Mention counts below measure name matches in tracked papers, not benchmark quality or downloads. A family count can cover multiple datasets; it is not a separate count for each comparison row. Choose a task and version first, preserve evaluation splits, and follow the linked release terms. The site's content license does not replace the datasets' own terms.
NLP dataset comparison: tasks, access and usage conditions
Dataset / version
Task and evaluation
Access and preparation
Usage conditions
SQuAD 1.1 / 2.0
Extractive reading comprehension; v2 adds questions unanswerable from the supplied passage.
Evaluation: Exact match and token F1; for v2 report the no-answer decision setup. Supplied-passage QA is different from corpus retrieval.
From Nov 2023 to Oct 2026, 15 archived papers mentioned SQuAD in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Jul 2026 (3).
No paper of the tracked journals mentioned SQuAD in the last 30 days (2026-10-07 to 2026-11-05).
From 2017 to 2026 (so far), SQuAD was mentioned in 26 papers in the tracked journals, most of them in AI (22), information systems (2) and management (1).
4 of them were published in 2025, against 0 in 2017.
Since 2023, the institutions with the most of these papers were American University (1), Anhui University (1) and BI Norwegian Business School (1).
The journals publishing most of them were Artificial Intelligence Review (3), Knowledge and Information Systems (2) and Technological Forecasting and Social Change (1).
The most-cited of them is “A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges” (Artificial Intelligence Review, 2025), with 275 citations.
Papers use SQuAD most often with Wikipedia (7), GLUE / SuperGLUE (5) and Retrieval and QA (MS MARCO, Natural Questions, HotpotQA) (5); it has 5 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.
Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)
Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.
Papers use SQuAD most often with Wikipedia (7), GLUE / SuperGLUE (5) and Retrieval and QA (MS MARCO, Natural Questions, HotpotQA) (5); it has 5 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok. The whole network →
What papers use SQuAD together with (at least 3 papers; lift 1 = chance)
Scholar4AI. (2026). SQuAD. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/squad
@misc{scholar4ai-datasets-squad,
author = {{Scholar4AI}},
title = {SQuAD},
year = {2026},
howpublished = {\url{https://scholar4ai.com/datasets/squad}},
urldate = {2026-11-05},
note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).