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

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

The Streaming Data Processing System Software market is experiencing robust growth, driven by the exponential increase in data volume from diverse sources and the need for real-time insights across various industries. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. Key drivers include the increasing adoption of cloud-based solutions offering scalability and cost-effectiveness, the growing demand for real-time analytics across sectors like finance (fraud detection, algorithmic trading), healthcare (patient monitoring, predictive diagnostics), and manufacturing (predictive maintenance, supply chain optimization). Furthermore, the rise of IoT devices and the proliferation of big data are significantly fueling market expansion. The dominance of established players like Google, Microsoft, and AWS is expected to continue, although the emergence of specialized niche players and open-source solutions poses a competitive challenge. Market segmentation reveals a significant preference for cloud-based solutions, reflecting the industry's shift towards flexible and scalable infrastructure. North America currently holds the largest market share, fueled by early adoption and a robust technology ecosystem, but Asia Pacific is projected to exhibit the highest growth rate over the forecast period driven by rapid digitalization and increasing government investments in digital infrastructure. While data security and privacy concerns represent a major restraint, innovative solutions focused on enhanced security and compliance are mitigating this risk. The competitive landscape is dynamic, with both established technology giants and specialized startups vying for market share. Strategic partnerships, acquisitions, and continuous technological innovation are defining the competitive dynamics. The future of the market is characterized by an increasing focus on AI and machine learning integration within streaming data processing platforms, enabling advanced analytics and predictive capabilities. The demand for efficient data governance and compliance solutions will also shape the market trajectory, driving the development of systems that ensure data quality, security, and privacy. Overall, the market's future growth prospects remain strong, driven by ongoing technological advancements and the ever-increasing need for real-time data insights across various industry verticals.

The Global Streaming Analytics Market size was valued at USD 9.91 billion in 2023 and is projected to reach USD 58.28 billion by 2032, exhibiting a CAGR of 28.8 % during the forecasts period. The global streaming analytics market’s primary area of specialization relates to the processing and analysis of real-time data from a range of streaming sources including IoT, social media, sensors, and transactional data sources. This market is driven by need for quick solutions of business intelligence and analysis, as the need for making decision in the short time periods is rising more and more. Real-time streaming analytics facilitate the processing of streaming data with analysis and reaction, which improves operational effectiveness, customer satisfaction, and shows proper options to develop. These are the evolution of artificial intelligence and machine learning for analytics, new cloud based streaming services and the concept of edge computing where information processing is done closer to the original source. The market leaders around the globe are focusing and leveraging on the effective and efficient streaming analytics solutions to support several industry domains such as retail, healthcare, finance, and telecommunications. Key drivers for this market are: Growing data volumes and velocity Need for real-time insights and decision-making Increasing adoption of IoT and connected devices Government initiatives and regulations. Potential restraints include: Data privacy and security concerns Lack of skilled professionals High cost of implementation. Notable trends are: Real-time data processing and analytics Cloud adoption and the rise of SaaS-based solutions Predictive analytics and machine learning Artificial intelligence (AI) and deep learning.

The Real-Time Streaming Processing Platform market is experiencing robust growth, projected to reach $1360.4 million in 2025. While a precise CAGR isn't provided, considering the rapid advancements in data analytics and the increasing need for real-time insights across diverse sectors, a conservative estimate would place the CAGR between 15% and 20% for the forecast period (2025-2033). This growth is fueled by several key drivers: the explosive growth of data volume from various sources (IoT, social media, etc.), the urgent need for immediate actionable intelligence in businesses, and the increasing adoption of cloud-based solutions that offer scalability and cost-effectiveness. Key trends shaping the market include the rise of serverless architectures, enhanced integration with AI/ML capabilities for advanced analytics, and the growing demand for edge computing to process data closer to its source for reduced latency. The market is segmented by service type (fully managed and self-managed) and application across diverse industries including financial services, healthcare, manufacturing, and more. The competitive landscape is highly dynamic, with established players like Google, Microsoft, and AWS alongside emerging innovative companies vying for market share. The market's segmentation reveals significant opportunities. The fully-managed services segment is likely to dominate due to its ease of use and reduced operational overhead. Financial services and healthcare and life sciences are leading adopters, driven by stringent regulatory compliance and the need for real-time fraud detection and personalized healthcare. However, restraints remain, including the complexity of implementing and managing real-time streaming platforms, data security and privacy concerns, and the skills gap in data engineering and analytics. Despite these challenges, the long-term outlook remains positive, with the market poised for substantial expansion driven by continuous technological innovation and the ever-increasing demand for real-time data-driven decision making across a broad spectrum of industries. Growth in the Asia Pacific region, particularly China and India, will contribute significantly to overall market expansion.

