Document and describe your data

Documentation and metadata

Digital data by definition are machine-readable, but understanding their meaning is a job for human beings.

Documenting your research includes descriptive and administrative information attached to your research data (metadata) and records.

• This documentation gives context and meaning to the data and makes it easier for you (and others) to understand now, and into the future.
• Documenting your data during the collection and analysis phases of your research is very important.  It documents your thinking, methodology and the research processes undertaken and how this may be reviewed during the research process. This is especially important if your research is going to be part of the scholarly record. It is also critical for the potential re-use of your data should this be an intended output.

Research data need to be documented at various levels.

• Project level: This is what the research sets out to do; the new knowledge it contributes to the field; the research questions/hypotheses tested; the methodologies used; the sampling frames used; the instruments and measures used, the phenomena it explores. etc.
• File or database level: How all the files (or tables in a database) that make up the dataset relate to each other; what format(s) they are in; whether they supersede or are superseded by previous files. A readme.txt file is the classic way of accounting for all the files and folders in a project. If there are also non-digital objects in your dataset, what are the relationships between these objects and the digital files?
• Variable or item level: The key to understanding research results is knowing exactly how an object of analysis came about. Not just, for example, a variable name at the top of a spreadsheet table, but the full label explaining the meaning of that variable.

Metadata

An important component of research record keeping and documentation is the management of different types of metadata associated with research data and records.

Metadata is structured information associated with an object for purposes of discovery, description, use, management and preservation (NISO, 2007) Metadata is data about data; information about information. Metadata adds value to documents or images. For scientific data, metadata is very important because it provides the context needed to make sense of what would otherwise be a collection of numbers.

Collection level metadata is used to describe an aggregation of objects. For example, a photo album (or folder) that contains a group of photographs might use descriptors that provide a connection or give context to the photos within. Collection metadata may contain information relating to the size of the collection, who took the photographs (there may be more than one person), the time period over which the photographs were taken, and so on. Collection level metadata can assist with the discovery of a large aggregation of objects.

There are many labels used for metadata but essentially there are three main types of metadata based on core functions that the information relates to:

Metadata schemas

You and your supervisor are best placed to find out what standard metadata schemas are being used in your discipline/community; check with your supervisor or colleagues to learn about what metadata standards they are using and the reasoning for their choices.

By choosing a well-supported metadata schema or standard, you will maximise the chance that your data can be re-used and understood by other researchers now, and into the future. If you don't know where to start, the Research Data Alliance have created a directory of metadata schemas that may get you started.

Below are links to collections of a number of well-known metadata schemas from a range of research areas put together by the Digital Curation Centre (DCC).

Follow the most appropriate link to find the schema most relevant to your area of research as well as additional tools, services and programs related to the schema. You may be surprised by how much work has already been done in your discipline. Even if a schema doesn't cover everything you want, you may be able to use a part of a schema, or a combination of schemas to describe your data.

• Biology
• Physical sciences
• Earth sciences
• Social sciences & humanities
• General research data (other research areas)

For more examples of metadata schemas or further information about metadata schemas refer to the Digital Curation Centre (DCC) metadata registry.

Research notebooks

Researchers can benefit by keeping a systematic record of their research. For many disciplines this may take the form of a diary or notebook to record ideas, articles and references. Laboratory-based researchers usually complete laboratory notebooks as crucial components of data management. Look to the local practices in your research group or lab for guidance.

Lab notebooks
Lab notebooks can play an important role in supporting claims relating to intellectual property developed by University researchers, and even defending claims of scientific fraud. This can also be particularly important for the Patent registration process. In less extreme circumstances, they demonstrate adherence to standards of good practice, academic and ethical integrity, and compliance with contractual provisions permitting sponsors to audit work carried out in pursuit of sponsored research. Therefore, thorough and effective management of laboratory data and the routine documentation of all lab procedures is a highly important responsibility for all laboratory researchers.

The University also provides access to an Electronic Laboratory Notebook (ELN) platform called LabArchives. Any student or staff member at the University can log into LabArchives using their University credentials and create their own electronic notebook to record, manage, and safely store their digital research data. LabArchives makes it easy to collaborate on research projects, even with external partners, and graduate research supervisors and students can keep track of a research project.