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Ann Arbor, MI 48109
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Contact Us
To arrange a consultation, or for more information, email us at THLResearchDataCore@umich.edu.
Benefits of Research Data Management
Good data management will save you time and increase the impact of your research. During a project, you can take steps to make your data:
- easier to use and interpret;
- accessible now and in the future;
- publishable and reusable; and
- compliant with funder and journal policies.
The Taubman Health Sciences Library provides services and expertise to help students, researchers, and faculty find, describe, organize, analyze, store, and share the data you produce at any point in the research data lifecycle.
- Article: Foundational Practices of Research Data ManagementBriney KA, Coates H, Goben A (2020) Foundational Practices of Research Data Management. Research Ideas and Outcomes 6: e56508. https://doi.org/10.3897/rio.6.e56508
"Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-consuming. By implementing foundational practices of data management, researchers set themselves up for success by formalizing processes and reducing common errors in data handling, which can free up more time for research." - Primer for Researchers on How to Manage DataArteaga Cuevas, Maria; Taylor, Shawna; and Narlock, Mikala. (2023). Introduction to Research Data Management for Researchers. Data Curation Network GitHub Repository.
This primer provides a high-level overview for researchers on research data management and sharing practices during the planning, implementation and closing phases of typical research projects.
The Research Data Management Lifecycle
The diagram below details stages in the research lifecycle. The lifecycle model is used to provide context in describing data stewardship activities that should take place over the course of a research project. For more detail on activities during each of these phases use the navigation menu on the left.
U-M's Research Data Stewardship Initiative
The U-M Research Data Stewardship Initiative (RDSI) was launched in 2022 to help the U-M research community navigate the evolving research data landscape. RDSI aims to facilitate institution-wide communication, education, and coordination related to data use, stewardship, and sharing.
Learn more by checking out the links below.
- Resources for Research DataAcross all stages of the research life cycle and all fields of study, researchers should consider the potential long-term impacts on the eventual storage and preservation of research data. This page has resources for each stage of the research life cycle.
- Research Data Stewardship FAQsAnswers to some common questions about RDSI and data sharing.
MICHR Resources
The Michigan Institute for Clinical and Health Research (MICHR) provides a variety of services to assist researchers with their clinical research.
- MICHR ResourcesView a comprehensive list of pre- and post-award services, educational opportunities, and tools offered by MICHR.
- Online TrainingDiscover a diverse catalog of online training modules designed to support clinical research professionals.
- InformaticsHarness MICHR-supported infrastructure for efficient and secure use of research applications and platforms, including REDCap.
- BiostatisticsConsult with or hire a statistician for services that span the project life cycle; from study design to publication.
- REDCap TrainingLearn the basics of building a database using REDCap for electronic data capture.
- Database Development & SupportHire an experienced professional to build your database for you and provide ongoing project support.
- ClinicalTrials.gov SupportEngage a clinical research specialist to register your clinical trial and receive guidance to manage your ongoing CT.gov responsibilities.
- Data Management MentoringWork with a mentor to implement best practices and use related tools designed to improve data quality.