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1,772 results for “Sensor”
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.
Sensor and nutrient data associated with the article Harrison et al. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression
This document describes a dataset used to produce Random Forests Regression models of stream nitrogen and phosphorus concentrations from high-frequency sensor data, as reported in: Harrison, J.W., Lucius, M.A., Farrell, J.L., Eichler, L.W., and Relyea, R.A. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of the Total Environment: https://doi.org/10.1016/j.scitotenv.2020.143005. The dataset consists of paired values of stream nitrogen and phosphorus concentrations and various high-frequency sensor parameters (water temperature, specific conductance, pH, fluorescent dissolved organic matter, turbidity, hydrostatic pressure, soil moisture) collected during baseflow and storm events from 2018 to 2019 as part of routine monitoring of eleven tributaries of Lake George, New York. This dataset does not include raw data; two levels of processing were performed: (1) erroneous values (extreme or otherwise outlying values with no apparent environmental cause) were removed from the sensor data as part of the routine QA/QC process of the Jefferson Project, and (2) one-hour rolling medians of the raw sensor data were calculated at a 1-minute timestep to maximize pairing of sensor data with nutrient concentrations. The resultant dataset was used to train and test the models presented in Harrison et al. 2020.
Hourly meteorological data gapfilled for sensor downtimes collected near Toolik Field Station, Alaska, summers 2012-2016
This data set includes meteorological parameters collected near Toolik Field Station from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). It also includes meteorological data collected by two additional entities that are available on public repositories. Toolik data reflect data collected by the Toolik Envronmental Data Center and Imnavait data reflect data collected by the Arctic Observatory Network (AON). These data have been modified such that that sensor downtimes have been gapfilled by pulling data from the next nearest station. These data are associated with publication DOI: 10.1111/jav.01712
Annual summer single-day measurements of the thermal environment with a bio-meteorological sensor under trees, shade structures, and sun-exposed areas in the Rio Salado Park in Tempe, AZ, USA
We have measured the thermal environment/bio-meteorological conditions under trees, shade structures, and at sun-exposed locations in the Rio Salado Park in Tempe, AZ, USA annually since 2018 on a clear sky, hot, sunny day in June/July to track shade performance of newly planted trees over time. Measurements were taken with a mobile bio-meteorological weather station, known as MaRTy (Middel & Krayenhoff, 2019; DOI: 10.1016/j.scitotenv.2019.06.085). Since the measurement campaign began, some of the trees have died, were removed, or their environment has changed due to external events such as a bridge collapse (2020-07-29).
Luquillo canopy trimming experiment - meteorological data, soil sensor, and cannopy measurements
The data archive is here: https://doi.org/10.2737/RDS-2019-0051 please use this DOI when citing this dataset. This data publication contains average daily means for field and satellite data from 3 treated and 3 control plots from the Canopy Trimming Experiment (CTE) located near El Verde Field Station (419 meters; 18°20’ N, 65°49’ W) in the Luquillo Experimental Forest (El Yunque National Forest), Puerto Rico collected from 2003 through 2019. In spring of 2005 (CTE1) and December of 2014 (CTE2), in 0.09 hectare (ha) square plots near the El Verde Field Station the forest canopy was trimmed and the canopy debris was littered to the forest floor. The plot size and trim amounts were based on the patch disturbance after the two most recent hurricanes before 2017, both category 3 hurricanes at the location of El Verde: Hugo in September 1989, and Georges in September 1998 (Zimmerman et al. 2014). Details of the trimming and littering treatment, as well as the biotic response to the 2005 experiment have been extensively documented (Richardson et al. 2010; Shiels et al. 2014, 2015; Shiels and González 2014). The data were collected in the inner 0.04 ha quadrants of the 0.09 ha trimmed plots to minimize edge effects. There were 3 sets of control and treated plots, with each set near El Verde field station. Field data include: solar radiation, throughfall, air temperature, soil temperature, relative humidity shallow soil, volumetric water content, soil profile volumetric water content, canopy leaf saturation, litter leaf saturation, and above canopy air temperature. Satellite data include: Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index converted into solar radiation, MODIS land surface temperature, and Advanced Microwave Scanning Radiometer (AMSR) soil volumetric water content. \<para\> Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952
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.
