Context

The aviation accident database throughout the world, from 1908-2019.

• All civil and commercial aviation accidents of scheduled and non-scheduled passenger airliners worldwide, which resulted in a fatality (including all U.S. Part 121 and Part 135 fatal accidents)
• All cargo, positioning, ferry and test flight fatal accidents.
• All military transport accidents with 10 or more fatalities.
• All commercial and military helicopter accidents with greater than 10 fatalities.
• All civil and military airship accidents involving fatalities.
• Aviation accidents involving the death of famous people.
• Aviation accidents or incidents of noteworthy interest.

There are similar dataset available on Kaggle. This dataset is cleaned versioned and source code is available on github.

Content

Data is scraped from planecrashinfo.com. Below you can find the dataset column descriptions:

• Date: Date of accident, in the format - January 01, 2001
• Time: Local time, in 24 hr. format unless otherwise specified
• Airline/Op: Airline or operator of the aircraft
• Flight #: Flight number assigned by the aircraft operator
• Route: Complete or partial route flown prior to the accident
• AC Type: Aircraft type
• Reg: ICAO registration of the aircraft
• cn / ln: Construction or serial number / Line or fuselage number
• Aboard: Total aboard (passengers / crew)
• Fatalities: Total fatalities aboard (passengers / crew)
• Ground: Total killed on the ground
• Summary: Brief description of the accident and cause if known

Acknowledgements

The original data is from the Plane Crash info website (http://www.planecrashinfo.com/database.htm). Dataset is scraped with Python. Source code is also public on Github

Inspiration

Find the root cause of plane crashes. Find any insights from dataset such as - Which operators are the worst - Which aircrafts are the worst

🛫 Airplane Crash Data (1919–2025) – Cleaned & Unified 📌 Overview This dataset is a comprehensive and manually curated collection of global aviation accidents and incidents from 1919 to 2025, sourced from five authoritative platforms. It combines historical and modern records into a single, clean, and analysis-ready .csv file — ideal for data science, machine learning, and aviation safety research.

📂 Sources Used The raw data was gathered from the following sources:

• https://asn.flightsafety.org/database/
• https://www.kaggle.com/datasets/saurograndi/airplane-crashes-since-1908
• https://www.panish.law/aviation_accident_statistics.html
• https://en.wikipedia.org/wiki/Aviation_accidents_and_incidents
• + 2 more manually scraped sources

Each source had unique attributes, structures, and formats. I manually extracted, cleaned, de-duplicated, and unified the datasets to generate this high-quality final version.

🧹 Data Cleaning & Curation The dataset preparation involved:

🧭 Date standardization across multiple formats (including parsing old historical dates)

🔍 Duplicate removal from overlapping sources

🛬 Location normalization (city, country, coordinates where possible)

📉 Fatality/injury counts harmonized into consistent columns

🧑‍✈️ Flight purpose categorization (commercial, military, training, etc.)

💥 Cause/description refinement to improve textual analysis usability

🏷️ Tagging & classification based on incident severity, aircraft type, etc.

📊 Columns in cleaned_data.csv(this is combination of all databased ,ready to work on) Below is a typical structure of the dataset:

Column Name Description Date :Date of the incident Location :City/Region/Country of the crash Operator :Airline or aircraft operator Flight No :Flight number (if available) Aircraft Type :Type/model of the aircraft Registration :Aircraft registration number Fatalities :Total number of fatalities Aboard :Total number of people on board Ground Fatalities :Number of people killed on the ground (if any) Summary :Short description or probable cause Source :Original source from which the data point was collected Crash Type :Categorized tag: e.g., Mid-air collision, engine failure, pilot error, etc. Year :Extracted year (useful for trend analysis)

Note: Not all columns are present in each original file; where possible, missing data has been filled or marked appropriately.

🔍 Why This Dataset Is Unique 📅 Over a century of aviation data (1919–2025)

🔄 Merged from five reputable sources

🧼 Thorough manual cleaning and validation

📚 Useful for:

Aviation safety analysis

Time-series forecasting

Natural Language Processing (NLP) on crash summaries

Machine learning (e.g., predicting crash causes or fatalities)

📌 Suggested Use Cases ✈️ Predictive modeling of aviation risk

📉 Trend analysis in global air safety

🗺️ Geographic visualization of accident hotspots

🤖 NLP classification of crash summaries

📊 Dashboard creation in Power BI or Tableau

📁 File Included cleaned_data.csv – Final cleaned dataset with unified schema

As a result of the continued annual growth in global air traffic passenger demand, the number of airplanes that were involved in accidents is on the increase. Although the United States is ranked among the 20 countries with the highest quality of air infrastructure, the U.S. reports the highest number of civil airliner accidents worldwide. 2020 was the year with more plane crashes victims, despite fewer flights The number of people killed in accidents involving large commercial aircraft has risen globally in 2020, even though the number of commercial flights performed last year dropped by 57 percent to 16.4 million. More than half of the total number of deaths were recorded in January 2020, when an Ukrainian plane was shot down in Iranian airspace, a tragedy that killed 176 people. The second fatal incident took place in May, when a Pakistani airliner crashed, killing 97 people. Changes in aviation safety In terms of fatal accidents, it seems that aviation safety experienced some decline on a couple of parameters. For example, there were 0.37 jet hull losses per one million flights in 2016. In 2017, passenger flights recorded the safest year in world history, with only 0.11 jet hull losses per one million flights. In 2020, the region with the highest hull loss rate was the Commonwealth of Independent States. These figures do not take into account accidents involving military, training, private, cargo and helicopter flights.

Airplane Crashes and Fatalities

This dataset showcases Boeing 707 accidents that have occurred since 1948. The data includes information on the date, time, location, operator, flight number, route, type of aircraft, registration number, cn/In number of persons on board, fatalities, ground fatalities, and a summary of the accident

How to use the dataset

This dataset includes information on over 5,000 airplane crashes around the world.

This is an absolutely essential dataset for anyone interested in aviation safety! Here you will find information on when and where each crash occurred, what type of plane was involved, how many people were killed, and much more.

This dataset is perfect for anyone interested in data visualization or analysis. With so much information available, there are endless possibilities for interesting stories and insights that can be gleaned from this data.

So whether you're a seasoned data pro or just getting started, this dataset is sure to give you plenty to work with. So get started today and see what you can discover!

Research Ideas

1. Plot a map of all flight routes
2. Analyze what type of aircraft is involved in the most crashes
3. Identify patterns in where/when crashes occur

Columns

• index: the index of the row
• Date: the date of the incident
• Time: the time of the incident
• Location: the location of the incident
• Operator: the operator of the aircraft
• Flight #: the flight number of the aircraft
• Route: the route of the aircraft
• Type: the type of aircraft
• Registration: the registration of the aircraft
• cn/In: the construction number/serial number of the aircraft
• Aboard: the number of people on board the aircraft
• Fatalities: the number of fatalities in the incident
• Ground: the number of people on the ground killed in the incident
• Summary: a summary of the incident

Acknowledgements

This dataset was obtained from the Data Society. If you use this dataset in your research, please credit the Data Society.

Columns: index, Date, Time, Location, Operator, Flight #, Route, Type, Registration, cn/In, Aboard, Fatalities Ground Summary

Data Source

Airplane Crashes and Fatalities Since 1908 (Full history of airplane crashes throughout the world, from 1908-present)

At the time this Dataset was created in Kaggle (2016-09-09), the original version was hosted by Open Data by Socrata at the at: https://opendata.socrata.com/Government/Airplane-Crashes-and-Fatalities-Since-1908/q2te-8cvq, but unfortunately that is not available anymore. The dataset contains data of airplane accidents involving civil, commercial and military transport worldwide from 1908-09-17 to 2009-06-08.

While applying for a data scientist job opportunity, I was asked the following questions on this dataset:

1. Yearly how many planes crashed? how many people were on board? how many survived? how many died?
2. Highest number of crashes by operator and Type of aircrafts.
3. ‘Summary’ field has the details about the crashes. Find the reasons of the crash and categorize them in different clusters i.e Fire, shot down, weather (for the ‘Blanks’ in the data category can be UNKNOWN) you are open to make clusters of your choice but they should not exceed 7.
4. Find the number of crashed aircrafts and number of deaths against each category from above step.
5. Find any interesting trends/behaviors that you encounter when you analyze the dataset.

