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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

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Published on: November 22, 2019

Building the biomedical data science workforce.

Michelle C Dunn¹, Philip E Bourne¹

¹Office of the Director, The National Institutes of Health, Bethesda, Maryland, United States of America.

|July 18, 2017

Summary

This summary is machine-generated.

The Big Data to Knowledge (BD2K) initiative trained a national biomedical data science workforce. It analyzed program strengths and weaknesses to guide future training efforts for researchers worldwide.

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Area of Science:

Biomedical Data Science
Computational Biology
Health Informatics

Background:

Biomedical research increasingly relies on computational, mathematical, and statistical skills.
A gap exists between the demand for and supply of trained biomedical data scientists.
The National Institutes of Health (NIH) Big Data to Knowledge (BD2K) program aimed to address this gap.

Purpose of the Study:

To analyze the strengths and weaknesses of the NIH's BD2K training program (2013-2016).
To provide insights for future biomedical data science training initiatives.
To inform funders and funding recipients globally.

Main Methods:

Review of extramurally funded BD2K training programs.
Analysis focused on national and international impact.
Evaluation of training across all career levels, from graduate students to senior researchers.

Main Results:

BD2K successfully initiated training across various levels of the biomedical research workforce.
Identified key strengths and areas for improvement in data science training methodologies.
The program contributed to narrowing the skills gap in biomedical data science.

Conclusions:

The BD2K program provided a foundational boost to biomedical data science training.
Future training efforts should build upon BD2K's successes and address identified weaknesses.
Sustained investment in data science education is crucial for advancing biomedical research.