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Biomedical Research Data Management: The Basics

Time limit: 90 days

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Full course description

Managing research data is the cornerstone of any successful research project in the health sciences. This course will delve into the landscape of biomedical data management to facilitate better understanding and organization of data planning, collection, analysis, and preservation. To make data findable, accessible, interoperable, and reusable (FAIR), we focus on best practices in data management and sharing to aid in scientific discovery.


For this course, we’ll delve into the biomedical data management life

cycle’s central areas:

• Storage (throughout each unit)

• Planning and designing

• Collection and creation

• Analysis

• Preservation


Module 1: Planning and Designing

We will start with Data Management Plans, what they are, and how they can help guide data storage, collection, analysis, and evaluation. We will then turn to security, sharing, and retention policies.

Module 2: Collection and Creation

We will examine various ways of gathering data through online tools, like E-Lab Notebooks and We will turn to data types and formats, focusing on what, how much, and the data access level. Next, we’ll look at the metadata that helps organize collected data. Finally, we will discuss data organization such as file naming conventions and structures, versioning, and record management.

Module 3: Analysis

We will discuss software and tools to prepare datasets for analysis. Then, we’ll discuss techniques for inspecting, transforming, and modeling data to find patterns. We will briefly discuss image data management, including using open-source image data management software packages.

Module 4: Preservation

We’ll discuss how data will be preserved and accessed for long-term use, including how retention requirements might differ depending on a funders’ policy. Within this framework, we’ll discuss intellectual property, copyright, and data ownership. Finally, we’ll discuss how to choose data for archiving.