Trust and Transparency in Reporting Machine Learning: The DOME-GigaScience Press Trial

Machine learning is increasingly applied to biological and biomedical data, and there is a need for sufficient detail to enable a researcher to understand the machine learning approach used in a research study. This is even more challenging due to Machine Learning studies being inherently difficult to interpret (the so-called “black box” effect).  To throw light on these methods, GigaScience Press (https://www.gigasciencepress.org/) has partnered with the DOME Consortium with the goal of encouraging authors to follow the DOME (Data, Optimisation, Model, Evaluation) recommendations.

The role of the GigaScience DataBase (GigaDB) Data Curation team is to ensure the Data Submission process runs as smoothly as possible. The DOME Consortium has generated the DOME Data Stewardship Wizard which enables researchers to submit their DOME annotations to a central repository and share them with reviewers. The GigaDB team scans submitted manuscripts for Machine Learning content, and performs checks to ensure that DOME annotations in support of GigaScience and GigaByte manuscripts are sufficiently complete.

To increase the visibility of the supporting DOME annotation, a link to DOME annotation is included in the GigaDB dataset that accompanies a GigaScience or GigaByte manuscript. The DOME annotations are a great asset to peer review, providing the necessary high-level overview to properly understand a machine learning study. We recommend that other journals follow our example in encouraging DOME annotations to be submitted early in the publication process and prior to peer-review.