HFR

## Overview

HFR is a dataset for object detection tasks - it contains Target annotations for 880 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A list of the top 50 HFR Wealth Management LLC holdings showing which stocks are owned by HFR Wealth Management LLC's hedge fund.

Highfield Resources reported AUD145.28M in Equity Capital and Reserves for its fiscal semester ending in December of 2024. Data for Highfield Resources | HFR - Equity Capital And Reserves including historical, tables and charts were last updated by Trading Economics this last December in 2025.

PSP FIELDS Radio Frequency Spectrometer, RFS, High Frequency Receiver, HFR, Data:The RFS is the high frequency component of the FIELDS experiment on the Parker Solar Probe spacecraft, see reference [1]. For a full description of the FIELDS experiment, see reference [2]. For a description of the RFS, see reference [3].The RFS produces auto and cross spectral data products in two frequency ranges, the Low Frequency Reciever range and the High Frequency Receiver range. Telemetered spectral data products for both HFR and LFR contain 64 frequency bins, with the LFR typically covering a frequency range from 10.5 kHz to 1.7 MHz, and the HFR covering from 1.3 MHz to 19.2 MHz, with approximately logarithmically spaced bins. LFR high-resolution spectra contain 32 finely spaced frequency bins near the plasma frequency. The exact frequency bins are selectable and are included as metadata variables in this file.The Level 2 data products contained in this data file have been calibrated for the preamp and RFS analog section response, the Polyphase Filter Bank, PFB, and the Fast Fourier Transform, FFT, spectral processing as described in reference [3]. Corrections for base capacitance and antenna effective length have not been applied. These corrections will be applied in Level 3 RFS data. Therefore, the units for all spectral quantities are given in V^2/Hz.The time resolution of the RFS data vary with instrument mode. During encounter, which is when PSP is within 0.25 astronomical units, AU, of the Sun, the cadence for RFS HFR and LFR spectra is typically about 7 s. During cruise mode, which is the default mode for operations outside of 0.25 AU, the cadence for HFR and LFR spectra is about 56 s.References:* 1) Fox, N.J., Velli, M.C., Bale, S.D., et al., Space Sci Rev (2016), 204:7, DOI:10.1007/s11214-015-0211-6* 2) Bale, S.D., Goetz, K., Harvey, P.R., et al., Space Sci Rev (2016), 204:49, DOI:10.1007/s11214-016-0244-5* 3) Pulupa, M., Bale, S.D., Bonnell, J.W., et al., JGR Space Physics (2017), 122, 2836-2854, DOI:10.1002/2016JA023345

This bar chart displays opening price by date using the aggregation sum. The data is filtered where the stock is HFR.AX and the date is the 6th of May 2025. The data is about stocks per day.

A dataset of public corporate filings (such as annual reports, quarterly reports, and ad-hoc disclosures) for HFR, Inc. (230240), provided by FinancialReports.eu.

This data set contains operating mode information for the CASSINI RPWS HFR instrument.

