NLM is building a workforce for data-driven research and health. To ensure a future of data-driven discovery and health, it is essential to have a biomedical informatics and data science workforce prepared to make conceptual and methodological advances in analytics, visualization, mining, and
other methods needed to use data for discoveries and to make it interoperable with existing knowledge.
Transcript:
[Lisa Federer] Data science is basically taking big data, sometimes combining multiple datasets,
sometimes different data types, and using that to extract knowledge from that data, so we can take all of the data that we've collected and that we hold here at NLM and use that to get new insights. And with things like AI on the rise, there's even more opportunities to take this data that we already have and get even more out of it.
[Maryam Zaringhalam] When I first heard "National Library of Medicine" and "Library," I thought, oh, libraries, stacks of books and journals. And when I came here, I realized that all of the data resources that I had used as a graduate student were actually housed here, being improved by the research
that is done here.
[Federer] So NLM is involved in data science in quite a lot of ways. We have the data that people need
to do data science, and all of that is freely available. We also have the literature (PubMed, PubMed Central) that people can use to do text analysis. And we also have the workforce development piece.
We are training the next generation of data scientists, whether that's fellows that are here at NLM learning with our staff scientists and researchers, whether it's through the Network of the National Library of Medicine that's providing training for librarians to engage with data science, and even some outreach to the public just to increase awareness and data literacy among the general public.
[Zaringhalam] Technology has enabled us to generate data at a clip that is faster
than ever before and for a much cheaper price point. Data that are born digital. We're now thinking about how can you take these very different types of data and bring them together to drive discovery, and preserves privacy, is cognizant of ethics and the responsibilities that we have as researchers
who are really committed to again advancing human health.
[Federer] We're really helping to envision the future of what data science is.
#datascience #biomedicalresearch #openscience #humanhealth #library