Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Data Curation Network Outputs

Persistent link for this collectionhttps://hdl.handle.net/11299/225718

Research and outputs from the Data Curation Network, a community of professional data curators, data management experts, data repository administrators, disciplinary scientists and scholars that represent academic institutions and non-profit data repositories who steward research data for future use. We strive to build a trusted community-led network of curators advancing open research by making data more ethical, reusable, and understandable.

Project website: https://datacurationnetwork.org/

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Browsing Data Curation Network Outputs by Issue Date

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    Annual Report 2021: End of Year Highlights of the Data Curation Network
    (2021) Johnston, Lisa R; Narlock, Mikala
    This report showcases the numerous endeavors the DCN supports, from shared curation to conducting research in Special Interest Groups, active collaborations, and education opportunities. This report also includes an overview of the newly adopted and implemented Governance Model.
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    Open for All, Reusable for Whom?: A Review of What Data Reusers Want and How Data Repositories Can Deliver
    (2021-06-10) Faniel, Ixchel M; Johnston, Lisa R; Wissel, Katie
    Understanding how data reusers seek and evaluate potential data for reuse will aid data curators, data managers, and developers in the open repository field. We will review past studies of data reusers, specifically a qualitative study of 105 researchers from three disciplinary communities: quantitative social science, archaeology, and zoology. The study identified 12 types of context information that data reusers mention needing when deciding whether to reuse data. Next, we will use the context types to create a feature set and assess how data repositories provide the needed context information to users. Finally, using findings from our assessment, we will showcase desirable features in use to prototype the design of a reuser-oriented data repository that developers can use to improve their data repository interface.
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    Data Curation Network End User Survey 2021
    (2021-09-28) Wright, Sarah; Johnston, Lisa; Marsolek, Wanda; Luong, Hoa; Braxton, Susan; Lafferty-Hess, Sophia; Herndon, Joel; Carlson, Jake; jw256@cornell.edu; Wright, Sarah; Data Curation Network
    This dataset includes the processed dataset from the 2021 End User Survey performed by the Data Curation Network.
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    Data Curation Network: Collaboratively Enhancing Capacity for Research Data Support
    (2021-12-14) Kozlowski, Wendy; Johnston, Lisa R
    Since its launch in 2016 the Data Curation Network (DCN) has grown into a radically collaborative network of institutions, data repositories, and organizations focused on the ethical sharing of research data. Growing beyond its original scope of shared staffing for data curation, the DCN has become a thriving community and sustainable organization that advocates for data curation and data curators and provides a unique platform for exploration and research. This presentation will highlight efforts underway in the DCN and provide project updates including research assessing the value of curation, an NSF-funded workshop to build and strengthen “data communities'' with our partners at Ithaka S+R, a multi-year NSF EAGAR grant to develop evidence-based models for campus data sharing infrastructure with our partners at ARL, a racial justice initiative to better incorporate inclusive and equitable data curation practice into our work, and the DCN strategic plan for the next three years.
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    Think Globally, Act Locally: The Importance of Elevating Data Repository Metadata to the Global Infrastructure
    (2022) Taylor, Shawna; Wright, Sarah; Narlock, Mikala R.; Habermann, Ted
    Inconsistent and incomplete applications of metadata standards and unsatisfactory approaches to connecting repository holdings across the global research infrastructure inhibit data discovery and reusability. The Realities of Academic Data Sharing (RADS) Initiative has found that institutions and researchers create and have access to the most complete metadata, but that valuable metadata found in these local institutional repositories (IRs) are not making their way into global data infrastructure such as DataCite or Crossref. This panel examines the local to global spectrum of metadata completeness, including the challenges of obtaining quality metadata at a local level, specifically at Cornell University, and the loss of metadata during the transfer processes from IRs into global data infrastructure. The metadata completeness increases over time, as users reuse data and contribute to the metadata. As metadata improves and grows, users find and develop connections within data not previously visible to them. By feeding local IR metadata into the global data infrastructure, the global infrastructure starts giving back in the form of these connections. We believe that this information will be helpful in coordinating metadata better and more effectively across data repositories and creating more robust interoperability and reusability between and among IRs.
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    Unpacking the structures of radical interdependence: The experience of the Data Curation Network
    (2022) Carlson, Jake; Cowles, Wind; Johnston, Lisa R.; Narlock, Mikala R.
