Data Standardization and Quality Management.

Important questions regarding the conduct of scientific research and data transparency have been raised in various scientific forums over the last 10 years. It is becoming clear, that in spite of published RIGOR guidelines, that improvement in the transparency of scientific research is required to focus on the discovery and drug development process so that a treatment can be provided to stroke patients. We have the unique privilege of conducting research using animal models of a disease so that we can address the development of a new therapy, and we should do this with great care and vigilance. This document identifies valuable resources for researchers to become Good Laboratory Practices compliant and increase and improve data transparency and provides guidelines for accurate data management to continue to propel the translational stroke research field forward while recognizing that there is a shortage of research funds worldwide. While data audits are being considered worldwide by funding agencies and they are used extensively by industry, they are still quite controversial for basic researchers. Due to the special exploratory nature of basic and translational science research, the current challenging funding environment, and independent and individualized laboratory activities, it is debatable if current individualized non-standardized data management and monitoring represents the best approach. Thus, herein, we propose steps to prepare research study data in an acceptable form for archival purposes so that standards for translational research data can be comparable to those that are accepted and adhered to by the clinical community. If all translational research laboratories follow and institute the guidelines while conducting translational research, data from all sources may be more comparable and reliable.