[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"examples-content:\u002Fexamples\u002Fscreens\u002Ff43668d7-d303-4d59-a9ce-7a0e7ac9a636":3,"content-store-meta":-1},{"canonical_path":4,"template":5,"family":6,"indexable":7,"seo":8,"breadcrumbs":12,"content":21,"examples":26,"showcases":109,"links":110,"dates":125},"\u002Fexamples\u002Fscreens\u002Ff43668d7-d303-4d59-a9ce-7a0e7ac9a636","detail","screens",true,{"title":9,"description":10,"og_image":11},"List - Nicelydone","Screen UI design example from Sift science. List view where fraud analysts explore risky users filtered by payment abuse score, review their order history…","https:\u002F\u002Fassets.nicelydone.club\u002Ff\u002Fsift-science_list_07f5ea12_nicelydone.jpg",[13,16,19],{"label":14,"to":15},"Examples","\u002Fexamples",{"label":17,"to":18},"SaaS screen examples","\u002Fexamples\u002Fscreens",{"label":20,"to":4},"List",{"title":20,"eyebrow":22,"description":23,"sections":24,"body":25},"Screen design","Screen UI design example from Sift science. List view where fraud analysts explore risky users filtered by payment abuse score, review their order history and attributes, and apply decisions or labels.",[],[],[27],{"uuid":28,"title":20,"description":29,"canonical_path":30,"image":11,"width":31,"height":32,"image_alt":33,"published_at":34,"kind":35,"patterns":36,"component_categories":74,"app":95},"f43668d7-d303-4d59-a9ce-7a0e7ac9a636","List view where fraud analysts explore risky users filtered by payment abuse score, review their order history and attributes, and apply decisions or labels.","\u002Fexamples\u002Fapps\u002Fsift-science\u002Fscreens",2880,9926,"List from Sift science","2016-10-21T08:09:48.000Z","screen",[37,46,55,60,66],{"id":38,"label":39,"slug":40,"rank":41,"group":42},13,"Filter & Sort","filter-and-sort",0,{"id":43,"label":44,"slug":45},2,"Actions","actions",{"id":47,"label":48,"slug":49,"rank":50,"group":51},41,"Dashboard","dashboard",1,{"id":52,"label":53,"slug":54},4,"Data & Analysis","data-and-analysis",{"id":56,"label":57,"slug":58,"rank":43,"group":59},47,"Table","table",{"id":52,"label":53,"slug":54},{"id":61,"label":62,"slug":63,"rank":64,"group":65},46,"Stats","stats",3,{"id":52,"label":53,"slug":54},{"id":67,"label":68,"slug":69,"rank":52,"group":70},71,"Workspace Switcher","workspace-switcher",{"id":71,"label":72,"slug":73},6,"Navigation & Commands","navigation-and-commands",[75,79,83,87,91],{"id":76,"title":77,"slug":78},233,"Tabs","tabs",{"id":80,"title":81,"slug":82},211,"Pagination","pagination",{"id":84,"title":85,"slug":86},174,"Card","card",{"id":88,"title":89,"slug":90},260,"Badge","badge",{"id":92,"title":93,"slug":94},175,"Button","button",{"slug":96,"title":97,"logo":98,"baseline":99,"categories":100},"sift-science","Sift science","https:\u002F\u002Fassets.nicelydone.club\u002Flogos\u002Fsift-science-116e211d.png","Fight fraud with machine learning",[101,105],{"id":102,"title":103,"slug":104},78,"Security","security",{"id":106,"title":107,"slug":108},80,"Artificial Intelligence","artificial-intelligence",[],[111,113,115,117,119,121,123],{"label":48,"to":112},"\u002Fexamples\u002Fscreens\u002Fdashboard",{"label":39,"to":114},"\u002Fexamples\u002Fscreens\u002Ffilter-and-sort",{"label":62,"to":116},"\u002Fexamples\u002Fscreens\u002Fstats",{"label":57,"to":118},"\u002Fexamples\u002Fscreens\u002Ftable",{"label":68,"to":120},"\u002Fexamples\u002Fscreens\u002Fworkspace-switcher",{"label":122,"to":30},"Sift science screen designs",{"label":124,"to":18},"All screen examples",{"published_at":34,"updated_at":126},"2026-06-13T21:48:01.007Z"]