Sentence examples for essential dataset from inspiring English sources

A consensus is needed on a minimum essential dataset to underpin effective national health information systems in sub-Saharan Africa.

Data sources for the second essential dataset on risk factor exposure can use standard adapted survey instruments for each risk factor (smoking, nutrition, high blood pressure, obesity, HIV sero-status, solid fuel smoke exposure, etc).

In this paper, we set out the principles of a minimum essential dataset that we believe all African countries should and could establish in order to guide health systems and policies through this epidemiological and health transition that is looming, and for which most sub-Saharan African countries are wholly unprepared.

Nevertheless, they are essential datasets to use as benchmark for examining the performance of the methods in a situation of closely related strains in the sample.

Streamlined data collection tools using evidence-based and agreed-upon essential datasets should be developed and deployed, reflective of broad health system considerations, minimum standards in humanitarian response, and essential health system analytics for coordinating a humanitarian response and health system recovery.

Using the more practical six point time-course, the coverage was still 84% versus 89.3% with the full non-essential dataset.

Another aim for sharing the datasets is to facilitate inter-laboratory comparisons of 3D imaging MS datasets, essential for raising the level of the technology and paving the way to open-access science.

It also allows an integrated estimate of uncertainties arising from different sources which is an essential diagnostic when datasets from various sources are considered.

Good practice in the assessment process is essential, particularly as datasets from many different sources may be combined together into a single database.

Just like several other existing views the cloud view allows to zoom and pan, which is particularly essential for large datasets.

Computational tools have the reputation of being hard to use and inaccurate compared to experimental methods in enzyme engineering, yet they are essential to probe datasets of ever-increasing size and complexity.