Estimates of various low-flow statistics were computed at 51 ungaged stream locations throughout New Jersey during the 2018 water year using methods in the published reports, Streamflow Characteristics and Trends in New Jersey, Water Years 1897-2003 (Watson and others, 2005) and Implementation of MOVE.1, Censored MOVE.1, and Piecewise MOVE.1 Low-Flow Regressions with Applications at Partial-Record Streamgages in New Jersey (Colarullo and others, 2018). The estimates are computed as needed for use in water resources permitting, assessment, and management by the New Jersey Department of Environmental Protection. The data release includes the stream name, location, method of estimation, drainage area, and intended use of the low-flow statistics computed during the 2018 water year. The data are provided as both a plain text file and ArcGIS shapefile format. References for publications cited: Watson, K.M., Reiser, R.G., Nieswand, S.P., and Schopp, R.D., 2005, Streamflow characteristics and trends in New Jersey, water years 1897-2003: U.S. Geological Survey Scientific Investigations Report 2005-5105, 131 p., https://pubs.usgs.gov/sir/2005/5105/pdf/NJsir2005-5105_report.pdf. Colarullo, S.J., Sullivan, S.L., and McHugh, A.R., 2018, Implementation of MOVE.1, censored MOVE.1, and piecewise MOVE.1 low-flow regressions with applications at partial-record streamgaging stations in New Jersey: U.S. Geological Survey Open-File Report 2018–1089, 20 p., https://doi.org/10.3133/ofr20181089.

We'll tailor a Twitch dataset to meet your unique needs, encompassing streamer profiles, viewer engagement metrics, streaming times, demographic data of viewers, follower counts, chat statistics, and other pertinent metrics.

Leverage our Twitch datasets for diverse applications to bolster strategic planning and market analysis. Scrutinizing these datasets enables organizations to grasp viewer preferences and streaming trends, facilitating nuanced content development and engagement initiatives. Customize your access to the entire dataset or specific subsets as per your business requisites.

Popular use cases involve optimizing content strategy based on streamer performance and viewer engagement, enhancing marketing strategies through targeted audience segmentation, and identifying and forecasting trends in the streaming community to stay ahead in the digital entertainment landscape.

The world of video gaming has evolved from a single player experience to a social occasion where players can meet to play games together and watch others play online. In the first quarter of 2021, a total of 8.8 billion hours of video game live streams were watched across the world, up from merely 3.6 billion hours two years previously.

About this Dataset

Twitch stream data collected from ~2500 popular Twitch streamers over 4 months (9/24/2020 to 2/05/2021). Real time data for live streamers updated approx. every 5 minutes. Dataset includes current timestamp, streamer name, stream title, game_id, stream start time, and viewership count. Contains 7,936,251 live stream data instances.

Category

Machine Learning

Keywords

social network,twitch,social media,Machine Learning,viewership

Row Count

7936132

Price

$40.00

Estimates of various low-flow statistics were computed at 66 ungaged stream locations throughout New Jersey during the 2021 water year using methods in the published reports, 1) Streamflow characteristics and trends in New Jersey, water years 1897-2003 (Watson and others, 2005) and 2) Implementation of MOVE.1, censored MOVE.1, and piecewise MOVE.1 low-flow regressions with applications at partial-record streamgaging stations in New Jersey (Colarullo and others, 2018). The estimates are computed as needed for use in water resources permitting, assessment, and management by the New Jersey Department of Environmental Protection. The data release includes the stream name, location, drainage area, method of estimation, lowest annual and winter average flows, and the 75 percent flow duration computed during the 2021 water year. The data are provided in plain text file and ArcGIS shapefile formats. References for publications cited: - Colarullo, S.J., Sullivan, S.L., and McHugh, A.R., 2018, Implementation of MOVE.1, censored MOVE.1, and piecewise MOVE.1 low-flow regressions with applications at partial-record streamgaging stations in New Jersey: U.S. Geological Survey Open-File Report 2018-1089, 20 p., accessed March 31, 2022, at https://doi.org/10.3133/ofr20181089. - Watson, K.M., Reiser, R.G., Nieswand, S.P., and Schopp, R.D., 2005, Streamflow characteristics and trends in New Jersey, water years 1897-2003: U.S. Geological Survey Scientific Investigations Report 2005-5105, 131 p., accessed March 31, 2022, at https://doi.org/10.3133/sir20055105.

Data release includes the following five data tables: (1) water-quality constituent outliers that were removed from the calibration of regression models used to estimate streamwater solute loads, (2) parameters used to model peak streamflow recurrence intervals, (3) models used to estimate streamwater constituent loads, (4) statistical summaries of water-quality observations, and (5) estimated annual streamwater constituent yields. An associated metadata file is included for each of the five data tables.

