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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.

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

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

The Trouble with Drugs: Possible Solution to the Opioid Problem

SCR Data Science - Wed, 2017-11-01 18:41
pills

“health medicine tablet pills” by Aloísio Costa Latgé ACL from Pixabay via CC0

The opioid epidemic has become a national crisis, one that may lead the White House to declare a national state of emergency. But there may be good news on the horizon about one possible solution to the rising number of overdose deaths.

Data from the Centers for Disease Control and Prevention shows that deaths from prescription drugs, heroin, and synthetic opioid like fentanyl have more than quadrupled in the last 20 years. Almost 30,000 deaths a year are attributed to illegal and legally prescribed opioids.

However, in the state of Colorado, the growth of overdose deaths has slowed over the past few years, an adjustment linked to the state’s legalization of recreational marijuana.

In a study published in the American Journal of Public Health and coauthored by researchers in the School of Public Health at the University of North Texas Health Science Center, an analysis of data from the year 2000 to 2015 shows a 6% reduction in Colorado’s number of opioid-related deaths after recreational marijuana was made legal in 2012.

The study, the first of its kind to look at short-term public health benefits of legalized marijuana, has garnered a huge amount of attention, trending on Google and ranked high on Web of Science for number of citations soon after it was released.

Despite the potential benefits demonstrated by the study, the lead author recommends caution for policymakers considering legal decisions, as further study is necessary to examine the long-term effects of expanded and legalized marijuana use. This is one story you’ll want to add to your saved folder and check back on in the future.

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

Categories: Data Science

Reflections on Librarianship and Big Data

MAR Data Science - Wed, 2017-11-01 08:00

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 Margaret (Peg) Burnette, Assistant Professor & Biomedical Sciences Librarian, University of Illinois at Urbana-Champaign

The world of librarianship is changing at what seems to be an ever-increasing rate. The librarian’s role has evolved from information organization and access to the provision of specialized services related to information and data quality, management, analysis, and application. Big data is here to stay and permeates both our professional and personal lives. In the era of digital content and libraries without walls, librarians grapple with new challenges in order to remain productive and relevant. And while users may no longer need help finding information, many likely need help with evaluation and management of increasingly large amounts of information and data.

In many ways, the demands of big data are the same as for small data. These demands afford opportunities for librarians that naturally complement librarians’ expertise. Traditional organization and classification skills are still needed to help researchers find, wrangle, and share research and data products of all kinds. More specialized skills, such as statistical or analytical expertise, subject or technical expertise, or advanced computer skills (coding, etc.), enhance the ability to provide highly sought after services that complement the research and education enterprise.

Despite these opportunities, librarians often lack the skills necessary to support research data in a holistic way. Libraries need to plan carefully to match services with librarian competencies and implement strategies to fill gaps. The research and data lifecycles may provide useful frameworks for determining and developing services. For example, an institution might decide to focus on the identification, procurement and application of existing data. Another might focus on infrastructure for data storage solutions which can be a huge challenge for researchers, particularly for big data initiatives. Support for data analysis and data visualization are additional support areas that researchers clamor for. SPSS and R are familiar tools but few have the skills necessary to provide robust support. The immersion that is necessary for mastery of tools like these is simply not realistic for librarians who often wear multiple hats.

A second framework that librarians might consider is big data’s five “Vs”. The Volume of data being produced can benefit from librarian expertise in the areas of organization, security, and storage options. Libraries that are not equipped to offer storage solutions can nonetheless provide information about options and respective implications. Velocity affords opportunities for librarian expertise in the areas of organization, access, and retrieval. For example, librarians can leverage expertise in controlled vocabularies and metadata for data mining projects. Additionally, librarians can apply organizational acumen to help wrangle the Variety of data, both structured and unstructured. Veracity of information is a mainstay of librarianship and data quality is no different. And finally, librarian contributions to data management, curation, and sharing strategies can contribute significantly to the Value of that data.

Ultimately, with all of these opportunities, it is vital to consider data services within the larger institutional context. Some of the services that libraries consider may be provided by other entities such as offices of research or IT units. Coordination is vital to ensure seamless and integrated services streams, shared and complementary responsibilities, and unified goals.

Categories: Data Science

Potential Roles for Health Sciences Librarians in Big Data

GMR Data Science - 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

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

MCR Data Science - 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

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

GMR Data Science - 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

Perspectives of Librarian Involvement in the Use of Big Data and Data Science

MAR Data Science - Wed, 2017-10-25 08:00

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 Mary Pat Harnegie, MLIS, AHIP, Medical Librarian, Cleveland Clinic Alumni Library and Manager, South Pointe Hospital Library

ostrich and man with their heads in the sand

This picture puts into words about how I might want to feel about Big Data and the role of the Librarian. After seeing the complexity of the Big Data processes and the unorganized systems that contribute to its disorder, I feel overwhelmed with the expansiveness of what needs to be done to make it usable. If I put my head in the sand, the problem(s) go away…Right?! Wrong!!

Sometimes, order comes out of organizing parts of disorder. So if you have a big picture of chaos, one way to attack the disorder is to pick a part that one can bring into order. When my family is faced with a seemingly insurmountable problem, I tell them that solving the problem is like eating an elephant. You can’t eat an elephant all in one sitting, but you have to deal with it in bite size chunks. The same thing can be applied to a problem: break down your problem in bite size chunks, identify facets of the problem, develop a solution to, and execute it. Look at the next facet of the problem, solve it. After a series of time, you have your elephant-sized problem solved because you dealt with it incrementally.

The class participants observed many examples of what is big data and its amazing applications in business and commerce. Several applications of Big Data and its use in medicine were exhibited in the videos of Kaelber, Longhurst and Meo. I found Dr. Longhurst’s examples of Big Data implications and adopted practices interesting. When given the opportunity of the supported research option and another “this is the way we have always done it” option in the EHR, his colleagues would often choose the second option. But when the EHR was defaulted to the supported research option, with the alternative option available as a “fill-in the blank”, researchers took the road of least resistance and checked the defaulted option. It seems that a lot of the success he described was in giving colleagues an easy-to-use default of the supported recommended action. This was the case in Dr. Kaelber’s examples.

Many of our readings utilized in the course discussed the nature of the unstructured data and its uselessness. The librarian has a place in the Big Data universe as a provider of organizational skills. We have experience in building ontologies like MeSH, where a controlled vocabulary can facilitate a uniform vocabulary through the use of related terms and automated relationship that can help build order in a data schema as well be used a format for use in machine learning. In our readings, we see that the massive amount of data will have to be parsed against standards of uniformity to be reliable and usable. This organizational skill can contribute to Big Data utilization in this way.

Librarians have database design and development skills that can be applied to the organization and data mining processes for Big Data processing. These skills can be adapted and refined for data management processes also. The use of a clinical decision making features, similar to the Green Button, will require organization, architecture design and prioritization that librarians have developed as a tool of their trade.

The enormity of the processes needed to happen is the reason for the picture of the ostrich and the man’s head in the sand. But in ignoring the elephant in the room, librarians will not serve their ultimate constituent well- the patient. The Big Data elephant presents a large and complex set of problems to be organized to be effective in patient care. Our skill sets can make us a team player in the organization, analysis and dissemination of great health care information and practices.

Categories: Data Science

Call for Feedback: NNLM Data Science and Data Management Training Needs Assessment

SEA Data Science - Tue, 2017-10-24 13:33

Are you interested in learning about biomedical and health research data management? Is there a specific area of data science/data management that you would like more information on?

The National Network of Libraries of Medicine (NNLM) Research Data Management Working Group requests feedback on the training needs throughout the country on data science and data management. The field of data science is broad in scope; encompassing a wide variety of areas including the generation, characterization, management, storage, analysis, visualization, integration, and use of large data sets relevant to biomedical and health research. Participation in this training needs assessment will provide NNLM direction for future educational opportunities.

To participate in this assessment, please visit: http://www.surveygizmo.com/s3/3937602/Data-Education-Needs-Assessment. This survey will close November 30, 2017.

 

Categories: Data Science

Finding a Foothold for Hospital Librarians in Big Data

MAR Data Science - Thu, 2017-10-19 08:00

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 Emily Schon, MLIS, AHIP, Librarian, Boston Children’s Hospital

“Big Data” seems to be a term used everywhere – from giant purchasing sites like Amazon and streaming services like Netflix, to government agencies and universities. It certainly seems useful to look at giant amounts of data, analyze it, and see how it can project outcomes or improve users’ experiences. Thanks to this growing trend, hospitals are making great strides toward utilizing Big Data. Many are now collecting and storing enormous amounts of data about their patients, which data scientists and other individuals around the hospital can utilize to improve and support clinical care.

As long-standing brokers of information, hospital librarians would seem to have a natural role in this new era of big data. Librarians possess many of the skills (e.g. data organization, management, etc.) that are and will be increasingly important in this realm. Yet, as things stand now, hospital librarians have neither the time nor the resources to add such a “big” responsibility to an already lengthy list of duties. Additionally, many hospitals do not include librarians in big data initiatives, such as EMR/EHR, where their skills could be most utilized to help change clinical decision making and ultimately clinical practice.

But that doesn’t mean this will always be the case. As big data becomes increasingly critical to hospital business – from clinical research to hospital operations – library departments could very well reorganize in order to prioritize the management of big data. For instance, dedicated librarians with skills and experience in data science could fill this role in hospitals. As hospitals’ big data efforts continue to grow, interdepartmental efforts may become more cohesive and integrated, and librarians will gain access to important parts along the whole big data process.  And it goes without saying that hospital librarians would need to be compensated at a level comparable to data scientists in order to attract top talent once they reach this point.

In the meantime, hospital librarians can make small measures to support data scientists and other researchers in their big endeavors hospital-wide. It would be worthwhile for hospital medical librarians to help researchers understand and prepare for sharing mandates, which would include finding repositories for data and providing guidance on where and how to share data in a reproducible/preservable manner. Librarians can do this through individual meetings and small classes that fit in with other daily operations, or by creating or adding to resource guides or pages on library websites. Librarians can also create general overview guides on what big data is, along with best practices, definitions, links to tools commonly used in big data, and suggested readings.

For the librarian who has more time, they can become better versed in statistical analysis tools (SAS, SPSS, R, Python, etc.) to provide instruction or assist researchers working on datasets on a consultation basis, similar to how they may assist with literature searches. They can also develop relationships with other departments, such as research computing groups within a hospital, to collaborate and find other fits for helping researchers in this manner.

Given the limited time and resources of many hospital librarians, and the often compartmentalized nature of hospitals, it is up to the hospital medical librarian to find and create a “role” within the world of hospital big data if one is desired. Librarians can draw upon their skillsets already in place, such as their superb organization and management skills, teaching, searching, and preservation. Since big data is a vast, quickly growing, and important field, it seems a natural fit for a librarian. But perhaps, for now, the role of the hospital librarian should only be a small role – one to start and find a foothold, and later look to grow.

Categories: Data Science

Infection Control Week

SCR Data Science - Tue, 2017-10-17 10:44
Bacteria

“Achromobacter xylosoxidans” by CDC/Todd Parker is licensed under CC0.

Every year, at least 2 million people in the U.S. become infected with bacteria that are resistant to antibiotics and at least 23,000 people die as a direct result of these infections. Bacteria adapt to the antibiotics designed to kill them, making our antibiotics less effective and limiting our treatment options. For more information on prevention, see the CDC’s Antibiotic/Antimicrobial Resistance page: https://www.cdc.gov/drugresistance/index.html

Also follow the “ABCs of Antibiotics”, provided by the Association for Professionals in Infection Control and Epidemiology (APIC):

  • Ask – “Are these antibiotics necessary?”
  • Bacteria – Antibiotics do not kill viruses. They only kill bacteria.
  • Complete the course – Take all of your antibiotics exactly as prescribed (even if you are feeling better).

For more information, check out the infographics at http://professionals.site.apic.org

Like NNLM SCR on Facebook and like us on Twitter.

Categories: Data Science

Biomedical & Health RDM Training for Librarians: Participant Applications

NTO Data Science - Wed, 2017-10-11 16:33

Health science librarians are invited to participate in a rigorous online biomedical and health research data management training course, sponsored by the National Library of Medicine (NLM) and the National Network of Libraries of Medicine Training Office (NTO). The course provides basic knowledge and skills for librarians interested in helping patrons manage their research data. Attending this course will improve your ability to initiate or extend research data management services at your institution. Familiarity with the research lifecycle is recommended but not required.

The major goal of this course is to provide an introduction to data issues and policies in support of developing and implementing or enhancing research data management training and services at your institution. This material is essential for decision-making and implementation of these programs, particularly instructional and reference services. 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.

Course Components

The online asynchronous component of the program is 8 weeks from January 8 – March 2, 2018. The format includes video lectures, readings, case studies, hands-on exercises, and peer discussions. Expect to spend up to 4 hours each week on coursework. Participants will be assigned a mentor, who will be available to guide and advise throughout the course and in the completion of a Capstone Project.

Between the end of the online component and the Capstone Summit, participants will complete a Capstone project, demonstrating improved skills, knowledge, and ability to support data management services at their institution. The experience will culminate with a Capstone Summit, to be held on April 10-11, 2018 at NIH in Bethesda, Maryland. Each participant will receive up to $900 to support travel to the Capstone Summit. At the Summit, participants will have the opportunity to share their Capstone projects, network with experts and each other, meet with NLM leaders in data science, and learn about cutting edge NIH data initiatives.

CE Credits

Participants who complete all modules, the Capstone Summit, and the course evaluation will receive MLA CE credit (exact number of hours to be determined). No partial CE credit is granted.

Instructors

The primary instructor is Shirley Zhao, MSLIS, MS, Data Science Librarian from the Spencer S. Eccles Health Sciences Library, University of Utah.

Each module will be co-taught by a practicing data librarian.

Who can apply?
  • Applications are open to health science librarians in the United States.
  • Applicants will be accepted from libraries currently looking to develop or enhance research data management training and services.
  • A letter of institutional support is required. See application instructions below.
  • Enrollment is limited to 40 participants.
What does it cost?

There is no charge for participating in the program. Participants will receive a stipend of up to $900 to cover travel costs to the Capstone Summit. Additional travel costs must be covered by the individual or their institution.

Important Dates
  • Application deadline: November 8, 2017
  • Notifications: Week of December 4, 2017
  • Online Course: January 8 – March 2, 2018
  • Capstone Summit: April 10-11, 2018
Application Details
  • Name and Contact Information
  • Current Role/Title
  • Place of Employment
  • Briefly describe your current experience or interest in research data management and why you would like to participate in this training.
  • Briefly describe the current status of research data management services at your library, including any barriers to implementation.
  • This training will have been worthwhile to you and your institution if…
Application Instructions

Please fill out the online Application Form, and upload a PDF of your current CV and your letter of institutional support. The letter of institutional support must be from your supervisor and address the following:

  1. time for participation in online course and Capstone Summit;
  2. the library’s commitment to or plans for adding or enhancing research data management services.

Please submit your application via the online form by November 8, 2017:
http://www.surveygizmo.com/s3/3894185/Biomedical-Health-RDM-Training-Participant-Application

Questions?

Contact NTO at nto@utah.edu

Categories: Data Science

Call for Reviewers: Biomedical and Health Research Data Management Training for Librarians

SEA Data Science - Thu, 2017-10-05 09:23

Are you an information professional experienced in research data management? Are you eager to share your knowledge with others and help expand the community of data librarians? The National Network of Libraries of Medicine Training Office has several opportunities for you to contribute to shaping a new training experience specifically for librarians.

This training is an 8-week online class with engaging lessons and practical activities, starting in January 2018. Students will complete a capstone project at the end of the course and the experience will culminate in a Capstone Summit at NIH on April 10-11, 2018.

Modules for the course may include, but are not limited to the following core research data management (RDM) areas:

  1. Data Lifecycle and RDM Overview
  2. Data Documentation
  3. Data Wrangling
  4. Data Standards, Taxonomies, and Ontologies
  5. Data Security, Storage, and Preservation
  6. Data Sharing and Publishing
  7. Data Management Plans
  8. RDM at Your Institution

We are looking for experienced data librarians to participate in this project as module reviewers, co-teachers, and/or mentors. You may (and are encouraged to) apply for more than one role, and for more than one module.

  • Reviewers: Critique module content, test exercises, make suggestions, add resources. Deliverable: written report of findings. (Due Nov 30) Paid $250.
  • Co-Teachers: Assigned to one or more modules. Work with course facilitator to create a case study related to module topic (due Nov 15). Provide feedback on student assignments and answer questions for your module(s) in a timely manner during the course (Jan-March 2018).
    Deliverables: Case study by deadline, written report of suggestions for class improvement (due April 2, 2018). Paid $750.
    Mentors: Participate in class discussions, sharing expertise as needed, during the course (January – March 2018). Provide at least 2 mentoring sessions to each assigned student (4-5) for completing the Capstone project, attend and participate in the Capstone Summit.
    Deliverables: written report of experience as mentor, suggestions for program improvement and sustainability of project. Paid $1250, and travel support to Capstone Summit up to $1250.

All reviewers, co-teachers, and mentors will be required to submit a W-9. Those receiving $1000 or more will also be required to complete a contract with the University of Utah.

Applications
Please submit your application via online form by October 20, 2017:
http://www.surveygizmo.com/s3/3873043/RDM-ReviewerApplication

Application Includes:

  • Name
  • Current Role/Title
  • Place of Employment
  • Please briefly describe your area(s) of interest, research, or primary expertise in data management.
  • Please summarize your qualifications to serve as a content reviewer, co-teacher, and/or mentor for this research data management class.
  • Indicate which modules you would like to serve as a content reviewer and/or co-teacher.
  • Would you like to serve as a mentor for 4-5 students in completing the Capstone Project?
  • Curriculum vitae (attachment)

For questions, please contact: Shirley Zhao, Training Development Specialist: Shirley.zhao@utah.edu

 

Categories: Data Science

Call for Reviewers, Co-Teachers, and Mentors

NTO Data Science - Wed, 2017-10-04 02:55

Are you an information professional experienced in research data management? Are you eager to share your knowledge with others and help expand the community of data librarians? The National Network of Libraries of Medicine Training Office has several opportunities for you to contribute to shaping a new training experience specifically for librarians.

Biomedical and Health Research Data Management Training for Librarians is an 8-week online class with engaging lessons and practical activities, starting in January 2018. Students will complete a capstone project at the end of the course and the experience will culminate in a Capstone Summit at NIH on April 10-11, 2018. A short description of the whole program can be downloaded here.

Modules for the course may include, but are not limited to the following core research data management (RDM) areas:

  1. Data Lifecycle and RDM Overview
  2. Data Documentation
  3. Data Wrangling
  4. Data Standards, Taxonomies, and Ontologies
  5. Data Security, Storage, and Preservation
  6. Data Sharing and Publishing
  7. Data Management Plans
  8. RDM at Your Institution

We are looking for experienced data librarians to participate in this project as module reviewers, co-teachers, and/or mentors. You may (and are encouraged to) apply for more than one role, and for more than one module.

Reviewers: Critique module content, test exercises, make suggestions, add resources.
Deliverable: written report of findings (due November 30, 2017).
Paid $250.

Co-Teachers: Assigned to one or more modules. Work with course facilitator to create a case study related to module topic (due November 15). Provide feedback on student assignments and answer questions for your module(s) in a timely manner during the course (January – March 2018).
Deliverables: Case study by deadline, written report of suggestions for class improvement (due April 2, 2018).
Paid $750.

Mentors: Participate in class discussions, sharing expertise as needed, during the course (January – March 2018). Provide at least 2 mentoring sessions to each assigned student (4-5) for completing the Capstone project, attend and participate in the Capstone Summit.
Deliverables: written report of experience as mentor, suggestions for program improvement and sustainability of project.
Paid $1250, and travel support to Capstone Summit up to $1250.

All reviewers, co-teachers, and mentors will be required to submit a W-9. Those receiving $1000 or more will also be required to complete a contract with the University of Utah.

Applications

Please submit your application via the online form by October 20, 2017:
http://www.surveygizmo.com/s3/3873043/RDM-ReviewerApplication

Application Includes:

  • Name
  • Current Role/Title
  • Place of Employment
  • Please briefly describe your area(s) of interest, research, or primary expertise in data management.
  • Please summarize your qualifications to serve as a content reviewer, co-teacher, and/or mentor for this research data management class.
  • Indicate which modules you would like to serve as a content reviewer and/or co-teacher.
  • Would you like to serve as a mentor for 4-5 students in completing the Capstone Project?
  • Curriculum vitae (attachment)
Questions

Please contact Shirley Zhao, Training Development Specialist.

Categories: Data Science

Request for Information: Next-Generation Data Science Challenges in Health and Biomedicine

MAR Data Science - Tue, 2017-10-03 15:47

On behalf of the National Institutes of Health (NIH), the National Library of Medicine (NLM) seeks community input on new data science research initiatives that could address key challenges currently faced by researchers, clinicians, administrators, and others, in all areas of biomedical, social/behavioral and health-related research. The field of data science is broad in scope, encompassing approaches for the generation, characterization, management, storage, analysis, visualization, integration and use of large, heterogeneous data sets that have relevance to health and biomedicine. Data science undergirds the broad and interdependent objectives of the NIH Strategic Plan.

Information about data science research directions that could lead to breakthroughs in any or all NIH interest areas is welcomed, whether applicable across wide swaths of health and biomedicine, or focused on particular research domains.

Information Requested:

NLM requests information on the three focal areas listed below:

  1. Promising directions for new data science research in the context of health and biomedicine.  Input might address such topics as Data Driven Discovery and Data Driven Health Improvement.
  2. Promising directions for new initiatives relating to open science and research reproducibility. Input might address such topics as Advanced Data Management and Intelligent and Learning Systems for Health.
  3. Promising directions for workforce development and new partnerships. Input might address such topics as Workforce Development and Diversity and New Stakeholder Partnerships.

Within these general topic areas, or others related to data science in health and biomedicine, NLM invites researchers, clinicians, organizations, industry representatives and other interested parties to provide input on:

  • Research areas that could benefit most from advanced data science methods and approaches;
  • Data science methods that need updating, or gap areas where new approaches are needed;
  • Priorities for new data science research;
  • Appropriate partnerships and settings for expanded data science research.

See the full notice of request for more background information and details on how to submit a response.

Inquiries:

Please direct all inquiries to Valerie Florance, PhD
National Library of Medicine (NLM)
Telephone: 301-496-4621
Email: NLMEPInfo@mail.nih.gov

Categories: Data Science

Urban Indian Health Institute: Decolonizing Data

PNR Data Science - Thu, 2017-09-28 05:00

The Urban Indian Health Institute (UIHI), a Division of the Seattle Indian Health Board located in Seattle, WA is one of 12 tribal epidemiology centers (TECs) funded by the Indian Health Service (IHS). TECs serve as a crucial component of the health care resources for all American Indians and Alaska Natives (AI/AN) by:

Managing public health information systems
Investigating diseases of concern
Managing disease prevention and control programs
Communicating vital health information and resources
Responding to public health emergencies
Coordinating these activities with other public health authorities

Although eleven of the TECs focus on regional AI/AN population health issues in IHS service areas, the UIHI addresses nationwide tribal AI/AN and urban Indian public health and disease surveillance needs. On their website, you will find the data-rich UIHI annual Community Health Profile, an overview of the health status of American Indians and Alaska Natives (AI/AN) within the 33 Urban Indian Health Programs (UIHPs) service areas. They also offer an excellent series of fact sheets, reports, and toolkits on chronic disease, communicable disease, and child and maternal health.

With her team of researchers, UIHI Director, Abigail Echo-Hawk, is passionate about decolonizing AI/AN data. For centuries, tribal information has been collected and interpreted by outside officials, government agencies, and researchers.  This has not only led to inaccurate and often harmful outcomes, but is also reflected in the wide gaps in accurate and reliable data available about AI/AN populations and their health trends. One of the overarching goals of the UIHI is to right this wrong and place health data back in the hands of the tribal and urban Indian population, where it can be used to help identify and ameliorate the health disparities that currently exist.

The NNLM PNR is proud to work with the UIHI and we look forward to bringing you more information on the amazing work they are doing.

Categories: Data Science

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