The Streaming Analytics Marketsize was valued at USD 18.10 USD Billion in 2023 and is projected to reach USD 80.22 USD Billion by 2032, exhibiting a CAGR of 23.7 % during the forecast period.Data Streaming analytics is the oxygenation of the online space turning the once operationg from two to three steps into one while providing near real-time insights into constantly shifting datasets. It's elemental to direct from your the center of the operations. Through calssification one can tap in the energy of continuous data streams from differnent sources, such as devices powered by IoT or social networking services (SNS). These applications are capable of highlighting repeating patterns, abnormalities, and trends in the given moment which help company’s executives make quick decisions and respond beforehand. Streaming analysis does all that is necessary during the performance of monitoring network performance, analysis of the customer behaviour and optimisation of logistics supply chain. For that matter, it gives the dynamic solution sufficient to track the continuous changes that are happening in the digital landscape. It associates all the aspects of data collection and use in a single process, which is eventual useful for any organization to reveal the value placed inside their data as it flows, thus discovering the new insights that enhance innovation and stand against the challenges that the fast-growing world brings. Recent developments include: September 2023: Timeplus announced that it has licensed Proton open sources for developers globally. Through this, companies can seamlessly create ad hoc reports over large datasets, using both live streaming and historical data and achieve faster results at a smaller cost than with other streaming frameworks., August 2023: Microsoft declared the acquisition of Activision Blizzard, Inc. to bring more resourceful and inventive games to performers everywhere and on any device. The acquisition with Activision Blizzard, Inc. focused on driving efforts to further strengthen the company’s culture and accelerate business growth., August 2023: Confluent, Inc. entered a partnership with Google Cloud. The expanded partnership helped more consumers transform their enterprises with real-time data and modernize their data platforms with a dependable bridge from their on-premise, multi-cloud data architectures to Google Cloud., May 2023: Qlik announced the acquisition of Talend, thereby combining its best-in-class capabilities for modern enterprises to transform, trust, access, analyze, and take action with data. The acquisition is claimed to offer significant benefits to clients, including enhanced support & services, expanded product offerings, and increased investments in innovation and R&D., December 2022: Microsoft and LSEG (London Stock Exchange Group) announced a partnership to develop new products and services for data and analytics. The partnership would help LSEG build a scalable and efficient platform for its Data & Analytics business to provide next-generation services to a variety of consumers in the financial markets value chain through enhanced workflow and better flexibility.. Key drivers for this market are: The Proliferation of Edge Computing Coupled with Technological Advancements to Fuel the Next Generation Computing Demand. Potential restraints include: Lack of Streaming Analytics Solutions Integration with Older Systems May Hinder Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

The U.S. Geological Survey South Atlantic Water Science Center, in cooperation with the South Carolina Department of Transportation, implemented a South Carolina StreamStats application in 2018. This shapefile dataset contains vector lines representing streams, rivers, and ditches that were used in preparing the underlying data for the South Carolina StreamStats application. Data were compiled from multiple sources, but principally represent lidar-derived linework from the South Carolina Department of Natural Resources and the South Carolina Lidar Consortium.The South Carolina hydrography lines were created from elevation rasters that ranged from 4 to 10 ft resolution, to produce a product of approximately 1:6,000-scale. Other sources include the 1:24,000 scale high resolution National Hydrography Dataset streamlines [for streamlines in Georgetown County (SC), NC, and GA] and the 1:4,800 scale local-resolution North Carolina Stream Mapping Project lines (mountain counties). These ...

