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59 results for “stream networks”
Studibach stream networks
<p>These files contain the data for the following publication:</p> <p>van Meerveld, H. J. I., Kirchner, J. W., Vis, M. J. P., Assendelft, R. S., and Seibert, J.: Expansion and contraction of the flowing stream network changes hillslope flowpath lengths and the shape of the travel time distribution, Hydrol. Earth Syst. Sci., <a href="https://doi.org/10.5194/hess-2019-218">https://doi.org/10.5194/hess-2019-218</a></p> <p>The data set includes:</p> <p>*) Mapped_networks.shp: Shapefile with mapped stream networks in the upper Studibach catchment (Alptal, Switzerland) for three different dates (columns in the attribute table, yyyy_mm_dd):</p> <ul> <li>Aug 21, 2018: Extremely dry conditions</li> <li>Nov 2, 2016: Dry conditions</li> <li>Oct. 25, 2016: Wetting-up conditions (during a low intensity rainfall event)</li> </ul> <p>The stream state was classified as:</p> <ol> <li>Dry streambed</li> <li>Standing water</li> <li>Weakly trickling</li> <li>Trickling</li> <li>Weakly flowing</li> <li>Flowing</li> </ol> <p>*) Topo_network.shp: The stream network shown on the topographic map (derived from the Federal Office of Topography (Swisstopo) National Map 1:25,000 (Pixelkarte 25))</p> <p>*) Boundary.shp: The catchment boundaries</p>
Simulation results of adaptive multicast streaming for videoconferences in software-defined networks
<p>Real-time applications, such as video conferences, have strong Quality of Service requirements for ensuring a decent Quality of Experience. Nowadays, most of these conferences are performed over wireless devices. Thus, an appropriate management of both heterogeneous mobile devices and network dynamics is necessary. Software Defined Networking enables the use of multicasting and stream layering inside the network nodes, two techniques able to enhance the quality of live video streams. In this paper, we propose two algorithms for building and maintaining multicast sessions in a software-defined network. The first algorithm sets up the initial multicast trees for a given call. It optimally places the stream layer adaptation function inside the core network in order to minimize the bandwidth consumption. This algorithm has two versions: the first one, based on shortest path trees is minimizing the latency, while the second one, based on spanning trees is minimizing the bandwidth consumption. The second algorithm adapts the multicast trees according to the network changes occurring during a call. It does not recompute the trees, but only relocates the stream layer adaptation functions. It requires very low computation at the controller, thus making our proposal fast and highly reactive. Extensive simulation results confirm the efficiency of our solution in terms of processing time and bandwidth savings compared to existing solutions such as multiple unicast connections, Multipoint Control Unit solutions and application layer multicast.</p>
LAGOS-US NETWORKS v1.0: Data module of surface water networks characterizing connections among lakes, streams, and rivers in the conterminous U.S
Knowing the degree of surface water connectivity among aquatic ecosystems can help scientists better understand and predict the movement of materials and biota across ecosystems. Methods to quantify surface water networks that include lake and stream connections at broad spatial scales are rare because it is difficult to balance accurate estimates of surface water connectivity and computational challenges. The LAGOS-US NETWORKS (NETS) module contains surface connectivity metrics for lake networks across the conterminous United States. We applied a graph theory approach to identify lake networks (i.e. a set of lakes connected by streams either upstream, downstream, or both) created from the medium resolution NHD lakes, streams, and rivers and subsequently derive surface water connectivity metrics for lakes and networks. Using this approach, we created a total of 898 networks that include 86,511 lakes. The NETS module includes a table with metrics for connections between lakes (both upstream and downstream), dams, network position, and whole networks. NETS also includes a flow table and bidirectional and unidirectional distance tables that provide the distances between every pair of connected lakes.
Stream and air temperature data from stream network in the Andrews Experimental Forest, 1997-2001
This study examines stream temperatures and associated air temperatures at multiple sites in stream networks within the Andrews Experimental Forest. Stream temperature sensors were placed at matched elevations in the main headwater streams of Lookout Creek, Mack Creek and McRae Creek as well as above and below major confluences in downstream reaches. Air temperatures were recorded 1.5 m above the stream at selected sites. Data were collected every half hour during late spring and summers. Some sites have data during fall and winter. Sensors were also placed in bottom of shallow piezometric wells in WS 3.
