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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

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