Make sure your data is Findable, Accessible, Interoperable and Reusable


FAIR Data Platform

The FAIR Data Platform is an interoperable research infrastructure, which ensures that all data generated within an UNLOCK-project is annotated, analyzed and safely stored according to the FAIR guiding principles. The platform supports the user community by providing access to the data. Additionally, it provides the necessary tools for data discovery and manipulation.

FAIR Data storage, compute, workflows


FAIR stands for Findable, Accessible, Interoperable, Reusable. Application of these four foundational principles will allow researchers to extract maximum benefit from the research investments made.

Yes, ofcourse. Our workflows ensure the conversion of data to information, which depends on the metadata provided by the user. The minimum requirement therefore is a detailed metadata document describing the experimental setup.

Most important is a good experimental design. That’s why within UNLOCK, we have developed a FAIR Data Station to guide the user in managing the (experimental) metadata. You can visit it here

While we prefer FAIR by Design, our FAIR Data Station (visit here) enables FAIRification of existing data. After FAIRification, data and metadata can still be analysed in the FAIR Data Platform.

There are multiple reasons. We have written a three-piece blog-post about this subject. Please read the second part to find out why


Schematic figure of the data infrastructure of the FAIR Data Platform.
A schematic representation of the data infrastructure used within UNLOCK. The iRODs data management system captures the experimental data streams. To enable the FAIR by Design principles, element- and data-wise experimental metadata generated by the lab equipment used and other required experimental meta-data is automatically linked with the data-streams and permanently stored within the iRODS infrastructure. Additionally, high throughput analysis of the data is done using a scalable cloud-based infrastructure and dockerized open source applications. Further, compute results and corresponding metadata are stored in the iRODS platform using the ISA data model. Further post-processing can be done using structured data analysis processes integrated in Jupyter Notebooks.

In brief, UNLOCK Knowledge management consist of four parts. 

  1. An integrated Rule-Oriented Data management System (iRODS) takes care of the collected (raw) assay data, transformed data and metadata.
  2. In the UNLOCK iRODS implementation, data files and folder are hierarchically organized through implementation of the Investigation/Study/Assay (ISA) format. This is an open general-purpose framework to collect and communicate complex metadata. In this set-up, an ‘Investigation’ is a collection of experiments revolving around a set of common research questions. Thus, the Investigation folder forms the root of a set of hierarchically organized folders and files containing data and metadata derived from experiments related to the research questions.
  3. Experimental design metadata is used to 1) automatically create the appropriate ISA folder structure at the start of the Investigation and 2) automatically start data crunching when raw data is obtained.
  4. Standardized workflows and container technology is used to transform the raw data in information.

Maintenance of the UNLOCK iRODs infrastructure and long-term preservation of data generated within the UNLOCK infrastructure is outsourced to SURF.

Below a 16-minute webinar explaining the technical aspects of the FAIR Data Platform