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Data Science

UC Libraries and IT@UC Host THIRD ANNUAL UC DATA Day

GMR Data Science - 10 hours 52 min ago

Data Day logoJanuary 22, 2018 – The University of Cincinnati Libraries and IT@UC announce the 3rd annual UC DATA Day. Scheduled for Tuesday, March 6 from 8:30 a.m. – 4:15 p.m. in Nippert Stadium West Pavilion on UC’s Main Campus (see directions), UC DATA Day 2018 offers a full schedule of engaging events that will reveal solutions to data challenges and foster a community of best practices around improved data management. All events are free and include lunch. The public is welcome.

Registration is now open at http://libapps.libraries.uc.edu/blogs/dataday/registration/.  Seats are limited, so register early.

The UC DATA Day 2018 keynote speaker is Patricia Flatley Brennan, RN, PhD, director of the National Library of Medicine (NLM). The NLM is the world’s largest biomedical library and the producer of digital information services used by scientists, health professionals and members of the public worldwide. Prior to her work at the NLM, she was the Lillian L. Moehlman Bascom Professor, School of Nursing and College of Engineering, at the University of Wisconsin–Madison.

The day will include panel discussions on “Game Changing Data: How Data is being used to affect change,” “Big Data” and “Data Solutions: Your Questions Answered.”

In addition, attendees can participate in two technical sessions on data analysis and data visualization with Python. During lunch, service providers will speak on how they support researchers and research data management.

For more information, contact Tiffany Grant, interim assistant director for research and informatics, at (513) 558-9153 or tiffany.grant@uc.edu.

Categories: Data Science

NLM Announces Participants in Biomedical Health Research Data Management Program

PSR Data Science - Tue, 2018-01-16 17:09

The National Library of Medicine has announced the list of participants in the National Network of Libraries of Medicine (NNLM)-created program, Biomedical and Health Research Data Management for Librarians. The program began January 8, introducing librarians to data issues and policies, with the goal of implementing or enhancing data services at their institution. The course topics include an overview of data management, choosing appropriate metadata descriptors or taxonomies for a dataset, addressing privacy and security issues with data, and creating data management plans.

The program offers an 8-week online class, mentoring by a data librarian, and completion of a capstone project at the end of the course. The experience culminates in a summit at the NIH campus in Bethesda, MD, on April 10-11. NLM Director Patricia Flatley Brennan, RN, PhD, said, “We need data-sophisticated librarians who can assist the research process, the enterprise, in developing the resources and data services around them. The Biomedical and Health Research Data Management for Librarians program will offer the kind of training that will develop librarians’ skills and develop practical and actionable data services at their own institutions.”

Program participants from NNLM PSR include Lynn Kysh, University of Southern California; Andrea Lynch, City of Hope; and Linda Murphy, University of California, Irvine. The program was developed and is led by Jessi Van Der Volgen (Assistant Director of NNLM Training Organization) and Shirley Zhao (Data Science Librarian of the Spencer S. Eccles Health Sciences Library, University of Utah), and is supported by co-teachers, reviewers, and mentors from libraries across the country: Marisa Conte, Anna Dabrowski, Christopher Eaker, Lisa Federer, Jen Ferguson, Jessica Gallinger, Patricia Gogniat, Tina Griffin, Margaret Henderson, Amy Koshoffer, Wladimir Labeikovsky, Tobin Magle, Sara Mannheimer, Hannah Norton, Peter Oxley, Zac Painter, Kevin Read, Franklin Sayre, Yasmeen Shorish, Vicky Steeves, Alisa Surkis, Jamie Wittenberg, and Mary Zide.

The NNLM Research Data Management Working Group will also participate in the summit and continue to serve as a resource for health sciences librarians and information professionals interested in improving their data management skills. The program complements the ongoing efforts of the NNLM RD3: Resources for Data-Driven Discovery which serves as a resource in fostering learning and collaboration in data science to support sharing, curating, and annotating biomedical data. A complete list of program participants is available on the NLM web site.

Categories: Data Science

DataFlash: You Spoke and We Listened!

PNR Data Science - Tue, 2018-01-16 14:57

Happy New Year from your data gurus, Annie and Ann!   And welcome to the first post in our new blog series, DataFlash!   We both monitor a wide range of listservs, social media accounts, news sites and other troves of data information, and we’ll be sharing what we come across, biweekly.

