The Integrated Animal Systems Biology Team is committed to implementing a research quality assurance (QA) program among collaborating laboratories. Quality Assurance best practices address the processes by which scientific data are generated, collected, secured, and used. These systems are implemented in order to provide assurance that data are fit for their intended purpose and that the processes under which they have been generated are transparent in order to facilitate reproducibility and reliability. We are working with a member of our team, Dr. Rebecca Davies, who is the Director of UM Quality Central (see How quality control could save your science, Nature, Jan. 27, 2016). We believe that QA is important within individual laboratories, and will be especially critical when collaborating and sharing data among laboratories for an integrated research project like ours. Furthermore, we believe that implementing QA best practices gives our team a competitive advantage in securing research funding, and will serve as a model for other University of Minnesota research teams that wish to improve research rigor. The frequent inability to reproduce data generated from federal and privately funded research projects is problematic, disappointing and expensive (Begley and Ellis, 2012; Economist, 2013; Prinz et al., 2011; Freedman et al., 2015). Integrating research QA best practices through the adoption and integration of electronic laboratory notebooks (ELN) is an innovative strategy to support this collaborative scientific effort and improve the rigor, reliability and reproducibility of the research data and inferences produced.
- Begley, C.G., and L.M. Ellis. 2012. Raise standards for preclinical cancer research. Nature 483 (7391):531-533. pdf
- Davies, R. 2013. Good research practices: It is time to do what others think we do. Quasar Magazine, July. pp. 21-23. pdf
- Economist. 2013. Unreliable research: Trouble at the lab. (Accessed 25 Feb 2016.) html
- Freedman, L.P., I.M. Cockburn, and T.S. Simcoe. 2015. The economics of reproducibility in preclinical research. PLoS Biol. 13(6):e1002165 doi:10.1371/journal.pbio. 1002165. pdf
- Prinz, F., T. Schlange, and K. Asadullah. 2011. Believe it or not: How much can we rely on published data on potential drug targets? Nat. Rev. Drug Discov. 10(9):712. pdf
- Quality Central - Sharpening the focus on sound science and quality practices. Postcard, Univ. of Minn., College of Vet. Med. pdf