Sentence examples for overlap of projects from inspiring English sources

"The agency also promises to do more to avoid overlap of projects across NIH institutes and with other agencies.

Disjointed reconstruction can lead to unnecessary competition, overlapping of projects, and excessively costly or incomplete implementation (Ophiyandri et al. 2013).

CCA and DRR integration could provide benefits at all scales, minimize overlap and duplication of projects and programs (Nalau et al. 2015), and move beyond vulnerability and resilience towards a vision of DRR that ends separation between the two issues and promotes working together towards simultaneous and common goals (Kelman et al. 2015).

This aspect of the course requires regular meeting of project leaders and the program leader to ensure a minimum of overlap between projects and a maximum of fruitful interaction between project proposals.

Her diverse range of projects overlap in their aim to present women from a distinctly female gaze.

Now, I want to make sure that I don't have a lot of projects overlapping because I want to spend time with my family, my friends and I hope to write another book.

The canopy model accounts for the projected overlap of the single plant leaves.

Of the three excluded studies, two (INTERHEART and INTERSTROKE) declined to participate because of overlap with existing projects, and a third (CoLaus) was excluded as the rs1229984 genetic variant was not directly genotyped.

There is also a risk of overlap between research projects that would result in wasted resources.

His workshop is a thoroughly messy overlapping of objects and projects that seems to have been crucial to his famously restless mind.

While survival probabilities based on data from Sweat are generally higher than those based on data from Albertsen, likely due to differences in the case-mix of the two studies, the point estimates are reasonably close and there is considerable overlap in the range of projected survival probabilities and absolute benefits, suggesting little sensitivity to baseline data set.