Success.ai’s Aviation Data provides verified access to professionals across the airlines, aviation, and aerospace industries. Leveraging over 700 million LinkedIn profiles, this dataset delivers actionable insights, contact details, and firmographic data for pilots, engineers, airline executives, aerospace manufacturers, and more. Whether your goal is to market aviation technology, recruit aerospace specialists, or analyze industry trends, Success.ai ensures your outreach is powered by accurate, enriched, and continuously updated data.

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Real-time updates reflect changes in roles, organizational affiliations, and professional achievements, ensuring relevant targeting. Tailored for Aviation and Aerospace Insights

Enriched profiles include work histories, areas of specialization, professional certifications, and firmographic data. Data Highlights: 700M+ Verified LinkedIn Profiles: Access a vast network of aviation and aerospace professionals worldwide. 100M+ Work Emails: Communicate directly with pilots, engineers, and airline executives. Enriched Professional Histories: Gain insights into career paths, certifications, and organizational roles. Industry-Specific Segmentation: Target professionals in commercial aviation, aerospace R&D, airport management, and more with precision filters. Key Features of the Dataset: Aviation and Aerospace Professional Profiles

Identify and connect with airline CEOs, aerospace engineers, maintenance technicians, flight safety experts, and other key professionals. Engage with individuals responsible for operational decisions, technology adoption, and aviation safety protocols. Detailed Firmographic Data

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Tailor datasets to specific aviation sectors, geographic regions, or professional roles to meet your strategic objectives. Strategic APIs for Enhanced Campaigns: Data Enrichment API

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Automate lead generation for a consistent pipeline of qualified professionals in the aviation sector, scaling your outreach efficiently. Success.ai’s Aviation Data empowers you to connect with the leaders and innovators shaping the aviation and aerospace industries. With verified conta...

This physiological data was collected from pilot/copilot pairs in and out of a flight simulator. It was collected to train machine-learning models to aid in the detection of pilot attentive states. The benchmark training set is comprised of a set of controlled experiments collected in a non-flight environment, outside of a flight simulator. The test set (abbreviated LOFT = Line Oriented Flight Training) consists of a full flight (take off, flight, and landing) in a flight simulator. The pilots experienced distractions intended to induce one of the following three cognitive states: Channelized Attention (CA) is the state of being focused on one task to the exclusion of all others. This is induced in benchmarking by having the subjects play an engaging puzzle-based video game. Diverted Attention (DA) is the state of having one’s attention diverted by actions or thought processes associated with a decision. This is induced by having the subjects perform a display monitoring task. Periodically, a math problem showed up which had to be solved before returning to the monitoring task. Startle/Surprise (SS) is induced by having the subjects watch movie clips with jump scares. For each experiment, a pair of pilots (each with its own crew ID) was recorded over time and subjected to the CA, DA, or SS cognitive states. The training set contains three experiments (one for each state) in which the pilots experienced just one of the states. For example, in the experiment labelled CA, the pilots were either in a baseline state (no event) or the CA state. The test set contains a full flight simulation during which the pilots could experience any of the states (but never more than one at a time). Each sensor operated at a sample rate of 256 Hz. Please note that since this is physiological data from real people, there will be noise and artifacts in the data.

This physiological data was collected from pilot/copilot pairs in and out of a flight simulator. It was collected to train machine-learning models to aid in the detection of pilot attentive states. The benchmark training set is comprised of a set of controlled experiments collected in a non-flight environment, outside of a flight simulator. The test set (abbreviated LOFT = Line Oriented Flight Training) consists of a full flight (take off, flight, and landing) in a flight simulator. The pilots experienced distractions intended to induce one of the following three cognitive states: Channelized Attention (CA) is the state of being focused on one task to the exclusion of all others. This is induced in benchmarking by having the subjects play an engaging puzzle-based video game. Diverted Attention (DA) is the state of having one’s attention diverted by actions or thought processes associated with a decision. This is induced by having the subjects perform a display monitoring task. Periodically, a math problem showed up which had to be solved before returning to the monitoring task. Startle/Surprise (SS) is induced by having the subjects watch movie clips with jump scares. For each experiment, a pair of pilots (each with its own crew ID) was recorded over time and subjected to the CA, DA, or SS cognitive states. The training set contains three experiments (one for each state) in which the pilots experienced just one of the states. For example, in the experiment labelled CA, the pilots were either in a baseline state (no event) or the CA state. The test set contains a full flight simulation during which the pilots could experience any of the states (but never more than one at a time). Each sensor operated at a sample rate of 256 Hz. Please note that since this is physiological data from real people, there will be noise and artifacts in the data.

Annual data on civil aviation employment. Details on employment include the average number of employees, and wages and salaries expenses, by category of employment (total, average number of employees, pilots and co-pilots, other flight personnel, general management and administration employees, maintenance personnel, aircraft and traffic servicing personnel, and all other employees). Data are for Canadian air carriers, Levels I and II combined, Level III, and Levels I to III combined. Data on wages and salaries are expressed in thousands of dollars.

SUMMARY This dataset Includes records of all SFPD deployed small Uncrewed Aerial Vehicles (sUAS) beginning May 16, 2024, to present month. These sUAS are used to enhance the safety of the community and officers by providing air support and situational awareness for law enforcement operations. Under Prop E (SF Admin Code 96I.2(e)(2)) the Department is authorized to use sUAS along with or in lieu of vehicle pursuits and to assist with active criminal investigations. Strategic Investigations has oversight of the program to ensure compliance with all federal, state, and local laws as well as Department policy and guidelines. HOW THE DATASET IS CREATED sUAS are deployed and operated exclusively by sworn personnel pilots licensed by the Federal Aviation Administration (FAA). The deployment of the departments sUAS occurs during active criminal investigations and/or vehicle pursuits, from which then, a flight log is generated by the UAS operator prior to the end of their shift. The designated program manager then reviews and approves flight logs for monthly publication of flight logs. After each flight, the pilot tags the Digital Media Evidence (DME) as having evidentiary value or not. If DME is found to have no evidentiary value, as it is not relevant to a criminal, civil or administrative matter, data may be deleted within 30 days. Contrastingly if there is evidentiary value, as it is relevant to a criminal, civil or administrative matter, it shall be retained for a minimum of 2 years and in accordance with federal/state laws and regulations. This retention schedule mirrors the SFPD policy for Body Worn Camera Footage. WHAT PRIVACY CONTROLS ARE THIS DATASET SUBJECT TO? The release of this data aims to balance public transparency with the protection of individual privacy, in full compliance with applicable laws. To protect individuals in this dataset from potential re-identification, incident locations are generalized to intersections or the 100-block level. If outside SFPD districts, locations are marked as “Out of SF” or “Unknown”. No other personal identifiable information (PII) is included for any individuals involved—such as suspects, victims, reporting parties, officers, or witnesses within this dataset. Incidents involving juveniles are excluded in accordance with California Government Code § 6254 and Welfare and Institutions Code § 827. UPDATE PROCESS The previous month’s data is uploaded monthly, no more than 14 days after data processing. HOW TO USE THIS DATASET This dataset can be used for the purpose of understanding the use of drones in law enforcement/public safety: correlating drone flight data with arrests, crime rates, types of incidents. Spatial analysis: mapping where drones are deployed to understand possible patterns (e.g. geographic clustering). Using the dataset for public awareness and enabling the community to have data informed discussions regarding drone use by law enforcement. RELATED DATASETS Law Enforcement Dispatched Calls for Service: Closed Police Department Incident Reports: 2018 to Present

In order to reduce subjective errors, two independent researchers conducted searches using the keywords "pilot psychology" and "SCL-90" respectively, and then judged the questionable literature through a third party. The time span of 13 articles is from 2004 to 2021, with a total of 7078 people. The sample size is large enough to provide accurate estimates and more stable results. All relevant literature before 2022 has been collected, and the deadline is January 1, 2022.

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

Aviation Safety Reports Text Classification

Using Reports to Discover Incidents and Problem Types

By US Open Data Portal, data.gov [source]

About this dataset

This U.S. Government Works Aviation Safety Reports Dataset for Text Mining is part of the SIAM 2007 Text Mining Competition dataset which has been used to create algorithms to classify documents according to the types of problems described. The documents in this dataset consist of reports on incidents that occurred during certain flights and are collected from human-generated reports as part of the Aviation Safety Reporting System (ASRS). The files for this competition come in raw text format, with each row representing a single document and its associated problem type label.

This dataset provides invaluable insights into aviation safety incidents and is an excellent resource for researchers interested in developing text mining techniques for categorizing documents by their contents. Analyzing these documents can help identify potential safety issues, both within individual aircrafts’ operations and more broadly online, driving domestic flying safety forward in an era when ever increasing numbers of people are travelling by air

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How to use the dataset

This dataset contains aviation safety reports which have been labelled according to the type of problem that occurred during a certain flight. It is a great resource for developing text mining algorithms for document classification.

Research Ideas

• Build an AI-powered Machine Learning classifier to identify problematic aviation incidents more quickly and accurately.
• Predict the risk of a particular flight, taking into consideration the type of incident that has occurred before on a similar flight.
• Construct an interactive searchable interface to allow users to better analyze and visualize aviation safety reports in order to uncover trends and suggest ways for improvement across all levels of relevant stakeholders within the sector, such as regulators, airlines, aircraft operators or pilots

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: testtruth-csv-gz-3.csv | Column name | Description | |:--------------|:------------------------------------| | -1 | Document Number (String) | | -1.1 | Aircraft Autopilot Problem (String) | | -1.2 | Auxiliary Power Problem (String) | | -1.3 | Avionics Problem (String) | | -1.4 | Cabin Pressure Problem (String) | | -1.5 | Communications Problem (String) | | -1.6 | Electrical System Problem (String) | | -1.7 | Engine Problem (String) | | -1.8 | Fire/Smoke Problem (String) | | -1.9 | Fuel System Problem (String) | | -1.10 | Ground Service Problem (String) | | -1.11 | Hydraulic System Problem (String) | | -1.12 | Ice/Frost Problem (String) | | -1.13 | Landing Gear Problem (String) | | -1.14 | Maintenance Problem (String) | | -1.15 | Navigation Problem (String) | | -1.16 | Oxygen System Problem (String) | | -1.17 | Structural Problem (String) | | -1.18 | Other Problem (String) |

File: traincategorymatrix-csv-gz-5.csv | Column name | Description | |:--------------|:------------------------------------| | -1 | Document Number (String) | | -1.1 | Aircraft Autopilot Problem (String) | | -1.2 | Auxiliary Power Problem (String) | | -1.3 | Avionics Problem (String) | | -1.4 | Cabin Pressure Problem (String) | | -1.5 | Communications Problem (String) | | -1.6 | Electrical System Problem (String) | | -1.7 | Engine Problem (String) | | -1.8 | Fire/Smoke Problem (String) | | -1.9 | Fuel System Problem (String) | | -1.10 | Ground Service Problem (String) | | -1.11 | Hydraulic System Problem (String) | | -1.12 | Ice/Frost Problem (String) | | -1.13 | Landing Gear Problem (String) | | -1.14 | Maintenance Problem (String) | | -1.15 | Navigation Problem (String) | | -1.16 | Oxyg...

Transportation and communication are crucial areas within analytics, especially in tackling safety and environmental challenges associated with the rapid expansion of urban centers and rising air traffic. One of the major hazards in aviation is bird strikes—collisions between aircraft and birds or other wildlife—which present a significant risk. These incidents can inflict severe damage on aircraft, particularly jet engines, and have even led to fatal accidents. Bird strikes are most common during critical flight stages such as takeoff, ascent, approach, and landing, when aircraft operate at lower altitudes where bird activity is more frequent.

The dataset provided by the FAA, covering incidents from 2019 to 2024, offers a comprehensive overview of bird strikes in the U.S. It includes detailed visualizations and analyses across several key areas:

• Trends Over Time: Yearly distribution of bird strike incidents.
• Airline Impact: Analysis of the top 10 U.S. airlines affected by bird strikes.
• Airport Incidents: Identification of the 50 U.S. airports with the highest frequency of bird strike incidents.
• Economic Impact: Yearly costs incurred by airlines and the aviation industry due to bird strikes.
• Timing and Altitude: When and at what altitude most bird strikes occur.
• Flight Phase: The phase of flight during which strikes are most likely to happen.
• Impact Analysis: How bird strikes affect flight operations, including aircraft damage.
• Pilot Awareness: Correlation between pilot knowledge of potential bird strike risks and the severity of the incidents.

This dataset offers valuable insights into bird strike patterns, focusing on factors such as aircraft type, location, flight phase, and the specific species involved. By analyzing these variables, it helps identify risk factors and trends, supporting the development of strategies to reduce the frequency and impact of bird strikes, ultimately enhancing aviation safety and risk mitigation.

Features: - AircraftType: The type of aircraft involved in the bird strike incident (e.g., "Airplane"). - AirportName: The name of the airport where the bird strike occurred (e.g., "LAGUARDIA NY", "DALLAS/FORT WORTH INTL ARPT"). - AltitudeBin: The altitude range (in feet) at which the bird strike occurred, divided into bins (e.g., "(1000, 2000]", "(30, 50]"). - MakeModel: The specific make and model of the aircraft involved (e.g., "B-737-400", "MD-80", "A-300"). - NumberStruck: The number of birds that were struck during the incident (e.g., "Over 100", "1", "26"). - NumberStruckActual: The actual number of birds that were struck during the incident (e.g., 859, 424, 261). - Effect: The effect of the bird strike on the aircraft, indicating whether it caused any damage or not (e.g., "Engine Shut Down", "No damage", "Caused damage"). - FlightDate: The date of the bird strike incident (e.g., "11/23/00 0:00"). - Damage: A description of the damage caused by the bird strike (e.g., "Caused damage", "No damage"). - Engines: The number of engines on the aircraft involved in the bird strike (e.g., 2 engines). - Operator: The airline or operator of the aircraft involved in the bird strike (e.g., "US AIRWAYS", "AMERICAN AIRLINES", "ALASKA AIRLINES"). - OriginState: The U.S. state where the aircraft originated (e.g., "New York", "Texas", "Washington"). - FlightPhase: The phase of flight during which the bird strike occurred (e.g., "Climb", "Landing Roll", "Approach", "Take-off run") - ConditionsPrecipitation: The weather condition related to precipitation at the time of the bird strike (e.g., "None", "Some Cloud"). - RemainsCollected?: Indicates whether bird remains were collected after the strike (e.g., "True" or "False"). - RemainsSentToSmithsonian: Indicates whether the bird remains were sent to the Smithsonian Institution for study (e.g., "True" or "False"). - Remarks: Additional comments or notes related to the incident, including specific details like the number of birds involved, actions taken, or other observations (e.g., "FLYING UNDER A VERY LARGE FLOCK OF BIRDS", "BIRD REMAINS ON F/O WINDSCREEN"). - WildlifeSize: The size of the bird or wildlife involved in the strike (e.g., "Small", "Medium"). - ConditionsSky: The sky condition at the time of the bird strike (e.g., "No Cloud", "Some Cloud"). - WildlifeSpecies: The species of the bird or wildlife involved in the strike (e.g., "European starling", "Rock pigeon", "Unknown bird - medium"). - PilotWarned: Indicates whether the pilot was warned about the potential for a bird strike (e.g., "Y" for Yes, "N" for No). - Cost: The cost incurred as a result of the bird strike (e.g., financial cost to repair damage or related expenses, usually in monetary value like 30,736). - Altitude: The specific altitude at which the bird strike occurred, typically in feet (e.g., 1500 feet, 50 feet). - PeopleInjured: The number of people injure...

Summary of 16 long-term care insurance pilot policy texts.