As a starting place, remember that survey we did last year, asking for your ideas about what the NNLM-PNR should be doing to help with your data needs? (The one with the fabulous NLM tape measure prizes?)  We completed the analysis and presented the findings to our Executive Committee last November (as well as sending out the tape measures), and now we’re ready to release it to you!   Here it is!

Briefly, here’s what you told us:

–You want training in data literacy and how to help patrons/users with data

–You want us to help with developing and sustaining collaborations

–You want us to provide specific assistance with things like how to help researchers with workflow, and by creating templates to help with assessing and managing data

–You’d also like training opportunities around informatics

We’re planning several trainings already around research data management, and will be working towards offering more on the other topics you’re interested in.  And we’ll be contacting the folks who generously gave us their email addresses and said they’d be happy to chat further.    Watch this space for further developments, and please feel free to reach out if there are any ways we can help, if you need a consultation, have an idea for a training, or whatever!  And deepest thanks to everyone who participated in the survey—it’s great for us to have this information!

Categories: Data Science

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

MCR Data Science - 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

Big Data in Healthcare: Exploring Emerging Roles

MAR Data Science - Tue, 2018-01-09 07:00

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. Register today!

DatesFebruary 5 – March 30, 2018

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.

About the Class

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

Dr. Patricia Flatley Brennan to Headline UC Data Day Event!

GMR Data Science - Thu, 2018-01-04 09:10

Researchers producing big data and small data face unique challenges in data management, data sharing, reproducible research and preservation. Data Day is a daylong event that will highlight these challenges and showcase opportunities for all researchers. This event promises to engage audience members, reveal solutions to these data challenges and foster a community of best practices around improved data management. This year, the keynote address will be given by Patricia Flatley Brennan, RN, PhD, Director of the National Library of Medicine. Panel topics include: Game Changing Data: How Data is being used to affect change, Big Data and Data Solutions. The event features some phenomenal and engaging panelists to present these topics. In addition, this year, two technical sessions will be hosted on Data Analysis and Data Visualization with Python. Data Day is free and open to the public, but registration is required.

When: March 6, 2018

Where: University of Cincinnati Libraries

More Info: http://libapps.libraries.uc.edu/blogs/dataday/

Categories: Data Science

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

SEA Data Science - 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: 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

The OHSU Library Data Science Institute Introduces Data Science Techniques to Librarians and Researchers

PNR Data Science - Thu, 2017-12-14 21:47

Today’s Dragonfly post comes to us from Nicole Vasilevsky, Letisha Wyatt, Robin Champieux, Laura Zeigen and Jackie Wirz

The Oregon Health & Science University Library in Portland, Oregon hosted the “OHSU Library Data Science Institute” (ODSI) from November 6-8, 2017 in downtown, Portland. The event was targeted towards researchers, librarians and information specialists with an interest in gaining beginner level skills in data science. The goal was to provide face-to-face, interactive instruction over a three-day workshop. The learning objectives for the training were:

  • Increase awareness of key skills in data science and how these can be applied to the participants own daily practices, such as research or serving patrons
  • Increase confidence with using data science techniques
  • Increase the ability of participants to use or apply data science techniques in problems outlined in the course

Over 75 participants attended this event, which was held over the 3 days. Participants came from within and outside Portland, Washington, Idaho, California, British Columbia and Kansas. The topics for the workshop included topics such as an introduction to version control and GitHub, exploratory data analysis and statistics, biomedical data standards; data description, sharing and reuse; quantitative and qualitative analysis, analyzing textual data, web scraping, data visualization and mapping and geospatial visualization. All of the materials are shared and openly available via our website.

The goals of the ODSI were to:

1) to increase skills of students and information professionals (e.g., librarians and research staff) so that they may be better equipped to work with data or meet the needs of the research communities that they work with

2) provide a venue for networking and relationship-building between local research community, libraries, and active information professionals.

As an outcome of this course, the majority of our participants that identified as librarians or information professionals reported they are more aware of, can actively teach or use key skills in data science and are more aware of how these can be applied to researchers. In addition, the respondents that identified as researchers reported that they have increased awareness of and confidence using data science knowledge; that they anticipate integrating skills derived from the Institute into their workflow (experimental design, data cleaning, analysis) and that they bring this information back to their laboratory, department, and peers.

Our full webpage, which includes links to session syllabi and instructional materials.

