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.

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.

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.

India All Scheduled Airlines: International: Number of Flight data was reported at 18,502.000 Unit in Mar 2025. This records an increase from the previous number of 16,668.000 Unit for Feb 2025. India All Scheduled Airlines: International: Number of Flight data is updated monthly, averaging 7,797.000 Unit from Apr 2001 (Median) to Mar 2025, with 283 observations. The data reached an all-time high of 18,574.000 Unit in Jan 2025 and a record low of 273.000 Unit in May 2020. India All Scheduled Airlines: International: 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.

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.

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This paper is a progress report of an effort whose goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advancedtechniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. This paper describes the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.

In prospective human exploration of outer space the need to maintain a species over several generations under changed gravity conditions may arise. This paper reports the analysis of the third generation of fruit fly Drosophila melanogaster obtained during the 44.5-day space flight (Foton-M4 satellite 2014 Russia) followed by the fourth generation on Earth and the fifth generation under conditions of a 12-day space flight (2014 in the Russian Segment of the ISS). The obtained results show that it is possible to obtain the third-fifth generations of a complex multicellular Earth organism under changed gravity conditions (in the cycle weightlessness - Earth - weightlessness) which preserves fertility and normal development. However there were a number of changes in the expression levels and content of cytoskeletal proteins that are the key components of the spindle apparatus and the contractile ring of cells.

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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.

Data Description

The available dataset is about Airplane Crashes throughout the world since 1908.

Variables:

Date: Date of crash Time: When in the day Location: of crash Operator: From which department? Flight: Kind of flight Route: Reason of flying Type: Which type? And other Variables.

This an easy dataset to know and it can be used for your EDA projects! Kindly upvote if you liked this dataset.😊

This dataset contains predictions of whether temperature inversions will occur at locations in Allegheny County.

This dataset is still under active development and should be considered to be in "beta".

Motivation

Temperature inversions occur when there is a warmer layer of air above the air at or near ground level. This represents a reversal of the normal flow of heat near the earth and results in the cooler air being trapped near the ground. Temperature inversions can lead to the formation of fog or dew. Pollution or smoke from fires, which would rise and dissipate in the atmosphere under normal conditions, become trapped near the ground in a temperature inversion, potentially leading to hazardous concentrations of pollutants in the air.

This dataset was extracted from NASA's Goddard Earth Observing System Forward-Processing (GEOS-FP) system as a collaboration between NASA's Goddard Space Flight Center and the Western Pennsylvania Regional Data Center, to provide access to 1-day, 3-day, and 5-day predictions of temperature inversions in Allegheny County.

Preprocessing/Formatting/Methodology

This dataset is generated using data-processing scripts written by partners at NASA Goddard Space Flight Center. The scripts extract from the GEOS-FP model the predicted air temperature as a function of latitude/longitude/date/height, and then, starting near surface level, search upward for the height of the local maximum in air temperature. This determines whether a temperature inversion is expected.

Each record is a prediction of whether there will be a temperature inversion, for a particular day at 12pm UTC (7am EST) within five days after the prediction, and for a particular cell in a coarse grid overlaying Allegheny County. If an inversion is predicted, the height of top of the inversion above the ground and the temperature difference between the ground and the top of the inversion are given, as well as an estimate of the inversion strength on a scale of 0 to 4 (where the strength of the inversion is calculated based on the value of the temperature difference). For some locations, we've also added the name of a place (e.g., "Pittsburgh" or "Monroeville") within that cell, to make look-ups easier.

Additionally, we've created forecast maps for the region and 5-day timeline forecasts (for particular locations) of both inversion strength and PM2.5 concentration.

Known Uses

If you are using this dataset, please write to the data steward (listed below) and let us know! Your stories support the development of future datasets like this.

Recommended Uses

This data could provide an early-warning system for certain kinds of unhealthy air-quality events, such as dangerously high PM_2.5 levels from wildfire-induced smog or pollution, trapped near the ground.

Known Limitations/Biases

The spatial resolution of the forecast is pretty coarse.

