Rocky Mountain Research Data Center

What is the RMRDC?

The Rocky Mountain Research Data Center (RMRDC) is a secure research environment where qualified researchers are allowed access to restricted data collected by the U.S. Census Bureau and other federal statistical agencies such as the National Center for Health Statistics and the Bureau of Labor Statistics.

The RMRDC is located in the Institute of Behavioral Science on the University of Colorado Boulder campus. It is set up according to the strictest federal guidelines for ensuring the data are used only for statistical purposes that advance research questions while protecting against disclosure of individual or business identities.

It is part of a national network of federal statistical research data centers(see list), and it serves the Rocky Mountain region (see map). It is financially supported by a consortium of research institutions including the host institution, University of Colorado Boulder, along with 8 member institutions: University of Colorado,  Denver and Anschutz Medical Campuses; Colorado State University, Ft. Collins and Pueblo Campuses, University of Wyoming, Colorado School of Mines, University of Denver, Rocky Mountain Poison and Drug Safety, and University of Texas El Paso.

What is a Federal Statistical Research Data Center?

 

 

Who Will Use It?

Researchers (faculty, staff, and graduate students) at all the consortium member institutions have free access to RMRDC services and the physical laboratory. Researchers not affiliated with a consortium institution are also able to use the RMRDC, but they will be charged an external user fee.  Contact the RMRDC director to negotiate this fee. All researchers are encouraged to generate collaborative and innovative restricted data projects in economics, demography, and the spatial, environmental, and health sciences.

How to Get Started?

Now is a good time to get started developing a proposal. The first step is to contact the RMRDC director or administrator. See contact information in the footer of this page. It will take a number of months to complete this process.  For information on proposal guidelines, visit Proposals.

Dec 2023 Newsletter

RMRDC Administrator: Catherine Talbot

  • Catherine Talbot's areas of expertise include demography, statistical methods, and examining social and environmental factors that shape health outcomes. She has worked as a Special Sworn Status researcher at the RMRDC since 2021 and is finalizing her PhD in Sociology at CU Boulder. With a prior decade-long career in hydrogeology and environmental consulting, she draws on these perspectives and experiences in her current research endeavors. She greatly enjoys working with a variety of data sources and collaborating with others.

Opportunities for Graduate Students and Early Career

RMRDC Sponsored Graduate Course: Federal Data for Health Research and Policy

Spring Semester, 2025

Taught Remotely with Zoom

Meeting time: Fridays 10am, MT:

1-credit option 10-11am; Lecture

3-credit option 10am-1pm; Lecture + SAS Lab

 

Course listed at CU Denver: Econ 6022; HBSC 6022; GEOG 5022; SOCY 5022;CU Denver|Anschutz Medical Campus: HSMP 6670

CU Boulder Students can enroll through Concurrent Enrollment

 Students from all other RMRDC consortium member institutions may enroll if a faculty member is willing to offer the course as an independent study.

 

Instructors: Jani Little, RMRDC Executive Director

Laura Argys, Prof. Econ, CU Denver

Catherine Talbot, RMRDC Administrator

Haeseong Park, RMRDC GRA

 

Abstract:

Restricted statistical data offer ways to investigate health research questions that are more nuanced and informative than most other available data sources. This course is designed to teach graduate students about federal data collections and how they are being used to address basic and policy-related health research questions.  Answering research questions with a federal dataset requires an understanding of how the data were collected, why they were collected, what population they represent and at what geographic scale.  Most critically, a researcher needs to know which parts of the question can be addressed with a public version of the dataset and which parts require a restricted version of the data. This course will approach these topics through analysis of published research papers that utilize public and restricted versions of the federal datasets.  Students enrolling in the 3-credit option will learn how to reproduce results using the public versions of the data with accompanying exercises in SAS, the predominant statistical language of the federal statistical agencies.