*Please note that FASEB, of which the Teratology Society is a member, will be submitting comments in response to this RFI. Teratology Society members who wish to independently submit comments are encouraged to do so using the link below.
Request for Information (RFI): Next-Generation Data Science Challenges in Health and Biomedicine
Notice Number: NOT-LM-17-006
Response Date: November 1, 2017
Issued by National Library of Medicine (NLM)
On behalf of the National Institutes of Health (NIH), the National Library of Medicine (NLM) seeks community input on new data science research initiatives that could address key challenges currently faced by researchers, clinicians, administrators, and others, in all areas of biomedical, social/behavioral and health-related research. The field of data science is broad in scope, encompassing approaches for the generation, characterization, management, storage, analysis, visualization, integration and use of large, heterogeneous data sets that have relevance to health and biomedicine. Data science undergirds the broad and interdependent objectives of the NIH Strategic Plan (https://www.nih.gov/about-nih/nih-wide-strategic-plan).
Information about data science research directions that could lead to breakthroughs in any or all NIH interest areas is welcomed, whether applicable across wide swaths of health and biomedicine, or focused on particular research domains.
During the past five years, and in response to a 2012 working group report on data and informatics issued by the Advisory Committee to the NIH Director (ACD) (https://acd.od.nih.gov/working-groups/diwg.html), NIH has made substantial investments in a data science infrastructure of tools, resources and workforce components of a digital research ecosystem for health and biomedicine. (https://commonfund.nih.gov/bd2k/). Building on this foundation, the landscape of data science research has evolved and is rapidly changing, within and beyond the NIH, its institutes, centers, and offices. In 2015, another ACD working group recommended that NLM become the programmatic and administrative home for data science at NIH, complementing NIH’s efforts to catalyze open science, data science and research reproducibility (https://acd.od.nih.gov/documents/reports/Report-NLM-06112015-ACD.pdf). Accordingly, to help NIH continue to strengthen and expand the scope of its investments in data science, NLM seeks information from public and private organizations (e.g., universities and industry) and individuals on promising data science research directions in health and biomedicine.
NLM requests information on the three focal areas listed below:
1. Promising directions for new data science research in the context of health and biomedicine. Input might address such topics as Data Driven Discovery and Data Driven Health Improvement.
2. Promising directions for new initiatives relating to open science and research reproducibility. Input might address such topics as Advanced Data Management and Intelligent and Learning Systems for Health.
3. Promising directions for workforce development and new partnerships. Input might address such topics as Workforce Development and Diversity and New Stakeholder Partnerships.
Within these general topic areas, or others related to data science in health and biomedicine, NLM invites researchers, clinicians, organizations, industry representatives and other interested parties to provide input on:
- Research areas that could benefit most from advanced data science methods and approaches;
- Data science methods that need updating, or gap areas where new approaches are needed;
- Priorities for new data science research;
- Appropriate partnerships and settings for expanded data science research.
How to Submit a Response
Response to this RFI must be submitted to https://www.research.net/r/NLMDataSci by November 1, 2017. Responses should be provided in a narrative form of up to 3 pages per topic, with links to pertinent supplemental information if needed. No attachments will be accepted. No proprietary, classified, confidential, or sensitive information should be included in your response.
Responses to this RFI are voluntary. This RFI seeks input for planning purposes only and should not be construed as a solicitation for applications or an obligation on the part of the Federal Government, the National Institutes of Health, or individual NIH Institutes or Centers. The information collected will not be considered confidential, but identifiers (names, institutions, emails, etc.) will be removed when responses are compiled. Processed, anonymized results may be shared internally or with members of health-related scientific work groups, or may be posted on an NIH public website, as appropriate. The NIH will acknowledge receipt of information submitted, but will not comment on the content.
Please direct all inquiries to:
Valerie Florance, PhD
National Library of Medicine (NLM)