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） 数据科学核心（DSC）旨在通过以下方式支持控制结核病大流行的研究工作 扩展和增强以TB为重点的数据科学。数据科学在广泛的 在生产性研究的迭代周期中的活动范围，包括研究设计、测定 开发、数据处理、统计分析、数据可视化、数学建模，以及 沟通调查结果。DSC旨在通过制定改善数据的举措来加强结核病的数据科学 提高科学素养，促进TRAC研究人员与结核病新病例数据之间的跨学科合作 科学家（目标1），以及直接增加结核病研究中数据科学方法的利用 (Aim 2）。目标1的目标也将通过虚拟培训和办公时间咨询来实现 作为年度半结构化的组织，以TB为重点的Hackweek和会议活动。DSC将 通过以以下形式向TRAC研究人员提供实际数据科学援助，实现目标2 有针对性的协商、有限范围的催化分析和更广泛的合作努力。我们将支持这些 通过直接支持FHCRC和SCRI的数据科学家以及联合董事的部分工资来支持这些活动。 新的TB数据科学合作也将受到DSC教师合作伙伴的鼓励- 数据科学专家，拥有丰富的结核病研究经验。教师合作伙伴已同意提供他们的 在包括随机建模、软件、 发展，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，和 流行病学另一个行业合作伙伴将帮助补贴这项工作的云计算成本，并帮助 培训TRAC研究人员如何以及何时利用云计算资源。在一起， DSC将建立的培训、社区建设和实践数据科学活动将协同工作 与其他TRAC核心一起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。