Data management: Recommendations

It is very important that data are searchable, findable, reusable, attributable, readable, accurate, and complete. Take one of your published figures from 5 years ago. Are you able to find the raw data? How long will it take you? According to our 2020 survey, 3 quarters of CFs cannot do this…

Use an ELN/LIMS or write a data management guideline for your users about how data will be managed at the CF. Define who does what and how, regarding data naming, saving, transferring, long-term storage and analysis.

• Data naming:

A unique and logical name is the first step to traceable data. Decide of a uniform convention that you impose on your users, also allowing some personalisation:

e.g. 201130-imk-experimentX [date]-[user acronym]-[free text from the user]

• Data saving:

Raw data files should be unmodifiable and should include metadata about the experimental setup and conditions.

• Data transferring:

Organize easy/practical data transfer to the user (eg. through network) to prevent data loss.

• Data storage:

Define who will store the raw data long-term (see responsibility). CF could store raw data additionally for the users on a server/repository, as a backup and to prevent fraud.

• Data analysis:

Analysed data should be linked to raw data, as well as published figures.

A data management plan (DMP) can also be implemented for each experience. It is a written document that describes the data you expect to acquire or generate during the course of a research project, how you will manage, describe, analyse, and store those data, and what mechanisms you will use at the end of your project to share and preserve your data. https://library.stanford.edu/research/data-management-services/data-management-plans https://en.wikipedia.org/wiki/Data_management_plan