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Science engagement can be a daunting prospect. This is especially true for scientists whose work involves animal models, and particularly nonhuman primates. Here, we show that openly explaining our rationale for our neuroscience work involving nonhuman primates — and the legal and ethical regulations that govern animal experimentation — increased public support and understanding, which is crucial for this essential research to continue.

The death of George Floyd in 2020 sparked intense emotion, and increased recognition of the need to take active measures in matters of race within science and academia. This piece considers the field’s immediate actions with regard to Black representation at neuroscience conferences, and whether we are rising to the occasion in an area under our control.

Academics are not immune to the biases contributing to persistent inequalities in society. We face an urgent need to overhaul and dismantle current evaluation practices that uphold inequities at multiple points along the academic pipeline. Graduate admissions and faculty advancement are two arenas of gatekeeping in which a reimagining and redistribution of weighting of commonly used evaluation metrics are warranted. We define and promote the use of dynamic, flexible holistic evaluation models that can be implemented by first recognizing and acknowledging the biases that contribute to racial and ethnic disparities in academia. Leaders of academic institutions must step up to drive adoption of these revised evaluation metrics.

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.