FOA Title: 
Request for Information (RFI): Next-Generation Data Science Challenges in Health and Biomedicine
Grant Type: 
NOT-LM-17-006
Primary IC: 
NLM
Release Date: 
Sep 26 2017
Expiration Date: 
N/A
AC Source: 
N/A
Purpose: 
Request Information RFI): Next-Generation Data Science Challenges Health Biomedicine Notice Number: NOT-LM-17-006 Key Dates Release Date: September 26, 2017 Response Date:   November 1, 2017 Related Announcements NOT-LM-18-001 Issued National Library Medicine NLM) Purpose behalf the National Institutes Health NIH), National Library Medicine NLM) seeks community input new data science research initiatives could address key challenges currently faced researchers, clinicians, administrators, others, all areas biomedical, social/behavioral health-related research. field data science broad scope, encompassing approaches the generation, characterization, management, storage, analysis, visualization, integration use large, heterogeneous data sets have relevance health biomedicine. Data science undergirds broad interdependent objectives the NIH Strategic Plan https://www.nih.gov/about-nih/nih-wide-strategic-plan). Information data science research directions could lead breakthroughs any all NIH interest areas welcomed, whether applicable across wide swaths health biomedicine, focused particular research domains. Background   During past five years, in response a 2012 working group report data informatics issued the Advisory Committee the NIH Director ACD) https://acd.od.nih.gov/working-groups/diwg.html), NIH made substantial investments a data science infrastructure tools, resources workforce components a digital research ecosystem health biomedicine. https://commonfund.nih.gov/bd2k/). Building this foundation, landscape data science research evolved is rapidly changing, within beyond NIH, institutes, centers, offices.  2015, another ACD working group recommended NLM become programmatic administrative home data science NIH, complementing NIH’s efforts catalyze open science, data science research reproducibility https://acd.od.nih.gov/documents/reports/Report-NLM-06112015-ACD.pdf). Accordingly, help NIH continue strengthen expand scope its investments data science, NLM seeks information public private organizations e.g., universities industry) individuals promising data science research directions health biomedicine. Information Requested NLM requests information the three focal areas listed below: 1. Promising directions new data science research the context health biomedicine.  Input might address such topics Data Driven Discovery Data Driven Health Improvement. 2. Promising directions new initiatives relating open science research reproducibility. Input might address such topics Advanced Data Management Intelligent Learning Systems Health. 3. Promising directions workforce development new partnerships. Input might address such topics Workforce Development Diversity New Stakeholder Partnerships. Within general topic areas, others related data science health biomedicine, NLM invites researchers, clinicians, organizations, industry representatives other interested parties provide input on: Research areas could benefit most advanced data science methods approaches; Data science methods need updating, gap areas where new approaches needed;  Priorities new data science research; Appropriate partnerships settings expanded data science research. to Submit Response Response this RFI must submitted https://www.research.net/r/NLMDataSci November 1, 2017. Responses should provided a narrative form up 3 pages per topic, links pertinent supplemental information needed. attachments be accepted. proprietary, classified, confidential, sensitive information should included your response. Responses this RFI voluntary. RFI seeks input planning purposes only should be construed a solicitation applications an obligation the part the Federal Government, National Institutes Health, individual NIH Institutes Centers. information collected not considered confidential, identifiers names, institutions, emails, etc.) be removed responses compiled. Processed, anonymized results be shared internally with members health-related scientific work groups, may posted an NIH public website, appropriate.  NIH acknowledge receipt information submitted, will comment the content. Inquiries Please direct inquiries to: Valerie Florance, PhD National Library Medicine NLM) Telephone: 301-496-4621 Email: NLMEPInfo@mail.nih.gov
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