Browsing by Author "Taylor, Shawna"

Now showing 1 - 7 of 7
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    Clinical Trials Data Primer
    (Data Curation Network, 2022) Gonzalez, Liliana; Narlock, Mikala R.; Taylor, Shawna
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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.
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    Institutional data repositories are vital
    (Science, 2024-09) Darragh, Jen; Narlock, Mikala R.; Burns, Halle; Cerda, Peter A.; Cowles, Wind; Delserone, Leslie; Erickson, Seth; Herndon, Joel; Imker, Heidi; Johnston, Lisa R.; Lake, Sherry; Lenard, Michael; Hofelich Mohr, Alicia; Moore, Jennifer; Petters, Jonathan; Pullen, Brandie; Taylor, Shawna; Wham, Briana
    As funding agencies and publishers reiterate research data sharing expectations, many higher-education institutions have demonstrated their commitment to the long-term stewardship of research data by connecting researchers to local infrastructure, with dedicated staffing, that eases the burden of data sharing. Institutional repositories are an example of this investment. They provide support for researchers in sharing data that might otherwise be lost: data without a disciplinary repository, data from projects with limited funding, or data that are too large to sustainably store elsewhere. The staffing and technical infrastructure provided by institutional repositories ensures responsible access to information while considering long-term preservation and alignment with international standards. To ensure continued access to invaluable research data, it is essential that publishers and funding agencies recognize institutional repositories as responsible and reliable data sharing solutions.
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    Make it explicit: Surfacing Power and Ethics in the CURATE(D) Protocol
    (2023) Woodbrook, Rachel; Taylor, Shawna; Dolan, Lana Tidwell; Murray, Reina Chano; Narlock, Mikala R.; Calvert, Scout
    In 2018, the Data Curation Network (DCN) developed the CURATE(D) model, a standardized set of steps for curating research data with an eye toward the FAIR and CARE principles. The CURATE(D) model has proven to be 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. The CURATE(D) model has been revised over the years to integrate feedback and keep pace with the evolving data curation profession. In the past year, the DCN has undertaken efforts to rework this model to be responsive to ethical and power considerations highlighted by data sovereignty and data justice movements. This has meant revising the guidance to make explicit the tacit, power-laden assumptions regarding data appraisal and selection criteria, sharing decisions, and the iterative nature of curation. In this presentation, attendees will be invited to compare the previous and current versions of this model, will learn about the revision process, and have the opportunity to provide feedback on the model. Presented at IASSIST 2023 in Philadelphia, PA.
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    Primer for Researchers on How to Manage Data
    (Data Curation Network, 2023) Arteaga Cuevas, Maria; Taylor, Shawna; Narlock, Mikala R.
    This work was created as part of a collaboration between the National Center for Data Services (NNLM) and the Data Curation Network.
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    Sensitive Biodiversity Essentials
    (Data Curation Network, 2023) Jordan, Jen; Ramirez-Reyes, Carlos; Taylor, Shawna; Thielen, Joanna; Wham, Briana
    This primer is intended to offer guidance to curators for assessing the sensitivity of biodiversity data associated with research on animals, insects, plants, fungi, microorganisms, etc. Sensitive data can be defined as any information which “would result in an ‘adverse effect’ on the taxon or attribute in question or to a living individual” if made publicly available (Chapman, 2020). This primer covers how to identify sensitive data, considerations for sharing these data, and points for discussion with data depositors. The objective is to support researchers in balancing the sharing of biodiversity data as a public good and protecting it from misuse.
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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.