Stream nitrate concentrations and discharge, stream nitrate uptake, and results of stream network nitrate model to determine lateral nitrate load from land to stream in Oak Creek, Arizona, USA
Data package associated with Handler et al. (2024) "Nitrate loads from land to stream are balanced by in-stream nitrate uptake across season in a dryland stream". The study describes the nitrate dynamics in Oak Creek watershed. Data include measurements from four seasonal synoptic sampling campaigns, nine seasonal stream nitrate uptake experiments on the main stem and tributaries, and the results of a network model that estimates the lateral load of nitrate from surrounding landscape to the stream network as well as network-level stream nitrate uptake and retention.
Dataset from VR Streaming Server (Emulated) and Radio Access Network for Streaming Traffic
<p>The dataset contains an experiment in a site where UEs attach to a gNodeB that provides access to a streaming server that is stressed with high demanding transcoding workloads to emulate VR/AR processes. The UEs are realized through the Remote UE mode enabled by Amarisoft Simbox emulator, and the gNodeB is realized through the Amarisoft Callbox, which also provides the user plane function. The emulated VR streaming server is deployed as a Nginx pod in a Kubernetes cluster.</p> <p>We rely on MonB5G sampling functions that feed monitoring data (CPU and RAN parameters) to the monitoring system. A streaming video server has been deployed with the help of a NGINX server. It provides video-on-demand and video streaming, which can be accessed by any user (or UE) for real-time reproduction. This VR video streaming emulation aids to assess the performance of the network and therefore the benefits that each solution has brought. The video “Big Buck Bunny” with h.264 encoding and a resolution of 1920x1080p has been used for the experiments.The description of dataset features are:<br> 1-) Index Number,<br> 2-) Time: Time of the experiment,<br> 3-) N: number of VR streaming clients,<br> 4-) C: Average CPU of VR streaming server [mc]<br> 5-) O: Outbound traffic at the server average outbound traffic (O) flowing from the data interface of the video server. <br> 6-) R: Instantaneous downlink bit rate [Mbps],</p> <p>The original video file information:</p> <table> <tbody> <tr> <td> <p>Video codec </p> </td> <td> <p>Advanced Video Codec (AVC) </p> </td> </tr> <tr> <td> <p>Width </p> </td> <td> <p>1920 pixels </p> </td> </tr> <tr> <td> <p>Height </p> </td> <td> <p>1080 pixels </p> </td> </tr> <tr> <td> <p>Display aspect radio </p> </td> <td> <p>16:9 </p> </td> </tr> <tr> <td> <p>Duration </p> </td> <td> <p>10 min 34 s </p> </td> </tr> <tr> <td> <p>Max Bitrate </p> </td> <td> <p>16.7 Mb/s </p> </td> </tr> <tr> <td> <p>Frame rate </p> </td> <td> <p>30 FPS </p> </td> </tr> </tbody> </table>
Stream network from 1997 survey and 2008 LiDAR flight, Andrews Experimental Forest
HJ Andrews stream network from 1994 base prepared from Lienkaemper's 1976 stream survey field data and contours generated from GSC Digital Elevation Model (DEM) maps, and generated from 2008 LiDAR 1 meter bare earth DEM. The 1994 base matches the 30 and 10 meter DEMs and the 2008 LiDAR version matches the LiDAR base layers. Stream order was added to hf01302.
Gulf Stream Daily Temperature, Salinity and Mixed Layer Depth fields from Ocean Stratification network (OSnet).
