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NIH Strategic Plan for Data Science

Mon, 2018-06-04 18:41

The National Institutes of Health (NIH) today released its first ever Strategic Plan for Data Science (PDF). The plan describes NIH’s overarching goals, strategic objectives, and implementation tactics for promoting the modernization of the NIH-funded biomedical data science ecosystem.

Wondering how libraries fit into the plan? NIH will partner with institutions to engage librarians and information specialists in finding new paths in areas such as library science that have the potential to enrich the data-science ecosystem for biomedical research. /da

Categories: Data Science

Save the date! Mountain West Data Librarian Symposium August 13-14, 2018

Wed, 2018-05-02 13:23

The Mountain West Data Librarian Symposium is a low-cost professional development opportunity for librarians and research data specialists. August 13-14 at the University of Colorado Boulder for the inaugural Mountain West Data Librarian Symposium!

Registration will be under $30 and open in June. The symposium will consist of morning workshops and afternoon unconference sessions. They are currently seeking workshop proposals for the symposium. Proposals are due Tuesday, May 22.

Workshops may run 55, 90 or 120 minutes. They are seeking hands-on training sessions that focus on active attendee participation. Proposals should focus on how to fulfill the job duties of research data specialists. Potential topics include, but are not limited to:

  • teaching research data management
  • data sharing and re-use
  • data curation and preservation
  • data literacy
  • research reproducibility
  • communication and outreach

Proposals of up to 250 words should be submitted to: https://goo.gl/forms/9VRH07JjVNYf9f8o2. Please view the full call for proposals prior to submitting.

Stay tuned for more announcements, follow @MountainWestDLS on Twitter, or check out the Mountain West Data Librarian Symposium Website. Please send any questions to MountainWestDLS@gmail.com. /da

Categories: Data Science

Librarians and Big Data: Should We Be Involved?

Thu, 2018-04-12 13:40

Written by: Caroline Marshall, MLS, AHIP, Senior Medical Librarian, Public Services, Cedars-Sinai Medical Library, Los Angeles, CA

There is a great deal of discussion about Big Data. We all think other people are doing it, we think we should be doing it, but we are not sure how to get involved (Tattersall & Grant, 2016).

There have been Calls to Action (Martin, 2016) about Big Data and an affirmation in several studies that librarians should get involved. It is almost as if we are going to miss the Big Data train if we don’t jump on board right away. Big Data is not going away but we, as librarians, need to ascertain how involved we can get depending on staffing and time.

Librarian skills for Big data have been identified more or less along the following bullet points

  • Information Curation
  • In-Depth research
  • Digital Scanning, Preservation
  • Cloud Data Expansion
  • Data Visualization
  • Collaboration, Teaching and Facilitation

Librarians are no strangers to Big Data and we often use these skills already; we use usage data in journal evaluation and renewals. We look at interlibrary loan data to ascertain how quickly we are turning requests around and as an indication of what journals we should purchase. We work with medical staff on citation management software teaching them how to manage, organize and share large quantities of citations for their publications. Librarians perform information curation such as creating digital archives and assigning metadata that will provide access points or cataloging different types of materials for easy retrieval. In-depth searching is something most of us do every day, defining the question or query to retrieve data is a common skill for many librarians.

Learning other skills such as Data Visualization, especially for some librarians who are mid-career, will mean outside workshops (Burton & Lyon, 2017) that will take away from our “regular” work and there is also the question of whether leadership will want to take us in this direction.

Burton & Lyon (2017) suggests librarians should be ‘Data Savvy’ but this is not a skill that can be taught. We cannot push roles onto staff that do not have the knowledge or the desire. Future Masters of Library Science Programs can incorporate more specific courses to create the data scientist librarian that can be part of the research team, but how will this look? How many projects can one person be embedded especially in an institution that has multiple research projects ongoing? Will that librarian be part of the library or employed by the research team?  

