Browsing by Subject "data sharing"

Now showing 1 - 8 of 8
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Ethical and Practical Considerations of Open Data Sharing when Conducting Research with Human Participants
    (2019-03) Bakker, Caitlin; Hunt, Shanda; Hofelich Mohr, Alicia
    The Libraries and LATIS will present on the conflicts that can arise when attempting to balance the protection of participant privacy with expectations for open data. Restrictive language in IRB protocols, consent forms, and participant agreements designed to protect privacy can limit researchers when sharing, storing, or archiving the resulting data. Ethically, what is the appropriate course of action
  • Thumbnail Image
    Item
    Follow the money: Getting chemists to share their data
    (Research Data Access & Preservation Summit, 2018-03-22) Lafferty, Meghan; Bishoff, Carolyn; Farrell, Shannon
    The University of Minnesota Libraries was invited to partner with an interdisciplinary research center to strengthen their data management practices & pilot a data project at the request of NSF. Data sharing is not the norm for chemistry & materials science researchers but we had the support of advocates in the center and made practical compromises that encouraged buy-in and participation, led to well-described and decently organized data, and a workflow that we adapted to their time constraints and publishing cycle. We faced familiar obstacles to fully FAIR data: friction with researchers, complex and heterogeneous data sets, and limitations of our repository systems. Our university has an established data curation process but each data submission was highly interdisciplinary and it was the first time most of us had encountered the data formats we curated. Additional pressure came from the NSF which expected the Center to be a leader in data management. The project is a scaled effort to change the data sharing culture of a large research center using a collaboration of embedded liaison librarians and data curators. We had no budget for this project and relied on existing infrastructure and staff. This presentation will examine several decisions and compromises that frame the tension between the FAIR ideal and the limitations we had on people, systems, and data.
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    MetroGIS Benefits Study.
    (1999) Craig, William J.; Bitner, David
  • Thumbnail Image
    Item
    R Code and Output Supporting: Computational reproducibility in The Wildlife Society's flagship journals
    (2019-06-05) ArchMiller, Althea A; Johnson, Andrew D; Nolan, Jane; Edwards, Margaret; Elliot, Lisa H; Ferguson, Jake M; Iannarilli, Fabiola; Velez, Juliana; Vitense, Kelsey; Johnson, Douglas H; Fieberg, John R; ALTHEA.ARCHMILLER@GMAIL.COM; ArchMiller, Althea A
    The goal of this study was to gauge the level of computational reproducibility, which is the ability to reach the same results using the same data and analysis methods, in the field of wildlife sciences. We randomly selected 80 papers published in the Journal of Wildlife Management and Wildlife Society Bulletin between 1 June 2016 and 1 June 2018. Of those for which we could obtain data, we attempted to reproduce their quantitative results using the original methods and data. The dataset shared in this repository is the de-identified results of our review, and the code provided here produces the results and figures in our published manuscript.