Research Data Management

This guide provides an introduction to research data management (RDM), including best practices for managing files, documentation and metadata, storage, sharing, reproducibility and preservation. It provides information on creating data management plans, citing data and more. 

Adapted with permission from Temple University's Research Data Management Guide

There are many reasons to have good research data management habits. Most federal funding agencies and more and more publishers are requiring that research data be made publicly available. That's much easier to accomplish if you know where your data is and how you can share it. It's also important to understand how your own data may change with time and by different lab staffing situations. Organization and documentation of your data and code is crucial to achieving consistently recorded data that is usable. Backing up your data and storing it in appropriate places helps ensure your data doesn't get accidently deleted during or after a project.

In short, managing and sharing research data helps you get funding, keep funding, and avoid retractions. It aids consistency in data collection and analysis, helps make your data make sense to your future self and colleagues, and helps advance science through replication and enabling new discoveries.

Good data management includes: 

• assigning responsibilities to research group members
• collecting data in a consistent and easily understood way
• following good file naming conventions
• using standard and non-proprietary file formats
• documenting your data thoroughly and consistently
• backing up data regularly
• making & following through with plans for storage of data for the long term