Reassessing data management in increasingly complex phenotypic datasets.

Area of Science:

• Plant biology
• Bioinformatics
• Data science

Background:

• Phenotypic datasets are complex, containing diverse data types like images, time-series, and measurements.
• Managing these datasets involves conflicting goals: facilitating analysis versus enabling data reuse (FAIR principles).
• Current methods for data analysis often lead to significant information loss.

Purpose of the Study:

• To propose a novel framework for managing heterogeneous phenotypic data.
• To enable the reuse of raw, synthesized, and computed data without information loss.
• To support diverse data analysis needs through theory-agnostic data organization.

Main Methods:

• Advocating for 'sensu stricto phenomic datasets' as an upstream data organization layer.
• Utilizing data-science tools for theory-agnostic data structuring.
• Distinguishing between 'sensu stricto phenomic datasets' and 'dedicated datasets'.

Main Results:

• The proposed 'sensu stricto phenomic datasets' preserve all raw data, avoiding information loss.
• This upstream organization allows multiple users to create tailored 'dedicated datasets'.
• Facilitates compliance with FAIR data principles for phenotypic information.

Conclusions:

• A theory-agnostic, upstream data organization strategy is crucial for phenotypic data management.
• This approach enhances data reusability and supports diverse analytical requirements.
• Enables robust data analysis while adhering to FAIR data principles.