Worldwide, **** percent of internet users watched content via streaming services each month as of the third quarter of 2023. People from the South Africa were using video streaming platforms the most, with ** percent of respondents to the survey stating to stream every month. Chile came second, with ** percent of people watching TV content on streaming platforms, followed by Mexico. By comparison, only **** percent of consumers from Russia indicated to use video streaming services, such as Netflix, on a monthly basis.

Multi Order Hydrologic Position (MOHP) raster datasets: Distance from Stream to Divide (DSD) and Lateral Position (LP) have been produced nationally for the 48 contiguous United States at 30-meter and 90-meter cell resolution for stream orders 1 through 9. These data are available for testing as predictor variables for various regional and national groundwater-flow and groundwater-quality statistical models. For quicker downloads, these data are available here nationally at a 90-meter cell resolution, as well as on the National Spatial Data Infrastructure (NSDI) Node at the higher 30-meter cell resolution ( https://water.usgs.gov/GIS/metadata/styles/landingPage/national_MOHP_Predictor.xml ). The concept behind MOHP is that for any given point on the earth’s surface there is the potential for longer and longer groundwater flow paths as one goes deeper and deeper beneath the land surface. These increasing depths correspond to increasing stream orders. Or in other words, with increasing depth these paths of groundwater flow travel further from divides to point of discharge which are to increasingly larger streams of higher stream order. DSD – Raster – Distance from Stream to Divide (DSD) rasters have cell values equal to the sum of the shortest distance to the stream or associated waterbody plus the shortest distance to the matching Thiessen divide. There are 9 rasters for streams orders 1 through 9. Units are in meters. LP – Raster -- the lateral position (LP) raster has cell values equal to the shortest distance to the stream or associated waterbody divided by the DSD. There are 9 rasters for streams orders 1 through 9. Combined, these two factors, DSD and LP, provide a measure or description of potential distance of groundwater flow to any location along the groundwater flow path.

The Daily Stream Flow Amounts Data Set contains daily measurements of stream flow for the four LTER stations and for the USGS stream-flow station located on tributaries to Kings Creek. This data set contains measurements from April 1979 to September 1988 for the USGS station, and from June 1985 to December 1987 for the 4 LTER stations. Five stream-flow gauges were placed across creeks in the Long-Term Ecological Research (LTER) section of the FIFE study area. Four of these five stations were maintained and monitored by the LTER staff while the fifth was part of the USGS network of stream flow gauges. The V-throated flume and standpipes used at the LTER weirs operated on the principle that the height of the water level in a standpipe at a specific location within a weir of known dimensions can be converted to volume of water in the stream. The change of this instantaneous volume with time could then be used to compute volumetric stream flow. The stilling pipe installation at the USGS stations operates on the principle that the height of the water level in a standpipe at a specific location within a streambed can be converted to volume of water in the stream. The tracking of the change in stream height with time then enables the calculation of stream flow.

Netflix's global subscriber base has reached an impressive milestone, surpassing *** million paid subscribers worldwide in the fourth quarter of 2024. This marks a significant increase of nearly ** million subscribers compared to the previous quarter, solidifying Netflix's position as a dominant force in the streaming industry. Adapting to customer losses Netflix's growth has not always been consistent. During the first half of 2022, the streaming giant lost over *** million customers. In response to these losses, Netflix introduced an ad-supported tier in November of that same year. This strategic move has paid off, with the lower-cost plan attracting ** million monthly active users globally by November 2024, demonstrating Netflix's ability to adapt to changing market conditions and consumer preferences. Global expansion Netflix continues to focus on international markets, with a forecast suggesting that the Asia Pacific region is expected to see the most substantial growth in the upcoming years, potentially reaching around **** million subscribers by 2029. To correspond to the needs of the non-American target group, the company has heavily invested in international content in recent years, with Korean, Spanish, and Japanese being the most watched non-English content languages on the platform.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. The MBSS assesses the ecological condition of 1st-4th order streams statwide (1995 - 2014). Biological - physical - and chemical pararmeters are reported as well site catchment data. See http://www.dnr.state.md.us/streams/MBSS.asp Last Updated: 2015 Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Hydrology/MD_StreamHealth/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Streaming Data Processing System Software Market Outlook

The global market size for Streaming Data Processing System Software was valued at approximately USD 9.5 billion in 2023 and is projected to reach around USD 23.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 10.8% over the forecast period. The surge in the need for real-time data processing capabilities, driven by the exponential growth of data from various sources such as social media, IoT devices, and enterprise data systems, is a significant growth factor for this market.

One of the primary growth drivers in this market is the increasing demand for real-time analytics across various industries. In a world where immediate decision-making can determine the success or failure of a business, organizations are increasingly turning to streaming data processing systems to gain instant insights from their data. This need for real-time information is particularly pronounced in sectors like finance, healthcare, and retail, where timely data can prevent fraud, improve patient outcomes, and optimize supply chains, respectively. Additionally, the proliferation of IoT devices generating massive amounts of data continuously requires robust systems for real-time data ingestion, processing, and analytics.

