Importance of data management

1. It’s a responsibility: scientists have a responsibility to accurately conduct, record and preserve research data.
2. Reproducibility: scientists are expected to communicate their findings so that others can reproduce it, validate it and build upon it. It’s therefore imperative that other scientists can rely on the integrity and quality of the data. A robust data management plan can help with this.
3. Quality: good data management is fundamental for producing high quality research.
4. Preservation: the majority of funders, research organisations and academic institutions actually ask that data should be preserved and accessible for a certain number of years after it’s created. So, for example, the research councils in the UK, the 2009 Code of Good Research Conduct states that research data needs to be preserved for at least 10 years, but institutions and other funders vary in their guidelines.
5. Publication: research data management is also important when it comes to publication because many leading journals require that the underlying datasets are also published or made accessible to encourage transparency and so that others can access the data when necessary.
6. Cost effectiveness: because planning how to collect and manage and preserve your scientific data in advance can help you avoid costlier mistakes further down the line and it allows you to more accurately budget your lab finances. And on top of that, it can help other people to avoid duplicating your work unnecessarily because if your data is already available, they can access your data and use it and build on it with their own research.
7. Funders: an important practical reason and that’s that research funders are increasingly mandating that researchers produce a data management plan when they apply for funding in their grant applications. So, for example, the National Science Foundation in the US and the Wellcome Trust in the UK, they both require grant applications to include a data management plan.