    In 2016, six academic libraries initiated the Data Curation Network (DCN) as an experimental model for collaboratively curating data sets deposited into their institutional repositories. The DCN model is centered on “radical interdependence”, opening up access to expertise of the data curators at any one institution to all participating member libraries. Under this model, DCN member institutions leverage the collective knowledge and skills of all member curators, creating a greater capacity to curate more data more effectively than any one institution could by themselves. The DCN is now a thriving community and has grown to 17 member institutions. The DCN recently conducted a project retrospective to better understand the enabling structures and tools that allowed the network to thrive. Representatives from the member institutions were recently asked to reflect on their experiences in developing the DCN and what elements contributed to its success. We asked them to consider not just the structures, tools, and workflows that we collectively designed, but the more intangible aspects of the DCN, such as generating trust, acknowledging vulnerability, and community building. In our presentation, we will explore the key enabling structures of “radical interdependence” that were identified by DCN members. We will then lead a discussion on how these structures could potentially be applied to other cross-institutional or multidisciplinary collaborations. Presented at the Coalition for Networked Information Fall 2022 member meeting.
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    Towards Authenticity: Critical Appraisal of Data Management Plans
    (2022) Carlson, Jake; Darragh, Jen; Fearon, Dave; Narlock, Mikala R.
    This workshop, geared towards program officers, will be an introduction to the many considerations of research data management and sharing, specifically around the role of data management plans (DMPs) in grant proposals. In addition to learning how to evaluate DMPs from the perspective of a data curator, attendees will learn how their roles fit into the entirety of the research data lifecycle, will learn how to balance sharing sensitive data while protecting participant privacy, and will brainstorm ways to integrate best practices for DMPs into the grant proposal process. This session will combine lecture, activities, and discussion -- attendees should come prepared to participate.
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    Researcher Approved: a Multi-institutional Survey of Depositors to Six Academic Data Repositories
    (2022) Wright, Sarah; Marsolek, Wanda; Luong, Hoa; Braxton, Susan; Lafferty-Hess, Sophia; Carlson, Jake
    The Data Curation Network (DCN) is a collaborative network of curators advancing open research by making data more ethical, reusable, and understandable. Institutional members participate in and learn from a community of expert data curators and curate data via a cross-institutional shared staffing model. This enables institutions to submit data sets to the network when they are outside of our local expertise or when local curators are busy or absent. The collaborative network model benefits our curators; however we questioned whether there is an impact on depositors. In order to evaluate end user satisfaction with data curation services, we surveyed recent depositors over the past year and a half, regardless of whether they received curation locally or from DCN curators. The result was overwhelmingly positive: we enjoyed a high response rate and consistently laudatory feedback including many free-text responses testifying to the value of curation. In times of tight budgets and constricting services, it is good to have researcher testimonials and survey data to indicate the added value of curatorial review to the data sharing process, and evidence that a collaborative network of data curators benefits us all.
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    A Network for People
    (Data Curation Network, 2022) Parkman, Aundria
    Blog post by Aundria Parkman, Data Curation Network (DCN) intern through the National Center for Data Services (NCDS). This post refers to the author's experience while attending the DCN annual All Hands Meeting held in June 2022. These internships are funded with Federal funds from the National Library of Medicine (NLM) and the National Institutes of Health (NIH).
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    DCN and the Chamber of Community
    (Data Curation Network, 2022) Gonzalez, Liliana
    Blog post by Liliana Gonzalez, Data Curation Network (DCN) intern through the National Center for Data Services (NCDS). This post refers to the author's experience while attending the DCN annual All Hands Meeting held in June 2022. These internships are funded with Federal funds from the National Library of Medicine (NLM) and the National Institutes of Health (NIH).
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    Groove is in the Heart: Trust and Vulnerability in Collaboration
    (2022) Blake, Mara; Marsolek, Wanda; Narlock, Mikala R.
    As members of a sustainable and successful collaborative network, we wanted to remove the magic and mystery of collaboration and unpack the emotional, mental, and technical labor that went into establishing this network. Through a project retrospective, we uncovered the need for radical interdependence, vulnerability and trust. This was presented at the 2022 Digital Library Federation Forum in Baltimore, Maryland.
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    New and Improved: Refining the CURATE(D) model and developing online modules
    (2022) Wham, Briana Ezray; Narlock, Mikala R.
    The CURATE(D) model for data curation is a useful teaching tool for demonstrating data curation best practices: while practical and structured enough to provide a foundation for learners, it also provides enough flexibility to be adaptive for different disciplines and data format needs. In the past year, the Data Curation Network has undertaken efforts to expand this model, incorporate ethical concerns, and make it accessible online via learning modules. In this presentation, attendees will learn more about the CURATE(D) 2.0 model and the collaborative revision process, as well as have early access to a beta version of the online learning modules. Participants will also be invited to provide asynchronous feedback on the modules during and following the session. Presented at the 2022 Midwest Data Librarian Symposium.
