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Data Science

Today (November 15th) is National Rural Health Day

SCR Data Science - Thu, 2018-11-15 18:29

National Rural Health Day exists to promote awareness of the unique healthcare needs in rural areas of America. 60 million people, or 1 in every 5 Americans, currently live in rural areas of the United States. Receiving medical care is not always easy for those living in these areas due to a potential shortage of providers.

In Arkansas, 44% of the population live in rural (or non-metropolitan) areas. This means there’s roughly 9.5 physicians for every 10,000 people living in rural areas. In Louisiana, there are 9 physicians to every 10,000 rural residents. For the other three states:

  •         New Mexico: 12.5 physicians per 10,000 rural residents
  •         Texas: 8 physicians per 10,000 rural residents
  •         Oklahoma: 10.3 physicians per 10,000 rural residents

This contrasts from metropolitan areas in Texas, for instance, where the average is 25.7 physicians for every 10,000 people.

A few strategies being used to bring more healthcare into these areas include virtual doctor visits, incentives for doctors, and affiliation with larger healthcare networks. Rural Health Information Hub provides several resources for those interested in learning about public health in rural areas. Knowing about the resources around your area could possibly save a life in times of emergency.

map of health shortages

Like NNLM SCR on Facebook and follow us on Twitter.

Sources:

https://medlineplus.gov/ruralhealthconcerns.html
https://www.ruralhealthinfo.org/topics/agricultural-health-and-safety

Categories: Data Science

Upcoming Webinar: Proposed Provisions for a Draft NIH Data Management and Sharing Policy

SEA Data Science - Tue, 2018-11-06 14:37

Date: Wednesday, November 7, 2018

Time: 11:30 AM – 1 PM ET

Registration: Details about the webinar, including how to register can be found by clicking here.

Description: On October 10, 2018, the National Institutes of Health (NIH) issued a Request for Information (RFI) in the NIH Guide to Grants and Contracts to solicit public input on proposed key provisions that could serve as the foundation for a future NIH policy for data management and sharing. The feedback obtained will help to inform the development of a draft NIH policy for data management and sharing, which is expected to be released for an additional public comment period upon its development.  To further engage stakeholders, NIH will be hosting a webinar on the proposed key provisions on November 7, 2018, from 11:30 a.m. – 1:00 p.m. ET.

Comments on the proposed key provisions will be accepted through December 10, 2018, and can be made electronically by visiting the National Institutes of Health, Office of Science Policy.

For a perspective on the importance of obtaining robust stakeholder feedback on this topic, please see the latest Under the Poliscope by Dr. Carrie D. Wolinetz.

Categories: Data Science

Data Flash: RIP PubMed Health

PNR Data Science - Sun, 2018-10-28 17:20

This is not exactly a data post, but, the loss of a trusted source for clinical effectiveness research will have its effects on the dataverse.  PubMed Health is being discontinued as of this coming Wednesday.  As any of my colleagues can tell you, I’m taking the loss of PubMed Health hard– I loved showing it to people at various conferences, and using it myself– I found it a wonderful mid-point between MedlinePlus.gov and PubMed.gov, and it also had some great methodology resources and a glossary.  All of its content will be findable in other ways though!

In thinking about how to proceed in future with finding clinical effectiveness research searching, I did some exploring and gathered my findings into a poster I presented recently at the Washington State Public Health Association conference.  Below, in list form, is the poster content–feel free to contact me at glusker (AT) uw.edu if you have any questions! And please send any suggestions for additions to these lists!

Check Out These Ways to Find Research on Clinical Effectiveness:

  • PubMed.gov has filters for systematic reviews and guidelines
  • Who cares? Seek out the organizations that care about the topic (Kids? American Academy of Pediatrics!)
  • If you or your local health sciences library have databases, check them out—for example, nursing database CINAHL has great content
  • NLM’s “Bookshelf” is becoming a good resource for guidelines https://is.gd/NLMBookshelf
  • The National Guidelines Clearinghouse will soon be re-released by ECRI and they have said it will be open access!
  • ClinicalTrials.gov records often link to related publications
  • For public health—try www.thecommunityguide.org and NICHSR OneSearch (a federated search of four public health databases)

Ramp Up Your Google Search Skills! 

  • Try this string, created by P.F. Anderson for a recent Twitter chat: guidelines|white-paper|standards|report|protocol| procedure|policy filetype:pdf (site:org OR site:gov) [fill in the condition])
  • Try GoogleScholar (scholar.google.com)

Search for Content from Reliable Guideline/Content Contributors (the Ones PubMed Health used):

  • Agency for Healthcare Research and Quality (US) (AHRQ)
  • Canadian Agency for Drugs and Technologies in Health (CADTH)
  • Centre for Reviews and Dissemination (CRD)
  • Cochrane
  • Department of Veterans Affairs’ Evidence-based Synthesis Program from the Veterans Health Administration R&D (VA ESP)
  • German Institute for Quality and Efficiency in Health Care (IQWiG)
  • Knowledge Centre for the Health Services at The Norwegian Institute of Public Health (NIPH)
  • National Cancer Institute (NCI)
  • National Institute for Health and Care Excellence guidelines program (NICE)
  • National Institute for Health Research (NIHR) Health Technology Assessment Programme (NIHR HTA)
  • Oregon Health and Science University’s Drug Effectiveness Review Project (DERP)
  • Swedish Council on Health Technology Assessment (SBU)
  • The TRIP database (TRIP)
  • World Health Organization (WHO)

AND IF ALL ELSE FAILS, ASK A LIBRARIAN!