Some lessons learned include:

  • Development of curricula for a diverse audience is a daunting challenge! To address this in the future we would consider tracks or ensuring that the content is focused and targeted to a specific career field/discipline.
  • A Train-the-Trainer event would help present a uniform approach towards pedagogy, hands-on-learning, and delivery. In addition, it might be helpful to host a demo day where instructors can test their content with either other instructors or a test audience.
  • More coffee and tea! Data Science is fueled by warm beverages, so we need to add more.
Categories: Data Science

NIH All of Us Research Program Traveling Exhibit Visits the University of Arizona in Tucson!

PSR Data Science - Wed, 2017-12-13 18:48
salam ahleh and yamila el-khayatAhlam Saleh and Yamila El-Khayat

by Yamila El-Khayat
Outreach Services Librarian
Health Sciences Library
University of Arizona
Tucson, AZ

The NIH All of Us Research Program traveling exhibit came to the University of Arizona’s Banner Health Hospital Campus on December 7, 2017. It provided an excellent opportunity to visit and learn more about the All of Us Program. At the entry, there was an introductory video that clearly and simply introduced the All of Us project. The video focused on two individuals of differing ethnicities and lifestyles, but with the same diagnosis. It focused on the importance of molding medicine to each individual because of their differences. It was a very creative way of simply defining the concept of precision medicine.

Next in the exhibit was an area to answer a couple of questions on a tablet computer. Then your picture was taken and you received a color identification from the spectrum of options. The picture was then shown framed in the identity color. No definitions were supplied regarding the colors, but I ended up being red, which according to the person giving us the tour was rare, and her first experience seeing that color. It was a further illustration of the differences in each of us. Finally, we were shown other activities and noises and had to identify what we thought they were and then shown what they really were. This was a way to learn about differing perceptions, again emphasizing the importance of uniqueness in individuals. All in all, the exhibit was an informative and entertaining way to learn more about the All of Us Research Program.

bus with all of us advertisementAll of Us Traveling Exhibit
Categories: Data Science

Coming soon—Survey results about DATA!

PNR Data Science - Wed, 2017-12-13 18:08

You may remember the email and picture below, which was sent out to various groups and promoted in various ways this past spring.  In it, we were asking you all to participate in the NNLM-PNR data needs assessment survey.  By doing the survey, Annie Madhavan and I (Ann Glusker) were hoping to get feedback on the directions you want us to go with teaching about and providing resources related to data.

Well, we had 60 people respond, which we are thrilled about, and we ended up sending out 14 fabulous prizes (the NLM tape measures shown in the photo–woo hoo!).  More importantly, we’ve completed the data analysis, got some VERY helpful information, and are putting the final touches on the report we promised! (a little later than we’d hoped, but better late than never?)

So, we just wanted to let you know that the report will be sent out to the HLIB-NW list in early January, as well as to all the people whose emails we have and/or who ask for a copy, with our best wishes for a very happy New Year.  See you online again soon!

Categories: Data Science

An NNLM RD3 Update

PNR Data Science - Mon, 2017-12-04 04:00

It’s been almost 6 months since the launch of the National Network of Libraries of Medicine’s new data website, NNLM RD3: Resources for Data-Driven Discovery, and since May, several new features have been added. When the site first launched at MLA 2017, it had only recently transitioned from the New England Region’s eScience Portal for Librarians. Since then, with the very generous assistance of both old and new volunteer content editors, we have updated many of the original physical science and engineering subject primers, and have also added a number of health sciences topics. The subject primers now provide a brief overview of each field followed by data related information, including pertinent articles on big data and data management, metadata, data repositories, and data standards and policies specific to each field.

We have also added a Twitter feed on the NNLM RD3 homepage that links to @NNLM_RD3’s Twitter page and highlights a wide range of data science and data management retweets. Also on the homepage, is a Data Science around the Regions blog feed that links to data related articles from across the NNLM’s eight regions.

In the coming months we are planning to feature information on innovative data librarians and data management initiatives across the country, update and add additional subject guides, reveal the Data Thesaurus, and report on the first cohort of NNLM Training Office’s Biomedical & Health RDM Training for Librarians course. We invite you to continue to explore NNLM RD3 and post your comments and suggests below or on website. RD3 continues to be a work in progress and one that could not exist without the support and expertise of many of our members. We are always on the lookout for content editors, so if you would like to contribute to a new or existing subject primer, or simply suggest a new feature or update, we would love to hear from you.

