The provenance for tracking mutations generates the linkage of biological data and its history for multitude various transformations which are implemented to several datasets provenant and obtained from derived multiple resources. When getting a meaningful trackable complete time mutations exposures being adaptable with a fair principles compliant to fairness of data provenance it will enhance the reproducibility, analysis and reusability of caracteristics which are crucial for confirming results and providing them in many other relevant outcoming projects. Open time series datasets resulted from both publicly accessible resources GEO omnibus and ArrayExpress are accessible and discoverable, but there are some missing tasks such their reproducibility and reusability which are fully requiring clear metadata assets for provenance fairness compliance. This research work assess the provenance of multitude features to be able to use a mutation Al timer tracker for enhancing the use of fairness provenance component implementation of algorithm to collect efficiently and in the right way compliant to Fair as Findable, Accessible, Interoperable and Reusable features in open time series and generating an associated provenance graph. We want to examine the FAIRness trackable provenance through an automatic mapping between the properties to generate a proper provenance mutation Al tracking system for exposures to create a data model graph for generating an approach arguably to encourage researchers to make their timely series enhancement analysis for biological datasets of similar features and characteristics, causing new research queries and insightful methods and investigations. As an output we will use this approach to provide both reusability and reproducibility, which are important features for enhancing and engaging more in deep details the scientific researches on different areas for dealing with cancer issues.

image for incoming piece for proof