Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
Description for each of the variables:
The global smartphone penetration in was forecast to continuously increase between 2024 and 2029 by in total 20.3 percentage points. After the fifteenth consecutive increasing year, the penetration is estimated to reach 74.98 percent and therefore a new peak in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smartphone penetration in countries like North America and the Americas.
Android maintained its position as the leading mobile operating system worldwide in the first quarter of 2025 with a market share of about ***** percent. Android's closest rival, Apple's iOS, had a market share of approximately ***** percent during the same period. The leading mobile operating systems Both unveiled in 2007, Google’s Android and Apple’s iOS have evolved through incremental updates introducing new features and capabilities. The latest version of iOS, iOS 18, was released in September 2024, while the most recent Android iteration, Android 15, was made available in September 2023. A key difference between the two systems concerns hardware - iOS is only available on Apple devices, whereas Android ships with devices from a range of manufacturers such as Samsung, Google and OnePlus. In addition, Apple has had far greater success in bringing its users up to date. As of February 2024, ** percent of iOS users had iOS 17 installed, while in the same month only ** percent of Android users ran the latest version. The rise of the smartphone From around 2010, the touchscreen smartphone revolution had a major impact on sales of basic feature phones, as the sales of smartphones increased from *** million units in 2008 to **** billion units in 2023. In 2020, smartphone sales decreased to **** billion units due to the coronavirus (COVID-19) pandemic. Apple, Samsung, and lately also Xiaomi, were the big winners in this shift towards smartphones, with BlackBerry and Nokia among those unable to capitalize.
The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.
This dataset was created by Muhammad Syidiq Abdjanur
According to our latest research, the global audio dataset market size reached USD 6.7 billion in 2024, driven by surging demand for machine learning and AI-powered audio applications. The market is experiencing robust expansion with a CAGR of 21.4% from 2025 to 2033, with forecasts indicating the market will attain USD 48.1 billion by 2033. Key growth factors include the proliferation of voice-activated technologies, increased adoption of smart devices, and the widespread integration of audio analytics in diverse sectors such as healthcare, automotive, and media & entertainment.
The primary growth driver for the audio dataset market is the exponential rise in the adoption of automatic speech recognition (ASR) and natural language processing (NLP) technologies. With businesses and consumers increasingly relying on voice assistants, chatbots, and virtual agents, the demand for high-quality, diverse, and annotated audio datasets has soared. These datasets are fundamental to training and refining AI models for voice recognition, transcription, and sentiment analysis. The integration of audio datasets into customer service, accessibility solutions for the differently-abled, and language learning platforms further amplifies market growth. Additionally, advancements in deep learning algorithms are enabling the extraction of more nuanced information from audio data, making datasets more valuable and broadening their use cases.
Another significant factor fueling the audio dataset market is the surge in smart device penetration and IoT adoption across industries. The proliferation of smart speakers, connected vehicles, wearable devices, and intelligent home appliances has created a massive influx of audio data. Organizations are leveraging this data to enhance user experience, personalize services, and enable real-time decision-making. In sectors like automotive, audio datasets are instrumental in developing advanced driver assistance systems (ADAS) and in-car voice assistants. In healthcare, audio datasets support the development of diagnostic tools and remote patient monitoring solutions. The convergence of audio datasets with big data analytics and cloud computing is unlocking new business models and revenue streams, further propelling market expansion.
The media & entertainment industry is also playing a pivotal role in the growth of the audio dataset market. The demand for music information retrieval, sound event detection, and content recommendation systems is at an all-time high. Streaming platforms, broadcasters, and content creators are increasingly utilizing audio datasets to optimize content delivery, improve audience engagement, and automate content moderation. The emergence of immersive audio experiences, such as spatial audio and 3D sound, is creating new opportunities for dataset providers. Furthermore, regulatory mandates for accessibility, such as closed captioning and audio descriptions, are compelling organizations to invest in robust audio datasets, driving further market growth.
