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Individual differences are an essential property of all living things, and personality provides a unique glimpse into the biology underlying behavioral variability. And yet, because of the lack of a systematic approach to personality, most works on animal personalities still end up examining a limited subset of subjectively chosen behavioral readouts. Lately, we have shown how personality can be inferred directly and objectively from high-dimensional natural behavioral space. While this approach is not species-specific, we have demonstrated it on mice as it is one of the most common model animals. The mice were videoed over several days, and their behavior automatically analyzed in depth. Altogether, the computer identified 60 separate behaviors such as approaching others, chasing or fleeing, sharing food or keeping others away from food, exploring, or hiding. We found the mice personalities by working backward from behavior and extracting the underlying traits that differ among individuals while being stable over time and across contexts. We validated that traits found this way (which we term identity domains) were stable across social context, do not change with age, explain the variability in performance in classical tests, and significantly correlates with gene expression in brain regions related to personality. Expanding this method to human behavior, by using location and physiological data from cellphones and smartwatches, revealed a highly structured personality space which resembles that of the mice. This method allows for better informed mechanistic investigations into the biology of individual differences, systematically comparing behaviors across species, as well as develop more personalized psychiatry. Recently we have also been employing this approach to subjectively quantify wellness and welfare in both people and animals, towards the biology of happiness.