Databases for research header image

Your data hub

The general idea

You’re likely to just start making your data with whatever software seems ‘natural’ to you. However, hold on for a moment, and consider the near future of your research project. Your table, notebook, network graph, or what have you, may quickly turn into a data hub. Perhaps you should consider a relational database.

Graph of a data hub with a relational database at the center

Basic pros and cons of a relational database

One important trademark of relational databases is that they are relatively flexible. To be precise, the data stored in tables and woven together through relationships, can easily be changed. Tables can be split up, data can be moved from one to another. Relationships can be redefined, et cetera.

Secondly, relational databases are also quite good at importing and exporting data. In particular exporting is very easy. One can bring data together in a particular view, if only for the export, and export it to the outside world, often in different file formats.

Thirdly, relational databases are not particularly suited for data analysis, although they often come packed with basic possibilities to summarize, do simple statistics, and generate graphs. However for more advanced scientific analysis they are simply not suited. The reverse is also true: the software that is suited for advanced analysis usually does not offer the same flexibility to rearrange data as relational databases do.

These traits make a relational database an excellent hub for your research data. You produce and maintain your data in the database and when the time comes, you export it as needed, import and import it in other software for analysis. This is illustrated in the figure.

Continue reading here for additional topics:
• But, you only use one type of software for analysis?
• Dealing with inflexibility
• Knowing when to start the hub

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