Fractal Clock by Mobilos CC BY-SA 3.0 https://commons.wikimedia.org/wiki/Category:Time#/media/File:Fractal_clock.jpg

In this next installment of the machine-actionable DMP blog series, we want to address the broader context of time to hone in on answering the following question:

How and when do you update some piece of information in a DMP?

This happens to be the substance of Principle 9 from our preprint, forthcoming in PLOS Miksa et al. 2018: maDMPs should be versioned, updatable, living documents.

DMPs should not just be seen as a “plan” but as updatable, versioned documents representing and recording the actual state of data management as the project unfolds. The act of planning is far more important than the plan itself, and to derive value for researchers and other stakeholders, the plan needs to evolve. DMPs should track the course of research activities from planning to sharing and preserving outputs, recording key events over the course of a project to become an evolving record of activities related to the implementation of the plan.

We can all agree that it’s important to treat maDMPs as living documents, but there are multiple approaches we might take to updating them, and multiple stakeholders who should be able to provide updates for particular pieces of information at particular points along the way. First we’ll provide a quick overview of the current state of DMP-time as represented in systems and policies related to our NSF EAGER project, plus a handful of other relevant systems and policies that extend the geographical and organizational scope. Then, we’ll pitch an idea for how we can handle DMP-time using Crossref/DataCite Event Data Service. We welcome, nay encourage your feedback about this and other ideas as we experiment and iterate and prove things out in practice.

Representing time in DMPs

So we built a graph database with seed data from our partners at BCO-DMO and the UC Gump Field Station on Moorea, and enriched it with information from the NSF Awards API and public plans created with the DMPTool. All of the projects represented in the database correspond with NSF awards and therefore the DMPs have an associated timeline of:

1. Create DMP and submit grant proposal (via institutional Office of Research, NSF Fastlane system)
2. Grant awarded (grant number issued by NSF)
3. Grant period ends, final report due (data deposited at appropriate repository)

This current grant/DMP workflow fails to capture information about actual data management activities as they unfold over the course of a project, however, data management staff at BCO-DMO and the Gump Field Station perform interventions and provide manual updates in their own repository systems opportunistically. These updates can occur during active stages of multi-year projects and most of them are done at the grant closeout stage when researchers are engaged with reporting activities and aware that they must deposit their data. Relevant NSF program officers from the Geosciences Directorate conduct manual compliance checks to ensure that grantees have deposited data prior to issuing a new award, which is a very useful feature of this case study.

In addition to the data repository systems, information about these projects flows through institutional grant management systems, NSF’s Fastlane system, and a subset is made publicly available via the NSF Awards API (example of our award). Each of these systems records the start data and end date for the award, and some include interim reporting dates. Our ongoing analysis for maDMP prototyping is focused on identifying additional milestones during the course of a project and which stakeholders should be responsible for updating which pieces of information…drilling into the original question of how and when do you update things?

DMP-time in European contexts

To avoid an overly narrow focus on one national context and one funding agency in this larger thematic discussion about time, we’ll also consider some European examples. The European Commission’s Horizon 2020 program acknowledges the fact that information about research data changes from the planning to final preservation stages; as a result, DMPs have built-in versioning. Horizon 2020 proposals that receive an award must submit a first version of the DMP within the first 6 months of the project. The DMP needs to be updated over the course of the project whenever significant changes arise, however, this “requirement” is somewhat vague and reads more like a best practice. Updated versions of the DMP are required at any periodic reporting deadline and at the time of the final report. DMPonline provides an optional set of Horizon 2020 templates that includes an 1) Initial DMP, 2) Detailed DMP, and 3) Final review DMP.

Our maDMP collaborators at the Technical University of Vienna are forging ahead with their own institutional prototyping efforts to automate DMPs and integrate them with local infrastructure. They just released this excellent interactive “mockups” tool and invite your feedback. Within the mockups system, time is represented through the concept of DMP Granularity and in some cases this is related to funding status. The level of granularity corresponds roughly with versions, which carry the labels “initial, detailed, or sophisticated.”

