Data Management for Microscopy for FAIR and Open Science

Bertrand Vernay included in recommendation

2023-02-09 519 words 3 minutes

Contents

A quick introduction to FAIR.

Data management is a critical element of laboratory work, unfortunately is often overlooked and deferred until comes the need for archiving at the end of a project’s life. Applying some simple clear rules will help you manage your research project in the long run and save you time, storage space and grant’s fund to publish and/or archive a research project.

This blog post is the first one in a series about the FAIR principles and OpenScience and how embracing it will help you manage your (microscopy) data without undue stress.

The FAIR principles are designed to enable OpenScience and are a good starting point to encourage you to think about how to optimally manage your research data. FAIR stands for Findable, Accessible, Interoperable and Reusable. For our purpose we will use our own interpretation of these rules in our first foray in data management.

Findable

The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.

In our case, being able to find you data when you need it thanks to a clear data management structure (for an excellent example of such an approach please see Marco’s MAIIA Webinar repository, presentation and video link (coming soon).

Action(s):

Establish clear data organisation rules in your team and stick to it!

Accessible

Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.

In our case, think about how your are going to share the data for the image processing and analysis with your favorite imaging facility.

Action(s):

Set the adequate read/write authorisation for your Mendel’s project.

Interoperable

The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.

In our case, ensure all documentations related to the data is available. That’s your material and methods written.

Action(s):

Document as much as possible as you go: sample preparation protocols, imaging settings, image analysis pipelines, etc.

Reusable

The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

In our case, do you have all the info (data + metadata) to know what is your data about and to reproduce your study?

Action(s):

Organise and document correctly as you go.

We will see in the next blog posts the practical aspects of (microscopy) data management.

Coming up next…

F-A-I-R in details
What’s in a file name? see Marco’s blog post
Manage research data: deciding upon a data structure for your team
Using tags to organise your data
Documenting your research with metadata, loads of it!
“One OpenLink to rule them all, one OpenLink to find them, One OpenLink to bring them all, and in the darkness bind them; In the Land of OpenScience where the FAIR principles lie”