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1,772 results for “sensors”
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2018 through December 2018
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from January 2018 through December 2018. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
Sensor data associated with Lucius et al. 2020 – Using machine learning to correct for nonphotochemical quenching in high-frequency in vivo fluorometer data.
This document describes a dataset used to produce Using machine learning to correct for nonphotochemical quenching in high-frequency, in vivo fluorometer data, as reported in: Lucius, M.A., Johnston, K.E., Eichler, L.W., Farrell, J.L., Moriarty, V.W. and Relyea, R.A. (2020), Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data. Limnol Oceanogr Methods, 18: 477-494. https://doi.org/10.1002/lom3.10378 The dataset consists of high-frequency water quality and meterological sensor data collected from two autonomous vertical profiling platforms deployed on Lake George, NY during the ice-free months of 2017-2019. Water quality data include depth-referenced measurements of chlorophyll fluorescence, water temperature and dissolved oxygen. Meteorological data include surface-incident total radiation as well as two derived values: solar azimuth and 1-hr rolling average of total radiation. Finally, using interpolated data from regularly collected subsurface profiles of photosynthetically active radiation, estimates of subsurface total radiation were estimated and included in this dataset. This dataset does not include raw data. The data used were subjected to quality control procedures of the Jefferson Project, as well as additional outlier removal measures and the creation of derived data (as previously described and described in detail in Lucius et al. 2020).
Deciphering the Origin of Riverine Phytoplankton Using In-Situ Chlorophyll Sensors
We monitored suspended chlorophyll concentration and turbidity during 48 storm events at two locations with contrasting hydraulic storage caused by milldams between the sites. Our study provides 15-min interval data on river flow, temperature, turbidity and chlorophyll-a concentration for each one of these events. These data have undergone a QA/QC process in which fouling drift was corrected by applying an offset linear interpolation before and after cleaning, and a discrete analytical correction was also applied based on extracted-chlorophyll analysis from grab samples. The dataset is provided in a wide csv format with one parameter per column.
Total and diffuse photosynthetically active radiation (PAR) recorded by a beam fraction (BF3) sensor during the summer of 2012 in vicinity of Toolik Lake, Alaska.
This file contains irradiance (PAR) and diffuse light data logged from a beam fraction (BF3) sensor near Toolik Lake, Alaska during the summer of 2012. The data comes from a compilation of automated datalogger readings as well as measurements taken during the field season in conjunction with the Delta-T SunScan wand to measure PAR in tall shrub canopies dominated by Betula nana or Salix pulchra species. The sensor was leveled and mounted to a 2m tripod in each location, and programmed to record instantaneous readings in 30 second to 5 minute intervals.
Hubbard Brook Experimental Forest: Log decomposition sensor data
This data set documents the temporal and spatial variation of soil and deadwood moisture, and nearby microclimate, for the a four-month period from June to October 2018. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and main tained by the USDA Forest Service, Northern Research Station.
El Verde Field Station Air temperature from automatic sensor
Hourly air temperature for El Verde Field Station (2007 – present). Temperature is measured in the same box where we measure manual min-max temperature. The box is mostly shaded by surrounding trees, but it is more exposed to sunlight after hurricanes defoliate the forest canopy (e.g., hurricane Maria in 2017). Data is measured hourly using a HOBO Pendant data logger placed inside a wooden box. Notes: • Daily maximum and minimum air temperatures are available in separate data sets. This is a long-term monitoring of air temperature in the same box where we measure manual min-max temperature at El Verde Field Station. The data set is meant as a back up to the long-term manual data set. Data is measured hourly using a HOBO Pendant data logger placed inside a wooden box: under the shade of trees. The min-max thermometer that is measured manually during work days is located next to the HOBO in the same box. 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 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
MCR LTER: North Shore Moored pH sensors, SeaFET 2012-2015 and SAMI 2015-ongoing
This dataset contains only the sensor data and only from the forereef mooring on the north shore. Full carbonate chemistry data derived from water samples where available are in a separate dataset. Sensor data from other locations are in a separate dataset. Near continuous measurements of seawater pH are made at two locations on the north shore of Moorea. On the fore reef, instruments are attached to a mooring cable at 10-m depth at LTER0. On the back reef, instruments are bottom-mounted at a location approximately 400 m landward of the reef crest at LTER0, such that pH of the same water mass is measured at both locations as water moves onto the reef crest and across the back reef (Hench et al. 2008). One SeaFET instrument and one SAMIpH instrument are deployed at each location. Measurements of pH by the SeaFETs are made every 30 minutes; pH measurements by the SAMIs are made every hour. SeaFET data (on the total pH scale) are output using both a constant salinity value (35 PSU) and using salinities measured in situ, if available. SeaFETs are calibrated using independent measures of pH from water samples taken adjacent to the instruments prior, during, and after deployment (using the m-cresol purple method). SAMIs are returned to the manufacturer each year for calibration. 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 2005-2019). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Snow depth sensor measurement data for Alpine site, 2010 - 2015
Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Alpine site, located just southwest of the Tundra Lab in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.
