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Data collection is expensive and time-consuming, meaning that detailed and accurate surveys are hard to achieve, said the authors.
Since data collection is expensive, it is essential to design an optimal network based on the minimal number of hydrometeorological stations in order to reduce costs.
This is because primary data collection is expensive and time-consuming, especially when a large number of individuals are to be investigated.
On the other hand, data collection is expensive, and given the large sample sizes necessary for successful molecular signature discovery, using existing datasets may be a more feasible approach.
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Traditional methods of data collection are expensive, time consuming, and lack spatial and temporal detail.
Although there can be potential bias in (any) recall method, this way of data collection is less expensive and relatively fast, and is still considered as a way to find out the extent of the problem in a poor community [ 24].
Since data collection is the most expensive part of the analysis, noise mapping data is ideally collected in such a way that it can be used for the calculation of the different scenarios, from which the most cost-effective action plans are chosen.
Retrospective data collection is not only expensive, but also prone to errors and in many cases the data cannot be reconstructed precisely.
To assess households' pharmaceutical expenditure, primary data collection is usually needed, which may be expensive and time consuming and therefore limited only to countries that have the resources to conduct such studies.
This data collection is time-consuming and expensive, and does not guarantee accurate and exhaustive datasets.
The cost of the original data collection is, however, much more expensive.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com