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9,152 results for “Networking”
Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Test Well Meteorological Station (TSWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.
The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Test Well Meteorological Station (TSWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmettswl/. These data complement and extend meteorological data recorded by an adjacent station (Met52b), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.
Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Tule 222 Well Meteorological Station (TUWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.
The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Tule 222 Well Meteorological Station (TUWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmettuwl/.
Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the West Mesa Meteorological Station (WSMS), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.
The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Tule 222 Well Meteorological Station (WSMS). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetwsms/.
Global Lake Ecological Observatory Network: Long term chloride concentration from 529 lakes and reservoirs around North America and Europe: 1940-2016
This dataset compiles long term chloride concentration data from 529 freshwater lakes and reservoirs in Europe and North America. All lakes in the dataset had greater than or equal to ten years of data. For each lake the following landscape and climate metrics were calculated: mean annual precipitation, mean monthly air temperatures, road density and impervious surface in 100 to 1500 m buffer zones, sea salt deposition. The dataset includes three files: 1) Descriptive data of lake sites (physical lake metrics, climate, land-cover characteristics), 2) Chloride time-series, and 3) GIS shapefiles.
Species persistence and mean rank abundance in global Nutrient Network plots from 2007-2019
This dataset uses data from the NutNet dataset to examine how temporal and spatial rarity are related, how temporal and spatial rarity predict species loss in ambient and experimentally perturbed plots. We use data from all sites that had NPK and/or fencing treatments with a minimum of five years of cover data when data was downloaded on August 2, 2019; 49 sites in all were used. Perturbations were NPK treatments (nitrogen, phosphorus, potassium and micronutrients) and fencing (vertebrate herbivore exclusion). Temporal rarity was assessed as the percentage of years a species was found in a plot (calculated in R script) and spatial rarity was assessed as the mean rank percentile of a species across all years in a plot (included in data table). We found that persistence (i.e. temporal rarity) was a better predictor than local abundance (i.e. spatial rarity) of whether a species would be absent in a neighboring plot, despite the rarity axes being correlated. Additionally, perturbations reduced persistence most strongly in highly persistent species and low abundance species, further suggesting these are unique dimensions of rarity.
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.
Vegetation composition for 84 sites in the Regional Site Network, compilation from previous surveys.
Many sites in the LTER regional site network (RSN) were adopted from preexisting site networks. Prior to becoming a part of the RSN, releve surveys were conducted at or near the current RSN site using the Braun-Bluanquet cover estimate scale. These data have been compiled for use as a baseline. It is important to note that many of the young successional sites were last surveyed in 2006, 2 years after fire.
Modeling CH4 and CO2 cycling using porewater stable isotopes in a thermokarst bog in Interior Alaska: Results from three conceptual reaction networks
Quantifying rates of microbial carbon transformation in peatlands is essential for gaining mechanistic understanding of the factors that influence methane emissions from these systems, and for predicting how emissions will respond to climate change and other disturbances. In this study, we used porewater stable isotopes collected from both the edge and center of a thermokarst bog in Interior Alaska to estimate in situ microbial reaction rates. We expected that near the edge of the thaw feature, actively thawing permafrost and greater abundance of sedges would increase carbon, oxygen and nutrient availability, enabling faster microbial rates relative to the center of the thaw feature. (full abstract available in supplemental file 610_NeumannPorewaterExtendedMetadataText.pdf)
Measurement of Fuel load (organic biomass) Quantities of Organic Soils, Understory Plants and Trees at Sites within the Bonanza Creek LTER Regional Site Network in Interior Alaska, 2019
This dataset contains fuel load measurements for a subset of the BNZ LTER's RSN sites (n = 28). The data for each site separted by 16 fuel types (or plant functional types) including organic soil layers (fibric, mesic), vascular understory (evergreen shrub, short deciduous shrub, graminoid, forb, tall deciduous shrub, tree seedling/sapling, dead-downed wood), nonvascular understory (feather moss, Sphagnum moss, colonizer moss, lichen) and trees (evergreen tree, deciduous tree).
Tree-Ring Data for Co-Occurring White Spruce and Paper Birch at an Intermediate Aged Stand in the Bonanza Creek LTER Regional Site Network - 2018
This dataset contains tree ring widths of co-occurring white spruce and Alaska paper birch. The data were published as part of a 2021 article in Journal of Ecology.
Daily precipitation from a network of weighing rain gauges on the Jornada Experimental Range (Jornada Basin LTER), 1976-2011.
This completed dataset contains daily precipitation measurements from a network of weighing rain gauges (Belfort Universal Precipitation Gauges, Series 5-780) at 58 locations on the Jornada Experimental Range in Dona Ana County, New Mexico, USA between January 1976 and December 2011. Locations and the dates during which data were collected were generally project-oriented. Temporal coverage per location is quite variable and ranges between 1,647 and 13,024 days. The network was maintained by USDA Agricultural Research Service personnel. After 2011 the remaining weighing rain gauges were replaced by tipping bucket gauges and those data are available separately.
