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.

As part of our work to extend the usability and interoperability of the new Networked DMP, we have partnered with Research Space (RSpace), a connected electronic lab notebook, and developed a prototype integration that allows users to track research data throughout the research life cycle. This new integration enables tri-directional data flows between RSpace, DMPTool, and data repositories, facilitating higher quality and more comprehensive research data capture and tracking.

The lack of interoperability between tools is arguably the most significant barrier to streamlining workflows throughout the research lifecycle. This gap prevents the comprehensive collection and incorporation of research data and metadata into the research record captured during the active research phase. Furthermore, it limits the scope for passing this data and metadata on to data repositories, thus undermining FAIR data principles and reproducibility. Bridging this gap is precisely what the integration seeks to address. 

Researchers are now able to reference and update their DMPs from within RSpace. The aims of this feature are:

1. To add value to DMPs to link to datasets generated throughout a study and become ‘living documents.’
2. Reduce the burden on researchers in keeping their DMPs up to date.
3. Append DOIs and links to datasets exported from RSpace to a repository to be associated with a DMP.

Learn More

To see this workflow in action, check out this short demo from FORCE 2021, where Julie Goldman from the Harvard Medical School explains how the DMPTool – RSpace – Dataverse workflow enhances research data capture in the Harvard environment. The user documentation also contains a walk-through of the feature with instructions. 

Additionally, an upcoming FORCE11 Community Call on January 25, 2022, will showcase this and other integrations and demonstrate ways in which data capture and reproducibility are enhanced by tool interoperability.  

Please reach out with any comments, suggestions, or ideas for future integrations!

Crossposted from the FAIR Island website

The California Digital Library (CDL), University of California Gump South Pacific Research Station, Berkeley Institute for Data Science (BIDS), Metadata Game Changers, and DataCite are pleased to announce that they have been awarded a 2- year NSF EAGER grant entitled “The FAIR Island Project for Place-based Open Science” (full proposal text). 

The FAIR Island project examines the impact of implementing optimal research data management policies and requirements, affording us the unique opportunity to look at the outcomes of strong data policies at a working field station. Building on the Island Digital Ecosystem Avatars (IDEA) Consortium (see Davies et al. 2016), the FAIR Island Project leverages collaboration between the Gump Station on the island of Moorea in French Polynesia (host of the NSF Moorea Coral Reef Long-Term Ecological Research site), and Tetiaroa Society, which operates a newly established field station located on the atoll of Tetiaroa a short distance from Moorea. 

The FAIR Island project builds interoperability between pieces of critical research infrastructure — DMPs, research practice, PIDs, data policy, and publications contributing to the advancement and adoption of Open Science.  In the global context, there are ongoing efforts to make science Open and FAIR to bring more rigor to the research process, in turn increasing the reproducibility and reusability of scientific results.  DataCite as a global partner in the project, has been working to recognize the importance of better management of research entities. This has led to critical advances concerning the development of infrastructure for Open Science. Increased availability of the different research outputs of a project (datasets, pre-registrations, software, protocols, etc.) would enable the reuse of research to aggregate findings across studies to evaluate discoveries in the field and ultimately assess and accelerate progress.

Key outcomes the FAIR Island team will develop include: 

1. CDL, BIDS, and the University of California Natural Reserve System will work together to build an integrated system for linking research data to their associated publications via PIDs. We will develop a provenance dashboard from field to publication, documenting all research data and research outcomes derived from that data. 

2. The project also facilitates further development of the DataCite Commons interface and extends connections made possible via the networked DMP that allows users to track relationships between DMPs, investigators, outputs, organizations, research methods, and protocols; and display citations throughout the research lifecycle.

3. Developing an optimal data policy for place-based research by CDL, BIDS, and Metadata Game Changers is the cornerstone component of the FAIR Island project.  A reusable place-based data policy template will be shared and implemented amongst participating UC-managed field stations and marine labs. In addition, we will be incorporating these policies into a templated data management plan within the DMPTool application and sharing it with the broader community via our website, whitepapers, and conferences such as the Research Data Alliance (RDA) Plenaries.

The FAIR Island project is in a unique position to demonstrate how we can advance open science by creating optimal FAIR data policies governing all research conducted at field stations. Starting with the field station on Tetiaroa, the project team plans to demonstrate how FAIR data practices can make the reuse of data and the collaboration of data more efficient. Data Management Plans (DMPs) in this “FAIR data utopia” will be utilized as key documents for tracking provenance, attribution, compliance, deposit, and publication of all research data collected on the island by implementing mandatory registration requirements, including extensive use of controlled vocabularies, personal identifiers (PIDs), and other identifiers.

The project will make significant contributions to international Open Science standards and collaborate with open infrastructure providers to provide a scalable implementation of best practices across services. In addition, DataCite seeks to extend the infrastructure services developed in the project to their member community across 48 countries and 2,500 repositories globally. 

We will continue to share details and feature developments related to the FAIR Island project via our blog. You can join the conversation at the next RDA plenary in November 2021. Feedback or questions are most welcome and can be sent directly to info@fairisland.org.