Three application examples, including data-independent far-field array design, nearfield array design, and pressure field interpolation, are presented.
Examples include data aggregation in databases, web crawlers, data mining, and others from many different scientific domains, like sky surveys, forecasting, RNA-sequencing, or seismology [1 5].
Examples include: data produced by public bodies and institutions (medical records, etc)., and data produced by the private sector (commercial transactions, banking/stock records, e-commerce, credit cards, etc).
Examples include data points for a child that cross centile lines on a growth curve quickly, or values >2 SDs from the mean (below the 2.3rd and above the 97.7th centiles).
Other examples include data-sources focused on collecting soil and yield data directly from farmers in order to make better commodity predictions.
Some examples include data-fitting problems for blood alcohol level, video tape counter readings and models for world population growth [19], obesity epidemics [20], Ebola epidemics [21], and bovine babesiosis disease and tick populations [22].
For example, including baseline data in a model which aims to predict post-baseline treatment differences may lead to misleading estimates.
There are dazzling examples of new technologies including data visualizations, the digital imagery that not only makes sense of huge quantities of complex information, but instantly reflects any changes.
Example topics include data acquisition and management; responsible authorship and allocation of credit; peer review; mentoring; conflicts of interest; and collaborative research.
If you were to utilize a supervised learning algorithm to automatically trade financial assets, for example, and included data from the Great Depression, the algorithm may notice that the market crashed right as a new chain of coffee shops opened.
