FAIR Island Project Receives NSF Funding

FAIR Island

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. 
  1. 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.
  1. 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

Connecting the DMP ID to an ORCID record

Recently we announced that the DMPTool can now generate persistent, unique IDs (the DMP ID) for plans created within the application. Building on this development, we are thrilled to share that the scholarly identifier service for researchers, ORCID, recently adopted the DMP as a resource type. As a result, DMPs are now a defined work type within an ORCID record and listed on an individual’s ORCID record. The connection between a DMP ID and ORCID is crucial for the Networked DMP, as ORCIDs play a key role in facilitating connections between researchers, institutions, outputs, and projects. It is precisely these types of relationships that we are enabling through our work on Networked DMPs.

Screenshot of manually adding a DMP as a work to an ORCID record

Additionally, DMP IDs generated via the DMPTool are now automatically linked to the DMP creator’s ORCID record. This means that when a DMPTool user “Registers” their plan, a DMP ID is generated, and this record is automatically pushed to ORCID and included as a work on their ORCID profile page. 

“Registering” a DMP will generate a DMP ID and push this work to the associated ORCID record
After a DMP ID is generated this work will be listed as a work on the researcher’s ORCID record

Together with Liz Krznarich from DataCite and DMPTool Editorial Board member ​​Nina Exner from Virginia Commonwealth University, I recently participated in an ORCID Community Call demonstrating this new integration and discussing our approach to building the Networked DMP. A recording of the webinar is available here, and our combined slide deck is available here.  

The DMPTool team continues to expand the Networked DMP. Development is currently underway for additional features within the DMPTool, including DMP versioning and advancing our API to facilitate external integrations. We look forward to sharing updates with you soon about these exciting advancements. In the meantime, as always, feedback or questions are most welcome and can be sent directly to maria.praetzellis@ucop.edu.

DMP Competition Winners: DMPs so good they go to 11

Last December we announced the inaugural Qualitative Data Management Plan (DMP) Competition, sponsored jointly by The Qualitative Data Repository, Princeton Research Data Service, and the DMPTool. As qualitative researchers writing such plans frequently ask for examples of excellent DMPs for qualitative research, we hoped that this competition would assemble a trove of exemplar DMPs that we could share with the research community. 

We received a wealth of excellent submissions. Many of the DMPs were so good in fact, that for that extra push over the cliff we decided to expand our pool of awardees from 10 to 11 outstanding Qualitative DMPs from a wide range of disciplines. We couldn’t be more excited to announce these winners today. We’re hugely thankful to everyone who submitted a DMP, and, of course, to the five data management experts who judged the entries (listed below).

Each entry was reviewed by three expert judges. They assessed DMPs on a 1-4 (not adequate to exemplary) scale for each item in an 18-item rubric rubric based on the DART Project  as well as guidance from the DMPTool. Judges also assigned an overall quality score from 1-10 to each DMP. You can find our rubric on OSF. Rubric scores and overall scores were closely correlated (r=.89), suggesting that the rubric closely aligned with experts’ assessments of overall quality. We also asked judges to include some overall observations about each DMP: we have included excerpts from these for each winner. 

And the awards for Outstanding Qualitative DMP go to:

Listed alphabetically by first author with summary comments from the judges

1. Amelia Acker, Ashley Bower, Emily Simpson, Bethany Radcliff, University of Texas at Austin, School of Information, “COVID-19 Oral Histories Project,” developed for research by Whitney Chappell, University of Texas at San Antonio 

“Wonderful DMP and approach to community-centered work”

2. Nicholas Bell, University of Pennsylvania and Georgetown University, “Why Do So Few Workers Take Trade Adjustment Assistance” 

“This is a strong DMP, and it’s clear the author has thought through and begun implementing good data management principles even in the composition of the DMP itself. Clear descriptions of data collection and plans for storing and sharing.”

3. Patricia Condon, Louise Buckley and Eleta Exline, University of New Hampshire,  “Teaching Quantitative Data in the Social Sciences at the University of New Hampshire: Data Management Plan”.

“Concise and straightforward descriptions of data formats, plans for storing and preserving … Wonderful DMP and acknowledgement that it’s a living document!”

