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University of Minnesota

Kathryn A. Martin Library

Research Data Management (RDM)

  • What standards will be used for documentation and metadata?
  • Are there good project and data documentation format/standards for your field?  
  • Is there a community standard for metadata sharing/integration?
  • What directory and file naming convention will be used?
  • What project and data identifiers will be assigned?

3 Ways to Organize your Files

Folder Structure

Folder Structure is an important part of Data Management.  When created an organization structure, consider the following to improve your navigation and ease of access to data and files

  1. Object type - e.g. My Research Project, My Music, My Photos, etc.
  2. Organizational structure - e.g. Department, sub-unit, or by individual.
  3. Combine: structure directory by unit then type.

Best Practices for Naming Files

  • Be Descriptive: 
    • 75092238.txt is not very useful to colleagues and is easily forgettable. 
    • 20120814_instrument8_rainyday_raw.txt (up to 255 characters) gives date, location condition and type of data collected.
  • Don’t rely on nesting in folders: 2012/august/instrument8/day14/raw.txt 
    • Folder is useful for storage, but ultimately file naming conventions are more important to long term storage.
  • Use consistent structure that falls into a useful order (for sorting) and decide on shared terminology
    • Speak with your colleagues/partners and agree on common terms and standards
  • ​List versions alphanumerically, eg. v1, v2, v3 rather than last, final, finalfinal, useTHISone. 
    • Number demonstrate a more logical order and cut down on your frustration.
  • Use numerical dates, eg. YYYYMMDD rather than Dec09.
    • This will keep your files ordered by number rather than alphabetically. Otherwise, you'll have files organized by 2015APRIL, 2015DEC rather than 201501xx, 201502xx
  • Some computers will not understand file names with UPPERCase letters, weird characters (/ , . # ?), or spaces between words.

Electronic Lab Notebooks

Not keen on using a physical notebook? Need a digital Notebook for documenting your laboratory and experiments?  Try these two Electronic Lab Notebooks.

Your Laboratory and Advisor may have alternatives, discounts and preferred tools in this process. Please consult with them for best options.

Documenting your data - Metadata

You have gathered and stored all this amazing data, and you want to share it with your colleagues and peers! Somehow, you have to take your notes, data fields and content and make it easy for others to understand, interpret and modify.  You will need to add Metadata!

Metadata  is "data about data".... Metadata also helps organize electronic resources, provide digital identification, and helps support archiving and preservation of the resource. (Source from Wikipedia, on 8/11/2015).  There are many metadata standards to consider and each discipline has its own preferred fields and identifying schema.  For the purpose of this guide, we'll just cover the basics for fields.

On the most basic level, your data preparation should have three components that will help others understand your data: Column headers, a Data Dictionary, and ReadMe.txt.

Column Headers - A spreadsheet should have a column headers with at the top of each column to identify the data fields. Don't share spreadsheet/.csv that doesn't include column identification.





























Data Dictionary/Key for Column Headers

Data Dictionary examples, shows acronym, full name of acronym, and then a definition of the complex term.

Researchers can quickly create a Research Key or data dictionary that provides definitions, expanded abbreviations, and other methodology that occurred in experiments.

Readme.txt Files

Example of the Read Me File, visualizing several paragraphs of text that offer in depth descriptions of what, when, where and why of a project.

Create a .txt file that offers a synopsis of the experiment that includes Time Frame, Subject, Location, Methods and Funding Institutions. This gives future researchers more context and documentation.

Data Testing and Validation