Interviews on Implementing Effective Data Practices, Part I: Why This Work Matters

Cross-posted from ARL News by Natalie Meyers, Judy Ruttenberg, and Cynthia Hudson-Vitale | October 28, 2020

In preparation for the December 2019 invitational conference, “Implementing Effective Data Practices,” hosted by the Association of Research Libraries (ARL), Association of American Universities (AAU), Association of Public and Land-grant Universities (APLU), and California Digital Library (CDL), we conducted a series of short pre-conference interviews.

We interviewed representatives from scholarly societies, research communities, funding agencies, and research libraries about their perspectives and goals around machine-readable data management plans (maDMPs) and persistent identifiers (PIDs) for data. We hoped to help expose the community to the range of objectives and concerns we bring to the questions we collectively face in adopting these practices. We asked about the value the interviewees see or wish to see in maDMPs and PIDs, their concerns, and their pre-conference goals.

In an effort to make these perspectives more widespread, we are sharing excerpts from these interviews and discussing them in the context of the final conference report that was released recently. Over the next three weeks, we will explore and discuss interview themes in the context of broad adoption of these critical tools.

Why This Work Matters

To start off this series of scholarly communications stakeholder perspectives, we need to position the importance of this infrastructure within broader goals. The overall goal of the conference was to explore the ways that stakeholders could adopt a more connected ecosystem for research data outputs. The vision of why this was important and how it would be implemented was a critical discussion point for the conference attendees.

Benjamin Pierson, then senior program officer, now deputy director for enterprise data, Bill and Melinda Gates Foundation, expressed the value of this infrastructure as key to solving real-world issues and making data and related assets first-class research assets that can be reused with confidence.

Clifford Lynch, executive director, Coalition for Networked Information, stated how a public sharing of DMPs within an institution would create better infrastructure and coordination at the university level for research support.

From the funder perspective, Jason Gerson, senior program officer, PCORI (Patient-Centered Outcomes Research Institute), indicated that PIDs are also essential for providing credit for researchers as well as for providing funders with a mechanism to track the impact of the research they fund.

Margaret Levenstein, director, ICPSR (Inter-university Consortium for Political and Social Research), spoke about the importance of machine-readable DMPs and PIDs for enhancing research practices of graduate students and faculty as well as the usefulness for planning repository services.

For those developing policies at the national level, Dina Paltoo, then assistant director for policy development, US National Library of Medicine, currently assistant director, scientific strategy and innovation, Immediate Office of the Director, US National Heart, Lung, and Blood Institute, discussed how machine-readable data management plan are integral for connecting research assets.

All of the pre-conference interviews are available on the ARL YouTube channel.

Natalie Meyers is interim head of the Navari Family Center for Digital Scholarship and e-research librarian for University of Notre Dame, Judy Ruttenberg is senior director of scholarship and policy for ARL, and Cynthia Hudson-Vitale is head of Research Informatics and Publishing for Penn State University Libraries.

Effective Data Practices: new recommendations to support an open research ecosystem

We are pleased to announce the release of a new report written with our partners at the Association of Research Libraries (ARL), the Association of American Universities (AAU), and the Association of Public and Land-grant Universities (APLU): Implementing Effective Data Practices: Stakeholder Recommendations for Collaborative Research Support.  

The report brings together information and insights shared during a December 2019 National Science Foundation sponsored invitational conference on implementing effective data practices. In this report, experts from library, research, and scientific communities provide key recommendations for effective data practices to support a more open research ecosystem. 

During the December conference, the project team developed a set of recommendations for the broad adoption and implementation of NSF’s recommended data practices as described in the NSF’s May 2019 Dear Colleague Letter.  The report focuses on recommendations for research institutions and also provides guidance for publishers, tool builders, and professional associations. The AAU-APLU Institutional Guide to Accelerating Public Access to Research Data, forthcoming in spring 2021, will include the recommendations.

The conference focused on designing guidelines for (1) using persistent identifiers (PIDs) for datasets, and (2) creating machine-readable data management plans (DMPs), both data practices that were recommended by NSF. Based on the information and insights shared during the conference, the project team developed a set of recommendations for the broad adoption and implementation of NSF’s preferred data practices. 

The report focuses on recommendations for research institutions and also provides guidance for publishers, tool builders, and professional associations. The AAU-APLU Institutional Guide to Accelerating Public Access to Research Data, forthcoming in spring 2021, will include the recommendations.

