By Brian Riley, California Digital Library (CDL), and Mary O’Brien Uhlmansiek, The Association of Research Libraries (ARL)

In March, the lead developer for the DMP Tool, Brian Riley, attended a workshop on “Scientometrics Using Open Data” offered by the Centre for Science and Technology Studies (CWTS) at Leiden University. Participation in this session allowed us to share the work we are doing as part of the MAP Pilot project funded by the NSF and IMLS, and to collaborate on scientometric analyses using open data sources such as Crossref and DataCite.

The MAP Pilot project involves working with 10 institutions across the US to test connecting machine-actionable data management and sharing plans (maDMSPs) with related research outputs. Using research project metadata and persistent identifiers to query open data sources, it is somewhat easy to find research articles produced by a particular project, but not the datasets, software and other artifacts that are described in a DMSP. We are investigating ways to improve their findability using automation including machine learning/AI.

When maDMSPs are created in the DMP Tool, users can enter useful project metadata to enable queries with other systems. This includes ORCIDs for contributors, funding opportunity identifiers, RORs for affiliations and funders, anticipated project start and end dates, and the planned data repository for storage. The DMP Tool then assigns a DMP ID to the DMSP.

DMSPs are often created years before the research outputs. The DMSPs in the DMP Tool with good metadata are only 2-3 years old, and their DMSP outputs have not yet been published. Therefore, the institutions contributing to our pilot have been asked to find older, funded research projects and their outputs to use as test cases. Using a new feature to upload an existing DMSP, they will enter basic information about the project (i.e., title, PI, grant identifiers) for research funded by 4 major US agencies (NSF, NIH, DOE, and NASA) and for which we have the most developed API integrations. As potential DMSP outputs are identified, the pilot teams will verify their relation to the research.

Identifying related DMSP outputs within the DMP Tool will give data librarians and research/grant management offices insight into the outputs of research projects, academic departments, and the institution. Users can generate reports for compliance checks (was the data shared according to the funder’s policy), grant reporting, and research management activities.

With sufficient metadata, how do we find related DMSP outputs? We start by exploring open data sources like Crossref, DataCite, and COKI. For example, we explore DataCite’s GraphQL API to extract DataCite metadata and compare it with DMP Tool projects. We use an algorithm to compare and score each field in the records. Each data source structures its metadata differently, though, so we must transform that metadata into a standardized format. We then weigh or score the confidence level of any matches found. A high confidence level is when grant IDs match, but this is rare currently. Confidence levels improve with additional identifiers like ORCIDs, RORs, and repository IDs.

Some development challenges discussed at the workshop include:

• US funding agencies lack a standard way of sharing metadata via their APIs and rarely include Grant IDs. Grant IDs are important but not reliable yet for identification purposes.
• Research/DMSP outputs associated with older projects frequently lack identifiers such as ROR and ORCIDs in their metadata record. 
• How can we find datasets and software related to published research articles in systems like COKI? Can we use an article’s references to find these artifacts? What other hooks will allow us to identify these related outputs, and how could improved metadata and the usage of identifiers help facilitate making these connections?  

We are exploring adding more data aggregators to combine findings and create a clearer picture of a research project and its outputs. We will also explore methods to identify related works from research article reference sections, like dataset or software references. We are experimenting with ML/AI techniques to determine if a research output might be related to a DMSP.

Findings from the MAP Pilot will be published as reports and best practices for implementing maDMSP workflows at research institutions after the project ends in 2025. If interested in collaborating on this important developmental work, please contact muhlmansiek [at] arl [dot] org for more information.

By Mary O’Brien Uhlmansiek, Project Director, The Association of Research Libraries (ARL)

This February, I joined the MAP Pilot team as Project Director, serving in a joint position with The Association of Research Libraries (ARL) and the California Digital Library (CDL). In this role, I will support ten research libraries in our pilot project, exploring ways to advance institutional coordination around machine-actionable data management and sharing plans (maDMSPs). The project will compile resources for research workflow improvements utilizing maDMSPs, such as for tracking compliance with funder data-sharing requirements or to initiate internal research infrastructure requests upon grant award, for example. Our pilot partners will also help drive improvements in the DMP Tool itself, providing valuable software testing and feedback as new interoperability features are developed, and using real-world examples to ensure the application will meet the needs of researchers and stakeholders alike.

Through my experiences serving as a data and repository manager for sensitive health-related information, in managing research software adoption and implementation at a large medical university, and as a facilitator for the adoption of outputs and recommendations at the Research Data Alliance, I can see the potential for the DMP Tool to provide critical research infrastructure for researchers and administrators alike as they navigate new data-sharing requirements from funders. I am excited to work with the project PIs, Cynthia Hudson Vitale and Maria Praetzellis, and the many other dedicated professionals from research library organizations in the open science movement. Projects such as the MAP Pilot are building blocks for the transition to more open science, and I look forward to the dissemination of the teams’ outputs to aid research institutions in adopting and continuing this important work. 

