IGE: Graduate Opportunities to Learn Data Science (GOLD): Empowering female and underrepresented graduate students through inclusive data science training

现代科学研究产生的数据量是前所未有的。虽然这导致了技术进步和科学发现，但它也创造了对具有数据科学技能的劳动力的迫切需求。此外，社会需要更多样化的科学工作队伍，因为创造性和强大的科学技术进步来自更多样化的科学团队。通过正确的方法，我们可以在数据科学方面培养不同的基础科学研究生，以满足这些劳动力需求。这项授予旧金山州立大学的国家科学基金会研究生教育创新（IGE）奖将评估一种新颖的模块化方法，用于包容性研究生教育，以提高基础科学领域的数据科学技能。研究生学习数据科学的机会（GOLD）战略将支持基础科学硕士学生，重点关注女性和历史上代表性不足的群体的学生，以(1)学习计算和统计技能，(2)将这些技能应用于他们的研究项目，以及(3)形成强大的学习和网络社区。这种新方法将创造包容性的环境，使不同的学生能够获得促进其职业发展所必需的跨学科技能。GOLD计划是模块化的，因此成功的部分可以很容易地被其他大学采用，从而对研究生教育产生重大影响。毕业生学习数据科学的机会（GOLD）战略将通过12个单元的跨学科证书课程来实施。该计划包括9个单元的系列课程，将教授编程和数据科学，使用来自生物和化学研究的作业和示例。一个单元的课程上了两次，将帮助学生将这些技能应用到他们自己的论文研究中，并得到同行导师的支持。一个单元的专业发展课程将学生与当地在学术界和工业界工作的计算生物学家和化学家联系起来，帮助学生建立网络并将他们的训练背景化。为了确保一个包容的环境，GOLD将(1)使用有效的方法来减少众所周知的障碍，这些障碍使许多女性和代表性不足的学生进入高科技领域；(2)为教师提供包容性教学策略、主动学习和指导方面的专业发展。GOLD战略的成功将根据五项学生成果进行评估，采用混合方法：获得计算技能、应用计算技能、职业目标、对包容性学习环境的感知和科学认同。结果将有助于研究生教育教育学的现状，并使类似项目的成功实施成为可能。研究生教育创新（IGE）项目专注于研究生教育的研究。IGE的目标是试点、测试和验证研究生教育的创新方法，并产生将这些方法推广到更广泛的社区所需的知识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The amount of data generated by modern scientific research is unprecedented. While this leads to technological advances and scientific discoveries, it also creates a dire need for a workforce with data science skills. In addition, society needs a more diverse science workforce, as creative and robust scientific and technological advances result from more diverse scientific teams. With the right approach, we can train diverse basic science graduate students in data science to fill these workforce needs. This National Science Foundation Innovations of Graduate Education (IGE) award to San Francisco State University will evaluate a novel, modular approach for inclusive graduate education to advance data science skills in the basic sciences. The Graduate Opportunities to Learn Data science (GOLD) strategy will support basic science Masters students, focusing on women and students from historically underrepresented groups, to (1) learn computational and statistical skills, (2) apply those skills to their research projects, and (3) form strong learning and networking communities. This new approach will create inclusive environments that prepare diverse students to obtain the interdisciplinary skills necessary to boost their career development. The GOLD program is modular, so successful components can easily be adopted at other universities, creating the potential for a significant impact on graduate education. The Graduate Opportunities to Learn Data science (GOLD) strategy will be implemented through a 12-unit interdisciplinary certificate program. The program includes a 9-unit series of courses that will teach programming and data science, using assignments and examples drawn from biology and chemistry research. A 1-unit class taken twice will help students to apply these skills to their own thesis research supported by peer-mentors. A 1-unit professional development class will connect students with local computational biologists and chemists working in academia and industry, helping the students network and contextualize their training. To ensure an inclusive environment, GOLD will (1) use validated methods to mitigate well-known barriers that alienate many women and underrepresented students entering high-tech fields and (2) provide faculty professional development on inclusive teaching strategies, active learning, and mentoring. The success of the GOLD strategy will be assessed based on five student outcomes using a mixed methods approach: acquisition of computational skills, application of computational skills, career goals, perception of inclusive learning environments, and scientific identity. Outcomes will contribute to the current state of pedagogy in graduate education and enable successful implementation of similar programs. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.