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Updated: 1 hour 28 min ago

Moodle Class Announcement: Big Data in Healthcare: Exploring Emerging Roles

Fri, 2018-01-12 10:03

The National Network of Librarians of Medicine (NNLM) invites you to participate in Big Data in Healthcare: Exploring Emerging Roles. This course will be primarily held via the Moodle platform with optional WebEx discussions. This course is designed to help health sciences librarians understand the issues of big data in clinical outcomes and what roles health sciences librarians can take on in this service area.

DatesFebruary 5 – March 30, 2018

Register: To register for this class, please visit: https://nnlm.gov/class/big-data-healthcare-exploring-emerging-roles/8113

The class size for this course is limited to 60 students. We will begin a waitlist if there are more interested in participating.

Course instructors for the winter session are Ann Glusker, Pacific Northwest RegionDerek Johnson, Greater Midwest RegionAlicia Lillich, MidContinental Region, Ann Madhavan, Pacific Northwest Region, Tony Nguyen, Southeastern/Atlantic Region, and Elaina Vitale, Mid-Atlantic Region.

Please contact Tony Nguyen with questions.

Description: The Big Data in Healthcare:  Exploring Emerging Roles course will help health sciences librarians better understand the issues of big data in clinical outcomes and what roles health sciences librarians can take on in this service area. Course content comes from information shared by the presenters at the March 7, 2016 NNLM Using Data to Improve Clinical Patient Outcomes Forum, top selections from the NNLM MCR Data Curation/Management Journal Club and NNLM PSR Data Curation/Management Journal Club’s articles, NINR’s Nursing Research Boot Camp, recommended readings from previous cohorts, and Big Data University’s Big Data Fundamentals online course.

Participants will have the opportunity to share what they learned with the instructor from each section of the course content either through WebEx discussions or Moodle Discussions within each Module. These submissions can be used to help support the student’s views expressed in the final essay assignment.

Objectives: Students who successfully complete the course will:

  • Explain the role big data plays in clinical patient outcomes.
  • Explain current/potential roles in which librarians are supporting big data initiatives
  • Illustrate the fundamentals of big data from a systems perspective
  • Articulate their views/options on the role health sciences sector librarians is in supporting big data initiatives

NOTE: Participants will articulate their views on why health sciences librarians should or should not become involved in supporting big data initiatives by sharing a 500-800 word essay. Students are encouraged to be brave and bold in their views so as to elicit discussions about the roles librarians should play in this emerging field. Participants are encouraged to allow their views to be published on a NNLM online blog/newsletter as part of a dialog with the wider health sciences librarian community engaging in this topic. Your course instructors will reach out to you following the completion of the course.

On top of information gained, being a part of the big data in clinical care dialog, and earning 9 continuing education credits from the Medical Library Association, students may earn an IBM Open Badge program from the Big Data University.

This is a semi-self-paced course (“semi” meaning there are completion deadlines). While offered primarily asynchronously, your course instructors plan to offer opportunities in which participants can join a WebEx discussion to discuss some of the content.

Course Expectations: To complete this course for nine hours of MLA contact hours, participants are expected to:

  • Spend 1-2 hours completed the work within each module.
  • Commit to complete all activities and articulate your views within each module.
  • Complete course requirements by the deadline established in each module.
  • Coordinate with a course instructor to publish your observations/final assignments on a NNLM blog/newsletter
  • Provide course feedback on the Online Course Evaluation Form

Grading: Grades for this course is simply a pass/fail grading system. When your submission meets the assignment’s expectations, you will receive full credit for the contact hours for that Module. For submissions that are unclear or incomplete, you may be requested for more information until your instructor approves.

  • For discussion posts, your activity will be marked as complete after you’ve submitted a discussion AND your instructor assigns a point to mark as complete
  • If you participate in WebEx Journal Club Discussions (when available), your instructor will assign points in the Discussions for that module.
  • Students have the option to accept fewer contact hours. However, you will need to inform your course instructors ahead of time.

 

Categories: Data Science

Central Plains Network for Digital Asset Management (CPN-DAM) Virtual Conference

Mon, 2017-10-30 13:49

Join the Central Plains Network for Digital Asset Management (CPN-DAM) for their one day conference on November 7th, 2017. This virtual event will include regular and poster presentations providing an opportunity to learn from the real-world experiences of others. With a focus on practical professional development in all stages of digital asset management, sessions will cover topics such as digital projects, embedded metadata, and digital archives. Learn, network, and share all from the comforts of your own desk!

For more information, visit the CPN-DAM Conference page.

About the network: “Central Plains Network for Digital Asset Management (CPN-DAM) was founded in October 2015. It has a regional focus encompassing Kansas, Missouri, Nebraska, Colorado and Oklahoma. The network’s vision is to provide professional development, networking and collaborating opportunities for professionals involved or interested in digital asset management. The network is open to all professionals from all backgrounds, including programmers, system administrators, librarians, digital humanities specialists and cultural heritage professionals.”

Categories: Data Science