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The goal for the future state of analysis results is that they are machine-readable

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, easily navigable, and highly reusable. The aim in creating the ARS was to provide a logical model that fully described analysis results and associated metadata to support

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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

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automationsupport 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.

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Many of the same metadata components are needed both to create a prospective technical specification of analyses to be performed and to give context to reported results. Therefore, a single logical model has been developed, which includes

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the components needed to specify analyses, represent contextualized results, and indicate how the results are displayed.

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The logical model acts as a blueprint or a set of rules describing how things should be organized and structured. For the ARS, it is being used to create a model that describes how analysis results data should be organized and structured. LinkML allows the creation of a standardized and consistent approach to describing analysis results data, enabling greater interoperability

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and collaboration across different stakeholders. Additionally, it enables converting them into various machine-readable formats (e.g., JSON, YAML, OWL, XML).

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,

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which  which is essential in the highly regulated pharmaceutical industry.

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