Research Data Management

McGill resources for selecting data storage options during the active phase of data management:

• McGill ITS knowledge-base article: Document storing and sharing - choosing the right solution (log in required)

• McGill Digital Research Services Hub (DRS)

  • FAQ covering topics such as data storage, research software, and data management

• McGill University ITS Cloud Directive

  • McGill Cloud Directive

  • Approved/Rejected Cloud Services (log-in required)

  • Cloud Directive “Cloud 101” One-Page Poster

• McGill Regulation on the Conduct of Research (Section 4: Research Data)

  • Research Data Retention Policy

• McGill ITS Full Disk Encryption (FDE) Service (McGill-owned devices only)

  • McGill Knowledge Base Article on FDE (Log In required)

• McGill ITS Secure Your Journey (Student and Academic)
  • McGill ITS Secure Your Journey

Documenting the data collection process is an important part of good research data management practices. It allows for sharing and reuse of data by others, enables the replication of the research and serves as a reference for the research team to verify findings and compare the research project with others. Depending on the data collected and the research project, different types of documentation can be useful.

• Readme Files: Provides information about a data file to ensure that it correctly interpreted. Cornell University has an excellent guide on Readme files.
• Codebooks: Describes the contents and structure of data collection. It provides information about the study, variables, data files, etc. The DDI Alliance has a comprehensive guide on how to create a codebook.
• Data Dictionaries: Makes data understandable by explain the meaning of the variables and values. The Open Science Framework has a guide on how to create a data dictionary.

What is Metadata?

Metadata is data to describe data. It's what enables others to understand the data produced in research projects. Metadata standards make data discoverable, citable, and usable by other researchers.

Some examples of metadata elements

• Principal investigator
• Title
• Founding sources
• Data collector / producer
• Project description
• Sample and sampling procedures
• Substantive, temporal, and geographic coverage of the data collected
• Data sources
• Units of analysis / observation
• Variables
• Related publications
• Technical information on files
• Data collection instruments
• Index or table of contents
• List of abbreviations and other conventions
• Interviewer guide
• Coding instrument

Metadata Standards

Metadata standards consist of elements to describe data. Different disciplines use different metadata standards that are adapted to the data that they produce. Some repositories also require the use of their own standard. Some examples of metadata standards are listed below.

• Dublin Core (general)
• DDI (social sciences)
• DCC Guide for Disciplinary Metadata