4. Dayna Cueva Alegría, University of Kentucky, NSF SBE, “Water Pollution Governance in Lake Titicaca: Creating Political Spaces of Democratization

“Strong DMP with a lot of attention and detail paid to data formats, storage, preservation, and sharing”

5. Laura Garbes, Brown University, NSF SBE, with Andrew Creamer, Science Data Specialist, Brown University,  “Analyzing Diversity Efforts in Public Radio Organizations – A comparative approach to performance standards in the workplace” 

“…this DMP is pretty much perfect. Includes different measures to avoid issues related to confidentiality and security as well as it is clearly committed to data discoverability, accessibility and reusability, specially when articulates about the storage/archiving options”

6. Christopher Hale, University of Alabama, NSF- SBE “Ethnic Diversity and Public Goods Provision Across Latin America”

“Strong plan for description of data collection, storage, and sharing, with good attention to considerations for de-identifying data during the entire process, prior to depositing with the repository. This DMP has a lot of great detail about the security and anonymity practices of the PI…”

7. Jaeci Hall, University of Oregon, NSF-SBE, “Text Analysis of Taldash (GAL) in Support of Nuu-wee-ya’ Language Revitalization: Indigenous-based linguistic analysis and methodological reflections

“This DMP is an excellent example of cultural sensitivity when working with indigenous materials… Good plans for handling sensitive data and the role of partner institutions with regards to data ownership and rights to share.”

8. Tina Nabatchi, PARCC, Syracuse University and  Rebecca McLain, Portland State University, NSF SBE, “The Atlas of Collaboration: Building the World’s First Large N Database on Collaborative Governance” 

“This DMP is strong in describing both how data will be gathered and maintained now, and how it will be appropriately archived in the future. Provides a great description of the expected data and roles and responsibilities with regard to data in a multi-institutional project. Fantastic DMP.”

9. Joshua Rubin, Bates College, NSF SBE, with Pete Schlax, Science and Data Librarian, Bates College, “Possibility Spaces and Possible Things

“Overall an excellent DMP… [T] he overall plan is strengthened by inclusion of QDR selection for data sharing” 

10. Carolina Seigler, Princeton University, Department of Sociology, NSF-SBE, “Religion and Sexual Violence” 

“Compelling DMP, really made the case why the data cannot be shared well and the security provisions were exemplary.” 

11. Ieva Zumbyte, Brown University,  NSF-SES, with Andrew Creamer, Science Data Specialist, Brown University, “Tracing the Quality of Public Childcare in the Neighborhoods of Chennai, India

“…carefully considers issues such as licensing and re-identification of de-identified data… Very good description of the chosen repository and the characteristics that backup such a choice, even though the raw data won’t be shared.”

Our panel of expert judges

  • Renata G. Curty, Social Sciences Research Facilitator, UCSB Library’s Research Data Services, University of California, Santa Barbara
  • Jennifer Doty, Research Data Librarian, Emory University
  • Celia Emmelhainz, Anthropology & Qualitative Research Librarian, University of California, Berkeley
  • Megan O’Donnell, Data Services Librarian, Iowa State University
  • Vicky Rampin, Research Data Management and Reproducibility Librarian, New York University Libraries

DMP IDs and the DMPTool: Announcing DMPTool v. 3.1

Image from "The Post-Office annual Glasgow directory" (1828)
Image from “The Post-Office annual Glasgow directory” (1828) (https://flic.kr/p/oe2ZFe)

Building the recent creation of “A Brave New PID” for DMPs, we are excited to announce that DMP creators can now receive IDs for their DMPs within the DMPTool. From the outset of our NSF-funded EAGER research project, the ability to generate DMP IDs has long been on the strategic roadmap for integrating DMPs into the scholarly knowledge sharing and persistent identifier ecosystem. 

Supporting NSF recommendations for data management 

As the NSF detailed in their May 2019 Dear Colleague Letter on Effective Practices for Data, it is recommended that researchers utilize PIDs for their data outputs and generate DMPs that allow for automated information exchange (machine-actionable DMPs, “maDMPs”). Expanding on the NSF Dear Colleague Letter, the recent report by ARL, AAU, APLU and CDL, Implementing Effective Data Practices: Stakeholder Recommendations for Collaborative Research Support, provided key recommendations for effective data practices to support an open research ecosystem emphasizing the adoption of PIDs and maDMPs. 

