This repository contains resources, namely TempTabQA, developed for the paper: Gupta, V., Kandoi, P., Vora, M., Zhang, S., He, Y., Reinanda R., Srikumar V., TempTabQA: Temporal Question Answering for Semi-Structured Tables. In: Proceeding of the The 2023 Conference on Empirical Methods in Natural Language Processing, Dec 2023. TempTabQA is a dataset which comprises 11,454 question-answer pairs extracted from Wikipedia Infobox tables. These question-answer pairs are annotated by human annotators. We provide two test sets instead of one: the Head set with popular frequent domains, and the Tail set with rarer domains. Files to access the annotation follow the below structure: Maindata qapairs: split into train, dev, head, and tail sets, in both csv and json formats Tables: Wikipedia category and tables metadata in csv, json and html formats Carefully read the ```LICENCE``` for non-academic usage. Note : Wherever required consider the year of 2022 as the build date for the dataset.