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With four components – distributive, procedural, interpersonal, and informational – organizational fairness provides a framework for examining institutional processes, including those that relate to pay. 

Ground rules reflect what is important to team members about how they interact and can promote inclusion within the team 

This series of questions can prepare you to learn more about your institution’s compensation system and illuminate potential areas for improvement. 

Communication is essential to teamwork and requires attention to be successful. By incorporating approaches that meet the needs of your deaf and hard-of-hearing (DHH) colleagues, you can improve access to communication for all. 

Fairness traps represent opportunities to fortify fairness by identifying guiding principles, raising awareness about assumptions being made, and inserting fairness checks into the process. 

Understand the collective mix of salary, other pay, benefits, and non-pay considerations for faculty in higher ed. 

The recent SARS-CoV-2 (COVID-19) pandemic exemplifies how newly emerging and reemerging viruses can quickly overwhelm and cripple global infrastructures. Coupled with synergistic factors such as increasing population densities, the constant and massive mobility of people across geographical areas and substantial changes to ecosystems worldwide, these pathogens pose serious health concerns on a global scale. Vaccines form an indispensable defense, serving to control and mitigate the impact of devastating outbreaks and pandemics. Towards these efforts, we developed a tunable vaccine platform that can be engineered to simultaneously display multiple viral antigens. Here, we describe a second-generation version wherein chimeric proteins derived from SARS-CoV-2 and bacteriophage lambda are engineered and used to decorate phage-like particles with defined surface densities and retention of antigenicity. This streamlines the engineering of particle decoration, thus improving the overall manufacturing potential of the system. In a prime-boost regimen, mice immunized with particles containing as little as 42 copies of the chimeric protein on their surface develop potent neutralizing antibody responses, and immunization protects mice against virulent SARS-CoV-2 challenge. The platform is highly versatile, making it a promising strategy to rapidly develop vaccines against a potentially broad range of infectious diseases. 

Abstract. Water quality in lakes is an emergent property of complex biotic and abiotic processes that differ across spatial and temporal scales. Water quality is also a determinant of ecosystem services that lakes provide and is thus of great interest to ecologists. Machine learning and other computer science techniques are increasingly being used to predict water quality dynamics as well as to gain a greater understanding of water quality patterns and controls. To benefit the sciences of both ecology and computer science, we have created a benchmark dataset of lake water quality time series and vertical profiles. LakeBeD-US contains over 500 million unique observations of lake water quality collected by multiple long-term monitoring programs across 17 water quality variables from 21 lakes in the United States. There are two published versions of LakeBeD-US: the “Ecology Edition” published in the Environmental Data Initiative repository (https://doi.org/10.6073/pasta/c56a204a65483790f6277de4896d7140, McAfee et al., 2024) and the “Computer Science Edition” published in the Hugging Face repository (https://doi.org/10.57967/hf/3771, Pradhan et al., 2024). Each edition is formatted in a manner conducive to inquiries and analyses specific to each domain. For ecologists, LakeBeD-US: Ecology Edition provides an opportunity to study the spatial and temporal dynamics of several lakes with varying water quality, ecosystem, and landscape characteristics. For computer scientists, LakeBeD-US: Computer Science Edition acts as a benchmark dataset that enables the advancement of machine learning for water quality prediction. 

This exploratory study examines the relationship between Aspire’s IThrive Collective counterspace community of support and the organizational transformation efforts of members of the IChange Network. Our study examines how a counterspace community of support could inform institutional transformation. We collected focus group data from participants in a IThrive counterspace conversation series, consisting of five gatherings from 2021-2022. Using Griffin’s (2020) institutional model for faculty diversity, we developed a codebook to capture areas of activity desired by faculty and university action plans. Preliminary results show an emerging framework to disaggregate impressions of faculty from dominant and underrepresented groups to inform transformation.