Sentence examples for data honest from inspiring English sources

Exact(1)

Public bodies (such as NHS trusts) have audit committees and pay auditors good money to check their books – why aren't they keeping the data honest?

Similar(59)

Confidentiality and intellectual property protection in such a competitive phase are ensured by an "honest data broker" data management system.

"Honest data would have earned Claremont McKenna the No. 18 ranking.

"For someone like me who has long campaigned for open and honest data in public life I could not have set foot on a battle bus that has at the heart of its campaign a figure that I know to be untrue," said Wollaston, who is a former GP.

The push that became a shove was, the former GP says, the claim by Vote Leave that quitting would free up £350m a week for the NHS – something she says "simply isn't true": For someone like me who has long campaigned for open and honest data in public life I could not have set foot on a battle bus that has at the heart of its campaign a figure that I know to be untrue.

Google is using this data in an honest attempt to find better recipes.

It's easier to get real, honest data through an independent third party than by asking the customer yourself.

The experiment was conducted for 50 simulation runs, each time with different randomly generated data set of honest and dishonest recommendations.

Third, Hayes's justification is based on an incorrect reading of the COPE guidelines [20], which recommend retraction if "findings are unreliable, either as a result of misconduct (e.g. data fabrication) or honest error (e.g. miscalculation or experimental error)…".

"According to the COPE [Committee on Publication Ethics] guidelines, 'Journal editors should consider retracting a publication if… they have clear evidence that the findings are unreliable, either as a result of misconduct (e.g. data fabrication) or honest error (e.g. miscalculation or experimental error).' [19] ….

It is believed that involvement and collaborative working can enable better recruitment, more honest data collection, minimise bias, provide a deeper level of interpretation and promote greater consideration of ethical issues [ 35, 36].

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