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Show me the data: a strategy for supporting sound science using research QA best practices

Date

2016-06-02

Authors

Davies, Rebecca, author
Society of Quality Assurance, publisher

Journal Title

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Abstract

Description

Presented at the National data integrity conference: data sharing: the how, why, when and when not to share held on June 2-3, 2016 at University of Colorado, Denver, Colorado. The National Data Integrity Conference is a gathering of people sharing new challenges and solutions regarding research data and integrity. This conference aims to provide attendees with both an understanding of data integrity issues and impart practical tools and skills to deal with them. Topics addressed will include data privacy, openness, policy, education and the impacts of sharing data, how to do it, when to do it, and when not to. Speakers and audience members come from diverse fields such as: Academic Research; Information Technology; Quality Assurance; Regulatory Compliance; Private Industry; Grant Funding; Government.
Quality Central: sharpening the focus on sound science and quality practices
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 collegiate research, service and training programs. Dr. Davies is actively encouraging scientists to use QA best practices to demonstrate the quality of the research data they generate, and to facilitate the reconstruction and reproducibility of their research results. Dr. Davies is leading the Data Reproducibility Initiative which is a national team tasked with creating tools and templates designed to make the integration of research QA into academic research programs easier. She is also developing research QA training curriculum for graduate students and post docs at the University of Minnesota.
PowerPoint presentation given on June 2, 2016.
Includes bibliographical references.

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Subject

quality assurance
QA
basic research data
strategy
collaborative approach
quality management systems
accuracy
reproducibility

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