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Updated: 42 min 19 sec ago

Love Data Week and Endangered Data Week in February

Thu, 2018-02-08 11:57

Love Data Week is February 12th – 16th and Endangered Data Week is February 26th – March 2nd. Data Weeks provide opportunities for researchers, scholars, data professionals, and the community to share stories about the data that shape our lives. Data Weeks encourage us to stop and think about the data that we use, manage, and create both professionally and in our everyday lives.

What is Love Data Week?

Love Data Week is February 12 – 16, 2018. Similar to Open Access Week, the purpose of Love Data Week is to raise awareness and build a community to engage on topics related to research data management, sharing, preservation, reuse, and library-based research data services. We are encouraged to share practical tips, resources, and stories to help researchers at any stage in their career use good data practices.

Topics covered are considered entry points to broad conversations about how data is being used to shape the world around us. This year the topics selected are:

  • Stories about data – Lessons learned about our own failures and successes in dealing with data.
  • Telling stories with data – How data is being used in creative and compelling ways to tell a clear story, whether to raise awareness, change behavior, or organize and action?
  • Connected conversations – There are lots of conversations about the challenges, potential, and strategies of working with data in various silos. How can we begin to facilitate conversations between communities that have similar challenges, but not interact in daily work?
  • We are data – Explore the personal, ethical, and practical implications of living in a culture that utilizes our every digital move.

What is Endangered Data Week?

Endangered Data Week (February 26 – March 2, 2018) is a new, collaborative effort, coordinated across campuses, nonprofits, libraries, citizen science initiatives, and cultural heritage institutions, to shed light on public datasets that are in danger of being deleted, repressed, mishandled, or lost. The week’s events can promote care for endangered collections by: publicizing the availability of datasets; increasing critical engagement with them, including through visualization and analysis; and by encouraging political activism for open data policies and the fostering of data skills through workshops on curation, documentation and discovery, improved access, and preservation.

A number of suggested activities are available in support of Endangered Data Week. Possible ideas include:

  • Subject-specific workshops or presentations using endangered datasets
  • Lectures or roundtables on issues of transparency, policy, or critical data literacy
  • Workshop/hackathon on organizing, reformatting, or visualizing endangered data
  • DataRescue events
  • Letter writing/advocacy campaigns
  • Data curation workshops or training
  • Data Expeditions
  • Workshops on ways to use archived websites for research
  • Web scraping/web archiving workshops
  • Data storytelling events, using tools like these, from DataRefuge

Are you celebrating Love Data Week or Endangered Data Week at your institution? What activities are you doing in support of these initiatives? Because we love data, we want to hear about your activities and lessons learned. If you’d like to share what your institution did during those weeks, please e-mail Tony Nguyen.

Categories: Data Science

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

Wed, 2018-01-03 14:46

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:

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