Hfr Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Surface ocean velocities estimated from High Frequency (HF)-Radar are representative of the upper 2.4 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Radial velocity measurements are obtained from individual radar sites through the U.S. HF-Radar Network. Hourly radial data are processed by unweighted least squares on a 6km resolution grid of the U.S. West Coast to produce near real-time surface current maps. acknowledgement=HFR-US-WestCoast HF Radar Network has been established within the Integrated Ocean Observing System (IOOS) program. area=US West Coast calibration_link=AGL1: hfrnet.administrators@sio.ucsd.edu, ANGL: hfrnet.administrators@sio.ucsd.edu, ARG1: hfrnet.administrators@sio.ucsd.edu, BIGC: hfrnet.administrators@sio.ucsd.edu, BML1: hfrnet.administrators@sio.ucsd.edu, BMLR: hfrnet.administrators@sio.ucsd.edu, BONI: hfrnet.administrators@sio.ucsd.edu, BRAG: hfrnet.administrators@sio.ucsd.edu, CBL1: hfrnet.administrators@sio.ucsd.edu, COMM: hfrnet.administrators@sio.ucsd.edu, COP1: hfrnet.administrators@sio.ucsd.edu, CRIS: hfrnet.administrators@sio.ucsd.edu, DCLR: hfrnet.administrators@sio.ucsd.edu, DCSR: hfrnet.administrators@sio.ucsd.edu, ESTR: hfrnet.administrators@sio.ucsd.edu, EXPL: hfrnet.administrators@sio.ucsd.edu, FBK1: hfrnet.administrators@sio.ucsd.edu, FORT: hfrnet.administrators@sio.ucsd.edu, GCVE: hfrnet.administrators@sio.ucsd.edu, GCYN: hfrnet.administrators@sio.ucsd.edu, LOO1: hfrnet.administrators@sio.ucsd.edu, LUIS: hfrnet.administrators@sio.ucsd.edu, MAN1: hfrnet.administrators@sio.ucsd.edu, MGS1: hfrnet.administrators@sio.ucsd.edu, MLML: hfrnet.administrators@sio.ucsd.edu, MONS: hfrnet.administrators@sio.ucsd.edu, NIC1: hfrnet.administrators@sio.ucsd.edu, NPGS: hfrnet.administrators@sio.ucsd.edu, PAFS: hfrnet.administrators@sio.ucsd.edu, PBON: hfrnet.administrators@sio.ucsd.edu, PILR: hfrnet.administrators@sio.ucsd.edu, PPNS: hfrnet.administrators@sio.ucsd.edu, PREY: hfrnet.administrators@sio.ucsd.edu, PSG1: hfrnet.administrators@sio.ucsd.edu, PSLR: hfrnet.administrators@sio.ucsd.edu, PTC1: hfrnet.administrators@sio.ucsd.edu, PTM1: hfrnet.administrators@sio.ucsd.edu, RAGG: hfrnet.administrators@sio.ucsd.edu, RFG1: hfrnet.administrators@sio.ucsd.edu, RTC1: hfrnet.administrators@sio.ucsd.edu, SAND: hfrnet.administrators@sio.ucsd.edu, SAUS: hfrnet.administrators@sio.ucsd.edu, SCCI: hfrnet.administrators@sio.ucsd.edu, SCDB: hfrnet.administrators@sio.ucsd.edu, SCDH: hfrnet.administrators@sio.ucsd.edu, SCI1: hfrnet.administrators@sio.ucsd.edu, SCNB: hfrnet.administrators@sio.ucsd.edu, SCPF: hfrnet.administrators@sio.ucsd.edu, SCRZ: hfrnet.administrators@sio.ucsd.edu, SCTB: hfrnet.administrators@sio.ucsd.edu, SDBP: hfrnet.administrators@sio.ucsd.edu, SDCI: hfrnet.administrators@sio.ucsd.edu, SDCP: hfrnet.administrators@sio.ucsd.edu, SDDP: hfrnet.administrators@sio.ucsd.edu, SDPL: hfrnet.administrators@sio.ucsd.edu, SDSC: hfrnet.administrators@sio.ucsd.edu, SDSE: hfrnet.administrators@sio.ucsd.edu, SDSL: hfrnet.administrators@sio.ucsd.edu, SDUT: hfrnet.administrators@sio.ucsd.edu, SDWW: hfrnet.administrators@sio.ucsd.edu, SEA1: hfrnet.administrators@sio.ucsd.edu, SHEL: hfrnet.administrators@sio.ucsd.edu, SLID: hfrnet.administrators@sio.ucsd.edu, SNI1: hfrnet.administrators@sio.ucsd.edu, SSD1: hfrnet.administrators@sio.ucsd.edu, STV2: hfrnet.administrators@sio.ucsd.edu, TRIN: hfrnet.administrators@sio.ucsd.edu, VATK: hfrnet.administrators@sio.ucsd.edu, VCOL: hfrnet.administrators@sio.ucsd.edu, VGPT: hfrnet.administrators@sio.ucsd.edu, VION: hfrnet.administrators@sio.ucsd.edu, VJOR: hfrnet.administrators@sio.ucsd.edu, VROC: hfrnet.administrators@sio.ucsd.edu, WIN1: hfrnet.administrators@sio.ucsd.edu, WLD2: hfrnet.administrators@sio.ucsd.edu, WSH1: hfrnet.administrators@sio.ucsd.edu, YHL1: hfrnet.administrators@sio.ucsd.edu, YHS2: hfrnet.administrators@sio.ucsd.edu calibration_type=AGL1: APM, ANGL: APM, ARG1: APM, BIGC: APM, BML1: APM, BMLR: APM, BONI: APM, BRAG: APM, CBL1: APM, COMM: APM, COP1: APM, CRIS: APM, DCLR: APM, DCSR: APM, ESTR: APM, EXPL: APM, FBK1: APM, FORT: APM, GCVE: APM, GCYN: APM, LOO1: APM, LUIS: APM, MAN1: APM, MGS1: APM, MLML: APM, MONS: APM, NIC1: APM, NPGS: APM, PAFS: APM, PBON: APM, PILR: APM, PPNS: APM, PREY: APM, PSG1: APM, PSLR: APM, PTC1: APM, PTM1: APM, RAGG: APM, RFG1: APM, RTC1: APM, SAND: APM, SAUS: APM, SCCI: APM, SCDB: APM, SCDH: APM, SCI1: APM, SCNB: APM, SCPF: APM, SCRZ: APM, SCTB: APM, SDBP: APM, SDCI: APM, SDCP: APM, SDDP: APM, SDPL: APM, SDSC: APM, SDSE: APM, SDSL: APM, SDUT: APM, SDWW: APM, SEA1: APM, SHEL: APM, SLID: APM, SNI1: APM, SSD1: APM, STV2: APM, TRIN: APM, VATK: APM, VCOL: APM, VGPT: APM, VION: APM, VJOR: APM, VROC: APM, WIN1: APM, WLD2: APM, WSH1: APM, YHL1: APM, YHS2: APM cdm_data_type=Grid citation=These data were collected and made freely available by the EuroGOOS European HFR Node. Data collected and processed by NOAA-NDBC. comment=Total velocities are