I see the librarian’s role not as being embedded in a research team but more in a collaborative, instructional, and facilitation role. This includes teaching classes on statistical or visualization software, and giving guidance on designing the query or on the creation of a database that will need to answer not just the immediate queries, but other queries that the researcher may not have thought of that may come up in the future. We can also identify data repositories that researchers can use that are in our own institutions but that are not gathered in any one place or provide advice on digitization and preservation. We can act as sounding boards in a more consultative manner as opposed to just classes.  

We cannot do everything and we need to be aware of staff, skills and time. Some of us are just getting our toes wet offering classes and so forth, but before scaling up to an institutional level we need to ascertain what we can offer and support.

References

Burton, M., & Lyon, L. (2017). Data Science in Libraries. Research Data and Preservation (RDAP) Review. Bulletin of the Association for Information Science and Technology. . Bulletin of the Association for Information Science and Technology, 43(4), 33-35.

Martin, E. R. (2016). The Role of the Librarian in Data Science. a Call to Action. Journal of eScience Librarianship, 4(2), E1092.

Tattersall, A., & Grant, M. J. (2016). Big Data – What is it and why it matters. Health Info Libr J, 33(2), 89-91. doi:10.1111/hir.12147

 

Categories: Data Science

Reflections on Big Data in Healthcare: Exploring Emerging Roles

Mon, 2018-04-09 12:21

Written by: Niala Dwarika-Bhagat, The Medical Sciences Library, The University of the West Indies, St Augustine, Trinidad and Tobago

Introduction: What is Big Data?

Technopedia defines Big Data as a process “that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying dataData that is unstructured or time sensitive or simply very large cannot be processed by relational database engines.” (Technopedia., 2018)

Over time, the different iterations of Big Data processing and application have been used to reflect, interpret and influence developmental change. The sanctity of this tacit operation has remained largely undisturbed, until the advent of social media. Layers of issues involving social media apps, now suggest that Big Data, in addition to its merits, can be manipulated to alter perception and reality. Notwithstanding the notoriety of current headlines, what is clear is that the Big Data is now a commonplace topic of conversation.

Do you think health sciences librarians should get involved with Big Data in healthcare? 

The health sciences librarian working with Big Data in the academic environment is potentially in a “safer” place away from the glare of mainstream media. And although the librarian has been traditionally and typically constrained by a much larger mandate to provide services and resources for curriculum support, this is set to change with data science featured on university curricula, as well as libraries’ strategic plans. So indeed, health librarians should and would inevitably get involved with big research data in healthcare even if it is to provide basic but essential data services support emanating from medical education.

Where should librarians get involved?

There is great potential for academic health librarians to do data services support. The roles that could be potentially played are:

  • As controlled vocabulary experts (cataloguers and indexers)
  • As systems experts, navigating through ILS and health research data sets
  • As marketers -doing outreach to garner support for data projects
  • As advisors e.g. creating data management plans
  • As trainers, embedded in data science courses
  • As search-experts aiding the discovery of health research data
  • As support for ongoing research projects
  • As programmers writing analyses using code
  • As advocates for the privacy of medical research data
  • As outreach experts, including creating research guides
  • As expert searchers, locating datasets for faculty research

How should librarians get involved?

The health sciences librarian need not become a data scientist but rather work in teams for maximum output and impact. Academic research data will form the crux of this work, operationalizing all of the above. As librarians accept a mandate to work with big research data, with their skills and training they can be the ultimate crucible for data discovery. I envisage their greater role would be as providers of information and trainers using their existing skills and expertise. This would involve activities such as engaging with faculty to harness research data, encourage researchers to deposit their research data into the library repository, collection development, data literacy instruction, creating online resource guides, assist with data management plans, and provide guidance on data tools. With advance training, they can even locate data sets that researchers require. Furthermore librarians with coding/programming skills, can definitely add value to data services support for research in healthcare data. As far as potential roles are concerned, there are a great many and, with time, there would be sophisticated and evolved workflows for the health science librarian.

REFERENCE

What is Big Data? – Definition from Techopedia. (n.d.). Retrieved March 28, 2018, from https://www.techopedia.com/definition/27745/big-data

Categories: Data Science