Abstract: Research data currently face a huge increase of data objects with an increasing variety of types (data types,formats) and variety of workflows by which objects need to be managed across their lifecycle by datainfrastructures. Researchers desire to shorten the workflows from data generation to analysis and publication,and the full workflow needs to become transparent to multiple stakeholders, including research administratorsand funders. This poses challenges for research infrastructures and user-oriented data services in terms of notonly making data and workflows findable, accessible, interoperable and reusable, but also doing so in a waythat leverages machine support for better efficiency. One primary need to be addressed is that of findability,and achieving better findability has benefits for other aspects of data and workflow management. In thisarticle, we describe how machine capabilities can be extended to make workflows more findable, inparticular by leveraging the Digital Object Architecture, common object operations and machine learningtechniques.