Above and below ground phenology for the Niwot Ridge sensor node array, 2021 - 2022.
The below-ground growing season often extends beyond the above-ground growing season in tundra ecosystems. However, we do not yet know where and when this occurs and whether these phenological asynchronies are driven by variation in local vegetation communities or by spatial variation in microclimate. We deployed root in-growth cores in 4 locations in Niwot Ridge’s sensor node array as part of a multi-site study of above- and below-ground tundra phenology.
Year 2020, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2020 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2021, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2021 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Nearshore DuraFET and HOBO Sensor Data: pH and Temperature (May–Nov 2021)
Data are pH (total scale, DuraFET) and temperature (degrees Celsius, DuraFET and HOBO TidbiT) from instruments deployed at three sites in the rocky intertidal zone in California, USA. pH data were collected every 15 minutes and HOBO temperature data were collected every 5 minutes and interpolated to a 15 minute interval. Sensor data collection began in May 2021 and was terminated in November 2021. DuraFET-based pH sensors were constructed with guidance from Dr. Francis Chan at Oregon State University.
Software for processing data from a fast-responding RINKO EC oxygen/temperature sensor (JFE Advantech Co, Ltd)
This dataset describes how data from a fast-responding JFE Advantech RINKO EC ARO-EC-CM sensor connected to a Nortek Vector is processed to obtain accurate aquatic eddy covariance measurements. The code and documentation are stored in a .zip file. It consists of a manual, Fortran source code, a definition file and a complied executable suitable for running on Microsoft Windows. The software development was supported by NSF funding to PI Berg (OCE-1824144, OCE-2223204).
iSCAPE Outdoor Sensor Deployment Data
<p><strong>Dataset Description</strong></p> <p>This dataset contains all the deployment data using low cost sensors during the iSCAPE project. The dataset is divided in a series of deployments, each of them described on a yaml file with the test name. Each csv file contains time series data of each experiment, and the yaml files contain the lists of devices used in each test. The tests are described in the comment of the yaml file, and are meant to be self explanatory. Two different types of tests are herein presented:</p> <ol> <li>Intervention monitoring tests (Guildford and Surrey). Two intervention monitoring results were conducted in the sites of Surrey (UoS) Living Lab and Hasselt (UH) Living Lab. The intervention in Surrey aimed at characterising the behaviour of green infrastructure and the effect on the pollutants dispersion next to traffic conditions. Two different sets of two stations were delivered and deployed, one set in the vicinity of Stoke Park, and the other in the vicinity of Sutherland Memorial Park (both in Guildford - UK). In the case of Hasselt, two Living Lab Stations were deployed. The first one was used to assess pollutant concentrations in the <em>Bassischool Kuringen</em> in Hasselt. The other station was deployed near the University of Hasselt.</li> <li>Sensor Calibration tests (CSIC, Dublin and Bologna). The tests conducted in Bologna (by UNIBO, 2018), Dublin (by UCD, 2019) and Barcelona (by IAAC, 2019) were intended as an assessment of the sensor technology in an outdoor environment scenario, by co-locating the iSCAPE LLSs with reference instrumentation.</li> </ol> <p>A complete description of these datasets and the result of their analysis is shown in D7.8 of iSCAPE which can be found in this url: <a href="https://www.iscapeproject.eu/results/">https://www.iscapeproject.eu/results/</a>.