Another major factor contributing to the market's growth is technological advancements and innovations in big data and artificial intelligence. With improvements in machine learning algorithms, data mining, and in-memory computing, modern streaming data processing systems are becoming more efficient, scalable, and versatile. These advancements enable businesses to handle larger data volumes and more complex processing tasks, further driving the adoption of these systems. Moreover, open-source platforms and frameworks like Apache Kafka, Apache Flink, and Apache Storm are continually evolving, lowering the entry barriers for organizations looking to implement advanced streaming data solutions.

The increasing adoption of cloud-based solutions is also a significant growth factor for the streaming data processing system software market. Cloud platforms offer scalable, flexible, and cost-effective solutions for businesses, enabling them to handle variable workloads more efficiently. The shift to cloud-based systems is especially beneficial for small and medium enterprises (SMEs) that may lack the resources to invest in extensive on-premises infrastructure. Cloud service providers are also enhancing their offerings with integrated streaming data processing capabilities, making it easier for organizations to deploy and manage these systems.

Regionally, North America holds the largest market share for streaming data processing system software, driven by strong technological infrastructure, high cloud adoption rates, and significant investments in big data and AI technologies. The Asia Pacific region is also expected to witness substantial growth during the forecast period, primarily due to the rapid digital transformation initiatives, growing internet and smartphone penetration, and increasing adoption of IoT technologies across various industries. Europe, Latin America, and the Middle East & Africa are also contributing to the market growth, albeit at differing rates, each driven by region-specific factors and technological advancements.

Component Analysis

The Streaming Data Processing System Software market is segmented by component into software and services. The software segment holds the lion’s share of the market, driven by the increasing need for sophisticated tools that facilitate real-time data analytics and processing. These software solutions are designed to handle the complexities of streaming data, providing functionalities like data ingestion, real-time analytics, data integration, and visualization. The continuous evolution of software capabilities, enhanced by artificial intelligence and machine learning, is significantly contributing to market growth. Furthermore, the availability of various open-source tools and platforms has democratized access to advanced streaming data processing solutions, fostering innovation and adoption across different industry verticals.

The services segment, while smaller in comparison to software, plays a critical role in the overall ecosystem. Services include consulting, integration, maintenance, and support, which are essential for the successful implementation and operation of streaming data processing systems. Organizations often require expert guidance to navigate the complexities of deploying these systems, ensuring they are optimally configure

This CSV file contains 21 dam metrics representing stream fragmentation and flow alteration for nearly 2.3 million stream reaches in the conterminous USA. Dam metrics fall into four main categories: segment-based, count and density, distance-based, and cumulative reservoir storage (described below). These data were developed using spatially verified large dam locations (n=49,468) primarily from the National Anthropogenic Barrier Dataset (NABD) that were spatially linked to the National Hydrography Dataset Plus version 1 (NHDPlusV1). These dam metrics have been summarized using the unique identifier field native to the NHDPlusV1 (COMID) which can be used to join this table to spatial layers and data tables of the NHDPlusV1. Non-fluvial features in the NHDPlusV1 (lake and reservoir flow paths, coastlines, etc.) are excluded (see NFHP metadata).
Please contact Arthur Cooper (coopera@msu.edu) for a copy of the publication associated with this data: Cooper, A.R., Infante, D.M., Daniel, W.M., Wehrly, K.E., Wang, L., Brenden, T.O. 2017. Assessment of dam effects for streams and fish assemblages of the conterminous USA. Science of the Total Environment doi:10.1016/j.scitotenv.2017.02.067

This data set includes information on sampling locations and physical conditions in lakes and streams across Ontario. It also includes meteorological conditions from monitoring stations in south-central Ontario.

Lakes

The data for lakes includes sampling location details and measurements such as:

• temperature (1976-2021)
• dissolved oxygen levels (1976-2021)
• water transparency (1976-2021)
• ice dates (1976-2022)

Streams

The data for streams includes measurements such as stream flow discharge from monitoring stations in south-central Ontario. (1976-2019)

Meteorological conditions

This dataset contains meteorological conditions for climate stations close to monitored lakes in south-central Ontario. It includes measurements such as:

• air temperature (1976-2020)
• humidity (1976-2020)
• precipitation (1976-2020)
• wind speed (1976-2020)

Data were collected since 1976 as part of routine monitoring of water quality of inland waters and for scientific and research purposes.

U.S. Rivers and Streams represents detailed rivers and streams in the United States. Due to the very large number of features in this dataset, it has a minimum draw scale of 1:400,000.To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to USA Detailed Rivers and Streams.