Regionally, North America holds the largest share of the audio dataset market, attributed to early technology adoption, high R&D investments, and the presence of major AI and tech companies. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digital transformation, increasing smartphone penetration, and government initiatives to promote AI research. Europe is also a significant market, driven by stringent data privacy regulations and a strong focus on innovation in automotive and healthcare sectors. Latin America and the Middle East & Africa are emerging markets, with growing investments in digital infrastructure and AI-driven applications. The global landscape is characterized by intense competition, continuous innovation, and a focus on developing multilingual and culturally diverse audio datasets.
The audio dataset market is segmented by dataset type into speech, music, environmental sounds,
https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy
According to our latest research, the global audio dataset market size reached USD 6.2 billion in 2024, driven by surging adoption of artificial intelligence and machine learning technologies across various industries. The market is experiencing robust growth, registering a CAGR of 18.7% from 2025 to 2033. By the end of 2033, the audio dataset market is forecasted to achieve a value of USD 33.1 billion. This impressive expansion is primarily attributed to the escalating demand for high-quality audio datasets to power applications in speech recognition, sound event detection, and music information retrieval, as organizations across sectors strive to enhance automation, customer experience, and operational efficiency.
The primary growth factor propelling the audio dataset market is the accelerated integration of AI-driven voice and sound technologies in both consumer and enterprise environments. The proliferation of smart devices, such as virtual assistants, smart speakers, and connected vehicles, has dramatically increased the need for diverse and well-annotated audio datasets. These datasets are essential for training robust machine learning models capable of understanding, interpreting, and generating natural language and environmental sounds. Furthermore, advancements in natural language processing (NLP) and deep learning algorithms have heightened the demand for larger, more complex, and multilingual audio datasets, enabling more accurate and context-aware applications in sectors like healthcare, automotive, and media & entertainment.
Another significant driver is the rising adoption of audio-based solutions in critical industries such as healthcare and automotive. In healthcare, audio datasets are the backbone of applications like automated transcription, remote patient monitoring, and diagnostic support systems that rely on voice analysis. The automotive sector is leveraging audio datasets to enhance in-car voice assistants, improve driver safety through sound event detection, and enable hands-free controls. Additionally, the education sector is increasingly utilizing audio datasets to develop adaptive learning platforms, language assessment tools, and accessibility solutions for students with disabilities. The convergence of these trends underscores the strategic importance of high-quality and diverse audio data in digital transformation initiatives.
The growing focus on multilingual and multicultural datasets is also catalyzing market expansion. As global businesses aim to cater to a wider audience, there is a pressing need for audio datasets that encompass a broad spectrum of languages, dialects, accents, and environmental conditions. This requirement is particularly pronounced in regions with diverse linguistic landscapes, such as Asia Pacific and Europe. The development and curation of such datasets are enabling more inclusive, accessible, and personalized user experiences, further fueling the growth of the audio dataset market. Moreover, increased investment in R&D and the emergence of open-source dataset initiatives are facilitating innovation and reducing entry barriers for smaller players and startups.
From a regional perspective, North America leads the audio dataset market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America is largely due to the presence of major technology companies, advanced AI research hubs, and substantial investments in digital infrastructure. Meanwhile, Asia Pacific is witnessing the fastest growth, propelled by rapid digitalization, increasing adoption of smart devices, and government initiatives supporting AI and data-driven innovation. Latin America and the Middle East & Africa are also emerging as promising markets, driven by expanding internet penetration and the growing adoption of audio-based applications in sectors such as media, education, and telecommunications.
