We live in a data-rich world with rapidly growing databases with zettabytes of data. Innovation, computation, and technological advances have now tremendously accelerated the pace of discovery, providing driverless cars, robotic devices, expert healthcare systems, precision medicine, and automated discovery to mention a few. Even though the definition of the term data science continues to evolve, the sweeping impact it has already produced on society is undeniable. We are at a point when new discoveries through data science have enormous potential to advance progress but also to be used maliciously, with harmful ethical and social consequences. Perhaps nowhere is this more clearly exemplified than in the biological and medical sciences. The confluence of (1) machine learning, (2) mathematical modeling, (3) computation/simulation, and (4) big data have moved us from the sequencing of genomes to gene editing and individualized medicine; yet, unsettled policies regarding data privacy and ethical norms could potentially open doors for serious negative repercussions. The data science revolution has amplified the urgent need for a paradigm shift in undergraduate biology education. It has reaffirmed that data science education interacts and enhances mathematical education in advancing quantitative conceptual and skill development for the new generation of biologists. These connections encourage us to strive to cultivate a broadly skilled workforce of technologically savvy problem-solvers, skilled at handling the unique challenges pertaining to biological data, and capable of collaborating across various disciplines in the sciences, the humanities, and the social sciences. To accomplish this, we suggest development of open curricula that extend beyond the job certification rhetoric and combine data acumen with modeling, experimental, and computational methods through engaging projects, while also providing awareness and deep exploration of their societal implications. This process would benefit from embracing the pedagogy of experiential learning and involve students in open-ended explorations derived from authentic inquiries and ongoing research. On this foundation, we encourage development of flexible data science initiatives for the education of life science undergraduates within and across existing models.

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改变定量生物学教育的性质：数据科学作为驱动力

我们生活在一个数据丰富的世界中，数据库数量迅速增长，数据量达到 zettabytes。创新、计算和技术进步现在极大地加快了发现的步伐，提供了无人驾驶汽车、机器人设备、专家医疗保健系统、精准医疗和自动发现等等。尽管数据科学一词的定义不断发展，但它已经对社会产生的广泛影响是不可否认的。我们正处于通过数据科学发现的新发现具有推动进步的巨大潜力的时候，但也可能被恶意利用，带来有害的道德和社会后果。或许在生物和医学科学中最能体现这一点。(1) 机器学习，(2) 数学建模，(3) 计算/模拟，(4) 大数据使我们从基因组测序转向基因编辑和个体化医疗；然而，关于数据隐私和道德规范的不稳定政策可能会导致严重的负面影响。数据科学革命放大了本科生物教育范式转变的迫切需要。它重申了数据科学教育在促进新一代生物学家的定量概念和技能发展方面相互作用并增强了数学教育。这些联系鼓励我们努力培养技术娴熟的问题解决者的广泛技能劳动力，擅长处理与生物数据有关的独特挑战，并能够跨科学、人文和社会科学的各个学科进行合作。为实现这一目标，我们建议开发开放课程，超越工作认证修辞，通过参与项目将数据敏锐度与建模、实验和计算方法相结合，同时提供对其社会影响的认识和深入探索。这一过程将受益于体验式学习的教学法，并使学生参与源自真实调查和持续研究的开放式探索。在此基础上，我们鼓励在现有模型内和跨现有模型为生命科学本科生的教育制定灵活的数据科学计划。同时还提供对其社会影响的认识和深入探索。这一过程将受益于体验式学习的教学法，并使学生参与源自真实调查和持续研究的开放式探索。在此基础上，我们鼓励在现有模型内和跨现有模型为生命科学本科生的教育制定灵活的数据科学计划。同时还提供对其社会影响的认识和深入探索。这一过程将受益于体验式学习的教学法，并使学生参与源自真实调查和持续研究的开放式探索。在此基础上，我们鼓励在现有模型内和跨现有模型为生命科学本科生的教育制定灵活的数据科学计划。