Advancing FAIR Principles for Research Software Implementing a Machine Actionable Software Management Plan into DMP OPIDoR

Software is one of the fundamental pillars of research, alongside publications and data. However, despite its essential contribution, research software remains difficult to discover, cite, and properly reference. While open-source practices are widely adopted in academia, they do not inherently ensure that software is easily findable or systematically documented - both of which are crucial for reproducibility, reuse, and long-term preservation. Due to the lack of proper mechanisms, software is frequently overlooked or poorly described in data management plans. To address this gap, a dedicated template has been implemented in the well-established DMP tool - DMP OPIDoR - to describe software, along with associated research data. Making the SMP machine-actionable and interoperable simplifies its maintenance throughout the research lifecycle and facilitates software tracking and documentation. DMP OPIDoR enables the display of customizable templates aligned with institutional, disciplinary, and international guidelines, and supports best practices in data management through community-driven recommendations. Our poster presents how this pioneering SMP template - in line with FAIR and machine actionable DMP principles - will promote good practices assuring source code quality. It encompasses the assignment of persistent identifiers, the use of software development platforms, the recognition of authors and contributors, as well as the need of a license. In the long term, maSMP will enhance software documentation, referencing, and ultimately its discovery, reuse, and citation — helping software gain the first-class status it deserves in open science.