This chapter examines the challenges involved in disseminating, integrating and analyzing large datasets collected on both human subjects and non-human experimental organisms, and within both clinical and research settings. I highlight some of the technical, ethical and epistemic concerns underlying current attempts to portray and use Big Data as revolutionary tools for producing biomedical knowledge and related interventions. When bringing together data collected on human subjects with data collected from other organisms, significant differences in the experimental cultures of biologists and clinicians emerge, which if left unchallenged risk to compromise the quality and validity of large scale, cross-species data integration. My study emphasises the complex conjunctions of biological and clinical practice, model organisms and human subjects, and material and virtual sources of evidence-thus emphasising the fragmented, localized and inherently translational nature of biomedical research, and the challenges underlying the assemblage and interpretation of big data in this domain.