Metadata

Metadata and documentation are different things: Documentation is meant to be read by humans; some metadata is designed more for machine processing than human readability. However metadata can be taken as a type of documentation. Create and generate metadata for your research data and datasets in your research lifecycle to preserve the data in the long run.

1. Consider what information is needed for the data to be read and interpreted in the future.

2. Understand your funder requirements for data documentation and metadata. Funder requirements for NSF, GBMF, IMLS, NEH, NIH and NOAA can be found at https://dmptool.org/public_templates.

3. Consult available metadata standards in your field. You may refer to Common Metadata Standards and Domain Specific Metadata Standards for details.

4. Describe data and datasets created in your research lifecycle, and use software programs and tools to assist in data documentation. Assign or capture administrative, descriptive, technical, structural and preservation metadata for the data. Some potential information to document:

• Descriptive metadata
  • Name of creator of data set
  • Name of author of document
  • Title of document
  • File name
  • Location of file
  • Size of file
• Structural metadata
  • File relationships (e.g. child, parent)
• Technical metadata
  • Format (e.g. text, SPSS, Stata, Excel, tiff, mpeg, 3D, Java, FITS, CIF)
  • Compression or encoding algorithms
  • Encryption and decryption keys
  • Software (including release number) used to create or update the data
  • Hardware on which the data were created
  • Operating systems in which the data were created
  • Application software in which the data were created
• Administrative metadata
  • Information about data creation (e.g. date)
  • Information about subsequent updates, transformation, versioning, summarization
  • Descriptions of migration and replication
  • Information about other events that have affected the files
• Preservation metadata
  • File format (e.g. .txt, .pdf, .doc, .rtf, .xls, .xml, .spv, .jpg, .fits)
  • Significant properties
  • Technical environment
  • Fixity information

5. Adopt a thesauri in your field or compile a data dictionary for your dataset.

6. Obtain persistent identifiers (e.g. doi) for datasets if possible to ensure data can be found in the future.

For your full data management plan, you may refer to Digital Curation centre’s Checklist for a Data Management Plan.

Please also refer to Data documentation & metadata and Data Documentation, Analysis & Statistical Software for more information on dataset metadata and its related services at UCF Libraries.

(Source: DMPTool: https://dmptool.org/. Digital Curation: A How-To-Do-It Manual; Digital Curation Centre: http://www.dcc.ac.uk/)