The scientific peer review process benefits greatly when the study section reviewers bring not only strong scientific qualifications and expertise, but also a broad range of backgrounds and varying scientific perspectives. Bringing new viewpoints into the process replenishes and refreshes the study section, enhancing the quality of its output.
What happens when a former colleague contacts you, a reviewer, out of the blue to ask if the application on which he is a principal investigator could be treated favorably at the review meeting? Do you brush off the investigator and figure you will not let the contact influence your review of that application? Or do you instead immediately notify NIH? Intrigued? We have a case for you (based on true stories, details have been changed slightly and names have been fictionalized). Read on.
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NIH’s National Library of Medicine has launched an effort to modernize ClinicalTrials.gov to deliver an improved user experience on an updated platform that will accommodate growth and enhance efficiency. Creating a roadmap for modernization requires feedback from a wide array of stakeholders on how to continue serving, balancing, and prioritizing their varied information needs. As ClinicalTrials.gov celebrates its 20th anniversary on February 29, 2020, we’re asking for your input on how it can best continue to serve your needs for many more years to come.
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Funding decisions rely heavily on peer review scores, but there is more to the story. NIH Institutes and Centers (ICs) weigh those scores together with ensuring their entire research portfolio addresses the wide array of diseases, conditions, or other research areas within its mission. They also account for unmet scientific needs and build on recent unexpected breakthroughs as part of prudent planning. When public health needs emerge, such as for the opioid epidemic or a microbial outbreak, ICs must be nimble enough to respond. Training, work force, and infrastructure needs are also thrown into the mix.
We present FY 2018 data on R01-equivalent applications and R56-Bridge awards, showing percentiles for both funded and unfunded applications.
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By looking to the past we may be able to better understand the flow of scientific knowledge going forward, and possibly even predict translational research outcomes. In their October PLOS Biology paper, Drs. Ian Hutchins and George Santangelo from the NIH’s Office of Portfolio Analysis devised a machine-learning strategy that taps into the trajectory of science by tracking knowledge flow from bench to bedside.
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NIH has considered a different approach to defining scientists from disadvantaged backgrounds. We reviewed a wide variety of criteria, looking for those that are relatively easy to self-evaluate and that capture a large proportion of affected people.
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This series aims to raise awareness and inspire creative problem solving of the challenges in maintaining integrity in peer review. In this case, Dr. Smith, who is being considered as a reviewer for the application, is a professional associate of Dr. Jones, the PI on the application. However, Dr. Smith had not declared a conflict with that application.
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My colleagues within the NIH Office of Portfolio Analysis sought to answer this call. Drs. Ian Hutchins and George Santangelo embarked on a hefty bibliometric endeavor over the past several years to curate biomedical citation data. They aggregated over 420 million citation links from sources like Medline, PubMed Central, Entrez, CrossRef, and other unrestricted, open-access datasets. With this information in hand, we can now take a better glimpse into relationships between basic and applied research, into how a researchers’ works are cited, and into ways to make large-scale analyses of citation metrics easier and free.
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In a paper recently published in Science Advances, we delved into the underlying factors associated with the funding gap between white and black researchers. We identified three decision points where disparate outcomes arose between white and black researchers: 1) the decision to bring applications to discussion during peer review study section meetings; 2) impact score assignments for those applications brought to discussion; and (3) a previously unstudied factor, topic choice – that is what topic the investigators chose to study.
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NIH is currently accepting public comments on the use of standards for capturing, integrating, and exchanging clinical data for research purposes (NOT-OD-19-150). This is a great opportunity to hear more from the community on ways to strengthen approaches that find, share, and access high-quality patient data, while also making it more interoperable and reusable. Such goals align with long-standing NIH data sharing policies and what was also called for in a related NIH strategic plan on data science.
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