You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: I am trying to use a machine to answer a question about the world around us.

Keyphrases: open-domain question answering text

Dataset description:
ARC: The AI2’s Reasoning Challenge (ARC) dataset is a multiple-choice question-answering dataset, containing questions from science exams from grade 3 to grade 9. The dataset is split in two partitions: Easy and Challenge, where the latter partition contains the more difficult questions that require reasoning.
NewsQA: The NewsQA dataset is a crowd-sourced machine reading comprehension dataset of 120,000 question-answer pairs. Documents are CNN news articles.
DuoRC: DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie. Why another RC dataset?
DAQUAR: DAQUAR (DAtaset for QUestion Answering on Real-world images) is a dataset of human question answer pairs about images.
SearchQA: SearchQA was built using an in-production, commercial search engine. It closely reflects the full pipeline of a (hypothetical) general question-answering system, which consists of information retrieval and answer synthesis.
SQuAD: The Stanford Question Answering Dataset (SQuAD) is a collection of question-answer pairs derived from Wikipedia articles. In SQuAD, the correct answers of questions can be any sequence of tokens in the given text.

Options:- A: ARC  B: NewsQA  C: DuoRC  D: DAQUAR  E: SearchQA  F: SQuAD
["ARC", "NewsQA", "SearchQA", "SQuAD"]