The global market for stream data pipeline processing tools is experiencing robust growth, driven by the increasing volume and velocity of data generated across diverse industries. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This significant growth is fueled by several key factors: the rising adoption of cloud-native architectures, the proliferation of real-time analytics applications (particularly in finance and security), and the increasing need for efficient and scalable data processing solutions to handle the ever-growing data streams from IoT devices, social media, and other sources. The demand for real-time insights is a major driver, pushing organizations to adopt tools capable of processing and analyzing data instantly, rather than relying on batch processing methods. Further, the continued expansion of cloud computing and the availability of sophisticated, managed services are simplifying implementation and reducing the total cost of ownership for these tools. The market is segmented by tool type (real-time, proprietary, and cloud-native) and application (finance and security, with other sectors like healthcare and logistics also showing increasing adoption). While North America currently holds a dominant market share, fueled by early adoption and a strong technology ecosystem, regions like Asia-Pacific are experiencing rapid growth due to increasing digitalization and investment in data infrastructure. However, factors such as the complexity of implementation, the need for skilled personnel, and data security concerns pose challenges to market expansion. The competitive landscape is highly fragmented, with a mix of established players like Google, IBM, and Microsoft, alongside emerging niche providers. The ongoing innovation in areas such as AI-powered data processing, serverless architectures, and enhanced security features will continue to shape the market landscape in the coming years.

The Major Water Sources list was created by IDNR to address concerns that new confinements would be constructed within view of "floatable/canoeable" rivers. The definition of major water source in Iowa Administrative Code - Natural Resource Commission - Chapter 65 is: "a water source that is a lake, reservoir, river or stream located within the territorial limits of the state, or any marginal river area adjacent to the state, if the water source is capable of supporting a floating vessel capable of carrying one or more persons during a total of a six month period in one out of ten years, excluding periods of flooding." The list was created by getting the counties to identify "canoeable" streams and with additional input from DNR staff.

Data consists of a python program, which generates a variety of strings simulates the behavior of multiple sources. The stream of data attains a fixed size of a window, where an aggregation function is applied as shown in program word count. The word count program read multiple input files and provide aggregated values. The processed results are shown in file output.

Notes: As of June 2020 this dataset has been static for several years. Recent versions of NHD High Res may be more detailed than this dataset for some areas, while this dataset may still be more detailed than NHD High Res in other areas. This dataset is considered authoritative as used by CDFW for particular tracking purposes but may not be current or comprehensive for all streams in the state.National Hydrography Dataset (NHD) high resolution NHDFlowline features for California were originally dissolved on common GNIS_ID or StreamLevel* attributes and routed from mouth to headwater in meters. The results are measured polyline features representing entire streams. Routes on these streams are measured upstream, i.e., the measure at the mouth of a stream is zero and at the upstream end the measure matches the total length of the stream feature. Using GIS tools, a user of this dataset can retrieve the distance in meters upstream from the mouth at any point along a stream feature.** CA_Streams_v3 Update Notes: This version includes over 200 stream modifications and additions resulting from requests for updating from CDFW staff and others***. New locator fields from the USGS Watershed Boundary Dataset (WBD) have been added for v3 to enhance user's ability to search for or extract subsets of California Streams by hydrologic area. *See the Source Citation section of this metadata for further information on NHD, WBD, NHDFlowline, GNIS_ID and StreamLevel. **See the Data Quality section of this metadata for further explanation of stream feature development. ***Some current NHD data has not yet been included in CA_Streams. The effort to synchronize CA_Streams with NHD is ongoing.

