Research data are not a mere by-product of scientific research, nor a simple means to (article) publication. They often have a much longer shelf life than the scientific publications they underpin:
Therefore, research data should be cared for as first-class research objects. RDM is precisely about that.
What is Research Data
Research data is any information collected or generated for the purpose of analysis to generate or validate scientific claims and original research findings.
They are the evidential basis that substantiates published research findings.
They may be primary data generated or collected by the researcher or secondary data collected from existing sources and processed as part of the research activity.
In addition to the 'raw' data, research data include information about the means necessary to generate data or replicate results, such as computer code, experimental methods and instruments used, and essential interpretive and contextual information, e.g. specifications of variables. Therefore, besides research data, RDM also requires you to manage the documentation needed to make those data understandable.
Types of research data
There is a huge variety of data types.
Research data can be classified in different ways, for example, based on their:
Research data can take many forms. It might be:
Research data management (RDM) is about collecting, caring for, using, preserving and sharing the data supporting your research. It encompasses all practices and actions to ensure that research data are secure, sustainable, easy to find, understand and reuse.
Key elements of RDM include:
RDM starts with data management planning. A number of funders ask for a data management plan (DMP) to be completed as part of a grant application, and it is always advisable to create a DMP for any research project involving the collection of primary data.
RDM is especially important when applied to primary data, i.e. new data collected or generated in the research activity. Because these are new and, in many cases, are essential to validating your research findings, it is important to ensure they are properly curated from the beginning.
While you are not responsible for the preservation and sharing of secondary data that you use in your research, you will still need to consider a number of issues, including: how and on what terms are the data to be accessed and used; where and how any copies of data will be stored; and whether the data provider allows copies of the data or derived data to be distributed.
The acronym FAIR is used to describe qualities that research data can have which maximises how beneficial it can be. They describes how research outputs should be organised so they can be findable, accessible, interoperable and reusable. Major international and national funding bodies, including the ARC and NHMRC, promote FAIR data to maximise the integrity and impact of their research investment.
The FAIR principles are designed to support knowledge discovery and innovation both by humans and machines. Making your research data FAIR can increase visibility and impact of yourself and your work, maximise potential from your data assets, and improve the reproducibility of your research. Following the FAIR guiding principles will also strengthen your research data management strategy. Even if you don’t intend to share your data with anyone yet, you will most likely reuse your own data.
8 steps to make your data more FAIR