Categories: Data Science

Seeing the Forest and the Trees: Why Librarians Can Make Valuable Contributions Working with Big Data

GMR Data Science - Fri, 2017-12-01 09:33

In the NNLM Big Data in Healthcare: Exploring Emerging Roles course, we asked participants, as they progressed through the course to consider the following questions: Do you think health sciences librarians should get involved with big data in healthcare? Where should librarians get involved, if you think they should? If you think they should not, explain why. You may also combine a “should/should not” approach if you would like to argue both sides. NNLM will feature responses from different participants over the coming weeks.

Written by: Heidi Beke-Harrigan, MLS, Health Sciences Librarian, Member Services Coordinator, OhioNET

There has been an explosion of conversation around the topic of big data. The potential for mining large sets of data in endless, customized combinations could revolutionize healthcare, patient outcomes and evidence-based medicine. At the same time, as with systematic reviews, effective data projects benefit from a collaborative environment and a team approach. One individual is not likely to possess the skills to formulate the right questions, write queries, extract the data, provide analysis and manage data storage/retrieval. Data without context is lifeless. Misused it can be exploited, misinterpreted and manipulated. Deriving meaning from data depends on someone’s ability to mine what’s there and make real connections to people’s lives. That’s where librarians excel. Our work has always been about cultivating connections, enabling access to raw information so that new ideas can ferment, providing access to those ideas and end products, and storing the results. Formats have come and gone, but it’s all data and librarians can play a key role in making data useful. Where individuals with specific expertise may focus on a very narrow aspect of data work (trees), librarians tend to see patterns, connections and possibilities (forest). Librarians like to create spaces where nuanced details and creativity can coexist and mingle in a place of infinite possibility.

What skills can librarians specifically bring to the table? Researchers have identified the need to recode data elements and challenges maintaining consistency of data over time as two barriers to big data work. Librarians with cataloging and metadata experience can work with teams to help bring about harmonizing of terminologies and standardize metadata descriptions. They are also able to ask important questions about storage and retrieval. Where will the coding that extracted the data live? Do the resulting data sets need to be stored? How can reproducibility or access points to the data be supported? What story does the data tell and who else might want to discover it?

Imagine further, a world where librarians are part of a new framework of front-line clinical teams and integral to using big data to improve patient outcomes. If we assist with research topic formulation, provide input regarding user experience design, help develop consult management tools, and support the creation of effective query forms and output displays, can we free up clinicians and partner with other colleagues to more fully explore the role of data in Practice Based Evidence (PBE)?

Librarians’ expertise in providing programming, informal learning opportunities and formal classroom instruction can serve us well to assist in citizen data scientist training and to prepare our students with critical skills for work in a data rich landscape. Part of that skill-set should also include an awareness for and appreciation for data literacy, data sharing, and transparency. As Dr. Brennan pointed out, there are certainly opportunities for data scientists and programmers in this information-rich world, but to give that data meaning, requires that we all bring the unique strengths and core values of our diverse professions to the table. In that realm, librarians have much to share.

Categories: Data Science

NIH’s All of Us Research Program Partners with NNLM to Reach Target Communities Through Local Public Libraries

PSR Data Science - Wed, 2017-11-29 11:45
diverse group of people with the All of Us Research Program logo and tagline, “The future of health begins with you.

The NIH All of Us Research Program and the National Library of Medicine (NLM) have teamed up to raise awareness about the program, a landmark effort to advance precision medicine. Through this collaboration, the National Network of Libraries of Medicine (NNLM) has received a $4.5 million award to support community engagement efforts by public libraries across the United States and to improve participant access. According to Eric Dishman, director of the All of Us Research Program: “We want to reach participants where they are. For many people in the country, including those with limited internet access, one of those places is the local library. We’re excited to work with the National Library of Medicine to make more people aware of All of Us and the opportunity to take part.”

The partnership is a three-year pilot program, running through April, 2020. Program objectives include:

  • To increase the capacity of public library staff to improve health literacy.
  • To equip public libraries with information about the All of Us Research Program to share with their local communities.
  • To assess the potential impact of libraries on participant enrollment and retention.
  • To highlight public libraries as a technology resource that participants can use to engage with the program, particularly those in underserved communities affected by the digital divide.
  • To establish an online platform for education and training about All of Us and precision medicine, with resources for members of the public, health professionals, librarians and researchers.
  • To help identify best practices in messaging and outreach that lead to increased public interest and engagement in the program.

