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Metadata Guide

Information on how to collect and maintain your metadata

What is Metadata and Why is it Important?

Staff and faculty in nearly every domain of the campus community are producing, manipulating, or analyzing data and digital objects as part of their day-to-day work. Because of this increase in data production and use, a need has been identified to describe data in order to make the data discoverable in repositories, understandable in context, and reusable by others. 

This guide provides a basic introduction to tools, resources, standards, and support for metadata and data documentation. For additional information, or to schedule a consultation on metadata and data documentation, contact Lina Spotts, Metadata Specialist, Carnegie Mellon University Libraries.

What is Metadata

Metadata describes the content, quality, condition, and other characteristics of data. Metadata is generally standardized, structured information that facilitates functions associated with data, such as:

  1. Organizing and managing data
  2. Preserving data for the long term
  3. Ensuring that data can be indexed and discovered in a data repository
  4. Retaining the context around which the data was captured or created, which is vital in facilitating comprehension and reuse of the data by other researchers

Types of Metadata

Descriptive metadata describes the object or data and gives the basic facts: who created it (i.e. authorship), title, keywords, and abstract.

Structural metadata describes the structure of an object including its components and how they are related.  It also describes the format, process, and inter-relatedness of objects. It can be used to facilitate navigation, or define the format or sequence of complex objects.

Administrative metadata includes information about the management of the object and may include information about: preservation and rights management, creation date, copyright permissions, required software, provenance (history), and file integrity checks.

Metadata Standards

Metadata standards --  

Metadata Content Standard: A Standard that defines elements users can expect to find in metadata and the names and meaning of those elements. 

Metadata Format Standard: A Standard that defines the structures and formats used to represent or encode elements from a content standard.

Controlled Vocabularies 

Metadata Element Sets

  • 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.

Resources for Finding Metadata Standards and Ontologies

Research Data Alliance Metadata Directory - The RDA Metadata Directory is a collaborative, open directory of metadata standards applicable to scientific data. Subject areas include arts and humanities, engineering, life sciences, physical sciences & mathematics, social & behavioral sciences, and general research data (multidisciplinary).

Linked Open Vocabularies (LOV) - LOV provides a searchable repository of vocabularies and ontologies used to describe many different disciplines and domains.

Data Documentation Initiative - DDI is an international standard for describing statistical and social science data. It contains a metadata specification, as well as a list of tools to help researchers work with DDI metadata.

FAIRsharing - FAIRsharing (formerly BioSharing) offers a searchable database of metadata standards, markup languages, taxonomies, and other resources for all disciplines.

BioPortal - BioPortal offers an extensive repository of biomedical ontologies, including a recommender tool to help choose the best ontology for your research.

Open Metadata Registry - The Metadata Registry provides services to developers and consumers of controlled vocabularies and is one of the first production deployments of the RDF-based Semantic Web Community's Simple Knowledge Organization System (SKOS).


Metadata for Research Data

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. 

You can learn more about Data Management here Intro to Data Management

The guidelines for CMU's Research Data Repository can be found here Kilthub



Best Practices and Further Reading

Best Practices for Metadata for social science data by the Inter-university Consortium for Political and Social Research.

Guide to writing "readme" style metadata by the Cornell Research Data Management Service Group.

Understanding Metadata by the National Information Standards Organization.

Metadata Basics by the Dublin Core Metadata Initiative.

Metadata Best Practices by DataONE.

MANTRA Research Data Management Training module on metadata, documentation, and citation.

Practical guidance for anyone working with research data by the UK Data Service.

Create & Manage Data: Documenting Your Data by the UK Data Archive.

Biomedical Ontologies and Controlled Vocabularies by the University of Michigan Taubman Health Sciences Library

Metadata Services at Hunt Library


The Metadata Specialist is available for consultations to help you start a new project, improve your metadata quality and consistency, and learn more about what metadata can do for you. 

We provide support for…


  • Reviewing project requirements and identifying metadata standards which best describe and enhance your research
  • Selecting appropriate controlled vocabularies to improve consistency in your project
  • Establishing workflows


  • Analyzing metadata and recommending transformation strategies to conform with project guidelines
  • Cleaning and validating your metadata to improve standardization and consistency
  • Inform on non-MARC metadata design, structure, workflows and standards to coordinate best practices across University Libraries.
  • Training and workshop opportunities


  • Recommending standards that encourage access, interoperability, and reuse
  • Identifying suitable repositories to connect your data to other research in your field
  • Exploring emerging trends in metadata, such as linked data, metadata harvesting, and automated creation.



Metadata Specialist
Hunt Library
Carnegie Mellon University
6555 Penn Avenue
Pittsburgh, PA 15206