Guest post by the Data Science @NLM Training Program team.
Regular readers of this blog probably know that NLM staff are expanding their expertise beyond library science and computer science to embrace data science. As a result, NLM—in alignment with strategic plan Goal 3 to “build a workforce for data-driven research and health”—is taking steps to improve the entire staff’s facility and fluency with this field so critical to our future.
The Library is rolling out a new Data Science @NLM Training Program that will provide targeted training to all of NLM’s 1,700 staff members. We are also inviting staff from the National Network of Libraries of Medicine (NNLM) to participate so that everyone in the expanded NLM workforce has the opportunity to become more aware of data science and how it is woven in to so many NLM products and services.
For some of our staff, data science is already a part of their day-to-day activities; for others, data science may be only a concept, a phrase in the strategic plan—and that’s okay. Not everyone needs to be a data scientist, but we can all become more data savvy, learning from one another along the way and preparing to play our part in NLM’s data-driven future. (See NLM in Focus for a glimpse into how seven staff members already see themselves supporting data science.)
Over the course of this year, the data science training program will help strengthen and empower our diverse and data-centric workforce. The program will provide opportunities for all staff to participate in a variety of data science training events targeted to their specific interests and needs. These events range from the all-hands session we had in late January that helped establish a common data science vocabulary among staff to an intensive, 120-hour data science fundamentals course designed to give select NLM staff the skills and tools needed to use data to answer critical research questions. We’re also assessing staff members’ data science skill levels and creating skill development profiles that will guide staff in taking the steps necessary to build their capacity and readiness for working with data.
At the end of this process, we’ll better understand the range of data science expertise across the Library. We’ll also have a much clearer idea of what more we can do to develop staff’s facility and fluency with data science and how to better recruit new employees with the knowledge and skills needed to advance our mission.
In August, the training program will culminate with a data science open house where staff can share their data science journey, highlight group projects from the fundamentals course, and find partners with whom they can collaborate on emerging projects throughout the Library.
But that final phase of the training initiative doesn’t mean NLM’s commitment to data science is over. In fact, it will be just the beginning.
In the coming years, staff will apply their new and evolving skills and knowledge to help NLM achieve its vision of serving as a platform for biomedical discovery and data-powered health.
How you are supporting the data science development of your staff? Let’s share ideas to keep the momentum going!
Co-authored by the Data Science @NLM Training Program team (left to right):
- Dianne Babski, Deputy Associate Director, Library Operations
- Peter Cooper, Strategic Communications Team Lead, National Center for Biotechnology Information
- Lisa Federer, Data Science and Open Science Librarian, Office of Strategic Initiatives
- Anna Ripple, Information Research Specialist, Lister Hill National Center for Biomedical Communications
3 thoughts on “Building Data Science Expertise at NLM”
Hello! While I am very supportive and excited for NLM staff to acquire greater data savvy, it would be helpful to have some additional clarification and guidance on parts of this initiative. I apologize for the long post, but I’m trying to be very clear and specific, since I have not been able to get answers to this set of questions anywhere so far.
First, for many of us in LO whose current duties do not involve data science skills, it isn’t clear what training goals we should have. It would be really helpful for many of us to have more guidance to figure out what kind of training will be *useful professionally,* rather than just *interesting personally.* It seems like there’s two likely basic scenarios here: Either it would be most useful to learn just what is applicable to our current work OR it would be most useful to learn skills that will be needed for new, different work that leadership has in mind for us to do. I imagine both things may happen to some extent, but it would be helpful if you could clarify which of these things you think the LO workforce should be doing for the most part.
1. Learn just what is useful to our current work: Some staff may see obvious ways to do their jobs better if they have more database skills and so on. However, if our current duties don’t already overlap with the relevant analytical and technical areas, should we just acquire sufficient contextual background information to understand and discuss what data scientists do and how to best serve them as a user base? I.e., is it your expectation that non-technological staff should mostly pursue the “Support Data Science” learner profile?
2. Learn skills beyond our current work: In contrast, do you hope that non-technological LO staff will actually acquire specific skills in things like programming, analytics, and visualization tools, because you expect we will take on a different set of work duties? If that case, can you provide any additional guidance on what that work might be? Are there particular skills that you expect to have more demand for than others? Would people would be reassigned or offered opportunities away from their current duties, based on the skills they acquire? In other words, if we put the effort into learning new skills, will be actually have the opportunities to use them here?
Second, along these same lines, could you please offer us some more information on the purpose and target audience for the Data Science Fundamentals course, and what criteria you are using to select the participants? Again, if this does not appear to overlap at all with our current work in LO, should we apply with a reasonable expectation that we could advance into new, different work after completion? Or is this meant more for training junior researchers, or maybe support staff who already work directly with researchers? Maybe it’s clear to some folks if the training is meant for them, but it isn’t clear right now who the training is NOT for. If you could comment more specifically on what the library hopes to achieve through this particular special training, in contrast to the more generally available training, that might be helpful.
Thank you for writing, Alex. I appreciate you taking me up on my invitation to engage on these topics so important to NLM.
The Data Science Readiness Survey provided the first step in determining how prepared we as an organization were to meet the challenges of data science.
As we implement the NLM Strategic Plan, we are literally building the road as we walk across it. This is especially true for data science and our workforce for all of NLM. We do anticipate that data science will fundamentally change many operations across the NLM, from purchasing and acquisitions to science and scholarship. Not everyone needs the same skill set, but the NLM needs a broad array of skills, and we need to invest in training to bring our workforce up to date. The Data Science @NLM Training Program will provide lots of opportunities, so it is important that staff work with their supervisors to not only figure out what specific trainings might be useful, but also to look at your current job duties for places where you can apply ideas presented in the data science training to current work and beyond.
All respondents participated in a self-assessment to determine their data science proficiencies and selected Skill Development Profiles that best characterized their aspirational goals. In addition, staff who wanted a more-in depth understanding of their data science readiness completed one or both of the data analytics and data science practical assessments to appraise their baseline skills in these areas. About 750 NLM staff members completed the self-assessment and will work with their supervisors to create personal learning plans that consider both professional development and departmental need.
Over 200 people indicated an interest in participating in the Data Science Fundamentals course. As was described in the February 26, 2019 NLM Town Hall meeting, Data Science Fundamentals is a 160-hour immersion designed to give staff more focused, structured hands-on skills and tools to study specific topics that will provide theory, demonstration, and independent practice opportunities. About 25 NLM staff will participate in this first offering. They will be drawn from across all NLM divisions and job series to foster a community of learning and practice. The Fundamentals course culminates in a capstone project, in which participants will build and present an original data model. Future training will be informed by this pilot experience.
Data science training opportunities abound, so I encourage all NLM staff to proactively pursue and help build the road as we walk across it.