Consistency in research experiments is hard to achieve. There are several factors that can affect data reproducibility. Publishing data in high impact journals requires repeating experiments and standardization of techniques and procedures.
4 Major Factors For Generating Robust And Data Reproducibility
As a rule, the brand and catalog numbers of products for a specific experiment or project must be fixed to ensure low variability in the data generated. One must use reagents from reputed brands with citations to maintain credibility of the results. Verifying the quality of the reagents will save both time and effort involved in planning and execution. This can be done by a thorough literature study and checking user reviews before ordering the product. If working with cell culture, the date of opening of cell culture media and its expiry must be labeled and checked before using it. This also applies to other reagents such as FBS, trypsin or PBS, etc. Old reagents must be discarded timely. For molecular biology experiments, antibodies and PCR reagents must be stored at an appropriate temperature and aliquoted from stock vials.
Equipment is another factor that can affect the endpoint analysis. Whether it is an analytical balance, pH meter or a microscope, servicing of the parts and calibration should be performed regularly. This is important to maintain accuracy and precision of the products. Variations can occur if switching from one equipment, therefore, validation is the key. Training and operator modules/workshops for various equipment must be organized.
User lab practices and training will be a strong determinant in generation of reliable data. Therefore, a strong training and mentorship are crucial to lay foundation of basic skills and lab practices. Another aspect of variability is the statistical analysis of the data generated. Cherry-picking the interesting results may seem promising but will not always be reproducible.
Different assays must be standardized and detailed SOPs should be drafted for all procedures. Right from pipetting to cell culture practices, laying down guidelines for the lab will help the newcomers to adapt faster in a lab. Sources of inherent variability in cell-based assays must be recognized and techniques must be regularly updated to ensure minimum variability.