Data Management Overview
Welcome to the data management pages for data providers to the ORNL Distributed Active Archive (DAAC). These pages provide an overview of data management planning and preparation and offer practical methods to successfully share and archive your data.
- Plan – write a short data management plan while preparing your research proposal,
- Manage – assign logical, descriptive file names, define the contents of your data files, and use consistent data values when preparing your data,
- Archive – create metadata and documentation while finalizing your data to enhance search visibility and usability, and
- DAAC Curation – submit your data to the DAAC for active archival and use by the scientific community.
Benefits of Good Data Management PracticesWhy should you worry about good data management practices? Here are some short- and long-term benefits:
- Spend less time doing data management and more time doing research
- Easier to prepare and use data for yourself
- Collaborators can readily understand and use data files
- Long-term (data publication)
- Scientists outside your project can find, understand, and use your data to address broad questions
- You get credit for archived data products and their use in other papers
- Sponsors protect their investment
Data Management Best PracticesThe ORNL DAAC has developed data management best practices for preparing environmental data sets for sharing and archival.
- Click on a best practice for more info
- Define the contents of your data files
- Assign descriptive data set titles
- Assign descriptive file names
- Use consistent data organization
- Use stable file formats
- Preserve information
- Protect your data
- Provide documentation and metadata
- Perform basic quality assurance
Data management best practices
Follow these practices to improve your data set's accessiblity and usability. These practices could be performed at any time during data set preparation, but are most useful when considered during the project planning and implemented during data collection. These practices need not be completed sequentially.
Click on a best practice to display more information.
Assign descriptive file names
Names should contain only numbers, letters, dashes, and underscores. Ideally, names may contain the project acronym, study title, location, investigator, year(s) of study, data type, version number, and file type.
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Use consistent data organization
One organizational style is multiple rows, each with common-separated values. Another style is individual columns for each value. Be sure to provide a definition for all coded values or abbreviations.
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Define the contents of your data files
Provide names, units of measure, formats, and definitions of coded values. Be consistent.
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Save raw data file with no transformations or analyses as "read-only". Use a scripted language to process data in a separate file.
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Use stable file formats
Text-based comma separated values are ideal. Avoid proprietary formats that may not be readable in the future.
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Perform basic quality assurance
Check that there are no missing values for key parameters. Scan and/or plot for impossible and anomalous values. Perform and review statistical summaries.
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Consider what a future investigator needs to know in order to obtain and use your data.
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Protect your data
Ensure that file transfers are done without error by comparing checksums before and after transfers. Create and test back-up copies often to prevent the disaster of lost data.
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Assign descriptive data set titles
A descriptive title should briefly describe your data set and will help workers search for and identify your data set as pertinent and useful for future research.
Jump to Assign descriptive data set titles for more information.
A more detailed explanation of our Data Management Best Practices can be found in DAAC Best Practices .