1 dataset found
  1. UFO Sightings Around The World (Better)

    • kaggle.com
    Updated Oct 25, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Jon Wright (2023). UFO Sightings Around The World (Better) [Dataset]. https://www.kaggle.com/datasets/jonwright13/ufo-sightings-around-the-world-better
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 25, 2023
    Dataset provided by
    Kaggle
    Authors
    Jon Wright
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Area covered
    World
    Description

    Context Extraterrestrials, visitors, little green men, UFOs, swap gas. What do they want? Where do they come from? Do they like cheeseburgers? This dataset will likely not help you answer these questions. It does contain over 80,000 records of UFO sightings dating back as far as 1949. With the latitude and longitude data it is possible to assess the global distribution of UFO sightings (patterns could aid in planetary defence if invasion proves to be imminent). The dates and times, along with the duration of the UFO's stay and description of the craft also lend themselves to predictions. Can we find patterns in their arrival times and durations? Do aliens work on weekends? Help defend the planet and learn about your fellow earthlings (and when they are most likely to see ET).

    Content Date_time - standardized date and time of sighting date_documented - when was the UFO sighting reported Year - Year of sighting Month - Month of sighting Hour - Hour of sighting Season - Season of the sighting Country_Code - Country code for the country of the sighting Country - Country name Region - More granular address than country (Includes state, province, region, etc) Locale - More granular address than Region (Includes city, town, village, etc) latitude - latitude longitude - longitude UFO_shape - a one word description of the "spacecraft" length_of_encounter_seconds - standardized to seconds, length of the observation of the UFO Encounter_Duration - raw description of the length of the encounter (shows uncertainty to previous column) description - text description of the UFO encounter. Warning column is messy, with some curation it could lend itself to some natural language processing and sentiment analysis.

    Note there are still some missing data in the columns. I've left it as is because depending on what the user is interested in the missing values in any one column may or may not matter.

    Acknowledgements Original Data source: https://github.com/planetsig/ufo-reports Previous dataset: https://www.kaggle.com/datasets/camnugent/ufo-sightings-around-the-world Geo-locate script: https://github.com/jonwright13/geo-locate

  2. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Jon Wright (2023). UFO Sightings Around The World (Better) [Dataset]. https://www.kaggle.com/datasets/jonwright13/ufo-sightings-around-the-world-better
Organization logo

UFO Sightings Around The World (Better)

80,000+ documented close encounters from the past 70 years with better addresses

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Oct 25, 2023
Dataset provided by
Kaggle
Authors
Jon Wright
License

Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically

Area covered
World
Description

Context Extraterrestrials, visitors, little green men, UFOs, swap gas. What do they want? Where do they come from? Do they like cheeseburgers? This dataset will likely not help you answer these questions. It does contain over 80,000 records of UFO sightings dating back as far as 1949. With the latitude and longitude data it is possible to assess the global distribution of UFO sightings (patterns could aid in planetary defence if invasion proves to be imminent). The dates and times, along with the duration of the UFO's stay and description of the craft also lend themselves to predictions. Can we find patterns in their arrival times and durations? Do aliens work on weekends? Help defend the planet and learn about your fellow earthlings (and when they are most likely to see ET).

Content Date_time - standardized date and time of sighting date_documented - when was the UFO sighting reported Year - Year of sighting Month - Month of sighting Hour - Hour of sighting Season - Season of the sighting Country_Code - Country code for the country of the sighting Country - Country name Region - More granular address than country (Includes state, province, region, etc) Locale - More granular address than Region (Includes city, town, village, etc) latitude - latitude longitude - longitude UFO_shape - a one word description of the "spacecraft" length_of_encounter_seconds - standardized to seconds, length of the observation of the UFO Encounter_Duration - raw description of the length of the encounter (shows uncertainty to previous column) description - text description of the UFO encounter. Warning column is messy, with some curation it could lend itself to some natural language processing and sentiment analysis.

Note there are still some missing data in the columns. I've left it as is because depending on what the user is interested in the missing values in any one column may or may not matter.

Acknowledgements Original Data source: https://github.com/planetsig/ufo-reports Previous dataset: https://www.kaggle.com/datasets/camnugent/ufo-sightings-around-the-world Geo-locate script: https://github.com/jonwright13/geo-locate

Search
Clear search
Close search
Google apps
Main menu