</p> <p><strong>Sensors</strong></p> <p>The sensors used are herein referred as Citizen Kits or Smart Citizen Kits, and the Living Lab Station or Smart Citizen Station. These are a set of modular hardware components that feature a selection of low cost sensors for environmental monitoring listed below. The Smart Citizen Station is meant to expand the capabilities of the Smart Citizen Kit, aiming to measure pollutants with more advanced sensors. The hardware is licensed under <a href="https://www.ohwr.org/licenses/cern-ohl/license_versions/v1.2">CERN Open Hardware License V1.2</a> and is fully described in the HardwareX Open Access publication: <a href="https://doi.org/10.1016/j.ohx.2019.e00070">https://doi.org/10.1016/j.ohx.2019.e00070</a>. The sensor documentation can be found at <a href="https://docs.smartcitizen.me">https://docs.smartcitizen.me</a> and with this DOI at Zenodo: <a href="https://doi.org/10.5281/zenodo.2555029">https://doi.org/10.5281/zenodo.2555029</a>.</p> <p>In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.</p> <ul> <li>Air temperature (ºC): Sensirion SHT-31 [TEMP]</li> <li>Relative Humidity (%rh): Sensirion SHT-31 [HUM]</li> <li>Noise level (dBA): Invensense ICS-434342 [NOISE_A]</li> <li>Ambient light (lux): Rohm BH1721FVC [LIGHT]</li> <li>Barometric pressure (kPa): NXP MPL3115A26 [PRESS]</li> <li>Particulate Matter PM 1 / 2.5 / 10 (µg/m3) Planttower PMS 5003 [EXT_PM_1,EXT_PM_25,EXT_PM_10]</li> </ul> <p>In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.</p> <ul> <li>Air Temperature (ºC) Sensirion SHT-31 [TEMP]</li> <li>Relative Humidity (% REL) Sensirion SHT-31 [HUM]</li> <li>Noise Level (dBA) Invensense ICS-434342 [NOISE_A]</li> <li>Ambient Light (Lux) Rohm BH1721FVC [LIGHT]</li> <li>Barometric pressure and AMSL (Pa and Meters) NXP MPL3115A26 [PRESS]</li> <li>Carbon Monoxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]</li> <li>Nitrogen Dioxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]</li> <li>Carbon Monoxide (ppm) Alphasense CO-B4 [GB_1W, GB_1A, final calculated valueCO_DELTAS_OVL_X-XX-XX - all the same]</li> <li>Nitrogen Dioxide (ppb) Alphasense NO2-B43F [GB_2W, GB_2A, final calculated value NO2_DELTAS_OVL_0-30-50 or NO2_DELTAS_OVL_0-5-50] </li> <li>Ozone (ppb) Alphasense OX-B431 [GB_3W, GB_3A, final value O3_DELTAS_OVL_0-30-50 or O3_DELTAS_OVL_0-5-50]</li> <li>Gases Board Temperature (ºC) Sensirion SHT-31 [GB_TEMP] or [EXT_TEMP]</li> <li>Gases Board Rel. Humidity (% REL) Sensirion SHT-31 [GB_HUM] or [EXT_HUM]</li> <li>PM 1 (µg/m3) Plantower PMS5003 [EXT_PM_1] or [EXT_PM_A_1], [EXT_PM_B_1] for each PM sensor in the case of the Living Lab Station</li> <li>PM 2.5 (µg/m3) Plantower PMS5003 [EXT_PM_25] or [EXT_PM_A_25], [EXT_PM_B_25] for each PM sensor in the case of the Living Lab Station</li> <li>PM 10 (µg/m3) Plantower PMS5003 [EXT_PM_10] or [EXT_PM_A_10], [EXT_PM_B_10] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 0.3um<0.5um particle size (#/l) Plantower PMS5003 [EXT_PN_03] or [EXT_PN_A_03], [EXT_PN_B_03] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 0.5um<1um particle size (#/l) Plantower PMS5003 [EXT_PN_05] or [EXT_PN_A_05], [EXT_PN_B_05] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 1m<2.5um particle size (#/l) Plantower PMS5003 [EXT_PN_1] or [EXT_PN_A_1], [EXT_PN_B_1] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 2.5m<5um particle size (#/l) Plantower PMS5003 [EXT_PN_25] or [EXT_PN_A_25], [EXT_PN_B_25] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 5m<10um particle size (#/l) Plantower PMS5003 [EXT_PN_5] or [EXT_PN_A_5], [EXT_PN_B_5] for each PM sensor in the case of the Living Lab Station</li> <li>PN between >10um particle size (#/l) Plantower PMS5003 [EXT_PN_10] or [EXT_PN_A_10], [EXT_PN_B_10] for each PM sensor in the case of the Living Lab Station</li> </ul> <p>The files with the _processed suffix, are processed files which:</p> <p>1. Resample the data using pandas resampling with mean() -<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.resample.html"> reference here</a></p> <p>2. Clean NaN and wrong readings.</p> <p>3. Add calculations for electrochemical sensors based on <a href="https://docs.smartcitizen.me/Components/Gas%20Pro%20Sensor%20Board/Electrochemical%20Sensors/#sensor-calibration">this methodology</a></p> <p><strong>How to find the data</strong></p> <p>Each yaml file contains the description of a test. Each test is comprised of recordings of several devices in the same location and during the same period. Each yaml file is comprised of the following fields:</p> <ul> <li>author: who has been in charge of performing the test (internal reference - not relevant)</li> <li>comment: describing in general terms what was done in the test, and with what purpose</li> <li>commit: the firmware commit (in the case of Smart Citizen devices) with which the test was performed, for development purposes only</li> <li>devices: a descriptor containing different fields for traceability (below)</li> <li>id: the test name</li> <li>project: within the test was performed, in this case it is always iscape</li> <li>report: if there is any report analysing the test</li> <li>type_test: indoor, oudoor test or other.