In the current landscape of pervasive smartphones and tablets, apps frequently exist across both platforms.Although apps share most graphic user interfaces (GUIs) and functionalities across phones and tablets, developers often rebuild from scratch for tablet versions, escalating costs and squandering existing design resources.Researchers are attempting to collect data and employ deep learning in automated GUIs development to enhance developers' productivity.There are currently several publicly accessible GUI page datasets for phones, but none for pairwise GUIs between phones and tablets.This poses a significant barrier to the employment of deep learning in automated GUI development.In this paper, we introduce the Papt dataset, a pioneering pairwise GUI dataset tailored for Android phones and tablets, encompassing 10,035 phone-tablet GUI page pairs sourced from 5,593 unique app pairs.We propose novel pairwise GUI collection approaches for constructing this dataset and delineate its advantages over currently prevailing datasets in the field.Through preliminary experiments on this dataset, we analyze the present challenges of utilizing deep learning in automated GUI development.