My solution was:

The following bar charts display the answers requested by point 1. of the assignment, in particular:

• the planes crashed per year
• people aboard per year during crashes
• people dead per year during crashes
• people survived per year during crashes

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F298505%2F37efb7629abf402544ddc46cc3a2d7bb%2F_results_0_0.png?generation=1587821759491827&alt=media" alt="">

The following answers regard point 2 of the assignment

• Highest number of crashes by operator: Aeroflot with 179 crashes
• By Type of aircraft: Douglas DC-3 with 334 crashes

I have identified 7 clusters using k-means clustering technique on a matrix obtained by a text corpus created by using Text Analysis (plain text, remove punctuation, to lower, etc.) The following table summarize for each cluster the number of crashes and death.

• Cluster 1: 258 crashes, 6368 deaths
• Cluster 2: 500 crashes, 9408 deaths
• Cluster 3: 211 crashes, 3513 deaths
• Cluster 4: 1014 crashes, 14790 deaths
• Cluster 5: 2749 crashes, 58826 deaths
• Cluster 6: 195 crashes, 4439 deaths
• Cluster 7: 341 crashes, 8135 deaths

The following picture shows clusters using the first 2 principal components: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F298505%2Fea73e0fe9ca12d594fd83f285d3eff62%2F_results_1_17.png?generation=1587821871806437&alt=media" alt="">

For each clusters I will summarize the most used words and I will try to identify the causes of the crash

Cluster 1 (258) aircraft, crashed, plane, shortly, taking. No many information about this cluster can be deducted using Text Analysis

Cluster 2 (500) aircraft, airport, altitude, crashed, crew, due, engine, failed, failure, fire, flight, landing, lost, pilot, plane, runway, takeoff, taking. Engine failure on the runway after landing or takeoff

Cluster 3 (211): aircraft, crashed, fog Crash caused by fog

Cluster 4 (1014): aircraft, airport, attempting, cargo, crashed, fire, land, landing, miles, pilot, plane, route, runway, struck, takeoff Struck a cargo during landing or takeoff

Cluster 5 (2749): accident, aircraft, airport, altitude, approach, attempting, cargo, conditions, control, crashed, crew, due, engine, failed, failure, feet, fire, flight, flying, fog, ground, killed, land, landing, lost, low, miles, mountain, pilot. plane, poor, route, runway, short, shortly, struck, takeoff, taking, weather
Struck a cargo due to engine failure or bad weather conditions mainly fog

Cluster 6 (195): aircraft, crashed, engine, failure, fire, flight, left, pilot, plane, runway
Engine failure on the runway

Cluster 7 (341): accident, aircraft, altitude, cargo, control, crashed, crew, due, engine, failure, flight, landing, loss, lost, pilot, plane, takeoff
Engine failure during landing or takeoff

Better solutions are welcome.

Thanks, Sauro

The number of flights performed globally by the airline industry has increased steadily since the early 2000s and reached **** million in 2019. However, due to the coronavirus pandemic, the number of flights dropped to **** million in 2020. The flight volume increased again in the following years and was forecasted to reach ** million in 2025.

Context

There are some big plane crashes recently. I want to know more about the crashes. For very first step, I need to collect data from somewhere, then I found http://www.planecrashinfo.com/database.htm You guys can pull new data from planecrashinfo.com by using https://github.com/hocnx/planecrashinfo_scraping

Content

Data format: Format

date:  Date of accident, in the format - January 01, 2001
time:  Local time, in 24 hr. format unless otherwise specified
location: location information
Airline/Op: Airline or operator of the aircraft
flight_no:  Flight number assigned by the aircraft operator
route:  Complete or partial route flown prior to the accident
ac_type:   Aircraft type
registration:  ICAO registration of the aircraft
cn_ln:  Construction or serial number / Line or fuselage number
aboard: Total aboard (passengers / crew)
fatalities: Total fatalities aboard (passengers / crew)
ground: Total killed on the ground
summary:   Brief description of the accident and cause if known

Acknowledgements

Many thanks to http://www.planecrashinfo.com

Inspiration

I want to know the trending of number plane crash and the damage volume each year

In 2023, the estimated number of scheduled passengers boarded by the global airline industry amounted to approximately *** billion people. This represents a significant increase compared to the previous year since the pandemic started and the positive trend was forecast to continue in 2024, with the scheduled passenger volume reaching just below **** billion travelers. Airline passenger traffic The number of scheduled passengers handled by the global airline industry has increased in all but one of the last decade. Scheduled passengers refer to the number of passengers who have booked a flight with a commercial airline. Excluded are passengers on charter flights, whereby an entire plane is booked by a private group. In 2023, the Asia Pacific region had the highest share of airline passenger traffic, accounting for ********* of the global total.

