This concern in metabolomics and other post-genomic platforms is a consequence of their success. A mechanism that may address these concerns is for the scientific community to take advantage of new online publishing media and associated data services, encouraging open science that recognises and aligns with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles (Wilkinson et al. We and many others in the metabolomics community hold the view that a lack of transparency and incomplete reporting has led to significant misinterpretation of data and a lack of trust in reported results (Broadhurst and Kell 2006 Considine et al. The intent of article content, and the corresponding review process, is to ensure adequate evidence of reproducibility however, a recent report highlights increasing and widespread concerns relating to reproduction and integrity of results, with 52% of responding scientists agreeing there is a significant ‘crisis’ of reproducibility (Baker 2016). Historically, journal articles have been the primary medium for sharing new scientific research. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. 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. Open data analysis platforms also exist however, they tend to be inflexible and rely on the user to adequately report their methods and results. 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. 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. 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.
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