aviation

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Crowdsourced air traffic data from The OpenSky Network 2020

Crowdsourced air traffic data from The OpenSky Network 2020

Stefan
Open Dataset
Creators: Olive, Xavier; Strohmeier, Martin; Lübbe, Jannis
Publication Date: 2022
Creators: Olive, Xavier; Strohmeier, Martin; Lübbe, Jannis

The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network’s more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic. Leveraging a network of over 2,500 members, the dataset aggregates ADS-B signals received by volunteers worldwide, ensuring a rich and diverse data source. The dataset includes records of 41,900,660 flights, capturing data from 160,737 unique aircrafts. Flight operations involving 13,934 airports across 127 countries are documented. In total, the dataset has a size of 7,0 GB. Each month is represented by a separate CSV file, containing flight data for that specific period. ​ Each file includes the following columns:​

  • callsign: Identifier used for air traffic control communications.​
  • number: Commercial flight number, if available.​
  • icao24: Unique 24-bit address assigned to the aircraft’s transponder.​
  • registration: Aircraft’s registration number.
  • typecode: Aircraft model code.
  • origin: ICAO code of the departure airport.​
  • destination: ICAO code of the arrival airport.​
  • firstseen: Timestamp of the first detection during the flight.​
  • lastseen: Timestamp of the last detection during the flight.​
  • day: Date of the flight.
Twitter US Airline Sentiment

Twitter US Airline Sentiment

Stefan
Open Dataset
Creators: (Makone, Ashutosh)
Publication Date: 2016
Creators: (Makone, Ashutosh)

The Twitter US Airline Sentiment dataset is a collection of tweets aimed at analyzing public sentiment toward major U.S. airlines. Compiled in February 2015, the dataset consists of 14,640 tweets directed at several U.S. airlines. It serves as a valuable resource for sentiment analysis and natural language processing research, particularly in understanding customer satisfaction, airline service quality, and issues reported by travelers. Each tweet in the dataset is labeled with one of three sentiment categories: positive, neutral, or negative. Tweets labeled as negative are further categorized into specific negative sentiment reasons, such as late flight, customer service issue, canceled flight, and lost luggage, providing deeper insights into common complaints. The dataset also identifies the airline mentioned in each tweet, covering six major U.S. carriers: United Airlines, US Airways, American Airlines, Southwest Airlines, Delta Air Lines, and Virgin America. Additional metadata is provided for each tweet, including tweet ID, tweet text, tweet coordinates (if available), user information, and location data, allowing for further contextual analysis. The dataset is relatively small, with a total size of 8,46 MB, making it easily manageable for sentiment analysis tasks and machine learning applications. It includes 14,640 tweets from 7,700 unique users, providing a broad yet concise representation of customer interactions with airlines on Twitter. The tweets were collected over a one-month period in February 2015, offering a snapshot of public sentiment during that specific timeframe.

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