Categories: Data Science

Health Sciences Librarians of Illinois Annual Conference – Rivers of Data, Streams of Knowledge

GMR Data Science - Tue, 2018-10-23 10:18

The Health Sciences Librarians of Illinois received a GMR Professional Development award for 3 CE courses at the annual conference, held September 26-28 at the Cliffbreakers Riverside Hotel and Conference Center in Rockford, Illinois.

Attendees learned how to plan and develop working relationships in Building Partnerships with Faculty, Clinicians, and Other Stakeholders, with Gwen Wilson, the Health Informatics Coordinator/Librarian at Washburn University in Topeka, KS. Erin Foster, Data Services Librarian at the Indiana University School of Medicine provided information on Data Management in the Wild. A trio from the University of Illinois-Urbana Champaign, including Peg Burnette, Assistant Professor and Biomedical Sciences Librarian, Erin Kerby, Veterinary Medicine Librarian and Amanda Avery, a student at the iSchool inspired us to create or improve Your Online Professional Identity – Using Professional Profile Systems to Your Best Advantage.

Erin Foster addresses Data Management learning objectives 

Erin Foster addresses Data Management learning objectives

Gwen Wilson overviewing the course

Gwen Wilson overviewing the courseCourse evaluations were very positive and many learners had immediate plans to make use of what they learned. HSLI is grateful to GMR for the professional development funding, which helped our small organization provide excellent continuing education for members.

Amanda Avery, Erin Kerby, Peg Burnette, being introduced by Ramune Kubilius

Amanda Avery, Erin Kerby, Peg Burnette, being introduced by Ramune Kubilius

Categories: Data Science

NIH Seeks Public Comment on Proposed Provisions for a Future Draft Data Management and Sharing Policy

SEA Data Science - Fri, 2018-10-12 15:45

On October 10, 2018, the National Institutes of Health (NIH) issued a Request for Information (RFI) in the NIH Guide to Grants and Contracts to solicit public input on proposed key provisions that could serve as the foundation for a future NIH policy for data management and sharing.  The feedback we obtain will help to inform the development of a draft NIH policy for data management and sharing, which is expected to be released for an additional public comment period upon its development.

Comments on the proposed key provisions will be accepted through December 10, 2018, and can be made electronically by visiting here.

To further engage stakeholders, NIH will also be hosting a webinar on the proposed key provisions on November 7, 2018, from 11:30 a.m. – 1:00 p.m. ET. Details about the webinar, including how to register can be found by clicking here.

For a perspective on the importance of obtaining robust stakeholder feedback on this topic, please see the latest Under the Poliscope by Dr. Carrie D. Wolinetz.

Questions about the proposed provisions may be sent to the NIH Office of Science Policy at SciencePolicy@od.nih.gov

Categories: Data Science

New Joint NSF/NLM Funding Announcement: Generalizable Data Science Methods for Biomedical Research

PSR Data Science - Thu, 2018-10-11 15:33

Significant advances in technology, coupled with decreasing costs associated with data collection and storage, have resulted in unprecedented access to vast amounts of health- and disease-related data. The National Library of Medicine and the Division of Mathematical Sciences in the Directorate for Mathematical and Physical Sciences (DMS) at the National Science Foundation (NSF) recognize the need to support research to develop innovative and transformative mathematical and statistical approaches to address important data-driven biomedical and health challenges. The goal of this interagency program is the development of generalizable frameworks combining first principles, science-driven models of structural, spatial and temporal behaviors with innovative analytic, mathematical, computational, and statistical approaches that can portray a fuller, more nuanced picture of a person’s health or the underlying processes.

Specific information concerning application submission and review process is through the National Science Foundation via solicitation NSF-19-500. Applicants may opt to submit proposals via Grants.gov or via the NSF FastLane system. For applications that are being considered for potential funding by NLM, the PDs/PIs will be required to submit their applications in an NIH-approved format. Anyone invited to submit to NIH will receive further information on submission procedures. Applicants will not be allowed to increase the proposed total budget or change the scientific content of the application in the submission to the NIH. The results of the first level scientific review will be presented to NLM Board of Regents for the second level of review. NLM will make final funding determinations and issue Notices of Awards to successful applicants. NLM and DMS anticipate making 8 to 10 awards, totaling up to $4 million, in fiscal year 2019. It is expected that each award will be between $200,000 to $300,000 (total costs) per year with durations of up to three years. The application submission window deadline is in early January, 2019.

Collaborative efforts that bring together researchers from the biomedical/health and the mathematical/statistical sciences communities are a requirement for this program and must be convincingly demonstrated in the proposal. While the research may be motivated by a specific application or dataset, the development of methods that are generalizable and broadly applicable is preferred and encouraged. Proposals should clearly discuss how the intended new collaborations will address a biomedical challenge and describe the use of publicly-available biomedical datasets to validate the proposed models and methodology. Applicants are expected to list specific datasets that will be used in the proposed research and demonstrate that they have access to these datasets. The Data Management Plan should describe plans to make the data available to researchers if these data are not in the public domain. Some of the important application areas currently supported by the National Library of Medicine include the following:

  • Finding biomarkers that support effective treatment through the integration of genetic and Electronic Health Records (EHR) data;
  • Understanding epigenetic effects on human health;
  • Extracting and analyzing information from EHR data;
  • Understanding the interactions of genotype and phenotype in humans by linking human sensor data with genomic data using dbGaP;
  • Protecting confidentiality of personal health information; and
  • Mining of heterogeneous data sets (e.g. clinical and environmental).

Inquiries should be directed to Jane Ye, PhD at the National Library of Medicine, (301) 594-4882.

