Managing research data
Philippa Frame
Getting started
introduction to research data management
Researchers have a responsibility to ensure their data is accurate, complete, authentic and reliable. Good research data management practices ensure that researchers and institutions can meet their obligations to funders, improve the efficiency of research, and make data available for sharing, validation and reuse.
Publication and re-usability of research data bring great benefits such as reproducibility and transparency, enhanced reputations of researchers and institutions, funder agreement compliance, and compliance with Open Access agendas. To support these objectives, it is imperative that research data management is done properly from the outset, through all stages of the research data lifecycle.
In Australia, all researchers are required to comply with the Australian Code for the Responsible Conduct of Research, 2018 (the Code) which outlines the responsibility researchers have to ensure the safe and secure storage and management of research data, records and primary materials, and where possible and appropriate, to allow access and reference to these data.
Adherence to the guidance set out within the QUT Manual of Policies and Procedures (MoPP) Management of research data and primary materials policy will allow QUT researchers to comply with the Code.
what is research data?
According to the MoPP, research data is :
- data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which an argument, theory, test or hypothesis, or other research output is based.
- It relates to data generated, collected, or used, during research projects, and in some cases may include the research output itself.
- Data may be numerical, descriptive, visual or tactile.
- It may be raw, cleaned or processed, and may be held in any format or media.
- Research data, in many disciplines, may by necessity include the software, algorithm, model and/or parameters, used to arrive at the research outcome, in addition to the raw data that the software, algorithm or model is applied to.
types of data
Depending on the kind of research or method of analysis, different types of research data may be created or collected. These types include:
Observational data are captured in real time, usually unique and irreplaceable.
e.g. brain images, survey data.
Experimental data are from lab equipment, often reproducible, but can be expensive.
e.g. chromatograms, microassays.
Simulation or model data are generated from test models where model and metadata may be more important than output data from the model.
e.g. economic or climate models.
Derived or compiled data result from processing or combining ‘raw’ data, often reproducible but expensive.
e.g. compiled databases, text mining.
Reference or canonical data are a (static or organic) conglomeration or collection of smaller (peer reviewed) datasets, most probably published and curated.