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: 9248431
• 负责人: John Quackenbush
• 金额: $ 28.32万
• 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/9248431
• 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（由适用提供）：20世纪下半叶，数字技术的前所未有的进步，通过提供了以前难以想象的速度和速度，通过提供了前所未有的复杂性，从而产生了一场革命，该革命正在改变科学，包括健康和生物医学研究。国家研究委员会（NRC）大规模数据分析委员会的成员在其2013年的“大规模数据分析的边界”中得出结论，与“大数据”相关的挑战远远超出了数据管理的技术方面，并强调，如果我们将这些数据利用这些数据对他们的优势使用，那么严格的定量和统计方法的发展至关重要。在 我们描述了一项综合计划，旨在为学生提供定量和计算技能和沟通和跨学科研究技能的培训，并为这些学生提供申请，使这些学生成为健康和生物医学研究领域的领先大数据科学家。在哈佛大学的公共卫生学院，我们进行了一项大量的投资，解决了这些挑战，包括启动新的形式硕士学位课程在计算生物学和定量基因组学方面，对生物统计学中的课程进行改进，以更加重视计算方法和大数据，以包括我们的核心竞争力，并包括我们的核心竞争力，并包括我们的核心竞争力。正在进行的资本运动。我们要求支持六名博士前学生，他们将从该计划中脱颖而出，具有尖端统计和计算方法开发方面的专业知识，对基本基础科学，公共卫生和临床科学的透彻理解，并在健康和生物医学研究领域广泛地应用了这些方法的技能。我们的学生将参加一个计划，旨在为他们提供跨学科的研究经验，培训他们以有效进行协作和沟通，并了解数据出处和复制研究的重要性。该培训计划涉及成熟和经验丰富的多学科教师的积极参与，包括生物统计学家，生物信息学科学家和计算生物学家，计算机科学家，分子生物学家，公共卫生研究人员和临床医生。它结合了课程工作中培训的要素，生物统计学，计算生物学，计算机科学，分子生物学，人口科学和临床科学的实验室旋转。学生将参加有指导性的独立方法论研究，将参与基于广泛的协作研究项目，并将在刺激和培育跨学科环境中拥有丰富的职业发展机会，这将使他们成为定量大数据健康科学研究的领导者。