Sentence examples for data quality skills from inspiring English sources

This study examines how genomics scientists' perceived priorities for data quality and data quality skills differ when assuming different roles played in genomics data curation work.

With regard to data quality skills, curators appeared to care more about understanding user's requirements and specific data management skills than end users, while end users valued the skills needed to deal with information overload more highly — those needed to identify useful, relevant information from large amounts of data.

The data quality, skill priorities, and tradeoffs identified by this study can inform the development of effective data curation mandates and policies, data quality assurance planning and training, and the design of curation role specific tool dashboards and visualization interfaces for genomics data.

'The robustness of crop model results depends on data quality, model skill prediction and model complexity.

Scientists with different curation roles (including that of end user) may focus on different data quality aspects and skill requirements in a community curation environment.

Conceptual basis, importance and bandwidth of variables, reliability and statistical properties, data and skill requirements, data quality and archiving, robustness under technology change, and cost/benefit issues are factors in indicator design.

In China, as part of the intervention, a training manual was developed to improve skills in checking data quality, data analysis and interpretation, feedback, advocacy and use of information (Aqil and Lippeveld 2007).

For data users, this can, in the medium term, improve concept harmonization and operationalization and thus yield higher data quality and new insights into causal mechanisms of skill formation and the outcomes of cognitive skills over the life course.

Scientists with different curation roles, given common curation tasks with the same skill requirements, prioritized different data quality criteria.

In fact, we observe that too many companies still ignore data quality problems, do not allow for synchronization of skills for big data and do not take the right investment approach to big data.

Another consultant to the project reviewed the team's interviewing skills and the project's data quality control measures just before the start of the survey.