Request for Information (RFI): Input to Advance Artificial Intelligence (AI)-ready Data Generation and Scalable Computational Approaches in NIAMS Research

Notice Number: NOT-AR-20-009

Key Dates
Release Date: March 3, 2020
Response Date: July 1, 2020

Related Announcements
NOT-AR-20-018
NOT-AR-20-021

Issued by
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)

Purpose

Through this Request for Information (RFI), the National Institutes of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) invites public comments on the needs and opportunities for developing, advancing, and implementing scalable computational approaches; and for generating new or re-purposing existing artificial intelligence (AI)-ready datasets in support of research across all NIAMS portfolios.

Background:

Rapid increase in the volume of biomedical data being collected brings both exciting opportunities and significant challenges. The complex nature of the underlying problems and of the data itself make modeling and computational analysis indispensable for generating hypotheses, interpreting results, and developing precision treatments. Proper annotation and metadata standards are essential to ensure that data are findable and reusable, as well as compatible with AI-based machine learning algorithms. Storage and analysis must be scalable and should ideally take advantage of cloud-based repositories. Adherence to the “FAIR” (Findable, Accessible, Interoperable, Reusable) data principles calls for well-developed indexing, sharing, and sustainability approaches. Tool development and computational analysis require collaboration between quantitative, biological, and clinical scientists. Through this RFI, NIAMS will identify challenges, opportunities, and specific needs pertaining to computational and modeling approaches (including large-scale AI and machine learning methods), as well as those related to the generation of new AI-ready datasets or re-purposing of existing datasets in musculoskeletal, rheumatic, and skin diseases research.

Information Requested:

NIAMS invites input from researchers in academia or industry, healthcare professionals, patient advocates and health advocacy organizations, scientific or professional organizations, Federal agencies, and other interested members of the public to help define community needs pertaining to computational and modeling approaches (including large-scale AI and machine learning methods) and their scalability, as well as those related to creating new datasets or re-purposing existing datasets in order to advance NIAMS-relevant research.

Scientific and professional organizations are strongly encouraged to submit a single response that reflects the views of their organization and membership as a whole.

Responses may include, but are not limited to, the following topics:

  • Computational/modeling needs relevant to your research topic specifically and/or to the field as a whole;
  • Your ability to constructively engage computational experts (as collaborators, consultants, or staff);
  • Nature of available analytical support (data management/LIMS, statistical analysis and annotation, integration of multiple data types, machine learning/AI, mathematical modeling, etc.);
  • Availability and ease of access to a computational center and to data/tool repositories;
  • Challenges and opportunities in scaling computational tools to analyze large, cloud-based datasets;
  • Metadata requirements for making datasets “AI-ready” (creating standardized datasheets, updating existing metadata, creating dataset indexes, etc.);
  • Opportunities for re-annotation, re-purposing, sharing, and secondary analysis of existing high-value datasets (biological, molecular, imaging, biomechanical, etc.);
  • Opportunities and needs for the generation of new, AI-ready, high-value datasets;
  • Sustainability of research data (access to or need for data repositories, resource requirements, sunsetting policies, etc.);
  • Computational needs in data analysis, interpretation, or sharing that are not being met, and what would be needed to address those needs;
  • Other relevant comments, suggestions, or concerns.

How to Submit a Response:

Responses to this RFI must be submitted electronically at https://grants.nih.gov/grants/rfi/rfi.cfm?ID=104

Responses must be received by July 1, 2020.

Responses to this RFI are voluntary. Do not include any proprietary, classified, confidential, trade secret, or sensitive information in your response. The responses will be reviewed by NIAMS staff, and individual feedback will not be provided to any responder. NIAMS will use the information submitted in response to this RFI at its discretion and will not provide comments to any responder’s submission. Respondents are advised that the Government is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted. The Government reserves the right to use any submitted information on public NIH websites, in reports, in summaries of the state of the science, in any possible resultant solicitation(s), grant(s), or cooperative agreement(s), or in the development of future funding opportunity announcements.

This RFI is for information and planning purposes only and shall not be construed as a solicitation, grant, or cooperative agreement, or as an obligation on the part of the Federal Government, the NIH, or individual NIH Institutes and Centers to provide support for any ideas identified in response to it. The Government will not pay for the preparation of any information submitted or for the Government’s use of such information. No basis for claims against the U.S. Government shall arise as a result of a response to this request for information or from the Government’s use of such information.

We look forward to your input and hope that you will share this RFI document with your colleagues.

Inquiries

Please direct all inquiries to:

Anthony Kirilusha, Ph.D.
NIAMS Division of Extramural Research
Phone: 301-594-5055
Email: anthony.kirilusha@nih.gov