Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

March 10, 2015

NIH RFI on National Library of Medicine

Filed under: BigData,Machine Learning,Medical Informatics,NIH — Patrick Durusau @ 2:16 pm

NIH Announces Request for Information Regarding Deliberations of the Advisory Committee to the NIH Director (ACD) Working Group on the National Library of Medicine

Deadline: Friday, March 13, 2015.

Responses to this RFI must be submitted electronically to: http://grants.nih.gov/grants/rfi/rfi.cfm?ID=41.

Apologies for having missed this announcement. Perhaps the title lacked urgency? 😉

From the post:

The National Institutes of Health (NIH) has issued a call for participation in a Request for Information (RFI), allowing the public to share its thoughts with the NIH Advisory Committee to the NIH Director Working Group charged with helping to chart the course of the National Library of Medicine, the world’s largest biomedical library and a component of the NIH, in preparation for recruitment of a successor to Dr. Donald A.B. Lindberg, who will retire as NLM Director at the end of March 2015.

As part of the working group’s deliberations, NIH is seeking input from stakeholders and the general public through an RFI.

Information Requested

The RFI seeks input regarding the strategic vision for the NLM to ensure that it remains an international leader in biomedical data and health information. In particular, comments are being sought regarding the current value of and future need for NLM programs, resources, research and training efforts and services (e.g., databases, software, collections). Your comments can include but are not limited to the following topics:

  • Current NLM elements that are of the most, or least, value to the research community (including biomedical, clinical, behavioral, health services, public health and historical researchers) and future capabilities that will be needed to support evolving scientific and technological activities and needs.
  • Current NLM elements that are of the most, or least, value to health professionals (e.g., those working in health care, emergency response, toxicology, environmental health and public health) and future capabilities that will be needed to enable health professionals to integrate data and knowledge from biomedical research into effective practice.
  • Current NLM elements that are of most, or least, value to patients and the public (including students, teachers and the media) and future capabilities that will be needed to ensure a trusted source for rapid dissemination of health knowledge into the public domain.
  • Current NLM elements that are of most, or least, value to other libraries, publishers, organizations, companies and individuals who use NLM data, software tools and systems in developing and providing value-added or complementary services and products and future capabilities that would facilitate the development of products and services that make use of NLM resources.
  • How NLM could be better positioned to help address the broader and growing challenges associated with:
    • Biomedical informatics, “big data” and data science;
    • Electronic health records;
    • Digital publications; or
    • Other emerging challenges/elements warranting special consideration.

If I manage to put something together, I will post it here as well as to the NIH.

Experiences with big data and machine learning, for all of the hype, have been falling short of the promised land. Not that I think topic maps/subject identity can get you there but certainly closer than wandering in the woods of dark data.

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