Data Management Plans (DMPs) and Data Availability Statements (DAS) provide context on activities and outcomes in Research Data Management Lifecycles (RDMLs) and encompass different aspects of the scholarly communication process. On the one hand, DMPs ensure researchers��� compliance with research funding and research organizations��� requirements, to carefully manage and appropriately share research data and outputs. On the other hand, DAS are integrated into scientific publishers' workflows to promote the availability of their publications��� underlying research data and outputs. Efforts are underway to standardize the structure and content of DMPs and DAS at both technical and policy levels. In this session you will hear from four key stakeholder groups regarding their approaches towards addressing challenges and opportunities in DMPs and DAS: Dutch Research Council (NWO) - Champions research data management policy alignment with research funders in Europe. One of the first organizations to implement Science Europe core requirements for DMPs, NWO actively promotes these requirements to other stakeholders. American Geophysical Union (AGU) - Advances data/software sharing policies and approaches in publishing through a broad coalition of partners and stakeholders. Beginning with its position statement on data (1997), AGU continues to improve its guidance and move towards machine-actionable DAS and research articles. Research Data Alliance (RDA) DMP Common Standards Working Group (WG) ��� Develops a community-wide information model, as well as specifications and access mechanisms for machine-actionable DMPs (maDMPs). The WG recently released its maDMP recommendations for fostering greater collaboration, integration, and automation of maDMPs across research workflows. Argos - An open extensible service through OpenAire that simplifies the management, validation, monitoring and maintenance of DMPs. Argos constructs DMPs as machine-actionable outputs that are also shared through Zenodo according to Open and FAIR practices. Furthermore, the Argos paradigm inspires discussions and solutions towards the automation, linking, and machine-actionability of DAS and DMPs.