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Describing the dynamic translational science landscape through Core Voucher utilization

Published online by Cambridge University Press:  14 June 2019

Elvira L. Liclican
Affiliation:
UCLA Clinical and Translational Science Institute, University of California, Los Angeles, Los Angeles, CA, USA
Scott G. Filler
Affiliation:
Division of Infectious Diseases, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
Jonathan Kaye
Affiliation:
Research Division of Immunology, Departments of Biomedical Sciences and Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
Christopher T. Denny*
Affiliation:
Division of Hematology/Oncology, Department of Pediatrics, Gwynne Hazen Cherry Memorial Laboratories, University of California, Los Angeles, CA, USA Molecular Biology Institute, University of California, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA California NanoSystems Institute, University of California, Los Angeles, CA, USA
*
Address for correspondence: C. T. Denny, MD, UCLA Clinical and Translational Science Institute, University of California, University of California Pediatrics, Hematology and Oncology BOX 951752, 10-240 Factor, Los Angeles, CA 90095-1752, USA. Email: cdenny@ucla.edu
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Abstract

Introduction:

Core facilities play crucial roles in carrying out the academic research mission by making available to researchers advanced technologies, facilities, or expertise that are unfeasible for most investigators to obtain on their own. To facilitate translational science through support of core services, the University of California, Los Angeles Clinical and Translational Science Institute (UCLA CTSI) created a Core Voucher program. The underlying premise is that by actively promoting interplay between researchers and core facilities, a dynamic feedback loop could be established that could enhance both groups, the productivity of the former and the relevance of the latter. Our primary goal was to give translational investigators what they need to pursue their immediate projects at hand.

Methods:

To implement this system across four noncontiguous campuses, open-source web-accessible software applications were created that were scalable and could efficiently administer investigator submissions and subsequent reviews in a multicampus fashion.

Results:

In the past five years, we have processed over 1400 applications submitted by over 750 individual faculty members across both clinical and nonclinical departments. In total, 1926 core requests were made in conjunction with 1467 submitted proposals. The top 10 most popular cores accounted for 50% of all requests, and the top half of the most popular cores accounted for 90% of all requests.

Conclusion:

Tracking investigator demand provides a unique window into what are the high- and low-priority core services that best support translational research.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Association for Clinical and Translational Science 2019
Figure 0

Fig. 1. Proposal distribution and diversity over time. (a) After an initial surge at the start of the program, the number of applications from each UCLA CTSI campus has been stable over time and approximates the size of the respective investigator pools. Data represent campus-specific and intercampus requests for applications (RFAs). (b) Over the last three intercampus RFAs, the proportion of new principal investigators (PI) per cycle has remained relatively stable, and equally split between young and senior investigators. (c) At the Westwood campus, a heterogeneous mix of basic science and clinical investigators has also remained relatively stable across RFAs. Data represent Westwood-specific and intercampus RFAs.

Figure 1

Fig. 2. Scoring distribution between intercampus and on-demand requests for applications (RFAs). When normalized, analysis of scoring distribution between intercampus and on-demand RFAs revealed a normal distribution for intercampus RFAs, whereas on-demand RFAs showed more skewing toward higher scores.

Figure 2

Table 1. Core requests by year. Heat-map representing color-coded levels of differentially requested cores over five years. Cores are listed in the order of data point representation on Fig. 3(a)

Figure 3

Fig. 3. Five-year overview of core requests. (a) Graphical representation of the absolute total number of requests (y-axis) per individual core (x-axis). Each data point represents a distinct core. The top 10 most popular cores accounted for 50% of all requests. Further, the top half of the most popular cores accounted for 90% of all requests. (b) Following normalization of the number of requests for an individual core, profiles of individual core requests demonstrate a near-linear trend for three representative cores: the Technology Center for Genomics & Bioinformatics, the DNA Microarray Core, and the Flow Cytometry Core.

Supplementary material: PDF

Liclican et al. supplementary material

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