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79 results for “sensor network”
Climate data for saddle catchment sensor network, 2017 - ongoing.
Spatial and temporal variability characterizes virtually all ecosystems, with resource supply changing over the course of growing season and across years due to climate variation. To better understand spatial heterogeneity in ecological response across landscape positions, we established a 16-node sensor array within a 45 hectare catchment landscape that measures temporal variability of important biogeochemical and hydrological controls on ecosystem processes. The array was established at the Niwot Saddle catchment in order to accompany long term water quality and discharge records taken at the top and bottom of this catchment. The region forms an important ecological linkage between the the terrestrial areas of the Niwot Ridge LTER and the aquatic component in the Green Lakes Valley.
Plant species composition for sensor network array, 2017 - ongoing.
Above-ground plant species cover was recorded for vegetation plots in the sensor network, starting in 2017. Cover was measured annually at peak biomass using a 100 point-intercept method.
Time-lapse camera (phenocam) imagery of sensor network plots, 2017 - ongoing.
Images from time-lapse cameras were analyzed to track the greenness curves of 16 plots in the Sensor Network at Niwot Ridge. Images were taken every 30 minutes during daylight hours throughout the growing season. Cameras were angled to view 1m^2 vegetation plots located at each sensor node. Pixels in the portion of the image capturing the vegetation plot were used to calculate the green chromatic coordinate (GCC). The change in GCC over the growing season represents the growth and phenology of the plant communities captured.
Soil moisture sensor network, design, location attributes and soil properties, Hainich, Germany, project AquaDiva
<p>This dataset contains information of the small scale highly resolved soil moisture measurement network that is part of the of the AquaDiva Critical Zone exploratory, Hainich National Park, Germany. The dataset contains information on soil measurement locations, as well as attributes to the location, the design type (random locations vs transects), as well as locations attributes like distance to the next tree and soil properties. Measurement design was first introduced by Metzger et al., (2017), and used in Fischer et al., 2023. See there for more information.</p> <p><strong>References</strong></p> <p>Fischer-Bedtke, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics – empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023.</p> <p>Metzger, J. C., Wutzler, T., Dalla Valle, N., Filipzik, J., Grauer, C., Lehmann, R., Roggenbuck, M., Schelhorn, D., Weckmüller, J., Küsel, K., Totsche, K. U., Trumbore, S., and Hildebrandt, A.: Vegetation impacts soil water content patterns by shaping canopy water fluxes and soil properties, Hydrological Processes, 31, 3783–3795, https://doi.org/10.1002/hyp.11274, 2017.</p>
New Hampshire Soil Sensor Network: Soil CO2 Fluxes
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes air temperature, soil temperature at 5 cm, and soil volumetric water content at 5 cm, and soil CO2 flux at the time of sampling, as well as gap-filled soil CO2 fluxes using non-linear least squares regression. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
LoRaWAN Dense Indoor Sensor Network (DISN) Transmission Meta Data
<p>We present a large data of indoor Long Range Wide Area Network (LoRaWAN) network metadata to study Dense Indoor Sensor Networks (DISN). We collected 14 million transmissions from 390 sensors between date February 2020 and date September 2020. The transmissions have been received by 3 gateways across 8 floors and distances up to 64 m. The prototype will run in the background throughout the project and the data set will be regularly updated.</p> <p> </p>
New Hampshire Soil Sensor Network: Snow depth (2012-2022)
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes data from snow depth sensors. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
MCR LTER: Coral Reef: Sensor Network: Bottom-mounted CTD Data - GUMPR, 2006-2012
Physical oceanographic data from bottom-mounted instrumentation (Seabird 16+ CTD) were sampled year-round on Gump reef in Cooks Bay on Moorea, French Polynesia (GUMPR site). Sampling began in 2006 until early 2012. The CTD measured conductivity, temperature, pressure, from which density and salinity were calculated. Data were collected every 5 minutes, processed and reported every 20 minutes. The instrument is mounted 2 m above the bottom in 6 m of depth. These data streamed near real-time as part of the Digital Moorea project (no longer active.) Daily, weekly, monthly and yearly means were calculated for temperature, salinity, and density. This is a completed timeseries which ended early 2012. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Alpine species transplant experiment within the sensor network, 2019 - 2021.
Weekly monitoring of survival and other fitness traits of 11 transplanted alpine species in addition to, non-transplanted, native, individuals. Individuals were monitored from early June to the end of August for survival and other fitness traits. Beginning in early July individuals from each species were transplanted to grids that were established near existing soil moisture sensors. Individuals were tracked until all had senesced however senescence was only indicated in transplants if it was irregular from native individuals.
Supplemental soil moisture and temperature data from the saddle catchment sensor network, 2019 - 2021.
Hand-held soil moisture measurements were taken at 8 of 16 soil moisture sensors within the sensor network at Niwot Ridge to supplement the continuous measurement system at these locations. The hand-held measurements occur much less frequently than the 10 min sensor data, but they are important in determining the spatial variability of soil moisture in the alpine.
Wireless Sensor Network Deployments (2013-2017)
<p><strong>Wireless Sensor Network Deployments (2013-2017)</strong></p> <p>This is an open data repositiory.</p> <p>It is concerned with systematically reviewing scientific publications containing actual Wireless Sensor Network deployments in five year span from 2013 to 2017.</p> <p><strong><em>Identification</em></strong></p> <p>Articles were first searched for in SCOPUS and Web of Science databases on 2018-06-12 using these queries/settings:</p> <p>SCOPUS</p> <p>Query: KEY({sensor network} OR {sensor networks}) AND TITLE-ABS-KEY(test* OR experiment* OR deploy*) AND NOT TITLE-ABS-KEY(review) AND NOT TITLE-ABS-KEY(simulat*) AND ( LIMIT-TO ( PUBYEAR,2017 ) OR LIMIT-TO ( PUBYEAR,2016 ) OR LIMIT-TO ( PUBYEAR,2015 ) OR LIMIT-TO ( PUBYEAR,2014 ) OR LIMIT-TO ( PUBYEAR,2013 ) )</p> <p>Raw results: 11536 articles</p> <p>De-duplicated results: 11374 articles</p> <p>Contained 4814 articles not found in Web of Science</p> <p>Web Of Science</p> <p>Querry: TS = ("sensor network" OR "sensor networks") AND TS = (test* OR experiment* OR deploy*) NOT TI="review" NOT TS=simulat*</p> <p>Additional query parameters: Indexes=SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC Timespan=2013-2017</p> <p>Raw results: 10204 articles</p> <p>De-duplicated results: 10196</p> <p>Contained 3636 articles not found in SCOPUS</p> <p>Final results</p> <p>When article results were merged from both databases finally 15010 articles were identified as possible candiates. Of those 6560 were found in both databases.</p> <p><em><strong>Screening</strong></em></p> <p>Data was exported as bibtex files and imported in Mendeley software for screening.</p> <p>4910 articles were left after the screening phase</p> <p><em><strong>Eligibility check</strong></em></p> <p>Then all screened included articles were checked for eligibility and 3017 eligible articles were identified.</p> <p><em><strong>Data extraction</strong></em></p> <p>In these articles 3059 wireless sensor network deployments were identified and codified data extracted from them.</p> <p><em><strong>Timeline</strong></em></p> <p>This data analysis took total time (including validation and error checking) from 2018-06-12 till 2020-05-29, after which the data was prepared for publiching till 2020-07-02.</p>
Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"
<p>This excel file contains the raw data used in the paper " LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225 " In particular it comprises sensor measurements and pollutant data from the automated air quality monitoring stations in the Tarragona area.</p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Data for publication "The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zürich"
<p>Please see README.md for a description of this package. </p> <p>This work was funded by the European Union's Horizon 2020 research and innovation programme, grant agreement number 101037319, named Pilot Applications in Urban Landscapes - towards integrated city observatories for greenhouse gases (PAUL) and is known as ICOS Cities.</p> <p> </p>
In-network convolution in grid-shaped wired sensor networks
<p>Data about the simulation of the in-network convolution in grid-shaped wired sensor networks. <br> We designed the simulation to examine the communication overhead of the technique applied on a wired sensor network at two different topologies.<br> Data include measurements of traveling time of packets and packet loss at varying of the link bitrate and kernel size. </p>
Real-Time Sensor Network of Detroit Green Infrastructure: Datasets and Code
<ol> <li><strong>MonitoredRainGardens_PaperTable.xlsx: </strong>Excel spreadsheet where the "Garden" sheet contains the 14 monitored green infrastructure (GI) sites with their design and physiographic features and the "Field Log" sheet contains the installation and field maintenance trips.</li> <li><strong>WaterWells.xlsx: </strong>an Excel file containing all of the water wells in the Detroit region uses for interpolating groundwater levels.</li> <li><strong>GI_GIS_Analysis.aparx:</strong> ArcGIS Pro project file which includes the 14 monitored GI sites and the GIS data for Detroit (percent imperviousness, elevation, slope, land use type, wells, interpolated groundwater levels, hydrologic soil group).</li> <li><strong>Code.zip: </strong>Zip folder containing another folder titled "Code" which holds: (1) a folder titled "SensorData" containing 16 csv files with the raw pressure transducer data for the 16 monitored GI sites during the measurement period (including the two excluded sites); (2) a csv file titled "MonitoredRainGardens.csv" containing the 14 monitored green infrastructure (GI) sites with their design and physiographic features used in the correlation analysis; (3) a csv file titled "storm_constants.csv" which contain the computed decay constants for every storm in every GI during the measurement period; (4) a csv file titled "GLWA_RainGaugesforStudy.csv" that contains rainfall from 9 rain gauges during the measurement period; (5) a Jupyter notebook titled "storm_constants_analysis.ipynb" which provides the code for calculating the decay constants for the monitored GI; (6) a Jupyter notebook titled "storm_constants.ipynb" which provides the code for analyzing the decay constants including the correlation analysis and surface plots; and (7) a Jupiter notebook titled "modeled_response.ipynb" which provides the code for plotting the drawdown curves based on the decay constant.</li> </ol>
New Hampshire Soil Sensor Network: Air Temperature, Soil Temperature, Soil Water Content, and Soil Electrical Conductivity, 2012 - ongoing
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes data from the air temperature, soil temperature, soil volumetric water content, and electrical conductivity sensors. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
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.
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>
SINS database - Node 4 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
ScienceDex guides
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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.