Data Management, or Research Data Management, covers the entire lifecycle of research. This includes the organization of data to dissemination and archiving of data.
Why does this matter?
For starters, Funding agencies, such as the National Science Foundation, National Institutes of Health, and National Endowment for the Humanities require data management and sharing plans in all grant applications.
Additionally, the White House Office of Science and Technology Policy released a directive (February 22, 2013) that all federal agencies are to make the results of federally funded research publicly available within one year of publication and to better manage data from federally funded research projects.
The DMPTool will help you create a Data Management Plan, which is required by most grant funders.
1. Simply click on the logo to access the login screen on the DMP site. Select your university from the list; if it is not listed select "Not in List."
2. If you don't already have a Net login; select "Create and Account."
Learn more about the DMP Tool by watching the overview video to the right or check out its QuickStart Guide.
The following are some examples of metadata standards that can be used to describe your data.
Depositing your data to a data repository is a great way to publish and share your data files with other researchers. Discipline specific repositories should be your first choice for data deposition. The MTSU institutional repository, JEWLScholar, may be an option for you if you can not find a discipline specific repository that meets your needs.
Examples of Discipline-Specific Repositories:
Computer Science: GitHub
Social Sciences: Inter-university Consortium for Political and Social Research (ICPSR)
For a listing of other discipline repositories, visit http://oad.simmons.edu/oadwiki/Data_repositories
It is best to consider long-term access to data before you choose which file type to use. This will help reduce the obsolescence of your data files by using file types that are designed for long-term access. Proprietary software can become obsolete and are not universally used across disciplines or regions. The following file types are preferable over proprietary software file types:
Text: PDF/A, TXT, XML, RTF (not Word or Pages)
Numerical Files: CSV, Tab Delimited (not Excel or Sheets)
Video Files: MPEG-4 (not Quicktime)
Image Files: TIFF, JPEG2000 (not GIF or JPG)
Audio Files: WAV (not mp3)
This video was produced by the University of Minnesota and addresses issues concerning the copyright of data sets.
1. Planning for effective data management
2. Data types and formats
3. Data documentation and metadata
4. File naming and organization
5. Sharing data sets
6. Citing data sets
7. Database best practices
8. Ownership and copyright
9. Incorporating other data into your research
10. Archiving data
Results from experiments
Images (e.g. scientific or medical)
Quantitative data (e.g. survey data)
Physical objects (e.g. artifcats)
Digitized photos / born digital photos
Social media data (e.g. tweets)
1. Use a scripted program for analysis of large data sets.
2. Always store an uncorrected data file. Do not make any corrections to this file. Give it an accurate raw file name and save it in a location away from the file you will edit.
3. Thoroughly document any processing done on a data set so that it can be duplicated. Keep track of this in a log or data management plan.
4. Save any scripts or code used to process so that it can duplicated. Always document what has been done, what has worked and what doesn't.
Data should be cited within a publication just as other literature are cited. Use the appropriate style citation format of your publication venue (eg. APA, Chicago, Turabian, etc.). The citation, at the least, should include the following information:
1. Author (s)
3. Date published
4. Universal, persistent Identifier (PID)
5. Some way to resolve the PID (DOIs and ARKs both have resolution services)
6. Date accessed
Be sure to provide a suggested citation whenever you share your data. This will encourage others to reuse your data sets in an appropriate and ethical manner.