12 datasets found
  1. 5G Coverage Worldwide

    • kaggle.com
    Updated Oct 14, 2023
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    dee dee (2023). 5G Coverage Worldwide [Dataset]. https://www.kaggle.com/datasets/ddosad/5g-coverage-worldwide
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 14, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    dee dee
    Description

    This comprehensive dataset provides a detailed overview of the global rollout of 5G networks in cities around the world.

    It is meticulously compiled from verified public sources and data obtained from Ookla. This dataset serves as a valuable resource for those who are interested in the state of 5G deployment and coverage.

    Key Features
    • Geographical Information: Gain insights into 5G availability and adoption in cities across the globe along with information on Latitude & Longitude
    • Operator Data: Explore 5G network information from various operators
    • Deployment type: Status of availability as Commercial Availability, Limited Availability , Pre-Release.
  2. Network Slicing

    • kaggle.com
    Updated Aug 8, 2022
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    Puspak Meher (2022). Network Slicing [Dataset]. https://www.kaggle.com/datasets/puspakmeher/networkslicing/code
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 8, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Puspak Meher
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Dataset Overview

    Cellular communications, especially with the advent of 5G mobile networks, demand stringent adherence to high-reliability standards, ultra-low latency, increased capacity, enhanced security, and high-speed user connectivity. To fulfill these requirements, mobile operators require a programmable solution capable of supporting multiple independent tenants on a single physical infrastructure. The advent of 5G networks facilitates end-to-end resource allocation through Network Slicing (NS), which allows for the division of the network into distinct virtual slices.

    Network slicing in 5G stands as a pivotal feature for next-generation wireless networks, delivering substantial benefits to both mobile operators and businesses. Developing a Machine Learning (ML) model is crucial for accurately predicting the optimal network slice based on key device parameters. Such a model also plays a vital role in managing network load balancing and addressing network slice failures.

    Dataset Characteristics and Target Classes

    The dataset is structured to support the development of an ML model that can classify the optimal network slice based on device parameters. The target output comprises three distinct classes:

    1. Enhanced Mobile Broadband (eMBB):

      • Focuses on high-bandwidth and high-speed data transmission.
      • Facilitates activities such as high-definition video streaming, online gaming, and immersive media experiences.
    2. Ultra-Reliable Low Latency Communication (URLLC):

      • Emphasizes extremely reliable and low-latency connections.
      • Supports critical applications like autonomous vehicles, industrial automation, and remote surgery.
    3. Massive Machine Type Communication (mMTC):

      • Aims to support a massive number of connected devices.
      • Enables efficient communication between Internet of Things (IoT) devices, smart cities, and sensor networks.

    File name: deepslice_data.csv

    Data Attributes (Columns Desc)

    • Device ID: Unique identifier for each device.
    • Connection Type: Specifies the type of connection (e.g., LTE, 5G).
    • Latency Requirements (ms): The maximum allowable latency for the device's operation.
    • Bandwidth Requirements (Mbps): The bandwidth needed for optimal device performance.
    • Reliability (%): The required reliability level for the device's connection.
    • Data Rate (Mbps): The data rate the device can handle.
    • Device Type: Categorizes the device (e.g., smartphone, IoT sensor).
    • Mobility (Low/Medium/High): Indicates the mobility level of the device.
    • Battery Life (hours): Expected battery life of the device.
    • Application Type: The primary application for the device's connection (e.g., video streaming, industrial control).

    Class Distribution

    The dataset includes labeled instances categorized into the three target classes: eMBB, URLLC, and mMTC. Each instance corresponds to a specific device configuration and its optimal network slice.

    Application and Relevance

    Network slicing in 5G is instrumental in provisioning tailored network services for specific use cases, ensuring optimal performance, resource utilization, and user experiences based on the requirements of eMBB, URLLC, and mMTC applications. This dataset is invaluable for researchers and practitioners aiming to design and implement ML models for network slice prediction, thereby enhancing the operational efficiency and reliability of 5G networks.