Five key takeaways from the report are:

  • Center the researcher by providing tools, education, and services that are built around data management practices that accommodate the scholarly workflow.
  • Create closer integration of library and scientific communities, including researchers, institutional offices of research, research computing, and disciplinary repositories.
  • Provide sustaining support for the open PID infrastructure that is a core community asset and essential piece of scholarly infrastructure. Beyond adoption and use of PIDs, organizations that sustain identifier registries need the support of the research community.
  • Unbundle the DMP, because the DMP as currently understood may be overloaded with too many expectations (for example, simultaneously a tool within the lab, among campus resource units, and with repositories and funding agencies). Unbundling may allow for different parts of a DMP to serve distinct and specific purposes.
  • Unlock discovery by connecting PIDs across repositories to assemble diverse data to answer new questions, advance scholarship, and accelerate adoption by researchers.

The report also identifies five core PIDs that are fundamental and foundational to an open data ecosystem. Using these PIDs will ensure that basic metadata about research is standardized, networked, and discoverable in scholarly infrastructure: 

  1. Digital object identifiers (DOIs) from DataCite to identify research data, as well as from Crossref to identify publications
  2. Open Researcher and Contributor (ORCID) iDs to identify researchers
  3. Research Organization Registry (ROR) IDs to identify research organization affiliations 
  4. Crossref Funder Registry IDs to identifier research funders 
  5. Crossref Grant IDs to identify grants and other types of research awards

The report is intended to encourage collaboration and conversation among a wide range of stakeholder groups in the research enterprise by showcasing how collaborative processes help with implementing PIDs and machine-actionable DMPs (maDMPs) in ways that can advance public access to research.

The full report is now available online

This material is based upon work supported by the National Science Foundation under Grant Number 1945938. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Project team:

  • John Chodacki, California Digital Library
  • Cynthia Hudson-Vitale, Pennsylvania State University
  • Natalie Meyers, University of Notre Dame
  • Jennifer Muilenburg, University of Washington
  • Maria Praetzellis, California Digital Library
  • Kacy Redd, Association of Public and Land-grant Universities
  • Judy Ruttenberg, Association of Research Libraries
  • Katie Steen, Association of American Universities

 

Additional report and conference contributors:

  • Joel Cutcher-Gershenfeld, Brandeis University
  • Maria Gould, California Digital Library

DMPRoadmap Team at the maDMP Hackathon

Research Data Alliance (RDA) recently hosted a three day (27-29 May 2020) machine-actionable DMP hackathon to build integrations and test the Common Standard for maDMPs. The event, coordinated through teams at RDA-Austria and TU Wien, was well attended with over 70 participants from Australia, Europe, Africa, and North America. 

The teams that work on DMP Tool (dmptool.org) and DMP Online (dmponline.org) were really pleased to represent our shared DMPRoadmap codebase and show our conformance with the standard and ability to exchange DMPs across systems. This blog post details the work of the DMPRoadmap group in the hackathon, for a full review of all outputs please visit the Hackathon GitHub.

What did we work on?

Maria Praetzellis and Sarah Jones, product managers from DMPRoadmap, joined the hackathon “TigTag” team and focused on mapping maDMPs to funder templates. During the hackathon, their group successfully mapped required questions from several funder specific DMPs including: 

  • Horizon 2020
  • Science Europe
  • National Science Foundation
  • U.S. Geological Survey

The goal of the exercise was to develop guidance on how to normalize the ways that fields from specific funder templates can be mapped to the standard, and, when necessary, develop extensions to incorporate template specific needs. The team came up with several proposals for changes to the documentation and structure of DMP Common Standard and made a few recommendations for extensions to the standard. The team is now assembling the recommendations and will submit ideas as issues to the Common Standard GitHub so work can be tracked going forward. 

Brian Riley and Sam Rust, developers from DMPRoadmap,  joined the hackathon “DMP Exchange team” and worked to determine how the RDA Common Standard JSON format could be used to exchange DMP metadata between tools. Their team provided a staging service and granted API keys to other development teams to allow testing of prototypes, which helped all participants debug issues. Over the course of the hackathon, our new maDMP API helped developers of the following DMP systems implement their own APIs:

Based on this work, we were able to exchange maDMP metadata between DMPTool and those three systems by the end of the hackathon.  Below are screenshots of DMP exports from the Data Stewardship Wizard that were imported into the DMPTool. Because we were each using the RDA Common Standard format, the new DMP was created within the DMPTool and the appropriate metadata was successfully mapped: title, description, project start/end dates, grant ID, contact information, and contributor information.

