+44 1865 259200
+44 1865 259200
Aether has developed a series of tools which facilitate efficient documentation and checking of datasets at any step in the data production process. These tools are adapted to and can be embedded within existing data management systems. They save time compared to conventional QA/QC approaches.
Aether’s Inventory Tools support the development of greenhouse gas and air pollution emission inventories. They are provided as part of clear and concise training for national inventory teams. The Excel based tools are designed to be easy to use and understand, making them perfect for new inventory compilers.
Aether’s Quality Comments (Q-Comments) tool has been developed to enable teams to carry out QA/QC processes in a highly efficient and transparent way. Through its tagging system, the tool improves communication and transparency, accelerating learning and aiding work handover. The tagging system is customisable and therefore offers a bespoke, consistent and efficient approach to completing and documenting QA/QC processes.
The Q-Comments tool is an advanced Excel tagging system that provides the user with a complete, transparent and documented record of the QA/QC procedure. This record is consistent across all spreadsheets in which the tool is used. Aether has implemented this system successfully, receiving excellent feedback from spreadsheet model users. The Q-Comments tool uses tags and comments to facilitate documentation and conversation between a spreadsheet author and a reviewer. The tags used can be customised to the needs of the user and the tools come with a user guide.

The Quality Analyst (Q-Analyst) tool can be used to verify timeseries datasets. It has been developed based on Aether’s experience in running quality assurance activities both as data compilers and reviewers. The Q-Analyst tool is an easy-to-use, Excel-based review tool that can be applied to all time series data.
Through the tool’s user-friendly interface, data are visualised to support QA/QC activities. Analysis of the data identifies attributes of the data that need checking, and the checking is enabled through data visualisation. Users can ensure datasets are comparable and consistent across a time series. Recalculations between versions of datasets can easily be reviewed in order to verify changes.

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