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

Abstract

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 DQassurance strategies, processes, procedures, and infrastructure. The study's findings may also inform educators in developing data quality assurance curricula and training courses.

Understanding metadata functional requirements in genome curation work

“…Historically, the genomics community may not create genome curation related metadata for global sharing. Many of these metadata were stored locally and disparately, and were designed by following the community's data standards (Huang, Stvilia, Jörgensen & Bass, 2012). This ranking result suggests that the metadata is expected to support the function of data reuse.…”

“…The increasing use of ultra‐high‐throughput sequencing methods generated a massive amount of raw genomic sequencing data. Metadata describing the sequencing data were attached to them to support data discovery, access, reuse, and dissemination functions standardized curation procedures, tools, and data quality models were developed to support curation work and meet the needs of the genomics research community (Klimke et al, 2011; Huang et al, 2012; Yang et al, 2011). The complexity of the genomic data and related yet disparate information resources poses challenges in developing metadata schemes that are both easy to use and effective in facilitating genomic data documentation, exchange, archiving, and reuse (Pagani et al, 2012).…”

Supporting Science through the Interoperability of Data and Articles

“…In addition, one of the participants mentioned that the skill set needed was dependent on the levels of complexity and maturity of data curation at a particular IR. The comparison of the IR staff's skills identified by this study to the set of data skills needed in genome annotation curation, one of data curation activities, identified by Huang et al (2012) points to some differences and similarities (see Figure 2). assessment.…”

Assessing the Importance of Data Factors of Data Quality Model in the Business Intelligence Area

“…Reference [36] reviewed data quality frameworks proposed over the past ten years and identified two pragmatic and analytic criteria with four common factors namely objectivity, accessibility, conciseness, and accuracy in the reviewed frameworks which show the importance of these factors for information quality. Reference [37] presented a PSP/IQ conceptual model based on reliability, usability, usefulness, and correct information to…”