[here comes the Title of the recipe]

Table of Contents

  1. Main FAIRification Objectives
  2. Graphical Overview of the FAIRification Recipe Objectives
  3. Requirements
  4. FAIRification Objectives, Inputs and Outputs
  5. Capability & Maturity Table
  6. Table of Data Standards
  7. Main Content goes here...
  8. License

Main Objectives

The main purpose of this recipe is:

Making self describing tabular data using Frictionless tools instead of dumping Excel files.

Graphical Overview of the FAIRification Recipe Objectives

Note: use this section to provide a decision tree for the overall process described in the recipe For more information about the syntax used to generate the diagram, please refer to the following documentation

graph LR; A(Data Acquisition):::box -->B(Raw Data):::box B --> C{FAIR by Design} C:::box-->|Yes| D(Standard Compliant Data):::box C:::box -->|No| E(Vendor locked Data):::box classDef box font-family:avenir,font-size:14px,fill:#2a9fc9,stroke:#222,color:#fff,stroke-width:1px linkStyle 0,1,2,3 stroke:#2a9fc9,stroke-width:1px,color:#2a9fc9,font-family:avenir;


  • technical requirements:
    • bash knowledge
    • ...
  • recipe dependency:
    • read Recipe 1 first!
  • knowledge requirement:
    • be sure to know what OBO is, or read it here: to knowledge...

Capability & Maturity Table

Capability Initial Maturity Level Final Maturity Level
Interoperability minimal repeatable

Help to fill this table out can be found ...(not yet)...

FAIRification Objectives, Inputs and Outputs

Actions.Objectives.Tasks Input Output
validation Structure Data File (SDF) report
calculation Structure Data File (SDF) InChi
calculation Structure Data File (SDF) SMILES
text annotation Human Phenotype Ontology annotated text

Table of Data Standards

Data Formats Terminologies Models
DICOM Human Phenotype Ontology OMOP

Main Content

This is the place for your actual content. You can also ...

... write executable code

import isatools
import json
import pandas as pd 
import holoview

... create workflow figures

one may use the following mermaid syntax:

graph LR;
    A[Data Acquisition] -->B(Raw Data)
    B --> C{FAIR by Design}
    C -->|Yes| D[Standard Compliant Data]
    C -->|No| E[Vendor locked Data]
graph LR; A["input data"]-->B["conversion to open format"]; A["input data"]-->C["automatic annotation"]; B["conversion to open format"]-->D(("output data")); C["automatic annotation"]-->D(("output data")); style A fill:#FF5733,stroke:#333,stroke-width:2px style D fill:#0A749B,stroke:#333,stroke-width:2px


Name Affiliation orcid CrediT role specific contribution
Philippe Rocca-Serra University of Oxford, Data Readiness Group 0000-0001-9853-5668 Writing - Original Draft original format
Susanna-Assunta Sansone University of Oxford, Data Readiness Group Writing - Review & Editing, Funding acquisition
Alasdair Gray Heriot-Watt Unviersity / ELIXIR-UK 0000-0002-5711-4872 Writing - Review & Editing minor improvements (see git)
Robert Giessmann Bayer AG 0000-0002-0254-1500 Writing - Review & Editing minor improvements (see git)


This page is released under the Creative Commons 4.0 BY license.