Representing time in maDMPs: Ideas for the future

The ability to update DMPs is central to our own plans for realizing machine-actionability and relies on infrastructure that already exists. In a nutshell, our idea is to insert DMPs and corresponding grant numbers into the sprawling web of information connecting people and their published outputs. We think the mechanism for accomplishing this is to issue DataCite DOIs for DMPs: this creates an identifier against which we can assert things programmatically. In addition, this hooks DMPs into Crossref/DataCite Event Data, which is a stream of assertions of relationships between research-related things. Existing and emerging registries of information are already leveraging this infrastructure—Scholix, ORCID, Wikidata, Make Data Count, etc. DMPs and grant numbers would provide a view of the connections between everything at the project level.

Documentation for Event Data explains that it “is a hub for the collection and distribution of a variety of Events and contains data from a selection of Sources. Every Event has a time at which it was created. This is usually soon after the Event was observed. In addition to this, every Event has a theoretical date on which it occurred…dates are represented as the occurred_at, timestamp and updated_date fields on each Event. The Query API has two views which allow you to find Events filtered by both occurred_at and timestamp timescales. It also lets you query for Events that have been updated since a given date.” This hub of information would therefore support versioning of the DMP as well as dynamic updating of key pieces of information (e.g. data types, volumes, licenses, repositories) by various stakeholders over time. Stakeholders could rely on this open hub of information and begin to make plans based on it (e.g., a named repository learns that a TB of data is expected within a specific timeframe).

In this scenario, the DMP would become an assertion store (cf. Wikidata and Wikibase). The assertion store would have a timeline component and anyone could use the DMP identifier to ping/query the Event Data Query API and find out what’s been asserted about the project. Various DMP stakeholders could also assert things about the project and update information over time. Each stakeholder could query and model DMP information based on the types of relationships and get the specific details they’re interested in… so an institution could discover who their PIs are collaborating with[o], a funder could check[p] if a dataset has been deposited in a named repository, a repository manager could search for any changes to a specific project or all relevant projects within a specific date range, etc. Wikidata has already begun indexing policies, in fact; once this happens at scale and is integrated with indexing of datasets, we could have automated dashboards displaying policy compliance and project progress.

That’s about it. Please tell us what you think about this approach to transforming a DMP into something active and updated, versioned and linked to research outputs.

From Flickr by Amber Case CC BY-NC 2.0 https://www.flickr.com/photos/caseorganic/4663192783/

Picking up where we left off from “Machine-actionable DMPs: What can we automate?”… Let’s unpack a couple of topics central to our machine-actionable DMP prototyping and automating efforts. These are the top rallying themes from all conversations, workshops, and working groups we’ve been privy to in the past few years. In addition, they feature in the “10 principles for machine-actionable DMPs” (principles 4 and 5):

• DMP common standards
• Persistent identifiers (PIDs)

DMP common standards
There’s community consensus about the need to first establish common standards for DMPs in order to enable anything else (Simms et al. 2017). Interoperability and delivery of DMP information across systems—to alleviate administrative burdens, improve quality of information, and reap other benefits—requires a common data model.

To address this requirement, the DMP Common Standards working group was launched at the 9th RDA plenary meeting in Barcelona. They’re making excellent progress and are on track to deliver a set of recommendations in 2019, which we intend to incorporate into our existing tools and emerging prototypes. Adoption of the common data model will enable tools and systems (e.g., CRIS, repositories, funder systems) involved in processing research data to read and write information to/from DMPs. The working group deliverables will be publicly available under a CC0 license and will consist of models, software, and documentation. For a summary of their scope and activities to date see Miksa et al. 2018.