    Conclusion

    This dataset is meticulously curated to facilitate the development of ML models for predicting the optimal 5G network slice. It encompasses a comprehensive set of attributes and target classes, ensuring that it meets the highest standards required for advanced research and practical applications in the field of cellular communications and network management.

  3. Z

    Outdoor NB-IoT and 5G coverage and channel information data in urban...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Feb 13, 2025
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    Marco Neri (2025). Outdoor NB-IoT and 5G coverage and channel information data in urban environments [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7674298
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    Dataset updated
    Feb 13, 2025
    Dataset provided by
    Marco Neri
    Maria-Gabriella Di Benedetto
    Özgü Alay
    Giuseppe Caso
    Luca De Nardis
    Anna Brunstrom
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G).

    Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements

    The dataset includes a metadata file providing the following information for each campaign:

    date of collection;

    start time and end time of collection;

    length;

    type (walking/driving).

    Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization.

    The dataset contains the following data for NB-IoT:

    Raw data for each campaign, stored in two .csv files. For a generic campaign , the files are:

    NB-IoT_coverage_C.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);

    NB-IoT_RefSig_cir_C.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.

    Processed data, stored in a Matlab workspace (.mat) file for each city: data are grouped in data points, identified by pairs. Each data point provides RF and CIR maximum delay measurements for each unique combination detected at the coordinates of the data point.

    Estimated positions of eNodeBs, stored in a csv file for each city;

    A matlab script and a function to extract and generate processed data from the raw data for each city.

    The dataset contains the following data for 5G:

    Raw data for each campaign, stored in two .xslx files. For a generic campaign , the files are:

    5G_coverage_C.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);

    5G_RefSig_cir_C.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.

    Processed data, stored in a Matlab workspace (.mat) file: data are grouped in data points, identified by pairs. Each data point provides RF and CIR maximum delay measurements for each unique combination detected at the coordinates of the data point.

    A matlab script and a supporting function to extract and generate processed data from the raw data.

    In addition, in the case of the Rome data additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches:

    A campaign-by-campaign linear interpolation (both NB-IoT and 5G);

    A bidimensional interpolation on all campaigns combined (NB-IoT only).

    A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach is detailed in:

    L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023. DOI: 10.3390/s23094266.

    The second interpolation approach is instead introduced and described in:

    L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation.

    Positioning using the 5G data was furthermore in investigated in:

    K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," IEEE Communications Magazine, Volume 62, Issue 5, pp. 44-49, May 2024. DOI: 10.1109/MCOM.011.2200707.

    G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: 10.1109/OJCOMS.2024.3499370.

    Please refer to the above publications when using and citing the dataset.

  4. e

    Mobile coverage 4G and 5G in the city of Hamm

    • data.europa.eu
    wms
    Updated Oct 26, 2024
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    (2024). Mobile coverage 4G and 5G in the city of Hamm [Dataset]. https://data.europa.eu/data/datasets/6dd0f556-e9d3-48ad-945f-95751de155cb?locale=en
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    wmsAvailable download formats
    Dataset updated
    Oct 26, 2024
    Area covered
    Hamm
    Description

    Mobile phone expansion, especially in cities like Hamm, is crucial for digital infrastructure and social and economic growth. A high-performance mobile network is indispensable in a networked world where digital communication and data transmission are becoming increasingly important. Hamm benefits enormously from a well-developed mobile network that enables fast telephony and Internet access via mobile devices. This is particularly important for companies that rely on smooth communication and for citizens in everyday life and at work. With the increasing demand for mobile services and new technologies such as the Internet of Things and 5G, comprehensive expansion is essential to meet demand and maintain Germany's competitiveness as a leading location for innovation and technology. The dataset contains information about 4G and 5G mobile coverage in the city of Hamm.

  5. m

    Data from: A multi-device and multi-operator dataset from mobile network...

    • data.mendeley.com
    Updated Jul 15, 2024
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    vandermi silva (2024). A multi-device and multi-operator dataset from mobile network coverage on Android devices [Dataset]. http://doi.org/10.17632/tjg9426ykr.1
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    Dataset updated
    Jul 15, 2024
    Authors
    vandermi silva
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The demand for mobile coverage with adequate signal quality has triggered criticism due to the maturity of the Internet's diffusion in today's society. However, with the deployment of 5G networks, even 5G NSA by 4G LTE, the complexity of the operating environment of mobile networks has increased. To evaluate the behavior of mobile networks in terms of signal quality and other important metrics for mobile telephony, we developed a dataset consisting of 33 radio parameters that can collect up to 736,974 records generated daily by smartphones and tablets. To create the dataset, an application was designed for the Android operating system using the Kotlin programming language, which can collect data in real time and generate a CSV file. The dataset has 10 samples collected from 9 cities located on the Amazon and Negro Rivers. The complete database covering all regions has 33 columns and 736,974 rows. In addition to the primary dataset, we divided the data into three regions: the metropolitan area of Manaus, the middle Solimões River, and the middle Amazonas River. During the scheduled trips, data were collected along rivers and roads that provide access to the locations selected for the experiment. The data was processed, indexed, and organized into a comprehensive database, then categorized by location. This organization allows experiments using the entire dataset across all cities or with data specific to an individual city. To access the database and conduct initial experiments, Python scripts were developed alongside the database to facilitate data loading and the generation of histograms and charts necessary for the initial investigation. In addition to the graph generation scripts, we also created heat maps based on the collected network variables.The data is organized in a folder named “network_dataset,” which contains a list of datasets. Each dataset is named according to the device ID concatenated with the timestamp at which it was collected.The raw dataset was stored inside the mobile device, and stored in the cloud after the preprocessing steps. The collected data contains mobile network variables such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal-to-Noise Ratio (SNR) and Channel Quality Indicator (CQI), collected in real-time and stored on the mobile device in Comma-separated Values ​​(CSV) data format. After completing the daily collection, the device automatically sends the file to the cloud.

  6. o

    Energy Cycle Characteristics for 5G/6G Networks Supported by RES, UAVs, and...

    • explore.openaire.eu
    • data.niaid.nih.gov
    Updated Mar 15, 2024
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    Adam Samorzewski (2024). Energy Cycle Characteristics for 5G/6G Networks Supported by RES, UAVs, and RISs [Dataset]. http://doi.org/10.5281/zenodo.10815397
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    Dataset updated
    Mar 15, 2024
    Authors
    Adam Samorzewski
    Description

    Overview The following dataset presents the energy cycle characteristics for 5G/6G mobile systems supported by Renewable Energy Sources (RES) and/or Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RISs). In addition, within the dataset, the energy gain related to the engagement of RES within the Radio Access Network (RAN) has also been distinguished. Scenario The considered network scenario includes 8 three- (_results_gcas.csv) or one-cell (_results_scas.csv & _results_kras.csv) base stations (BSs) placed within the Poznan city (surroundings of the old market) and supported by Renewable Energy Sources — photovoltaic panels (PVs) and/or wind turbines (WTs). The aforementioned base stations can be treated as stationary towers or mobile access points (e.g., drones/UAVs). Those latter have been additionally equipped with RIS devices, which are able to reflect and manipulate a radio signal to influence occurrences such as interferences, coverage, or human exposure. However, the use of RISs has been taken into account only to evaluate the impact of the engagement of such devices on the energy side of the mobile system, omitting the changes in radio characteristics. The network traffic has been assumed to be fixed (64 mobile users (UEs) with 100 Mbps downlink — DL, and 25 Mbps uplink — UL, per each), however, its density in specific parts of the city is modeled randomly for each simulation run. The simulation runs have been performed for 4 dates (vernal equinox, summer solstice, autumn equinox, winter solstice), each one from a different season of the year. The aim of such an approach was to highlight the impact of the time of the day and the year on the energy gain obtained thanks to enabling RES generators. The weather conditions assumed within the simulation are typical for the climate in Poland. Methodology The energy-cycle calculations (system's power consumption, renewable energy production, and excessive energy storage) have been based on the mathematical formulas from the scientific literature and performed within the digital simulation runs by using the Green Radio Access Network Design (GRAND) tool (developed by teams from the Ghent University & Poznan University of Technology). The UE-BS association process within the mobile system has been done by doing multi-objective optimization using the Gurobi software, which has taken into account parameters like path loss, predicted power consumption of BSs, and guaranteed DL & UL bit rates for UEs. Simulation setup The setup of the input parameters for used mathematical models (power consumption, energy generation, energy storage) has been done in accordance with the values attached within the delivered literature positions (cited within the publications included in the Related works section of the following dataset) and adjusted to the considered study. Furthermore, the data used to model the network environment (building distribution, coverage area, base stations' locations) as well as to predict weather conditions are the real data (for the year 2022) collected by the city hall of Poznan, one of the Polish mobile operators, and weather stations placed in Poznan, respectively. The number of simulation runs performed has been equal to 10 (each run has included energy-cycle calculations for 4 seasons of the year), with the time step of a single run set to 1 hour of the day. Results The results of the aforementioned investigations have been included in the attached files, which can be described as follows: File _results_gcas.csv The first column denotes the date (season of the year), for which the values have been obtained. The columns from second to fifth present observed values of the State of Charge (SoC) of a battery system (in %) for a single network cell on average in a time step. Those columns are the obtained values for the RAN, in which no RES, only PVs, only WTs, and both types of RES generators have been enabled, respectively. Files _results_scas.csv & _results_kras.csv The first column denotes the date (season of the year), for which the values have been obtained. The second and third columns denote the number of drone base station (DBS) exchanges within the wireless system on average in a particular time step, where no RES and only PVs are enabled, respectively. The fourth and fifth columns present the conventional (fossil-fuels-based) energy consumption (in kWh) for the whole system in a specific time step, in which no RES and only PVs are engaged for all the access nodes. The sixth column is the energy savings (in kWh) related to the use of RES generators within the mobile network. Furthermore, the seventh and eighth columns represent the amount of renewable energy harvested from the solar radiation in total and the peak value of this amount observed during the entire day, respectively. Acknowledgment More details about the conducted studies have been described within the attached papers (Related works sect...

  7. a

    Data from: Small Cell

    • gisdata-csj.opendata.arcgis.com
    • data.sanjoseca.gov
    Updated Aug 28, 2020
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    City of San José (2020). Small Cell [Dataset]. https://gisdata-csj.opendata.arcgis.com/datasets/CSJ::small-cell
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    Dataset updated
    Aug 28, 2020
    Dataset authored and provided by
    City of San José
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    5G cellular antennas operated by AT&T, Verizon, or Mobilitie. The City of San Jose rents street light poles to telecom companies for their 5G technology antennas to provide faster and better wireless data coverage.Data is published on Mondays on a weekly basis.

  8. i

    Milan Dataset

    • ieee-dataport.org
    Updated Jun 30, 2022
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    Mariam Abdullah (2022). Milan Dataset [Dataset]. https://ieee-dataport.org/documents/milan-dataset
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    Dataset updated
    Jun 30, 2022
    Authors
    Mariam Abdullah
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    sent SMS

  9. N

    LinkNYC Kiosk Locations

    • data.cityofnewyork.us
    • datasets.ai
    • +1more
    application/rdfxml +5
    Updated Apr 12, 2016
    + more versions
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    Office of Technology and Innovation (OTI) (2016). LinkNYC Kiosk Locations [Dataset]. https://data.cityofnewyork.us/w/s4kf-3yrf/25te-f2tw?cur=ZBWxmXRNlp7
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    csv, xml, json, tsv, application/rssxml, application/rdfxmlAvailable download formats
    Dataset updated
    Apr 12, 2016
    Dataset authored and provided by
    Office of Technology and Innovation (OTI)
    Description

