Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Section 3: Core Principles of Data Modernization
Table 7
FAIR PRINCIPLE
DEFINITION
GENERAL EXAMPLE
USE CASE
Findable
Ideally, data are easily located and understood by humans and machines, which includes metadata, standardized tagging, and centralized access points. Systems allow secure but user-friendly access to data, including explicit permissions, documentation, and compatibility with assistive technology.
A state public health agency creates a
Local community organizations use the tool to find data for grant applications and environmental justice efforts.
Modernizing environmental health data systems involves more than technology and is a strategic process informed by a clear set of principles. These core principles ensure that modernization efforts are purposeful, sustainable, and capable of delivering value to public health profes - sionals and the communities they serve. A well-structured modernization strategy can enhance service accessibility, improve operational efficiency, and strengthen long-term resilience through effective data management practices. The FAIR Principles for Optimal Data Use A foundational framework for data modernization is the FAIR principles—findable, accessible, interoperable, and reusable. Initially introduced in 2016 in Scientific Data to guide scientific data stewardship, these principles serve as a global standard for making digital resources more valuable and meaningful across different systems and users. In EPH, these principles help ensure data can be used across jurisdictions, are accessible, and apply in both routine operations and emergency response. Table 7 il- lustrates how each component of the FAIR framework is applied in EPH, with practical examples and real-world use cases to support its implementation. The FAIR framework improves not only internal data manage - ment but also external transparency, making environmental health data more discoverable, shareable, and impactful.
searchable online map showing neighborhood- level health burdens (e.g., pollution, asthma rates), supported by indicator definitions and metadata. A city government offers an open data portal with downloadable datasets on air quality complaints, dumping incidents, and water quality— accompanied by plain- language user guides. A regional health department publishes lead and PM2.5 data in standard geospatial formats via an API, enabling other entities to overlay with housing or hospital data. A university-led air monitoring project releases datasets with complete documentation (e.g., sensor
Accessible
Neighborhood coalitions and journalists access these data to track local environmental trends and advocate for policy changes.
Interoperable Data integrates smoothly across systems, agencies, and tools
Urban planners and researchers combine datasets to identify
through standardized formats, vocabularies, and APIs (application programming interfaces).
neighborhoods needing infrastructure upgrades or public health interventions.
Reusable
Datasets are well- documented with
Advocacy groups and educators repurpose the data in public forums, policy briefs, and student projects to raise awareness.
metadata, methods, and licensing so they can be reused in multiple contexts.
calibration, collection dates, reuse terms).
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