Categories: Data Science

ICYMI Webinar Recap for September 2018: Using US Census Bureau Data

SCR Data Science - Tue, 2018-10-09 10:07

The In Case You Missed It (ICYMI) Webinar Recap series will provide a summary of our monthly SCR CONNECTions webinars. We’ll go over highlights from our guest speakers’ presentations and give some additional thoughts about the connections our attendees could be making from the presented topics!

Our September guest speaker, Susana Privett, Data Dissemination Specialist with the US Census Bureau, is no stranger to online webinar presentations. A large part of her duties include giving online and in-person trainings and workshops on data and the census bureau’s online tools. And it’s a good thing, because September was one of our highest attended webinars yet!

For those not aware, all of the data collected from the US census, performed every 10 years, is posted online and available for access from census.gov. The website was recently revamped to be more user friendly and provide more opportunities for learning about census data, including additional training, news, infographics, and stories about data.

Susana demonstrated many of the features of the census website, such as QuickFacts to compare geographical data and American Factfinder, a data search tool that locates tables of population data. She also explained how census data is collected and categorized, with a breakdown of the geographic area types and an overview of census tracts and blocks. “They’re really like Russian nesting dolls,” she said, with a combination of legal and statistical geography.

Data and assessment are increasingly important topics in an era of big data and with the growth of digital data collection. Certainly anyone applying for grant funding knows the importance of data in showing evidence of need and potential for impact! The census bureau provides one possible source of data that can be utilized, and it’s freely available for anyone to use.

Susana just scratched the surface of what data the census bureau has to offer, and we hope to offer another session from her in the future for those looking to enhance their census data searching skills. Be on the lookout for that future session, and catch up with her webinar in the meantime:

Don’t forget to mark your calendars for our next SCR CONNECTions webinar, Game On! Motivate and Engage Your Staff with Gaming Strategies, scheduled for Wednesday, October 10th at 10am CT / 9am MT!

Like NNLM SCR on Facebook and follow us on Twitter.

Categories: Data Science

Clem McDonald, MD, Named as First NLM Chief Health Data Standards Officer

PSR Data Science - Wed, 2018-10-03 19:41
Dr. Clem McDonald

Clem McDonald, MD

NLM Director Patricia Flatley Brennan, RN, PhD, has announced the appointment of Clem McDonald, MD, to the newly created position of Chief Health Data Standards Officer for the National Library of Medicine. His appointment will be effective November 1, 2018. The new position demonstrates NLM’s strong and enduring commitment to health data standards. The Chief Health Data Standards Officer’s responsibilities will involve integrating standards efforts across the Library, including the Fast Healthcare Interoperability Resources (FHIR) interoperability standard, Common Data Elements, and the vocabularies specific to clinical care (e.g., RxNORM, LOINC, SNOMED). The chief will also develop partnerships with industry, academia, and other federal agencies to advance the use of health data standards in clinical practice, public health, and observational data, including sensors.

For the last 12 years, Dr. McDonald served as Director of the Lister Hill National Center for Biomedical Communications (LHNCBC) and Scientific Director of its intramural research program. His research focuses on clinical informatics; tools based on HL7’s FHIR to facilitate the use of electronic health records and research bases; the analysis of large clinical databases; the promotion, development, enhancement, and adoption of clinical messaging and vocabulary standards; and text de-identification. Prior to coming to NLM, Dr. McDonald served as the Regenstrief Professor of Medical Informatics at the Indiana University School of Medicine and the Director of the Regenstrief Institute for Health Care, a privately endowed research institute working to integrate research discovery, technological advances, and systems improvement into the practice of medicine. Dr. McDonald developed the Regenstrief Medical Record, one of the first electronic health record systems, and introduced the use of randomized trials to study health information systems. With NLM support, he and his colleagues developed the first Health Information Exchange, now loaded with 6 billion results from hospitals across Indiana. He also initiated the Logical Observation Identifier Names and Codes (LOINC) database observations for laboratory tests, clinical measurements, and clinical reports, and he was one of the founders of the Health Level 7 (HL7) message standards, used in hospitals today.

Effective November 1, Milton Corn, MD, Deputy Director of NLM for Research and Education, will also assume the responsibilities of Acting Scientific Director, LHNCBC. Olivier Bodenreider, MD, PhD, Chief of the Cognitive Science Branch at LHNCBC and a Principal Investigator in NLM’s Intramural Research Program, has been selected to become Acting Director, LHNCBC. Jerry Sheehan, NLM Deputy Director, will provide executive oversight and guidance.

Categories: Data Science

NLM Funds Study to Forecast Long-Term Costs of Data

PSR Data Science - Wed, 2018-10-03 19:39

The National Library of Medicine has teamed up with the National Academies of Sciences, Engineering, and Medicine (NASEM) to conduct a study on forecasting the long-term costs for preserving, archiving, and promoting access to biomedical data. The study is being conducted as part of the NLM’s efforts to develop a sustainable data ecosystem, as outlined in both the NLM Strategic Plan and the NIH Strategic Plan for Data Science. Such an ecosystem is possible because the products and processes of research are now digital by default, and increasingly sophisticated and powerful computation can now be brought to data, rendering meaning that had previously been hidden. Across the biomedical sciences, decisions must be made about where in this ecosystem to invest limited resources to maximize the value of the data for scientific progress; strategies are needed to address question such as: What is the future value of research data? For how long must a dataset be preserved before it should be reviewed for long-term archiving? And what are the resources necessary to support long-term data storage?

For this study, NASEM will appoint an ad hoc committee to develop a framework for forecasting these costs and estimating potential benefits to research. The committee will examine and evaluate:

  • Economic factors to be considered when examining the life-cycle cost for data sets (e.g., data acquisition, preservation, and dissemination);
  • Cost consequences for various practices in accessioning and de-accessioning data sets;
  • Economic factors to be considered in designating data sets as high value;
  • Assumptions built in to the data collection and/or modeling processes;
  • Anticipated technological disruptors and future developments in data science in a 5- to 10-year horizon; and
  • Critical factors for successful adoption of data forecasting approaches by research and program management staff.

