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Introduction webinar january 16 2019

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PUSH and HSI hosted the Readying Your University to Open Data Compliance Webinar on January 18, 2019. In this webinar, Utah State University representatives shared their policy, procedures, and guidance for complying with funders’ data management and open data requirements.

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Introduction webinar january 16 2019

  1. 1. Hosted by Hunger Solutions Institute (HSI) and Presidents United to Solve Hunger (PUSH) January 16, 2019 Readying Your University to Open Data Compliance #PUSH4opendata and #OpenData
  2. 2. Presenters: Kara Newby, Auburn University and HSI Kevin Peterson, Utah State University Betty Rozum, Utah State University Jaime Adams, USDA (furloughed) Host: Anne Mims Adrian, Auburn University & PUSH Readying Your University to Open Data Compliance #PUSH4opendata and #OpenData
  3. 3. Presidents United to Solve Hunger (PUSH)
  4. 4. Presidents United to Solve Hunger (PUSH) An international consortium of universities have the collective mission to end hunger and poverty, both locally and globally. Over 100 university presidents from 5 continents have agreed to make food and nutrition security a priority on their campus— through research, instruction, student engagement, and outreach. www.pushtosign.org
  5. 5. Questions? PUSH and how can your university become a PUSH university Kara Newby, Operations Manager, Hunger Solutions Institute, kara.newby@auburn.edu Advancing open data at universities Anne Mims Adrian, PhD, PUSH Open Data Project Manager, aadrian@auburn.edu
  6. 6. PUSH 2018 Study & Report: in partnership with GODAN http://wp.auburn.edu/push/?page_id=1087 Of 99 PUSH schools, 15 have open access policies. Most of open access policies provide licensing requirements for sharing scholarly works and allow for faculty to waive the licensing requirements unless the funder requires open access. No school has an open data policy. Open data and data management planning are driven by funders’ requirements for researchers to receive funding.
  7. 7. Both GODAN and PUSH Found http://wp.auburn.edu/push/?page_id=1087 Grantees struggle to ensure data quality, effective data management, and provisions to responsible data reuse. Lack of proper budgeting and institutional open data policies hinder processes of opening and publishing data correctly. Lack of clear directives on where to publish and low awareness to access published data create barriers to compliance. This is especially true for researchers without a mature disciplinary repository.
  8. 8. Reasoning for Open Data Requirements Enhance and accelerate research and innovation. Provide more transparency to research. Connect researchers from various locations and disciplines. Increase collaboration opportunities. Help provide solutions to wicked problems.
  9. 9. Funders’ Open Data Requirements White House Office of Science and Technology Policy (OSTP) executive order and memo of 2013. Many U.S. federal agencies have been requiring open access and open data for awhile. Requirements from federal agencies different. ======================================== USDA intends to complete a USDA-wide data policy in 2019 that will align all of the agency data reporting requirements for USDA customers.
  10. 10. Open Access and Open Data Definitions • Open Access: • Research articles and information that are free online, coupled with the rights to use these articles fully in the digital environment. • Open Data: • Research data that anyone can access, use, or share freely available on the internet permitting any user to – download, copy, analyze, re-process, pass to software, and use for any other purpose. • With no financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself.
  11. 11. Open Access and Open Data Definitions • Open access and open data are sometimes used interchangeably. • Regardless of how the terms are defined, open data must be released responsibly with clear ownership and licensing. The FAIR Principles, developed by the Dutch TechCentre for the Life Sciences (DTL) specify that data should be • Findable, • Accessible, • Interoperable, and • Re-usable to enhance the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.

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