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Updated: Jan 19, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Published on: December 15, 2023

Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing.

Kevin M Mendez¹, Leighton Pritchard², Stacey N Reinke³

¹Centre for Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia.

Metabolomics : Official Journal of the Metabolomic Society

|September 16, 2019

Summary

This tutorial introduces interactive notebooks for metabolomics researchers to enhance data reproducibility and transparency. It promotes open science by integrating data analysis with published work for broader community use.

Keywords:

Cloud computing Data science Jupyter Open access Reproducibility Statistics

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Quaternary Structure Modeling Through Chemical Cross-Linking Mass Spectrometry: Extending TX-MS Jupyter Reports

Published on: October 20, 2021

Area of Science:

Metabolomics
Data Science
Scientific Reproducibility

Background:

Lack of transparency and reporting standards in science impacts result integrity.
Metabolomics, as an omics science, is vulnerable to irreproducibility due to large data volumes and reliance on data science.
Existing efforts align with FAIR data standards, but analysis platforms lack flexibility and adequate reporting.

Purpose of the Study:

To encourage metabolomics researchers to manage their data science workflows.
To promote the sharing of resources through open science principles.
To enable transparent dissemination of methods and results for metabolomics research.

Main Methods:

Introduction to interactive web-based computational laboratory notebooks.
Experiential tutorials using Jupyter Notebook, GitHub, and Binder.
Guidance for metabolomics researchers on data science integration.

Main Results:

Provides a framework for transparently disseminating metabolomics methods and results.
Enables rapid, reusable, and integrated data science within published work.
Offers an inclusive and intuitive solution for computational novices and experts.

Conclusions:

Interactive notebooks can significantly improve transparency and reproducibility in metabolomics.
Empowering researchers with user-friendly tools fosters open science and data sharing.
This approach supports the FAIR data science principles within the metabolomics community.