MAY 24, 2017 10:30 AM PDT

Standing up for the quality of our data with Research Quality Assurance

Presented at: Lab Automation 2017
Speaker
  • Associate Professor, Department of Veterinary Population Medicine (VPM), University of Minnesota
    Biography
      Rebecca L Davies, PhD is the Director of Quality Central at the University Of Minnesota College Of Veterinary Medicine. Quality Central is an internal service organization that provides tools and support for integrating quality assurance (QA) best practices into academic research, service and training programs. Dr. Davies is actively encouraging and training scientists to use science-centered and risk-based QA best practices to provide credible assurance that research records and data are complete and reliable throughout the research life cycle. Dr. Davies received her PhD in comparative animal physiology from the University of Minnesota and is an associate professor in department of Veterinary Population Medicine at the College of Veterinary Medicine. Her interests include test method validation, the adoption of voluntary QA practices within non-regulated research programs, and the use of laboratory error data and QA metrics to drive continuous improvement in laboratory and research settings.

    Abstract

    Those that fund and publish scientific data are actively proposing or requiring strategies to improve research conduct in the hopes of improving research outcomes and scientific deliverables. Early responders to research reproducibility concerns are rightly focusing on study design, statistical approaches, characterization of critical reagents and attention to bias and research premise. While these measures are promising, they will likely fail to address the risk associated with inconsistent research data management and record keeping that is typically unaddressed in our research training
    programs.

    Research Quality Assurance (RQA) systems are used to monitor and support the processes by which scientific data are generated and managed. Quality systems provide assurance through the generation of credible evidence (documentation) that data are fit for their intended purpose and that the processes under which they have been generated can be reconstructed through effective record keeping. Responsible agencies mandate robust research QA systems in formally regulated research environments as a way of supporting data reliability and transparency. However, scientists are not typically implementing RQA systems within basic research settings because RQA training and expertise is rarely available to guide the implementation and use of quality management systems. This is unfortunate because the adoption of RQA best practices has been recommended for years (Volsen SG Kent JM, et al. 2004. Drug Discovery Today.9:21;903-905; WHO. Quality Practices in Basic Biomedical Research. World Health Organization. Geneva, Switzerland: WHO/TDR) as a reasonable strategy for supporting research quality.

    Biomedical research scientists must be prepared to demonstrate the quality of their data to ensure that their work stands the test of time and advances human, animal or environmental health. In addition, trainees deserve to leave our institutions prepared to transition into careers where a seamless integration into a QA environment is expected. This presentation will provide an introduction to research quality assurance as a strategy for addressing known threats to research quality. It will also encourage scientists to promote scientific excellence, improve research training, and stand up for the quality of their work by introducing RQA best practices into their individual, collegiate or institutional
    research settings.
     


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    Standing up for the quality of our data with Research Quality Assurance

    Presented at: Lab Automation 2017

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