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.

• RDA Metadata Standards Directory lists hundreds of standards, extensions, tools, and use cases. The directory can be browsed by discipline and subject area.
• Seeing Standards: A Visualization of the Metadata Universe provides a visualization of relationships between over 100 metadata standards used by cultural heritage organizations (libraries, museums, archives, galleries, etc.) The glossary provides links and brief descriptions for each of the standards represented.
• Digital Curation Centre's Disciplinary Metadata links to information about these disciplinary metadata standards, including profiles, tools to implement the standards, and use cases of data repositories currently implementing them.

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!

General Purpose:

• DublinCore (DC) Metadata Element Set is a generic set of 15 properties for describing a wide range of resources.
• Metadata Object Description Schema (MODS) is a descriptive standard used to describe a variety of types of resources; it is maintained by the Library of Congress.

Sciences: 

• Darwin Core (DwC) is used in the biological sciences to describe collections of biological objects or data and includes a glossary of terms intended to facilitate the sharing of information about biological diversity.
• Access to Biological Collection Data (ABCD) is used in the biological sciences to describe specimens and scientific observations.
• Astronomy Visualization Metadata Standard (AVM)  is used to describe data-derived astronomical images. 
• Ecological Metadata Language (EML) is used  to formalize and standardize concepts necessary when describing ecological data.
•  FDGC Federal Geographic Data Committee Standard is a standard for documenting digital geospatial data; it is especially relevant to researchers in the field of Geographic Information Systems.

Social and Behavioral Sciences:

• Data Documentation Initiative (DDI) is a standard for describing observational and survey data in the social, behavioral, economic, and health sciences; also useful for structuring research data documentation.
• OLAC is a standard used by the Open Language Archives Community for describing language resources in linguistics research.

Arts and Humanities:

• Text Encoding Initiative Guidelines (TEI) is a standard for the representation of texts in digital form, and has been used by researchers in the humanities, social sciences, and linguistics since 1994.
• VRA Core is a standard created by the Visual Resources Association for describing cultural objects, such as images and works of art.
• PBCore, also known as the Public Broadcasting Metadata Dictionary, is a standard designed for the description of audiovisual resources in digital and analog formats.

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.