The astute among you (or the inveterate blog watchers) caught me blogging on a new outlet last week: DataScience@NIH. You’re not seeing double, and I haven’t abandoned NLM Musings from the Mezzanine.
In January, 2017 I assumed the role of the NIH Interim Associate Director for Data Science (iADDS), as Dr. Phil Bourne stepped down from his position as the inaugural Associate Director for Data Science.
I now have two distinct but related roles: iADDS and the Director of NLM. As the iADDS, I have responsibilities to the whole of NIH to work with my fellow institute and center directors to guide NIH’s future investments in data science. As the NLM Director I must lead the Library in its day-to-day operations and the creation of its new strategic plan.
The two jobs are a natural pairing.
The challenges of big data—its size, variability, and accessibility—align with the strengths of the library. In fact, recognizing how big data’s technical complexity leveraged NLM’s core strengths, the Advisory Committee to the NIH Director recommended making NLM the intellectual hub for data science in June 2015. They wrote, “NLM is now poised to build on its activities in computational-based research, data dissemination, and training to assume the NIH leadership role in data science.”
My taking on the iADDS role is one step toward making that recommendation a reality. Fortunately I am guided by an outstanding NLM leadership team (I’ll introduce you to them later), and terrific colleagues across the NIH, particularly those in the Scientific Data Council.
It’s an exciting time and having a view from above (iADDS) and a view from within (Director, NLM) helps me see both the challenges and the pathways to resolution. I’ll keep these two blogs going but sometimes point back and forth, so that you can see where my thoughts are going.
I welcome your thoughts. How should the NLM help NIH meet the challenges of data science? What is the role of the NLM in ensuring that data are FAIR (Findable, Accessible, Interoperable, and Reusable)? And most importantly, how do we accelerate discovery through data? The NLM needs your questions and your ideas!