A novel framework for assessing metadata quality in epidemiological and public health research settings

Metadata are critical in epidemiological and public health research. However, a lack of biomedical metadata quality frameworks and limited awareness of the implications of poor quality metadata renders data analyses problematic. In this study, we created and evaluated a novel framework to assess metadata quality of epidemiological and public health research datasets. We performed a literature review and surveyed stakeholders to enhance our understanding of biomedical metadata quality assessment. The review identified 11 studies and nine quality dimensions; none of which were specifically aimed at biomedical metadata. 96 individuals completed the survey; of those who submitted data, most only assessed metadata quality sometimes, and eight did not at all. Our framework has four sections: a) general information; b) tools and technologies; c) usability; and d) management and curation. We evaluated the framework using three test cases and sought expert feedback. The framework can assess biomedical metadata quality systematically and robustly

The evaluation and harmonisation of disparate information metamodels in support of epidemiological and public health research

BACKGROUND: Descriptions of data, metadata, provide researchers with the contextual information they need to achieve research goals. Metadata enable data discovery, sharing and reuse, and are fundamental to managing data across the research data lifecycle. However, challenges associated with data discoverability negatively impact on the extent to which these data are known by the wider research community. This, when combined with a lack of quality assessment frameworks and limited awareness of the implications associated with poor quality metadata, are hampering the way in which epidemiological and public health research data are documented and repurposed. Furthermore, the absence of enduring metadata management models to capture consent for record linkage metadata in longitudinal studies can hinder researchers from establishing standardised descriptions of consent. AIM: To examine how metadata management models can be applied to ameliorate the use of research data within the context of epidemiological and public health research. METHODS: A combination of systematic literature reviews, online surveys and qualitative data analyses were used to investigate the current state of the art, identify current perceived challenges and inform creation and evaluation of the models. RESULTS: There are three components to this thesis: a) enhancing data discoverability; b) improving metadata quality assessment; and c) improving the capture of consent for record linkage metadata. First, three models were examined to enhance research data discoverability: data publications, linked data on the World Wide Web and development of an online public health portal. Second, a novel framework to assess epidemiological and public health metadata quality framework was created and evaluated. Third, a novel metadata management model to improve capture of consent for record linkage metadata was created and evaluated. CONCLUSIONS: Findings from these studies have contributed to a set of recommendations for change in research data management policy and practice to enhance stakeholders’ research environment