Advancing the Promise of Open Science: We Want to Hear from You!

From the NLM Director:

Today, I am delighted to join with my colleagues across the National Institutes of Health (NIH) to invite you to join with us to advance the promise of open science. The tenets of open science undergird the many offerings and services of NLM, including PubMed, our biomedical literature database containing more than 35 million citations, and PubMed Central (PMC), our full-text literature repository containing more than 8.8 million articles, as well as our many molecular databases, such as the Sequence Read Archive (SRA) and GenBank, which respectively hold partial and complete genomic sequences. I am proud of NLM’s efforts to advance the promise of open science and hope that you will take this opportunity to share your perspectives on the next step in this journey by providing comment on the NIH’s Public Access Plan. 

Here is a blog being posted by colleagues from across NIH that provides more information.

It is only February, but this has already been a busy year with respect to open science. First off, the NIH Policy for Data Management and Sharing (DMS Policy) became effective January 25, 2023! As you most likely know by now, the DMS Policy requires NIH-supported researchers to prospectively plan for how scientific data will be preserved and shared. We know that sharing scientific data accelerates biomedical research discovery, leads to cures, and supports transparency, so we see this as a huge step forward for open science.

Implementation of the DMS Policy has been a big undertaking over the last few years, and we are grateful to our colleagues throughout the scientific enterprise for your continued engagement. Your feedback has resulted in providing valuable resources to support the community at, including Frequently Asked Questions and other guidance. We also want to acknowledge our NIH colleagues who worked across the agency to seamlessly ensure that WE were ready to meet this important moment.

Open science is a priority at NIH and across the U.S. Federal Government. Earlier this year, the White House Office of Science and Technology Policy (OSTP) declared 2023 to be the Year of Open Science. This OSTP announcement included details on actions being taken across the Federal Government to advance national open science policy, provide access to the results of taxpayer-supported research, accelerate discovery and innovation, promote public trust, and drive more equitable outcomes. Keen observers on this topic will also remember that OSTP issued guidance in August 2022 on Ensuring Free, Immediate, and Equitable Access to Federally Funded Research, asking agencies to accelerate access to data and publications.

Today, we are pleased to announce that the “NIH Plan to Enhance Public Access to the Results of NIH-Supported Research” (NIH’s Public Access Plan) is now available for public review and comment. We are issuing this Plan in response to the OSTP memo and also because it is consistent with NIH’s longstanding commitment to open science. This Plan builds upon the strong foundation of the NIH Public Access Policy which, since 2008, has made over 1.4 million articles describing NIH-supported research available to the public through PubMed Central. As you will see, the Plan builds on what we currently do, and we expect to maintain many current practices. But importantly, we ultimately plan to institute a zero-embargo period on publications so that research results are freely available to the public without delay.

It is important to keep in mind that this Plan is not a proposed policy, but a roadmap of steps NIH will take to enhance access to research products.  Any future updates to the NIH Public Access Policy will, in turn, be released as a draft for public comment. Also, to loop back to the DMS Policy—we expect that the DMS Policy will meet all expectations related to data sharing in the OSTP memo.

The NIH Public Access Plan also provides preliminary considerations on the issue of metadata and persistent identifiers, as described in the OSTP memo. Persistent identifiers contribute to the findability of research products (publications, data, software, etc.) and ensure that appropriate credit for use of those products is maintained. This is another area where public input is needed to inform NIH’s future plans. We will ensure that there will be lots of opportunities to engage on this topic and others over the next months and years.

We also want to take a moment to let you know how the Intramural Research Program at NIH is doing its part to ensure that the research NIH conducts meets the expectations of open science and data sharing.  All scientists at NIH must submit and have an approved data management and sharing plan for all research studies.  Studies involving human participants must have an approved data management and sharing plan in place as a prerequisite for Institutional Review Board review.  Additionally, annual reports of studies must indicate how the investigators have complied with their approved plans.

So far 2023 has been a productive beginning to what is shaping up to be a great year for open science. NIH is fully committed to realizing the expectations of the Biden Administration when it comes to open science. We encourage anyone with an interest in this space to review the NIH Public Access Plan and provide feedback.

Comments on the NIH Public Access Plan will be accepted until 11:59 PM on April 24, 2023.  Comments can be submitted via our online portal at:

Authors from left to right:

Patricia Flatley Brennan, RN, PhD, Director, National Library of Medicine

Nina F. Schor, MD, PhD, NIH Deputy Director for Intramural Research

Susan K. Gregurick, PhD, NIH Associate Director for Data Science

Michael Lauer, MD, NIH Deputy Director for Extramural Research

Lyric Jorgenson, PhD, Acting NIH Associate Director for Science Policy

4 thoughts on “Advancing the Promise of Open Science: We Want to Hear from You!

  1. Excited for “Open Science” ( AI Researcher Perspective)
    AI models can be used to help identify and prioritize gaps in access and understanding of research products and services, as well as to identify areas where educational resources and tools can be developed to better enable individuals to access and interpret research materials. AI models can also be used to create data analysis tools that can help researchers identify correlations and trends in research data, which can help to more effectively target open science efforts and resources. Additionally, AI models can be used to develop natural language processing-based applications to make research content more accessible and understandable to non-specialists.
    AI to facilitate public access and understanding

    1. Natural Language Processing (NLP) Models can be used to create summaries of research papers and articles, making them easier to understand and more accessible to the public.

    2. AI-driven algorithms can be implemented to search through large volumes of data quickly, enabling more efficient and accurate searches of the PubMed and PMC databases.

    3. AI can be used to generate visualization and infographics from the research data, making the information more easily understandable and digestible and reaching a wider audience.

    4. AI-based tools can be used to identify gaps in the research and to suggest areas of further exploration.

    5. AI can be used to detect and remove bias in the research data, leading to more equitable outcomes.

    6. AI can be used to assist in the development of new methods and techniques for conducting research and analyzing data.
    To ensure equitable access to research products AI models can:
    1. Natural language generation (NLG) to automatically generate summaries of scholarly publications.
    2. Translation engines to provide access to research products across multiple languages and dialects.
    3. AI-driven search algorithms to identify and prioritize research products from underrepresented populations.
    4. Machine learning algorithms to detect and address potential issues of bias, such as in the selection of reviewers.
    5. Automated annotation tools to facilitate the sharing and curating of research products.
    6. AI-powered recommendation systems to suggest research products to users.

  2. The results are always good. If when we take the time to work better than ever, it means Things that we don’t yet know or can’t think through. So when did we meet? Exchange life experiences, ideas of each person, results. always beautiful

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