Research data are the data generated, gathered, used, or produced during any systematic investigation, and in some cases include the research output itself. Data can be found in any discipline from the humanities to social and natural science, and in different formats e.g. digital and non-digital, numerical, textual, or audio-visual, etc.
Research Data Management (RDM) refers to all aspects of data management throughout the research data lifecycle, from planning through collection and storage, to preserve, sharing and reuse. RDM aims to ensure that research data is findable, accessible, understandable and reusable over time. It is an essential component of responsible research, and researchers have a responsibility to follow good RDM practices to ensure the sustainability of their research data.
Good RDM not only helps to ensure the long-term availability of data and to maximize the value of research data, but also brings significant benefits to researchers:
Source: Hanson, K., Surkis, A., & Yacobucci, K. (2012, December 20). Data Sharing and Management Snafu in 3 Short Acts [Video file]. YouTube. https://youtu.be/N2zK3sAtr-4
The research data lifecycle is a key concept within RDM. It describes different phases research data go through from the start to the end of a research project, which includes: Planning, Collecting, Processing & Analysis, Storage & Preservation, Sharing and Reuse.
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Research Data Lifecycle |
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Planning is the important first step towards successful research data management. Research funders now increasingly require researchers to submit a Data Management Plan (DMP) for grant applications. A proper DMP not only helps researchers to comply with funders' data plan requirements, but also allows them to think through their research data workflow at the early stage of a research project.
Data collection is a systematic process of gathering and measuring information on variables of interest. In addition to collecting data from primary sources, researchers can also choose to reuse secondary data that was collected from previous studies to save time and effort.
After collecting necessary data, the next stage is data processing and analysis. Data processing and analysis involves inspecting, cleansing, transforming, and modelling data in order to turn data into meaningful insight.
During a research project, active data should to be stored safely in a structured way to enable easy retrieval and prevent data loss. Also, data with long-term value should be properly preserved to ensure the long-term access to data.
Researchers can choose to share their data after the completion of the research projects. Sharing research data is the key improve research visibility. It also benefits the whole research community by accelerating new scientific discovery, and avoiding unnecessary duplication of scientific efforts.
Reusing existing data can save researchers time and efforts, and hence accelerate the pace of scientific discovery. There are various sources of open data including public data repositories, governmental and organizational websites, dataset search engines, etc.
Throughout the research data lifecycle, it is recommended that data management should follow the FAIR Principles in order to make the data to be Findable, Accessible, Interoperable and Reusable.
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