To validate the forecast, a comparison was made of its predictions with actual temperature-inversion measurements made by weather balloon (or sodar/RASS acoustic upper air profiler) by the Allegheny County Health Department Air Quality Office. The results are shown in this table, which is accompanied by some additional analysis. When the 1-day forecast predicted a strong or moderate inversion, there was about a 90% chance that it was historically correct, and when the 3-day or 5-day forecast confirmed this forecast for the same date, the accuracy increased, with more than 96% historical accuracy when confirmed by the 5-day forecast.

Also, sometimes the model results can not be computed on the expected schedule. (These delays are reported on the "geos5-fp-users" mailing list.) In these instances, the WPRDC's automated processes fall back to the previous day's forecasts; the forecast_version field provides the date and hour that the forecast simulation was started.

Related Datasets

The Allegheny County Health Department's measurement of pollutant concentrations (and other parameters) at several measurements stations are published in the Allegheny County Air Quality dataset.

We are also publishing a dataset that forecasts concentrations of three air quality parameters: carbon monoxide (CO), nitrogen dioxide (NO2), and fine particulate matter (PM2.5).

Credits

This work is the result of a collaboration between the WPRDC and NASA's Goddard Space Flight Center. This dataset would not have been possible without the efforts of NASA Goddard Space Flight Center personnel to apply NASA's atmospheric models and domain expertise to the problem of forecasting temperature inversions, yielding this prototype forecast, tailored to Allegheny County. Thanks also to Jason Maranche and Angela Wilson of the Allegheny County Health Department's Air Quality Program for providing us with, and helping us understand, their historical temperature-inversion measurement data (used to validate the predictions).

Geospatial Dataset of GNSS Anomalies and Political Violence Events

Overview

The Geospatial Dataset of GNSS Anomalies and Political Violence Events is a collection of data that integrates aircraft flight information, GNSS (Global Navigation Satellite System) anomalies, and political violence events from the ACLED (Armed Conflict Location & Event Data Project) database.

Dataset Files

The dataset consists of three CSV files:

1. Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv
• Description: Contains all grids and dates that had aircraft traffic during 2023.
• Number of Records: 6,777,228
• Purpose: Provides a complete view of aircraft movements and associated data, including grids without any GNSS anomalies.
2. Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv
• Description: A filtered version of V1, including only the grids and dates where GNSS anomalies (jumps or gaps) were reported.
• Number of Records: 718,237
• Purpose: Focuses on areas and times with GNSS anomalies for targeted analysis.
3. Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv
• Description: Contains aggregated monthly data for each grid cell, combining GNSS anomalies and ACLED political violence events. Summarizes aircraft traffic, anomaly counts, and conflict activity at a monthly resolution.
• Number of Records: 25,770
• Purpose: Enables temporal trend analysis and spatial correlation studies between GNSS interference and political violence, using reduced data volume suitable for modeling and visualization.

Data Fields: Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv and Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv

1. grid_id
• Description: Unique identifier for a grid cell on Earth measuring 0.5 degrees latitude by 0.5 degrees longitude.
• Format: String combining latitude and longitude (e.g., -10.0_-36.0).
2. day
• Description: Date of the recorded data.
• Format: YYYY-MM-DD (e.g., 2023-03-28).
3. geometry
• Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.
• Format: POLYGON((longitude latitude, ...)) (e.g., POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))).
4. flights
• Description: Number of aircraft flights that passed through the grid on that day.
• Format: Integer (e.g., 28).
5. GPS_jumps
• Description: Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.
• Format: Integer (e.g., 1).
6. GPS_gaps
• Description: Number of reported GNSS "gap" anomalies, indicating gaps in aircraft routes, in the grid on that day.
• Format: Integer (e.g., 0).
7. gaps_density
• Description: Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.
• Format: Decimal (e.g., 0).
8. jumps_density
• Description: Density of GNSS jumps, calculated as the number of jumps divided by the number of flights.
• Format: Decimal (e.g., 0.035714286).
9. event_id_cnty
• Description: ACLED event ID corresponding to political violence events in the grid on that day.
• Format: String (e.g., BRA69267).
10. disorder_type
• Description: Type of disorder as classified by ACLED (e.g., "Political violence").
• Format: String.
11. event_type
• Description: General category of the event according to ACLED (e.g., "Violence against civilians").
• Format: String.
12. sub_event_type
• Description: Specific subtype of the event as per ACLED classification (e.g., "Attack").
• Format: String.
13. acled_count
• Description: Number of ACLED events in the grid on that day.
• Format: Integer (e.g., 1).
14. acled_flag
• Description: Indicator of ACLED event presence in the grid on that day (0 for no events, 1 for one or more events).
• Format: Integer (0 or 1).

