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 scientists' perceptions of and priorities for data quality and data quality assurance skills needed in genome annotation. This study was guided by a previously developed general framework for assessment of data quality and by a taxonomy of data-quality (DQ) 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 5-factor constructs, and 17 relevant skills were grouped into 4-factor constructs. The constructs defined by this study advance the understanding of data quality relationships and are 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.