A second round of consultation is underway currently to tease out more details and gather additional requirements about what DMP info is needed when for each stakeholder group. This international, multi-stakeholder working group is open to all; check out their session at the next RDA plenary in Botswana and contribute to the DMP common data model (6 Nov; remote participation is available).

Current/traditional DMPs - model questionnaires

<administrative_data>
    <question>Who will be the Principal Investigator?</question>
    <answer>The PI will be John Smith from our university.</answer>
</administrative data>

Machine-actionable DMPs - model information

“dc:creator”:[ {
         “foaf:name”:”John Smith”,
         “@id”:”orcid.org/0000-1111-2222-3333”,
         “foaf:mbox”:”mailto:jsmith@tuwien.ac.at”,
         “madmp:institution”:”AT-Vienna-University-of-Technology”
} ],

Caption: An example of data models for traditional DMPs (upper part) and machine-actionable DMPs (lower part). (Miksa et al. 2018: Fig. 1)

PIDs and DMPs
The story of PIDs in DMPs, or at least my involvement in the discussion, began with a lot of hand waving and musical puns at PIDapalooza 2016 (slides). After a positive reception and many deep follow-on conversations (unexpected yet gratifying to discover a new nerd community), things evolved into what is now a serious exploration of how to leverage PIDs for and in DMPs. The promise of PIDs to identify and connect research-relevant entities is tremendous and we’re fortunate to ride the coattails of some smart people who are making significant strides in this arena.

For our own PID-DMP R&D we’re partnering with one of the usual PID suspects, Datacite, to draw from their expertise and technical capabilities. Datacite contributed to the timely publication of the European Commission-funded FREYA report, which provides the necessary background research and a straightforward starting point(s). There’s also an established RDA PID interest group that we plan to engage with more as things progress.

A primary goal of FREYA is the creation and expansion of the “PID Graph.” The PID Graph “connects and integrates PID systems, creating relationships across a network of PIDs and serving as a basis for new services.” The report summarizes the current state of PID services as well as some emerging initiatives that we hope to harness (each is classified as mature, emerging, or immature):

• ORCID iDs for researchers (mature)
• DOIs for publications and data (mature), and software (emerging; also see SWH IDs)
• Research OrgIDs for organizations (aka ROR; emerging and CDL is participating so we have an intimate view)
• Global grant IDs (emerging and very exciting to track the prototyping efforts of Wellcome, NIH, and MRC!)
• Data repository IDs (immature but on the radar as we address DMPs)
• Project IDs/RAiDs (emerging and we see a lot of overlap with DMPs)

It also describes a vast array of PIDs for other things, all of which are potentially useful for maDMPs as we reconfigure them as an inventory of linked research outputs (Table 1: RRIDs, protocols, research facilities, field stations, physical samples, cultural artifacts, conferences, etc. etc.). Taken together, these efforts are aimed at extending the universe of things that can be identified with PIDs and expanding what can be done with them. This, in turn, supports automation and machine-actionability to achieve better research data management and promote open science.

Summing up
For now we’ll continue exploring our graph database and interviewing stakeholders who contributed seed data to dive deeper into their workflows, challenges, and use cases for maDMPs. This runs parallel to the activities of the RDA DMP Common Standards WG and various emerging PID initiatives. Based on this overlapping community research, we can move forward with outlining what to implement and test. The recommendations of the RDA group for DMP common standards are a given, and below is a high-level plan for PID prototyping:

PIDs for DMPs and PIDs in DMPs:

• DOIs for DMPs: define metadata
• PIDs in DMPs: What can we achieve by leveraging mature PID services? How do we make the information flow between stakeholders and systems?

Stay tuned as the story develops here on the blog! I’ll also be presenting on maDMPs in a data repositories session convened by our BCO-DMO partners at the upcoming American Geophysical Union meeting in DC (program here, 11 Dec). And Daniel Mietchen will be at PIDapalooza 2019 (Dublin, 23-24 Jan) promoting a highly relevant initiative: PIDs for FAIR ethics review processes.