Snow depth sensor measurement data for Lower Sub Alpine site, 2010 - 2015
Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Lower Sub Alpine site, located southeast of the Tundra Lab, below treeline in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.
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.
Year 2018, 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 2018 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2019, 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 2019 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Dataset for: Diurnal patterns in solute concentrations measured with in situ UV-Vis sensors: natural fluctuations or artefacts?
<p>This dataset contains high-resolution (10-minute interval) data for nitrate, dissolved organic carbon, precipitation and discharge at four measurement stations (NF, SHA, TTP, OUT) in the South West Mau, Kenya. This data was used for the analysis of diurnal patterns in nitrate and dissolved organic carbon concentrations. The zipped folder contains the following files and data:</p> <ul> <li>Calibration.csv: <ul> <li>site = name of measuring site</li> <li>date = date and time of grab sample (yyyy-mm-dd hh:mm:ss)</li> <li>DOC = dissolved organic carbon concentration in grab sample (mg C/L)</li> <li>nitrate = nitrate concenctration in grab sample (mg N/L)</li> </ul> </li> <li>Files with suffix ".ts.csv" (time series data from 1-11-2014 to 31-10-2019; prefix indicates measuring site): <ul> <li>date = date and time of measurement (yyyy-mm-dd hh:mm:ss)</li> <li>nit.raw = nitrate concentration measured by sensor (mg N/L)</li> <li>nit.flag = indication of validity of nitrate measurement (if NA, measurement is valid; for explanation of flags, see supplement of <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR021592">Jacobs et al. 2018</a>)</li> <li>nit.proc = processed nitrate concentration (mg N/L)</li> <li>nit.bg = background concentration of nitrate (48-h moving median; mg N/L)</li> <li>nit.patt = deviation from background concentration of nitrate (nit.proc minus nit.bg; mg N/L)</li> <li>doc.raw = dissolved organic carbon concentration measured by sensor (mg C/L)</li> <li>doc.flag = indication of validity of dissolved organic carbon measurement (if NA, measurement is valid)</li> <li>doc.proc = processed dissolved organic carbon concentration (mg C/L)</li> <li>doc.bg = background concentration of dissolved organic carbon (48-h moving median; mg C/L)</li> <li>doc.patt = deviation from background concentration of dissolved organic carbon (doc.proc minus doc.bg; mg C/L)</li> <li>p = precipitation (mm/10 mins)</li> <li>q = discharge (m³/s)</li> <li>sensor = serial number of sensor</li> </ul> </li> <li>Files with suffix ".exp.csv" (data for sensor comparison experiment from 5-9-2017 to 1-12-2017; prefix indicates measuring site): <ul> <li>date = date and time of measurement (yyyy-mm-dd hh:mm:ss)</li> <li>prec = precipitation (mm/10 mins)</li> <li>nit.orig = processed nitrate concentration measured by fixed sensor (mg N/L)</li> <li>nit.bg.orig = background concentration of nitrate measured by fixed sensor (48-h moving median; mg N/L)</li> <li>nit.patt.orig = deviation from background concentration of nitrate measured by fixed sensor (nit.orig minus nit.bg.orig; mg N/L)</li> <li>nit.dup = processed nitrate concentration measured by mobile sensor (mg N/L)</li> <li>nit.bg.dup = background concentration of nitrate measured by mobile sensor (48-h moving median; mg N/L)</li> <li>nit.patt.dup = deviation from background concentration of nitrate measured by mobile sensor (nit.dup minus nit.bg.dup; mg N/L)</li> <li>doc.orig = processed dissolved organic carbon concentration measured by fixed sensor (mg C/L)</li> <li>doc.bg.orig = background concentration of dissolved organic carbonmeasured by fixed sensor (48-h moving median; mg C/L)</li> <li>doc.patt.orig = deviation from background concentration of dissolved organic carbonmeasured by fixed sensor (doc.orig minus doc.bg.orig; mg C/L)</li> <li>doc.dup = processed dissolved organic carbonconcentration measured by mobile sensor (mg C/L)</li> <li>doc.bg.dup = background concentration of dissolved organic carbonmeasured by mobile sensor (48-h moving median; mg C/L)</li> <li>doc.patt.dup = deviation from background concentration of dissolved organic carbon measured by mobile sensor (doc.dup minus doc.bg.dup; mg C/L)</li> <li>set = experimental treatment</li> </ul> </li> </ul>
Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))
<p>Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))</p>
Dataset for sound source localization with 101 Blinky sound-to-light conversion sensors
<p>Blinkies are sound-to-light conversion devices that can be used to monitor the sound level over large areas. The data from the sensors is harvested using a video camera. This dataset contains seven videos that were recorded in the gymnastical hall of Tokyo Metropolitan University, Hino Campus on July 3rd 2018. In the video, 101 Blinkies are spread on the ground of the gymnastic hall. A bluetooth speaker mounted on a remote controlled car runs between the Blinkies, causing them to change intensity. The file `pyramic_json` is a JSON format file containing all the meta-data necessary such as sensor locations, room dimensions, and segmentation information.</p> <p>This dataset was used to demonstrate sound source localization in the paper "Blinkies: Open source sound-to-light conversion sensors for large-scale acoustic sensing and applications" by Robin Scheibler and Nobutaka Ono (to appear).</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>
Perception Sensor Dataset For Bioinspired Landing Trajectories Of An Ornithopter Robot
<p>The dataset contains the measurements captured by several onboard sensors during the landing maneuvers of an ornithopter robot. Each dataset contains a ROS bag file with the sensor measurements, a file with the bioinspired trajectory, a file with the events generated by the simulated event-based sensor, and a README file with the instructions to use the dataset.</p> <p>The bioinspired landing trajectories are computed using Tau Theory. Each landing trajectory test was performed in a simulated scenario. The object models of each scene can be found in the /model/meshes folder of each scene. There are two testing scenes: (i) a warehouse and (ii) a refinery. The file object_pose.csv includes the position and orientation of each object in the scene. The sensor measurements were saved in rosbag file that contains a topic for each sensor measurement. The dataset includes information from the following simulated sensors:</p> <ul> <li>Velodyne HDL-32E</li> <li>Sonar sensor with a range of 20 m</li> <li>IMU</li> <li>Frame based monocular camera</li> <li>Event camera</li> </ul>
SMART2: Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys
<p>To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area.</p> <p> </p> <p>Enrollment in the Social Mixing and Respiratory Transmission (SMART) study operated on an opt-out basis, and all students registered in a participating school before the start of the study were eligible to participate. Students in kindergarten (typically aged 5 years) to 12<sup>th</sup> grade (typically aged 18 years) from two elementary (K to 4<sup>th</sup> grade, K to 5<sup>th</sup> grade), two middle (5<sup>th</sup> to 6<sup>th</sup> grade, 7<sup>th</sup> to 8<sup>th</sup> grade), two elementary-middle (K to 8<sup>th</sup> grade), and two high (both 9<sup>th</sup> to 12<sup>th</sup> grade) schools were eligible to participate in SMART. Participation rates were high in all schools (82 to 99%). Each school provided aggregate demographic information about the school population, and individual grade and sex of participating students.</p> <p><em>Proximity sensor deployments</em></p> <p>The details of proximity sensor deployments have been described in detail elsewhere (60). In brief, participating students were given proximity sensors in plastic pouches and instructed to wear the pouch around their neck for the duration of the school day without removing or otherwise tampering with the sensor. In six of the eight schools, all participating students were given a sensor; in two schools, the large student population limited the deployment to randomly selected classrooms in each grade. Deployments typically lasted from the first class period (08:00 – 09:00) to the last class period (14:00 – 15:00). Deployment days in each school were chosen to be representative of a typical school day, without any special schoolwide or grade-specific activities that could modify normal contact patterns.