<p>The OSnet Gulf Stream product consists of 4D daily Temperature, Salinity and Mixed Layer Depth, available on a 1/4 degree regular grid and on 51 depth levels from the surface down to 1000m, in the Gulf Stream region from 01-01-1993 to 31-12-2019.<br> We introduce OSnet (Ocean Stratification network) in the Gulf Stream region, a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The interpolation scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T-S) profiles down to 1000m and the Mixed Layer Depth (MLD) from satellite data covering 1993 to 2019. The inputs are sea surface temperature and sea level anomaly, complemented with mean dynamic topography, bathymetry, longitude, latitude and the day of the year. The in-situ profiles are from the CORA database and include Argo floats and ship-based profiles. The prediction of the MLD is used to adjust a posteriori the vertical gradients of predicted T-S profiles, thus increasing the accuracy of the solution and removing vertical density inversions. The prediction is generalized on a 1/4 degree daily grid, producing four-dimensional fields of temperature, salinity and mixed layer depth, with their associated confidence interval issued from the bootstrap.</p> <p>The full dataset is downloadable with the <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> tool and the command :<code> zenodo_get 6011144</code></p>
Data and code for Freshwater corridors in the conterminous US: a coarse-filter approach based on lake-stream networks
<p>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.</p>
An environmental resistance model to inform the biogeography of aquatic invasions in complex stream networks
<p>Freshwater invasions are a global conservation issue. Emerging tools for biogeographical analyses can provide critical information for their effective management and monitoring. Here, we propose a method to assess the distribution of environmental resistance of stream ecosystems to biological invasions by coupling multi‐stage habitat potential models for non‐native species. Location: Andean Patagonia (Chile and Argentina).Taxa: North American beaver (<em>Castor canadensis</em>), Chinook salmon (<em>Oncorhynchus tshawytscha</em>), and coho salmon (<em>O. kisutch</em>). Methods: Environmental resistance to invasive species was mapped throughout a large region of Patagonia by stacking multi‐stage habitat relationships for each target species and assessing the complementation between critical habitats at multiple scales. We generated an environmental model of stream networks derived from high‐resolution topographic and climatic data representing 15,406 drainage basins (>1 km2) covering an area of 369,791 km2. We quantified the intrinsic potential of stream reaches (100 m and 1000 m) to sustain high‐quality habitats and assessed habitat complementation (i.e., abundance and proximity) at the sub‐basin scale as a proxy for environmental resistance. Results: Our model revealed high heterogeneity in the distribution of environmental resistance to invasions throughout the study region, providing case‐specific insights for the research and management of invaders. Conclusions: Environmental resistance modelling is a novel method to study the biogeography of riverine invasions. Our approach is compatible with additional sources of information about species and the environment and shows versatility to diverse invasion scenarios and data sources. This method can be useful in prioritising research and management of incipient and spreading invasions, especially for large and data‐poor regions.</p>
Data accompanying Using Neural Networks to Learn the Forced Response of the Jet-Stream to Tropospheric Temperature Tendencies
<p>Data used to train and evaluate a CNN. Details about data and the preprocessing can be found in the citation given below</p> <p>Charlotte Connolly, Elizabeth A. Barnes, Pedram Hassanzadeh, and Mike Pritchard: Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability, accepted to Artificial Intelligence for the Earth Systems 03/2023. Preprint available at <a href="https://arxiv.org/abs/2301.00496">https://arxiv.org/abs/2301.00496</a>.</p> <p>Code found at https://doi.org/10.5281/zenodo.7796266.</p> <p> </p>
Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)
<p>Described in 2009, the Patch-nosed Salamander (<em>Urspelerpes brucei</em>) is a miniature species of lungless salamander with a geographic range of only ~45 km<sup>2</sup>. This species is endemic to the foothills of the Appalachian Mountains in extreme northeastern Georgia and northwestern South Carolina. The Tugaloo River—a waterway of some 50 m in width that forms the political boundary between the two states—bisects the tiny range of <em>U. brucei</em> and likely acts as a barrier to gene flow. Using RADcap data and a suite of complementary population genomic analyses, we evaluated the role that this river and its tributaries may play in enabling and/or interrupting gene flow among populations of <em>U. brucei</em>, and we investigated patterns of within-population and between-population genetic variation. Our results revealed a general pattern of isolation-by-stream distance and indicated that a population separated by the Tugaloo River is moderately more differentiated than what is explainable by stream distance alone. Unique in both its physiography and geologic history, this region in which <em>U. brucei</em> lives also harbors more than a dozen other species of lungless salamanders. Therefore, the genetic patterns that we have elucidated may have larger implications for differentiation among populations of other species with similar dispersal abilities.</p>
An environmental resistance model to inform the biogeography of aquatic invasions in complex stream networks
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Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)
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International Long-Term Ecological Research Network (ILTER) Atmospheric Deposition and Stream Nitrogen Synthesis
