Data produced from research that can be fully shared, re-used, and validated in the broader scientific data ecosystem have increased utility and impact. The National Institutes of Health (NIH) stresses this point by urging researchers to produce data that meets its FAIR principle—defined as data that is “findable, accessible, interoperable, and reproducible.” The ability to effectively share data begins with including standard measurement protocols when designing a new study (e.g. survey forms, questionnaires, bioassays). If standard protocols are widely adopted, then the data collected will be consistent and directly comparable. This makes it easy to share data with other investigators who have used the same measurement protocols.
In the increasingly collaborative and data-centric scientific environment, use of established standard protocols lends scientific credibility and reliability to any research study. Many protocols exist to measure a wide variety of phenotypes, and choosing a well-established standard measure can be challenging, as well as time consuming. By providing access to well-established protocols, the PhenX Toolkit alleviates the need for time-intensive literature reviews and developing questionnaires from scratch. When it comes time for data analysis, use of standard measurement protocols allows for direct data comparison and combination with many published studies in the database of Genotypes and Phenotypes (dbGaP) and other public data repositories.