The audio dataset market is segmented by dataset type into speech, music, environmental sounds, and others. Among these, the speech dataset segment commands the largest market share, owing to the widespread use of automatic speech recognition (ASR) systems in consumer electronics, customer service automation, and virtual assistants. The demand for speech datasets is further amplified by the growing trend towards voice-enabled applications in smart homes, automotive infotainment, and
DescriptionFour tables have been published which provide summary information on the level of Smart Meter penetration in the SPEN SPD and SPM licence areas at different level of aggregation. The four files which have been published are: 1 - Smart Meter Penetration by Postcode Sector - aggregated information giving the % Smart Meter penetration (also broken down by Smart Meter Type i.e. SMETS1 or SMETS2) by Postcode Sector 2 - Smart Meter Penetration by Census Zone - aggregated information giving the % Smart Meter penetration (also broken down by Smart Meter Type i.e. SMETS1 or SMETS2) by Census Zone. In Scotland the census zones are at Datazone level, in England and Wales the census zones are at Output Area level. 3 - Smart Meter Penetration by LV Feeder - aggregated information giving the % Smart Meter penetration (also broken down by Smart Meter Type i.e. SMETS1 or SMETS2) by LV Feeder (i.e. the Circuit ID at LV) 4 - Smart Meter Penetration by LV Transformer - aggregated information giving the % Smart Meter penetration (also broken down by Smart Meter Type i.e. SMETS1 or SMETS2) by LV TransformerEach file gives the following information: 1 - a unique identifier for each records (the Postcode Sector, the Census Zone Name, the LV Feeder ID, or the LV Transformer ENID) 2 - The total number of SPEN properties for each record 3 - The total number of Smart Meters associated with these properties, also broken down by Smar Meter type (SMETS1 or SMETS2) 4 - The % Smart Meter penetration for each recordThe datasets are updated on a quarterly basis. DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this).Records where the Total SPEN population is 4 properties or less have been removed from the published tables. Data Triage As part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Smart Meter Penetration dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information page. Download dataset metadata (JSON)
https://researchintelo.com/privacy-and-policyhttps://researchintelo.com/privacy-and-policy
According to our latest research, the Global Time-Series Database for OT Data Market size was valued at $1.7 billion in 2024 and is projected to reach $6.3 billion by 2033, expanding at an impressive CAGR of 15.7% during the forecast period of 2025–2033. One of the primary factors driving this robust growth is the accelerating digital transformation across operational technology (OT) environments, especially in sectors such as manufacturing, energy, and utilities. As organizations increasingly deploy IoT devices and smart sensors within their OT infrastructure, the volume and velocity of time-series data generated have surged, necessitating advanced database solutions tailored for real-time analytics, predictive maintenance, and process optimization. The demand for scalable, high-performance time-series databases is further amplified by the growing emphasis on Industry 4.0 initiatives and the need for seamless integration between IT and OT systems, enabling enterprises to unlock actionable insights from their operational data.
North America currently holds the largest share of the global time-series database for OT data market, accounting for nearly 38% of total revenue in 2024. This dominance is attributed to the region’s mature industrial sector, early adoption of digital transformation strategies, and a robust ecosystem of technology providers. The United States, in particular, has been at the forefront of deploying advanced OT data management systems, driven by stringent regulatory requirements for asset monitoring, a high concentration of manufacturing and energy enterprises, and significant investments in R&D. Additionally, the presence of leading time-series database vendors and cloud service providers has fostered a competitive landscape that accelerates innovation and market penetration. North America’s proactive policy environment, promoting smart manufacturing and energy efficiency, further cements its leadership position in this market.
In contrast, the Asia Pacific region is emerging as the fastest-growing market, projected to register a remarkable CAGR of 19.2% from 2025 to 2033. This rapid expansion is underpinned by the region’s ongoing industrialization, significant investments in smart infrastructure, and the proliferation of IoT devices across manufacturing, transportation, and utilities. Countries such as China, Japan, South Korea, and India are witnessing accelerated adoption of time-series database solutions to support predictive maintenance, asset management, and process optimization initiatives. Government-led digitalization programs, coupled with a surge in foreign direct investment in industrial automation, are propelling market growth. The increasing focus on energy efficiency, grid modernization, and smart city projects further boosts the demand for real-time OT data management platforms in the Asia Pacific.
Meanwhile, emerging economies in Latin America, the Middle East, and Africa are gradually embracing time-series databases for OT data, albeit at a slower pace due to infrastructural limitations and budgetary constraints. Adoption in these regions is often driven by localized demand in sectors such as oil & gas, mining, and utilities, where real-time monitoring and asset optimization are critical. However, challenges such as limited access to advanced technologies, skill shortages, and inconsistent regulatory frameworks can impede widespread deployment. Despite these hurdles, increasing awareness of the benefits of digital transformation and ongoing policy reforms to attract foreign investment are expected to gradually improve adoption rates, positioning these regions as potential growth frontiers over the long term.
Attributes | Details |
Report Title | Time-series database for OT data Market Research Report 2033 |
By Component | Software, Services |
By Deployment Mode </ |
https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy
The global Artificial Intelligence (AI) Training Dataset market is experiencing robust growth, driven by the increasing adoption of AI across diverse sectors. The market's expansion is fueled by the burgeoning need for high-quality data to train sophisticated AI algorithms capable of powering applications like smart campuses, autonomous vehicles, and personalized healthcare solutions. The demand for diverse dataset types, including image classification, voice recognition, natural language processing, and object detection datasets, is a key factor contributing to market growth. While the exact market size in 2025 is unavailable, considering a conservative estimate of a $10 billion market in 2025 based on the growth trend and reported market sizes of related industries, and a projected CAGR (Compound Annual Growth Rate) of 25%, the market is poised for significant expansion in the coming years. Key players in this space are leveraging technological advancements and strategic partnerships to enhance data quality and expand their service offerings. Furthermore, the increasing availability of cloud-based data annotation and processing tools is further streamlining operations and making AI training datasets more accessible to businesses of all sizes. Growth is expected to be particularly strong in regions with burgeoning technological advancements and substantial digital infrastructure, such as North America and Asia Pacific. However, challenges such as data privacy concerns, the high cost of data annotation, and the scarcity of skilled professionals capable of handling complex datasets remain obstacles to broader market penetration. The ongoing evolution of AI technologies and the expanding applications of AI across multiple sectors will continue to shape the demand for AI training datasets, pushing this market toward higher growth trajectories in the coming years. The diversity of applications—from smart homes and medical diagnoses to advanced robotics and autonomous driving—creates significant opportunities for companies specializing in this market. Maintaining data quality, security, and ethical considerations will be crucial for future market leadership.
The "Smart Meter Penetration by Census Zone" data table provides aggregated information giving the percentage of Smart Meter penetration (also broken down by Smart Meter Type i.e. SMETS1 or SMETS2) by Census Zone. In Scotland the census zones are at Datazone level, in England and Wales the census zones are at Output Area level. The table gives the following information:An unique identifier for each records (the Postcode Sector, the Census Zone Name, the LV Feeder ID, or the LV Transformer ENID)The total number of SPEN properties for each recordThe total number of Smart Meters associated with these properties, also broken down by Smar Meter type (SMETS1 or SMETS2)The % Smart Meter penetration for each recordFor additional information on column definitions, please click the Dataset schema link below.DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this). Records where the total SPEN population is 4 properties or less have been removed from the published tables. Data Triage As part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Smart Meter Penetration dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information page. Download dataset metadata (JSON)
In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.
Smartphone penetration rate still on the rise
Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.
Smartphone end user sales
In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Ebit-Interest-Coverage Time Series for Xiaomi Corp. Xiaomi Corporation, an investment holding company, engages in the development and sales of smartphones in Mainland China and internationally. It operates through Smartphones, IoT and Lifestyle Products, Internet Services, and Others segments. The company also offers internet of things (IoT) and lifestyle products comprising smart large home appliances, smart TVs, tablets, wearables and other IoT and lifestyle products; hardware repairment services for products; installation services for certain IoT products; and sale of materials. In addition, it provides internet services, such as advertising, online game, and fintech services; intermediary services to the borrowers and third-party funding parties; and development, manufacture, and sales of smart electric vehicles. Further, the company in the wholesale and retail of smartphones and ecosystem partners' products; investment activities; sales of smart hardware and e-book; software and hardware development; procurement and sales of smartphones, ecosystem partners' products and spare parts, and raw materials; operation of retail stores; and commercial factoring and e-commerce business. Additionally, it provides intra-group capital supervision, collection, remittance, credit guarantee, and interest rate risk management; customer; software related; promotion; electronic payment technology; and technical services. Xiaomi Corporation was incorporated in 2010 and is headquartered in Beijing, the People's Republic of China.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The Smartwatch Price Dataset contains information about the features and prices of popular smartwatch models from various brands. The dataset includes columns such as Brand, Model, Operating System, Connectivity, Price (USD), Display Type, Display Size (inches), Resolution, Water Resistance (meters), Battery Life (days), Heart Rate Monitor, GPS, and NFC.