While the data models used by many systems do not yet offer full support of the RDA Common Standard model, progress was made towards mapping the high level DMP information across the board. Also, the confirmation that these systems could exchange information using RDA Common Standard JSON was encouraging and will likely open the door for future integrations. 

Other outcomes

We also collaborated with members of the DMP Melbourne, University of Cape Town and Stockholm University on an integration with their institutional repository platform. The teams were interested in pushing both DMP metadata and the physical DMP document into that repository. However, they did not yet support the maDMP standard. So the team created two separate prototype scripts. The first script extracts DMPs from a DMPRoadmap system and creates a placeholder Project that future datasets can be connected to and also uploads a PDF copy of the DMP. The second script converts their JSON into RDA Common Standard compliant JSON. While their institutional repositories do not contain many DMPs at this point, a service like this could help extract DMPs for import into DMP systems that utilize the RDA Common Standards in the future. We hope to build upon this work to facilitate integrations with additional repositories in the future. 

Future work 

Hackathon participants are now collating work produced during the hackathon into a final report. In addition, participants expressed interest in:

  • More communities. Most of the attendees at this hackathon were developers from DMP-focused tools. In the future, it would be great to have participants from other communities, including developers of CRIS systems, data repository platforms, and ethics tools.  This would help us expand the types of use cases being served.
  • More PIDs. The power of connected information replies on persistent identifiers.  We would like to increase our connection with various standards and integrate with the Research Organization Registry (ROR), the Funder Registry, and the Contributor Roles Taxonomy (CRediT) to provide more structured information to support such integrations.

Thank you again to the team at RDA Austria and TU Wein for organizing the hackathon.  If you’re interested in tracking future development and outputs of this work please follow the GitHub and consider joining the RDA Common Standard Working Group or Active DMPs Interest Group

What’s new with our machine actionable DMP work?

Building on the conceptual framework laid out in articles such as Ten principles for machine-actionable data management plans and prior blog posts covering such topics as what maDMPs are, what they can do to support automation, utilizing common standards and PIDs, and maDMPs as living documents, we are now moving into active development on the technical aspects of our NSF funded EAGER research project

A phased approach: building a plan for maDMPs

The goal of our EAGER research project is to explore the potential of machine-actionable DMPs as a means to transform the DMPs from a compliance exercise based on static text documents into a key component of a networked research data management. This ecosystem will not only facilitate, but also improve the research process for all stakeholders. 

We will be laying out the phases of work in the coming months and will continue to use this blog to keep the community informed of our progress, and to solicit your feedback and ideas.

Phase 1 Workplan

maDMP_phase1

Phase 1 of of our research entails exploring the following three high level ideas:

  1. How to best restructure the DMPTool metadata to utilize the RDA Working Group Common Standard
  2. How to optimize the Digital Object Identifiers (DOI) metadata schema for DMPs 
  3. How to best incorporate other Persistent identifiers (PIDs) into DMPs

Common Standards

The common data model for the creation of machine-actionable DMPs, produced by the RDA working group on DMP Common Standards, was recently released for community feedback. Our partners at the Digital Curation Center (DCC) have now implemented this model into the DMPRoadmap codebase. A big thank you to Sam Rust from DCC for his work on this! Those interested in learning more about the Common Standard in DMPRoadmap may want to view a recent webinar recording of Sam detailing this work. This was a fundamental step towards machine actionable DMPs, as it forms the foundation to enable information flow between DMPs and affiliated external systems in a standardized manner.

DOIs for DMPs

With our partners at the Digital Curation Center (DCC), we are working to incorporate the common standards into the shared DMPRoadmap codebase and our DMPTool development plans. As part of this work, we have partnered with DataCite to update their metadata schema to better support DMPs and to optimize a workflow for generating DOIs for DMPs. By relying on the DOI infrastructure, we will then be able to utilize the Event Data service from DataCite to record when assertions have been made on the DOI. More on the workflows surrounding this aspect of the project below. 

DMPs and the PID graph

Projects such as Freya have been working to connect research outputs through a PID graph.  A key question underpinning much of our work is how we can best leverage the PID graph (see Principle 5: Use PIDs and controlled vocabularies) within the DMP ecosystem. To connect DMPs to the larger PID ecosystem, our first phase will also include incorporating the following persistent identifiers into the DMP as a baseline for future work:

Phase 1 workflows

As discussed above, in Phase 1, we are building a system to mint DOIs for DMPs and creating a landing page for DMP DOIs to record updates to the DOI that occur over time. Although the system can be thought of as a giant API, pulling and pushing data from various sources, we are also building a landing page for these DOIs in order to visually demonstrate the types of connections made possible by tracking a research project over time from the point of DMP creation. 

