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Welcome to the CLDR

The Center for Large Data Research and Data Sharing in Rehabilitation (CLDR) is an extension of the previously funded, Center for Rehabilitation Research using Large Datasets. The Center continues to build scientific capacity in large data research by focusing on education and learning experiences designed to promote collaborative research. The CLDR has developed an innovative program that advances collaborative rehabilitation science research, information policy, and evidence-based rehabilitation practices.

The Center's mission is to build rehabilitation research capacity by increasing the number of investigators conducting rehabilitation and disability outcomes research using large administrative and research datasets. This mission has expanded to include an important focus on data sharing and archiving information from completed rehabilitation research studies.


 Pilot Project Program  Visiting Scholars Program

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Services Offered

  • Capacity Building in Large Data Research
  • Data Sharing and Archiving
  • Workshops, Webinars and Independent Training
  • Rehabilitation Data Directory and Variable Catalog
  • Pilot Project and Visiting Scholar Programs

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 Event Highlights

 

 


 News & Announcements

News and Announcements Archive

Purdue Big Data  Big Data Training at Purdue University (BigTaP) Group   1/2/19

Big Data Training for Translational Omics Research: Online course February 4 - April 19, 2019
Applications Due: Jan 28, 2019

The course is supported by the NIH “Big Data to Knowledge (BD2K)” program and was built to meet the needs of busy biomedical researchers who need entry-level training in big data science.

The format of the course includes a combination of short instructional videos, tool demonstrations, and problem-based activities. The goal of the course is to build familiarity and basic competency with established tools and publicly available resources to analyze and interpret genomic and genetic data. The course was developed to accommodate self-paced learning, especially for physician-scientists. However, applications will be considered from all qualified faculty, post-doctoral researchers, and post-prelim graduate students. The cost of tuition for the on-line course is covered for accepted applicants!

For additional information and the online application please visit: http://www.stat.purdue.edu/bigtap/online/index.html
group of older people   Inside NIA Blog  12/20/18

The Gateway to Global Aging Data is an NIA-funded website at the University of Southern California that enables longitudinal and cross-national comparisons of the health, social, and economic status of older people. Read NIA Blog post, Open the door to a world of data by Dr. Partha Bhattacharyya Partha.
Ken Ottenbacher, PhD, OTR The Rheumatologist  12/12/18

In 2013, the National Institutes of Health (NIH) launched Big Data to Knowledge (BD2K), an initiative to facilitate the use of large biomedical data sets for research, the design of new research tools and methodologies, and training researchers. The initiative promotes discovery in this new landscape, said Kenneth J. Ottenbacher, PhD, OTR, Russell Shearn Moody Distinguished Chair in Neurological Rehabilitation at the University of Texas Medical Branch, Galveston.

Accessible, Shareable Data
“Data need to be findable, accessible, interoperable and reusable. That means the data are out there and can be used again,” said Dr. Ottenbacher. “This [approach] is a new concept of data science within the NIH. There are so many new, different ways we can use these new, different types of data. We should be trying to take advantage of that.” Read the article.
older african american couple  SCoA Press Release  11/11/18
For patients who need rehabilitation before they can leave the hospital, does the hospital make a difference in whether they go home at discharge?

That was the question Kenneth Ottenbacher, PhD, OTR and colleagues attempted to answer in their new paper published in JAMA Network Open on November 9th,  “Facility and Geographic Variation in Rates of SuccessfulCommunity Discharge after Inpatient Rehabilitation amongMedicare Fee-for-Service Beneficiaries." The article looks at the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014. Dr. Addie Middleton, an assistant professor at the Medical University of South Carolina and a former faculty member at UTMB, is the lead author on the study. She is also a scholar in the Rehabilitation Sciences Career Development Program. Dr. Janet Bettger, another author on this study, is a former pilot project awardee of the Center for Large Data Research and Data Sharing in Rehabilitation.

Currently, more than 3 million of the approximately 9 million Veterans enrolled in VA care are over the age 65 and receive some care from the Veterans Health Administration (VHA). The VA Office of Geriatrics and Extended Care (GEC) offers a comprehensive array of programs and services with a unique responsibility for programs in all care settings. In addition, GEC has programs that support the transition of Veterans between each of these settings.

Join us on November 8 from 1:00 p.m. - 2:30 p.m. ET for a free webinar, Data and Analyses for VA Geriatrics & Extended Care and "Choose Home," to address the question of how the VA can better serve Veterans at home as they age and how data is used to guide the GEC in its development of a maintainable balance of services and needs?

