Research Data Management

“It can be a chore, but good research data management is like good dental hygiene. It’s healthy, attractive and makes people want to know you.”

Laurence Horton, LSE Data Librarian

Research Data Cycle

Research data lifecycle

The digital age has dramatically changed social science research. Today we can do more research with more data in less time and at a lower cost. Computing power allied to digital storage and transmission allows us to combine existing data with new forms of digitally generated administrative and transactional data. This creates new opportunities for social science research, for example in terms of reusing data that were thought only for answering one research question, expanding their research potential.

Meanwhile, such opportunities for research also bring to light the challenge of correctly managing the data used. Researchers need to be aware of the importance of getting it right when it comes to data collection, organisation, contextualisation, storage, and dissemination. While research data management has always been integral to good research practice, it becomes more and more important in the context of the digital society in which we now live.

Research data management is about looking after your data. It concerns the development and implementation of practices, procedures, and policies to protect, validate, and describe data. Doing this ensures its quality, thereby facilitating potential reuse.

Practicing good research data management will keep your data alive for generations, creating an impact long after your original research. Regardless of whether you intend to share your data or not, making sure that you think about how you will manage the collection, use, and store data early on in your research project is fundamental.

Why? Because a little time spent on research data management at the start of a project means a lot more time for writing and publishing at the end.

The advantage for researchers in addressing research data management early on in their project is that it provides them with a strategy for confronting issues such as:

  • consent, data ownership and licensing:
    If you are reusing data, what can you do with that data and what should you not do?
    If you are creating data, are there any restrictions on future reuse you that you need to justify?
  • research integrity and replication:
    Good research is replicable research, meaning that context is critical. Have you described the process of data creation and analysis so that others can understand, evaluate, and reuse the data or methodology without having to ask you for further information?
  • data security and the risk of data loss:
    Think about how you are going to share data within a research team. Do you know what happens to your data when you press “Save”? Is it being backed-up, where is it stored, and who can access the data?
  • safe and secure disposal of data:
    Copies of data, or data not suitable for long-term preservation, need to be disposed of without compromising guarantees of confidentiality given to participants and funders.

In short, designing and implementing a research data management strategy increases and extends the value of your research, which will mean that you save time and resources. Moreover, funding bodies increasingly view how a proposal addresses research data management as an essential component of any funding request.

The CESSDA User Guide on research data management can be downloaded below.

CESSDA User Guide RDM
Name Type Size
1_Research data management PDF 109.23 kB
2_Research data management plan PDF 102.78 kB
3_Data sharing PDF 100.74 kB
4_Documentation and metadata PDF 111.71 kB
5_File formats PDF 104.10 kB
6_Data security PDF 138.58 kB
7_Intellectual property rights PDF 106.44 kB
8_Data consent and ethics PDF 122.77 kB
9_Research data management checklist PDF 98.65 kB

Last updated: Monday 01 June 2015

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