This data set contains 5 minute averages for the energetic particle detector rate data obtained from the LEMMS telescope during the time the detector was operated during the Venus encounter.

This data is provided by the Korea Expressway Corporation to check the toll fees between the departure and arrival points of the expressway. The fees for each route are categorized by vehicle type, and include vehicle fees for types 1 to 6. This data allows users to check the fee information for a specific section in advance, which is useful for establishing operation plans and calculating expenses. It can also be used by transportation companies and general drivers when calculating fares, and contributes to increasing the transparency and predictability of the toll system. This data can be used to promote economic use of road infrastructure and to establish highway operation policies and respond to civil complaints. It may differ from real-time information, and is provided based on fixed-rate standard data.

This is a list search service provided by the public data portal. Provides metadata and aggregate figures for keywords searched on public data portals.

This data set includes full resolution electric and magnetic wave spectra from the Galileo plasma wave receiver recorded during Jupiter orbital operations. In addition waveform survey data (uncalibrated) and all instrument housekeeping data are included. The parameters provided for the electric field spectral data are uncalibrated data numbers. Software and calibration tables provided as part of this data set allow for fully calibrated data for the electric field measurements in raw data numbers, voltage at the antenna inputs (V), electric field (V/m), electric field spectral density (V2/m2/Hz), or power flux (W/m**2/Hz). The sources of these data are the High Frequency Receiver, Sweep Frequency Receiver, and Spectrum Analyzer which make up the Low Rate Science portion of the PWS.

This data set includes wideband waveform measurements from the Galileo plasma wave receiver obtained during Jupiter orbital operations. These data were obtained during selected observation periods near perijove, satellite encounters and other select times. These measurements are electric waveforms obtained by rapidly sampling the potential at the input to the receiver from the electric dipole antenna. The sample rates are 201,600/s, 25,200/s, or 3,150/s taken through bandpass filters of 80, 10, or 1 kHz, respectively.

finbarr/rlvr-code-data-go-code-edit dataset hosted on Hugging Face and contributed by the HF Datasets community

Large go-around, also referred to as missed approach, data set. The data set is in support of the paper presented at the OpenSky Symposium on November the 10th.

If you use this data for a scientific publication, please consider citing our paper.

The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from OpenSky Network's historical data base for the year 2019. The published data set contains multiple files:

go_arounds_minimal.csv.gz

Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight

The last two columns, n_approaches and n_rwy_approached, are useful to filter out training and calibration flight. These have usually a large number of n_approaches, so an easy way to exclude them is to filter by n_approaches > 2.

go_arounds_augmented.csv.gz

Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight


    registration
    string
    Aircraft registration


    typecode
    string
    Aircraft ICAO typecode


    icaoaircrafttype
    string
    ICAO aircraft type


    wtc
    string
    ICAO wake turbulence category


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection

string

    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometre


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)


    operator_country
    string
    ISO Alpha-3 country code of the operator


    operator_region
    string
    Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)


    wind_speed_knts
    integer
    METAR, surface wind speed in knots


    wind_dir_deg
    integer
    METAR, surface wind direction in degrees


    wind_gust_knts
    integer
    METAR, surface wind gust speed in knots


    visibility_m
    float
    METAR, visibility in m


    temperature_deg
    integer
    METAR, temperature in degrees Celsius


    press_sea_level_p
    float
    METAR, sea level pressure in hPa


    press_p
    float
    METAR, QNH in hPA


    weather_intensity
    list
    METAR, list of present weather codes: qualifier - intensity


    weather_precipitation
    list
    METAR, list of present weather codes: weather phenomena - precipitation


    weather_desc
    list
    METAR, list of present weather codes: qualifier - descriptor


    weather_obscuration
    list
    METAR, list of present weather codes: weather phenomena - obscuration


    weather_other
    list
    METAR, list of present weather codes: weather phenomena - other

This data set is augmented with data from various public data sources. Aircraft related data is mostly from the OpenSky Network's aircraft data base, the METAR information is from the Iowa State University, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the WMO's Aerodrom Reports and Forecasts handbook.

go_arounds_agg.csv.gz

Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:

    Column name
    Type
    Description




    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    n_landings
    integer
    Total number of landings observed on this runway in 2019


    ga_rate
    float
    Go-around rate, per 1000 landings


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection
    string
    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometres


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)

This aggregated data set is used in the paper for the generalized linear regression model.