The committee will provide a consensus report and two case studies illustrating the framework’s application to different biomedical contexts relevant to NLM’s data resources. Relevant life-cycle costs will be delineated, as will any assumptions underlying the models. To the extent practicable, NASEM will identify strategies to communicate results and gain acceptance of the applicability of these models. As highlighted in a recent blog post, NASEM will host a two-day public workshop in late June 2019 to generate ideas and approaches for the committee to consider. Further details on the workshop and public participation will be made available in the coming months.

NLM is supporting NASEM’s efforts to solicit names of committee members, as well as topics for the committee to consider. Suggestions should be sent to Michelle Schwalbe, Director of NASEM’s Board on Mathematical Sciences and Analytics, or Elizabeth Kittrie, NLM Senior Planning and Evaluation Officer.

Categories: Data Science

Data Flash: Who doesn’t love going to the fair?

PNR Data Science - Fri, 2018-09-28 05:45

In this case, a free, online DATA FAIR!  Next week, October 1 through 5, ICPSR (the international data consortium/data archive/data education and research organization) will be holding the 2018 ICPSR Data Fair .  The number of offerings is impressive, and there’s enough variety that there’s something for everyone—diversity and inclusion, training, sharing, tools, and more.  You register for each session individually (but don’t forget the Tweetchats!).   Best of all, no costs for registration or travel!

And, if online learning and participation is appealing, you might also consider involving your library or organization in International Open Access Week, October 22-28.  You can share a blog post about your open access “successes, challenges and ideas”, and see what others are doing around the world.

Last but not least, let us know if you come across other opportunities like this that our Pacific Northwest colleagues might be interested in–we’re always happy to spread the word!

Categories: Data Science

Upcoming Webinar: Planning, Developing, and Evaluating R Curriculum at the NIH Library – October 12 2 PM ET

SEA Data Science - Thu, 2018-09-27 12:11

Join NNLM for the next iteration of the Research Data Management webinar series: Planning, Developing, and Evaluating R Curriculum at the NIH Library October 12, from 2-3 pm ET. To register for this free webinar, visit: https://nnlm.gov/class/Rtraining. Can’t make it on the 12th? Don’t worry, the webinar will be recorded!

This webinar will describe a pilot project to evaluate current R training at the NIH Library, and based on an evaluation of the data, revise the library’s R training curriculum. This will include a discussion of the development of a training plan, weekly R check-in sessions, managing documents using Open Science Framework (OSF), and an evaluation of the pilot.

Learning Objectives:

By the end of this webinar participants should have a better understanding of:

  1. R curriculum before the pilot project
  2. Our evaluation of data-related training before the pilot project
  3. The components of the pilot project
  4. The development of our training plan
  5. How OSF was used for project management
  6. Format and frequency of classes during the pilot project
  7. Post-pilot evaluation

Instructor Bios:

Doug Joubert joined the National Institutes of Health (NIH) Library in 2004. He is a customer-oriented practitioner with extensive experience in providing comprehensive research and information services support to researchers working in the areas of public health and health care policy. In this role, Doug provides his clients with services that support of the missions of the NIH and select HHS staff divisions. As part of his duties at the NIH Library, he identifies and provides guidance on the effective use of emerging technologies and recommends strategies to capitalize on them. Practice areas include data analytics, data visualization, GIS, and teaching.

Candace Norton joined the National Institutes of Health (NIH) Library as a National Library of Medicine (NLM) second year Associate Fellow in 2017. Prior to joining the NLM Associate Fellowship Program, Candace managed a small corporate library for a pharmaceutical and life sciences consulting company in Bethesda, MD. During her fellowship appointment, she has pursued projects and training in areas related to pharmacovigilance monitoring, systematic reviews, bibliometric analysis, and data visualization.

Categories: Data Science

Upcoming webinar: Planning, Developing, and Evaluating R Curriculum at the NIH Library

MCR Data Science - Wed, 2018-09-26 16:32

Join NNLM for the next iteration of the Research Data Management webinar series: Planning, Developing, and Evaluating R Curriculum at the NIH Library October 12, from 2-3 pm ET. To register for this free webinar, visit: https://nnlm.gov/class/Rtraining. Can’t make it on the 12th? Don’t worry, the webinar will be recorded!

This webinar will describe a pilot project to evaluate current R training at the NIH Library, and based on an evaluation of the data, revise the library’s R training curriculum. This will include a discussion of the development of a training plan, weekly R check-in sessions, managing documents using Open Science Framework (OSF), and an evaluation of the pilot.

Learning Objectives:
By the end of this webinar participants should have a better understanding of:
1. R curriculum before the pilot project
2. Our evaluation of data-related training before the pilot project
3. The components of the pilot project
4. The development of our training plan
5. How OSF was used for project management
6. Format and frequency of classes during the pilot project
7. Post-pilot evaluation

Instructor Bios:
Doug Joubert joined the National Institutes of Health (NIH) Library in 2004. He is a customer-oriented practitioner with extensive experience in providing comprehensive research and information services support to researchers working in the areas of public health and health care policy. In this role, Doug provides his clients with services that support of the missions of the NIH and select HHS staff divisions. As part of his duties at the NIH Library, he identifies and provides guidance on the effective use of emerging technologies and recommends strategies to capitalize on them. Practice areas include data analytics, data visualization, GIS, and teaching.