The global market for stream data pipeline processing tools is experiencing robust growth, driven by the exponential increase in real-time data generated from various sources, including IoT devices, social media, and e-commerce platforms. The demand for immediate insights and actionable intelligence from this data is fueling the adoption of these tools across diverse industries, such as finance, healthcare, and manufacturing. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 20% from 2025 to 2033, reaching approximately $60 billion by 2033. This growth is propelled by several factors, including the increasing adoption of cloud-based solutions, the need for enhanced data security and governance, and the growing prevalence of advanced analytics techniques like machine learning and AI, all requiring efficient stream processing capabilities. Key players like Google, AWS, Microsoft, and IBM are leading the market, driving innovation through continuous product enhancements and strategic acquisitions. However, challenges such as data complexity, integration complexities across diverse systems, and the need for skilled professionals to manage these systems act as restraints. The market segmentation reveals a strong preference for cloud-based solutions due to their scalability and cost-effectiveness. The North American region currently holds the largest market share, followed by Europe and Asia-Pacific. However, the Asia-Pacific region is witnessing the fastest growth, fueled by rising digitalization and technological advancements. The competitive landscape is highly dynamic, with established players and emerging startups vying for market share. This necessitates continuous innovation in areas like enhanced real-time analytics capabilities, improved data security features, and integration with other business intelligence platforms. The future of the stream data pipeline processing tool market appears promising, with continued growth driven by the increasing volume and velocity of data generated in a rapidly digitalizing world.

This data release collates stream water temperature observations from across the United States from four data sources: The U.S. Geological Survey's National Water Information System (NWIS), Water Quality Portal (WQP), Spatial Hydro-Ecological Decision Systems temperature database (EcoSHEDS), and the U.S. Fish and Wildlife's NorWeST stream temperature database. These data were compiled for use in broad scale water temperature models. Observations are included from the contiguous continental US, as well as Alaska, Hawaii, and territories. Temperature monitoring sites were paired to stream segments from the Geospatial Fabric for the National Hydrologic Model. Continuous and discrete data were reduced to daily mean, minimum, and maximum temperatures when more than one measurement was made per site-day. Various quality control checks were conducted including inspecting and converting units, eliminating some duplicate entries, interpreting flags and removing low quality observations, fixing date issues from the WQP, and filtering to expected water temperature ranges. However, we expect data quality issues persist and users should conduct further data quality checks that match the intended use of the data. This data release contains four core files: - site_metadata.csv contains information about each site at which water temperature observations are reported in this dataset. - national_stream_temp_code.zip contains the R code used to derive the data in this data release. - daily_stream_temperature.zip is a compressed comma separated file of observed water temperatures. - spatial.zip contains the geographic information about each site at which water temperature observations are reported in this dataset.

Description:

This zipfile contains linear geometries showing a channel network across the SAFE Project landscape. The network was calculated using the GRASS hydrology tool r.stream.extract from SRTM elevation data and the resulting flow accumulation predictions (see https://zenodo.org/record/3490488 and https://zenodo.org/record/3490687).

Note that these networks are derived entirely from remotely sensed data, but do form a single interconnected network across the wider SAFE landscape. Other stream network data are available () but the provenance of these are not well known and they only cover part of the SAFE network.

Details of the geoprocessing can be found here: https://www.safeproject.net/dokuwiki/safe_gis/stream_networks.

Project: This dataset was collected as part of the following SAFE research project: SAFE CORE DATA

XML metadata: GEMINI compliant metadata for this dataset is available here

Files: This dataset consists of 2 files: SAFE_SRTM_Stream_network_metadata.xlsx, SRTM_Channels_network.zip

SAFE_SRTM_Stream_network_metadata.xlsx

This file only contains metadata for the files below

SRTM_Channels_network.zip

Description: Shapefile containing 54413 calculated segments forming a channel network across the wider SAFE landscape.

This file contains 1 data tables:

1. Feature properties (described in worksheet Properties)

   Description: Field descriptions for shapefile properties

   Number of fields: 17

   Number of data rows: Unavailable (table metadata description only).

   Fields:

   • cat: Identity of segment (Field type: id)
   • type_cd: Segment type (see also type) (Field type: numeric)
   • ID: Identity of segment (Field type: id)
   • length: Length of network segment (Field type: numeric)
   • n_ponts: Number of points in segment (Field type: numeric)
   • sourceX: X coordinate of source point of segment (Field type: numeric)
   • sourceY: Y coordinate of source point of segment (Field type: numeric)
   • sinkX: X coordinate of sink point of segment (Field type: numeric)
   • sinkY: Y coordinate of sink point of segment (Field type: numeric)
   • type: Segment type - is the segment a source or outflow segment (connected at one end only, type_cd = 0) or internal (connected at both ends, type_cd=1) (Field type: categorical)
   • snkChnn: Identity of sink channel for this segment (Field type: id)
   • srcChnn: Identities of source channels for this segment (Field type: id)
   • nSourcs: Count of source channels (Field type: numeric)
   • sorcElv: Elevation of start point of segment (Field type: numeric)
   • sinkElv: Elevation of sink point of segment (Field type: numeric)
   • sorcFlw: Incoming flow at segment source (Field type: numeric)
   • sinkFlw: Outgoing flow at segment sink (Field type: numeric)

