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Metadata and describing data

Metadata: the who, what, when, where, why, how of your research.

This resource will help you identify the best metadata strategy for your research, discipline, and data needs.

 

Metadata and describing data

Metadata is documentation that describes data. Properly describing and documenting data allows users (yourself included) to understand and track important details of the work. Having metadata about the data also facilitates search and retrieval of the data when deposited in a data repository.

Metadata can include content such as contact information, geographic locations, details about units of measure, abbreviations or codes used in the dataset, instrument and protocol information, survey tool details, provenance and version information and much more. In a lab setting, much of the content used to describe data is initially collected in a notebook. When possible, structure your metadata using an appropriate, agreed-upon metadata standard format. (See below for examples and guidelines.)

When no appropriate metadata standard exists, you may consider composing a "readme" style metadata document, as described in this guide.

 

Metadata standard by discipline

To find an appropriate metadata standard for your discipline, consider the Disciplinary Metadata guide (via the Digital Curaiton Center).

Additionally, a community-driven project manges an open directory of metadata standards (via Research Data Alliance).

 

Metadata formats and standards

Metadata can take many different forms, from free text to standardized, structured, machine-readable, extensible content. Specific disciplines, repositories or data centers may guide or even dictate the content and format of metadata, possibly using a formal standard. Because creation of standardized metadata can be difficult and time consuming, another consideration when selecting a standard is the availability of tools that can help generate the metadata (e.g. Morpho allows for easy creation of EML, Nesstar for DDI data, etc.).

Some specific examples of metadata standards, both general and domain specific are:

  • Dublin Core - domain agnostic, basic and widely used metadata standard
  • DDI (Data Documentation Initiative) - common standard for social, behavioral and economic sciences, including survey data
  • EML (Ecological Metadata Language) - specific for ecology disciplines
  • ISO 19115 and FGDC-CSDGM (Federal Geographic Data Committee's Content Standard for Digital Geospatial Metadata) - for describing geospatial information
  • MINSEQE (MINimal information about high throughput SEQeuencing Experiments) - Genomics standard
  • FITS (Flexible Image Transport System) - Astronomy digital file standard that includes structured, embedded metadata
  • MIBBI - Minimum Information for Biological and Biomedical Investigations

 

Related information

Best Practices in Creating Metadata. ICPSR. http://www.icpsr.umich.edu/icpsrweb/content/deposit/guide/chapter3docs.html. Part of the ICPSR's Guide to Social Science Data Preparation and Archiving.

Metadata Best Practices. DataONE. http://www.dataone.org/best-practices/metadata

Metadata Services. Cornell Research Data Management Service Group. http://data.research.cornell.edu/services#Metadata 

Minimum Information for Biological and Biomedical Investigations. MIBBI Project. https://biosharing.org/standards/?selected_facets=isMIBBI:true&view=table. Minimum Information guidelines from diverse bioscience communities.