derived using least square fit that maps radial velocities measured from individual sites onto a cartesian grid. The final product is a map of the horizontal components of the ocean currents on a regular grid in the area of overlap of two or more radar stations. contributor_email=brian.zelenke@noaa.gov; hfrnet.administrators@sio.ucsd.edu; hfrnet.administrators@sio.ucsd.edu; hfrnet.administrators@sio.ucsd.edu contributor_name=Brian Zelenke; Lisa Hazard; Hugh Roarty; Mark Otero contributor_role=Program Manager; HFR expert; HFR expert; HFR expert Conventions=CF-1.11, EuroGOOS European HFR Node, COARDS, ACDD-1.3 data_assembly_center=European HFR Node data_character_set=utf8 data_language=eng data_mode=R data_type=HF radar total current data distribution_statement=These data are public and free of charge. User assumes all risk for use of data. User must display citation in any publication or product using data. User must contact PI prior to any commercial use of data. doa_estimation_method=AGL1: Direction Finding, ANGL: Direction Finding, ARG1: Direction Finding, BIGC: Direction Finding, BML1: Direction Finding, BMLR: Direction Finding, BONI: Direction Finding, BRAG: Direction Finding, CBL1: Direction Finding, COMM: Direction Finding, COP1: Direction Finding, CRIS: Direction Finding, DCLR: Direction Finding, DCSR: Direction Finding, ESTR: Direction Finding, EXPL: Direction Finding, FBK1: Direction Finding, FORT: Direction Finding, GCVE: Direction Finding, GCYN: Direction Finding, LOO1: Direction Finding, LUIS: Direction Finding, MAN1: Direction Finding, MGS1: Direction Finding, MLML: Direction Finding, MONS: Direction Finding, NIC1: Direction Finding, NPGS: Direction Finding, PAFS: Direction Finding, PBON: Direction Finding, PILR: Direction Finding, PPNS: Direction Finding, PREY: Direction Finding, PSG1: Direction Finding, PSLR: Direction Finding, PTC1: Direction Finding, PTM1: Direction Finding, RAGG: Direction Finding, RFG1: Direction Finding, RTC1: Direction Finding, SAND: Direction Finding, SAUS: Direction Finding, SCCI: Direction Finding, SCDB: Direction Finding, SCDH: Direction Finding, SCI1: Direction Finding, SCNB: Direction Finding, SCPF: Direction Finding, SCRZ: Direction Finding, SCTB: Direction Finding, SDBP: Direction Finding, SDCI: Direction Finding, SDCP: Direction Finding, SDDP: Direction Finding, SDPL: Direction Finding, SDSC: Direction Finding, SDSE: Direction Finding, SDSL: Direction Finding, SDUT: Direction Finding, SDWW: Direction Finding, SEA1: Direction Finding, SHEL: Direction Finding, SLID: Direction Finding, SNI1: Direction Finding, SSD1: Direction Finding, STV2: Direction Finding, TRIN: Direction Finding, VATK: Direction Finding, VCOL: Direction Finding, VGPT: Direction Finding, VION: Direction Finding, VJOR: Direction Finding, VROC: Direction Finding, WIN1: Direction Finding, WLD2: Direction Finding, WSH1: Direction Finding, YHL1: Direction Finding, YHS2: Direction Finding Easternmost_Easting=-115.8329 format_version=v3 geospatial_lat_max=49.98244 geospatial_lat_min=30.2596 geospatial_lat_units=degrees_north geospatial_lon_max=-115.8329 geospatial_lon_min=-130.3327 geospatial_lon_resolution=0.07038737864077667 geospatial_lon_units=degrees_east history=Data collected at 2025-11-10T07:00:00Z. netCDF file created at 2025-11-10T08:48:49Z by the European HFR Node. id=HFR-US-WestCoast-Total_2025-11-10T07:00:00Z infoUrl=https://www.hfrnode.eu/ institution=NOAA-NDBC, SLO, NPS, BML, ONC, OSU, UCSB, USC, SIO institution_edmo_code=1463, 3790, 3805, 3645, 3716, 3807, 3820, 3836, 1390 institution_references=https://www.ndbc.noaa.gov/, https://www.calpoly.edu/, http://www.codar.com/, https://nps.edu/, https://marinescience.ucdavis.edu/bml/about, https://www.oceannetworks.ca/, https://oregonstate.edu/, https://www.ucsb.edu/, https://www.usc.edu/, http://sio.ucsd.edu/ keywords_vocabulary=GCMD Science Keywords last_calibration_date=AGL1: N/A, ANGL: N/A, ARG1: N/A, BIGC: N/A, BML1: N/A, BMLR: N/A, BONI: N/A, BRAG: N/A, CBL1: N/A, COMM: N/A, COP1: N/A, CRIS: N/A, DCLR: N/A, DCSR: N/A, ESTR: N/A, EXPL: N/A, FBK1: N/A, FORT: N/A, GCVE: N/A, GCYN: N/A, LOO1: N/A, LUIS: N/A, MAN1: N/A, MGS1: N/A, MLML: N/A, MONS: N/A, NIC1: N/A, NPGS: N/A, PAFS: N/A, PBON: N/A, PILR: N/A, PPNS: N/A, PREY: N/A, PSG1: N/A, PSLR: N/A, PTC1: N/A, PTM1: N/A, RAGG: N/A, RFG1: N/A, RTC1: N/A, SAND: N/A, SAUS: N/A, SCCI: N/A, SCDB: N/A, SCDH: N/A, SCI1: N/A, SCNB: N/A, SCPF: N/A, SCRZ: N/A, SCTB: N/A, SDBP: N/A, SDCI: N/A, SDCP: N/A, SDDP: N/A, SDPL: N/A, SDSC: N/A, SDSE: N/A, SDSL: N/A, SDUT: N/A, SDWW: N/A, SEA1: N/A, SHEL: N/A, SLID: N/A, SNI1: N/A, SSD1: N/A, STV2: N/A, TRIN: N/A, VATK: N/A, VCOL: N/A, VGPT: N/A, VION: N/A, VJOR: N/A, VROC: N/A, WIN1: N/A, WLD2: N/A, WSH1: N/A, YHL1: N/A, YHS2: N/A manufacturer=AGL1: Codar, ANGL: Codar, ARG1: Codar, BIGC: Codar, BML1: Codar, BMLR: Codar, BONI: Codar, BRAG: Codar, CBL1: Codar, COMM: Codar, COP1: Codar, CRIS: Codar, DCLR: Codar, DCSR: Codar, ESTR: Codar, EXPL:

This dataset provides daily air temperature and precipitation data from Harvard Forest for long-term climate studies. It combines measurements from the original manual weather station (Shaler, 1964-2002, dataset HF000) with measurements from the current automated weather station (Fisher, since 2001, dataset HF001). Gaps in the Shaler data were filled using data from nearby stations. Data from a 12-month period when both stations were operational were used to adjust the Shaler data to more closely match the Fisher data. This dataset is updated annually.

HFR Streaming Market Outlook

According to our latest research, the global HFR Streaming market size is valued at USD 2.84 billion in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a CAGR of 21.7% from 2025 to 2033, reaching an estimated USD 21.73 billion by 2033. The primary growth factor driving this expansion is the surging demand for ultra-smooth and immersive digital experiences across live sports, gaming, and entertainment, enabled by advancements in high frame rate (HFR) streaming technologies.

The rapid proliferation of high-speed internet infrastructure and increasing consumer appetite for premium visual content are central to the growth of the HFR Streaming market. As global audiences demand more lifelike and engaging experiences, broadcasters and streaming platforms are compelled to adopt HFR solutions to deliver content at 60 FPS, 120 FPS, and even 240 FPS. This shift is further catalyzed by the widespread adoption of 4K and 8K displays, which require higher frame rates to fully leverage their capabilities. The integration of HFR streaming into live sports and gaming has particularly accelerated, as these segments benefit the most from reduced motion blur and enhanced clarity, driving user engagement and satisfaction.

