Committing to Data Quality Review

Abstract

Amid the pressure and enthusiasm for researchers to share data, a rapidly growing number of tools and services have emerged. What do we know about the quality of these data? Why does quality matter? And who should be responsible for data quality? We believe an essential measure of data quality is the ability to engage in informed reuse, which requires that data are independently understandable. In practice, this means that data must undergo quality review, a process whereby data and associated files are assessed and required actions are taken to ensure files are independently understandable for informed reuse. This paper explains what we mean by data quality review, what measures can be applied to it, and how it is practiced in three domain-specific archives. We explore a selection of other data repositories in the research data ecosystem, as well as the roles of researchers, academic libraries, and scholarly journals in regard to their application of data quality measures in practice. We end with thoughts about the need to commit to data quality and who might be able to take on those tasks.

More Data, Less Process? The Applicability of MPLP to Research Data

“…They include completeness (contextual), accessibility (accessibility), ease of operation (representational), and credibility (intrinsic) of the data. These aspects of data quality are often summed up in the notion of data being 'independently understandable' to their intended users (CSSDS, 2012;King, 1995;Lee, 2010;Peer, Green, & Stephenson, 2014). This is the quality standard to which research data are being held, particularly those subject to data management and sharing mandates that have grown in popularity among funders and journals (e.g., National Endowment of the Humanities, 2012; National Institutes of Health, 2003;National Science Foundation, 2010;Nature, 2015;PLOS, 2014;Science, n.d.).…”

“…Data are complex, and the archival processing-or what we equate to data curation-required to make them available and usable for researchers has been informed by uncompromising standards of quality. Achieving these quality standards requires data archives to complete a laundry list of skill-intensive, labor-intensive, and time-intensive data curation tasks including normalizing file formats, mitigating confidentiality risks, checking for and correcting data errors, generating and enhancing descriptive metadata, assembling contextual documents, recording checksums, defining undefined variable and value codes, reconciling discrepancies between datasets and codebooks, and so on (Peer, Green, & Stephenson, 2014). Not so different from the backlog situation in traditional archives, compromises in data curation processes are inevitable given the nature of tightly resourced environments in which many data archives operate.…”

Improving Data Quality in a Resource Constraint Public Health Organization in Nigeria with Divide and Conquer and Lot Quality Assurance Sampling Approach

“…The verification and validation was the examining and enhancing of the actual data (Peer et al 2014). However, as of 2011 CareWare was deployed to only 14 of the 84 facilities initially intended to implement the electronic medical records system.…”

“…Data quality review was advocated for by (Peer et al, 2014). Peer et al (2014) described data quality review as "a process whereby data and associated files are assessed and required actions are taken to ensure files are independently understandable for informed reuse".…”

“…Data quality review was advocated for by (Peer et al, 2014). Peer et al (2014) described data quality review as "a process whereby data and associated files are assessed and required actions are taken to ensure files are independently understandable for informed reuse". Improving and maintaining data quality initiative included the onsite data verification and validation: intrinsic, contextual and representation of data quality (Chang and Strong, 2013).…”