Below is a high level overview of this workflow and whiteboarding of its potential architecture. (For those that would like a more detailed view, please check out our GitHub).maDMPRegistry

  1. maDMP system accepts common standard metadata from DMPTool (DMP Roadmap) 
  2. maDMP system sends that metadata to DataCite to mint a DOI (which it then returns to the DMPTool)
  3. A landing page is generated for the DMP DOI
  4. A separate harvester application queries outside APIs to check for assertions recorded against the DOI. For this phase of work we will work with the NSF awards API, and return any award information into the maDMP system. 
  5. The maDMP system then sends any award info returned to DataCite 

Our goal is to leverage the work being done by the RDA Exposing DMP working group to help inform the privacy concerns of exposing certain types of assertions on this landing page.  

Next Steps

Looking ahead, we plan to produce a basic prototype ready for testing and feedback by the end of October. I will be presenting on our work thus far at the upcoming RDA and CODATA meetings. During these meetings, I look forward to continuing our work with the RDA Common Standards Working Group (and to meeting many of those active in this space for the first time in-person)! 

Once we establish the workflow to record assertions to a DMP DOI, our next phase of work will include pilot projects with domain-specific and institutional stakeholders to test the flow and integration of relevant information across services and systems. With these partners we plan to test how maDMPs can help track data management activities as they occur during the course of a grant project. 

Finally, it’s important to note that all of our development work is being done in a test environment where we will continue to iterate for the next several months as we determine how best to deploy new features to the DMPTool and DMPRoadmap codebase. 

Interested in contributing?

Lastly, we realize that maDMP is far from the most euphonious or creative name for this service (nor is our original idea of the DMPHub much better). We are open to any and all ideas for naming this work so if you have any ideas, however strange or off the wall, please do let us know. If we use your idea we promise to shower you with accolades for your denomination genius. Also, free stickers galore.

To review or contribute to the technical components of the project check out our GitHub. And most importantly, please send any and all feedback, questions, or ideas for names to maria.praetzellis@ucop.edu.

 

Representing time in machine-actionable DMPs

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.

Common standards and PIDs for machine-actionable DMPs

QR code cupcakes

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.

Roadmap back to school edition

Summer activities and latest (major 2.0.0) release
The DMPRoadmap team is checking in with an overdue update after rotating holidays and work travels over the past few months. We also experienced some core team staff transitions and began juggling some parallel projects. As a result we haven’t been following a regular development schedule, but we have been busy tidying up the codebase and documentation.

This post summarizes the contents of the major release and provides instructions for those with existing installations who will need to make some configuration changes in order to upgrade to the latest and greatest DMPRoadmap code. In addition to infrastructure improvements, we fixed some bugs and completed some feature enhancements. We appreciate the feedback and encourage you to keep it coming since this helps us set priorities (listed on the development roadmap) and meet the data management planning needs of our increasingly international user community. On that note, we welcome Japan (National Institute for Informatics) and South Africa (NeDICC) as additional voices in the DMP conversation!

Read on for more details about all the great things packed into the latest release, as well as some general updates about our services and of course machine-actionable DMPs. The DCC has already pushed the release out to its services and the DMPTool will be upgrading soon – separate communications to follow. Those who run their own instances should check out the full release notes and a video tutorial on the validations and data clean-up (thanks Gavin!) to complete the upgrade.

DMPRoadmap housekeeping work (full release notes, highlights below)

  • Instructions for existing installations to upgrade to the latest release. Please read and follow these carefully to prevent any issues arising from invalid data. We highly recommend that you backup your existing database before running through these steps to prepare your system for Roadmap 2.0.0!
  • Added a full suite of automated unit tests to make it easier to incorporate external contributions and improve overall reliability.
  • Added data validations for improved data integrity.
  • Created new and revised existing documentation for coding conventions, tests, translations, etc (Github wiki). We can now update existing translations and add new ones more efficiently.

