UNM FIRST: Administrative Core

项目摘要-行政核心 新墨西哥州大学（UNM）是新墨西哥州的旗舰和最大的大学，是四所 少数民族国家。它也是该国最贫穷和资源最不足的州之一。新 墨西哥有大量的西班牙裔（49.3%）和美洲印第安人（11%）人口。UNM是一个西班牙裔服务 卡内基非常高的研究活动大学，拥有16，000多名本科生和6，000名 研究生。学生群体的民族/种族多样性密切反映了国家的多样性， 女生占本科和研究生入学人数的57.4%。UNM有一个非凡的NIH团队- 资助的研究人员在神经科学和数据科学研究方面享有国际声誉， 培养和提升下一代多元化科学家的巨大潜力。生物医学系的多样化 在UNM将与NIH的目标，即多样化的生物医学劳动力在各级，以实现其 我们的使命是发现和创新，以改善人类健康。此外，它将促进包容性 卓越，并为女性和URM学员提供榜样以及相关的职业道路 在STEM方面取得进展。集群雇用教师，这将是新的新墨西哥州，似乎是一个有效的方式， 建立一个教师队伍，使教授队伍多样化。因此，UNM FIRST计划将实施一个集群， 教师招聘，指导和专业发展的设计模型，嵌入UNM的 持续致力于包容性卓越。旨在促进多样性的招聘和雇用战略 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 显著增加我们的URM教师谁是NIH资助的主要研究者的百分比。的 中心假设是，UNM FIRST计划将成功地雇用，促进和保留多样化的教师 队列，以及该计划产生的政策/实践，例如创建机构 创新执行委员会，将通过UNM，并最终作出重大改进， 包容性卓越。UNM FIRST行政核心的具体目标是： 招募和雇用神经科学和数据科学领域（AIM）的多样化UNM FIRST教师队伍 1），分配启动研究资金，并提供行政支持，以UNM第一教师队列 促进成功获得NIH资助（AIM 2），并为UNM教师提供持续支持， 部门主席和UNM领导实施新的创新校园政策和流程， 增加女性和多元化教师的招聘，晋升和保留，否则会导致更具包容性的 文化（AIM 3）。鉴于重点是使用循证医学， 增加神经科学和数据科学领域教师多样性的方法。

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.