Prioritization of data quality dimensions and skills requirements in genome annotation work

The rapid accumulation of genome annotations, as well as their widespread reuse in clinical and scientific practice, poses new challenges to management of the quality of scientific data. This study contributes towards better understanding of scientist perception and priorities for data quality and data quality assurance skills needed in genome annotation. Our study was guided by a previously developed general framework for assessment of data quality and by a taxonomy of data quality skills, and intended to define context-sensitive models of criteria for data quality and skills for genome annotation. Analysis of the results revealed that genomics scientists recognize specific sets of criteria for quality in the genome-annotation context. Seventeen data quality dimensions were reduced to five factor constructs, and 17 relevant skills were grouped into four factor constructs. The constructs defined by this study advances the understanding of data quality relationships and is an important contribution to data and information quality research. In addition, the resulting models can serve as valuable resources to genome data curators and administrators for developing data-curation policies and designing DQ-assurance strategies, processes, procedures, and infrastructure. The study's findings may also inform educators in developing data quality assurance curricula and training courses.