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

2021874 NRT-HDR数据驱动决策解决复杂资源问题“没有人会因为一个数字而做出决定。他们需要一个故事，”诺贝尔奖得主丹尼尔卡尼曼解释说。讲故事，同时保持对数据的真实性，是这个世纪的一个决定性挑战。叙述具有说服力，但不客观;数字往往客观，但不具有说服力。为了使科学对社会产生最广泛的有益影响，其研究结果需要以科学的完整性和真正的同理心进行传播。虽然越来越多的跨学科职业需要这种双流利性，目前的研究生课程没有充分准备学生掌握这些技能。在许多机构，培训仍然是孤立的，课程划分将那些具有数据科学理论，深入的特定领域知识和政策分析技能的人分开。这些划分并不能很好地映射到真实的世界，在这个世界上，最紧迫的社会和环境挑战需要跨学科的创新和合作。有效的跨学科合作需要一定程度的曝光，经验和实践，在现实世界中解决问题，结合数字和叙述。塔夫茨大学的国家科学基金会研究培训（NRT）奖将通过教育数据专业人员来解决这一需求，这些数据专业人员将综合数字和叙述来设计和实施技术上有效且适合上下文的数据驱动解决方案。该项目预计将培训140多名硕士和博士生：20名NRT研究员，50名NRT问题专注沉浸式研究员，60名NRT旅行获奖者和12名NRT模块开发人员。该NRT将培训两种类型的数据专业人员。精通政策的数据专家-主要来自STEM学科-将推进数据科学的前沿，并将能够（a）识别，分析和解决问题与适当的数据驱动的理论，工具和技术;和（B）适应和获得技能集，以利用新兴的数据为重点的技术，技术和工具。与此同时，他们将接受与政策有关的技能培训，并能够在其工作场所与技术培训较少的决策者合作。精通数据的决策者-主要来自非STEM学科-将在政策制定和决策中使用数据。他们将能够（a）在包括数据用户和数据生产者的团队中进行有效合作，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）以可行的方式提供基于数据的咨询意见，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换为可操作的信息。培训将立足于从丰富的数据（即，大数据）和数据稀缺的背景下，共同的许多现实世界的资源问题-包括那些在粮食，能源，水和生态系统的交叉点。这种NRT模式将通过建立两个潜在的变革性组件提供跨学科的理论-实践综合：（1）模块化课程元素（MCE）;和（2）以问题为中心的沉浸式（PFI）。这个NRT的一个独特之处是在MCE中使用一个通用的数据库，类似于在整个课程计划中采用一本通用的书。提出的以问题为中心的理论实践综合策略将促进数据科学专家、领域专家、从业者和决策者之间更深入的可操作合作。通过该项目的承诺和计划，教育来自STEM和非STEM学科的数据专业人员，它将积极寻求扩大代表性不足的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他们跨学科和机构和可持续超越NRT资助期限转移。NSF研究培训（NRT）计划旨在鼓励开发和实施大胆的，新的潜在变革模型STEM研究生教育培训。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相一致的综合培训模式，在高优先级的跨学科或融合研究领域对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价值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.