Managing Research Data: Data Management Resources
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Managing Your Research Data
Every discipline will have its own best practices for file naming conventions, versioning, metadata, and archiving. The information on this page can serve as a starting point to help you learn more about these standards of data management in your field.
If you need further information, please submit a request for a Data Management Consultation.
Describing Your Data
Sharing your research data is impossible without proper data documentation. Metadata is data about data - structured information that describes the content and makes it easier to find or use. Metadata can be embedded within the data itself, or stored separately. Metadata can be included in any data file or file format.
From DataQ: Documentation for your dataset should be focused on allowing others to understand and use your data without assistance from you. Key components to include are: overall context for the data (e.g. a project abstract), methods used to collect or acquire the data (or a reference to these methods), descriptive information about each data field or measure (including units of measure and any other important notes), and any required information about the formatting or accessibility of the dataset. If there are multiple data files, a guide (or readme file) that gives an overview of each file is very useful. In the end, it is important to remember that the reason you are sharing your data is so that it will be usable to others. As with other types of scholarship, it may be helpful to ask a colleague to review the data package you plan to share to see if they have any suggestions for your documentation.
There are many metadata standards, and which one you chose will depend on type, scale, and discipline of your research data. To determine metadata standards for your discipline, you can search Google for "your discipline and metadata" (for example: botany and metadata or psychology and metadata).
Examples of metadata standards include:
- Darwin Core (Biology)
- Data Documentation Initiative (Social/Behavioral Sciences)
- Ecological Metadata Language (Ecology)
- Dublin Core (General)
If you just need a simpler system to keep track of data within your lab, there are three main types of metadata addressed by most standards:
- Descriptive: described the resource for identification and discovery
- Structural: how objects are related to one another
- Administrative: Creation date, file type, rights management, etc.
Data Management Resources
Some excellent guides to managing research data: