Unlocking the Power of Data Harmonization in Environmental Health Sciences: A Comprehensive Exploration of Significance, Use Cases, and Recommendations for Standardization Efforts.

backgroundThe field of environmental health sciences increasingly demands comprehensive and diverse data sets, particularly in response to emerging research areas such as climate change, mixtures, and exposomics. The data needed to address the complexity of environmental health research questions often extend beyond the boundaries of a single study or data resource. Traditional data management approaches struggle to harmonize the ever-expanding and heterogeneous data sources needed for research in the environmental health sciences. Harmonization may help address this issue as it involves aligning and standardizing various elements of data to allow comprehensive analysis, data pooling, and interpretation across studies.

objectivesThe primary objective is to inform researchers about the transformative potential of embracing harmonization methodologies and to motivate contributions to ongoing efforts, thereby fostering advancements.

methodsUsing the Environmental Health Language Collaborative's Data Harmonization Use Case, we provide a practical illustration of existing data harmonization approaches, identify gaps, and emphasize future research and application directions. We selected two publicly available environmental epidemiology studies on the topic of childhood asthma and three studies on the topic of biomarkers of metals exposure during pregnancy and birth outcomes and applied several existing harmonization approaches to assess interoperability. DISCUSSION: Our process revealed the potential limitations of many existing harmonization approaches, with notable failures to identify common variables across independent data sets and lack of agreement between human and computer-based approaches. This use case identified various challenges with existing approaches, including reliance on often incomplete data documentation and large amounts of manual effort. To address these challenges, we recommend the continued advancement and dissemination of community data standards, the development of software and tools to facilitate harmonization through automation, and strategic efforts to promote engagement in data harmonization within the environmental health sciences community. Collaborative science is needed to advance our understanding of environmental contributors to health, and realizing the harmonization potential of our scientific data is a step toward improved collaboration.