How to establish and maintain a multimodal animal research dataset using DataLad

Area of Science:

• Animal research
• Data science
• Bioinformatics

Background:

• Sharing experimental data and processing tools is crucial but rarely implemented in animal studies.
• Existing FAIR guidelines and publisher demands highlight the need for better data management.
• Lack of standardized protocols hinders collaboration and reproducibility in large multimodal datasets.

Purpose of the Study:

• To present a step-by-step protocol for version control and remote collaboration on large multimodal animal study datasets.
• To ensure data security and facilitate the adoption of FAIR data logistics and processing workflows.
• To provide a reproducible data handling infrastructure applicable to diverse research areas.

Main Methods:

• Implemented a data management plan for data security and a homogeneous file/folder structure.
• Utilized DataLad for automatic tracking of data changes and version control.
• Shared all data on the research data platform GIN for accessibility and collaboration.

Main Results:

• Developed a simple, cost-effective workflow for managing and sharing large, multimodal animal study data.
• Enabled automatic tracking of data modifications and facilitated remote collaboration.
• Made raw and processed data available, supporting independent reproduction of data processing steps.

Conclusions:

• The presented workflow simplifies FAIR data logistics and processing for animal research.
• This infrastructure supports the collection of heterogeneously acquired datasets and enhances reproducibility.
• The protocol serves as a blueprint for improving data handling across research sites and disciplines.