</li> </ul> <p><strong>Description of devices entry</strong></p> <p>For each device that was used in the test, two generic types are used:</p> <ul> <li>low cost sensors (type: STATION or KIT)</li> <li>high end sensors (type: REFERENCE)</li> </ul> <p>For <strong>low cost Smart Citizen sensors</strong>, the fields are:</p> <ul> <li>alphasense: electrochemical sensors device ids, by pollutant (for manufacturer calibration) and slots in which they were placed</li> <li>device_id: device id in Smartcitizen API</li> <li>fileNameInfo: not used</li> <li>fileNameProc: (only if source = csv is specified) 2019-03_EXT_UCD_URBAN_BACKGROUND_API_CITY_COUNCIL_REF.csv</li> <li>fileNameRaw: (only if source = csv is used) raw file name</li> <li>frequency: original recording frequency</li> <li>location: for timezone correction only, not accurate</li> <li>max_date: last recording date</li> <li>min_date: first recording date</li> <li>name: self-explanatory</li> <li>pm_sensor: if there was a pm sensor connected (all of them are PMS5003 if no sensor is specified)</li> <li>source: api or csv</li> <li>type: STATION (KIT + Alphasense + PM board with two PMS5003) or KIT</li> <li>version: smartcitizen hardware version</li> </ul> <p>For <strong>high end</strong> sensors, the fields are:</p> <ul> <li>channels: which channels the device was recording for internal convertion <ul> <li>names: which are the columns in the csv file</li> <li>pollutants: which pollutants do they respectively refer to</li> <li>units: the units of these pollutants</li> </ul> </li> <li>equipment: the brand of the analyser</li> <li>fileNameProc: same as above</li> <li>fileNameRaw: same as above</li> <li>index: format in which the timeindex is done, for parsing purposes <ul> <li>format: (example '%Y-%m-%d %H:%M:%S')</li> <li>frequency: frequency at which the device was recorded</li> <li>name: column name</li> </ul> </li> <li>location: same as above</li> <li>name: name of the device</li> <li>type: REFERENCE (always for these devices)</li> <li>source: csv</li> </ul> <p><strong>iSCAPE Dataset Reference Numbers:</strong></p> <p>The datasets here presented are related to the following iSCAPE dataset reference numbers:</p> <ul> <li>DS_TS_049</li> <li>DS_TS_050</li> <li>DS_TS_051</li> <li>DS_TS_052</li> <li>DS_TS_053</li> <li>DS_TS_055</li> <li>DS_TS_056</li> <li>DS_TS_057</li> <li>DS_TS_058</li> </ul>
Evaluation and Calibration of a Low-cost Particle Sensor in Ambient Conditions Using Machine Learning Methods
<p>Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low-cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was co-located with a reference instrument, named Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, in Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine learning algorithms using random search techniques for the best model architectures. The two machine learning algorithms are XGBoost and feedforward neural network (NN). Field evaluation showed that the Pearson r between the low-cost sensor and the SHARP instrument was 0.78. Fligner and Killeen (F-K) test indicated a statistically significant difference between the variances of the PM<sub>2.5 </sub>values by the low-cost sensor and by the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset, compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F-K test using the test dataset showed that the variances of the PM<sub>2.5</sub> values by the NN and the XGBoost and by the reference method were not statistically significantly different. From this study, we conclude that feedforward NN is a promising method to address the poor performance of the low-cost sensors for PM<sub>2.5</sub> monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.</p>
DATALOG_Lambertseter_VGS_20190329_Sensor_2
