Research Data Management: Implementing: Metadata and Documenting Your Process

 Metadata is data that describes your data. Metadata is used to structure actual data sets - like the column headings of simple tabular data - as well as to describe features of data sets. Some examples of metadata include information that answers the questions of when, who, what, why, how, etc:

• date the data was created
• creators of the data
• the source of the data
• purpose for which the data was collected
• structure of data files
• changes made between different versions of data
• codes used for variables and missing values
• data collection methods and instruments used
• steps taken to anonymize the data

Sometimes metadata is contained in the data files produced by the software used to collect or analyze the data, other times it is included in a codebook or lab notebook. Every effort needs to be made to keep this information with the data set with which it is affiliated.

Why should you care about metadata?

It provides the means for organizing and describing your data. Metadata facilitates data collection, processing, archiving, discovery, re-use, and analysis.

Metadata standards not only facilitate use of your data in its native environment, but maximize its usability in other environments. For example, standardized metadata will allow you to more easily move your data from one data repository to another.  Check into whether there are standards commonly employed by your department or your organization. Perhaps your research domain commonly employs a metadata standard. It may be that the repository into which you will be depositing your data has metadata requirements. You will have to do a little research. To help get you started, we have provided a list of metadata standards sorted by discipline below:

Writing good documentation for your research project is essential for clarity, reproducibility, and transparency. It allows others to understand and replicate your work. The following strategies can serve as a guide on how to write effective documentation for your research project:

• Detail Your Methodology: Enable others to replicate your work by including detail about where, when, and how data was collected as well as any information about samples, sampling methods, and/or data sources. This should also include any equipment, software, or instruments used, including specific models or versions. Finally, outline the steps for data processing and the statistical methods used. Include any data cleaning procedures and transformations.
• Document Data Management: Include information on how and where the data is stored, managed, and shared.
• Use Documentation Tools: Consider using tools designed for documentation to improve organization and collaboration. This includes electronic research notebooks (ERN) or code notebooks like Jupyter Notebook. If your project involves code, using version control systems like Git can help you track changes. Read our guide to Electronic Lab Notebooks.
• Use Clear and Concise Language: Avoid jargon where possible and define clearly when specialized language is necessary. Keep sentences and paragraphs concise.

If you have questions about metadata or would like to request a metadata consultation with a member of the Data Services Team, please submit a request here.