Columns
Brand: the manufacturer of the smartwatch
Model: the specific model of the smartwatch
Operating System: the operating system used by the smartwatch (e.g. watchOS, Wear OS, Garmin OS, Fitbit OS, etc.)
Connectivity: the types of connectivity supported by the smartwatch (e.g. Bluetooth, Wi-Fi, Cellular)
Display Type: the type of display technology used by the smartwatch (e.g. AMOLED, Retina, E-Ink, LCD)
Display Size (inches): the size of the smartwatch's display in inches
Resolution: the resolution of the smartwatch's display in pixels
Water Resistance (meters): the depth to which the smartwatch can be submerged in water without damage
Battery Life (days): the estimated battery life of the smartwatch in days
Heart Rate Monitor: whether or not the smartwatch has a built-in heart rate monitor
GPS: whether or not the smartwatch has built-in GPS for location tracking
NFC: whether or not the smartwatch has NFC (Near Field Communication) for contactless payments or other wireless data transfer.
Price (USD): the price of the smartwatch in US dollars
The dataset provides a comprehensive overview of the different smartwatches available in the market and can be used for various purposes such as price comparison, feature analysis, and market research. The data is gathered from various sources such as official brand websites, online retailers, and tech blogs. This dataset can be useful for individuals or businesses interested in the smartwatch industry, as well as researchers and data analysts.
Cover image: https://pin.it/13TyoYn
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Brenner, B., Fabini, J., Offermanns, M., Semper, S., & Zseby, T. (2024). Malware communication in smart factories: A network traffic data set. Computer Networks, 255, 110804. or in BibTeX: @article{brenner2024malware, title={Malware communication in smart factories: A network traffic data set}, author={Brenner, Bernhard and Fabini, Joachim and Offermanns, Magnus and Semper, Sabrina and Zseby, Tanja}, journal={Computer Networks}, volume={255}, pages={110804}, year={2024}, publisher={Elsevier}} Context and methodology Machine learning-based intrusion detection requires suitable and realistic data sets for training and testing. However, data sets that originate from real networks are rare. Network data is considered privacy-sensitive, and the purposeful introduction of malicious traffic is usually not possible. In this paper, we introduce a labeled data set captured at a smart factory located in Vienna, Austria, during normal operation and during penetration tests with different attack types. The data set contains 173 GB of PCAP files, representing 16 days (395 hours) of factory operation. It includes MQTT, OPC UA, and Modbus/TCP traffic. The captured malicious traffic originated from a professional penetration tester who performed two types of attacks:(a) Aggressive attacks that are easier to detect.(b) Stealthy attacks that are harder to detect. Our data set includes the raw PCAP files and extracted flow data. Labels for packets and flows indicate whether they originated from a specific attack or from benign communication. We describe the methodology for creating the dataset, conduct an analysis of the data, and provide detailed information about the recorded traffic itself. The dataset is freely available to support reproducible research and the comparability of results in the area of intrusion detection in industrial networks. Technical details readme.txt Information about the data collection, format, necessary software and versions to access it.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Depreciation Time Series for Xiaomi Corp. Xiaomi Corporation, an investment holding company, engages in the development and sales of smartphones in Mainland China and internationally. It operates through Smartphones, IoT and Lifestyle Products, Internet Services, and Others segments. The company also offers internet of things (IoT) and lifestyle products comprising smart large home appliances, smart TVs, tablets, wearables and other IoT and lifestyle products; hardware repairment services for products; installation services for certain IoT products; and sale of materials. In addition, it provides internet services, such as advertising, online game, and fintech services; intermediary services to the borrowers and third-party funding parties; and development, manufacture, and sales of smart electric vehicles. Further, the company in the wholesale and retail of smartphones and ecosystem partners' products; investment activities; sales of smart hardware and e-book; software and hardware development; procurement and sales of smartphones, ecosystem partners' products and spare parts, and raw materials; operation of retail stores; and commercial factoring and e-commerce business. Additionally, it provides intra-group capital supervision, collection, remittance, credit guarantee, and interest rate risk management; customer; software related; promotion; electronic payment technology; and technical services. Xiaomi Corporation was incorporated in 2010 and is headquartered in Beijing, the People's Republic of China.
