Research Data Management: Managing research data

Data management is not just the responsibility of the researcher who has created or collected the data. Various parties are involved in the research process and may play a role in ensuring good quality data, safeguarding them and facilitating data sharing. It is crucial that roles and responsibilities are assigned and not just presumed. For collaborative research, assigning roles and responsibilities across partners is important.

People involved in data management and sharing can include:

• The project director designing and overseeing the research
• Research staff designing research, collecting, processing and analyzing data
• Laboratory or technical staff generating metadata and documentation
• A database designer
• External contractors involved in data collection, data entry, transcribing, processing or analysis
• Support staff managing and administering research and research funding, providing ethical review and assessing Intellectual Property rights
• Institutional IT services staff providing data storage, security and backup services
• External data centers or web archives that facilitate data sharing

You should also consider whether any particular training may be needed for any staff involved. Your institution or specialist organizations may coordinate or provide training in various aspects of research data management.

Research centers and large-scale projects can also consider how best to support researchers through a framework of shared best practices, guidance and policies.

Sharing data (with others or within a lab over time) is impossible without proper data documentation. "Metadata" is data about data. It's structured information that describes content and makes it easier to find or use. A metadata record can be embedded in data or stored separately. Any data file in any format can have metadata fields. In social science, this record is called the "codebook" or "data dictionary."

There are many metadata standards and which one is right for your data will depend on the type, scale, and discipline of your research project.

Some examples of metadata standards are:

• Astronomy Visualization Metadata
• Content Standard for Digital Geospatial Metadata (more on metadata from the FGDC)
• Darwin Core
• Data Documentation Initiative
• Dublin Core
• Ecological Metadata Language

For more examples, see the Research Data Alliance Metadata Directory.

If your field doesn't have a metadata standard (it may not be listed above) or if you just need a simpler system to keep track of data within your own lab, consider that there are three main types of metadata addressed by most standards:

• Descriptive: describes the resource for identification and discovery
• Structural: how objects are related or put together
• Administrative: creation date, file type, rights management

Also consider this advice from the UK Data Archive [pdf]:

Good data documentation includes information on:

• The context of data collection: project history, aim, objectives and hypotheses
• Data collection methods: sampling, data collection process, instruments used, hardware and software used, scale and resolution, temporal and geographic coverage and secondary data sources used
• Dataset structure of data files, study cases, relationships between files
• Data validation, checking, proofing, cleaning and quality assurance procedures carried out
• Changes made to data over time since their original creation and identification of different versions of data files
• Information on access and use conditions or data confidentiality

 At the data-level, documentation may include:

• Names, labels and descriptions for variables, records and their values
• Explanation or definition of codes and classification schemes used
• Definitions of specialist terminology or acronyms used
• Codes of, and reasons for, missing values
• Derived data created after collection, with code, algorithm or command file
• Weighting and grossing variables created
• Data listing of annotations for cases, individuals or items