DMPRoadmap new features and bug fixes

  • Comments are now visible by default without having to click ‘Show.’ Stay tuned for additional improvements to the plan comments functionality in upcoming sprints.
  • Renamed/standardized text labels for ‘Save’ buttons for clarity.
  • Added a button to download a list of org users as a csv file (Admin > ‘Users’ page)
  • Added a global usage report for total users and plans for all orgs (Admin > ‘Usage’ page)
  • Admins can create customized template sections and place them at the beginning or end of funder templates via drag-and-drop
  • Removed multi-select box as an answer format and replaced with multiple choice

DCC/DMPonline subscriptions [Please note: this does not apply to DMPTool users] Another recent change is in the DMPonline service delivery model. The DCC has been running DMP services for overseas clients for several years and is now transitioning the core DMPonline tool to a subscription model based on administrator access to the tool. The core functionality (developing, sharing and publishing DMPs) remains freely accessible to all, as well as the templates, guidance and user manuals we offer. We also remain committed to the Open Source DMPRoadmap codebase. The charges cover the support infrastructure necessary to run a production-level international service. More information is available for our users in a recent announcement. We’re also growing the support team to keep up with the requests we’re receiving. If you are interested in being at the cutting edge of DMP services and engaging with the international community to define future directions, please apply to join us!

Machine-actionable DMPs
Increasing the opportunities for machine-actionability of DMPs was one of the spurs behind the DMPRoadmap collaboration. Facilities already exist via use of a number of standard identifiers and we’re moving on both the standards development tracks and code development and testing.

The CDL has been prototyping for the NSF EAGER grant and started a blog series focused on this work (#1, #2, next installation forthcoming), with an eye to seeding conversations and sharing experiences as many of us begin to experiment in multiple directions. CDL prototyping efforts are separate from the DMPRoadmap project currently but will inform future enhancements.

We’re also attempting to inventory global activities and projects on https://activedmps.org/ Some updates for this page are in the works to highlight new requirements and tools. Please add any other updates you’re aware of! Sarah ran a workshop in South Africa in August on behalf of NeDICC to gather requirements for machine-actionable DMPs there and the DCC will be hosting a visit from DIRISA in December. All the content from the workshop is on Zenodo and you can see how engaged the audience got in mapping our solutions. The DCC is also presenting on recent trends in DMPs as part of the OpenAIRE and FOSTER webinar series for Open Access week 2018. The talk maps out the current and emerging tools from a European perspective. Check out the slides and video.

You can also check out the preprint and/or stop by the poster for ‘Ten Principles for Machine-Actionable DMPs’ at Force2018 in Montreal and the RDA plenary in Botswana. This work presents 10 community-generated principles to put machine-actionable DMPs into practice and realize their benefits. The principles describe specific actions that various stakeholders are already undertaking or should take.

We encourage everyone to contribute to the session for the DMP Common Standards working group at the next RDA plenary (Nov 5-8 in Botswana). There is community consensus that interoperability and delivery of DMP information across systems requires a common data model; this group aims to deliver a framework for this essential first step in actualizing machine-actionable DMPs.

Machine-actionable DMPs: What can we automate?

Following on some initial thoughts about Scoping Machine-Actionable DMPs (maDMPs), we’re keen to dive into the substance. There are plenty of research questions we plan to explore here and over the course of our maDMP prototyping efforts. Let’s begin with these:

What can we automate?
What needs to be entered manually?

One of the major goals for maDMPs is to automate the creation and maintenance of some pieces of information.

Automation stands to alleviate administrative burdens and improve the quality of information contained in a DMP.

Thankfully, we’re not starting from scratch since Tomasz Miksa crafted an assignment for his CS students at the Technical University of Vienna to build an maDMP prototype tool and answer these very questions (course details; assignment). The student reports provide valuable insights that will help guide our own and others’ work on the topic. Read on for a brief overview of the assignment and a discussion of the key results; the results are woven into answers to the questions above.

I will also note that our own project includes grant numbers as a key piece of project metadata, which is not part of this assignment. We’re currently exploring the NSF Awards API and institutional grants management systems in the context of these questions, more on this anon.

Assignment
Students were instructed to build a tool that gathers information from external sources and automatically creates a DMP. Modeled on the European Commission’s DMP requirements for Horizon 2020, students could choose to create a DMP when a project starts (first version upon receiving funding) or when a project ends and all products have been preserved/published (final report). For the first option, the tool should help researchers estimate their storage needs and select a proper repository to store their research outputs. For the second option, the tool should connect to services where data is stored to retrieve information for creating a DMP.

External (or controlled) sources of information included:

  1. Administrative info (researcher name, project title): Use one or both of these inputs to search the university profile system and/or ORCID API to retrieve additional info (affiliation, contact email, etc).
  2. Find a repository (option 1): Use the OpenDOAR API or re3data API to recommend a repository based on sample data types and location (Europe, Austria)
  3. Get metadata about things deposited in a repository (option 2): Collect as much info as possible from the GitHub API about software products and OAI-PMH compliant repositories (e.g., license, format, size, etc) for other products.
  4. Select a license (if not provided in step 3): EUDAT license selector, reuse existing code.
  5. Preservation details: Allow users to tag all research products (e.g., input data, output data, software, documentation, presentation, etc.). Group them if appropriate. Provide a combo-box to define how long each product will be preserved (5, 10, 20 years).

