**6. Conclusions**

Enterprise-level function of comprehensive clinical trial datasets is closer to reality than it has been in the past. The quality of the dataset will significantly influence the quality of our understanding of the applied information and how we use clinical information moving forward. Efforts need to be made to optimize existing datasets in the NCTN and industry to help move knowledge forward in a manner we can validate and trust.

**183**

*Acquisition and Management of Data for Translational Science in Oncology*

\*, Maryann Bishop-Jodoin1

1 University of Massachusetts/IROC Rhode Island, Lincoln, RI, USA

6 University of Pennsylvania/IROC Philadelphia, Philadelphia, PA, USA

8 Washington University of St. Louis/IROC St. Louis, St. Louis, MO, USA

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

9 University of Massachusetts Medical School, Worcester, MA, USA

, Mark Rosen6

, James Shen9

, Kathryn Karolczuk1

2 University of Arkansas/TCIA, Little Rock, AR, USA

3 Stony Brook University/TCIA, Stony Brook, NY, USA

5 Ohio State University/IROC Ohio, Columbus, OH, USA

7 MD Anderson/IROC Houston, Houston, TX, USA

\*Address all correspondence to: tjfitzgerald@qarc.org

provided the original work is properly cited.

, Michael Knopp5

, Ameer Elaimy<sup>9</sup>

4 Emory University/TCIA, Atlanta, GA, USA

, Fran Laurie1

, Ying Xiao6

, Peter Lee9

, Fred Prior2

, Sandra Kessel1

, Richard Hanusik1

, David Followill7

, Maria Giulia Cicchetti1

, Joel Saltz<sup>3</sup>

,

,

,

*DOI: http://dx.doi.org/10.5772/intechopen.89700*

**Author details**

Matthew Iandoli1

Ashish Sharma4

and Janaki Moni1

Jeff Michalski8

Thomas J. FitzGerald1
