Innovative Solutions for Data Harmonization, Mobile Analytics, and End-User Support
Institute or Center: Office of Strategic Coordination (OSC), Office of the Director
Project: Innovative Solutions for Data Harmonization, Mobile Analytics, and End-User Support
Skills Sought: Applicants should possess technical skills in one or more of the following areas, as relevant to their proposed project area(s) (see below):
- artificial intelligence (AI)
- cloud computing
- data engineering
- data science
- software design
- mobile app development
About the position: OSC, which leads the NIH Common Fund, seeks an innovative solutioneer to develop AI approaches to data harmonization, mobile computing apps, and intelligent training bots. End users include clinicians, clinician scientists, industry scientists, computational biologists, bioinformaticists, and other researchers. The Scholar will lead one or more of the following important activities:
- a pilot effort to develop and apply AI or other tools to “learn” how to harmonize data sets more efficiently. The goal is to develop a successful algorithm/learning tool for metadata harmonization that: reduces manual curation (compared with existing baseline data), responds to crowd-sourcing methods for harmonization, and increases its learning over time.
- efforts to convert at least one workflow that would typically rely on egressed data from the cloud and a CPU into a mobile app (IOS and Android) to initiate a workflow in Terra or Cavatica.
- efforts to automate interactions with users to provide an interactive and intelligent “bot” so that routine queries can be answered without devoting person power to a helpdesk. The goal is to use existing help manuals, FAQs, and guidance documents to produce an intelligent agent that will help a dataset user navigate datasets, tools, workflows, analysis packs, results, and the like.
About the work: The NIH Common Fund programs address emerging biomedical opportunities and challenges that no single NIH Institute or Center can address on its own, but are of high priority for the NIH as a whole. The OSC is developing the Common Fund Data Ecosystem (CFDE), where Common Fund-supported biomedical datasets will be stored and used in the digital cloud environment. The Scholar would work to address rate-limiting steps in three areas of technological innovation:
- creating interoperable datasets is an area of increasing need that currently is addressed by trained developers, scientists, and considerable time and other resources;
- mobile computing platforms available to datasets that have been harmonized to conserve fiscal resources and speed analyses; and
- end-user help or bots that learn from and adapt to clinical, computational, and other questions could help Data Coordinating Centers manage the growing number of queries they expect to receive.
- Gabriella Miller Kids First Pediatric Research Program (primary use case)
- Genotype-Tissue Expression (primary use case)
- Undiagnosed Diseases Network
- Library of Integrated Network-based Cellular Signatures (LINCS)
Depending on the DATA Scholar’s area of interest, he/she may require access to different data sets supported by the NIH Common Fund initiatives.
Why this project matters: These innovations are intended to open new and exciting areas of scientific inquiry within and between Common Fund data sets and are expected to transform the CFDE and other areas of NIH. The Scholar will have a unique opportunity to contribute to biomedical research and human health by making Common Fund data sets in physiology and pediatric science more amenable to new discoveries.
Work location: Bethesda, MD
Work environment: Scholar will work alongside CFDE team members, including industry and academic professionals who are members of the CFDE coordinating center, technical team, training team, and individual data coordinating centers. The Scholar also will have opportunities to interact with NIH offices specializing in policy, data science, and other areas and to engage with senior NIH leadership who are heavily invested in data science. The Scholar will have ready access to Common Fund data sets and be encouraged to interact with leading academic scientists to satisfy the goals of the project.
To apply to this or other DATA Scholar positions, please see instructions here: datascience.nih.gov/data-scholars
This page last reviewed on January 29, 2020