    LinkNYC is the City’s program to provide free high-speed Wi-Fi, nationwide calling, a dedicated 911 button, charging ports for mobile devices, and access to social services. The City has recently begun to roll out a new and improved design of the original LinkNYC kiosk: Link5G. This new design will provide all of the amenities of LinkNYC kiosks, with the added benefit of 4G and 5G connectivity to enhance mobile telecommunications networks. This dataset lists locations for LinkNYC kiosks plus four public payphones in the five boroughs.

  10. N

    LinkNYC New Site Permit Applications

    • data.cityofnewyork.us
    • catalog.data.gov
    Updated Jun 6, 2025
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    Office of Technology and Innovation (OTI) (2025). LinkNYC New Site Permit Applications [Dataset]. https://data.cityofnewyork.us/Social-Services/LinkNYC-New-Site-Permit-Applications/xp25-gxux
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    xml, csv, application/rdfxml, tsv, application/rssxml, kml, application/geo+json, kmzAvailable download formats
    Dataset updated
    Jun 6, 2025
    Dataset authored and provided by
    Office of Technology and Innovation (OTI)
    Description

    LinkNYC is the City’s program to provide free high-speed Wi-Fi, nationwide calling, a dedicated 911 button, charging ports for mobile devices, and access to social services. The City has recently begun to roll out a new and improved design of the original LinkNYC kiosk: Link5G. This new design will provide all of the amenities of LinkNYC kiosks, with the added benefit of 4G and 5G connectivity to enhance mobile telecommunications networks. This dataset lists proposed new locations for LinkNYC kiosks that are currently open for public comment.

  11. B2B Technographic Data in China

    • kaggle.com
    Updated Sep 13, 2024
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    Techsalerator (2024). B2B Technographic Data in China [Dataset]. https://www.kaggle.com/datasets/techsalerator/b2b-technographic-data-in-china
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 13, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Techsalerator
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Area covered
    China
    Description

    Techsalerator’s Business Technographic Data for China: Unlocking Insights into China’s Technology Landscape

    Techsalerator’s Business Technographic Data for China provides an exhaustive and detailed collection of information essential for businesses, market analysts, and technology vendors aiming to understand and engage with companies operating in China. This dataset offers a thorough exploration of the technological landscape, capturing and categorizing data related to technology stacks, digital tools, and IT infrastructure within Chinese businesses.

    For inquiries, please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

    Top 5 Most Utilized Data Fields

    1. Company Name: This field lists the names of companies being analyzed in China. Understanding these companies helps technology vendors target their solutions effectively and enables market analysts to evaluate technology adoption trends within specific businesses.

    2. Technology Stack: This field details the technologies and software solutions a company utilizes, such as ERP systems, CRM software, and cloud services. Knowledge of a company’s technology stack is crucial for understanding its operational capabilities and technology needs.

    3. Deployment Status: This field indicates whether the technology is currently in use, planned for deployment, or under evaluation. This status helps vendors gauge the level of interest and current adoption among businesses in China.

    4. Industry Sector: This field identifies the industry sector in which the company operates, such as manufacturing, technology, finance, or retail. Segmenting by industry sector helps vendors tailor their offerings to specific market needs and trends within China.

    5. Geographic Location: This field provides the geographic location of the company's headquarters or primary operations within China. This information is vital for regional market analysis and understanding local technology adoption patterns.

    Top 5 Technology Trends in China

    1. Artificial Intelligence (AI) and Machine Learning: AI and machine learning are at the forefront of technological advancements in China, with businesses integrating these technologies for data analysis, automation, and enhancing customer experiences.

    2. 5G Technology: China is a global leader in the deployment and adoption of 5G technology. This advancement is driving innovations in various sectors, including telecommunications, smart cities, and autonomous vehicles.

