Research fabrication and data fraud are one of the major concerns worldwide which are rising and evident from increasing number of retractions in peer reviewed Journals. If not checked, this can impact the reputation of a research organisation as well as the costs of translation of research data. In basic research extremely publi–cized cases of falsification of data have been reported and it is possible, that there are many unreported or un–detected cases. In order to improve the quality standards, the validation processes were implemented for data quality at Neuroscience research lab in India for verifying PhD thesis results. Good Laboratory practices could be implicated in every research institute so that data impostures were prevented. We aimed to establish superior data quality by randomly verifying raw data in multiple projects funded by national agencies before publica–tion of results at Neuroscience Research Lab