The DMPTool team continues to work towards supporting these recommendations by building new features and services for an open, automatically updated, interconnected system for data management of research projects. 

Our new feature of generating IDs for DMPs represents tangible progress towards achieving our shared goal of moving DMPs from static text documents into structured, interoperable data that is able to be fed across stakeholders, linking metadata, repositories, and institutions, and allowing for notifications, verification, and reporting in real-time. 

What’s included in this latest release? 

Below is an outline of three new features included in this release. For technical details and a few additional features included, please see our v3.1.0 documentation. These improvements have also been distributed to the larger community within our shared open source codebase, DMPRoadmap. Thank you to the DMPTool Editorial Board for their guidance and feedback as we developed this feature set. We are also appreciative of the DMPTool Administrators who submitted feedback on an early iteration of this release. We intend on incorporating many of these suggestions in future releases and building off the many good ideas shared by all as we continue to expand our support for Networked DMPs

1. IDs for DMPs

Within the Finalize/Publish tab users can “Register” their plan and generate a DMP ID. The DMP ID will then display within the tool and link to a landing page for the plan. For further details on this feature please see our DMP ID documentation.

2. DMP ID Landing Page

After receiving a DMP ID, the system will generate a DMP landing page that includes high level details about the plan. The DMP ID metadata does not include the narrative components of a DMP. For an example of a DMP ID landing page please see this DMP

The landing pages also demonstrate the types of connections made possible by tracking a research project over time from the point of DMP creation. As a project progresses over time, updates to the plan can be connected to the DMP ID and will display on the associated landing page. 

3. Research Outputs Tab

The new Research Outputs tab allows researchers to describe specific project outputs at a more granular and controlled manner than was previously possible solely via the text narrative. In designing this new section, we strived to utilize as many controlled vocabularies and PIDs as possible. Here are some highlights of the new tab:

  • Repository selector tool utilizing the Registry of Research Data Repositories (re3 data registry) that allows researchers to define where they anticipate depositing a specific output 
  • License selector (from SPDX) that allows researchers to define the associated license for specific outputs 
  • Ability to flag outputs as containing sensitive data and/or PII

Researchers can create an unlimited number of specific research outputs. All entered outputs are included in the downloaded version of the plan, placed after the narrative component of the plan so as not to interfere with funder page count limits. 

What’s up next?

With the ability to generate DMP IDs now in place, we are one step closer to creating networked, living DMPs. While this is a great start, we have many additional features in development that will extend the usability and interoperability of this new generation of DMPs. In the coming months, we will be working on developing these additional features:

  • Connecting DMPs to other related research outputs such as datasets and journal articles via the PID Graph
  • Connecting DMP IDs to corresponding ORCID records 
  • Incorporating additional PIDs including research resource identifiers (RRIDs
  • Sponsor and funder approval workflow wherein these stakeholders can review, comment, and approve submitted DMPs
  • Integration with the Electronic Lab Notebook, RSpace
  • Adding the ability for DMPTool admins to curate a list of recommended repositories for the new repository selector tool 

Additionally, in response to several DMPTool admin requests for outreach materials supporting adoption of the DMP ID, we are developing materials to share with the DMPTool admin community in order to promote these data practices amongst their users. 

We will continue to share details on this work and the development of new features to support the networked DMP. Stay tuned for more developments over the coming months for further advancements.

As always, feedback or questions are most welcome and can be sent directly to maria.praetzellis@ucop.edu.

A Brave New PID: DMP-IDs

Cross-posted from DataCite written by Kristian Garza and Matt Buys

Original post: https://doi.org/10.5438/j22a-5d79

Despite the challenges over the last year, we are pleased to share some exciting news about launching the brave new PID, DMP IDs. Two years ago we set out a plan in collaboration with the University of California Curation Center and the DMPTool to bring DMP IDs to life. The work was part of the NSF Eager grant DMP Roadmap: Making Data Management Plans Actionable and allowed us to explore the potential of machine-actionable DMPs as a means to transform the DMP into a critical component of networked research data management.

The plan was to develop a persistent identifier (PID) for Data Management Plans (DMPs). We already have PIDs for many entities, such as articles, datasets etc. (DOIs), people (such as ORCID iDs) and places (such as ROR IDs). We knew that it would be important for DataCite to support the community in establishing a unique persistent identifier for DMPs. Until now, we had no PID for the document that “describes data that will be acquired or produced during research; how the data will be managed, described, and stored, what standards you will use, and how data will be handled and protected during and after the completion of the project”. There was no such thing as a DMP-ID; and today that changes.

Over the last few years, there has been lots of community effort towards establishing a standard data model under the Research Data Alliance (RDA) DMP Common Standards Working Group and we are now able to bring this all together in the form of a new identifier.

DMP schema example

DMP IDs at a fundamental level are registered as a DOI with the resourceTypeGeneral “OutputsManagementPlan.” Since the DataCite release of schema 4.4, the resourceTypeGeneral controlled vocabulary now includes this as a controlled list item. DMP IDs are created in the same way as registering any DOI, with the same required fields, but must include the “OutputsManagementPlan” resourceTypeGeneral to be identifiable.

Generating DMP IDs creates an unbreakable link between a data plan to the project outputs and allows access to DataCite’s supporting services such as Event Data to facilitate connections via the PID Graph.

Assigning DOIs to persistently identify DMPs is a trend that we have seen already. Since 2019, more than 200 DMPs have been assigned a DOI for their identification. Repositories such as Zenodo made this possible by allowing users to select Data Management Plans as one of the many types of resources.

Distribution of DOIs assigned to DMPs by year(creation of the DOI).

We know through our work with the DMP community that the introduction of the formal DMP ID, will allow for DMP IDs to proliferate and serve downstream use cases.

Besides persistently identifying DMPs, the assignment of DMP IDs realizes the promises of machine-actionable DMPs. The DataCite GraphQL API can now expose Data Management Plans and all their connections. Other applications can use the same APIs to build machine-actionable DMPs-based applications such as visualizations or summary statistics.

From today, it is possible for DataCite members to use the MDS API and Fabrica to assign DMP IDs to your Data Management Plans. Our team has created documentation to support the community in registering DMP IDs, understanding best practices and exploring related connections in the PID Graph.

We are really pleased to have reached this milestone and look forward to tracking the downstream impact.

DMPRoadmap Annual Planning Meeting

This is a joint blog post between DMPonline and the DMPTool

In February we conducted our annual strategic planning meeting between DCC and CDL to discuss joint plans for the upcoming year. We were joined from DCC by: Kevin Ashley, Patricia Herterich, Magdalena Drafiova, Marta Nicholson, Ray Carrick, Angus Whyte, Diana Sisu and from CDL: John Chodacki, Marisa Strong, Catherine Nancarrow, Brian Riley and Maria Praetzellis.

This meeting was a follow up to our 2019  meeting, where we had a chance to meet for three days with our colleagues and we wanted to replicate this in our half day online meeting. This time around we had to swap to Zoom for the lovely city of Edinburgh and only met for a half day instead of three days. Nonetheless, we managed to accomplish some important high level planning discussions regarding the work of continuing our collaboration on the Roadmap codebase. In this blog post we provide you with the summary of what we discussed and share our plans for the coming months. 

Celebrating the achievements of 2020

We all agreed that despite the many challenges of 2020 (not to mention the departure of Sarah Jones and Sam Rust), this was a very successful year for our collaboration. Our team of developers completed several large developments a few of which are highlighted below: 

  • Completed the Rails5 migration 
  • Developed an API that is compliant with the RDA Common Standard for DMPs
  • Released a new feature allowing for conditional questions and notifications within DMP templates
  • Improved the usage dashboard
  • Integrated with Google Analytics
  • Integrated with translation.io to facilitate several languages

Several new features surrounding machine-actionable DMPs were also released of the past year including: 

  • RORs Identifiers for research organizations
  • Funder Registry Identifiers for funders
  • ORCiDs for DMP creators and collaborators
  • API compliant with RDA Common Standard Metadata Schema 
  • Ability to export plans as RDA Common Standard compliant JSON

Highlights of our 2021 Development Plans 

During the first quarter of 2021, DMPonline will focus on consolidating the code base, making sure the various changes both the DMPTool and DMPonline team have developed over the past year are integrated and any new work is carried out on top of a shared code base. 