<p>Measurements taken by a DIY sensor (Sensor 2) designed by the project air:bit (http://airbit.uit.no/#english). Measurements were taken by students of the school Lambertseter VGS, located in the district of Nordstrand in Oslo, Norway.</p>
Associated dataset for "Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The conducted instrumental and perceptual evaluation utilize the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. However, the provided execution configurations (see below) are probably not exactly in accordance with the latest ReTiSAR code base. Hence, the at the time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way you should obtain exactly the same Python setup as utilized in the instrumental and perceptual evaluation in the publication:</p> <pre><code>conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code>source activate ReTiSAR_ICASSP_freeze</code></pre> <p>Directory "SNR":</p> <ul> <li>Tools for instrumental evaluation (Section 4)</li> <li>Shell script to capture input and output signals of rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all specified configurations</li> <li>Matlab script to analyse captured signal and generate system transfer plots (Figure 1 to Figure 3 and further configurations)</li> </ul> <p>Directory "Relative Output Levels":</p> <ul> <li>Tools for preparation of perceptual evaluation (Section 5)</li> <li>Shell script to capture rendered uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse and level align captured signals and generate plot result plot (Figure 4)</li> </ul> <p>Directory "Absolute Output Levels":</p> <ul> <li>Tools for specification of perceptual evaluation (Section 5)</li> <li>Shell script to capture reproduced uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse the calibrated captured signals yielding the average level in the ear signals of 58.2 dBSPL (Section 5.1)</li> </ul> <p>Files in base directory and directory "Study Results":</p> <ul> <li>Tools for perceptual evaluation / user study (Section 5)</li> <li>Matlab GUI to conduct perceptual user study (employ by executing "ICASSP_gui.m", respective ReTiSAR instances are started and remote controlled by the GUI, raw study results will be stored in "results" directory)</li> <li>Matlab script to "calculate_conclusion.m" to analyse the raw study results and generate individual and conclusive result plots (Figure 5, Figure 6 and more)</li> </ul>
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>
NYU FloodSense street sign mounted distance sensor
<p>Ultrasonic distance data in mm from a sensor mounted on a street sign post at the corner of 5th Street and Hoyt, Brooklyn, NY (40.676640, -73.994595). The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Data is collected at ~5min intervals. Time fields are in local time (New York).</p> <p>Two types of erroneous data has been observed:</p> <ul> <li>Large spikes in distance that always manifest at 5000mm - can be excluded</li> <li>There are ~1% rises in distance measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is prelimary and is for prototyping purposes. Not to be used as a reliable data source as it is.</p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></p>
NYU FloodSense street sign mounted flood depth sensor
<p>Water depth level in mm from a sensor mounted on a street sign post at the corner of 5th Street and Hoyt, Brooklyn, NY (40.676640, -73.994595). The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. Ultrasonic technology is used to detect flood water depth.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Depth data is collected at ~5min intervals. Time fields are in local time (New York). Date format is: 2020-10-04 20:11:45.742594232-04:00</p> <p>Two flood events have been observed in this dataset between these date ranges:</p> <ol> <li> <p>"2020-11-15 19:37:00.000000000-05:00" to "2020-11-16 00:30:00.000000000-05:00"</p> </li> <li> <p>"2020-11-30 10:20:00.000000000-05:00" to "2020-11-30 13:30:00.000000000-05:00"</p> </li> </ol> <p>Erroneous data has been observed:</p> <ul> <li>There are ~1% decreases in depth measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is preliminary and is for prototyping purposes. </p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></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.