Data Fields: Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv

The file contains monthly aggregated GNSS anomaly and ACLED event data per grid cell. The structure and meaning of each field are detailed below:

1. grid_id
• Description: Unique identifier for a grid cell on Earth measuring 0.5° latitude by 0.5° longitude.
• Format: String combining latitude and longitude (e.g., -0.5_-79.0).
2. year_month
• Description: Month and year of the aggregated data.
• Format: String in Mon-YY format (e.g., Jan-23).
3. geometry
• Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.
• Format: POLYGON((longitude latitude, ...))
  (e.g., POLYGON((-79.0 -0.5, -78.5 -0.5, -78.5 0.0, -79.0 0.0, -79.0 -0.5))).
4. flights
• Description: Total number of aircraft flights that passed through the grid cell during the month.
• Format: Integer (e.g., 1230).
5. GPS_jumps
• Description: Total number of GNSS "jump" anomalies (possible spoofing events) in the grid cell during the month.
• Format: Integer (e.g., 13).
6. GPS_gaps
• Description: Total number of GNSS "gap" anomalies, indicating interruptions in aircraft routes, during the month.
• Format: Integer (e.g., 0).
7. event_id_cnty
• Description: Semicolon-separated list of ACLED event IDs associated with the grid cell during the month.
• Format: String (e.g., ECU3151;ECU3158;ECU3150).
8. disorder_type
• Description: Semicolon-separated list of disorder types (e.g., "Political violence", "Demonstrations") reported by ACLED in that grid cell during the month.
• Format: String.
9. event_type
• Description: Semicolon-separated list of high-level ACLED event types (e.g., "Riots", "Protests").
• Format: String.
10. sub_event_type

• Description: Semicolon-separated list of detailed subtypes of ACLED events (e.g., "Mob violence", "Armed clash").
• Format: String.

1. acled_count

• Description: Total number of ACLED conflict events in the grid cell during the month.
• Format: Integer (e.g., 2).

1. acled_flag

• Description: Conflict presence indicator: 1 if any ACLED event occurred in the grid cell during the month, otherwise 0.
• Format: Integer (0 or 1).

1. gaps_density

• Description: Monthly density of GNSS gaps, calculated as GPS_gaps / flights.
• Format: Decimal (e.g., 0.0).

1. jumps_density

• Description: Monthly density of GNSS jumps, calculated as GPS_jumps / flights.
• Format: Decimal (e.g., 0.0106).

Data Sources

• GNSS Anomalies Data:
• Calculated from ADS-B (Automatic Dependent Surveillance-Broadcast) messages obtained via the OpenSky Network's Trino database.
• GNSS anomalies include "jumps" (potential spoofing incidents) and "gaps" (interruptions in aircraft route data).
• Political Violence Events Data:
• Sourced from the ACLED database, which provides detailed information on political violence and protest events worldwide.

Temporal and Spatial Coverage

• Temporal Coverage:
• From January 1, 2023, to December 31, 2023.
• Daily records provide temporal granularity for time-series analysis.
• Spatial Coverage:
• Global coverage with grid cells measuring 0.5 degrees latitude by 0.5 degrees longitude.
• Each grid cell represents an area on Earth's surface, facilitating spatial

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.

India Passenger Traffic: All Airports data was reported at 36,106,614.000 Person in Mar 2025. This records an increase from the previous number of 34,911,116.000 Person for Feb 2025. India Passenger Traffic: All Airports data is updated monthly, averaging 18,787,598.000 Person from Nov 2009 (Median) to Mar 2025, with 185 observations. The data reached an all-time high of 37,541,465.000 Person in Dec 2024 and a record low of 61,861.000 Person in Apr 2020. India Passenger Traffic: All Airports data remains active status in CEIC and is reported by Airports Authority of India. The data is categorized under Global Database’s India – Table IN.TA007: Airport Authority of India: Passenger Traffic. [COVID-19-IMPACT]

The volume of air-freight transport in the United Arab Emirates was forecast to decrease between 2024 and 2029 by in total 0.02 billion ton-kilometers. This overall decrease does not happen continuously, notably not in 2026 and 2027. The volume of air-freight transport is estimated to amount to 14 billion ton-kilometers in 2029. As defined by Worldbank, air freight refers to the summated volume of freight, express and diplomatic bags carried across the various flight stages (from takeoff to the next landing). The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the volume of air-freight transport in countries like Oman and Israel.

ARGOS PTTs were fitted to 99 flying-foxes, n=49 males, 50 females.We managed data from deployed PTTs in a standardized format in Movebank (http://www.movebank.org/node/2). Prior to analysis, we examined the datasets for inconsistencies, and fixes with ARGOS code Z, along with fixes with longitudes >140 or latitudes <0, were removed. We used daytime fixes (between 10 am and 4 pm) to assign animals to a “roost site” (as mainland Australian flying-foxes do not forage during the day). If high resolution (ARGOS location code 3) daytime fixes occurred within 3.5 km of a “known colony”, we assumed animals were roosting at that site. Where accurate daytime fixes were more than 3.5 km from a known roost location, we manually assigned animals to a new “roost site” located at the center of the cluster of fixes. If multiple tracked individuals roosted at the same location, this new roost site was confidently considered to be a previously unidentified ‘colony’ of flying-foxes.

Data cons...

Original provider: Greenland Institute of Natural Resources

Dataset credits: Greenland Institute of Natural Resources

Abstract: The study of long-distance migration provides insights into the habits and performance of organisms at the limit of their physical abilities. The Arctic tern Sterna paradisaea is the epitome of such behavior; despite its small size (<125 g), banding recoveries and at-sea surveys suggest that its annual migration from boreal and high Arctic breeding grounds to the Southern Ocean may be the longest seasonal movement of any animal. Our tracking of 11 Arctic terns fitted with miniature (1.4 g) geolocators revealed that these birds do indeed travel huge distances (more than 80,000 km annually for some individuals). As well as confirming the location of the main wintering region, we also identified a previously unknown oceanic stopover area in the North Atlantic used by birds from at least two breeding populations (from Greenland and Iceland). Although birds from the same colony took one of two alternative southbound migration routes following the African or South American coast, all returned on a broadly similar, sigmoidal trajectory, crossing from east to west in the Atlantic in the region of the equatorial Intertropical Convergence Zone. Arctic terns clearly target regions of high marine productivity both as stopover and wintering areas, and exploit prevailing global wind systems to reduce flight costs on long-distance commutes.

Purpose: The Arctic tern is known to make the longest annual migration in the animal kingdom. During its breeding season, it is found far to the north where summer days are long, and it winters far south in the southern hemisphere, where the days are longest during November to February. This means that the Arctic tern probably experiences more sun light during a calendar year than any other creature on Earth. The long-distance travel of the Arctic tern is well-known both amongst researchers and in the broader public. Now, for the first time, technological advances allow us to follow the Arctic tern on its immense journey, practically from pole to pole.

Supplemental information: Four erroneous points were removed from the original dataset: ARTE_410, 9/17/2007 noon; ARTE_370, 9/13/2007 noon; ARTE_373, 9/15/2007 noon and 9/16/2007 noon. Sand Island (74.263 degrees N, 20.160 degrees W), northeast Greenland, is the breeding colony for these Arctic terns and was placed on the map (red-orange square). Sand Island can be used as the beginning and end of all tracks, but since exact dates of the starting and ending of the migration were not available (high-Arctic zone = continuous day light during summer = poor positions when using geolocators), the tracklines for each animal were not mapped to and from the breeding colony.

Realtime micro-scale weather simulations were performed to support UAV (Uncrewed Aerial Vehicle) flights during the ISARRA Lower Atmospheric Process Studies at Elevation a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field deployment. These simulations were performed by driving a nested grid configuration of the Weather Research and Forecasting model with its innermost mesh being run at 111 m grid spacing. The innermost grid was nested within a grid with 1 km grid spacing. The outermost grid being driven using operational forecast models data as described below. While the MYNN2 PBL scheme is used to parameterize turbulence in the 1 km grid, the PBL scheme is turned off within the 111 m grid, thus, allowing large-scale turbulent eddies to be resolved by WRF primitive equations. Details of the model configuration and data formats are given in Pinto et al. (2021).

LAPSE-RATE took place in the San Luis Valley of Colorado during July of 2018. Goals of LAPSE-RATE were to sample the finescale evolution of the boundary layer and associated sub-mesoscale flows across a sub-alpine desert valley using a combination of surface-based instrumentation and in situ data collected using numerous, low-flying small UAVs. The realtime simulations were produced twice per day in order to support mission planning and UAVs flight operations. The simulation used for next-day planning was run using forcing data from NCEP's Global Forecast System (GFS) while the simulation available each morning of the experiment to support in flight operations was run using data from the NCEP High Resolution Rapid Refresh (HRRR), Version 3. Both simulations were valid between 04:00 and 16:00 MDT. The dataset consists of two sets of files: 3D grids and high temporal resolution time series and profiles for a select group of grid points. The 3D grids consist of all relevant basic state parameters (P, T, U, RH) and diagnostics (e.g., sub-grid scale TKE, ceiling height, visibility) that have been interpolated to flight levels AGL using the Unified Post-Processor (UPP). The UPP was used to de-stagger the mass and wind fields, interpolate forecast data to flight levels AGL and to compute diagnostics such as visibility, ceiling height, and radar reflectivity. Point data were stored for select grid points coincident with 3 fixed observation sites set up during LAPSE-RATE (i.e., Saguache, Moffat and Leach Airfield). The 3D grid files are stored every 10 minutes, while grid point data have a time resolution of 0.666 and 6 seconds for the 111 m grid spacing domain and 1 km grid spacing domain, respectively.

; Please see the README files for more details describing the dataset.

This publication provides digital flight line data for a high-resolution horizontal magnetic gradient and radiometric survey over an area of southeast Missouri and western Illinois. The survey represents the first airborne geophysical survey conducted as part of the U.S. Geological Survey (USGS) Earth Mapping Resource Initiative (Earth MRI) effort (Day, 2019). Earth MRI is a cooperative effort between the USGS, the Association of American State Geologists, and other Federal, State, and private sector organizations to improve our knowledge of the geologic framework of the United States. Data for this survey were collected by Terraquest, Ltd. under contract with the USGS using a fixed wing aircraft with magnetometers mounted in the tail stinger and each wing tip pod and a fully calibrated gamma ray spectrometer. The survey operated out of the Farmington, Missouri airport from December of 2018 to May of 2019. The survey covers a 146-kilometer x 154-kilometer area centered on the town of Ironton, Missouri. Data were collected along north-south flight lines spaced 300 meters (m) apart with east-west tie lines flown every 3000 m. A mean terrain clearance of 117 m was maintained except where safety dictated a higher elevation. A total of 68,375-line kilometers (km) of data were collected. Files that are available in this publication include flight line data for the magnetic gradient survey, flight line data for the radiometric survey and a report describing the survey parameters, field operations, quality control and data reduction procedures. A zip file is provided that contains the contractor's deliverable products that includes Geosoft databases and grids for the magnetic and radiometric survey and the report describing the survey and data reduction. The 2018-2019 survey was designed to augment and connect two previous USGS airborne geophysical surveys. Adjacent surveys include a magnetic and gravity gradiometry helicopter survey flown in 2014 (McCafferty, 2016a) centered on the Pea Ridge iron mine and a magnetic and radiometric survey flown in 2016 and centered on Ironton, Missouri (McCafferty, 2016b).