Candace Norton joined the National Institutes of Health (NIH) Library as a National Library of Medicine (NLM) second year Associate Fellow in 2017. Prior to joining the NLM Associate Fellowship Program, Candace managed a small corporate library for a pharmaceutical and life sciences consulting company in Bethesda, MD. During her fellowship appointment, she has pursued projects and training in areas related to pharmacovigilance monitoring, systematic reviews, bibliometric analysis, and data visualization.

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Categories: Data Science

“Big Data in Healthcare: Exploring Emerging Roles” Guest Essay

PNR Data Science - Tue, 2018-09-25 21:01

One of the more popular courses we at the NNLM offer is called “Big Data in Healthcare: Exploring Emerging Roles“.  This nation-wide course “explains the role big data plays in clinical patient outcomes, explains current/potential roles in which librarians are supporting big data initiatives, and illustrates the fundamentals of big data from a systems perspective”.  The assignments in the course build over the nine weeks until the participants can use them as the basis for a final essay.  The essays are really wonderful thought-pieces both about how librarians can enter the big data world, and about how the participants themselves see that world differently after having taken the course.

And then we in the NNLM regions post the essays from those in our regions who wrote them!  In our most recent offering there were no participants from the Pacific Northwest Region, but Luz Crespo from the Southeastern Atlantic region has graciously allowed us to post her final essay here.   Enjoy!

“Big Data and the Role for Health Science Librarians” by Luz Crespo

As health science librarians get involved with big data, they develop the skills that can assist end-users. Librarians who learn about the processes of big data can evaluate how data originate, how the amount of data is constantly being produced for larger capacities, and generally how it works. It is interesting to learn that data can be accessed from various resources. Librarians learning about big data can comprehend how the information is accessed, obtained, accumulated and the formats that initiate this process. For example, clinicians may insert a wide variety of data that may include patient demographics, which then can be accessed in the patient’s electronic health record (the digital format versus paper documentation). Health professionals are able to access the patient information quickly and find health diagnoses and health documentation; such as the patient’s medical history, current conditions, and lab results to determine the patient’s quality of care.

Where I work, the Electronic Health Record (EHR) is the system that is used within the facility. Though I do not have access to this software, I am confident that as technology continues to improve,  medical librarians who are knowledgeable with these types of software can achieve the skills to communicate, connect and educate healthcare professionals that need assistance within the healthcare system. The EHR is used strictly for healthcare professionals.. Earlier in this course, it was interesting to learn that the Metro Health System was one of the first to utilize the EHR. I’m not sure how medical librarians would have access to the EHR since the HIPAA policy is established to protect the patient information.

Dr. Brennan’s presentation on the BD2K engages individuals to comprehend that data sciences, providing the tools and training, can allow individuals the awareness to communicate and learn the techniques that permits them to better serve others in locating information, which can make a difference, for example, for, researchers who are needing assistance in this area. I agree with Tara Douglas-Williams on the importance of nurses actively contributing in big data initiatives across various health care systems. She expressed how Dr. Brennan is an advocate in assisting librarians in building data science relationships. There is an old saying, “For the things we have to learn before we can do them, we learn by doing them.” (Aristotle, from The Nicomachean Ethics). This illustrates that as information professionals or librarians it is important to adapt and learn the skills that provide the tools that can assist others with their life-long learning.

In my opinion, health sciences librarians that fulfill the goal to gain knowledge and gather the information that is needed to support researchers and healthcare professionals can succeed in meeting the needs of the end-users and surrounding community. Overall, learning about big data  allowed me to see the big picture and how it can benefit me as a new librarian, and how I can share what I have learned with others.

Categories: Data Science

Creating our own pathway

MCR Data Science - Tue, 2018-09-25 16:33

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: Kristin Whitehair, Director of Library Services, St. Luke’s Health System

During the rise of evidence-based medicine, there was a clear link to the health sciences library.  With evidence, usually as published in the literature, creating the foundation of practice, the library was a natural partner for clinicians, researchers, administrators, and students.

Now with the growth of big data and data science, we are seeing a similar transition.  Organizations are devoting significant resources and energy to data science initiatives.  The potential of data science appears huge, and largely untapped.  The potential of data science has mostly focused on health research.  However, data science can also look internally within the organization, especially for larger health systems.  For example, retail corporations study online customer behavior, product offerings, and facility design.  This same potential holds true for the health sector.  Libraries can support data science in both health research and for organizations internally seeking to optimize business operations.

While there was a clear path to the library with evidence-based medicine, with data science librarians must build their own pathway.  Part of this lies in how we define ourselves.  Libraries can be a collection of literature, physical space, research expertise, and so much more.  In general, libraries avoid limited definitions of their function.  My library is a digital library, and I stress that we are a service with 24/7 access.  This is an attempt to combat the stereotype of libraries as a room with books.  By thoughtfully identifying our function and mission we can position libraries to take advantage of new opportunities such as supporting the organization’s data science initiatives, and whatever else may come next.

Additionally, libraries can provide resources to support data science initiatives.  Some ideas that come to mind are coordinating coding boot camps and organizing regular interest group meetings.  Throughout my career I’ve witnessed how the library can bring people with similar interests from different disciplines together.  Public health researchers may be encountering the same technical problems as biostatisticians.  The library can provide a forum for them to connect.  All of these can be done by the library connecting people with similar interests.

Moreover, library staff can also develop knowledge and skills in the data science field.  Broadly, there are two types of knowledge.  First, there is definitional knowledge, to have an understanding of the meaning of terms.  This is similar to a librarian having a broad understanding of cardiac terminology to better help cardiovascular researchers find information.  Secondly, there is functional knowledge needed to perform data science tasks.  This can focus on hands-on experience with data sets and popular data analysis programming languages.  Over the course of the “Big Data In Healthcare” class we’ve seen several examples of both types of knowledge.

