10 simple rules for machine-actionable DMPs
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The document outlines 10 simple rules for creating machine-actionable data management plans (DMPs). The rules were developed by a group of 4 people from different perspectives through an iterative process. The rules include making DMPs FAIR, following a defined workflow, enabling automation using supporting systems, using persistent identifiers and controlled vocabularies, following a common data model, making DMPs available for both human and machine consumption, supporting evaluation and monitoring, ensuring DMPs are updatable and versioned, and making DMPs publicly available in part or whole. The document asks readers which rules they agree with and what they would add or remove.
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10 simple rules for machine-actionable DMPs
- 1. 10 simple rules for machine- actionable data management plans Sarah Jones | Digital Curation Centre |sarah.jones@glasgow.ac.uk Stephanie Simms | California Digital Library | stephanie.simms@ucop.edu Daniel Mietchen | University of Virginia | daniel.mietchen@virginia.edu Tomasz Miksa | TU Wien & SBA Research | tmiksa@sba-research.org
- 2. Developing the rules • 4 people coming from different perspectives • Each developed our own set of rules • Merged into ‘allourideas’ and voted • Multiple iteration cycles • Still draft
- 3. 10 simple rules for machine-actionable DMPs 1. Be FAIR 2. Follow a well-defined workflow 3. Enable automation using supporting systems 4. Components of ecosystem need ma- descriptions 5. Use PIDs and controlled vocabularies 6. Follow a common data model for maDMPs 7. Be available for human and machine consumption 8. Support evaluation and monitoring 9. Be updatable, living, versioned documents 10. Be publicly available, in part or whole
- 4. What do you think? • What do you agree with? • What would you add? • What would you remove?
- 5. Tabling activity DMP authors DMP reviewers Funders Infrastruct. providers Support staff DMP tool providers 6. Follow a common data model for DMPs Write plans using templates & tools that adopt a common model Adhere to data model when proposing templates Follow model when exchanging data across services Recommen d services that follow model Adopt/impl ement model in tools
- 6. FAIR DMPs working group Force2017, Berlin, Germany Wrap up
Editor's Notes
- Be FAIR – double meaning: data being FAIR but also DMP itself also being FAIR Workflow – shared responsibility to create DMPs – all relevant stakeholders should be involved and engaged at appropriate time Enable automation – connecting up and implementing the workflow to exchange data across systems at relevant points e.g. data volumes to generate costs or allocate storage Ecosystem – DMP depend on info provided by other systems and this also needs to be in a structured form Funder policies describing requirements Repository constraints e.g. formats accepted, charged levied PIDs – not only DOIs, ORCIDs, funder IDs, repository IDs etc. Leveraging these to feed info into and out of DMPs. Controlled vocabularies to provide options users can select from Common data model for DMPs being developed which is a protocol kind of like “TCP IP” for the internet (RDA working group), ideally services and tools should adopt these as they emerge to benefit from data exchange Human + machine – move away from free text to more machine-actionable structure at the technical level. Will also need a human interface so stakeholders can view specific info they seek Evaluation – allow you to model good and bad practice, improve review process and provide a tool to support monitoring in an automated fashion (check actions have been executed e.g. query DOI to check data deposited) Live docs – DMP not a one-time action, something that lives throughout the course of the project. Plan should be updated Publicly available – trend towards publishing DMPs and the more info that is open the more it can be queried and exchanged across systems. Parts can be closed though or need authorisation to access
- DMP authors – researchers, coauthors or collaborators who may not author DMP directly DMP reviewers – usually other researchers or program officers reviewing a DMP w/grant proposal Funders Infrastructure providers – repositories other computing and storage providers, many campus stakeholders (IT, libraries, HR) Support staff – data managers, research administrators, data librarians, ethics/legal boards DMP tool providers – CDL, DCC, Australian DMRs, other locally developed tools Others?