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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
Published on: March 15, 2011
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Making waves: Report your quality assurance and control (QA/QC) information when publishing analytical data.
Gretchen L Lescord1, Adam T Lepage2
1Florida LAKEWATCH & the School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USA.
Water Research
|March 2, 2025
Summary
Transparent quality assurance and control (QA/QC) reporting in quantitative analytical chemistry is crucial for aquatic sciences. This study provides eight recommendations to improve QA/QC reporting, enhancing data reliability and research impact.
Area of Science:
- Quantitative analytical chemistry
- Aquatic sciences
- Environmental monitoring
Background:
- Analytical chemistry data is vital for aquatic science research and waterbody management.
- Current scientific literature often lacks comprehensive quality assurance and control (QA/QC) reporting.
- Interdisciplinary researchers may benefit from explicit guidance on QA/QC practices.
Purpose of the Study:
- To inform interdisciplinary researchers about QA/QC theory and approaches in analytical chemistry.
- To provide actionable recommendations for effective QA/QC reporting in scientific publications.
- To emphasize the importance of QA/QC for the broader aquatic science community.
Main Methods:
- Review of QA/QC theory and common practices in analytical chemistry.
- Development of eight specific recommendations for QA/QC reporting.
- Analysis of current literature for examples of QA/QC reporting deficiencies and successes.
- Discussion of reporting detection limits and sample sizes.
Main Results:
- Identified consistent deficiencies in QA/QC reporting in recent aquatic science literature.
- Proposed eight recommendations for transparent and concise QA/QC summaries.
- Highlighted the importance of reporting detection limits and sample sizes.
- Presented positive examples of effective QA/QC reporting.
Conclusions:
- Improved QA/QC reporting enhances the reach, usefulness, and longevity of scientific data.
- Transparent QA/QC practices are essential for robust scientific research and data integrity.
- Adoption of these recommendations will benefit students, researchers, data managers, and editors.
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