The Stroke Dataset is a result of a study describing the cause/effect relationship between neural output and the biomechanical functions being executed in walking post-stroke patients. The dataset includes kinematic, kinetic, and electromyography (EMG) data collected from 27 post-stroke subjects and from 17 healthy control subjects.

Learn more about Medical University of South Carolina Stroke Data (ARRA).

Visit the Archive of Data on Disability to Enable Policy and research (ADDEP) to learn more on how to access these and other archived datasets.

The EI-CO study database was generated in collaboration with a large, urban EI program in Colorado, a community partner for an NIH/NCMRR R03 study. This academic-community research partnership provided researchers with access to an urban EI program's electronic administrative database. The dataset included as part of this collection includes 2045 cases for 44 variables; demographic variables include: race, ethnicity, language, sex, age, and developmental condition type.

Learn more about Early Intervention Colorado (EI-CO) Participant Characteristics, Service Use, and Outcomes, Colorado, 2014-2016.

Visit the Archive of Data on Disability to Enable Policy and research (ADDEP) to learn more on how to access these and other archived datasets.

NIH releases strategic plan for data science - June 4, 2018

Storing, managing, standardizing and publishing the vast amounts of data produced by biomedical research is a critical mission for the National Institutes of Health. In support of this effort, NIH today released its first Strategic Plan for Data Science that provides a roadmap for modernizing the NIH-funded biomedical data science ecosystem.  Over the course of the next year, NIH will begin implementing its strategy, with some elements of the plan already underway. NIH will continue to seek community input during the implementation phase. Learn more at the NIH website.

photoFebruary, 2018
The UTMB Office of Biostatistics, Clinical & Translational Science Awards (CTSA) and CLDR sponsored this TRCC (Texas Regional CTSA Consortium) Quantitative Seminar: The Hope, Hype and Reality of EHR for Research, presented by Gulshan Sharma, MD, MPH, Vice President, Chief Medical & Clinical Innovation Officer.

photoFebruary, 2018

Dr. Sook-Lei Liew is the lead author of recent publication about ATLAS, "A large, open source dataset of stroke anatomical brain images and manual lesion segmentations." She is a pilot project recipient at CLDR and a phase 2 scholar at the Rehabilitation Research Career Development (RRCD) program.

brainThe Anatomical Tracings of Lesions after Stroke (ATLAS) dataset is now available for download through the Archive of Data on Disability to Enable Policy and research (ADDEP), supported by CLDR. Researchers globally are using the scans to develop and test algorithms that can automatically process MRI images from stroke patients. In the long run, scientists hope to identify biological markers that forecast which patients will respond to various rehabilitation therapies and personalize treatment plans.

Read More: MRI stroke data set released - Science Daily | USC releases MRI stroke dataset to spur AI research - Health Data Management | Researchers hope to use MRI for stroke treatment, recovery - HSC News
photoFebruary, 2018
Nancy Baker, ScD, OTR/L is lead author on a new publication, What Types of Treatment Are Provided for Patients With Carpal Tunnel Syndrome? A Retrospective Analysis of Commercial Insurance in the journal of the American Academy of Physical Medicine and Rehabilitation. Dr. Baker is a CLDR Visiting Scholar.
More News & Announcements

An Interview with Dr. Ottenbacher

Big Data Collaboration Fuels Rehabilitation Research

Learn More

Contact Us

CLDR is supported by:
 Division of Rehab Sciences
 (409) 747-1637
 CLDR.info@utmb.edu

MR3: Medical Rehabilitation Research Resource Network

CLDR is part of the Medical Rehabilitation Research Resource Network (MR3 Network), funded by The National Institute of Child Health and Human Development (NICHD), through the National Center for Medical Rehabilitation Research (NCMRR), the National Institute for Neurological Disorders and Stroke (NINDS), and the National Institute of Biomedical Imaging and Bioengineering (NIBIB).
The Center for Large Data Research and Data Sharing in Rehabilitation involves a consortium of investigators from the University of Texas Medical Branch, Colorado State University, Cornell University, and the University of Michigan. The CLDR is funded by NIH - National Institute of Child Health and Human Development, through the National Center for Medical Rehabilitation Research, the National Institute for Neurological Disorders and Stroke, and the National Institute of Biomedical Imaging and Bioengineering. (P2CHD065702).
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