The "Smart Meter Penetration by Postcode" data table provides aggregated information giving the percentage of Smart Meter penetration (also broken down by Smart Meter Type i.e. SMETS1 or SMETS2) by Postcode SectorThe table gives the following information:An unique identifier for each record (the Postcode Sector, the Census Zone Name, the LV Feeder ID, or the LV Transformer ENID)The total number of SPEN properties for each recordThe total number of Smart Meters associated with these properties, also broken down by Smar Meter type (SMETS1 or SMETS2)The % Smart Meter penetration for each recordDisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this). Records where the total SPEN population is 4 properties or less have been removed from the published tables. Data Triage As part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Smart Meter Penetration dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information page. Download dataset metadata (JSON)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Debt-To-Assets-Ratio Time Series for Xiaomi Corp. Xiaomi Corporation, an investment holding company, engages in the development and sales of smartphones in Mainland China and internationally. It operates through Smartphones, IoT and Lifestyle Products, Internet Services, and Others segments. The company also offers internet of things (IoT) and lifestyle products comprising smart large home appliances, smart TVs, tablets, wearables and other IoT and lifestyle products; hardware repairment services for products; installation services for certain IoT products; and sale of materials. In addition, it provides internet services, such as advertising, online game, and fintech services; intermediary services to the borrowers and third-party funding parties; and development, manufacture, and sales of smart electric vehicles. Further, the company in the wholesale and retail of smartphones and ecosystem partners' products; investment activities; sales of smart hardware and e-book; software and hardware development; procurement and sales of smartphones, ecosystem partners' products and spare parts, and raw materials; operation of retail stores; and commercial factoring and e-commerce business. Additionally, it provides intra-group capital supervision, collection, remittance, credit guarantee, and interest rate risk management; customer; software related; promotion; electronic payment technology; and technical services. Xiaomi Corporation was incorporated in 2010 and is headquartered in Beijing, the People's Republic of China.
https://www.zionmarketresearch.com/privacy-policyhttps://www.zionmarketresearch.com/privacy-policy
Global Cloud Mobile Backend as a Service (BaaS) Market size was $3.0 Billion in 2022 and is slated to hit $7.3 Billion by the end of 2030 with a CAGR of nearly 24.1%.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Machine learning-based intrusion detection requires suitable and realistic
data sets for training and testing. However, data sets that originate from
real networks are rare. Network data is considered privacy sensitive and the
purposeful introduction of malicious traffic is usually not possible. In this
paper we introduce a labeled data set captured at a smart factory located
in Vienna, Austria during normal operation and during penetration tests with different
attack types. The data set contains 173 GB of PCAP files, which represent 16 days (395 hours) of factory operation. It includes MQTT, OPC UA, and Modbus/TCP traffic. The captured malicious traffic was originated
by a professional penetration tester who performed two types of attacks: (a)
aggressive attacks that are easier to detect and (b) stealthy attacks that are
harder to detect. Our data set includes the raw PCAP files and extracted
flow data. Labels for packets and flows indicate whether packets (or flows)
originated from a specific attack or from benign communication. We describe
the methodology for creating the data set, conduct an analysis of the data
and provide detailed information about the recorded traffic itself. The data
set is freely available to support reproducible research and the comparability
of results in the area of intrusion detection in industrial networks.
File description:
a_day1, a_day2, s_day1, s_day2, tf_a and tf_s: Main data set, where files starting with "tf" are training files containing only benign, operational data and all other files are attack files containing both, operational data and attack data.
images.zip: Contains descriptive images about the data.
extractions.zip: Contains extracted packets, flows in both labeled and unlabeled form.
a_day_tuesday_dos.zip: additional day of attack traffic containing benign and attack data, including a DoS attack. This day is not labeled.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
Description for each of the variables: