New! Duke is participating in the Federal Demonstration Partnership (FDP) NIH DMSP Template Pilot, which is testing the effectiveness and usability of two DMSP templates developed in collaboration with representatives from participating NIH Institutes and Centers (ICs). The ultimate goals of the pilot are to harmonize DMSP requirements across NIH ICs and programs and to mitigate the administrative burden for researchers associated with DMSP development and implementation. Further information is available via the FDP website. New!
If you are applying for a competing proposal to NIH after January 25, 2023 you are eligible to participate and will be contacted by the Duke Office of Scientific Integrity.
Research data management involves the activities researchers do to organize, describe, preserve, and share their data. Good data management practices are integral to the entire research lifecycle, from planning for what kind of data you will collect to depositing your data set in a repository.
But what are data? Research data are the original sources or materials (born digital or converted to digital) that were created or gathered in the process of your research. They serve as the foundation from which you draw conclusions and produce results/findings (test hypotheses, study trends, provide evidence, refute claims). They may be numeric or qualitative, structured or unstructured. Among many possible forms, data may take the form of notebooks, statistical or spatial data tables, audio or visual recordings, photographs or models. If a research funding agency requires a formal Data Management Plan, they will often provide some guidance as to what they consider data.
As technology has made it easier to share digital files, and as funders (public and private) and journals increasingly require data sharing for reproducibility and transparency, data management has become an important practice for researchers to follow.
Sharing your research data allows for greater visibility and recognition of your body of work. Beyond traditional scholarly metrics, methods are being developed to track research data re-use to measure research impact. In addition, you are working to further science by allowing others to explore your data and use it in novel ways.
The FAIR Guiding Principles were established in 2016. They are a set of standards agreed upon by stakeholders across the research landscape in academia, industry, publishing and federal and private funding agencies. The goal of these principles is to make scientific (and, really all data) more easily accessible for research transparency, reproducibility and to facilitate new discovery. One of the ways to ensure your data is FAIR is to deposit it in an established data archive or repository.