4 extractive question-and-answer pairs built from UCI Machine Learning Repository, published by archive.ics.uci.edu. Every answer is a verbatim span of text the source prints, and each row carries the passage it sits in, its offset in that passage, the source quote, the page and the location in the document, so any row can be checked against the original. 4 of the 4 pairs (100.0%) are explanatory questions and 0 restate a figure. 100.00% of rows pass the corpus quality gate.
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https://www.desidata.in/api/datasets/bike-sharing-dataset-question-and-answer-dataset/downloadDataset downloads are free. For Python or API downloads, sign in once and create a free DD token; set it as DD_TOKEN or save it in your notebook's secrets. Requests are linked to your account so your download history and counts stay accurate.
# One-time install: pip install desidata
# Set DD_TOKEN in your environment first (create a free token in Profile & settings).
import desidata
df = desidata.load("bike-sharing-dataset-question-and-answer-dataset")
df.head()Sign in with Google to download.
Usable for analysis, but expect some cleaning before you rely on it.
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First 4 of 4 rows
| question | answer | context | answer_start | question_type | knowledge_quality_score | source_quote | source_page | source_location | confidence | validation_status |
|---|---|---|---|---|---|---|---|---|---|---|
| What permissions does the Creative Commons Attribution 4.0 license grant for the dataset? | the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given | Import in Python Hadi Fanaee-T This dataset is licensed under aCreative Commons Attribution 4. International(CC BY 4.0) license. This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given. By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository. APAMLAChicagoVancouverIEEEBibTeX | 145 | definition | 1.000 | This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given. | p[27] | 0.700 | valid | |
| What type of data is provided along with the rental bike counts in the dataset? | the corresponding weather and seasonal information | This dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information. Multivariate Social Science Regression Integer, Real 17389 | 126 | summary | 0.970 | This dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information. | p[0] | 0.700 | valid | |
| What can be identified by analyzing the data from the bike sharing system? | most of important events in the city | This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. it is expected that most of important events in the city could be detected via monitoring these data. Has Missing Values? No By Hadi Fanaee-T, João Gama. 2013 Published in Progress in Artificial Intelligence | 140 | summary | 0.970 | it is expected that most of important events in the city could be detected via monitoring these data. | p[7] | 0.700 | valid | |
| What does the bike sharing system function as in the city? | a virtual sensor network | This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. it is expected that most of important events in the city could be detected via monitoring these data. Has Missing Values? No By Hadi Fanaee-T, João Gama. 2013 Published in Progress in Artificial Intelligence | 44 | definition | 0.940 | This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. | p[7] | 0.700 | valid |
Read straight from the file — download or use the API URL for the full dataset.