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    The DCN Listens
    (Data Curation Network, 2022) Arteaga Cuevas, Maria
    Blog post by Maria Arteaga Cuevas, Data Curation Network (DCN) intern through the National Center for Data Services (NCDS). This post refers to the author's experience while attending the DCN annual All Hands Meeting held in June 2022. These internships are funded with Federal funds from the National Library of Medicine (NLM) and the National Institutes of Health (NIH).
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    Checklist of CURATED Steps Performed by the Data Curation Network, v.2
    (2022) Data Curation Network
    The DCN developed a standardized set of C-U-R-A-T-E-D steps and checklists to ensure that all datasets submitted to the Network receive consistent treatment. The CURATE(D) Checklists were revised to include more information about how to use the workflow and incorporates ethical considerations at each step. The most up to date version is available at: https://z.umn.edu/CURATE
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    Data Curation Network Strategic Framework 2022-2025 v1.0
    (2022) Data Curation Network
    Our three year “living” strategic framework outlines the Data Curation Network's vision for continued growth while further cementing our sustainability as an organization and within the open research community. The purpose of this document is to: -Publicly and clearly articulate the Data Curation Network’s mission, vision, and values -Capture members’ ideas and aspirations for the next few years -Anticipate (in order to respond to) spending priorities -Help all DCN members understand what they can do to move us forward
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    Data Curation Network Governance Model v 2, revised June 2022
    (Data Curation Network, 2022) Data Curation Network;
    A revised version of the Data Curation Network's Governance Model for fiscal year 2022-2023 (July 1-June 30). This version of the DCN's Governance Model amends the description of the Executive Committee and it's roles and responsibilities as a DCN structure; redesigns the role of DCN Director, now that it is a full time position; removes elections as the means of determining committee chairs and Executive Board committee members in favor of seeking volunteers; and removes Beta Membership as an option from our website, but retaining it as an option to consider when recruiting members to provide greater flexibility. The DCN Governance model will be reviewed on no less than an annual basis by the DCN Governance Board to ensure it accurately reflects and represents the culture, practices and needs of the DCN.
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    Peer Compare Preservation Practices Benchmark Report
    (2022) Data Curation Network
    DCN members participated in a pilot project in early 2022 in which partner representatives were invited to individual meetings with then DCN Assistant Director, Mikala Narlock, to share and discuss their current preservation practices, specifically with regards to research data. This report details the background, project methodology, results, shared challenges and opportunities, and potential future projects for the DCN and similar peer organizations. These conversations revealed that many members share challenges, such as creating and managing preservation metadata, drafting and sharing retention and review policies, or collaborating on the complex preservation of blended software and code. The overall sentiment of interviews is that each institution is preserving content to the best of their abilities now, while watching research data preservation best practices develop. This report should be understood as a snapshot in time and a benchmark of current practices.
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    “We’re all doing the best we can with what we’ve got": Preservation practices of Data Curation Network members
    (2022) Luong, Hoa; Narlock, Mikala R.; Petters, Jonathan
    Over the course of six weeks, members of the Data Curation Network were interviewed by then Assistant Director to discuss their research data preservation practices. Through these semi-structured interviews, several commonalities emerged, including key challenges that will need to be addressed to ensure the long-term reusability of research data as well as the similar mentality many institutions expressed: that they are doing the best they can with what they have. The authors conclude by identifying areas of potential future research as well as practical collaboration opportunities. This presentation was presented at iPRES (the International Digital Preservation Conference) in Glasgow, Scotland, September 2022.
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    Ensuring long-term reusability and reproducibility: Collaborative Curation for FAIR data
    (2022) Narlock, Mikala R.; Taylor, Shawna
    Through the Data Curation Network (DCN), members enable findable, accessible, interoperable, and reusable (FAIR) data through a shared curation model, education and outreach efforts, and research and advocacy. This work exists within a member- funded and member-driven organization, with a focus on sustainability and long-term growth. Members of the DCN help shape the future of data curation and enable FAIR data by sharing best practices, collaboratively addressing shared challenges, empowering and educating one another, and advocating for data curation broadly. Poster created for and presented at the 17th International Digital Curation Conference (IDCC) June 2022. Includes a sixty second audio file describing the poster.