The All of Us Research Program aims to build one of the largest, most diverse datasets of its kind for health research, with one million or more volunteers nationwide who will sign up to share their information over time. Researchers will be able to access participants’ de-identified information for a variety of studies to learn more about the biological, behavioral and environmental factors that influence health and disease. Their findings may lead to more individualized health care approaches in the future.

Amanda J. Wilson, head of NLM’s National Network Coordinating Office (NNCO), and Dara Richardson-Heron, M.D., chief engagement officer of the All of Us Research Program, will lead the new partnership. Each NNLM region’s funding includes one FTE for an All of Us Point of Contact. Kelli Ham, formerly NNLM PSR Consumer Health Librarian, will fill the role in the Pacific Southwest Region. Her new title will be Community Engagement Librarian. Over the course of the pilot program, Kelli will focus her outreach efforts on various designated target geographic areas in the region, beginning with Sacramento, CA.

The All of Us Research Program is currently in beta testing. To learn more, sign up to receive updates. Precision Medicine Initiative, All of Usthe All of Us logo, and “The Future of Health Begins with You” are service marks of the U.S. Department of Health and Human Services.

Categories: Data Science

NIH Blood Pressure Study Data Supports AHA/ACC Hypertension Guidelines

SCR Data Science - Tue, 2017-11-21 13:22

1952924 by Myriam from Pixabay via CC0

The National Institutes of Health (NIH) funded a landmark study that supports a crucial component of the 2017 Hypertension

Clinical Practice Guidelines put forth by the American Heart Association (AHA) and the American College of Cardiology (ACC).  The AHA and ACC guidelines state that high blood pressure should be treated earlier by changes in lifestyle and medications for some.  The new guideline recommends treatment at 130/80 instead of 140/90.

Recommendations are the result of the Systolic Blood Pressure Intervention Trial (SPRINT) that was designed to determine how to best treat adults with high blood pressure, over the age of 50, and at risk for heart disease.  SPRINT was sponsored in part by the National Heart, Lung, and Blood Institute (NHLBI), and the National Institute of Diabetes and Digestive and Kidney Disease (NIDDK), the National Institute of Neurological Disorders and Stroke (NINDS) and The National Institute of Aging (NIA), divisions of the National Institutes of Health.

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Categories: Data Science

Reflections on Big Data in Healthcare: Exploring Emerging Roles

GMR Data Science - Thu, 2017-11-16 08:57

In the NNLM Big Data in Healthcare: Exploring Emerging Roles course, we asked participants, as they progressed through the course to consider the following questions: Do you think health sciences librarians should get involved with big data in healthcare? Where should librarians get involved, if you think they should? If you think they should not, explain why. You may also combine a “should/should not” approach if you would like to argue both sides. NNLM will feature responses from different participants over the coming weeks.

Written by by Emily B. Kean, MSLS, Research and Education Librarian, Donald C. Harrison Health Sciences Library, University of Cincinnati Libraries.

I believe that health sciences librarians can positively contribute to big data in healthcare, to an extent. After completing this course, I certainly have a much better understanding of what big data is, and I can also see some overlap between traditional functions of librarianship and several of the concepts of big data. In my opinion, the areas where librarians could most significantly contribute are in areas such as creating and developing taxonomies for machine learning. From some of the readings in the class, it seems like some of the positions which were described as data managers are roles that librarians could easily fill; however, as was also demonstrated in the literature, non-librarian professionals are rarely identifying librarians as capable of filling these roles. I feel that if librarians are striving to fill the role of data managers or data scientists, based on some of the readings from this class and some of the discussion that has taken place, a serious effort would have to be made to educate colleagues and peers about the role that librarians can play.

Overall, I find that after completing this course it seems to me that the approach described by Dr. Patti Brennan regarding nursing in the field of data science is also incredibly applicable to the field of librarianship and data science. I think Dr. Brennan’s approach that nurses have an understanding and appreciation for what data science can do for their profession but also the idea that not all nurses will become data scientists is a very healthy approach and it’s one that is also applicable to the field of librarianship. I can easily see a future where librarians could potentially participate on teams that might involve healthcare professionals and data scientists, but I don’t know that it’s realistic that all librarians will develop the skills of a true data scientist. Along the mindset presented in Dr. Brennan’s lecture, I don’t think it’s desirable that all librarians should become data scientists. As Dr. Brennan describes, there will still be a need for nurses to fill traditional nursing roles and there will still be a need for librarians to fill traditional librarian roles, with a small percentage from each profession adopting the role of data scientist.

