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Research Data Management

This guide is intended to assist researchers with data management and provide access to data management tools and services.

What is Metadata?

Metadata is the information that describes and documents research data. Metadata will make your datasets searchable in an archive or repository, easily located from a citation, and easily understood by people who might want to use your data. There are many metadata elements that you should consider when describing and documenting your research data, including:

  • Title
  • Creator (Principal Investigators)
  • Date Created (also versions)
  • Format
  • Subject
  • Unique Identifier (ex. doi)
  • Description of the specific data resource
  • Coverage of the data (spatial or temporal)
  • Publishing organization
  • Type of resource
  • Rights (ethics/legal/etc)
  • Funding or Granting Agency

Metadata Standards

Metadata standards or schemas consist of specific elements used to describe or document your data. Some disciplines have established metadata standards. In addition, some data repositories have their own standards. There are also several general purpose schemas that you can adapt to fit your needs. Below are some metadata standards:

 
 
 
 
 
 
 
 
 
 
 
 
 


Creative Commons License
Metadata Concept Map by Amanda Tarbet is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

General: Dublin Core | MODS
Social Science: DDI
Humanities: TEI | VRA
Sciences: Darwin Core | ITIS | EML | DIF | SEED | FGDC | ISO 19115

The UK's Digital Curation Centre (DCC) also maintains an inventory of discipline-specific metadata standards

(Adapted from Queen's University Research Data Management LibGuide)

README

A README file is a plain text file that includes descriptive information used commonly for software, games, and code. It is a supplementary document that exists so the creator can explain the contents to the user. When working with data, it can be useful to create and include a README file with your data. This ensures that future users will understand the data, any terms, and more. 

There are no standards for writing a README text file, but it is recommended to include: 

  • Title
  • Principle Investigator(s)
  • Dates/Locations of data collection
  • Keywords
  • Language
  • Funding
  • Descriptions of every folder, file, format, data collection method, instruments, etc. 
  • Definitions
  • People involved
  • Recommended citation

For more information about writing README files for data, Cornell University has a comprehensive guide