data science
Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
- Categories:
Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
- Categories:
Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19”, Proceedings of the 25th International Conference on Human-Computer Interaction (HCII 2023), Copenhagen, Denmark, July 23-28, 2023 (Accepted for Publication)
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
- Categories:
This dataset is a set of eighteen directed networks that represents message exchanges among Twitter accounts during eighteen crisis events. The dataset comprises 645,339 anonymized unique user IDs and 1,396,709 edges that are labeled with respect to Plutchik's basic emotions (anger, fear, sadness, disgust, joy, trust, anticipation, and surprise) or "neutral" (if a tweet conveys no emotion).
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By querying open data of notorious scientific databases via representational state transfers, and subsequently enforcing data management practices with a dynamic topic modeling approach on the referred metadata available, this work achieves a feasible form of article set analysis and classification. Research trends for a given field in specific moments are identified, and also the referred trends evolution throughout the years.
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This simulated combat reports dataset combines fictional headings, reporting units, and attack times with real data from 551 records of terrorist attacks in Afghanistan (2009–2010) [1]. The dataset combines selected attributes from the DA Form 1594 [2] and U.S. Army Spot Report [3]. The dataset also includes additional attributes for tactical context.
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