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GMR Data Science

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The blog of NNLM Greater Midwest Region
Updated: 1 hour 35 min ago

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

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:

Categories: Data Science

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

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

Reflections on Big Data in Healthcare: Exploring Emerging Roles

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

Potential Roles for Health Sciences Librarians in Big Data

Tue, 2017-10-31 10:00

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 Beth Whipple, Assistant Director for Research and Translational Sciences at the Ruth Lilly Medical Library, Indiana University School of Medicine

Big data is one of the directions in which the field of healthcare is moving, and to continue to support and collaborate with our colleagues outside of the library, we need to understand trends and how to provide relevant resources and support. As experts in information retrieval, information organization, and as folks who interface both with end users and back end developers, we are uniquely positioned to be involved with big data in healthcare. I see roles for health sciences librarians in four general areas: programming/coding, information organization, end-user/usability feedback on systems, and data management.

As an undergraduate math major (who also had to take computer programming classes), I find it interesting to see how my previous training now relates to what librarians are starting to do, in particular the involvement of some data librarians in programming/coding instruction (e.g., teaching R, Python). That being said, there is a reason I went to library school and did not get an advanced degree in math. While librarians can build roles in this area, I believe it is not for everyone, and there are other ways that librarians can be involved in big data and data science work in healthcare.

Information organization is a big area where I see librarians involved with big data moving forward. While we are most familiar with literature databases, I often explain to patrons that if they understand how one database is set up, they can use those organizational principles to understand other databases. For example, as part of an NLM Informationist project at my institution, three librarians created a map of all the rules for a clinical decision support system to show how items were connected and to identify gaps. While we did have to learn how to read through the rule syntax, which presented a learning curve, we really were using our information organization skills to create maps of different concept areas and visually present that information to the pediatricians we partnered with on the project. The clinicians looked to us for expertise in the area of information organization in order to better understand their clinical decision support system.

The third area in which we can contribute related to big data is through our end-user and usability skills with our patrons and clients in how systems are designed. We are familiar with straddling the line between understanding the technical side of systems and translating them to our users. I also sometimes see our expertise acting as a squeaky wheel to try and explain to technical folks why something they think is “so cool” isn’t 1) practical, 2) useful, or 3) necessary. As a knitter, just because there are many things that I could make, doesn’t mean I should. Sometimes designers can get carried away with something technically interesting that is totally useless. Our role in that instance is to speak up, reiterate the desired outcomes of the project, and help make sure the end goal is reached.

The fourth area we can provide support for big data is through data management. I taught a Tableau class yesterday, and in the debrief with my colleagues, it was pointed out that I was teaching data management without even realizing it. As part of the class, I pointed out a sample dataset’s naming conventions and mentioned that those outside the project might not understand those conventions. I highlighted the importance of considering naming conventions when working with datasets, in order to ensure clarity. Additionally, my Data Services Librarian colleague related recently how, in working with our Clinical Informationist, she learned that he keeps a “diary” for each systematic review he’s involved with where he records details about the search strategy, databases searched, and documents other pieces of the review process. She talked with him about that practice being a form of data management, which hadn’t occurred to him previously. Many librarians are already practicing data management and teaching those skills in their everyday work, without realizing it’s “data management”. Librarians can easily expand their roles to support big data through this area, as information organization skills are underlying aspects of big data and librarianship.

As health sciences librarians, we are connectors – helping to bring the right people together, leading the right people to the right resources, and bridging the gaps between silos. We can demonstrate this through offering classes at the library – taught by library staff or other experts – on data topics, sponsoring data talks through the library, and in general doing what we do best—serving all patrons that are part of the mission of our institutions, sharing information, and connecting people, in order to make things more efficient and productive overall.

Categories: Data Science

NNLM Professional Development Awardee, Noreen Mulcahy attends Pure Information, the 2017 Midwest Chapter/MLA Conference

Mon, 2017-10-30 11:46
Noreen Mulcahy

Noreen Mulcahy

The NNLM Professional Development Award made it possible for me to attend Pure Information, the 2017 Midwest Chapter/MLA Conference in Ypsilanti, MI.  The event was held from Saturday, October 14-Monday, October 16 at the Marriott at Eagle Crest.

As part of the award, I had the opportunity to take the class Data Management for Librarians, presented by Caitlin Bakker, Research Services Liaison, University of Minnesota Twin Cities.  She discussed how librarians can incorporate research data services to clients.  Some hands-on exercises gave participants the opportunity to develop data management plans as well as assess research projects.  Her in-depth insight and knowledge of these topics provided me with a better understanding of research and data management.

The contributed paper sessions had something for everyone.  Stevo Roksandic, director of the Mount Carmel Health Sciences Library (MCHSL – where I work) and our former co-worker Allison Erlinger presented  “Rethink, Redo, Repurpose”: Transforming Library Space to Meet Clients’ Needs.  They outlined how MCHSL met the needs of our diverse users, focusing mainly on millennials.  Changes in physical spaces and updating terminology on the Library website are some examples of these transformations.

Marilia Y. Antúnez and Kathy Schupp from the University of Akron discussed how they developed a journal club for undergraduate students in nutrition and dietetics.  The program demonstrated how a journal club can teach students how to critically appraise scientific literature.  In the same vein, Jenny Taylor from the University of Illinois talked about how tracking student citations and interviews gave her an overview of literacy skills of first year medical students.

It was the first conference for me since receiving credentialing from the Academy of Health Information Professionals (AHIP), Senior Level.  While visiting the MLA booth, Tomi Gunn regaled my badge with an AHIP ribbon and sticker.  It was a proud moment.

I want to personally thank the Greater Midwest Region of the NNLM for this Professional Development Award.  Besides all the learning opportunities it provided, the most beneficial part of the conference was networking.  Catching up with long-time friends like Jennifer Herron from Indiana and meeting new people like Anna Liss Jacobsen from Miami University/Ohio gave me energy and an affirmation that I chose the right line of work!

Posted on behalf of Noreen Mulcahy, MLIS, AHIP, Lead Health Sciences Librarian – Technical Services, Mount Carmel Health Sciences Library, Columbus, OH

Categories: Data Science