Building strong relationships throughout the organization is the key to creating services and developing skills that meet the organization’s needs.  In general, library services are not “one size fits all.”  It only makes sense that library services supporting data science would also not be.  Strong organizational relationships are important to knowing what the key challenges and opportunities are for your organization, and are the key to ensuring that the library is best serving stakeholders.

In library efforts with data science, it is important to acknowledge where a library may not be a good fit.  This depends on individual staff skills and attitudes.  Much data science work is done using command line programming, which can be challenging to some.  Personally, I have a strong grasp of descriptive statistics, but my knowledge of calculus is lacking.  This creates a notable knowledge gap in the supporting data science. I need to know my limits in interpreting models.  This is not a unique situation for libraries, as it is similar to when a library staff member is asked for medical or legal advice.  We can provide information, but if lacking the appropriate qualifications should be careful when we offer an interpretation.

Overall, the growth in data science is an opportunity for health care in general, and health sciences libraries.  We can all create our own path supporting these initiatives that is the best fit for our individual organizations.

Categories: Data Science

Big Data Science: What Librarians Offer

SEA Data Science - Tue, 2018-09-25 12:17

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 Ansell, Nursing and Consumer Health Liaison Librarian, George A. Smathers Libraries, University of Florida, Gainesville, FL

Throughout the history of the profession, librarians have questioned the scope and breadth of their role.  With every new technology comes an opportunity for new services and a threat to old ones.  An example: thanks to the advent of electronic resources and searchable databases, librarians spend much less time retrieving materials for patrons now, and more training patrons how to retrieve materials themselves.  Each time a disruptive technology makes itself known, librarians have to collectively decide how to accommodate it.  Whether such accommodation is considered an evolution of the profession, or a mutation, depends very much on your perspective.  Faced with the disruption created by big data technologies, librarians, and medical librarians in particular, must decide how to accommodate it, and in what ways big data is both an opportunity for and a threat to our services.

Many librarians choose to view big data technologies as less of a disruptive technology and more of the same techniques/technologies currently being used, simply at a larger scale.  Data Management has always been an essential research skill, big data just makes the necessity more evident.  And while data management is a newer part of the average library’s service repertoire, it is overall well understood as a natural part of the library’s expertise, if you consider data as just another type of material that libraries can collect, organize, and preserve.  While the specific tools and techniques used to manage data require computer science skills beyond that of most public service librarians, it is not outside the realm of expertise of many technical services librarians and library information technology staff, who, in collaboration with an institution’s researchers, can create tools, repositories, and templates that ease the burden of the data management process.  The California Digital Library’s DMP Tool is perhaps the strongest example of what such collaborations can create.

However, I think that only viewing big data technologies through the lens of data management ignores entirely new potential opportunities for service and outreach.  As library data scientists like Lisa Federer demonstrate, big data is not simply the result of researchers using the same methods on a larger scale, but truly a new type of science, with new challenges.  It is similar to the revolution in evidence synthesis that occurred when systematic reviews emerged as a premier methodology – to conceive of systematic reviews as simply a more expansive kind of narrative review is to misunderstand fundamental differences in their nature.  Some examples of issues to big data approaches include: the creation and management of searchable, multi-institutional data repositories to support big data techniques; the ethics of the kind of surveillance/data gathering techniques required to create big data (this latest report on fitbit heart rate data is a prime example, particularly because it is not published in any academic journal); or whether current statistical methods are appropriate for the kinds of heterogeneous data sets common to big data.  Now, I don’t think that library science has the answer to all of these questions, or that we should be held responsible for answering even one of them.  What I am saying is that the values of librarianship – accessibility, transparency, and accuracy/rigor – give librarians an important perspective on big data initiatives that expertise in Python or R won’t necessarily bring.

Sadly, while I believe our perspective is valuable even without expertise in big data research techniques, I fear that the voice of librarians is likely to be ignored as irrelevant by researchers and administrators as repositories, tools, and analysis techniques are established, if our perspectives are the only things we have to offer.  Tangible skillsets and resources, of recognizable value to stakeholders in the big data process, may be the only way we will be given a seat at the big data decision-making table.  If nothing else, librarians must learn the language of big data, in order to be part of the conversation.

Categories: Data Science

Understanding How Librarians can Support Data Science and Big Data

MAR Data Science - Mon, 2018-09-24 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 Cathryn Miller, Social Sciences Librarian, Duquesne University, Pittsburgh, PA

Supporting data science and big data means supporting a new form of research.  Researchers engaging in data science often find or collect big data (large volumes of data), wrangle (prepare) the data, analyze it, and create reports (Federer, 2018).  A common technique used in data science is machine learning in which machines (computers) learn how to cluster, make recommendations, predict outcomes etc based on what the machines learn from the data.  In a healthcare setting, big data and data science can transform the clinical decision-making process.

How can librarians support researchers engaging in data science?  By no means do I think that librarians must learn advanced statistics or computer programming to support data science and big data.  We can support data science and big data by extending our strengths in providing access to information and in providing instruction.  In addition, librarians may want to consider learning about research data management, “the active management and appraisal of data over the lifecycle of scholarly and scientific interest” (Jones & Pickton, 2013).

Providing Access to Information: Focusing collection development efforts on data science methodology could be very helpful, especially for researchers who are venturing into data science for the first time.  Topics for books and ebooks might include machine learning, research data management, data visualization, text mining, algorithms, R programming language, python, data wrangling etc.  Curating those resources on a LibGuide or website, along with links to websites that help people learn about data science and obtain support (eg stackoverflow.com) might be especially useful.