The final reports describe the architecture and implementation of the tool; demonstrate how it works; include a human-readable and an maDMP created with the tool; and answer some questions about the benefits and limitations of automation.

Results
The student reports emphasize that a mixture of automation and manual processes is necessary to produce DMPs that meet all of the requirements outlined by funders. They demonstrate how we can leverage automation for maDMPs and provide thoughtful analyses about how we can consume available sources of information.

Portions of a DMP that can be automated easily include:

  • Basic project details such as title, names/authors, DMP creation date
  • Information (including metadata) about the research products associated with the project (e.g., data, software…)
  • Repository details: e.g., Zenodo, Github for software

Other automated portions of a DMP enable some inference but aren’t adequate by themselves:

  • Licenses: can be derived from a Github/Zenodo link
  • Software and data preservation details: some data is given for each file; some assumptions can be made based on the repository
  • Data sharing, access, and security details: some data is given for each file; some assumptions can be made based on the repository
  • Costs/resources: estimations can be made based on the size and type of data

Portions of a DMP that cannot be completed via automation:

  • Roles and responsibilities (although at TU Wien this is partially automated; they assume the project uses their infrastructure and provide details to designate individuals responsible for backups, final data deposit, etc)
  • Licenses and policies for reuse, derivatives (complete answers must be provided manually)
  • Ethical and privacy questions

Check out this example of a human-readable landing page for the DMP produced by one student team (Rafael Konlechner and Simon Oblasser) and the corresponding json output for the maDMP version. Some other examples of maDMP-creation tools for both assignment options are available here (ex 1, ex 2, ex 3, ex 4, ex 5, ex 6); they’re provided as Docker containers that can be launched quickly.

Discussion
The student prototypes and some other projects in this arena (e.g., UQRDM) inform larger maDMP goals surrounding automation and maintenance/versioning (i.e., keeping info in a DMP up to date). They identify sources/systems of existing information, mechanisms (APIs, persistent identifiers) for consuming and connecting it, and some important limitations regarding the informational content that require manual interventions and enrichment.

Our own prototype is following a similar trajectory as the student assignment. We’re defining existing data sources/systems and exploring the possibilities for moving information between them. The good news is that there are lots of sources and APIs out there in the wild with implications for maDMPs. There are also lots of existing initiatives to connect all the things that could become part of an maDMP framework (e.g., Scholix, ORCIDs, OrgIDs).

By taking this approach, we want to make the creation and maintenance of a DMP an iterative and incremental process that engages all relevant stakeholders (not just researchers writing grant proposals). Researchers need guides and translators to find the best resources and do their research efficiently, and in a manner that complies with open data policies. As we noted in the previous blog post, we want to enable repository operators, research support staff, policy experts, and many others to contribute to DMPs in order to achieve good data management.

Up next
Some related questions that we’re mulling over, but won’t endeavor to answer in this post:

  • Which stakeholders and/or systems should be able to make and update assertions (in a DMP) about a grant-funded project?
  • What is required to put it all together?

A teaser for the second question: interoperability and delivery of the DMP information across systems requires a common data model for DMPs. You can join the RDA DMP Common Standards working group to contribute to this ongoing effort. We’ll unpack this one in a future blog post.

Thanks to Tomasz (also a co-chair of the RDA group) and his students for taking an inspirational lead in maDMP prototyping!

Scoping Machine-Actionable DMPs

Machine-actionable data management plans (maDMPs) are happening. Over the past several years we’ve contributed to community discussions and various events to suss out what we all mean by this term and why we think maDMPs are important. In the midst of these efforts, we (California Digital Library) also received an NSF EAGER grant to prototype maDMPs and are now in the process of designing that work.

To connect our prototyping with the constantly evolving maDMP landscape, we remain active in the Research Data Alliance, Force11, domain-based efforts (e.g., AGU Enabling FAIR Data), and of course we run the DMPTool service as part of an international policy/support initiative called the DMP Roadmap project. We also recently helped launch a website activedmps.org to identify all of the people and projects across the globe working on maDMPs.

In keeping with this community thread, as well as for our own edification, we’re kicking off an maDMP blog series. The primary goal is to offer some framing documents so other stakeholders, especially those who’ve invested as much time as we have thinking about such an obscure topic (!), can help us ask and answer the many outstanding questions about maDMPs. A secondary motivation is to respond to the frequent queries from our users and other stakeholders about how to envision and plan for an maDMP future, which seems inevitable as more of us begin to prototype in different directions.

