CyberTraining: Implementation: Medium: C2D - Cybertraining for Chemical Data scientists

该项目为数据化学领域的劳动力发展建立了一个可扩展和可持续的培训平台，名为C2D（化学数据科学家的网络培训）。机器学习（ML）的使用将彻底改变合成化学和许多依赖于它的领域，包括材料和能源科学、信息技术和健康科学。为了实现这种转变，现有和未来的劳动力需要培训，考虑到他们的培训需求、背景和学习偏好的多样性。C2D是一个自适应和个性化的培训平台，根据学习者的学习进度和学习状态的自动评估，从精心策划的学习材料中选择个性化的任务。它组织培训视频、阅读和评估，以满足学习者的需求，并将免费提供给学术界、工业界和一般人群。具体的征聘机制将确保代表性不足的群体的参与。通过在线和现场培训的结合，C2D将使当前和未来的劳动力能够在合成化学中使用ML。化学数据科学家的网络培训（C2D）平台将自适应学习和评估与关于机器学习在合成化学中的应用的个性化培训材料推荐相结合，从而实现数据化学领域的个性化教学。该平台是心理学家、计算机科学家和化学家跨学科合作的结果，旨在围绕最新的心理测量原理和推荐系统开发C2D，以提供个性化指导。C2D将从一项调查和涉及领域专家的焦点小组分析开始，制定一份规划课程材料的蓝图，这些材料将被输入一个基于持续适应性评估的个性化推荐系统。C2D与许多学术和工业伙伴合作，以确保平台的广泛采用和可持续性。它将促进整合核心机器学习素养和化学特定网络技能的研究人员的发展，使机器学习方法在化学中得到广泛采用，并确保依赖合成化学的部门的持续经济竞争力。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 project establishes a scalable and sustainable training platform for workforce development in data chemistry, named C2D (Cybertraining for Chemical Data scientists). The use of machine learning (ML) will revolutionize synthetic chemistry and many fields that depend on it, including materials and energy science, information technology and health sciences. To enable this transformation, the existing and future workforce needs training that takes their diversity of training requirements, backgrounds, and learning preferences into account. The C2D is an adaptive and personalized training platform, entailing personalized task selection from curated learning materials based on learners' progress and automated assessment of their learning status. It organizes training videos, reading, and assessments in a way that is responsive to the needs of the learners and will be offered to the academic, industrial, and general population free of charge. Specific recruitment mechanisms will ensure the participation of underrepresented groups. Using a combination of online and in-person training, C2D will empower the current and future workforce to use ML in synthetic chemistry. The Cybertraining for Chemical Data scientists (C2D) platform integrates adaptive learning and assessment with personalized recommendations for training materials regarding the application of machine learning in synthetic chemistry, thereby enabling personalized instruction in the field of data chemistry. The platform is the result of an interdisciplinary collaboration of psychologists, computer scientists and chemists, and aims to develop C2D around the latest psychometric principles and recommender systems for providing personalized instruction. Starting from a survey and focus groups analysis involving domain experts, the C2D will develop a blueprint for curating curricular material that is fed into a personalized recommender system based on continuous adaptive assessment. The C2D works with a number of academic and industrial partners to ensure the wide adoption and sustainability of the platforms. It will promote the development of research workforce integrating core ML literacy and chemistry-specific cyber skills to enable the wide adoption of ML methods in chemistry and ensure the continued economic competitiveness of the sectors that depend on synthetic chemistry.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.