Convergence Accelerator Phase I (RAISE): Upskilling for Future Jobs through NLx Talent Demand Data

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。这一融合加速器第一阶段项目的更广泛影响/潜在好处将是根据汇总的职位空缺信息创建公开可用的数据集。团队成员将跨越不同的学科，包括私营部门招聘专家、公共劳动力发展系统的工作人员、劳动经济学家、数据科学家和软件开发人员。该项目将向一个日益动态、开放、互联和以个人为中心的劳动力数据生态系统提供可操作的实时劳动力市场信息(LMI)。由此产生的研究和衍生数据产品将有助于为求职者、学习者、过渡服役人员(退伍军人)和军人配偶及其公共劳动力系统中的职业顾问设计职业规划工具。雇主可以利用研究的洞察力和工具，通过更清晰的工作描述来改善工作匹配，从而增加基于技能的招聘。随着时间的推移，公平获得以工人为中心的职业规划工具将增强美国工人的就业弹性。融合加速器第一阶段项目将建立基础设施和合作伙伴关系，以提高国家实时LMI的质量、数量和可用性。自动化和频繁变化的技能要求要求有必要访问人才管道每一部分的实时信息。然而，十多年来，公共劳动力系统中补充“传统”LMI的一套资源--关于职位空缺的实时信息--的生产基本上局限于私营部门。为了实现开放和可访问的未来劳动力数据生态系统，该团队将(1)创建开放的代码来收集、验证、清理和存储数据；(2)建立治理程序，向研究人员提供原始数据；(3)测试纳入外部数据集的技术和统计可行性；(4)探索开发预测性人工智能工具，从职位描述中提取技能和能力短语；以及(5)探索为美国公众开发以工人为中心的资源。通过强调透明度和开源工具，该项目将促进围绕技能框架和方法的创新，以在不断发展的劳动力中协调劳动力市场数据集。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。

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/potential benefit of this Convergence Accelerator Phase I project will be the creation of publicly available datasets based on aggregated job vacancy information. Team members will converge across disciplines to include private sector recruitment experts, staff of the public workforce development system, labor economists, data scientists, and software developers. The project will deliver actionable real-time labor market information (LMI) to an increasingly dynamic, open, connected, and individual-centered workforce data ecosystem. The resulting research and derived data products will contribute to the design of career planning tools for job seekers, learners, transitioning service members (veterans), and military spouses, as well as their career counselors in the public workforce system. Employers may utilize the research insights and tools to improve job matching through clearer job descriptions, thus increasing skills-based hiring. Over time, equitable access to worker-centered tools for career planning will enhance the employment resiliency of American workers.The Convergence Accelerator Phase I project will establish infrastructure and partnerships to increase the quality, quantity, and availability of real-time LMI for the nation. Automation and frequently shifting skill requirements necessitate access to real-time information along every part of the talent pipeline. However, production of the set of resources that complements "traditional" LMI in the public workforce system - real-time information on job vacancies - has been largely confined to the private sector for over a decade. To enable an open and accessible workforce data ecosystem of the future, the team will (1) create open code to collect, validate, clean, and store data; (2) establish governance procedures to make raw data available to researchers; (3) test the technical and statistical feasibility of incorporating external data sets; (4) explore the development of predictive artificial intelligence tools to extract skill and competency phrases from job descriptions; and (5) explore the development of worker-centered resources for the American public. By emphasizing transparency and open-source tools, the project will promote innovation around skill frameworks and methods of harmonizing labor market data sets in a constantly evolving workforce.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.