What is inside: An air accidents dataset extracted from ICAO (International Civil Aviation Organization) API, occurred in the last 15 years (jan2008-may2022). 15 datasets (1 per each year of occurrences) were merged to create this single one, making a total of 6109 events.

Original ICAO API datasets link is available below. API key required, with 100 free calls available using a company email for subscription. Every accidents year dataset requires one different call (available in CSV or JSON format).

https://applications.icao.int/dataservices/default.aspx

IMPORTANT DEFINITION - Air Incident / Air accident difference: Air Incident: It's any event involving one or more aircraft in which some how put goods or human lives at risk. Air Accident: It's an incident which causes serious damages, injury or fatal victims.

** Only ACCIDENTS were selected in this dataset **

Data analytics/science possibilities: * Data Cleaning * Data Manipulation * EDA * Binary classification ML model creation to estimate probabilities of fatal victims and serious injuries in an air crash, in different aircraft categories (commercial jets, smaller airplanes, helicopters).

Columns: * Date: Date of occurrence in datetime format (time is default 00:00:00.000Z) * StateOfOccurrence: Country of occurrence * Location: City or nearest city of occurrence * Model: Aircraft type * Registration: Aircraft registration code * Operator: Name of Operator (airline, institution or responsible for the aircraft operation) * StateOfOperator: Operator's country * StateOfRegistry: Country of aircraft registration * FlightPhase: Flight Phase of occurrence (take off, landing, approach, etc.) * Class: Accident (unique value). rmk: only 'Accident' class was selected in this dataset (no incidens) * Fatalities: Number of fatal victims * Over2250: If aircraft basic operational weight (empty) is greater than 2250KG * Over5700: If aircraft basic operational weight (empty) is greater than 5700KG * ScheduledCommercial: If it was a scheduled commercial flight (airlines mostly) * InjuryLevel: Level of injuries on person/people involved in the occurrence * TypeDesignator: Aircraft type code (https://cfapps.icao.int/doc8643/reports/Part2-By%20Type%20Designator(Decode).pdf) * Helicopter: if it was a helicopter * Airplane: If it was an airplane * Engine: Number of engines in the aircraft * EngineType: Aircraft engine type (jet, turboprop or piston) * Official: If ICAO was designated to participate in the accident investigation * OccCats: All risk types involved in the occurrence, coded according to HRCs ( High Risks Categories codes) - ICAO * Risk: Main risk type involved in the occurrence, coded according to HRCs(( High Risks Categories codes) - ICAO * Year: Year of occurrence

Any comments and additional knowledge are very welcome..

Enjoy!

Daily data showing UK flight numbers and rolling seven-day average, including flights to, from, and within the UK. These are official statistics in development. Source: EUROCONTROL.

Rigid solid body dynamics is a key element of the undergraduate mechanical engineering curriculum. In a context of reverse engineering and/or sustainable development, being able to analyze the mechanical and material properties of a system without damaging it is a required skill. In this dataset, an unbalanced hollow cylinder rolling over horizontal path without sliding is studied. Four generations of last year bachelor students in mechanical engineering, representing a hundred people a year, followed a total of 12 hours of practical sessions working on such systems. This work aims at showing how computer tools can help and improve a rigid solid body dynamics course.

India All Scheduled Airlines: Domestic: Number of Flight data was reported at 102,319.000 Unit in Mar 2025. This records an increase from the previous number of 92,291.000 Unit for Feb 2025. India All Scheduled Airlines: Domestic: Number of Flight data is updated monthly, averaging 48,100.000 Unit from Apr 2001 (Median) to Mar 2025, with 288 observations. The data reached an all-time high of 102,319.000 Unit in Mar 2025 and a record low of 188.000 Unit in Apr 2020. India All Scheduled Airlines: Domestic: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

Plane Crash Information Data

It is really sad and very disheartening when we heard news about aeroplane crashes. People travel to meet their love ones, to explore new places and are always happy when we think about travelling.

I have gathered some information's for all the aircraft accident records from the year 1908 to 2019. The dataset consist of 5242 rows and 13 columns.

NOTE: The dataset is really messy. Some cleaning effort will be required.

Contents : plane_crash_data.csv

The dataset consist of the following columns: 'date', 'time', 'location', 'operator', 'flight_number', 'route', 'aircraft_type', 'registration', 'cn_ln', 'aboard', 'fatalities', 'ground' and 'summary'

Acknowledgements

The original data source is from the Plane Crash info website (http://www.planecrashinfo.com/database.htm). I have used Python and Pandas web-scraping techniques to gather these informations.

Inspiration

With the intent to find the major root cause of why the aircraft accidents are happening and how we can prevent it, lets explore and analyze this dataset to find these insights.

License

Copyright (c) 2018 Budhajit Roy Chanamthabam

Passengers enplaned and deplaned at Canadian airports, annual.

This dataset represents active recreational flyer fixed sites (commonly referred to as flying fields) that are established by an agreement with the FAA. The fixed sites depicted here are located in controlled airspace two or more miles from an airport. At these sites, recreational UAS operations are authorized up to the unmanned aircraft system (UAS) facility map (UASFM) altitudes. If you fly at the fixed sites depicted in this dataset within controlled airspace, you must adhere to the operating limitations of the agreement, which is available from the fixed site sponsor.The FAA currently is upgrading LAANC (Low Altitude Authorization and Notification Capability) to enable recreational flyers to obtain automated authorization to controlled airspace. The FAA is committed to quickly implementing LAANC for recreational flyers. The FAA also is exploring upgrades to DroneZone to enable access for recreational flyers. Until LAANC is available for recreational operations, the FAA is granting temporary airspace authorizations to operate at certain fixed sites (commonly referred to as flying fields) that are established by an agreement with the FAA. For fixed sites that are located in controlled airspace two or more miles from an airport, operations are authorized up to the unmanned aircraft system (UAS) facility map (UASFM) altitudes. The FAA is reviewing fixed sites located within two miles of an airport and will make individualized determinations of what airspace authorization is appropriate. Aeromodelling organizations that sponsor fixed sites, regardless of their location within controlled airspace, can obtain additional information about requesting airspace authorization by email at UAShelp@faa.gov. During this interim period, you may fly in controlled airspace only at authorized fixed sites. The list of authorized fixed sites is available on the FAA’s website at www.faa.gov/uas and will be depicted on the maps on the FAA’s UAS Data Delivery System, which is available at https://udds-faa.opendata.arcgis.com. Agreements establishing fixed sites may contain additional operating limitations. If you fly at a fixed site in controlled airspace, you must adhere to the operating limitations of the agreement, which is available from the fixed site sponsor.As a reminder, existing FAA rules provide that you may not operate in any designated restricted or prohibited airspace. This includes airspace restricted for national security reasons or to safeguard emergency operations, including law enforcement activities. The easiest way to determine whether any restrictions or special requirements are in effect as well as the authorized altitudes where you want to fly is to use the maps on the FAA’s UAS Data Delivery System, which is available at https://udds-faa.opendata.arcgis.com, and to check for the latest FAA Notices to Airmen (NOTAMs). This information may also be available from third-party applications.The FAA will provide notice when LAANC is available for use by recreational flyers.Alternatively, during this interim period, the FAA directs recreational flyers to existing basic safety guidelines, which are based on industry best practices, on its website (faa.gov/uas): • Fly only for recreational purposes • Keep your unmanned aircraft within your visual line-of-sight or within the visual line of sight of a visual observer who is co-located and in direct communication with you • Do not fly above 400 feet in uncontrolled (Class G) airspace • Do not fly in controlled airspace without an FAA authorization • Follow all FAA airspace restrictions, including special security instructions and temporary flight restrictions • Never fly near other aircraft • Always give way to all other aircraft • Never fly over groups of people, public events, or stadiums full of people • Never fly near emergency response activities • Never fly under the influence of drugs or alcoholYou also should be able to explain to an FAA inspector or law enforcement official which safety guidelines you are following if you are flying under the exception for limited recreational unmanned aircraft operations.Please do not contact FAA Air Traffic facilities for airspace authorization because these facilities will no longer accept requests to operate recreational unmanned aircraft in controlled airspace.Please continue to check faa.gov/uas on a regular basis for the most current directions and guidance.

China Air: Passenger Traffic: Domestic data was reported at 664.657 Person mn in 2024. This records an increase from the previous number of 590.516 Person mn for 2023. China Air: Passenger Traffic: Domestic data is updated yearly, averaging 95.618 Person mn from Dec 1970 (Median) to 2024, with 42 observations. The data reached an all-time high of 664.657 Person mn in 2024 and a record low of 0.210 Person mn in 1970. China Air: Passenger Traffic: Domestic data remains active status in CEIC and is reported by Civil Aviation Administration of China. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TI: Air: Passenger Traffic.

Airline on-time performance Have you ever been stuck in an airport because your flight was delayed or canceled and wondered if you could have predicted it if you'd had more data? This is your chance to find out.

The results We had a total of nine entries, and turn out at the poster session at the JSM was great, with plenty of people stopping by to find out why their flights were delayed.

The data The data consists of flight arrival and departure details for all commercial flights within the USA, from October 1987 to April 2008. This is a large dataset: there are nearly 120 million records in total and takes up 1.6 gigabytes of space when compressed and 12 gigabytes when uncompressed.

The challenge The aim of the data expo is to provide a graphical summary of important features of the data set. This is intentionally vague in order to allow different entries to focus on different aspects of the data, but here are a few ideas to get you started:

When is the best time of day/day of week/time of year to fly to minimise delays? Do older planes suffer more delays? How does the number of people flying between different locations change over time? How well does weather predict plane delays? Can you detect cascading failures as delays in one airport create delays in others? Are there critical links in the system? You are also welcome to work with interesting subsets: you might want to compare flight patterns before and after 9/11, or between the pair of cities that you fly between most often, or all flights to and from a major airport like Chicago (ORD). Smaller subsets may also help you to match up the data to other interesting datasets.

Columns | Name|Description| | --- | --- | |year| 1987-2008| |month| 1-12| |day of month| 1-31| |day of week| 1 (Monday) - 7 (Sunday)| |DepTime| actual departure time (minutes)| |CRSDepTime| scheduled departure time (minutes) |ArrTime| actual arrival time (minutes)| |CRSArrTime| scheduled arrival time (minutes)| |UniqueCarrier| unique carrier code| |FlightNum| flight number| |TailNum| plane tail number| |ActualElapsedTime| in minutes| |CRSElapsedTime| in minutes| |AirTime| in minutes| |ArrDelay| arrival delay, in minutes| |DepDelay| departure delay, in minutes| |Origin| origin IATA airport code| |Dest| destination IATA airport code| |Distance| in miles| |TaxiIn| taxi in time, in minutes| |TaxiOut| taxi out time in minutes| |Cancelled| was the flight cancelled?| |CancellationCode| reason for cancellation (A = carrier, B = weather, C = NAS, D = security)| |Diverted| 1 = yes, 0 = no| |CarrierDelay| in minutes| |WeatherDelay| in minutes| |NASDelay| in minutes| |SecurityDelay| in minutes| |LateAircraftDelay| in minutes|

On behalf of Australia, and in support of the Malaysian accident investigation, the Australian Transport Safety Bureau (ATSB) led search operations for missing Malaysian Airlines flight MH370 in the Southern Indian Ocean. Geoscience Australia provided advice, expertise and support to the ATSB to facilitate marine surveys, which were undertaken to provide a detailed map of the sea floor topography and to aid navigation during the underwater search.

This dataset comprises Side Scan Sonar (SSS), Synthetic Aperture Sonar (SAS) and multibeam sonar backscatter data at 5 m resolution. Data was collected during Phase 2 marine surveys conducted by the Governments of Australia, Malaysia and the People’s Republic of China between September 2014 to January 2017. The data was acquired by Echo Surveyor 7 (Kongsberg AUV Hugin 1000), Edgetech 2400 Deep Tow and SLH PS-60 Synthetic Aperture Sonar Deep Tow deployed from the following vessels: Fugro Supporter, Fugro Equator, Fugro Discovery, Havila Harmony, Dong Hai Jiu 101 and Go Phoenix.

All material and data from this access point is subject to copyright. Please note the creative commons copyright notice and relating to the re-use of this material. Geoscience Australia's preference is that you attribute the datasets (and any material sourced from it) using the following wording: Source: Governments of Australia, Malaysia and the People's Republic of China, 2018. MH370 Phase 2 data.
We honour the memory of those who have lost their lives and acknowledge the enormous loss felt by their loved ones.

Content

Male Flight Attendants

This folder contains the data behind the story Dear Mona, How Many Flight Attendants Are Men?

male-flight-attendants.tsv contains the percentage of U.S. employees that are male in 320 different job categories.

Source: IPUMS, 2012

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.