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FAIR Data Cube

Approximately 80% of the time and effort in multi-omics data analysis is dedicated to data preparation. Integrating non-interoperable datasets can be challenging. The FAIR-Data Cube (FDCube) provides an effective solution, simplifying the combination of data from various sources. It adheres to the FAIR principles, ensuring data is findable, accessible, interoperable, and reusable. 

Developed as part of the X-Omics initiative, the FAIR Data Cube (FDCube) supports omics researchers in three key areas: 

  1. Creating and publishing multi-omics metadata via the FAIR Data Station and the FAIR Data Point. 
  2. Querying and accessing data from the triple store. 
  3. Analyzing access-protected data. 

For more information on the FAIR data cube, please see the documentation page - FAIRDataCube wiki

FAIR principles

The FAIR principles were proposed to guide researchers to describe and share their data to increase data reuse and research reproducibility. FAIR stands for Findable, Accessible, Interoperable, and Reusable data.

The different principles can be explained in the following way:

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 is essential for the automatic discovery of datasets and services.

Accessible: Once the user finds the required data, she/he needs to know how it can be accessed, including authentication and authorization.

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.

Reusable: The goal of FAIR is to optimize 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.

Multi-omics data integration using the FAIR Data Cube

This video  shows a practical example of how the FAIR Data Cube facilitates multi-omics data analysis. It shows the added value of FAIR data for integration of data from different sources. It demonstrates the mapping of a SARS-CoV-2 pathway to multi-omics data represented in a FAIR Data Cube.

Set – up FDCube locally
Cloud Implementation

Fair Data Cube Components

FAIR Data Station

The FAIR Data Station is a platform for metadata ingestion designed to enhance metadata quality. It ensures metadata complies with minimum information standards, supporting FAIR scientific data management from the start.

FAIR Data Point

FAIRDataPoint is a REST API and web interface designed for creating, storing, and sharing FAIR metadata.

GraphDB

GraphDB is a Semantic Graph Database, compliant with W3C Standards.