Statistical and Quantitative Training in Big Data Health Science

大数据健康科学统计与定量培训

• 批准号: 9115368
• 负责人: John Quackenbush
• 金额: $ 28.02万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9115368
• 关键词: Big Data; Health Sciences; Training

 DESCRIPTION (provided by applicant): Unprecedented advances in digital technology during the second half of the 20th century have produced a revolution that is transforming science, including health and biomedical research, by providing data of unprecedented complexity in volumes and at a rate that was previously unimaginable. Members of National Research Council's (NRC's) Committee on Massive Data Analysis concluded in their 2013 "Frontiers of Massive Data Analysis" report that the challenges associated with "Big Data" go far beyond the technical aspects of data management and emphasized that development of rigorous quantitative and statistical methods was crucial if we are to use these data to their advantage. In this application we describe an integrated program designed to provide students with training in the quantitative and computational skills and communication and interdisciplinary research skills-and their application-required for those students to become the next generation of leading Big Data scientists in health and biomedical research. At the Harvard TH Chan School of Public Health, we have made a substantial investment is addressing these challenges, including launching a new formal Master's Degree program in Computational Biology and Quantitative Genomics, revamping the curriculum in Biostatistics to include a greater emphasis on computational methods and Big Data, a proposal undergoing internal review to include computation as an area of core competency for our students, and the inclusion of Big Data analytics as a central focus of the School's ongoing capital campaign. We are requesting support for six pre-doctoral students who will emerge from the program with expertise in cutting-edge statistical and computational methods development, a thorough understanding of fundamental basic science, public health, and clinical science, and demonstrated skills in the application of those methods in a wide range of areas in health and biomedical research. Our students will participate in a program designed to provide them with interdisciplinary research experience, to train them to collaborate and communicate effectively, and to understand the importance of data provenance and reproducible research. The training program involves active participation by accomplished and experienced multidisciplinary faculty members, including biostatisticians, bioinformatics scientists and computational biologists, computer scientists, molecular biologists, public health researchers, and clinicians. It combines elements of training in coursework, lab rotations in biostatistics, computational biology, computer science, molecular biology, population science and clinical science. Students will participate in directed and independent methodological research, will be involved in broad-based collaborative research projects, and will have rich career development opportunities in a stimulating and nurturing interdisciplinary environment that will prepare them to be leaders in quantitative Big Data health science research.

 描述：在20世纪下半叶，Havoluti的数字技术的前所未有的进步正在改变科学，包括健康和生物医学研究，以前是无法想象的。强调，如果我们要到达标准，那么严格的定量和统计方法至关重要 我们对量化技能和国际研究技能进行培训的学生描述了该计划。在公共卫生方面，我们进行了投资正在解决这些挑战，包括启动Compuology Biology in Compulogy Biology和Cuantititiatition基因组学方面的计算方法和大数据，一项提案，内部评论，以作为核心核心核心核心核心核心核心领域核心核心核心核心我们确定的学生抽动尽可能多地尽可能高，我们要求支持六个学前班学生方法，很多基础科学，公共卫生和临床科学，以及在健康和健康和生物医学研究中的各种领域的应用方面的技能。为他们提供有效的跨学科合作和同情，并通过完成的数据来源和可重复的研究的重要性。计算机科学，分子生物学，人口科学和临床科学。