</p> <p> </p> <p>We used TelosB wireless sensors (61) programmed in the NesC language to send beacons every 20 seconds (beacon frequency 3 per minute). The receiving sensor recorded the contacting sensor’s identity, an internal time stamp, and a radio strength signal indicator (RSSI). Signal strength provided an estimate of physical proximity, but was highly dependent on the orientation of the two sensors and any obstructions between them and therefore could not be used to define an exact distance between contacts. Based on pilot studies and previous work on effective distances of respiratory virus transmission (29, 62), we chose a signal threshold (-80 dBm) that should correspond to contacts of relevance to respiratory disease transmission.</p> <p> </p> <p>The number of unique proximity sensor contacts recorded for a participant was defined as the total number of other participants with whom their proximity sensor recorded at least one interaction during each deployment. To explore patterns of contacts of varying length, we considered several values of the contact threshold, or the minimum number of recorded interactions between two proximity sensors required to be considered a unique contact. The number of interactions between any given pair of sensors was taken to be the maximum number of interactions recorded by either sensor, to account for battery failure, measurement error, or other malfunctions.</p> <p> </p> <p><em>Contact survey design</em></p> <p>Contact surveys were completed by participants in school under the supervision of project staff and teachers. Each sheet of the paper version allowed for information on up to 30 contacts to be recorded; additional sheets could be requested. Two versions were designed: one for middle- and high-school students, and a simplified version for elementary school students (although some elementary school children completed the middle- and high-school version, upon consultation with school administrators and teachers). Classrooms were randomly selected to participate from each grade, and students of several classrooms completed more than one contact survey over the course of the study period.</p> <p> </p> <p>Participants were asked to report information about any individual they talked with, played with, or touched the previous day, including the contact’s age and sex, whether they attended the same school as the participant, the context in which the contact was made, whether the contact involved direct or indirect (through a shared object) touch, and approximate duration of the contact. Students reported the total number of contacts made in the previous day, without detailed information, and additional demographic information about themselves and their household. The surveys were completed either on paper or by computer, depending on resources available in each school.</p> <p> </p> <p>We defined total survey contacts as the total number of individuals a student reported having interacted with on the day before the survey was completed. Detailed contacts were the subset of total contacts for which the student reported contact age, sex, duration, and context. We considered further subsets of detailed survey contacts, including those occurring within school, those reported to have lasted more than 10 minutes over the course of the day, and those occurring on the same day as a sensor deployment.</p> <p> </p>
Robust step detection from different waist-worn sensor positions – implications for clinical studies
<p>The dataset contains tri-axial acceleration and gyroscope data (100 Hz sampling) from walks from 19 healthy volunteers, each walking up to three times a parcours of 20 meters with self-selected speed, slow speed or with five soft turns at self-selected speed. Each participant wore 11 time-synchronized sensors during these tests: left/right foot, 5 around waist, non-dominant wrist and upper arm and collar and pocket. In addition to the sensor recordings each 20 meter walk was timed with a stop-watch. </p> <p>Also see: <a href="https://doi.org/10.1159/000511611">https://doi.org/10.1159/000511611</a></p>
Experimental data to the publication "Genetic-optimised aperiodic code for distributed optical fibre sensors"
<p>The source data underlying Figs. 3-5 and Supplementary Figs. 6, 8-14 are provided as a Source Data file.</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.