UNM FIRST: Administrative Core

项目总结--行政核心 新墨西哥大学(UNM)是新墨西哥州的旗舰大学和最大的大学，是四所大学之一 少数族裔占多数的州。它也是美国最贫穷、最缺乏资源的州之一。新的 墨西哥有大量的西班牙裔(49.3%)和美国印第安人(11%)。UNM是一名拉美裔服务人员 学院和卡内基研究活动非常活跃的大学，有超过16,000名本科生和6,000名学生 研究生。学生人口的族裔/种族多样性密切反映了国家的多样性，以及 女性占本科生和研究生入学人数的57.4%。北卡罗来纳大学有一群不同寻常的NIH- 资助在神经科学和数据科学研究方面享誉国际的研究人员，每个人都有 培养和提升下一代多元化科学家的巨大潜力。使生物医学教师队伍多样化 在UNM将与NIH的目标保持一致，即使各级生物医学工作人员多样化，以实现其 发现和创新的使命，以改善人类健康。此外，它还将催化包容性 卓越，并为女性和URM实习生提供榜样和相关的职业道路 STEM的进展。对新墨西哥州州立大学来说，集中招聘教员似乎是一种有效的方式 建立一支教师队伍，使教授队伍多样化。因此，UNM First方案将实施一个集群 嵌入到UNM的教师招聘、指导和专业发展的设计模式 对包容性卓越的持续承诺。旨在促进多样性的招聘和招聘战略 并将使用减少歧视。招聘将在六个部门进行。我们的长期目标是 采用集群/队列招聘模式，增加UNM生物医学教师的多样性，这反过来又将 显著增加我们URM教职员工的百分比，他们是NIH资助的首席调查人员。这个 中心假设是，UNM First项目将成功地聘用、提拔和留住多样化的教师 队列，以及由该计划产生的政策/实践，例如创建机构 创新执行委员会，将被UNM采纳，并最终在 包罗万象的卓越。联合国管理机制第一行政核心的具体目标是：领导 招聘和聘用神经科学和数据科学(AIM)领域的UNM第一批不同的教员 1)，分配启动研究资金，并向新墨西哥州大学第一批教员提供行政支助 为了促进成功获得国立卫生研究院的资助(目标2)，并为新墨西哥州州立大学的教职员工提供持续的支持， 部门主席和UNM领导层实施新的创新校园政策和流程，以 增加女性和多样化教职员工的招聘、晋升和留住，否则将导致更具包容性的 制度文化(目标3)。鉴于重点是使用循证证据，拟议的工作具有重要意义 在神经科学和数据科学领域增加教员多样性的方法。

PROJECT SUMMARY – ADMINISTRATIVE CORE The University of New Mexico (UNM) is the flagship and largest university in New Mexico, which is one of four majority-minority states. It is also one of the poorest and most under-resourced states in the nation. New Mexico has a large Hispanic (49.3%) and American Indian (11%) population. UNM is a Hispanic-Serving Institution and a Carnegie Very High Research Activity University with over 16,000 undergraduates and 6,000 graduate students. The ethnic/racial diversity of the student population closely reflects that of the state, and women make up 57.4% of undergraduate and graduate enrollments. UNM has an extraordinary group of NIH- funded researchers who are internationally renowned for research in neuroscience and data science, each with great potential to train and elevate the next generation of diverse scientists. Diversifying the biomedical faculty at UNM will align with NIH’s goal of diversifying the biomedical workforce at all levels in order to accomplish its mission of discovery and innovation toward improving human health. Furthermore, it will catalyze inclusive excellence, and provide role models for female and URM trainees and a relatable path toward career advancement in STEM. Cluster hiring of faculty, which will be new for UNM, appears to be an effective way to build a faculty cohort and diversify the professoriate. Thus, the UNM FIRST program will implement a cluster design model of faculty hiring, mentoring, and professional development that is embedded within UNM’s ongoing commitment to inclusive excellence. Recruitment and hiring strategies designed to promote diversity and reduce discrimination will be used. Hiring will occur across six departments. Our long-term goal is to employ a cluster/cohort hiring model that increases the diversity of biomedical faculty at UNM, which in turn will significantly increase our percentage of URM faculty who are Principal Investigators with NIH funding. The central hypothesis is that the UNM FIRST program will successfully hire, promote, and retain a diverse faculty cohort, and the policies/practices resulting from this program, such as the creation of the Institutional Innovation Implementation Board, will be adopted by UNM and ultimately make significant improvements in inclusive excellence. The Specific Aims of the UNM FIRST Administrative Core are: to lead the process of recruiting and hiring the diverse UNM FIRST faculty cohort in the neuroscience and data science areas (AIM 1), to distribute start-up research funding and provide administrative support to the UNM FIRST faculty cohort to facilitate success in obtaining NIH funding (AIM 2), and to provide ongoing support for UNM faculty, department chairs, and UNM leadership to implement new innovative campus-wide policies and processes to increase hiring, promotion, and retention of female and diverse faculty and otherwise lead to a more inclusive institutional culture (AIM 3). The proposed work is highly significant, given the focus on using evidence-based approaches to increase diversity among faculty in the areas of neuroscience and data science.