    3. Cloud Computing and Big Data: The adoption of cloud computing and big data analytics is growing rapidly in China. Businesses are leveraging these technologies for scalable storage solutions, data-driven decision-making, and improved operational efficiency.

    4. E-Commerce and Digital Payments: E-commerce continues to thrive in China, supported by sophisticated digital payment systems and platforms. Companies are enhancing their online presence and payment solutions to meet the demands of a tech-savvy consumer base.

    5. Cybersecurity: With the rise of digital transformation, cybersecurity is becoming increasingly crucial in China. Businesses are investing in advanced security measures to protect against cyber threats and ensure data privacy.

    Top 5 Companies with Notable Technographic Data in China

    1. Alibaba Group: A major player in e-commerce and cloud computing, Alibaba Group is known for its extensive technology stack, including cloud services, big data analytics, and AI solutions.

    2. Huawei Technologies: As a leading telecommunications company, Huawei is at the cutting edge of 5G technology, AI, and network infrastructure, playing a significant role in shaping China's digital landscape.

    3. Tencent: Tencent is a key player in digital services, including social media, gaming, and fintech. The company’s technology stack includes advanced cloud computing, AI, and data analytics.

    4. ByteDance: Known for its popular apps like TikTok, ByteDance is leveraging AI and machine learning for content recommendation and user engagement, making significant strides in the tech industry.

    5. JD.com: A major e-commerce and logistics company, JD.com utilizes sophisticated technology solutions for its operations, including AI-driven logistics, cloud computing, and data analytics.

    Accessing Techsalerator’s Business Technographic Data

    If you’re interested in obtaining Techsalerator’s Business Technographic Data for China, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide a customized quote based on the number of data fields and records you need, with the dataset available for delivery within 24 hours. Ongoing access options can also be discussed as needed.

    Included Data Fields

    • Company Name
    • Technology Stack
    • Deployment Status
    • Industry Secto...
  12. LinkNYC Kiosk Locations

    • nycopendata.socrata.com
    application/rdfxml +5
    Updated Apr 12, 2016
    + more versions
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    Office of Technology and Innovation (OTI) (2024). LinkNYC Kiosk Locations [Dataset]. https://nycopendata.socrata.com/Social-Services/LinkNYC-Locations/s4kf-3yrf
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    tsv, xml, csv, application/rssxml, json, application/rdfxmlAvailable download formats
    Dataset updated
    Apr 12, 2016
    Dataset provided by
    New York City Office of Technology and Innovationhttps://www.nyc.gov/content/oti/pages/
    Authors
    Office of Technology and Innovation (OTI)
    Description

    LinkNYC is the City’s program to provide free high-speed Wi-Fi, nationwide calling, a dedicated 911 button, charging ports for mobile devices, and access to social services. The City has recently begun to roll out a new and improved design of the original LinkNYC kiosk: Link5G. This new design will provide all of the amenities of LinkNYC kiosks, with the added benefit of 4G and 5G connectivity to enhance mobile telecommunications networks. This dataset lists locations for LinkNYC kiosks plus four public payphones in the five boroughs.

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dee dee (2023). 5G Coverage Worldwide [Dataset]. https://www.kaggle.com/datasets/ddosad/5g-coverage-worldwide
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5G Coverage Worldwide

5G rollouts in cities across the globe- Updated till Sept. 2023

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2 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Oct 14, 2023
Dataset provided by
Kagglehttp://kaggle.com/
Authors
dee dee
Description

This comprehensive dataset provides a detailed overview of the global rollout of 5G networks in cities around the world.

It is meticulously compiled from verified public sources and data obtained from Ookla. This dataset serves as a valuable resource for those who are interested in the state of 5G deployment and coverage.

Key Features
  • Geographical Information: Gain insights into 5G availability and adoption in cities across the globe along with information on Latitude & Longitude
  • Operator Data: Explore 5G network information from various operators
  • Deployment type: Status of availability as Commercial Availability, Limited Availability , Pre-Release.
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