Date range: 2010-10-01 to 2019-10-01

Latitudinal extent: 4.0223 to 5.9761

Longitudinal extent: 116.0242 to 117.9758

This repository contains various datasets used to map and analyze freshwater connectivity (i.e., corridors) in the conterminous US based on networks of lakes, streams and rivers. We considered lake-stream networks as analogous to habitat corridors. Hub lakes are individual lakes that are disproportionately important for maintaining intact networks. We also analyzed the protection status of freshwater connectivity using the US Protected Areas Database v. 2.0. R analysis scripts can also be found in this repository. Much of the data we used came from published or soon-to-be published sources, which are referenced below.

This data set contains streamflow data from the ALERT stream gages overseen by the Arizona Department of Water Resources. There are a total of 6 stations included in the data set. The data are collected on an event basis. The stations are located throughout Cochise, Gila, Pinal, Santa Cruz and Yuma Counties in Arizona. This data set covers the period from 1 June to 30 September 2004. The data are in columnar ASCII format. The data are provided as is in their original format.

This resource contains the data and code associated with the manuscript "STICr: An open-source package and workflow for Stream Temperature, Intermittency, and Conductivity (STIC) data" by Zipper et al.

Full citation: [ADD HERE]

Paper abstract: Non-perennial streams constitute over half the world’s stream miles but are not commonly included in streamflow monitoring networks. Stream Temperature, Intermittency, and Conductivity (STIC) loggers are widely used for characterizing flow presence or absence in non-perennial streams. To facilitate ‘FAIR’ (findable, accessible, interoperable, and reusable) stream intermittency science, we present an open-source R package, STICr, for processing STIC logger data. STICr includes functions to tidy data, calibrate sensors, classify data into wet/dry readings, and perform quality checks and validation. We also show a reproducible STICr-based workflow for an interdisciplinary project spanning multiple watersheds, years, and research groups. In South Fork Kings Creek (Konza Prairie, Kansas, USA), we show that stream intermittency is driven by the balance between monthly precipitation inputs, seasonal evapotranspiration fluxes, and underlying geology. Overall, STICr can be used to create FAIR stream intermittency data and enable advances in hydrologic and ecosystem science.

Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573Metadata and Downloads

This dataset contains data used to train the models.

This dataset contains the 2012 version of the anadromous fish streams (polylines) for Southeast Alaska and is pull from the Anadromous Waters Catalog. The Alaska Department of Fish and Game's (ADF&G) Anadromous water bodies data is derived from the ADF&G's GIS shape files for the "Catalog of Waters Important for Spawning, Rearing or Migration of Anadromous Fishes" (referred to as the "Catalog") and the "Atlas to the Catalog of Waters Important for Spawning, Rearing or Migration of Anadromous Fishes" (referred to as the "Atlas"). It is produced for general visual reference and to aid users in generating various natural resource analyses and products. The shape files depict the known anadromous fish bearing lakes and streams within Alaska (from the mouth to the known upper extent of species usage). It incorporates data from a variety of sources including: USGS Digital Line Graph (DLG) and National Hydrography Dataset (NHD) hydrography data; Alaska Department of Natural Resources hydrography layer; and ADF&G shape files for the "Atlas" and "Catalog". ADF&G updates the Anadromous Streams data regularly. Note that stream numbers, locations, extent of cataloged habitat or species utilization of a given stream may change from year to year. Data for the shape files are current as of the 2012 revision of the "Atlas to the Catalog of Waters Important for the Spawning, Rearing or Migration of Anadromous Fishes" and the "Catalog of Waters Important for the Spawning, Rearing or Migration of Anadromous Fishes" effective June 1, 2012. This particular data layer is for the Southeastern Region of Alaska.