As　big　data　and　artificial　intelligence　continue　to　gain　importance,　the　reliable　quality　of　datasets　has　be-come　a　crucial　factor　in　algorithm　performance　and　result　reliability.　However,　many　datasets　lack　standardization　and　quality　control,　which　can　lead　to　potential　issues　in　software　development　and　data　analysis.　In　this　paper,　we　propose　an　objective　dataset　evaluation　algorithm　that　utilizes　multiple　metrics,　including　unified　naming　conventions　and　document　annotations,　for　statistical　analysis　and　filtering　to　ensure　reliable　data.　Our　approach　scores　datasets　using　reasonable　criteria,　and　we　use　visualization　techniques　inspired　by　remote　sensing　to　compare　and　visualize　reliable　dataset　quality.　Our　research　demonstrates　that　our　evaluation　method　is　feasible　and　can　assist　software　developers　in　enhancing　the　reliable　quality　and　efficiency　of　software　development　by　improving　dataset　quality.　Our　study　concentrates　on　datasets　obtained　from　GitHub　using　a　web　crawler,　and　our　approach　establishes　a　standardized　technique　for　evaluating　reliable　dataset　quality.　©　2023　IEEE.