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Research Data Management

What is Metadata

Metadata is structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource.

"Metadata facilitates and supports the discovery, identification, organisation and interoperability of research outputs. Having rich metadata will help maximise exposure, reuse and citation of your research findings." (Adapted from Source: Australian Research Data Commons)

Types of Metadata

Here is the metadata type table adapted from National Information Standards Organization United States.

Metadata Type Example Properties Primary

Descriptive metadata

Common fields which help users to discover online sources through searching and browsing





Publication date




Technical metadata

Fields which describe the information required to access the data

File type

File size

Creation date/time

Compression scheme


Digital object management


Administrative Metadata for Preservation

Fields that facilitate the management of resources


Preservation event


Digital object management


Administrative Metadata on Rights

Fields which deal with intellectual property rights

Copyright status

License terms

Rights holder


Digital object management

Structural metadata

Fields which describe how different components of a set of associated data relate to one another


Place in hierarchy


Markup languages

Languages which integrate metadata and flags for other structural or semantic features within content


Heading List





Introduction to Metadata

The following is a video created by EDINA, University of Edinburgh, explaining what metadata is.

Metadata Formats and Standards

Metadata standards/schemas may vary from discipline to discipline. Dublin Core is one of the most commonly-used generic metadata standards.

Simple Dublin Core involves 15 elements (optional & repeatable):
















Source: Dublin Core Metadata Initiative (DCMI

Here are some useful resources for you to explore metadata schema in your research areas:


All research datasets should have an accompanying README file that provides comprehensive information about the dataset, such as contact details of the research team, data collection methods, methodological information and data files and folder organisational structure etc. A README file is intended to help ensure that your research data can be correctly interpreted and re-used by others. It is intended to not only help other researchers, but also yourself, to ensure research reproducibility and maximise reuse of your data and accrue data citations.

Here are some best practices in creating comprehensive README files.

  • Create a separate README file for each individual data file or a single README file for the dataset as a whole
  • Write your README document as a plain text file
  • Name your README file as ""
  • Use standardized date formats e.g.W3C/ISO 8601 date standard YYYY-MM-DD or YYYY-MM-DDThh:mm:ss

The table below is adapted from Cornell University's Guide to writing "readme" style metadata, and highlights possible contents that you could include in your README file. Fields indicated in bold are mandatory for inclusion in your readme file, and can be adapted for your own unique dataset and discipline.

General information

  • Title of the dataset
  • Names, institution, address and contact information of people responsible for the research dataset
    • Principle Investigator (PI)
    • Co-PIs and research team
    • Contact person for questions
  • Date(s) of data collection
  • Geographic location of data collection, if applicable
  • Keywords used to describe the data
  • Language of datasets
  • Funding information that supported the collection of the data.
Data and file overview
  • For each file, list the file name and provide a short description of what data it contains
  • Date that the file was created or updated
  • File formats
  • Relation of the file to other data files in the dataset, if any.
Sharing and access information
  • Licenses or restrictions related to the data
  • Links to publications that cite or use the data
  • Links to other publicly accessible locations where the data was also uploaded to
  • Recommended citation for the dataset
Methodological information
  • Description of methods for data collection/generation 
  • Description of methods used for data processing and analysis
  • Any instrument-specific information needed to understand or interpret the data, e.g., details of any particular operating system/software required to make use of the data
  • Quality-assurance procedures (if applicable)
  • People involved with data collection, processing, analysis and/or submission
  • Links to other publicly accessible locations where such methodological information can also be found
Data-specific information

You may repeat this section as needed for each data file or dataset 

  • Count of number of variables, cases, rows
  • List of variables, including full names and definition of column headings for tabular data
  • Units of measurement
  • Definitions of codes or symbols used to record missing data
  • Specialised formats or other abbreviations used
  • Any other useful information specific to the data