Data Quality Control. Data shall be collected and maintained in accordance with contractor’s Quality Assurance Plan as incorporated in the contract (Section F.5.3). The overall goal of quality control is to ensure the effectiveness and efficiency of collection efforts as well as the quality of data collected. Data quality is of utmost importance. As such the contractor shall ensure the highest quality in data collected by its At-Sea Monitors. NMFS will provide a data quality rating of At-Sea Monitors to the provider on a bi-annual basis (Section J, Attachment 19, Data Quality Rating). The contractor shall use the data quality rating of At-Sea Monitors in their Quality Assurance Plan (F.5.3).
Data Quality Control. ODV facilitates quality control of multi-parameter datasets by providing a range of automat- ic and visual checks for easy identification and flagging of outliers and suspicious data. There is support for automated range checks of any basic variable (use option Tools>Find Outliers (Range Check)) as well as automatic statistical outlier checks for any data window currently applying the VG gridding method (use option Extras>Find Outliers (Field Check) of the data window’s popup menu). In both cases, ODV generates lists of suspicious data points and allows user controlled (point by point) or automatic flagging of the identified data. In addition to automatic quality control procedures, ODV also provides a wide range of easy-to-use visual and interactive methods for the identification and editing of outliers. For instance, you can plot all data from a given region or along a given section using data win- dows of SCATTER or SECTION scope, easily revealing outliers and questionable data in the entire dataset. Flagging or editing the numerical values of the spurious data is as easy as clicking on such a data point in one of the data windows and invoking the Edit Data option of the variable that you want to modify. Note that all changes made to a data collection are logged in the collection logfile. The log record includes information about the sample that was changed, the date and time of the modification, the user who made the change and the computer on which the operation was performed. You can browse the collection logfile at any time using option Collection>Browse Log File. In the example below, outliers for salinity and oxygen can easily be spotted in the plots showing the data of an entire cruise. Clicking on such an outlier point selects it as current sample, which can then be edited and/or flagged. You can hide and exclude bad or ques- tionable data from the analysis by establishing data quality sample filters. Figure 6-1: Identification of outliers for a zonal section in the North Atlantic 7 Importing XYZ Data Irregularly spaced or gridded data for some quantity Z at given X and Y coordinates are commonly provided in files using three-columns for the X, Y and Z values, respectively. Ex- amples of such XYZ datasets are (1) maps of a given Z variable (X represents longitude, Y represents latitude), (2) vertical sections (X=along section coordinate, Y=depth) or (3) time- evolution plots (X=some geographical coordinate, Y=time or vice versa). You can load a...
Data Quality Control. Forsta is responsible for providing overall Service functionality and the ability to configure the Service to present data within the core capabilities of the Software. Client is responsible for checking the data and the “quality control” process needed to ensure data accuracy. Forsta is responsible for providing guidance (per Data Files section above) to ensure the structure of the data file will be compatible with the Software. Forsta is not responsible for data integrity due to inaccurate or incomplete data files and will not check nor is responsible for accuracy or complete data.
Data Quality Control. The City shall apply the following procedures for quality control of H2S data measured by different logging equipment:
Data Quality Control. Confirmit is responsible for providing overall Dapresy Service functionality and the ability to configure the Dapresy Service to present data within the core capabilities of the Dapresy Software. Client is responsible for checking the data and the “quality control” process needed to ensure data accuracy. Confirmit is responsible for providing guidance (per Data Files section above) to ensure the structure of the data file will be compatible with the Dapresy Software. Confirmit is not responsible for data integrity due to inaccurate or incomplete data files and will not check nor is responsible for accuracy or complete data.
Data Quality Control. A. To the extent that clients and other agencies supplying information have provided accurate data, HMIS users are responsible for the accuracy of the data they enter into the HMIS.
Data Quality Control. The quality of data checked by reviewing questionnaires for completeness and relevance by the supervisors and principal investigator.