If you would like to learn more about maDMSPs or to get involved in future work in this area, please consider joining a group such as the Active Data Management Plans Interest Group at the Research Data Alliance.

The Association of Research Libraries (ARL) and the California Digital Library (CDL) have selected five institutional teams to pilot the integration or creation of prototypes and possible workflows for machine-actionable data management and sharing plans (maDMSPs). The pilot project will run January–December 2024. This project is funded by an Institute of Museum and Library Services (IMLS) National Leadership Grant. Additional information about the project is on our project webpage.

Machine-actionable data management and sharing plans are structured, machine-readable documents that allow for dynamic reporting on the intentions and outcomes of a research project, enabling streamlined information exchange across relevant parties and systems. These plans go beyond traditional static document-based DMSPs, and contain an inventory of key metadata about a project and its outputs (not just datasets), with a change history that stakeholders can query for information over the lifetime of the research. Implementing maDMSPs can be a key piece of establishing interconnected, automated systems for research data management and compliance.

The maDMSPs pilot institutions will help shape the development of maDMSPs and gain valuable early experience with new approaches to enable more automated and connected research data management. The institutions are:

• Arizona State University
• Northwestern University Feinberg School of Medicine
• Pennsylvania State University
• University of California, Riverside
• University of Colorado, Boulder

An additional five institutions have been selected for the maDMSP extended cohort that will engage closely with the pilot cohort.

The Association of Research Libraries (ARL) and the California Digital Library (CDL) are seeking four institutional teams to pilot the integration or creation of prototypes and possible workflows for machine-actionable data management and sharing plans (maDMSPs). The pilot project will run January–December 2024. This project is funded by an Institute of Museum and Library Services (IMLS) National Leadership Grant. Additional information about the project is on our project webpage.

Interested organizations should submit their expression of interest here.

Machine-actionable data management and sharing plans are structured, machine-readable documents that allow for dynamic reporting on the intentions and outcomes of a research project, enabling streamlined information exchange across relevant parties and systems. These plans go beyond traditional static document-based DMSPs, and contain an inventory of key metadata about a project and its outputs (not just datasets), with a change history that stakeholders can query for information over the lifetime of the research. Implementing maDMSPs can be a key piece of establishing interconnected, automated systems for research data management and compliance.

This pilot provides an exciting opportunity for selected institutions to help shape the development of maDMSPs and gain valuable early experience with new approaches to enable more automated and connected research data management.

By agreeing to be part of this pilot program, institutions will:

• Define a set of success measures for institutional pilot projects of maDMSPs at their organization.
• Gather a sample set of data management plans from funded research projects to use as test cases for connecting plans with associated datasets and other research outputs.
• Provide engaged feedback on the maDMSP features and uses at their organization.
• Conduct ongoing work to meet the locally defined success measures.
• Attend and actively participate in project meetings every other month.
• Participate in project communication, outreach, and engagement (such as conference panels, webinars, reports and articles, etc.).
• Coordinate and manage one program team site visit.

Pilot projects should include a team of three to five people representing institutional stakeholders who will work together to test or prototype an institutional solution to support public access to research data leveraging the maDMSP. Teams may include representatives from the offices of several institutional stakeholders, such as the research office, library, information technology, institutional review board (IRB), high-performance computing units, and/or faculty.

Examples of possible pilot projects include, but are not limited to:

• Modeling notification workflows that could be automated through maDMSPs to alert stakeholders to key events over the data life cycle. Example use cases include alerts around sensitive data, managing big data, enabling data transfer, and linking datasets to published outputs.
• Building prototype integrations connecting maDMSPs with existing research information management systems (RIMS) or researcher profile systems. For example, automatically updating and exchanging key metadata between maDMSPs and other research systems.
• Engaging academic or administrative departments to test the utility of maDMPs for their research workflows and data management needs. Departmental testing would provide feedback to inform the optimization of maDMSP systems.
• Demonstrating and improving communication workflows between key campus units involved in research data management using maDMSPs as a connecting platform. Example stakeholders include the library, research office, IT/security, IRB, research computing, and high-performance computing units.

Pilot institutions will:

• Gain early access to new maDMSP features and functionality.
• Influence technical development and workflow processes of the maDMSP platform.
• Be reimbursed for up to $6,000 per institution to attend conferences or workshops to communicate pilot project goals or outcomes.

The ARL/CDL project team will produce all required reporting to IMLS; there are no federal grant reporting requirements for pilot partners.

We are seeking a range of institutions that are diverse in size, research activity, and level of development of services and infrastructure for research data management and sharing. Even if your institution has just begun planning for research data management and sharing, we invite you to apply.

Applications will remain open until Friday, November 10, 2023, and we anticipate notifying applicants by the end of November.

If you are interested in learning more, you are invited to register to attend an optional, informational webinar on Thursday, November 2, at 10:00 a.m. PDT/1:00 p.m. EDT.

Please direct any questions to Cynthia Hudson Vitale cvitale@arl.org or Maria Praetzellis maria.praetzellis@ucop.edu.