(Modified from Data Management Plans: Types of Data (California State University Long Beach)
Managing data is important for you, your research group and the greater research community. Access to data in the future depends on steps you take at the time of data collection. Some data and resource types will have specific considerations, and not may fit the guidelines suggested here. Consult with the head of your research group for the best practices in your field.
1. Save data as components, not as compound variables.
Ex. save weight and height, not the calculated BMI (body mass index)
2. Make sure all data collectors know what the units are and what the variable is actually measuring
3. Tips for creating spreadsheets: http://bit.ly/DM-SS
File Names – set up file naming conventions
1. Choose descriptive names
2. Use keywords for searching
3. Start name with characters other than zeroes: ET-00002, not 00002
4. Use underscore instead of spaces: ET_00002, not ET 00002
5. Ex. Project-name_experiment#_keyword_keyword_yyyy/mm/dd - recommended date format
File Formats - Open, uncompressed formats will be more accessible in the future.
Consider both access and long term preservation when appropriate.
See examples at http://scholar.uc.edu/format_advice_request
1. Documents: .pdf or .txt or open office vs. .doc or .docx
2. Tabular data / spreadsheets: .csv vs. .xlsx
3. Images: .tiff vs. Proprietary formats such as .psd for Adobe Photoshop files
Folder Structure – Choose an organization scheme for your files according to a criteria that makes sense to you.
Two suggested methods are based on projects or function of files. Choose names that are based on search terms, but are not too long. Remember that the folder names are actually the whole directory name.
Back up and archiving data
Back up - Apply the “3-2-1” or “Here – Near – Far” rule:
2 different storage media – hard drive, cloud storage
1 off-site/cloud backup
(Sign up for UC’s Box.com cloud storage: https://kb.uc.edu/kbarticles/ucbox-landing.aspx - unlimited storage)
Archiving - Use a repository for long-term storage and data sharing, such as Scholar@UC http://scholar.uc.edu
The best choice will depend on the type of research and data collected.
What does a future investigator need to know in order to use the data you have generated?
The goal is to provide enough context that the data can be understood without the researcher being there to explain.
Ask yourself can your data explain itself if you are not there to do it? You may still have work to do so the data can.
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