Another significant driver is the evolution of hardware and software ecosystems supporting HFR Streaming. Innovations in GPU and CPU technologies, coupled with sophisticated encoding and compression algorithms, have made it feasible to stream high frame rate content without excessive bandwidth consumption. This has opened new avenues for content creators, broadcasters, and OTT platforms to differentiate their offerings and capture premium audiences. Additionally, the emergence of cloud-based services has lowered the barriers to entry for smaller players, fostering greater competition and innovation within the market. The services segment, in particular, is witnessing rapid growth as enterprises seek managed solutions for seamless HFR content delivery.

The increasing integration of HFR streaming in non-entertainment sectors such as education and corporate communications is further expanding the market’s horizon. Educational institutions are leveraging HFR streaming for interactive virtual classrooms, while corporates utilize it for high-fidelity video conferencing and training. These applications underscore the versatility of HFR technology and its potential to redefine digital interactions beyond traditional media and entertainment. As a result, the addressable market for HFR streaming continues to broaden, encompassing diverse end-user segments and unlocking new revenue streams.

From a regional perspective, North America currently leads the global HFR Streaming market, attributed to its early adoption of advanced streaming technologies and a mature digital content ecosystem. Europe and Asia Pacific are also witnessing significant growth, driven by increasing investments in digital infrastructure and rising consumer demand for high-quality streaming experiences. The Asia Pacific region, in particular, is poised for the fastest CAGR over the forecast period, fueled by the rapid expansion of internet connectivity and the proliferation of smart devices. Latin America and the Middle East & Africa, while currently smaller in market share, are expected to register steady growth as digital transformation initiatives gain momentum in these regions.

Component Analysis

The HFR Streaming market is segmented by component into hardware, software, and services, each playing a pivotal role in the value chain. The hardware segment encompasses advanced streaming servers, encoders, graphics processing units (GPUs), and network infrastructure that form the backbone of HFR content delivery. With the relentless pursuit of higher frame rates and resolutions, hardware manufacturers are focusing on developing robust, low-latency devices capable of handling intensive data streams. The increasing adoption of edge computing and specialized hardware accelerators further enhances real-time processing capabilities, ensuring minimal lag and optimal viewer experiences. As streaming platforms and broadcasters strive to deliver content at 120 FPS and above, demand for cutting-edge hardware solutions is anticipated to remain strong throughout the forecast period.

The software segment is equally critical, encompassing video encoding and decoding applications, content management syst

huent189/unified-HFR-HDR dataset hosted on Hugging Face and contributed by the HF Datasets community

Highfield Resources reported 27 in Employees for its fiscal semester ending in December of 2023. Data for Highfield Resources | HFR - Employees Total Number including historical, tables and charts were last updated by Trading Economics this last December in 2025.

HFR 120 Hz Live Production Market Outlook

According to our latest research, the Global HFR 120 Hz Live Production market size was valued at $1.8 billion in 2024 and is projected to reach $6.4 billion by 2033, expanding at a CAGR of 14.7% during 2024–2033. The primary driver for this robust growth is the surging demand for ultra-smooth, high-quality video content across live broadcasting, sports, and entertainment sectors worldwide. As audiences increasingly expect immersive viewing experiences, broadcasters and production companies are rapidly upgrading their infrastructure to support High Frame Rate (HFR) 120 Hz live production, which delivers superior motion clarity and reduced latency. This technological evolution is not only enhancing viewer engagement but also setting new standards for content delivery, thereby fueling market expansion.

Regional Outlook

North America currently dominates the HFR 120 Hz Live Production market, accounting for the largest market share in 2024, estimated at over 38% of the global revenue. The region’s leadership is attributed to its highly mature broadcasting infrastructure, early adoption of cutting-edge video technologies, and the presence of leading industry players such as Grass Valley, EVS, and AJA Video Systems. Additionally, robust investments in sports broadcasting, coupled with the proliferation of OTT platforms, have accelerated the deployment of HFR 120 Hz solutions. Favorable government policies supporting digital transformation and a strong appetite for live sports and entertainment content further underpin North America’s market dominance.

Asia Pacific is emerging as the fastest-growing region in the HFR 120 Hz Live Production market, projected to register a CAGR of 17.2% from 2024 to 2033. The region’s rapid expansion is driven by increasing investments in media infrastructure, rising disposable incomes, and a burgeoning youth population with a strong preference for high-definition live content. Countries like China, Japan, South Korea, and India are witnessing significant upgrades in broadcasting and live event production capabilities. Major sporting events, government initiatives promoting digitalization, and the expansion of 5G networks are further accelerating the adoption of HFR 120 Hz technologies across the region.

