Curriculum
Computer Science for Societal Challenges and Innovation, XXXIX series
Grant sponsor
Dipartimento di Scienze Biomediche - DSB
Supervisor

Silvio Tosatto
Co-supervisor
s
TBD
Contact
omarabdelghani.attafi@studenti.unipd.it

Project description
The DOME registry facilitates adherence to "DOME: recommendations for supervised machine learning validation in biology" published in Nature by transforming the standards into an accessible online platform. This platform provides guidelines and streamlines the reporting of machine learning methods in biology, thereby enhancing reproducibility and reliability within the broader machine learning community. The DOME registry offers a curated set of annotations for machine learning papers that conform to the DOME Recommendations. Each curated annotation receives a score based on the extent to which the recommendations are followed. Additionally, the DOME registry provides users with the opportunity to contribute annotations using the Data Stewardship Wizard, making collaboration, sharing, and annotation management a simple task. Bringing the DOME Recommendations to reality through this online platform is a significant step towards enabling critical assessment and improving the reproducibility of published research. The guidelines for curating annotations are fully explained in the DOME Registry: (https://registry.dome-ml.org).