Substrate Induced Respiration (SIR) from 26 sites across vegetation community gradient in and near sensor network, 2017
In alpine ecosystems, small-scale variations in topography determine the spatial and temporal “patchiness” of snow accumulation, snowmelt, vegetation, and biological activity. In the Niwot Ridge Long-term Ecological Research Program VII proposal, Suding and colleagues specifically articulate a need to determine how asynchronous responses across a landscape affect catchment-scale export of water and nutrients in the context of changing climate (H4). Accordingly, we must develop an understanding of how asynchronous responses in patch-scale behavior including microbial activity and decomposition are connected hydrologically, how they aggregate at the catchment scale, and how those relationships may change in the future. To address this, we measured substrate induced respiration (SIR; analogous to microbial biomass) from alpine tundra soils at 26 locations across a soil moisture and corresponding vegetation community composition gradient that included NWT sensor network nodes 6 through 21 in the Saddle stream catchment. These data help to constrain interactions between patch-scale alpine biogeochemical and hydrological processes over space and time.
Learning Naturalistic Temporal Structure in the Posterior Medial Network
Open the record for dataset details and reuse information.
Differentiation of functional networks during long-term memory retrieval in children and adolescents
Open the record for dataset details and reuse information.
Ascending arousal network connectivity during recovery from traumatic coma
Open the record for dataset details and reuse information.
Curated Research on Network Traffic Analysis
<p>With the NTA Database we aim to collect relevant information about the research in network traffic analysis conducted during the last years. To this end, we have curated related papers from journals and conferences and stored the extracted data in JSON files. </p>
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
CNNpredIM - Dataset for Rapid Prediction of Earthquake Ground Shaking Intensity Using Raw Waveform Data and a Convolutional Neural Network
<p>The <strong>dataset</strong> available here is the dataset used in the <a href="https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggaa233/5836721"><strong>paper</strong> <em>"Rapid Prediction of Earthquake Ground Shaking Intensity Using Raw Waveform Data and a Convolutional Neural Network".</em></a></p> <p>The <strong>abstract</strong> of the <strong>paper</strong>:</p> <blockquote> <p>This study describes a deep convolutional neural network (CNN) based technique for the prediction of intensity measurements (IMs) of ground shaking. The input data to the CNN model consists of multistation 3C broadband and accelerometric waveforms recorded during the 2016 Central Italy earthquake sequence for M ≥ 3.0. We find that the CNN is capable of predicting accurately the IMs at stations far from the epicenter and that have not yet recorded the maximum ground shaking when using a 10 s window starting at the earthquake origin time. The CNN IM predictions do not require previous knowledge of the earthquake source (location and magnitude). Comparison between the CNN model predictions and the predictions obtained with Bindi et al. (2011) GMPE (which require location and magnitude) has shown that the CNN model features similar error variance but smaller bias. Although the technique is not strictly designed for earthquake early warning, we found that it can provide useful estimates of ground motions within 15-20 sec after earthquake origin time depending on various setup elements (e.g., times for data transmission, computation, latencies). The technique has been tested on raw data without any initial data pre-selection in order to closely replicate real-time data streaming. When noise examples were included with the earthquake data, the CNN was found to be stable predicting accurately the ground shaking intensity corresponding to the noise amplitude.</p> </blockquote>
VLBNs - Very Large Boolean Networks
<p>This archive contains instances of Boolean networks (in BoolNet format) generated from scale-free random network structure, on which has been applied the inhibitor-dominant rule to devise the logical functions.</p> <p>The generated networks range from 1,000 to 100,000 nodes, with in-degrees ranging up to 1400.</p> <p>Contents:</p> <ul> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3714876/files/Analysis.ipynb/%3Fdownload%3D1">Analysis.ipynb</a>: in/out-degree scatter plots of the generated networks</li> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3714876/files/Generation.ipynb/%3Fdownload%3D1">Generation.ipynb</a>: notebook used to generate the networks (requirs colomoto_jupyter python module)</li> <li>random-{N}-{id}.bnet: generated Boolean networks with N components</li> </ul>
Emergence of cooperative bistability and robustness of gene regulatory networks
<p>Simulation and analysis source codes and obtained data set for "Emergence of cooperative bistability and robustness of gene regulatory network" (<a href="https://doi.org/10.1371/journal. pcbi.1007969">PLoS Comput Biol 16 (2020) e1007969</a> and <a href="https://arxiv.org/abs/1907.12030">arXiv:1907.12030</a>) by Nagata and Kikuchi. </p> <p>Source codes and figures are compiled in Jupyter notebook. Detailed discription of data sets is found in "readme.txt" file.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.