Data Management and Broader Impact – Satisfying the new NSF Merit Review criteria

NSF LogoEarlier this year the National Science Foundation released an updated version of the Merit Review process, which among other items includes modifications to the criteria used to assess Broader Impact. The following explores a few ideas on how data management strategies can be leveraged towards expanding your broader impact.

The fundamental purpose of the Merit Review process is to ensure that proposals are reviewed in a fair and equitable manner. Recently, after more than a decade since the last in-depth review of these criteria, a task force was established in 2010 to evaluate and revise the principles and descriptions of the Merit Review process. A final report was published by the task force in 2012, and the new criteria have been in effect for all NSF proposals submitted since January 2013.

As stated in the Proposal and Award Policies and Procedures Guide, “the Intellectual Merit criterion encompasses the potential to advance knowledge” and “the Broader Impacts criterion encompasses the potential to benefit society and contribute to the achievement of specific, desired societal outcomes.” While previous guidelines required proposals to address intellectual merit and broader impact within the one-page summary preceding the main proposal, the new guidelines are more explicit, requiring proposers to now include individual stand-alone statements on intellectual merit and broader impacts within the Project Summary. Additionally, proposers must also include a specific section within the Project Description that directly addresses the broader impact of the proposed research.

Keeping in mind that proposals also require a supplemental document describing your Data Management Plan, consider the potential benefits and advantages of interconnecting your data management strategy with your objectives for achieving broader impact. For example:

  • Data sharing. Data that is openly shared with the community can be utilized by multiple researchers for a variety of applications and thus have greater impact than just a single project. Data sharing also increases the awareness of and number of publications citing the research that created the data.
  • Class development. Project data that is utilized for class development and classroom exercises expands impact related to student engagement and education. Student involvement can also be extended to incorporate different aspects of data collection and processing tasks.
  • Learning modules. The development of training tools and learning modules based on project data can add even greater dimension to the impact on education, particularly when shared openly with the greater scientific community.
  • Additional projects. Utilizing data across multiple projects, as well as for multiple proposal efforts, increases impact across a greater range of scientific objectives. Exploring alternative uses for data can also spur new research ideas and encourage interdisciplinary project development.

Data can be extremely valuable, so be sure to leverage its full potential when proposing new projects and expanding the impact of your current research. It benefits both you and the community.

This is Part 3 of a discussion series on data management and data sharing related to government funded research. Visit Part 1 and Part 2 to read the earlier installments of this storyline.

For more information on the NSF Merit Review process: