A standard for machine-actionable data management plans is only useful if researchers have tools that put it into practice. Over the past decade three platforms have come to dominate the data-management-planning landscape, each developed and maintained by a significant open-science organisation, and each now working towards interoperability through the same common standard. For an institution choosing how to support its researchers, or a researcher trying to understand the options, it helps to see how DMPTool, DMPonline and Argos compare — what they share, where they differ, and what unites them. This article surveys that landscape through the machine-actionable DMP domain of the CASRAI Dictionary.
Why dedicated tools exist
It is reasonable to ask why data management planning needs special software at all, when a plan could be written in a word processor. The answer lies in everything a good tool does beyond capturing text. A dedicated DMP platform guides researchers through funder and institutional templates so they answer the right questions; it supplies guidance at the point of need; it allows plans to be shared, reviewed and collaboratively edited; and, increasingly, it exports plans in structured, machine-readable formats so the commitments they contain can be acted on by other systems rather than read once and filed. This last capability — producing a machine-actionable plan rather than a static document — is what distinguishes a modern DMP tool from a template in a folder.
DMPTool
The first of the three, DMPTool, is developed and operated by the California Digital Library. It emerged to help researchers, particularly in the United States, meet the data-management-planning requirements of funders, and it provides funder and institutional templates, tailored guidance and a collaborative environment for producing plans. DMPTool has been a leading voice in the move towards machine-actionable planning, contributing to the development of the standards and infrastructure that allow plans to become connected, living objects rather than text deliverables. Its institutional adoption across many universities has made it a familiar part of the research-support landscape, and its development sits within the broader work of the California Digital Library on open scholarship and research infrastructure.
DMPonline and DMP Roadmap
The second platform, DMPonline, is developed by the Digital Curation Centre, a long-standing centre of expertise in research-data curation. Like DMPTool, it offers funder and institutional templates, embedded guidance and collaborative editing, and it is widely used across the United Kingdom, Europe and beyond. DMPonline and DMPTool are closely related at a deeper level: they share a common open-source codebase known as DMP Roadmap, jointly developed by the two organisations. This shared foundation means the two services have a great deal in common under the surface even as each is tailored to its own community of funders and institutions. The collaboration behind DMP Roadmap is itself a notable feature of the landscape: rather than building competing systems from scratch, two major infrastructures pooled effort into a common platform, which has helped align their approach to machine-actionable planning.
Argos
The third platform, Argos, comes from the European open-science ecosystem and is developed in association with OpenAIRE and EUDAT. Argos was designed from the outset with machine-actionability and openness in mind, and with close integration into the wider European research-infrastructure landscape. It supports the creation of plans against templates and, in keeping with its origins, emphasises producing plans as structured, openly available outputs that connect into the broader graph of European research information. Its provenance in OpenAIRE and EUDAT positions it naturally within an ecosystem oriented towards linking outputs, projects and funding, and it reflects a vision in which the DMP is not an isolated document but a connected node in the research record.
What unites them: the RDA DMP Common Standard
For all their differences in origin and community, the three platforms are converging on a shared foundation for interoperability: the RDA DMP Common Standard, developed through the Research Data Alliance. The common standard defines a shared model and structure for expressing the information a DMP contains, so that a machine-actionable plan can be exported from one system and understood by another. This matters because plans do not live in isolation: a plan created in one tool may need to be read by a funder’s system, harvested into a repository, or connected to the persistent identifiers for the people, projects and outputs it describes. Without a common structure, every such exchange would require bespoke translation. With it, a maDMP exported from DMPTool, DMPonline or Argos can in principle flow into the wider ecosystem and be acted upon. The standard is what turns three separate tools into parts of a connected planning landscape.
Choosing between them
For an institution or researcher, the choice often comes down to context rather than a verdict on which platform is best. Existing institutional adoption, the funders one works with, the surrounding national infrastructure and integration with other systems all weigh on the decision. Because all three are moving towards the same common standard, the choice is less consequential than it once was: the goal is interoperable, machine-actionable planning, and each platform is a credible route to it. The decision is one of fit, not of compatibility.
A consistent vocabulary across tools
For plans to move between these platforms and the systems that consume them, the elements they contain must mean the same thing everywhere — the data types, the licences, the identifiers, the contributor roles. That consistency is what the CASRAI Dictionary provides, complementing the structural interoperability of the RDA standard with shared meaning for the terms that flow through it. And because data management planning is part of the wider research record, the contributions it documents can be described in the same shared framework — the CRediT taxonomy and its full set of contribution roles. To weigh the platforms side by side in more detail, our comparison resources set out their features against one another. The tools differ in origin and emphasis, but they share a destination: planning that machines as well as people can act upon.
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