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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.
Data Management Glossary
Cornell University's Research Data Management Service has complied a basic glossary of data management terms.
Data Sharing & Management Snafu in 3 Short Acts
A data management horror story by Karen Hanson, Alisa Surkis and Karen Yacobucci. This is what shouldn't happen when a researcher makes a data sharing request! Topics include storage, documentation, and file formats.
DataONE Best Practices
The DataONE Best Practices database provides individuals with recommendations on how to effectively work with their data through all stages of the data lifecycle.
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
A Deeper Dive Into Data Management
Data Curation Lifecycle
The DCC Curation Lifecycle Model provides a graphical, high level overview of the stages required for successful curation and preservation of data from initial conceptualisation through the iterative curation cycle. The model can be used to plan activities within a specific research project to ensure all necessary stages are undertaken, each in the correct sequence.
These resources can provide you with more data management information, training, and tools.
MANTRA - Research Data Management Training
MANTRA is a free, non-assessed course with guidelines to help you understand and reflect on how to manage the data you collect throughout your research. The course is particularly appropriate for those who work with digital data.
Nature Magazine: Special Issue on Data Sharing (Sept. 2009)
"Sharing data is good. But sharing your own data? That can get complicated. As two research communities who held meetings in May on the issue report their proposals to promote data sharing in biology, a special issue of Nature examines the cultural and technical hurdles that can get in the way of good intentions."