Long overlooked in neuroscience research, sex and gender are increasingly included as key variables potentially impacting all levels of neurobehavioral analysis. Still, many neuroscientists do not understand the difference between the terms “sex” and “gender,” the complexity and nuance of each, or how to best include them as variables in research designs. This TechSights article outlines rationales for considering the influence of sex and gender across taxa, and provides technical guidance for strengthening the rigor and reproducibility of such analyses. This guidance includes the use of appropriate statistical methods for comparing groups as well as controls for key covariates of sex (e.g., total intracranial volume) and gender (e.g., income, caregiver stress, bias). We also recommend approaches for interpreting and communicating sex- and gender-related findings about the brain, which have often been misconstrued by neuroscientists and the lay public alike.
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Why multilayer networks instead of simple graphs? modeling effectiveness and analysis flexibility and efficiency!
Making a case for multilayer Networks for modeling and analysis of data with multiple entity and relationship types.
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- Award ID(s):
- 1955798
- PAR ID:
- 10251436
- Editor(s):
- S. Madria, P. Fournier-Viger
- Date Published:
- Journal Name:
- Big Data Analytics - 6th International Conference, BDA
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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