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Why do we care about data management?
This guide will provide general information about data management, including
- data management plans (DMPs)
- file naming conventions
- security and backup
- publication and preservation
Explore the tabs across the top of this page to learn more.
Why Manage Data?
Record amounts of data are being generated on a daily basis. With constant improvements in technology, higher precision and coverage in data creation, collection, and acquisition and a higher capacity for storage, there is an increased importance in managing, integrating, and re-using data. As more and more publishers and funding agencies require researchers to share their data, developing good data management practices early in you career will make it easier to keep your data organized, meet funder requirements, and prepare data for sharing with others.
Data management addresses the lifecycle of your research output including its creation, organization, accessibility, archiving and distribution. "Data" types can include:
- Observational: data that is captured real-time such as sensor data and survey data
- Experimental: data collected from lab equipment such as gene sequences and magnetic field readings
- Simulation: data generated from test models such as climate models and economic models
- Derived or Compiled: data that is aggregated or analyzed such as data mining and compiled databases
- Reference: data that is collected, reviewed, and published such as databanks and data portals