We wanted to develop a tool that allows easy identification of individual calls for which researchers are eligible and have realistic success chances for (smart support). One way to achieve our goal was to compile data about seniority (age, PhD age, position), bibliometric data (citations, number of publications, h-index, gender, research field, and more) of researchers in our institution – coupled with information about previous funding activities. This allows us to individually tailor and encourage researchers to apply for specific call that fits their research area, PhD age and career level. This however is not sufficient to guide researchers to funding options that would allow realistic success chances. Therefore we compiled seniority (age, PhD age, position), bibliometric data (citations, number of publications, h-index, gender, research field, and more) from researchers who received grants – and we did this on national scale in Denmark - this provided us information about at which career level, PhDage, bibliometric profile, etc researchers successfully receive certain grants. By cross-linking the data on internal researchers with data on "successful" researchers we could predict the likely hood of success for individual researchers for specific calls. This approach lead to more stratified and focused funding activity - mainly as researchers now aware of their individual expected success rates. Researchers are now applying to “the right call at the right time” they spend more time on research and writing proposals for calls they can actually win - rather than applying for random calls (and trying their luck). In this petcha kucha we present the setup of our tool, we present some examples of successful and unsuccessful approaches and finally we critically discuss the limitation of a data & evidence based approach.
