Browsing by Author "Woodbrook, Rachel"

Now showing 1 - 8 of 8
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    Accessibility Data Curation Primer
    (Data Curation Network, 2023) Oxford, Emily; Woodbrook, Rachel
    Data curators are uniquely positioned to help improve access not just to individual datasets, but to the world of research data at large. As guides to and stewards of data, curators can counsel researchers on how to build accessibility into data planning, collection, analysis, and archiving. This primer is intended as a starting point for data curators who are invested in improving the accessibility of individual files or datasets, rather than as definitive guide. There is far more work to be done than can be addressed in the scope of this primer. Disability is also a complex concept with a diversity of possible presentations, which will present varying (sometimes even conflicting) accessibility needs.
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    Consent Forms Data Curation Primer
    (Data Curation Network, 2021-02-26) Hunt, Shanda; Hofelich Mohr, Alicia; Woodbrook, Rachel
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    CURATE(D) Fundamentals Workshop
    (Data Curation Network, 2024) Hudson Vitale, Cynthia; Hadley, Hannah; Wham, Briana; Borda, Susan; Carlson, Jake; Darragh, Jennifer; Fearon, David; Herndon, Joel; Hunt, Shanda; Johnston, Lisa R.; Kalt, Marley; Kozlowski, Wendy; Lafferty-Hess, Sophia; Marsolek, Wanda; Moore, Jennifer; Narlock, Mikala; Scott, Dorris; Wheeler, Jon; Woodbrook, Rachel; Wright, Sarah; Yee, Michelle; Lake, Sherry
    Slides developed for the CURATE(D) Fundamentals workshops. These materials only include the lecture slides and have removed associated exercises and institutional examples used in synchronous trainings. See the CURATE(D) modules [https://datacurationnetwork.github.io/CURATED/] for exercises to work through the CURATE(D) steps or contact the DCN to discuss partnering on a in-person workshop.
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    FITS Data Curation Primer
    (Data Curation Network, 2024-10-31) McKone, Lubov; DeRocchis, Robyn; Orozco, Rebecca; Woodbrook, Rachel
    From the primer: FITS, or Flexible Image Transport System, is a data format most widely used in astronomy to transfer scientific data and their associated metadata. FITS was developed in the late 1970s by astronomers in the USA and Europe to facilitate international data transfer between observatories. In astronomy, images of the night sky are treated as arrays of data to be analyzed. For this reason, FITS was designed to be a highly flexible format that can be used to store and transfer any number of n-dimensional arrays. This means that although its name contains “image,” FITS files often contain only non-image data such as one-dimensional spectra or tabular information. Most commonly, FITS files contain a combination of images and 2-dimensional data tables stored in rows and columns. In fact, FITS files can contain almost anything. Although the building blocks are simple, FITS files can have unlimited components, and therefore can become complex quickly.
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    Human Subjects Data Essentials Data Curation Primer
    (Data Curation Network, 2020) Darragh, Jen; Hofelich Mohr, Alicia; Hunt, Shanda; Woodbrook, Rachel; Fearon, Dave; Moore, Jennifer; Hadley, Hannah
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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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    Summit for Academic Institutional Readiness in Data Sharing (STAIRS) Complete Slide Deck
    (Data Curation Network, 2024) Narlock, Mikala; Carlson, Jake; Cowles, Wind; Herndon, Joel; Petters, Jon; Kozlowski, Wendy; Delserone, Leslie; Chandramouliswaran, Ishwar; Federer, Lisa; Wham, Briana; Woodbrook, Rachel; Johnston, Lisa; Lafia, Sara; Ivey, Susan; Downey, Moira; Hofelich Mohr, Alicia; Stollar Peters, Catherine; Fernandez, Rachel
    The complete, main slide deck from the Summit for Academic Institutional Readiness in Data Sharing (STAIRS). Includes slides from individual presenters as well as STAIRS organizers.
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    Training, Consulting and Curation Services: Curation as Education
    (2024) Woodbrook, Rachel
    Presented at the Summit for Academic Institutional Readiness in Data Sharing (STAIRS), this presentation provides insight into how curation can be used as education.