Lack of standardization for data quality
Open data platforms may integrate information from multiple sources, they may provide access to raw data, or both. There are few standardized ways to integrate information from multiple sources, which can make it difficult to both ensure data accuracy and build user trust. Many open hardware tools also use proprietary, platform-specific, or non-standard data formats, which limits transparency and collaboration. In the long term, these challenges require high-level policy guidance and coordination to harmonize QA/QC practices across platforms or networks, as well as an acknowledgement that data quality practices may depend on the goals of the research (e.g., interpretation for policy making, advocacy, or science). In the meantime, data portal maintainers are left to navigate a difficult balance between transparency, data quality, and usability in the absence of such standards.
Solutions
1.
Ask for user feedback
Enable users to interact transparently with data by flagging anomalies, commenting on data points, and submitting proposed corrections or metadata clarifications.
2.
Follow standard operating procedures (SOPs)
Define and implement clear SOPs for data collection, processing, and quality control. Provide public documentation and versioning so users can trace decisions in order to better understand the reasoning behind the processes.
3.
Define tiers of data quality
Provide different quality levels (e.g., raw, processed, validated) to accommodate various user needs while maintaining transparency.
4.
Track data provenance
Track and display metadata on data origin, processing steps, timestamps, and responsible parties to support interpretability and trust.
Know of another resource or solution?
Resources
GitHub Issues and Discussion Platform
GitHub provides mechanisms for flagging issues on open software and files as well as creating a space for discussion.
Protocols.io
Protocols.io is a platform for sharing community-reviewed, reproducible protocols. It enables researchers to publish, discover, and collaboratively improve experimental methods, ensuring transparency and repeatability in scientific workflows.
Data Management Skillbuilding Hub
Data Management Skillbuilding Hub provides comprehensive guidance on data management, curation, and quality control. The hub offers practical resources, tutorials, and best practices to help researchers enhance the integrity and usability of their data throughout the research lifecycle.
Open Geospatial Consortium
Open Geospatial Consortium set global benchmarks for geospatial data sharing and integration. These standards facilitate seamless data exchange and ensure high-quality, interoperable geospatial information across different platforms and applications.
Research Data Alliance
Research Data Alliance is a global community dedicated to advancing data standardization and sharing best practices. Their recommendations and outputs help researchers to overcome barriers to data sharing, foster collaboration among researchers, and promote open science worldwide.
Wilson Center's Open Science Hardware
The Wilson Center’s Open Science Hardware: A Shared Solution to Environmental Monitoring Challenges outlines the potential of open science hardware to benefit environmental monitoring efforts in tribal, local, state, and federal governments. It also explores strategies to address current barriers and accelerators to impact.
Open Knowledge Open Data Editor
Open Knowledge has developed the Open Data Editor which helps users detect errors, check for correct data formats, and even publish their own data on open data portals.