• [ ] Don't Touch the Raw Data - back it up somewhere reasonable and keep a read-only copy.

  • [ ] Have a plan! - Decide where your data is going to be stored, what its called, when/if it needs to be deleted

    BEFORE you start collecting it and note it down in a data management plan. If you collect sensitive data, plan for
    consent for sharing from the start!
  • [ ] Document Everything - You know who the worst person at replying to emails is about what the sampling frequency of

    channel X was. Nope not him, it's actually yourself from a year ago. Keep the documentation with the data!
  • [ ] Create the data you want to see in the word - Imagine someone was about to give you a dataset that you needed to

    analyse well in order to get that job you've been dreaming about. What would you want it to look like? That's how
    yours should look.
  • [ ] Try not to re-invent the wheel. Before you start creating some crazy new schema, storage format or naming

    protocol - have a quick google, ask your colleagues. Something that's already being used is likely going to be
    better in the long run even if you think there's a better solution. <a name="What-Next"></a>

What to learn next

If you haven't read the chapters on Open Research and Credit yet, you might want to read it now for more context on how research data management supports Open Research and how these practises can result in getting attribution for your work (Credit).

Further reading


RDM: - Research Data Management DMP: - Data Management Plan FAIR: - Findable, Accessible, Interoperable and Reusable METADATA: - Data that describes other data Data Repository - Online archive or platform that stores and curates your data, ideally issuing a Digital Object Identifier (DOI) so your data is easily findable and citable. DOI Digital Object Identifier