Just as the traditional approach to schooling for librarians has evolved to encompass the ideas of information science, I do see a future where a Masters in Library Science program would encompass the ideas of data science as well. One of the areas that was touched upon by this course but we didn’t really get into in great detail are all of the different programming languages used by data scientists. I don’t know that it’s entirely feasible to re-train the majority of current working health sciences librarians, but I do believe that exposing library science students to data science concepts as part of their masters-level education will better prepare future librarians – in the health sciences and other areas – to be perceived as experts in this field and be approached as team members for interdisciplinary collaborations.

Categories: Data Science

Call for Participation: NNLM SEA Data Management Program Advisory Committee

SEA Data Science - Wed, 2017-11-15 12:00

The National Network of Libraries of Medicine (NNLM), Southeastern/Atlantic Region (SEA) is extending an invitation for network members to join and participate in the Data Management Program Advisory Committee (PAC).

The Data Management PAC will work cooperatively with Tony Nguyen, Technology and Communications Coordinator in planning and carrying out committee work. Members are volunteers who share an expert knowledge on the topic.

The responsibility of PACs includes:

  • Advise NNLM staff on the need for and relative priority of education within the program area.
  • Assist with program evaluation.
  • Ensure that programming is aligned with local needs.
  • Evaluate technology and data related award applications.

The PAC will meet a few times a year via web conferencing software. NNLM SEA will select up to 7 members to participate in this PAC.

If you would like to nominate yourself or a colleague as a member, please visit: http://www.surveygizmo.com/s3/3898415/SEAPAC. The deadline to apply is December 1, 2017.

Categories: Data Science

The National Institutes of Health (NIH) Launches a Crowdsourcing Project Called PregSource to Better Understand Pregnancy

SCR Data Science - Tue, 2017-11-14 16:55

PregSource, collects information from pregnant women to increase knowledge about pregnancy.  The research project delves into emotional, physical, labor, and delivery aspects to identify specific challenges experienced by subsets of women, to include those with physical disabilities.  The overarching goal of the research program is to form better strategies to improve maternal health care in the United States.

Participants of PregSource answer online surveys to share information about their experiences like sleep, mood, weight changes, morning sickness, and others.  According to the NIH, by collecting this data, the NIH hopes to answer the following research questions:

  • How many women experience morning sickness? How long does it generally last?
  • How much does pregnancy affect women’s sleep patterns? How do these patterns change over the course of the pregnancy?
  • What are the patterns of weight gain during pregnancy, and how do they affect health?
  • How do women with challenges, such as physical disabilities or chronic diseases, experience pregnancy and new motherhood?

Pregnant women ages 18 years and older can enroll.  Enrollment is free.  Information from participants will not be sold to third parties.  Personal information is de-identified, meaning names and addresses are removed from data collected.  The information is then shared with researchers to be used in future studies.

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Categories: Data Science

Reflections on: Big Data in Healthcare: Exploring Emerging Roles

SEA Data Science - Tue, 2017-11-07 09:13

In the NNLM Big Data in Healthcare: Exploring Emerging Roles course, we asked participants, as they progressed through the course to consider the following questions: Do you think health sciences librarians should get involved with big data in healthcare? Where should librarians get involved, if you think they should? If you think they should not, explain why. You may also combine a “should/should not” approach if you would like to argue both sides. NNLM will feature responses from different participants over the coming weeks.

Reflections on: Big Data in Healthcare: Exploring Emerging Roles

Written by: Meaghan Muir, MLIS, Manager, Library Services, Boston Children’s Hospital

“Big Data in Healthcare: Exploring Emerging Roles” has been a valuable introduction to discovering how physicians, nurses, researchers, and librarians are using big data and data science. It has been interesting to explore the different ways in which big data is being used, especially in our day-to-day lives, such as how Netflix and online retailers are using big data to interact with their customers. Data of all kinds is being created every second of the day, and the exponential growth is overwhelming and difficult to comprehend.