We identified variables controlling stream nitrogen concentrations and fluxes, and how they have changed over time, by synthesizing 20 time series ranging from 5 to 51 years of data collected from forest and grassland dominated watersheds across Europe, North America, and East Asia and across four climate types (tropical, temperate, Mediterranean, and boreal) using the International Long-Term Ecological Research Network. We found declining trends in bulk ammonium and nitrate deposition, with ammonium contributing significantly more to atmospheric nitrogen deposition over time. Among sites, there were significant positive relationships between (1) precipitation and stream ammonium and nitrate fluxes and (2) atmospheric nitrogen inputs and stream nitrogen concentrations and fluxes. There were no significant relationships between air temperature and stream nitrogen export. Our long-term data shows that although nitrogen deposition is declining over time, atmospheric nitrogen inputs and precipitation remain the main predictors for nitrogen exported from forested and grassland watersheds. Overall, we also demonstrate that long-term monitoring provides understanding of ecosystems and biogeochemical cycling that would not be possible with short-term studies alone. Acknowledgements: We thank the organizers and funders of the ILTER Nitrogen Initiative Training Course and Workshop in Hokkaido, Japan in June 2016, which brought together many of the participants in this project. Templer was supported by a US National Science Foundation LTER grant NSF DEB 1637685. McDowell was supported by US National Science Foundation LTER grant NSF DEB 1831592. We are grateful to the EU Horizon 2020 funded eLTER PLUS project (Grand Agreement No. 871128) for financial support to Haase and Dirnböck. Dirnböck was also funded by the LTER-CWN project (FFG project number 858024). This study was partly supported by the Research Initiative Grants of the ILTER, Grants-in-Aid for Scientific Research (1
Seasonality Drives Carbon Emissions along a Stream Network
Headwater stream networks contribute a significant amount to the global carbon dioxide terrestrial flux because of high turbulence and coupling with terrestrial environments. Heterogeneity within headwater stream networks, both spatially and temporally, makes measuring and upscaling these emissions challenging because measurements of carbon dioxide in streams are often limited to a few monitoring points. We modified a stream network model to reflect real measurements made under base flow and high flow conditions at Martha Creek in Stabler, WA in the US Pacific Northwest. We found that under high flow conditions, the stream network had much greater total carbon emissions than during low flow conditions (1.22 Mg C day-1 vs. 0.034 Mg C day-1). We attribute this increase to a larger overall stream network area (0.04 km2 vs 0.01 km2) and discharge (1.9 m3/s vs. 0.005 m3/s) in November versus August. Our results demonstrate the need to understand the nonperennial nature of streams when calculating carbon emissions. We compared the stream network emissions with the terrestrial net ecosystem exchange (NEE) estimated by local eddy covariance measurements per area of the watershed (-5.5 Mg C day-1 in August and -2.2 Mg C day-1 in November). Daily stream emissions in November accounted for a much larger percentage of NEE in August (54% vs. 0.62%). We concluded that the stream network can emit a large percentage of the forest NEE in the winter months, and annual estimates of stream network emissions must consider the flow regime throughout the year.
Stream stage and water table elevation in hyporheic and ground water from McRae Ck well network, Andrews Experimental Forest, 1989-1993
Measurements of stream stage of McRae Ck and water table heights from a network of shallow wells located adjacent to the stream. Data were collected from September 1989 to September 1992 on an irregular basis to sample both baseflow periods and storm events across seasons of the year. Stage height was read from staff plates permanently located at several location in the mainstem channel and in a small back channel; water table elevations were measured with a weighted measuring tape with a visible signal when the tape touched the water in the well.
Artifacts related to "Using Informed Access Network Selection to Improve HTTP Adaptive Streaming Performance"
<p>This archive contains data related to in the following paper:</p> <p>"Using Informed Access Network Selection to Improve HTTP Adaptive Streaming Performance"</p> <p>(published at the ACM MMSys 2020 conference)</p> <p>Copyright (c) 2020, Theresa Enghardt <theresa@tenghardt.net>, Fachgebiet INET - TU Berlin.</p> <p><br> See https://github.com/fg-inet/MMSys2020_Informed-Access-Network-Selection for more information.</p> <p>This data is released under the Creative Commons Attribution 4.0 International license.</p>
Aggregated Network and Application Twitch.tv Live Streaming Dataset
<p>This dataset contains application and network measurements of 6,982 individual Twitch.tv streaming sessions of 222 different streamers summing up to more than 1,000h live streaming. The data are aggregated to uplink requests.</p>
Data from: Improving governance outcomes for water quality: insights from participatory social network analysis for chalk stream catchments in England
<p>Globally important chalk streams in England are in poor ecological health, in part due to inadequate water quality. Addressing this issue requires an understanding of the governance systems that surround water quality. The complexity and uncertainty inherent in hydrological systems has led to the emergence of integrated and adaptive forms of governance. In these multi-actor governance systems, the structure of the relationships between actors (the social network) has been shown to affect governance processes and outcomes.</p> <p>Using participatory social network analysis, we mapped and analysed the social networks for the River Test and River Itchen in Hampshire, UK, to identify actors and their roles, determine the network characteristics, and identify interventions to improve governance.</p> <p>Although the results suggest a well connected network of actors from the state, private sector and civil society, we find that decision making is not decentralised. Bureaucratic governance by central state actors dominates. However, trust in these central state actors and private actors in the networks is low, which undermines collaboration and co-ordination in the network.</p> <p>Devolving authority to local actors, building trust in the networks, and improving connections to important actors could help to improve governance outcomes for water quality.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.