Downloading the trajectories

Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the Traffic library for easy access to the database:

import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic

load minimum data set

df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"])

select London City Airport, go-arounds, and 2019-01-04

airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc )

df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)")

iterate over flights and pull the data from OpenSky Network

flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time

# fetch the data from OpenSky Network
flights.append(
  opensky.history(
    start=start_time.strftime("%Y-%m-%d %H:%M:%S"),
    stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"),
    callsign=row["callsign"],
    return_flight=True,
  )
)

The flights can be converted into a Traffic object

Traffic.from_flights(flights)

Additional files

Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:

validation_table.xlsx: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.

validation_sample.zip: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The GOES-R PLT Field Campaign Cloud Radar System (CRS) dataset provides high-resolution profiles of reflectivity and Doppler velocity at aircraft nadir along the flight track. The CRS was flown aboard a NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT field campaign took place from March 21 to May 17, 2017 in support of post-launch product validation of the Advanced Baseline Image (ABI) and the Geostationary Lightning Mapper (GLM) aboard the GOES-R, now GOES-16, satellite. The CRS data files are available in netCDF-3 format with browse imagery available in PNG format.

It provides the number of downloads and API utilization requests by year (2011-2023) of file data registered in the public data portal, and is useful for analyzing the trend of increase in public data utilization. The file format is provided in CSV format, and the meta items are statistical year, registration agency, list name, data name, file downloads, and API utilization requests. You can download file data from the public data portal without logging in, and to utilize the open API, you must register as a public data portal member and log in to apply for utilization.

This data collection consists of archived GOES-R Series Space Environment In-Situ Suite (SEISS) Level 0 data from the operational GOES-East and GOES-West satellites. The Geostationary Operational Environmental Satellite-R (GOES-R) series provides continuity of the GOES mission through 2035 and improvements in geostationary satellite observational data. GOES-16, the first GOES-R satellite, began operating as GOES-East on December 18, 2017, and GOES-18 began operating on March 1, 2022 replacing GOES-17 as GOES West in early January 2023. GOES-19 began operational service April 7, 2024, replacing GOES-16.  SEISS is comprised of four sensors that monitor proton, electron, and heavy ion fluxes in the magnetosphere: the Energetic Heavy Ion Sensor (EHIS), the Magnetospheric Particle Sensors - High and Low (MPS-HI and MPS-LO), and the Solar and Galactic Proton Sensor (SGPS). The SEISS Level 0 data are composed of Consultative Committee for Space Data Systems (CCSDS) packets containing the science, housekeeping, engineering, and diagnostic telemetry data downlinked from the instrument. The Level 0 data files also contain orbit and attitude/angular rate packets generated by the GOES spacecraft. Each CCSDS packet contains a unique Application Process Identifier (APID) in the primary header that identifies the specific type of packet, and is used to support interpretation of its contents. Users may refer to the GOES-R Series Product Definition and Users’ Guide (PUG) Volumes 1 (Main) and 2 (Level 0 Products) for Level 0 data documentation. Related instrument calibration data and Level 1b processing information are archived and available for order at the NOAA CLASS website. The SEISS Level 0 data files are delivered in a netCDF-4 file format, however, the constituent CCSDS packets are stored in a byte array making the data opaque for standard netCDF reader applications. The SEISS Level 0 data files are packaged in daily tar files (data bundles) by satellite for the archive. Recently ingested archive tar files are available for 14 days on a CLASS-hosted anonymous FTP server for users to download. Data archived on tape are available to users by special order through NCEI customer service.

Non-traditional data signals from social media and employment platforms for GO stock analysis

This is the full disk L1b data in the visible, near infrared, and infrared spectral region from the Advanced Baseline Imager (ABI), onboard NOAA's GOES-19 satellite.

Context

This is the national traffic data measured at every expressway gate operated and updated in public data portal by Korea Expressway Corporation (KEC). The aggregated data has been updated a matrix of daily regional Traffic Control System (TCS) by every month as of November 2020.

Content

To respect originality, this kaggle dataset has consisted of Korean labels which encoding 'euc-kr' and you may want to apply 'unicode_escape' option for encoding, but eventually the matrix would be recommendable to convert Korean to English and add new columns for sum when your work need to merge with other data such as the edit version formatting as below sample table. | date | region | arrival_Capital | arrival_Gangwon | arrival_DaejeonChungnam | arrival_GwangjuJeonnam | arrival_DaeguGyeongbuk | arrival_BusanGyeongnam | arrival_Jeonbuk | arrival_Chungbuk | sum_bydeparture | sum_byarrival | | --- | --- | | 20200101 | Capital | | | | | | | | | sum(c2:j2) | sum(c2:c9) | | 20200101 | Gangwon | | | | | | | | | sum(c3:j3) | sum(d2:d9) | | 20200101 | DaejeonChungnam | | | | | | | | | sum(c4:j4) | sum(e2:e9) | | 20200101 | GwangjuJeonnam | | | | | | | | | sum(c5:j5) | sum(f2:f9) | | 20200101 | DaeguGyeongbuk | | | | | | | | | sum(c6:j6) | sum(g2:g9) | | 20200101 | BusanGyeongnam | | | | | | | | | sum(c7:j7) | sum(h2:h9) | | 20200101 | Jeonbuk | | | | | | | | | sum(c8:j8) | sum(i2:i9) | | 20200101 | Chungbuk | | | | | | | | | sum(c9:j9) | sum(j2:j9) |

You may refer to the translation table by author as follow: | Korean | English | | --- | --- | | 집계일자 | date | | 출발권역명 | region | | 수도권 | Capital | | 강원 | Gangwon | | 대전충남 | DaejeonChungnam | | 광주전남 | GwangjuJeonnam | | 대구경북 | DaeguGyeongbuk | | 부산경남 | BusanGyeongnam | | 전북 | Jeonbuk | | 충북 | Chungbuk |

Acknowledgements

Public Data Portal (https://www.data.go.kr/) has linked to the KEC Portal (https://www.ex.co.kr/eng/) [Online]. Available at: http://data.ex.co.kr/portal/fdwn/view?type=TCS&num=C5&requestfrom=dataset# [Accessed 6 January 2021]

Inspiration

This study aimed at data exploration how the government policy had influenced on human movements and presents how traffic volume in transportation had changed at the South Korea county level.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This data set contains the 4km resolution all channel data over the southeastern United States from the GOES-13 satellite during the VORTEX-SE_2017 field campaign. The GOES-13 channels are visible (channel 1), near-IR (2), water vapor (3), thermal IR (4), and 13 micron (6). The data are in gzipped McIDAS AREA format files. This is a large data set, consisting of approximately 6 GB daily. Please consider the size of the data set when making your order. You can order 9 days in a single order.

This dataset contains 1-km resolution GOES-14 visible channel satellite data collected during the PECAN project. GOES-14 was brought out of storage during the first part of PECAN (1-12 June) for Super Rapid Scan Operations for GOES-R. These data are in McIDAS AREA file format and were provided by CIRA at Colorado State University. During this period SRSO would be called daily and the regional focus may vary from day to day depending on the weather. During SRSOR operations data are available every minute. NCAR/EOL collected and archived these data. During PECAN, GOES-14 was stationed at 105W. All files are combined into hourly tar files to minimize size and download time. Each filename contains the UTC date/time stamp at which the sector was broadcast.

Korea Southern Power Co., Ltd. _ Korea Southern Power Company provides power generation performance information by power plant/unit/period as data for power generation performance inquiry.

This data set provides energetic (MeV) ion count rates and events measured by the Heavy Ion Counter (HIC) instrument on the Galileo spacecraft. These data are derived from high time resolution raw data that were recorded to tape and then played back later in the orbit. There are two basic types of data files associated with the full-rate reduced data: Detector Count Rates and Events (Pulse Heights).