Data science is multidisciplinary, and there absolutely is a role for health sciences libraries. However, we cannot assume that all health sciences libraries, and especially all health sciences librarians, can readily become involved. There are clear opportunities, but there are also significant barriers to offering library-based support of data science activities. Hospital libraries, may have unique challenges and opportunities. Some challenges that have been discussed in this course that are specific to hospital libraries/librarians include:

  • Lack of competencies to use data science tools.
  • No dedicated library staff/position for data science.
  • Lack of knowledge about researchers work and data life cycle.
  • Getting buy in from stakeholders/partners
  • Lack of experience, have never worked with a big data project.
  • Lack of time resources to implement data science support services.

The good news for hospital librarians is that there are plenty of opportunities and various ways to engage with clinicians and researchers working with big data. Librarians already possess skills to assist clinicians and researchers. We are accustomed to educating user populations on how to use resources such as databases and other library-related programs. Taking literature searches a step further by not only searching for published literature, but also searching directly in the associated data set (if applicable) is a possible role for health sciences librarians. Librarians are also well-versed in advising on open access/information sharing policies which can be translated to helping researchers comply with data sharing policies. This includes talking to researchers about mandates to share their data and helping them prepare it in a shareable form as well as educating others on existing hospital specific data management policies. Focusing on specific populations that are engaging in big data projects is an opportunity. For example, nurses will often turn to a hospital library as their sole resource because they might not be connected to an academic library. Libraries working with nurses who are involved or getting involved with big data endeavors is an obvious partnership seeing as the library is already their go to for help with various projects. Libraries can help people who are new to big data by teaching them about how big data is generated and collected. It’s also a natural fit for librarians to help others learn how to organize information of all types, including big data.  

Getting started is somewhat daunting.  The JMLA article (Read KB, Surkis A, Larson C, McCrillis A, Graff A, Nicholson J, Xu J. Starting the data conversation: informing data services at an academic health sciences library. J Med Libr Assoc. 2015 Jul;103(3):131-5) is one way to approach this. Simply, librarians can start a conversation with groups within the hospital that might be potential partners. Ideally a conversation would be started with a clinical research and a basic science research group, as the JMLA article discussed. This conversation ideally would assess current practices and potential needs, and introduce to the stakeholders what a librarian might bring to the table. Keeping in mind what Dr. Brenner said about not needing to be data scientists to do data science. It is unlikely that the typical hospital library will have a data science librarian on staff (as of this moment in time) but as described above there are many ways in which health sciences librarians can complement activities of clinicians and researchers engaging in data science efforts. It is rather encouraging to see that the number of opportunities discussed far outnumbers the challenges.

Categories: Data Science

The NIH Data Science Releases a Case Study Underscoring the Value of Librarianship in the Patient Care Setting

SCR Data Science - Tue, 2017-11-07 05:00
NLM

The National Library of Medicine

A NIH Data Science published a report titled A Case Study in NIH Data Science: Open Data and Understanding the Value of Libraries and Information Services in the Patient Care Setting.  In short, the NIH used other research studies to learn where and how clinicians reported using PubMed/MEDLINE as an information resource influencing clinical decision making.

Journals and PubMed/MEDLINE were the two resources most used by clinicians according to the NIH data analysis.  In addition, the NIH discovered that when clinicians use a greater number of information resources, the probability of changes to patient care were higher and so is the prevention of negative events.

According to the NIH, the advantage of using research that is already available saves time, money, increases collaboration, and extends the life of the original work.  This has direct implications for researchers and librarians, in particular.  Leveraging information service skills is an important part of affecting patient care.

Who best to provide that service than a librarian?

Remember to like the NNLM SCR on Facebook and follow us on Twitter to get the latest updates and health news!

Categories: Data Science

Reflection: Should Health Science Librarians Be Involved in Big Data?

SEA Data Science - Thu, 2017-11-02 07:58

In the NNLM Big Data in Healthcare: Exploring Emerging Roles course, we asked participants, as they progressed through the course to consider the following questions: Do you think health sciences librarians should get involved with big data in healthcare? Where should librarians get involved, if you think they should? If you think they should not, explain why. You may also combine a “should/should not” approach if you would like to argue both sides. NNLM will feature responses from different participants over the coming weeks.

Should Health Science Librarians Be Involved in Big Data?

