Research Data Management¶
Prerequisites / recommended skill level¶
The following sections in this handbook provide useful context and complementary information to this chapter:
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Summary¶
Research Data Management (RDM) covers how research data can be stored, described and reused. Data here is used as a generic term to encompass all digital objects. RDM is a key part in enabling reproducible research. RDM ensures efficiency in research workflows, and also greater reach and impact, as data become FAIR (Findable, Accessible, Interoperable and Reusable). Data should be stored in multiple locations and backed-up regularly to prevent loss or data corruption. Clearly describing data using documentation and metadata ensures that others know how to access, use and re-use your data, and also enable conditions for sharing and publishing data to be outlined.
What are the benefits of Research Data Management?¶
Managing your data allows you to always find your data and ensure the quality of scientific practice
Storing your data properly and and backing-up regularly prevents data loss
It can help with recognition for all research outputs
It stimulates collaboration with others, who will find it easier to understand and reuse your data
RDM is cost/time efficient, as you will always be able to find and use your data
What Is Data?¶
Data are all digital objects that you use and produce during your research life cycle, encompassing datasets, software, code, workflow, models, figures, tables, images and videos, interviews, articles. Data are your research asset. In some fields it’s obvious what data means - you have observations or results of simulations. However, in other fields, particularly in Social Sciences, Humanities or Arts, you may be thinking “I don’t think I have any data”. A good way of thinking about what might be classed as data that needs to be managed is to ask yourself the questions “What is the information that I need to use and write about in my paper or book?”, “What information would I need to back up my conclusions?” and “What information is needed by others to understand and possibly replicate the research that I’ve done?”. This information is your data.
The Research Data Lifecycle - A Model for Data Management¶
Research data often follows a ‘lifecycle’ which follows the research project as it evolves; here is a video that describes it. This model provides a useful basis on which to plan for research data management, from data creation at the start of a research project, through to publishing and sharing research at the end of the project, and archiving any research data for the long-term and for future re-use once the project has ended.
The research data lifecycle involves data creation, data use, data publication and sharing, data archiving and data re-use or destruction. However, data have a longer lifespan than the research project that creates them.