UX Improvements 

Based on the extensive usability testing that both DMPTool and DMPonline have conducted over the past year, we will select pieces of work that will have significant impact for both services. Initially we will focus on the creation of a new plan wizard making the creation of new plans and the selection of templates and appropriate guidance easier.

Expanded machine-actionable DMP features

  • The ability to generate a unique identifier for a DMP with an associated landing page that connects the DPM to eventual research outputs
  • A new Research Outputs tab will allow for more granular description of specific research outputs 
  • Integration with the Registry of Research Data Repositories (re3data)
  • Integration with FAIRsharing
  • Plan versioning

DMPRoadmap for funders

In 2021, we will also work on making DMPRoadmap more useful to funders. This will include:

  • A different dashboard view
  • Easier ways to integrate grant numbers and other funder specific information
  • Tagging of institutional DMP templates as funder compliant

Other collaborations

The DMPonline team will also work with the TU Delft on a project that will integrate the system more with institutional login options to automatically get more information about users and use that to improve workflows and reporting for institutional admins.

RSpace integration

The electronic lab notebook, RSpace, and the DMPTool are currently working on an integration allowing for the bi-directional linking of data between DMPTool and RSpace. The first phase of this work is currently in development and utilizes OAuth so that users can connect accounts. Once we get this initial connection running, the team will look at bi-directional notifications and updates between the two systems.

For a more detailed description of our upcoming development plans please see our wiki page. This promises to be another busy but exciting year of work for both teams and we look forward to continuing to share our progress with you!

Furthering Open Science through Research Data Management Services

As I begin my second year at CDL, I am excited to outline the objectives and key activities for my work: furthering research data management (RDM) practices that support open science at the University of California and beyond. 

I conceptualize our work in the larger context of what an ideal RDM ecosystem might be: wherein open science practices are universally understood and implemented by data creators and stewards and built upon the bedrock of simple, interoperable RDM infrastructure and optimal open data policy. Below are four key ways in  which RDM services at CDL contribute to this overall effort in 2021.

  1. Facilitating Communication Between Data Librarians and Researchers

For almost ten years now, the DMPTool web application has provided accessible, jargon-free, practical guidance for researchers to create and implement effective data management plans for 30+ funding agencies. Thanks to our dedicated Editorial Board we are able to keep the tool up in sync with current funder requirements and best practices. 

In 2021, we will be expanding our outreach to the library community by offering quarterly community calls with DMPTool users in order to discuss new features, highlight community use, and facilitate feedback. Additionally, the DMPTool Editorial Board will analyze existing guidance within the tool to identify aspects that need to be updated or new topics that should be included. The DMPTool has long been a community-supported application and we will continue to expand our engagement with the community as we grow the application. 

  1. Serving as an Interoperable Partner in Essential RDM Services

Our work developing the next generation of machine-actionable, networked DMPs builds upon community developed standards and is rooted in collaboration. In order to create the new networked DMP, these partnerships will continue to be essential to our success. Last year’s release of the RDA DMP Common Standard for machine-actionable Data Management Plans and the recent report Implementing Effective Data Practices: Stakeholder Recommendations for Collaborative Research Support (written by CDL, ARL, AAU & APLU) are testament to the power of these partnerships. We simply get more done when we work together. Additionally our continued collaboration with DMPonline allows us to share resources as we co-develop via the DMPRoadmap codebase, share best practices, and advance new features jointly. 