In contrast, emerging economies in Latin America, the Middle East, and Africa are experiencing a more gradual uptake of HFR 120 Hz Live Production due to infrastructure limitations, higher initial investment costs, and fragmented regulatory frameworks. While there is growing localized demand for enhanced live production in sports and entertainment, challenges such as limited technological expertise, inconsistent broadband connectivity, and budget constraints continue to impede widespread adoption. However, targeted government policies, international collaborations, and the gradual rollout of high-speed internet are expected to create new growth avenues in these regions over the forecast period.

Report Scope

This pie chart displays stocks over time per date using the aggregation count. The data is filtered where the stock is HFR.AX and the date is the 6th of May 2025. The data is about stocks per day.

The data set consists of maps of radial velocity of the sea water surface current collected at Cabo Vilán (VILA) site along the Galician coast (North Western Spain). Data are averaged over a time interval of 1 hour around the cardinal hour. High Frequency (HF)-RADAR measurements of ocean velocity are radial in direction relative to the radar location and representative of the upper 0.3-2.5 meters of the ocean. acknowledgement=Galicia HF Radar Network has been established within RAIA and MyCoast projects. area=NW Spain calibration_link=gis@intecmar.gal calibration_type=APM cdm_data_type=Grid citation=These data were collected and made freely available by the EuroGOOS European HFR Node. These data are jointly collected and processed by Intecmar-Xunta de Galicia and Puertos del Estado. comment=These data may contain inaccuarecies or errors, thus we decline every responsability for their use. Please, contact Puertos del Estado to request permission to use these data. contributor_email=pmontero@intecmar.gal;maribel@puertos.es;plorente@puertos.es;gayensa@intecmar.gal; ih.snig.metadados@hidrografico.pt contributor_name=INTECMAR; PUERTOS DEL ESTADO; IH contributor_role=INTECMAR; PUERTOS DEL ESTADO; IH Conventions=CF-1.11, EuroGOOS European HFR Node, COARDS, ACDD-1.3 data_assembly_center=European HFR Node data_character_set=utf8 data_language=eng data_mode=R data_type=HF radar radial current data distribution_statement=These data are public and free of charge. User assumes all risk for use of data. User must display citation in any publication or product using data. User must contact PI prior to any commercial use of data. doa_estimation_method=Direction Finding format_version=v3 history=Data measured at 2025-11-10T20:00:00Z. netCDF file created at 2025-11-11T08:56:53Z by the European HFR Node. id=HFR-Galicia-VILA_2025-11-10T20:00:00Z infoUrl=https://www.hfrnode.eu/ institution=INTECMAR - Xunta de Galicia institution_edmo_code=4841 institution_references=http://www.intecmar.gal keywords_vocabulary=GCMD Science Keywords last_calibration_date=2021-11-26T00:00:00Z manufacturer=CODAR Ocean Sensors. SeaSonde metadata_character_set=utf8 metadata_contact=lorenzo.corgnati@sp.ismar.cnr.it metadata_date_stamp=2025-11-11T08:56:53Z metadata_language=eng naming_authority=eu.hfrnode netcdf_format=NETCDF4_CLASSIC netcdf_version=4.9.3 network=HFR_Galicia oceanops_ref=6204727 platform_code=HFR-Galicia-VILA processing_level=2B project=Observatorio RAIA; MyCoast qc_manual=Recommendation Report 2 on improved common procedures for HFR QC analysis: https://dx.doi.org/10.25607/OBP-944 reference_system=EPSG:4326 references=Recommendation Report 2 on improved common procedures for HFR QC analysis: https://dx.doi.org/10.25607/OBP-944 sensor_model=CODAR Ocean Sensors. SeaSonde site_code=HFR-Galicia software_name=EU_HFR_NODE_NRTprocessor software_version=v3 source=coastal structure source_platform_category_code=17 sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v70 testOutOfDate=now-1day time_coverage_duration=PT1H time_coverage_end=2025-11-10T20:00:00Z time_coverage_resolution=PT1H time_coverage_start=2019-04-09T12:00:00Z topic_category=oceans update_interval=void wigos_id=0-22000-0-6204727 wmo_platform_code=6204727

These hourly surface current velocities are a combined product derived from 8 monostatic radars (4 CODAR and 4 WERA) operated as part of the PEACH program. Level 2 data have been quality-controlled and gridded to an hourly time-base. A detailed description of processing methods and analysis is provided by Seim, et al. (2022) and a brief outline in the documentation uploaded with this dataset.

Seim, H., Savidge, D., Muglia, M., Haines, S., & Han, L. (2022). Surface current observations from a combined CODAR/WERA high-frequency radar array along the North Carolina coast during the Processes Driving Exchange at Cape Hatteras (PEACH) Project. IEEE/MTS Proceedings Oceans 2022.

The data set consists of maps of total velocity of the surface current in the Skagerrak Strait averaged over a time interval of 1 hour around the cardinal hour. Surface ocean velocities estimated by High Frequency (HF) Radar are representative of the upper 0.3-2.5 meters of the ocean. acknowledgement=HFR-Skagerrak Radar Network has been designed, implemented and managed through the efforts of the Norwegian Meteorological Institute. area=Skagerrak calibration_link=JOMF: terjeb@met.no, TORU: terjeb@met.no calibration_type=JOMF: APM, TORU: APM cdm_data_type=Grid citation=These data were collected and made freely available by the EuroGOOS European HFR Node. Data collected and processed by the Norwegian Meteorological Institute. comment=Total velocities are derived using least square fit that maps radial velocities measured from individual sites onto a cartesian grid. The final product is a map of the horizontal components of the ocean currents on a regular grid in the area of overlap of two or more radar stations. contributor_email=terjeb@met.no; martinai@met.no contributor_name=Terje Borge; Martina Idzanovic contributor_role=Site manager; Administrative contact Conventions=CF-1.11, EuroGOOS European HFR Node, COARDS, ACDD-1.3 data_assembly_center=European HFR Node data_character_set=utf8 data_language=eng data_mode=R data_type=HF radar total current data distribution_statement=These data are public and free of charge. User assumes all risk for use of data. User must display citation in any publication or product using data. User must contact PI prior to any commercial use of data. doa_estimation_method=JOMF: Direction Finding, TORU: Direction Finding Easternmost_Easting=11.99795 format_version=v3 geospatial_lat_max=59.48898 geospatial_lat_min=57.01102 geospatial_lat_resolution=0.02693434782608691 geospatial_lat_units=degrees_north geospatial_lon_max=11.99795 geospatial_lon_min=7.50205 geospatial_lon_resolution=0.05108977272727273 geospatial_lon_units=degrees_east history=Data collected at 2025-10-04T02:00:00Z. netCDF file created at 2025-10-05T12:07:36Z by the European HFR Node. id=HFR-Skagerrak-Total_2025-10-04T02:00:00Z infoUrl=https://www.hfrnode.eu/ institution=Norwegian Meteorological Institute institution_edmo_code=1212 institution_references=http://www.met.no keywords_vocabulary=GCMD Science Keywords last_calibration_date=JOMF: 2020-06-12T00:00:00Z, TORU: 2020-07-10T00:00:00Z manufacturer=JOMF: Codar, TORU: Codar metadata_character_set=utf8 metadata_contact=lorenzo.corgnati@sp.ismar.cnr.it metadata_date_stamp=2025-10-05T12:07:36Z metadata_language=eng naming_authority=eu.hfrnode netcdf_format=NETCDF4_CLASSIC netcdf_version=4.9.3 network=MetNo HFR-Skagerrak Northernmost_Northing=59.48898 platform_code=HFR-Skagerrak-Total processing_level=3B qc_manual=Recommendation Report 2 on improved common procedures for HFR QC analysis: https://dx.doi.org/10.25607/OBP-944 reference_system=EPSG:4326 references=Recommendation Report 2 on improved common procedures for HFR QC analysis: https://dx.doi.org/10.25607/OBP-944 sensor_model=JOMF: Codar, TORU: Codar site_code=HFR-Skagerrak software_name=EU_HFR_NODE_NRTprocessor software_version=v3 source=coastal structure source_platform_category_code=17 sourceUrl=(local files) Southernmost_Northing=57.01102 standard_name_vocabulary=CF Standard Name Table v70 testOutOfDate=now-1day time_coverage_duration=PT1H time_coverage_end=2025-10-04T02:00:00Z time_coverage_resolution=PT1H time_coverage_start=2020-09-17T11:00:00Z topic_category=oceans update_interval=void Westernmost_Easting=7.50205

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An image dataset for video interpolation focusing on video games

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[More… See the full description on the dataset page: https://huggingface.co/datasets/animadot/gaming-hfr.

