DSC 将建立的培训、社区建设和实践数据科学活动将同时进行 与其他 TRAC 核心直接扩大结核病科学，促进结核病相关问题的新合作， 并扩大我们社区中结核病科学家的数量。