Work with data

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Good data management makes it possible for project members to access the right versions of the project data when they need them, without unnecessary effort, or risks of error or data loss. When the project gets under way and data are collected in folders or databases, that’s when the plans you made for data management in your DMP are made real. Carefully considered storage solutions and documentation keep the data material safe, and by following the project's naming conventions and folder structures, you keep the data ordered and easy to understand for all parties involved.

Regardless of how well you plan a research project, things may still go wrong. The data management routines you create have to be simple enough to follow without taking too much time or effort. You need to strive for order, logical structures, intuitive file naming conventions, and rules for versioning that are easy to follow. There are no universal solutions; research data and research projects can be hugely different. But with good documentation and routines for easy data management, you make the work with the data material simpler and more secure.

This section contains information about how you can work with data without risking data errors or loss of data. We will discuss how you can organise data to make it easy to find what you need when you need it, and to make it possible for other people, too, to analyse the data. We will also provide some suggestions to how you may give access to data to those need to have access to them without the risk for unauthorised access. You will find useful tips of what to consider when you choose software, and recommendations if you are several people working with the same files. Some questions you may want to consider are:

  • How do I make sure that the data are backed up?
  • In which ways can the data material become corrupt?
  • How do I best organise the data?
  • How can we solve issues of access to well-protected data?
  • How can I make sure that I will still be able to read the files from my analysis program in the future?

 

Data loss

Important considerations to avoid corruption or loss of data.

Data errors

Useful advice to avoid common errors in data management.

Well-organized data

Organize the data to make them understandable to you and others, now and in the future.

Access

Data should be protected from unauthorized access, but easy to access for you and your project partners.

Shared work files

Are you a group of people who collaborate on the same data material? In that case, good order is of extra importance.

Software

Your choice of software determines how you can store and document data, and to which degree you can save the data in a persistent file format.