Organizing Workshops:  Librarians can facilitate learning by organizing workshops.  Librarians have created and shared workshop materials on a variety of data science topics; Kristin Briney at The University of Wisconsin Milwaukee made her principles of data visualizations workshop available to be reused (Briney 2017).  There are also many workshops about research data management that librarians can use such as the Research Data Management Essentials workshop created by Alisa Surkis and Kevin Read at New York University (Read & Surkis, 2018).

Services Supporting Research Data Management:  Librarians’ specialized knowledge in finding, storing and preserving information could be particularly helpful for data scientists.  Consulting with researchers to help them create data management plans, think about the way their data are documented and organized, protected, stored and shared is a task that relates to librarian skillsets.   

Librarians don’t have to become experts in data science and big data to help those collecting and analyzing big data.  By providing access to information and organizing workshops, librarians can support data scientists.  Librarian support is key to helping researchers thrive, regardless of whether their data is big or small, and regardless of the methodologies they use.

REFERENCES:

Briney, K. (2017). Data Visualization Camp Instructional Materials (2017). UWM Libraries Instructional Materials. 4.
https://dc.uwm.edu/lib_staff_files/4

Federer, L. (2018). Data Science 101

.

Jones, S., Guy, M., & Pickton, M. (2013). Research data management for librarians [training booklet]. Digital Creation Centre.

Read, K & Surkis, A. (2018). Research Data Management Teaching Toolkit. Retrieved from: https://figshare.com/articles/Research_Data_Management_Teaching_Toolkit/5042998

Categories: Data Science

Librarians and Research Data Management Services: Branching Out Into Big Data

MCR Data Science - Tue, 2018-09-18 10:47

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: Rose Fredrick, Digital Repository Librarian, Health Sciences Library, Creighton University

Big data has a different nature than traditional research data. It is more immediate and ephemeral which creates large, eclectic datasets that are not easily categorized or managed with traditional data science tools.  It is changing the way research is done and the health sciences in particular are discovering new possibilities for studies by aggregating multiple sources of patient data, like wearable health trackers and electronic health records. These transformative studies also give health science librarians an opportunity to support data scientists by building upon existing research data management services.  The librarian’s role in research data management is well-established and this creates a natural launching point for librarians to expand into big data research services.

Many libraries already provide a full array of data services, such as advising on data management plans, metadata and organization, public access mandates, data security, and the preservation and archival of data sets.  Although big data has different needs when it comes to storage and analysis, many of the same services apply.  Librarians have expertise in the ethical implications of data privacy, publisher and funder requirements, and in curating, organizing and preserving data.  All of these skills and services can benefit big data researchers, but librarians do need to be aware of the challenges of big data.

While the knowledge base of librarianship and research data management can clearly be used advantageously for big data services, there can be barriers to librarians implementing these new services.  Perhaps the biggest barrier is training. Depending on the services being offered, at a minimum librarians will need to become familiar with the nature of big data and how that shapes the research process, the correct terminology, and what resources are available to researchers.  Furthermore, to offer the most robust services, librarians may need data science training or advanced technical training to assist with data processing. Not all institutions are prepared to train librarians so extensively nor will they experience enough demand to require a full-time data science librarian .

Librarians can offer more basic services without intensive data science and technical training, however.  A first step could be to become familiar with the terminology, issues, and processes of using big data and be ready to refer researchers with questions to useful resources.  Another option that requires a bit more investment is to offer instruction on crafting data management plans, understanding funder/publisher requirements for data, or choosing a data preservation platform.  Librarians with more time could offer one-on-one advisory sessions on the data management plan for their research projects.  Librarians without a data science background could also take advantage of training geared towards them, like the Data and Visualization Institute for Librarians or the Data Sciences in Libraries Project.

Additionally, as a digital repository librarian, I wanted to determine whether my library would be able to offer services for archiving big data.  Currently, our institutional repository would not be able to house such large sets of data, so while we can advise researchers on preparing for preservation and selecting a platform, we will not be able to archive the data sets in-house.  In the future, it may be possible to collaborate with our information technology department and create an archival system using Apache Hadoop . Some libraries with enough technical resources may already be able to take that step. In the meantime, I think libraries can offer counseling on choosing from the available platforms and perhaps offer data preparation advice based on their experience from archiving smaller sets of research data. In summary, health sciences librarians have relevant expertise and services to offer to big data research and they should consider what combination of services will be the best fit for their institutions.

Categories: Data Science

Big Data in Healthcare: Finding Your Niche

GMR Data Science - Mon, 2018-09-17 12:39

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: Brenda Fay, Library Specialist, Aurora Libraries – Aurora St. Luke’s Medical Center

For librarians in health science libraries, big data in healthcare might be something of a stranger. Sure, we know that data is being collected about patients, but how do we librarians fit in? Depending on what type of library you work in, whether you’re a solo librarian, and perhaps even your comfort level learning new skills, knowledge and familiarity with data and data practices may or may not be something in your wheelhouse. I work in a large healthcare system within a team of fourteen librarians and library staff. Our institution has a research arm that is growing and growing, and yet none of us have really been involved in big data or data management practices at our institution. I don’t think that’s very unusual for a place that isn’t also an academic medical center. Can healthcare big data be overwhelming? Yes. Is big data in healthcare worth all the fuss? Yes.