Looking ahead, in 2021, we will expand on our collaborations including:

  • Partnering with DataCite to encourage adoption of the new DMP ID, a resource made possible by the forthcoming metadata scheme update. Expect more updates on this soon!
  • A new integration between the DMPTool and electronic lab notebook platforms, starting with RSpace.
  • Partnering with the UC Natural Reserve System and the Tetiaroa Society to advance data policies supporting open science at working field stations.
  1. Supporting a Transparent Research Process 

Much of our work last year was focused on developing the backend infrastructure necessary to confidently be able to say DMPTool DMPs are machine-actionable. 

With the infrastructure in place and development completed, in 2021 we will be releasing several new features to expand the possibilities of the new networked DMP and help ensure transparency in the research process. Many of these new features are currently being pilot tested as part of the FAIR Island Project. We will also be conducting webinars in the coming weeks to gather feedback from the community to further inform our iterative feature development and release cycles.

  1. Developing Optimal Open Data Policies 

The FAIR Island project is a real-world use case evaluating the impact of implementing optimal research data management policies and requirements; the project will help demonstrate and publicize the outcomes of strong data policies in practice at a working field station. 

With the recent addition of Erin Robinson to the team, the FAIR Island project is making swift progress towards implementing a data policy that will govern data collected on the Tetiaroa atoll. This data policy is still open to community feedback so if you are interested in contributing, now is your chance! Please share your thoughts via this survey

In 2021, the FAIR Island project team will continue to advance and iterate on the data policy, working with additional field stations to advance data policies supporting open science. In partnership with the UC Natural Reserve System and 4Site network, we aim to move toward a common, optimal data policy that can be shared amongst UC field stations and other partner sites. To keep abreast of our progress please check out our project website where we are tracking project work in our blog. 

How to contribute
Building on a solid foundation of community developed standards for DMPs and FAIR data, this year we will be moving much of this work from theory into real world implementation. 

It’s an exciting time for these developments and we welcome all questions, comments, and advice.  Please reach out with your thoughts!

Call for Submissions to the Inaugural Qualitative Data Management Plan (DMP) Competition

QDR-DMPTool-Princeton

Data Management Plans (DMPs) play an integral role in ensuring that data are collected, organized, stored, and shared responsibly. Qualitative researchers writing such plans frequently ask for examples of excellent DMPs for qualitative research. To respond to this need, and to celebrate excellence in managing and sharing qualitative data, we are excited to announce the inaugural Qualitative Data Management Plan Competition.

If you have a DMP for a qualitative research project, you are invited to submit it for a chance to win one of 10 “outstanding qualitative DMP” awards, each of which includes a prize of $100. The competition is a joint initiative of the Qualitative Data Repository, DMPTool, and the Princeton Research Data Service

Rules for Submission:

  1. The DMP must describe a research project that is either primarily qualitative in nature, or is multi-method and qualitative data form a significant part of the project  (“qualitative” is conceived broadly; see this non-exhaustive list of types of qualitative data).
  2. The DMP should be about 2 or 3 single-spaced pages in length.
  3. The DMP must be publicly available online. We recommend sharing the DMP directly through the DMPTool or publishing on the Zenodo platform.
  4. The competition is open to DMPs from current or past proposals.
  5. If the DMP was written for a particular funding opportunity, please include a link to the funder’s requirements for DMPs.
  6. The author(s) must complete the submission form no later than 11:59 PM EDT March 15, 2021.

Because of legal restrictions beyond our control, while anyone submitting a DMP will be considered for an award, the monetary component of the award is only available to participants who are eligible to work in the US.

Please submit your entry through this form

Valid submissions will be reviewed by a panel of five judges, and their evaluations will be guided by the DMP rubric from the DART Project (https://osf.io/kh2y6/). We expect to notify the 10 winners via email by April 30, 2021 and publicly announce them on our websites and social media. Please contact the competition organizers at qdr@syr.edu with any questions.

Our panel judges are:

  • Renata G. Curty, Social Sciences Research Facilitator, UCSB Library’s Research Data Services, University of California, Santa Barbara
  • Jennifer Doty, Research Data Librarian, Emory University
  • Celia Emmelhainz, Anthropology & Qualitative Research Librarian, University of California, Berkeley
  • Megan O’Donnell, Data Services Librarian, Iowa State University
  • Vicky Steeves, Research Data Management and Reproducibility Librarian, New York University Libraries

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