This session will share information about current practices and provocative questions about issues of equity and diversity in Research Development by sharing experiences from two different faculty development/mentoring programs, one at Saint Louis University (SLU) and the other at Appalachian State University (ASU).  At SLU, the BILCN-Research helps faculty in Building Integrated Learning and Coaching Networks for Research.  The Appalachian State University PREPARE program (Prepare for Appalachian Research Experiences) is a similar, more established program.
The purpose of this session is to stimulate discussion about different approaches to cohort-based faculty research development programs and more specifically the role of such programs in addressing research needs for underrepresented groups of faculty and linking such programs directly to Justice, Diversity, Equity, and Inclusion (JEDI) initiatives.  Current cohorts of PREPARE specifically address gender underrepresentation in STEM disciplines and the program has been supported by the NSF.  The initial BILCN-Research@SLU did not have a requirement for DEI categorization, though all the faculty participants have been in intersectional underrepresented categories. Future iterations of the BILCN cohort program will explicitly aim to provide faculty researchers not only with networking and proposal development tools, but also guidance on how to contribute to an effective and inclusive research ecology.
BILCN-Research is a 6-9 month, cohort-based research development program that develops SLU faculty’s networks and skills needed to advance their research agendas and effectively compete for external funding. Faculty participants are selected by a committee of faculty members and are high-potential, full-time SLU faculty who have not received major external funding awards in the past 3-5 years, but who have active research agendas and a project for which they will seek major external funding. The expected outcome for all participants is submission of a major external grant proposal within approximately 1 year. 
PREPARE is a customized research mentoring program for research-interested junior faculty at Appalachian State University.  Each cohort is introduced to a mentor network which assists these individuals with establishing a realistic research agenda, improving grantsmanship skills, seeking appropriate external funding sources, and promoting compliance with internal and external protocols to increase successful grant submissions. Participants anticipate preparing and submitting a grant proposal within 12-24 months.
The program will invite participants to examine Research Development practices at their own institutions, specifically cohort-based programs and those that target JEDI-impacted faculty.

Research development (RD) is a relatively new area of professional practice in and outside higher education for which “the full extent of practitioner purposes and practices is yet to be delineated” (Preuss, Eck, Fechner & Walker, 2018, p. 2). To address this gap, data was compiled from 442 position descriptions, two surveys that asked about the background, experience, employers, roles, responsibilities, and salaries of RD professionals, and the membership roll of the National Organization of Research Development Professionals (NORDP). Quantitative and qualitative analyses were completed as well as comparisons of findings from each of the sources. The result was an overview of the field and the persons who work in it. The average research development professional was a middle-aged, White, female with six or less years of experience in RD who held an advanced degree, who worked full time in a position she had occupied for four years or less and for an entity where she had been employed for longer than four years. More than 75% of the RD professionals were women but minorities were underrepresented as in many other areas of higher education and in STEM-related fields. More than half of the informants were recruited to, grew into, or had positions created for them in RD by their employer rather than being recruited from outside the organization. Approximately 72% of research development professionals in the United States worked at public colleges or universities in 2017, with the same percent working for doctorate-granting institutions. Their employers were located in 49 US states and one territory but there was a group of 14 states in which two-thirds of all known RD professionals could be found. There was also a tendency for large state and private universities that have more selective admission policies and a strong and extended focus on conducting research to employ groups of five or more RD professionals. RD professionals hold positions with a wide range of responsibilities, 62 were demonstrated to be part of the field. The data substantiated that these can be grouped in four categories already used by practitioners, strategic research advancement, communicating about research and research priorities, enhancing collaboration, and support of proposal production. Evidence of employment tiers and development of areas of specialization was found with statistically significant differences in responsibilities demonstrated between persons with the title Director and Proposal Developer/Grant Writer. RD offices tended to be small, most commonly staffed by one to two persons. Centralized offices were the type most frequently reported, 37.6% of respondents, but seven other organizing patterns existed. RD offices with five or more employees occurred at larger, flagship institutions that have had an historic focus on externally-funded research yet, over half of research development offices were reported to have existed for six years or less. Compensation reported for research development professionals was comparable to that noted by McDonald and Sorensen (2015) for Assistant and Associate Professors. Combined, the data facilitated formulation of the first detailed definition of research development that is fully evidence-based including identifying RD’s contribution to the mission of higher education and an operational description of how RD is practiced.

This article analyzes the impact of research unit size on the academics’ scientific output and research related communication behaviors with peers, controlling for individual and organizational characteristics, including the academics engagement in teaching. Results show that research unit size does not influence total scientific output, but rather the scientific output profile of individual academics. Academics at larger research units publish more in international peer-review journals than in national peer-reviewed journals notwithstanding the disciplinary fields they belong to. Departing from the literature assumption that publishing in international journals demands greater research efforts and scope, this finding suggests that research unit size positively affects research international visibility and may also affect positively research quality. In fact, the analysis shows that the larger the size of the research unit, the more frequent is the overall communication of individual academics but also the greater tends to be the information exchange with international peers. Perhaps more indicative is that the academics information exchange with peers at both national and international level is highest at larger research units suggesting that research unit size facilitates more frequent contacts with academics based at national and international institutions.

This module explores tensions that exist between the commitment to take ethical approaches in global research and to abide by UK/EU-based research governance standards. The course considers how researchers can meet these evolving funder guidelines realistically without compromising their research objectives and their relationships with local partners and communities. It provides academics, doctoral students and research development staff with a platform to reflect on these issues and with tools to constructively navigate them.This is a paper version of the online module offered to SOAS staff and students through the BLE platform as part of the Research Office training essentials. A paper version is made available open access to be of wider benefit to UK-based and international researchers. The online module has been designed to be dynamic and the developers commit to incorporating comments and feedback from users to improve and to enhance it. Please be aware that this paper version may not reflect the latest version online.The open access version can be found at the link: https://eprints.soas.ac.uk/32038/

Resumen
Se propone un modelo para varios enfoques alternativos de la predicción de las probabilidades involucradas a partir de procesar las señales obtenidas enviadas por los jugadores, que pueden ser empresas compitiendo en el mercado. Ellos fijan un nivel mínimo de la utilidad esperada y se interesan en la probabilidad de que se satisfaga esta expectativa. Estos enfoques son analizados usando experimentos de Monte Carlo, desarrollado en el marco de la Teoría de Juegos y diferentes modelos estadísticos para hacer predicciones sobre la probabilidad de éxito o de obtener una cierta utilidad. Se establece el comportamiento de las distintas estrategias propuestas considerando que los errores siguen una distribución normal, doble exponencial y Cauchy. Se hace una comparación de los resultados dados por los algoritmos programados el R y SAS, encontrando que son mejores los de R en términos de, sin embargo GLIMIX PRC MIXED de SAS tiene valores menores de V2 en todos los casos.
Abstract
We propose a model for several alternative approaches to the prediction of the probabilities involved by processing the obtained signals sent by the players, which may be companies competing in the market. They set a minimum level of expected utility and are interested in the probability that this expectation will be satisfied. These approaches are analyzed using Monte Carlo experiments, developed in the framework of Game Theory and different statistical models to make predictions about the probability of success or to obtain a certain utility. The behavior of the different proposed strategies is established, considering that the errors follow a Normal, double Exponential and Cauchy distribution. A comparison is made of the results given by the algorithms programmed R and SAS, finding that R are better in terms of, however GLIMIX PRC MIXED of SAS has lower values of V2 in all cases.