COGITATE aims to accelerate research on consciousness by building on open science practices, such as adversarial collaboration, internal replication, open experimental protocols, open instrument validation, in addition to multiverse analysis practices. We aim to understand consciousness, that is, what makes us feel, taste, suffer, have joy, etc. Consciousness is intimate to us, we all know what it means to be consciousness, and how it feels when we lose consciousness, such as in falling to deep sleep or anesthesia. Yet, the footprints of consciousness in the brain are not well understood. We are guided by theory and are testing critical hypotheses of two of the most prominent theories of consciousness: Global Neural Workspace Theory and Integrated Information Theory. By building on adversarial collaboration we aim to experimentally eliminate theories thereby increasing confidence in the remaining ones. As a consortium, we believe in transparency, and acknowledge the existence of implicit biases. As such, all experiments are performed by a set of theory-impartial specialists and their expert teams. We aim to create large, reproducible, transparent, and open data that drive trustworthy science; and also encourage community participation which will enable further discoveries beyond the theories being tested. An integral part of our efforts aims at technical innovation to enable efficient and reliable data sharing based on the FAIR principles; but also robust and generalizable (not just reproducible and replicable) science to enable firm conclusions and steady advancement. We believe that large-scale collaboration will transform our understanding of the mind/brain by providing unique high quality, multimodal datasets that will enable discoveries, while also making science credible and transparent, thereby increasing public trust and the capacity to effectively inform policy. As a whole, we believe in global, democratic, diverse, and inclusive participation as well as in responsible and sustainable science by preserving knowledge and minimizing unnecessary efforts.