The data set consists of maps of radial velocity of the sea water surface current collected by the HF radars of the CALYPSO High Frequency Radar Network at Pozzallo (POZZ) site in the Malta Channel. Data are averaged over a time interval of 1 hour around the cardinal hour. Surface ocean velocities estimated by HF Radar are representative of the upper 0.3-2.5 meters of the ocean. acknowledgement=Surface current data were obtained by the HF radar network installed as part of the CALYPSO, CALYPSO FollowOn, and CALYPSO SOUTH projects under partial sponsorship of the EU Operational Programme Italia‑Malta 2007‑2013. The network is managed by the University of Malta (Dr Adam Gauci), the University of Palermo (Prof. Giuseppe Ciraolo and Dr Fulvio Capodici), the CNR-IAS (Dr. Salvatore Aronica) and the ARPA-Sicilia (Dr. Salvatore Campanella). More information can be obtained from: http://calypsosouth.eu area=Sicily-Malta Channel calibration_link=fulvio.capodici@unipa.it calibration_type=APM cdm_data_type=Grid citation=These data were collected and made freely available by the EuroGOOS European HFR Node. Surface current data were obtained by the HF radar network installed as part of the CALYPSO, CALYPSO FollowOn, and CALYPSO SOUTH projects under partial sponsorship of the EU Operational Programme Italia‑Malta 2007‑2013. The network is managed by the University of Malta (Dr Adam Gauci), the University of Palermo (Prof. Giuseppe Ciraolo and Dr Fulvio Capodici), the CNR-IAS (Dr. Salvatore Aronica) and the ARPA-Sicilia (Dr. Salvatore Campanella). More information can be obtained from: http://calypsosouth.eu comment=Total velocities are derived using least square fit that maps radial velocities measured from individual sites onto a cartesian grid. The final product is a map of the horizontal components of the ocean currents on a regular grid in the area of overlap of two or more radar stations. contributor_email=adam.gauci@um.edu.mt; giuseppe.ciraolo@unipa.it; fulvio.capodici@unipa.it, salvatore.aronica@cnr.it, scampanella@arpa.sicilia.it contributor_name=Adam Gauci; Giuseppe Ciraolo; Fulvio Capodici, Salvatore Aronica, Salvatore Campanella contributor_role=Project manager; Project manager; Project manager; Project manager; Project manager; Conventions=CF-1.11, EuroGOOS European HFR Node, COARDS, ACDD-1.3 data_assembly_center=European HFR Node data_character_set=utf8 data_language=eng data_mode=R data_type=HF radar radial current data distribution_statement=These data are public and free of charge. User assumes all risk for use of data. User must display citation in any publication or product using data. User must contact PI prior to any commercial use of data. doa_estimation_method=Direction Finding format_version=v3 history=Data measured at 2025-11-29T06:00:00Z. netCDF file created at 2025-11-29T06:50:18Z by the European HFR Node. id=HFR-CALYPSO-POZZ_2025-11-29T06:00:00Z infoUrl=https://www.hfrnode.eu/ institution=University of Palermo institution_edmo_code=5527 institution_references=https://www.unipa.it/ keywords_vocabulary=GCMD Science Keywords last_calibration_date=2021-08-19T00:00:00Z manufacturer=Codar metadata_character_set=utf8 metadata_contact=lorenzo.corgnati@sp.ismar.cnr.it metadata_date_stamp=2025-11-29T06:50:18Z metadata_language=eng naming_authority=eu.hfrnode netcdf_format=NETCDF4_CLASSIC netcdf_version=4.9.3 network=HFR-CALYPSO oceanops_ref=6103592 platform_code=HFR-CALYPSO-POZZ processing_level=2B project=CALYPSO, CALYPSO FO, CALYPSO SOUTH qc_manual=Recommendation Report 2 on improved common procedures for HFR QC analysis: https://dx.doi.org/10.25607/OBP-944 reference_system=EPSG:4326 references=Recommendation Report 2 on improved common procedures for HFR QC analysis: https://dx.doi.org/10.25607/OBP-944 sensor_model=Codar site_code=HFR-CALYPSO software_name=EU_HFR_NODE_NRTprocessor software_version=v3 source=coastal structure source_platform_category_code=17 sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v70 testOutOfDate=now-1day time_coverage_duration=PT1H time_coverage_end=2025-11-29T06:00:00Z time_coverage_resolution=PT1H time_coverage_start=2024-09-02T00:00:00Z topic_category=oceans update_interval=void wigos_id=0-22000-0-6103592 wmo_platform_code=6103592