NRT-HDR Data Driven Decision Making to Address Complex Resource Problems

奖2021874NRT-HDR数据驱动的决策，以解决复杂的资源问题“没有人因为数字而做出决定。他们需要一个故事。“诺贝尔奖得主丹尼尔·卡纳曼解释说。讲故事，同时保持数据的真实性，是本世纪的一个决定性挑战。叙述具有说服力，但不客观；数字通常是客观的，但不具有说服力。为了让科学为社会提供最广泛的有益影响，它的发现需要以科学的正直和真实的同理心进行交流。尽管越来越多的跨学科职业需要这种双重流利，但目前的研究生课程并没有为学生掌握这些技能做好充分的准备。在许多机构，培训仍然是孤立的，课程划分将那些拥有数据科学理论技能、深入的特定领域知识和政策分析的人分开。这些分割并不能很好地映射到现实世界，在现实世界中，最紧迫的社会和环境挑战需要跨学科的创新和合作。有效的跨学科协作需要一定程度的曝光度、经验和实践，将数字和叙述结合起来解决现实世界中的问题。这项授予塔夫茨大学的国家科学基金会研究培训(NRT)奖将通过培训数据专业人员来满足这一需求，这些专业人员将综合数字和叙述，以设计和实施技术上高效且适合背景的数据驱动的解决方案。该项目预计将培训140多名硕士和博士生：20名有津贴的NRT研究员、50名NRT专注于问题的沉浸式研究员、60名NRT旅行获奖者和12名NRT模块开发人员。NRT将培养两类数据专业人才。精通政策的数据专家-主要来自STEM学科-将推动数据科学的前沿，并将能够(A)利用适当的数据驱动理论、工具和技术来识别、分析和解决问题；以及(B)调整和获得技能集，以利用新兴的数据重点技术、技术和工具。与此同时，他们将接受与政策相关的技能培训，并能够与工作场所受过较少技术培训的决策者合作。精通数据的决策者--主要来自非科学、技术和经济领域的决策者--将在政策制定和决策中使用数据。他们将能够(A)在包括数据使用者和数据生产者在内的团队中进行有效协作，包括具有不同背景和观点的科学家、工程师、从业人员和决策者；(B)以可操作的方式提供了解数据的建议，并广泛传播这些结果，以便采取有效行动。学员将拥有越来越多的数据科学工具和方法组合的实践经验，这些工具和方法有助于将数据快速转化为可操作的信息。培训将立足于从数据丰富(即大数据)和数据稀缺的环境中发现、定义和解决许多现实世界资源问题--包括食品、能源、水和生态系统交叉问题--的共同问题。这种NRT模式将通过建立在两个潜在的变革性组成部分上提供跨学科的理论-实践综合：(1)模块课程要素(MCEs)；(2)以问题为中心的沉浸(PFI)。这种NRT的一个独特之处是使用了一个跨MCES的通用数据库，类似于采用了一本跨课程计划写作的通用书籍。拟议的以问题为中心的理论-实践综合战略将促进数据科学专家、领域专家、从业人员和决策者之间更深层次的可操作合作。随着该项目致力于培养STEM和非STEM学科的数据专业人员，它将积极寻求扩大代表不足群体对数据科学的参与。内容丰富、模块化和适应性强的计划元素将使它们可以跨学科和机构转移，并在NRT资助期后可持续。NSF研究培训(NRT)计划旨在鼓励为STEM研究生教育培训开发和实施大胆的、具有潜在变革性的新模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科或趋同研究领域对STEM研究生进行有效培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Award 2021874NRT-HDR Data Driven Decision Making to Address Complex Resource Problems“No one ever made a decision because of a number. They need a story,” explained Nobel Laureate Daniel Kahneman. Telling stories, while remaining true to the data, is a defining challenge of this century. Narratives are persuasive but not objective; numbers are often objective but not persuasive. For science to provide the broadest beneficial impact to society, its findings need to be communicated with both scientific integrity and authentic empathy. Although a growing number of interdisciplinary careers demand this dual fluency, current graduate curricula do not adequately prepare students to master these skills. At many institutions, training remains siloed, and curricular partitions separate those with skills in data science theory, deep domain-specific knowledge, and policy analysis. These partitions do not map well to the real world, where the most pressing social and environmental challenges demand interdisciplinary innovation and collaboration. Effective interdisciplinary collaboration requires a level of exposure, experience, and practice in real-world problem-solving that combines numbers and narratives. This National Science Foundation Research Traineeship (NRT) award to Tufts University will address this need by educating Data Professionals who will synthesize numbers and narratives to design and implement data-driven solutions that are technically efficient and contextually appropriate. The project anticipates training over 140 masters and doctoral trainees: 20 NRT Fellows with stipends, 50 NRT Problem-Focused Immersion Fellows, 60 NRT Travel Awardees, and 12 NRT Module Developers. This NRT will train two types of data professionals. Policy-Savvy Data Experts—primarily from STEM disciplines—will advance the frontiers of data science and will be able to (a) identify, analyze, and solve a problem with the appropriate data-driven theory, tools, and techniques; and (b) adapt and acquire skill sets to harness emerging data-focused technologies, techniques, and tools. At the same time, they will have training in policy-relevant skills and be able to collaborate with the less technically trained decision-makers in their workplace. Data-Proficient Decision Makers—primarily from non-STEM disciplines—will use data in policy development and decision making. They will be able to (a) collaborate effectively on teams that include users and producers of data, including scientists, engineers, practitioners, and decision-makers with different backgrounds and perspectives; and (b) provide data-informed advice in an actionable way as well as broadly communicate those results for effective action. Trainees will have hands-on experience with a growing portfolio of data science tools and methods that facilitate the rapid translation of data into actionable information. Training will be grounded in finding, defining and resolving problems from both data-rich (i.e., big data) and data-scarce contexts common to many real-world resource problems – including those at the intersection of Food, Energy, Water, and Ecosystems. This NRT model will provide an interdisciplinary theory-practice synthesis by building on two potentially transformative components: (1) Modular Course Elements (MCEs); and (2) Problem-Focused Immersion (PFI). A unique aspect of this NRT is the use of a common database across the MCEs, akin to the adoption of a common book in Writing Across the Curriculum programs. The proposed problem-focused and theory-practice synthesis strategy will foster deeper actionable collaboration among data science experts, domain experts, practitioners, and decision-makers. With this project’s commitment and plan to educate Data Professionals from STEM and non-STEM disciplines, it will actively pursue broadening participation in data science from under-represented groups. Content-rich, modular and adaptable nature of program elements will make them transferrable across disciplines and institutions and sustainable beyond the NRT grant period.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.