For this inaugural scoping piece we want to address the following high-level questions. And just to reiterate, the answers herein are distilled from our own thinking; by no means do we think that these are the correct or only answers. We invite others to challenge our ideas at any/every step along the way.

  1. What are maDMPs?
  2. What are they not? 
  3. Who are they for?
  4. How are they different from “traditional” DMPs?
  5. What does this mean for the future of DMPs and support services?

…What comes next?

 

1. What are maDMPs?
maDMPs are a vehicle for reporting on the intentions and outcomes of a research project that enable information exchange across relevant parties and systems. They contain an inventory of key information about a project and its outputs (not just data), with a change history that stakeholders can query for updated information about the project over its lifetime. The basic framework requires common data models for exchanging information, currently under development in the RDA DMP Common Standards WG, as well as a shared ecosystem of services that send notifications and act on behalf of humans. Other components of the vision include machine-actionable policies, persistent identifiers (PIDs) (e.g., ORCID iDs, funder IDs, forthcoming Org IDs, RRIDs for biomedical resources, protocols.io, IGSNs for geosamples, etc), and the removal of barriers for information sharing.

2. What are they not?
maDMPs are not a collection of best practices for creating a data management plan (those exist already, Michener 2015) nor are they a comprehensive record of every detail about a research project and how it was conducted (i.e., they are not the Open Science Framework). It is out of scope to use maDMPs to connect all the things in the universe and try to solve reproducibility. Instead they are a plan and instructions about how to implement the plan, as well as a report about the completion of the plan; this plan includes an inventory/registry of research outputs and information about what to do with each thing (e.g., length of time to retain a dataset in a repository).

3. Who are they for?
maDMPs are focused primarily on infrastructure providers, systems, and those responsible for creating and enforcing research data policies. maDMPs are not focused primarily on researchers, data librarians, or other research support staff. However, broad adoption by all stakeholders in the research enterprise is required to achieve the the goals of the policies and ideally everyone will reap the benefits. Here is a (roughly) ranked-order list of the target audience for maDMPs:

  • Funder: funding agencies and foundations that specify requirements for DMPs and monitor compliance.
  • Repository Operator: General (e.g., Zenodo, Dryad), disciplinary (e.g., GenBank, ICPSR), and institutional data repositories.
  • Infrastructure Provider: Providers of systems for creating DMPs (DMPTool, DMPonline), grants administration, researcher profiles (RIMS/CRIS), etc. .
  • Institutional Administrator: Office of Research/Sponsored Programs, Chief Information Officers, University Librarians, others.
  • Ethics Review: Institutional Review Boards (IRB)/Research Ethics Boards (REB) that authorize human subjects research.
  • Legal Expert: Technology transfer offices; copyright and patent experts.
  • Publisher: Purveyors of article and data publication services.
  • Researcher: Principal Investigator and collaborators, including postdoctoral researchers, graduate and undergraduate students.
  • Research Support Staff: Data managers/curators, research administrators, and data librarians.

machine-actionable DMP info flows

Examples of stakeholder interactions within the ecosystem of machine-actionable DMPs. Stakeholders communicate with each other by exchanging information through DMPs. For example, a repository operator can select a proper repository, set an embargo period, and assign a correct license to data submitted by researchers. In return, a system acting on behalf of a repository operator provides a list of DOIs assigned to the data and provides information on costs of storage and preservation. This in turn can be accessed by a funder to check how the DMP was implemented.

4. How are they different from “traditional” DMPs?
The vision for maDMPs is to automate certain pieces of the DMP process, especially to alleviate the administrative burden of entering the same information in multiple places (e.g. it would be great if a researcher could recycle part or all of an IRB application for a DMP, or generate a Biosketch/CV automatically from their ORCID profile, or automatically generate a data availability statement when publishing data/articles). There is still a need for a human-readable narrative that describes digital research methods and outputs, but the main difference is that it should be updatable so that DMPs can become useful beyond the grant application stage.

5. What does this mean for the future of DMPs and support services?
We get asked this question often, most recently in the form of a provocative email from Dr. Devan Ray Donaldson as he was designing the curriculum for his digital curation course at Indiana University Bloomington.