Real Time Data Streaming Tool Market Outlook

The global Real Time Data Streaming Tool market size was valued at approximately USD 10.2 billion in 2023 and is projected to grow at a robust CAGR of 18.5% from 2024 to 2032, reaching an estimated market size of USD 35.3 billion by 2032. The primary growth factor driving this market is the increasing need for businesses to gain quick insights from massive amounts of data to make informed decisions in a competitive landscape.

One of the significant growth factors in the Real Time Data Streaming Tool market is the exponential increase in data generation from various sources such as social media, IoT devices, and enterprise applications. As businesses seek to harness this data to gain real-time insights, the demand for efficient data streaming tools is escalating. Organizations across sectors are recognizing the competitive advantage that real-time data analytics can provide, such as enhancing customer experiences, optimizing operations, and identifying new revenue opportunities.

Another crucial factor propelling growth in this market is the widespread adoption of advanced technologies like artificial intelligence (AI) and machine learning (ML). These technologies rely heavily on data, and the ability to process this data in real-time is paramount for their effective deployment. For instance, in sectors such as healthcare and finance, real-time data processing can lead to improved predictive analytics, fraud detection, and personalized services, thereby driving the adoption of real-time data streaming tools.

The increasing investment in cloud-based infrastructure is also a significant driver for the Real Time Data Streaming Tool market. Cloud platforms offer scalable and flexible solutions that can handle large volumes of data with minimal latency. This is particularly beneficial for small and medium enterprises (SMEs) that may not have the resources to invest in extensive on-premises infrastructure. The shift towards cloud-based solutions is further accelerated by the growing prevalence of remote work, which necessitates efficient and reliable data streaming capabilities.

From a regional perspective, North America is expected to dominate the Real Time Data Streaming Tool market, owing to the early adoption of advanced technologies and the presence of numerous key market players. However, the Asia Pacific region is anticipated to witness the highest growth rate due to rapid digital transformation in emerging economies like China and India, coupled with increasing investments in IT infrastructure. Europe also represents a significant market, driven by stringent data regulations and the growing need for real-time analytics in various industries.

Real Time Analytics is becoming an indispensable tool for organizations aiming to stay ahead in today's fast-paced market environment. By leveraging real time analytics, businesses can analyze data as it is generated, allowing for immediate insights and actions. This capability is crucial for sectors such as finance and healthcare, where timely data-driven decisions can significantly impact outcomes. Real time analytics not only enhances operational efficiency but also enables companies to personalize customer experiences and optimize supply chain processes. As the volume of data continues to grow, the demand for real time analytics solutions is expected to rise, driving further innovation and adoption in the market.

Component Analysis

In the Real Time Data Streaming Tool market, the component segment is broadly categorized into software, hardware, and services. The software segment is expected to hold the largest market share due to the extensive adoption of various data streaming platforms and tools. These software solutions offer a range of functionalities such as data integration, processing, and visualization, which are crucial for real-time analytics. Vendors are continuously enhancing their software offerings with advanced features like AI and ML capabilities, further driving their adoption.

Hardware components, although a smaller segment compared to software, play a critical role in the Real Time Data Streaming Tool market. Specialized hardware solutions, such as high-speed data servers and network accelerators, are essential for managing the substantial volumes of data generated in real-time. These hardware solutions ensure minimal latency and high processing speeds, which are crucial for sectors that rely on i

This resource from Ecocat is a catalogue containing each stream's location, spawning distribution, barriers and points of difficult ascent, escapement records, and other general data pertaining to the stream. The catalogue also includes a topographical map of the stream's location and in some cases, a sketch which further describes the surrounding area.

Percentage of Canadians' time spent online and using video streaming services and video gaming services, in a typical week.

In-stream habitat data include measurements of a variety of physical and aquatic stream attributes that collectively reveal a great deal about stream condition for salmonids and trout. Characterizing and inventorying the physical conditions that define stream habitat for salmonids is an important part of the habitat restoration process. The California Department of Fish and Wildlife (CDFW) collects data on a number of physical attributes of streams and classifies these streams by one of several habitat types. The in-stream habitat data collection process involves two distinct steps; identifying channel type and assigning a habitat type. These in-stream habitat data are used for a variety of purposes including analysis of stream suitability for supporting salmonid populations, as part of larger and more complex watershed assessments, and to establish baseline conditions against which future assessments can measure change. They are a critical part of determining restoration priorities and identifying salmonid refugia. The California Salmonid Stream Habitat Restoration Manual published by the CDFW, describes the process of using in-stream habitat data and other data and information for identifying streams with restoration potential and working through the stream restoration process.The objective of stream inventory reports are to document the current habitat conditions and recommend options for the potential enhancement of salmonid habitat. Recommendations for habitat improvement activities are based upon target habitat values suitable for salmonids in Californias streams.