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The past 50 years have been witnessing a steady increase in the number of experiments on rodent brain. With over 650,000 publications in total and 20,000 indexed publications annually, neuroscience appears to be one of the most predominant areas to utilize rats and mice for research. Thus, neuroscientists have now joined the big-data club, a domain that was traditionally reserved for astronomers and high-energy physicists. While challenging, the systematic curation and analysis of biomedical data has already been shown as a powerful tool to generate new hypotheses. Cochrane collaboration is a great example for the success of these approaches. Inspired by such clinical meta-analysis strategies, my research group and I have been developing and utilizing modern data mining methods, machine/deep learning techniques and mathematical analysis to extract and integrate data from animal experiments into new hypotheses. By screening 338,982 publications over the past 7 years, we have systematically collected data of 147,138 rats, which has been integrated into two open-access databases; www.syphad.org and www.chemnetdb.org. Thereby, we have been able to characterize the multi-scale neurochemical connectome of the rat brain as well as a novel evidence-based classification for neuropsychiatric drugs. The success of our pioneering project has generated an observable momentum in using existing preclinical data for hypothesis generation.