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

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 transparent

SUBMITTER: Mendez KM 

PROVIDER: S-EPMC6745024 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature