Sentence examples for data in other fields from inspiring English sources

However, little work, so far, exists in synthetic mobile phone data representations and the development of representative and useful synthetic data in other fields has proven challenging18.

Funders were beginning to look at standards for large-scale data in other fields of the life sciences [ 18].

These records had been checked during the matching process and had sufficient matching data in other fields, including COB, to indicate that they were a genuine match.

The difference between the prevalence of missing data in other fields between records with correctly vs. erroneously missing XRT start date was also highly significant (Table 1).

Missing data in other fields (Gender, DOB, Cancer Site) was most common among records with missing XRT start date that was found on manual review (at least one of the three fields was missing in 34.7% of the records) and least common among records (7.6%) where XRT start date was not missing or the missing XRT start date could not be subsequently identified on manual verification (p < 0.0001).

This has been a problem for data analytics in other fields; there is no reason why change management will be any different.

Pharmacological prophylaxis for preventing venous thromboembolism (VTE) is a worldwide established procedure in hip and knee replacement surgery, as well as in the treatment of femoral neck fractures, but few data exist in other fields of orthopaedics and traumatology.

Thus, it may be of use for other problems of network inference in systems biology outside of the area of sequential gene expression time series data, or in other fields where networks that change with time are encountered.

The system proposed shows how the use of a schematic spatial organization of data, widely exploited in other fields (e.g. Geographical Information Systems) can be extremely useful to develop efficient tools for research and teaching in neurosciences.

Records with a discrepancy were kept as a match (and assumed to be due to typographic error) if they had sufficient data in agreement in other fields, such as viral load and HIV-diagnosis date, to indicate that they were a genuine match.

The Collaboratory@Columbia is responding to this global trend by embedding data literacy across Columbia's curricula, pairing data scientists with professors in other fields to develop and teach new courses.