Written by Adelia Grabowsky, MLIS, Health Sciences Librarian, Ralph Brown Draughon Library, Auburn University

I think that health science librarians are able to support big data in the same way that they are involved in supporting any type of data. Chandrasekaran (2013) illustrates the variety and complexity of skills required to work with data. He includes additional requirements for big data, including the necessity of working with specialized software like Hadoop, which permits collection and analysis of data sets spread out across multiple computers (Chandrasekaran, 2013). Most librarians do not have all or even most of the skills enumerated on Chandrasekaran’s (2013) map. However, during a talk at a National Institute of Nursing Big Data Boot Camp, Brennan (2015) suggests that not every nurse needs to be or has the time to be a data scientist. Instead, she believes that all nurses should have an understanding of data science with a small number of nurses developing the skills and knowledge to actively engage in big data studies (Brennan, 2015). I think this premise also holds true for librarian support for big data. It is important that all librarians have a basic understanding of the research data life cycle and of the vocabulary of data. However, involvement that is more extensive may depend on the fit of data needs to more traditional librarian roles and/or the skills and interests of the specific librarian.

Federer (2016) presents a research data life cycle which begins with data-specific planning for research projects and proceeds to data collection or acquisition, data analysis or interpretation, data preservation and curation, and finally, sharing of data. Many librarians already support these stages of the data life cycle, with the exception of data analysis or interpretation, in some way. Although librarians have not traditionally been involved with data collection, they have often been involved with data acquisition by assisting in finding free or acquiring fee-based data sets. Librarians have also traditionally been part of the process of making results of research more “findable” by attaching metadata. As funding agencies have begun to require planning, which includes how data will be stored and shared; librarians have used those same skills to assist in the planning process, increase findability by attaching metadata to data sets and find suitable spaces (either in-house or subject or agency-based) in which to store and preserve data. All of these activities should translate to work with big data. The exception to library support of the research data life cycle is data analysis/visualization. For most librarians, this area will require an upgrading of skills in order to provide support. I think the decision to provide support for data analysis will depend on an individual librarian’s interest and the time they have to devote to new support activities. One example of a likely requirement in this area is a knowledge of programming languages like R or Python (Federer, 2016). For librarians that are interested in providing support for data analysis, there are many training opportunities ranging from learning R through an institutional subscription like Lydia.com to specialized short courses like the Data and Visualization Institute for Librarians (NCSU Libraries, n.d.).

One thing to remember is the use of big data in healthcare is still in its infancy, with continuing discussions about how and when data should be used (Cohen et al., 2015; Iwashyna & Liu, 2014; Krumholz, 2014) and about how current patient privacy protections impact the effective use of big data (Longhurst, Harrington, & Shah, 2014). As the use of big data grows and evolves, decisions made today about librarian support may not be as applicable in the future. Instead, librarians must stay informed about changes that are occurring and remain flexible in offering support and in willingness to update skills if needed.

References

Brennan, P. (2015). NINR Big Data Boot Camp part 4: Big data in nursing research. Retrieved from https://www.youtube.com/watch?time_continue=2101&v=KOFLQ5z05f8

Chandrasekaran, S. (2013). Becoming a data scientist – Curriculum via metromap. Retrieved from http://nirvacana.com/thoughts/wp-content/uploads/2013/07/RoadToDataScientist1.png

Cohen, B., Vawdrey, D. K., Liu, J., Furuya, E. Y., Mis, F. W., Larson, E., & Hospital, N. Y. (2015). Challenges associated with using large data sets for quality assessment and research in clinical settings, 16(0), 117–124. https://doi.org/10.1177/1527154415603358.Challenges

Federer, L. (2016). Research data management in the age of big data: Roles and opportunities for librarians. Information Services and Use, 36(1–2), 35–43. https://doi.org/10.3233/ISU-160797

Iwashyna, T. J., & Liu, V. (2014). What’s so different about big data?: A primer for clinicians trained to think epidemiologically. Annals of the American Thoracic Society, 11(7), 1130–1135. https://doi.org/10.1513/AnnalsATS.201405-185AS

Krumholz, H. M. (2014). Big data and new knowledge in medicine: The thinking, training, and tools needed for a learning health system. Health Affairs, 33(7), 1163–1170. https://doi.org/10.1377/hithaff.2014.0053

Longhurst, C. A., Harrington, R. A., & Shah, N. H. (2014). A “green button” for using aggregate patient data at the point of care. Health Affairs, 33(7), 1229–1235. https://doi.org/10.1377/hlthaff.2014.0099

NCSU Libraries. (n.d.). Data Science and Visualization Institute for Librarians. Retrieved from https://www.lib.ncsu.edu/datavizinstitute

Categories: Data Science

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