A metadata standard is a high level document which establishes a common way of structuring and understanding data, and includes principles and implementation issues for utilizing the standard.
Metadata can be organized into four general types. Metadata element sets or schema, sometimes called data structure standards, are the categories of data that make up a record or other information object. Controlled vocabularies and name authorities, sometimes called data value standards, are lists of standardized terms and names used to create metadata (see our guide on Taxonomies and Controlled Vocabularies for more information). Data content standards are guidelines for inputting data into metadata elements. Data exchange standards are specifications for encoding data
There are many metadata standards purposed for specific disciplines. The following resources may assist in locating a standard suitable for your project.
This page is not intended to be an exhaustive list. Some common standards are presented below, listed by discipline. To find the right standards for your projects, contact us!
Social and Behavioral Sciences:
Arts and Humanities:
Metadata has value to both the original creator of a data set and other potential users. Complete metadata allows researchers to locate data they created and recall the circumstances and context under which they created and analyzed the data. It allows researchers outside of the original research team to discover, understand and use the data.
The Library's guide on Research Data Management, particularly the section Describing Data, provides additional support and resources for learning more about metadata and research data. The library has also published the white paper "Data Sharing with D-Scholarship@Pitt", which provides best practices for depositing data in D-Scholarship@Pitt.