Why should health science librarians get involved with big data in healthcare? With the ever-growing interest and use of data all around us, data isn’t going away anytime soon. Librarians are great at continually staying on top of trends and changes in our field, and I truly believe that health science librarians will become more and more involved, in one way or another, with data initiatives at their institutions. It’s better to be in front of the curve and helping guide the conversation, than trying to catch up when the ship has sailed. Learning about big data will keep librarians relevant. If we look at skills librarians already have, like organization and classification, taxonomies and metadata, those could immediately be leveraged into increasing the quality of research data management practices at our institutions by working with researchers on their data management plans, which many need to include on grant and funding applications. We should also get involved because there are so many free training opportunities available to us from MLA, NLM, and others. If MLA and NLM/NNLM think big data is worth supporting on such a large scale, I am onboard, too.

How might health science librarians get involved with big data in healthcare? This is much trickier and depends a lot on the situation you find yourself in. You might not be able to start any of these activities today or even next year, but knowing how other health science librarians work with big data in their institutions can inspire you to find a way where you are. Reference questions might lead you to big data. If you’ve ever been asked to find data, Kevin Read and his NYU librarian colleagues have created a data catalog (NYU Health Sciences Library, n.d.) for those looking for data sets to use, or for researchers to publish their own data. Assisting on systematic reviews or publications might lead you to big data. A 2018 study looked at Google Trends, an online source for accessing trends in Google’s search data, and laypeople’s searches for asthma (Mavragani, A, K, & KP., 2018). It had some methodological issues that a librarian would have likely pointed out right away. Building relationships with library users might lead you to big data. Librarians at NU Health Sciences Library had conversation with basic and clinical researchers at their institution to learn more about their data needs. These conversations allowed them to tailor library services to fill a gap in “community’s data issues including, but not limited to, the challenges they face when collecting, organizing, and sharing their research” (Read, Surkis, Larson, McCrillis, & Nicholson, 2015).

I firmly believe that working with big data in healthcare will raise the profile of health science librarians and the libraries they work in.

Bibliography

Mavragani, A., A, S., K, S., & KP., T. (2018). Integrating smart health in the US health care system: Infodemiology study of asthma monitoring in the Google era. JMIR Public Health and Surveillance, e24.

NYU Health Sciences Library. (n.d.). Data catalog. Retrieved August 29, 2018, from https://datacatalog.med.nyu.edu/

Read, K. B., Surkis, A., Larson, C., McCrillis, A. G., & Nicholson, J. X. (2015). Starting the data conversation: informing data services at an academic health sciences library. Journal of the Medical Library Association, 131-135.

Categories: Data Science

Upcoming Training for Health Sciences Library Staff

MAR Data Science - Wed, 2018-09-12 11:00

Did you know that you can get free training from the National Network of Libraries of Medicine right from your desktop? Nearly every day, there is a new webinar from NNLM or the National Library of Medicine. Other classes are available through Moodle. Since webinars are available nationally, make sure to take note of time zones. Some upcoming classes that may be of interest to health sciences library staff include:

  • Clinical Information, Librarians and the NLM: From Health Data Standards to Better Health – This interactive webinar series focuses on the roles and products of the National Library of Medicine related to applied medical informatics, particularly as applied to electronic health records systems and clinical research. Sessions are held weekly on Thursdays through October 4, from 12:00-12:40 PM ET.
  • PubMed for Librarians – PubMed for Librarians is made up of six 90-minute segments. These six segments will be presented via WebEx and recorded for archival access. Each segment is meant to be a stand-alone module designed for each user to determine how many and in what sequence they attend. Register for the next live session (part 4) coming up on September 19 from 2:00-3:30 PM ET, or watch a recording.
  • Accessible Library Customer Service – September 19, 1:00-2:00 PM ET – Gain knowledge and tools to provide accessible customer service in your library by joining us for this one-hour webinar! This presentation will give an overview of disability including appropriate terminology, creating an accessible environment, and evaluating current practices for way-finding, emergency preparedness, and web resources. Other topics include budgeting for accessibility, accessible employment, specific service needs, potential partner organizations, and a plethora of tips and resources for future use.
  • ClinicalTrials.gov – September 26, 3:00-4:00 PM ET – This presentation will help you learn how to navigate the site and understand the nuances and limitations of information available on ClinicalTrials.gov.
  • NNLM Research Data Management Webinar Series – The NNLM Research Data Management (RDM) webinar series is a collaborative, bimonthly series intended to increase awareness of RDM topics and resources. The series aims to support RDM within the library to better serve librarians and their institutional communities. The next webinar in this series, Planning, Developing, and Evaluating R Curriculum at the NIH Library, is coming up on October 12 from 2:00-3:00 PM ET.
  • LinkOut for LibrariesNovember 1, 2:00-3:00 PM ET – LinkOut for Libraries provides journal access to PubMed users. Join us for an informational webinar to learn more about this service from the National Library of Medicine. Erin Latta, from the National DOCLINE Coordination Office, will lead this webinar.

In addition to scheduled courses, NNLM has a number of “on-demand” self-paced classes via Moodle, such as:

Most webinars are recorded, so you are encouraged to register for a session of interest, even if you cannot make the live webinar. To register for classes, you just need to create an account.

You can find additional opportunities on our training schedule.

The best way to find out about upcoming trainings, NLM updates and other information from the Network is to subscribe to MAR Weekly Postings, which come out on Fridays.

Categories: Data Science

Apply Today! Biomedical and Health Research Data Management Training for Librarians

SEA Data Science - Tue, 2018-09-11 12:03

Health sciences librarians are invited to apply for the online course, Biomedical and Health Research Data Management Training for Librarians, offered by the NNLM Training Office (NTO). The course is a free, 7-week online class with engaging lessons,  practical activities and a final project. The course runs October 15 – December 14, 2018.

The 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. 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.

Applications are due September 20, 2018.

Additional details and the online application are available here.

For questions, please contact the NNLM Training Office

Categories: Data Science

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