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The ARS Model has several possible implementations, including leveraging analysis results metadata to aid in automation as well as representing analysis results as data in a dataset structure. The creation of an ARS technical specification could be used to support
automation, traceability, and the creation of data displays. An analysis results dataset could support reuse and reproducibility of results data. Figure 2 is an example of how the ARS Model could be used in a modernized workflow that shifts the focus from retrospective reporting to prospective planning.Jira showSummary false server Issue Tracker (JIRA) serverId 85506ce4-3cb3-3d91-85ee-f633aaaf4a45 key ARSP-8
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Further, LinkML is a flexible and extensible tool; the model can be easily modified as needed to incorporate new data elements or requirements. By creating a machine-readable model, analysis results data can be more easily understood, shared, and reused by both humans and machines. LinkML also supports the development of validation rules that can be used to ensure the integrity and quality of the data,
which is essential in the highly regulated pharmaceutical industry.Jira showSummary false server Issue Tracker (JIRA) serverId 85506ce4-3cb3-3d91-85ee-f633aaaf4a45 key ARSP-7
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