Our response: Librarians and other digital curation experts absolutely have a role to play in supporting researchers with DMPs and data management issues more broadly. At CDL we spend a lot of time digging into the weeds of digital curation issues with librarians and researchers at all 10 UC campuses and we noticed that a major barrier to effectively supporting researchers is that they don’t recognize the language/jargon of digital curation. At the risk of self-promotion I’ll direct you to this guide that we created based on our collective experiences as researchers, and now as people who support researchers, called “Support Your Data.” John Borghi was the main driver of the project (more details from him here) and we’re now developing more attractive resources and a website to adapt for your purposes if you find these materials useful. The goal is to educate researchers about good data management practices by relating to their current practices, and demonstrate how small habits (e.g., file naming conventions) can amount to better/more efficient research.

… What comes next?
maDMPs present an opportunity to move DMPs beyond a compliance exercise by providing needed structure, interoperability, and added-value functionality to support open, reusable research data. We’re designing and developing an open framework for maDMPs that builds on existing initiatives and infrastructure. There are numerous efforts focused on connecting people and outputs (e.g., ORCID, Wikidata, Scholix, NCBI accession numbers). We want to link this information with grant numbers to create a dynamic inventory of assertions about a grant-funded research project (note: in the future we’ll also consider DMPs not associated with grants).

Step 1 for us is to get seed data from our partners at BCO-DMO and the UC Berkeley Gump Field Station on Moorea and structure it to define native maDMPs. We’ll discuss subsequent steps in future blog posts. Stay tuned!

Set the controls for the heart of the sun

Our DMPTool and DMPonline services have been humming along with the same underlying code for a couple of months now. Since our MVP release, we’ve shifted gears to more regular sprints. We’re also pleasantly surprised by how eager the wider DMP community has been to join forces in migrating, translating, and even contributing new features already! Here’s a brief retrospective and a glimpse into the future.

Post MVP Backlog
There is a modest backlog of work that didn’t make into the MVP release. We’ve prioritized these issues and are focused on tying up the loose ends over the coming months. Those following the DMPRoadmap Github repository will notice regular releases. The goal is to settle into a steady two-week rhythm, but in the near term we’re working on slightly shorter or longer cycles to address critical bugs and some minor refactoring. Many thanks to our users on both sides of the pond who have reported issues and provided overwhelmingly positive feedback so far!

Evolving processes
We’ve been communicating with our respective user communities about new fixes and features as things pertain to them. Some things to note about our evolving development process:

  • DMPRoadmap GitHub repo: this is where most development work happens since the majority of fixes and features apply to the core codebase. This repository also contains all technical documentation, release notes, and other info for those interested in deploying their own instances or contributing to the project.
  • The DMPRoadmap wiki has a list of potential future enhancements. We’re collating ideas here and will define priorities and requirements in consultation with the community via user groups and listserv discussions. If you have other desired new features please let us know.
  • Any service-specific customizations reside in separate GitHub repos. For example, you can find the custom Single-Sign-On code in the DMPTool GitHub repo. The way that we handle helpdesk functions varies too. DMPTool users can report issues directly in the DMPTool repo or via the helpdesk. If something pertains to the common codebase, Stephanie will tag the issue and transfer it to DMPRoadmap. For DMPonline users we ask you to report issues via the helpdesk.

External contributions
Our core dev team is test driving the external contributor guidelines with the French team from DMP OPIDoR. They developed a new feature for a global notification system (e.g., to display maintenance messages, updates to funder templates) that happens to be in our backlog. The new feature looks great and is exactly the kind of contribution we’d like from others. You’ll see it in the next release. Thanks Benjamin and Quentin!

We’re also keen to commence monthly community dev calls to learn about other new features that folks might be planning and keep track of how we collaborate on DMP support across the globe.

Translations
We’ll be adding new translations for Brazilian Portuguese (thanks to Benilton de Sá Carvalho and colleagues at UNICAMP) and Finnish thanks to DMPTuuli. We’re also reaching out to fill in missing portions of existing translations for other languages since we added so many new features. New translations are always welcome; more information is available on the GitHub wiki and/or contact us.

A machine-actionable future
With the launch milestone behind us, we’re devoting more attention and resources to creating a machine-actionable future for DMPs. Two working groups hosted productive sessions at the recent RDA plenary (DMP Common Standards, Exposing DMPs) that included lightning talk presentations by members of the DMPRoadmap project (slides 1 and slides 2). Both of the groups are on track to provide actionable outputs in the next 12 months that will bolster wider community efforts on this front. We’ll continue participating in both groups as well as begin prototyping things with the NSF EAGER grant awarded to the California Digital Library. Stay tuned for more details via future updates and check out the activedmps.org site to get involved.