Dell Medical School

Biomedical Data Science Hub

Date stamp: 01.15.2020

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The Hub is a collaborative resource for data science activity, including both clinical and population health biostatistics and bioinformatics, among other quantitative areas.

Principles Driving the BDS Hub 

Dell Medical School has the opportunity to rethink how health care is delivered. This should also rethink how data drives that process.

Key components:

  • Modern notions of data science applied to biomedicine
  • Benefit from strong campus, and build strong homebase
  • Balance methods with collaborations for long term sustainability
  • Build ongoing experimental design into what we do

Please review our policies listed below. Please contact with questions or if you would like some human interaction around your project/problem.

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Faculty and Staff

Affiliate Faculty

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Policies for BDS Hub Collaborative Activity


We are a very lean group and would like to remain that way.

For grant applications with a deadline:

  • We need to know about and have a preliminary sense of the aims, nature, and scope of the project at least 4 weeks (and preferably 6) before it is due.  (Here, "due" means when the science / programmatic part of the project is due to local funding officials.)  This usually requires either a meeting or a well-constructed specific aims page along with some email exchanges.  More is obviously better.
  • We need a very good draft project plan, sufficient to develop a statistical analysis plan and/or a power or sample size estimation issuing therefrom at least 3 weeks before the project is due. 
  • For manuscripts, analyses, and other projects, we will do our best to scope, schedule, and implement (and communicate about all that) on a case-by-case basis.

Effort and Funding 

If the Biomedical Data Science Hub members are working on projects that will lead fairly directly to external funding, then the Biomedical Data Science Hub is expected to cover that effort with its funds, and for those individuals to also participate on the funded sequelae projects.    

Faculty and staff effort on sponsored projects should have salary recovered at the same level as effort expended.

Estimating biostatistics and biomedical informatics effort on sponsored projects.

  • As the researchers conducting the data science (biostatistics or bioinformatics) components of a project, those faculty and staff are in the best position to make an accurate estimate of the effort required, perhaps, in the case of staff, with the help of their supervisor.
  • In particular, it is not appropriate for the PI on a collaborative project to do this estimation in isolation.

Generally, on collaborative projects, PhD level BDS members should appear as Co-I, and MS members should appear as research staff, e.g., "Biostatistician".

Priorities for BDS Hub Effort

For BDS Hub faculty and staff time and effort on projects that are not (yet) externally funded:

1. Grant or contract applications

2. Work leading to grant or contract applications, e.g.:

  • preliminary data analysis
  • manuscripts leading to preliminary data

3. Study design or data analysis for junior or other "starting up" faculty

4. Unfunded work on funded projects (this should be avoided)

5. Unfunded work on unfunded projects (this should also be avoided)

Note: If another UT or Dell Medical unit wants to fund work internally, then the Biomedical Data Science Hub considers that to be “funded” and it would not figure into this calculus.

Prioritization of Health Equities Research

In alignment with the Dell Medical School mission we are prioritizing equity in research.  Studies especially involving under served populations, health disparities and healthcare inequities will be given higher priority.  

Ground Rules for Data Reproducibility

1.    **We prefer to receive data in a statistical package, however if data come to us in Excel, data should Follow standards in article** Data Organization in Spreadsheets

2.  When we get your data or other files, we are going to date stamp your file name with yymmdd, so it might get returned to you as such.

3.  Unless otherwise arranged we expect a “data dictionary” to be included with data.

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Resources & Training

Local Didactic Courses

From Great Idea to Clear Results: A year long course offered in conjunction with Department of Women's HealthDell Med Office of Research, and  UTHealth School of Public Health in Austin for those who could benefit from learning research study design.

Research Nuts & Bolts (offered by the Dell Med Office of Research) is a one-hour monthly forum focusing on topics related to execution of clinical and population research.

Looking for a more specialized course on the UT-Austin campus? Contact the BDS Hub and we will try to help you locate one.

General Texts and Asynchronous Training

The Art of Data Science, by Roger D. Peng, and Elizabeth Matsui. Note you can pay as little as $0 if you decline the lecture videos and choose only the book option.

The Data Science Salon: A Collaborative Learning Experience, by Roger D. Peng, Elizabeth Matsui, and Corinne Keet. 

 Understanding data and statistics in the medical literature, by Jeffrey Leek, Lucy D'Agostino McGowan, and Elizabeth Matsui.

Useful Articles

Broman, K. W., & Woo, K. H. (2018). Data Organization in Spreadsheets. The American Statistician, 72(1), 2-10.  

Leek, J., & Peng, R. (2015).  What is the question?  Insights, 347(6228) 1314-1315.

Haukoos, J.& Lewis, R. (2015). The Propensity Score. JAMA, 314(15) 1637-1638.

Norton, E., Dowd, B. & Maciejewski, M. (2018) Odds Ratios-Current Best Practice and Use. JAMA 320(1) 84-85.

Have questions?  Please email: