Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing
Mendez, Kevin M. and Pritchard, Leighton and Reinke, Stacey N. and Broadhurst, David I. (2019) Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing. Metabolomics, 15. 125. ISSN 1573-3882 (https://doi.org/10.1007/s11306-019-1588-0)
Preview |
Text.
Filename: Mendez_etal_Metabolomics_2019_Toward_collaborative_open_data_science_in_metabolomics.pdf
Final Published Version License: Download (1MB)| Preview |
Abstract
BACKGROUND: A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. AIM OF REVIEW: To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science. KEY SCIENTIFIC CONCEPTS OF REVIEW: This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform.
ORCID iDs
Mendez, Kevin M., Pritchard, Leighton ORCID: https://orcid.org/0000-0002-8392-2822, Reinke, Stacey N. and Broadhurst, David I.;-
-
Item type: Article ID code: 70046 Dates: DateEvent31 October 2019Published14 September 2019Published Online7 September 2019AcceptedSubjects: Medicine > Therapeutics. Pharmacology
Science > MicrobiologyDepartment: Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences Depositing user: Pure Administrator Date deposited: 08 Oct 2019 08:14 Last modified: 11 Nov 2024 12:27 URI: https://strathprints.strath.ac.uk/id/eprint/70046