Benefits of data management
Research data management refers to how you handle, organise, and structure your research data throughout the research process. Data management:
- Begins with your initial considerations regarding what will be necessary for using or collecting your particular type of data;
- Includes measures for maintaining the integrity of the data, making sure that they are not lost due to technical mishaps, and that the right people can access the data at the appropriate time;
- Looks forward to the future, making it clear that you should provide detailed and structured documentation to be able to share your data with other colleagues and prepare them for long-term availability.
To make your research as time-efficient, reproducible and safe as possible, it is important that your data management is well thought through, structured, and documented. A good data management strategy takes into account technical, organisational, structural, legal, ethical and sustainability aspects. The time invested in setting up a good data management strategy pays off when the time comes to reproduce your analysis and results. You will be able to easily find and understand your data, increase your data's reuse potential and comply with funder mandates at the same time.
Data Management Plan
Data Management Plans (DMPs) are a key element of good data management | European Commission, 2016.
Information regarding your data management needs to be easily found and understood, not least if you are working on a project that runs over several years and involves a large team of people. In order to simplify data management, a Data Management Plan (DMP) can be created early in the research process. A DMP is a formal document that provides a framework for how to handle the data material during and after the research project. The way a DMP will look once it is finished isn't universal. It is a "living" document that changes with the needs of a project and its participants. It is updated throughout the project to make sure that it tracks such changes over time and that it reflects the current state of your project.
A lot of diversity exists in DMPs because they are always built around the particular needs of the data collected within your project. Sometimes there are particular requirements that has to be answered in the DMP from stakeholders such as:
The added value of a Data Management Plan
Several researchers I’ve been talking to who have looked at the Data Management Planning checklist of the Swedish National Data Service (SND) have said that doing so made them start thinking of data security, ownership, file formats etc. before they started their project. By doing so they avoided some possible problems that would otherwise appear later on | Ulf Jakobson, Data manager humanities, SND.
A Data Management Plan (DMP) offers added value in the following ways: