To date, GPS tracking data for minibus taxis has only been captured at a sampling frequency of once per minute. This is the first GPS tracking data captured on a per-second (1 Hz) basis. Minibus taxi paratransit vehicles in South Africa are notorious for their aggressive driving behaviour characterised by rapid acceleration/deceleration events, which can have a large effect on vehicle energy consumption. Infrequent sampling cannot capture these micro-mobility patterns, thus missing out on their effect on vehicle energy consumption (kWh/km). We hypothesised that to construct high fidelity estimates of vehicle energy consumption, higher resolution data that captures several samples per movement would be needed. Estimating the energy consumption of an electric equivalent (EV) to an internal combustion engine (ICE) vehicle is requisite for stakeholders to plan an effective transition to an EV fleet. Energy consumption was calculated following the kinetic model outline in "The bumpy ride to electrification: High fidelity energy consumption estimates for minibus taxi paratransit vehicles in South Africa".

Six tracking devices were used to record GPS data to an SD card at a frequency of 1Hz. The six recording devices are based on the Arduino platform and powered from alkaline battery packs. The device can therefore operate independently of any other device during tests. The acquired data is separately processed after the completion of data recording. Data captured is initiated with the press of a button, and terminated once the vehicle reached the destination. Each recorded trip creates an isolated file. This allows for different routes to be separately investigated and compared to other recordings made on the same route.

There are 62 raw trip files, all found in the attached 'raw data' folder under the corresponding route and time of day in which they were captured. The raw data includes date, time, velocity, elevation, latitude, longitude, heading, number of satellites connected, and signal quality. Data was recorded on three routes, in both directions, for a total of six distinct routes. Each route had trips recorded in the morning (before 11:30AM) , afternoon (11:30AM-4PM) and evening (after 4PM).

The processed data is available in the 'Processed Data' folder. In addition to the raw data, these processed data files include the displacement between observations, calculated using Geopy's geodesic package, and the estimated energy provided by the vehicle's battery for propulsion, braking, and offload work. The python code for the kinetic model can be found in the attached GitHub link https://github.com/ChullEPG/Bumpy-Ride.

Future research can use this data to develop standard driving cycles for paratransit vehicles, and to improve the validity of micro-traffic simulators that are used to simulate per-second paratransit vehicle drive cycles between minutely waypoints.

This dataset production would not be possible without the work of Morales Ferre, Ruben, Lohan, Elena Simona, & De la Fuente, Alberto. (2019). Image datasets for jammer classification in GNSS [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3783969 .

Raw IQ Dataset –

Contains 1000 training samples and 250 testing samples for DME, narrowband, single AM, single chirp, single FM jamming signals and no jamming signal present.

To generate new raw files –

Download and extract ‘Jamming_Classifier.zip’ from https://zenodo.org/record/3783969

Place ‘signal_generation.m’ into the ‘Jamming_Classifier’ folder.

When you run signal generation you can choose whether to create training or test data and the number of samples. They will be saved in the folders Image_training_database and Image_testing_database.

Since the early 1990s, the GFZ has operated a global GNSS station network with currently about 70 stations for precise satellite clock & orbit determination, realization of the terrestrial reference frame, radio occultation measurements or studies on crust dynamics. A subset of these stations contributes also to the tracking networks of the International GNSS Service (IGS) and the EUREF Permanent GNSS Network (EPN). Other stations contribute to GFZ observatories (IPOC, DESERVE, TERENO), to the GPS Atmosphere Sounding Project (GASP), to WMO Global Climate Observing System Reference Upper-Air Network (GRUAN) or to other external cooperations. We offer data of 51 GFZ GNSS stations under this DOI. Nearly all stations are equipped with Javad or Septentrio hardware. Depending on the location and hardware they provide data of GPS (L1 / L2 / L5), GLONASS (L1 / L2 / L3), Galileo (E1 / E5a / E5b / E6), BeiDou (B1 / B2 / B3), QZSS (L1 / L2 / L5 / L6), NAVIC (L5), and SBAS (L1 / L5). The GNSS Station Nework Site (https://isdc.gfz-potsdam.de/gnss-station-network/) provides direct access to the 1s and 30s sampled RINEX data (near real-time, file based) and to real-time streams. Real-time streams are available for stations contributing to the IGS. Raw data GNSS binary raw observations are available upon request. All GFZ Stations follow the site guidelines of the International GNSS Service (https://kb.igs.org/hc/en-us/articles/202011433-Current-IGS-Site-Guidelines) Station specific metadata can be found at our metadata portal SEMISYS. An overview of the list of stations with direct links to the station specific metadata in semisys is available via ftp://datapub.gfz-potsdam.de/download/10.5880.GFZ.1.1.2020.001/2020-001_Ramatschi-et-al_List-of-GFZ-GNSS-Stations-with-links-to-SEMISYS.pdf.

NOTE: The data was recorded erroneously and will be updated soon.

This dataset contains high quality GNSS and jammer raw data generated during the in-lab validation activities of jamming detection and localization performed in the frame of the GATEMAN project. These files are grouped for each type of validation scenario defined. The scenarios are combinations of three different GNSS and three different jamming signals.

Content:

The archive contains a folder for each validation scenario. Each folder contains two files. One contains the GNSS data and the second file contains jamming data. A function to read the data with GNU Octave/Matlab is also included.

├── GALE1+AMtone
│ ├── AMtone@40MSps.bin
│ └── GALE1@40MSps.bin
├── GALE1+Chirp10MHz
│ ├── Chirp10MHz@40MSps.bin
│ └── GALE1@40MSps.bin
├── GALE1+Chirp20MHz
│ ├── Chirp20MHz@40MSps.bin
│ └── GALE1@40MSps.bin
├── GPSL1+AMtone
│ ├── AMtone@40MSps.bin
│ └── GPSL1@40MSps.bin
├── GPSL1+Chirp10MHz
│ ├── Chirp10MHz@40MSps.bin
│ └── GPSL1@40MSps.bin
├── GPSL1+Chirp20MHz
│ ├── Chirp20MHz@40MSps.bin
│ └── GPSL1@40MSps.bin
├── GPSL5+AMtone
│ ├── AMtone@40MSps.bin
│ └── GPSL5@40MSps.bin
├── GPSL5+Chirp10MHz
│ ├── Chirp10MHz@40MSps.bin
│ └── GPSL5@40MSps.bin
├── GPSL5+Chirp20MHz
│ ├── Chirp20MHz@40MSps.bin
│ └── GPSL5@40MSps.bin
└── readBinData.m

Data format:

Each file stores the received baseband samples as an array of complex, 16-bit signed integer data (range -32,768 to 32,767) of the corresponding signal. The data is stored in Big-endian, network order format, i.e. the most-significant byte occupies the lowest memory address. The real and imaginary components of the data correspond to the in-phase (I) and quadrature-phase (Q) data, respectively. I and Q-samples are interleaved [I, Q, I, Q, ...] in the array.

Data:

The recorded data of each validation scenario consists of two files, one containing the GNSS signal (L1, L5 or E1 band), the other one containing the jamming signal (tone, chirp 10 & 20 MHz bandwidth). The RF GNSS signal was generated with a GNSS signal generator, it contains only a single spreading sequence. The RF jamming signal was generated with a Vector Signal Transceiver. Both signals of a scenario were recorded synchronously with an USRP at an I\Q rate of 40 MS/s.

GNSS signals:

• Startime: 30.05.2018 at 12:00 (approximately)
• Latitude: N 50 deg, 50.4864 min
• Longitude: E 4 deg, 21.3499 min
• Altitude: 50m
• Trajectory: Static
• Ephemeris file: brdc0150.18.n

GNSS band PRN Elevation (deg) Azimuth (deg) Power (dBm)
GPS L1 G7 68 85 -70
GPS L5 G23 70 172 -70
Galileo E1 E15 76 229 -68.5

Jamming signals:

• The tone is simple a sinosoidal waveform in the centre of the GNSS bands.
• The chirps are linear frequency modulated waveforms (sawtooth) in the centre of the GNSS bands with sweep range of 10 MHz and 20 MHz and sweep period of 8.64 µs.

The data uploaded here are a truncated samples. Longer samples of data are available upon request.

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market. Veraset Movement (Mobile Device GPS / Foot Traffic Data) offers unparalleled insights into footfall traffic patterns across North America.

Covering the United States, Canada and Mexico, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's Movement data helps in shaping strategy and making data-driven decisions.

Veraset’s North American Movement Panel: - United States: 768M Devices, 70B+ Pings - Canada: 55M+ Devices, 9B+ Pings - Mexico: 125M+ Devices, 14B+ Pings - MAU/Devices and Monthly Pings

Uses for Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

This data repository contains sample datasets of raw DInSAR time series (NSBAS_PARAMS.h5), raw, interpolated GNSS time series maps (GPS_East/North/Up.h5) , errors associated with the GNSS data (GPS_East/North/Up_sigma.h5), and integrated DInSAR + GNSS time series (fused.h5). Details about the data can be read about in the following publication: [Corsa, B. "Integration of DInSAR Time Series and GNSS data for Continuous Volcanic Deformation Monitoring and Eruption Early Warning Applications" Remote Sens. 2022, 14(3), 784; https://doi.org/10.3390/rs14030784]. The raw DInSAR time series spans 245 dates between 2015-11-11 to 2021-04-13 over the Big Island of Hawaii. The current raw GPS data and fused time series used 22 data points between those same dates.

Monitoring the impact of sea storms on coastal areas is fundamental to study beach evolution and the vulnerability of low-lying coasts to erosion and flooding. Modelling wave runup on a beach is possible, but it requires accurate topographic data and model tuning, that can be done comparing observed and modeled runup. In this study we collected aerial photos using an Unmanned Aerial Vehicle after two different swells on the same study area. We merged the point cloud obtained with photogrammetry with multibeam data, in order to obtain a complete beach topography. Then, on each set of rectified and georeferenced UAV orthophotos, we identified the maximum wave runup for both events recognizing the wet area left by the waves. We then used our topography and numerical models to simulate the wave runup and compare the model results to observed values during the two events. Our results highlight the potential of the methodology presented, which integrates UAV platforms, photogrammetry and Geographic Information Systems to provide faster and cheaper information on beach topography and geomorphology compared with traditional techniques without losing in accuracy. We use the results obtained from this technique as a topographic base for a model that calculates runup for the two swells. The observed and modeled runups are consistent, and open new directions for future research.

Leverage the most reliable and compliant global mobility and foot traffic dataset on the market. Veraset Movement (Mobile Device GPS Mobility Data) offers unparalleled real-time insights into footfall traffic patterns globally.

Covering 200+ countries, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement.

Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's mobile location data helps in shaping strategy and making data-driven decisions.

Veraset Global Movement panel (mobile location) includes: - 1.8+ Billion Devices Monthly - 200 Billion Pings Monthly Device and Ping counts by Country are available upon request

Common Use Cases of Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Please visit: https://www.veraset.com/docs/movement for more information and schemas

GNSS Dataset (with Interference and Spoofing) consists of three parts: Part I (Raw data of 2023 12 to 20, Sept, 2023, clean data), Part II (Raw data of 2023 21 to 30, Sept, 2023, clean data) and Part III (Processed data 12 to 30, Sept, 2023; data collected with spoofing and jamming on 21 Dec, 2023; Scripts and Material) .

The data were recorded by a GNSS receiver installed on the 5th floor of the Science Hall of Yunnan University. HackRF One emits spoofing signals and the commercial jammer emits suppression jamming to attack the receiver on 21 Dec, 2023. The provided datasets are interesting for the GNSS monitoring, GNSS security, anti-jamming and anti-spoofing mechanisms based scientific communities.

These data provide the most comprehensive information available on the spatial and temporal patterns of GNSS satellites, observation and receiver parameters, referring to five constellations (GPS, Campass, Galileo, GLONASS and QZSS) and eight signal bands (L1C/A, L2C, E1, E5b, B1, B2, L1, L2). The dataset provides observations of the receiver three scenarios: normal state, affected by commercial jammers, and spoofed by SDR HackRF One. These observations include more details such as carrier-to-noise density ratio (C/N0), signal spectrum, Doppler shift, pseudorange, carrier phase, satellite health indicator, real-time position data and dilution of precision (DOP), etc. These data can be used to analyze navigation satellite operation rules, satellite covering time above the receiver, satellite overhead time prediction and GNSS monitoring system construction, provides a large amount of fine-grained data that can be used as an example to study safeguards at civil aviation airports, monitoring for harmful radio interference.

Global Navigation Satellite System (GNSS) Station unavco/gnss/GPS Soil Moisture/AMES/6052/L0/00:00:01 Name: ames Processing Level: L0 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2012-04-04T23:00:00 data_stop_time:2016-04-05T20:41:12 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Global Navigation Satellite System (GNSS) Station unavco/gnss/PBO/P265/1018/L1/00:00:15 Name: PutahCreekCN2005 Processing Level: L1 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2005-08-26T23:33:15 data_stop_time:2018-03-22T05:59:45 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Level 1A Data Products are the result of a non-destructive processing applied to the Level 0 data at NASA/JPL. The sensor calibration factors are applied in order to convert the binary encoded measurements to engineering units. Where necessary, time tag integer second ambiguity is resolved and data are time tagged to the respective satellite receiver clock time. Editing and quality control flags are added, and the data is reformatted for further processing. The Level 1A data are reversible to Level 0, except for the bad data packets. This level also includes the ancillary data products needed for processing to the next data level. The Level 1B Data Products are the result of a possibly destructive, or irreversible, processing applied to both the Level 1A and Level 0 data at NASA/JPL. The data are correctly time-tagged, and data sample rate is reduced from the higher rates of the previous levels. Collectively, the processing from Level 0 to Level 1B is called the Level 1 Processing. This level also includes the ancillary data products generated during this processing, and the additional data needed for further processing. The Level 2 data products include the static and time-variable (monthly) gravity field and related data products derived from the application of Level 2 processing at GFZ, UTCSR and JPL to the previous level data products. This level also includes the ancillary data products such as GFZ's Level 1B short-term atmosphere and ocean de-aliasing product (AOD1B) generated during this processing. GRACE-A and GRACE-B Level 1B Data Product: Satellite clock solution [GA-OG-1B-CLKDAT, GB-OG-1B-CLKDAT, GRACE CLKDAT]: Offset of the satellite receiver clock relative to GPS time, obtained by linear fit to raw on-board clock offset estimates GPS flight data [GA-OG-1B-GPSDAT, GB-OG-1B-GPSDAT, GRACE GPSDAT]: Preprocessed and calibrated GPS code and phase tracking data edited and decimated from instrument high-rate (10 s (code) or 1 s (phase)) to low-rate (10 s) samples for science use (1 file per day, level-1 format) Accelerometer Housekeeping data [GA-OG-1B-ACCHKP, GB-OG-1B-ACCHKP, GRACE ACCHKP]: Accelerometer proof-mass bias voltages, capacitive sensor outputs, instrument control unit (ICU) and sensor unit (SU) temperatures, reference voltages, primary and secondary power supply voltages (1 file per day, Level 1 format) Accelerometer data [GA-OG-1B-ACCDAT, GB-OG-1B-ACCDAT, GRACE ACCDAT]: Preprocessed and calibrated Level 1B accelerometer data edited and decimated from instrument high-rate (0.1 s) to low-rate (1s) samples for science use (1 file per day, Level 1 format) Intermediate clock solution [GA-OG-1B-INTCLK, GB-OG-1B-INTCLK, GRACE INTCLK]: derived with GIPSY POD software (300 s sample rate) (1 file per day, GIPSY format) Instrument processing unit (IPU) Housekeeping data [GA-OG-1B-IPUHKP, GB-OG-1B-IPUHKP, GRACE IPUHKP]: edited and decimated from high-rate (TBD s) to low-rate (TBD s) samples for science use (1 file per day, Level 1 format) Spacecraft Mass Housekeeping data [GA-OG-1B-MASDAT, GB-OG-1B-MASDAT, GRACE MASDAT]: Level 1B Data as a function of time GPS navigation solution data [GA-OG-1B-NAVSOL, GB-OG-1B-NAVSOL, GRACE NAVSOL]: edited and decimated from instrument high-rate (60 s) to low-rate (30 s) samples for science use (1 file per day, Level 1 format) OBDH time mapping to GPS time Housekeeping data [GA-OG-1B-OBDHTM, GB-OG-1B-OBDHTM, GRACE OBDHTM]: On-board data handling (OBDH) time mapping data (OBDH time to receiver time Star camera data [GA-OG-1B-SCAATT, GB-OG-1B-SCAATT, GRACE SCAATT]: Preprocessed and calibrated star camera quaternion data edited and decimated from instrument high-rate (1 s) to low-rate (5 s) samples for science use (1 file per day, Level 1 format) Thruster activation Housekeeping data [GA-OG-1B-THRDAT, GB-OG-1B-THRDAT, GRACE THRDAT]: GN2 thruster data used for attitude (10 mN) and orbit (40 mN) control GN2 tank temperature and pressure Housekeeping data [GA-OG-1B-TNKDAT, GB-OG-1B-TNKDAT, GRACE TNKDAT]: GN2 tank temperature and pressure data Oscillator frequency data [GA-OG-1B-USODAT, GB-OG-1B-USODAT, GRACE USODAT]: derived from POD product GRACE-A and GRACE-B Combined Level 1B Data Product Preprocessed and calibrated k-band ranging data [GA-OG-1B-KBRDAT, GB-OG-1B-KBRDAT, GRACE KBRDAT]: range, range-rate and range-acceleration data edited and decimated from instrument high-rate (0.1 s) to low-rate (5 s) samples for science use (1 file per day, Level 1 format) Atmosphere and Ocean De-aliasing Product [GA-OG-1B-ATMOCN, GB-OG-1B-ATMOCN, GRACE ATMOCN]: GRACE Atmosphere and Ocean De-aliasing Product. GRACE Level-2 Data Product: GAC [GA-OG-_2-GAC, GB-OG-_2-GAC, GRACE GAC]: Combination of non-tidal atmosphere and ocean spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on Level 1 AOD1B product (1file per time span, Level 2 format) ...

Global Navigation Satellite System (GNSS) Station unavco/gnss/BARGEN/REP3/1307/L0/00:00:15 Name: Repository 3 Processing Level: L0 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2006-04-05T21:54:30 data_stop_time:2010-08-27T05:59:45 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Overview High-precision barometers (Paroscientific 6000-16B-IS) are combined with Nishiyama-Bedard Quad Disk pressure probes, measuring pressure (mb) at the surface, nominally 2 m above ground level. Data are sampled at 20 Hz for potential studies of turbulence. The sensors' high accuracy makes them useful for determining horizontal pressure gradients and their relation to wind ramp events, as well as the temporal variability of pressure associated with mountain wakes and waves. Note different ASCII file formats for Goldendale (z04) and Walla Walla (z09) sites. Data Details ASCII Format Field 1: DataloggerID Field 2: Year Field 3: Julian Day Field 4: Hour and Min (UTC) Field 5: Seconds Decimal (UTC) Field 6: GPS Lock Identifier (A = Locked Signal; V=Insufficient Satellite Coverage) Field 7: GPS Clock String (UTC) Field 8: Pressure (mb) Example of data file: 101,2016,243,000,0.04,0001A,2016/08/29 23:59:58.150,913.314500 101,2016,243,000,0.09,0001A,2016/08/29 23:59:58.200,913.315652 101,2016,243,000,0.14,0001A,2016/08/29 23:59:58.250,913.313351 101,2016,243,000,0.19,0001A,2016/08/29 23:59:58.300,913.315626 101,2016,243,000,0.24,0001A,2016/08/29 23:59:58.350,913.315255 101,2016,243,000,0.29,0001A,2016/08/29 23:59:58.400,913.315267 101,2016,243,000,0.34,0001A,2016/08/29 23:59:58.450,913.315430 101,2016,243,000,0.39,0001A,2016/08/29 23:59:58.500,913.312698 101,2016,243,000,0.44,0001A,2016/08/29 23:59:58.550,913.315139 101,2016,243,000,0.49,0001A,2016/08/29 23:59:58.600,913.314793 101,2016,243,000,0.54,0001A,2016/08/29 23:59:58.650,913.317083 101,2016,243,000,0.59,0001A,2016/08/29 23:59:58.700,913.316959 101,2016,243,000,0.64,0001A,2016/08/29 23:59:58.750,913.312730 101,2016,243,000,0.69,0001A,2016/08/29 23:59:58.800,913.315043 101,2016,243,000,0.74,0001A,2016/08/29 23:59:58.850,913.318476 101,2016,243,000,0.79,0001A,2016/08/29 23:59:58.900,913.312417 101,2016,243,000,0.84,0001A,2016/08/29 23:59:58.950,913.317606 101,2016,243,000,0.89,0001A,2016/08/29 23:59:59.000,913.316681 101,2016,243,000,0.94,0001A,2016/08/29 23:59:59.050,913.314978 101,2016,243,000,0.99,0001A,2016/08/29 23:59:59.100,913.318996 Goldendale (z04) and Walla Walla (z09) ASCII Format Field 1: GPS Clock String (UTC) Field 2: Pressure (mb) Example of data file: 2016/08/29 23:59:58.150,913.314500 2016/08/29 23:59:58.200,913.315652 2016/08/29 23:59:58.250,913.313351 2016/08/29 23:59:58.300,913.315626 2016/08/29 23:59:58.350,913.315255 2016/08/29 23:59:58.400,913.315267 2016/08/29 23:59:58.450,913.315430 2016/08/29 23:59:58.500,913.312698 2016/08/29 23:59:58.550,913.315139 2016/08/29 23:59:58.600,913.314793 2016/08/29 23:59:58.650,913.317083 2016/08/29 23:59:58.700,913.316959 2016/08/29 23:59:58.750,913.312730 2016/08/29 23:59:58.800,913.315043 2016/08/29 23:59:58.850,913.318476 2016/08/29 23:59:58.900,913.312417 2016/08/29 23:59:58.950,913.317606 2016/08/29 23:59:59.000,913.316681 2016/08/29 23:59:59.050,913.314978 2016/08/29 23:59:59.100,913.318996 Data Quality No special data quality control is needed. Uncertainty 0.0001% Resolution ±0.08 hPa Accuracy Stability better than 0.1 hPa per year.

This metadata record pertains to processed dual-frequency geodetic quality Global Positioning System (GPS) data from six sites deployed on the surface of the Totten Glacier between approximately late 2016 and early 2019. The data captures the three-dimensional motion/trajectory of the glacier over a period of approximately 2 years, with a temporal sampling rate of 1 coordinate estimate every 5 minutes. The raw GPS data is covered in a separate metadata record.
Site description:
• At each site, aluminium towers were erected on the glacier surface – these towers housed a GPS antenna connected to GPS receiver, batteries and solar panel. The approximate height of the GPS antenna was ~3 m above the snow surface at the time of deployment. The GPS receivers to logged data to all GPS satellites in view at a data rate of 1 observation every 15 seconds.
• The sites were revisited at the midpoint of the project during the 2017/18 austral summer field season. Given the expect large snow accumulation rate in the region of the deployment, the aluminium towers were extended to elevate the antenna ~3 m above the snow surface at that time.
• The GPS equipment used were Trimble NETR9 receivers and Trimble TRM57971.00 antennas.
Processing description:
• Dual frequency code and carrier phase GPS data from each site was processed using a kinematic Precise Point Positioning (kPPP) approach with the NASA/JPL Gipsy software package (v 6.3). Processing was undertaken by Christopher Watson (University of Tasmania).
• The processing approach followed the standard conventions in the geodetic community (IERS2010 conventions for tidal deformation of the solid Earth, VMF1 mapping function for the resolution of tropospheric zenith delay, JPL final orbits and clocks). Site coordinates were computed once every 5 minutes.
• Processed site trajectories are provided at a temporal resolution of 1 sample per 5 minutes. Coordinates are relative to the ITRF2014 terrestrial reference frame.
• The data archive with file name AAS_4287_GPS_txyz_llh_sigma is considered the first level of processing it contains the most basic output of antenna position for each epoch in time. These files provide Earth-Centred Earth-Fixed (ECEF) cartesian coordinates (XYZ) as well as latitude, longitude and ellipsoidal height coordinates (expressed on the GRS80 ellipsoid). The formal uncertainty from Gipsy v6.3 processing is provided for each epoch in the form of a 3D sigma (expressed in units mm). No outlier detection has been undertaken. The time standard used is UTC, and the temporal sampling is 1 coordinate estimate per 5 minutes. The change in antenna height at the mid-point of the data collection (to raise the tower given snow accumulation) has not been corrected for in any way.
• The data archive with file name AAS_4287_GPS_tllh_al_ac_pos_vel is considered the next level of post-processed data as it contains quantities derived from the GPS trajectories and appropriate correction of the antenna height change at the mid-point of the data collection. These files contain the site latitude, longitude and ellipsoidal height (expressed on the GRS80 ellipsoid), the along flow position and velocity, the across flow position and velocity. Outlier detection using a threshold of 25 mm on the 3D sigma (see previous file description) has been undertaken. To resolve the along and across flow coordinate transformation, coordinates were first transformed into a topocentric (north, east) system. The origin of this transformation is provided in the header of each data file. From the local topocentric system, an along and across flow system was derived using piecewise linear fitting of n and e components and temporal knots every 3 months. The temporal knots used in the piecewise linear fitting are provided in the header of each data file. The antenna height correction was estimated using a regression process – offsets and their uncertainty in north, east and up directions are provided in the header of each data file. Again, the time standard used is UTC, and the temporal sampling is 1 coordinate estimate per 5 minutes.

Six GPS sites were deployed in the 2016/17 austral summer season. The sites were revisited in the 2017/18 austral summer season and then retrieved in the 2018/19 austral summer field season. Dates for specific sites are below (yyyy/mm/dd):
Site TG01: 2016/11/22 – 2018/12/23
Site TG02: 2016/11/25 – 2019/01/09
Site TG03: 2016/12/03 – 2019/01/28
Site TG04: 2016/12/03 – 2018/12/24
Site TG05: 2016/11/25 – 2019/01/08
Site TG06: 2016/12/03 – 2018/12/24
Note that data has temporal gaps due to a) lack of solar power over winter and b) equipment failure at TG03.

Overview High-precision barometers (Paroscientific 6000-16B-IS) are combined with Nishiyama-Bedard Quad Disk pressure probes, measuring pressure (mb) at the surface, nominally 2 m above ground level. Data are sampled at 20 Hz for potential studies of turbulence. The sensors' high accuracy makes them useful for determining horizontal pressure gradients and their relation to wind ramp events, as well as the temporal variability of pressure associated with mountain wakes and waves. Note different ASCII file formats for Goldendale (z04) and Walla Walla (z09) sites. Data Details ASCII Format Field 1: DataloggerID Field 2: Year Field 3: Julian Day Field 4: Hour and Min (UTC) Field 5: Seconds Decimal (UTC) Field 6: GPS Lock Identifier (A = Locked Signal; V=Insufficient Satellite Coverage) Field 7: GPS Clock String (UTC) Field 8: Pressure (mb) Example of data file: 101,2016,243,000,0.04,0001A,2016/08/29 23:59:58.150,913.314500 101,2016,243,000,0.09,0001A,2016/08/29 23:59:58.200,913.315652 101,2016,243,000,0.14,0001A,2016/08/29 23:59:58.250,913.313351 101,2016,243,000,0.19,0001A,2016/08/29 23:59:58.300,913.315626 101,2016,243,000,0.24,0001A,2016/08/29 23:59:58.350,913.315255 101,2016,243,000,0.29,0001A,2016/08/29 23:59:58.400,913.315267 101,2016,243,000,0.34,0001A,2016/08/29 23:59:58.450,913.315430 101,2016,243,000,0.39,0001A,2016/08/29 23:59:58.500,913.312698 101,2016,243,000,0.44,0001A,2016/08/29 23:59:58.550,913.315139 101,2016,243,000,0.49,0001A,2016/08/29 23:59:58.600,913.314793 101,2016,243,000,0.54,0001A,2016/08/29 23:59:58.650,913.317083 101,2016,243,000,0.59,0001A,2016/08/29 23:59:58.700,913.316959 101,2016,243,000,0.64,0001A,2016/08/29 23:59:58.750,913.312730 101,2016,243,000,0.69,0001A,2016/08/29 23:59:58.800,913.315043 101,2016,243,000,0.74,0001A,2016/08/29 23:59:58.850,913.318476 101,2016,243,000,0.79,0001A,2016/08/29 23:59:58.900,913.312417 101,2016,243,000,0.84,0001A,2016/08/29 23:59:58.950,913.317606 101,2016,243,000,0.89,0001A,2016/08/29 23:59:59.000,913.316681 101,2016,243,000,0.94,0001A,2016/08/29 23:59:59.050,913.314978 101,2016,243,000,0.99,0001A,2016/08/29 23:59:59.100,913.318996 Goldendale (z04) and Walla Walla (z09) ASCII Format Field 1: GPS Clock String (UTC) Field 2: Pressure (mb) Example of data file: 2016/08/29 23:59:58.150,913.314500 2016/08/29 23:59:58.200,913.315652 2016/08/29 23:59:58.250,913.313351 2016/08/29 23:59:58.300,913.315626 2016/08/29 23:59:58.350,913.315255 2016/08/29 23:59:58.400,913.315267 2016/08/29 23:59:58.450,913.315430 2016/08/29 23:59:58.500,913.312698 2016/08/29 23:59:58.550,913.315139 2016/08/29 23:59:58.600,913.314793 2016/08/29 23:59:58.650,913.317083 2016/08/29 23:59:58.700,913.316959 2016/08/29 23:59:58.750,913.312730 2016/08/29 23:59:58.800,913.315043 2016/08/29 23:59:58.850,913.318476 2016/08/29 23:59:58.900,913.312417 2016/08/29 23:59:58.950,913.317606 2016/08/29 23:59:59.000,913.316681 2016/08/29 23:59:59.050,913.314978 2016/08/29 23:59:59.100,913.318996 Data Quality No special data quality control is needed. Uncertainty 0.0001% Resolution ±0.08 hPa Accuracy Stability better than 0.1 hPa per year.

This data set comprises GPS high rate ground data of a sample rate of 1 sec, generated by decoding the original measurement data. This raw data passed no quality control. The data are given in the Rinex 2.1 format

Global Navigation Satellite System (GNSS) Station unr/gnss/WLWN/16278/L2/24:00:00 Name: WLWN Processing Level: L2 measurement_technique: gnss variable_measured: position creator:Thomas Yan data_start_time:2016-08-31T00:00:00 data_stop_time:2018-03-14T00:00:00 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Update: New version includes additional samples taken in November 2022.

Dataset Description

This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters.

The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus.

Dataset Description Sample Count Receiver Count

No-Tx Samples 46 10 to 25

1-Tx Samples 4822 10 to 25

2-Tx Samples 346 11 to 12

The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows:

(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) )

Measurement Parameters Description

Frequency 462.7 MHz

Radio Gain 35 dB

Receiver Sample Rate 2 MHz

Sample Length N=10,000

Band-pass Filter 6 kHz

Transmitters 0 to 2

Transmission Power 1 W

Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device.

Usage Instructions

Data is provided in .json format, both as one file and as split files.

import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f)

The json data is a dictionary with the sample timestamp as a key. Within each sample are the following keys:

rx_data: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name.

tx_coords: A list of coordinates for each transmitter. Each entry contains latitude and longitude.

metadata: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in tx_coords

File Separations and Train/Test Splits

In the separated_data.zip folder there are several train/test separations of the data.

all_data contains all the data in the main JSON file, separated by the number of transmitters.

stationary consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers.

train_test_splits contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with unused.json, is equivalent to the file all_data/single_tx.json.

The random split is a random 80/20 split of the data.

special_test_cases contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus.

The grid split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the grid split.

The seasonal split contains data separated by the month of collection, in April, July, or November

The transportation split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The non-driving.json file contains the union of the walking and cycling data.

campus.json contains the on-campus data, so is equivalent to the union of each split, not including unused.json.

Digital Surface Model

The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR survey. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models.

To read the data in python:

import rasterio as rio import numpy as np import utm

dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T')

Dataset Acknowledgement: This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product.

DSM DOI: https://doi.org/10.5069/G9TH8JNQ

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market.

Veraset Movement (Mobile Location Data) offers unparalleled insights into footfall traffic patterns across dozens of European countries.

Covering 45+ European countries, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail, and transportation logistics, Veraset's Movement data helps shape strategy and make impactful data-driven decisions.

Veraset’s European Movement Panel includes the following countries: - United Kingdom-GB - Germany-DE - France-FR - Spain-ES - Italy-IT - The Netherlands-NL - Switzerland-CH - Belgium-BE - Sweden-SE - Austria-AT - Denmark-DK - Finland-FI - Cyprus-CY - Poland-PL - Ireland-IE - Portugal-PT - Romania-RO - Hungary-HU - Czech Republic-CZ - Greece-GR - Bulgaria-BG - Lithuania-LT - Croatia-HR - Norway-NO - Latvia-LV - Luxembourg-LU - Slovakia-SK - Estonia-EE - Cayman Islands-KY - Slovenia-SI - Vatican city-VA - Turks and Caicos Islands-TC - Bermuda-BM - Malta-MT - Iceland-IS - Liechtenstein-LI - Monaco-MC - British Virgin Islands-VG - Anguilla-AI - Andorra-AD - Greenland-GL - San Marino-SM - Federated States of Micronesia-FM - Montserrat-MS - Pitcairn islands-PN

Common Use Cases of Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting