Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,772
datasets available to search
ShareScore release 0.9.0
Dataset results
1,772 results for “sensors”
EO4WildFires: An Earth Observation multi-sensor, time-series machine-learning-ready benchmark dataset for wildfire impact prediction
<p>This paper presents a benchmark dataset called EO4WildFires; a multi-sensor (multi spectral; Sentinel-2, Synthetic-Aperture Radar - SAR; Sentinel-1, meteorological parameters; NASA Power) time-series dataset that spans 45 countries, which can be used for developing machine learning and deep learning methods targeted for the estimation of the area that a forest wildfire might cover.</p> <p>This novel EO4WildFires dataset is annotated using EFFIS (European Forest Fire Information System) as forest fire detection and size estimation data source. A total of 31,742 wildfire events are gathered from 2018 to 2022. For each event, Sentinel-2 (multispectral), Sentinel-1 (SAR) and meteorological data are assembled into a single data cube. The meteorological parameters that are included in the data cube are: ratio of actual partial pressure of water vapor to the partial pressure at saturation, average temperature, bias corrected average total precipitation, average wind speed, fraction of land covered by snowfall, percent of root zone soil wetness, snow depth, snow precipitation, as well as percent of soil moisture.</p> <p>The main problem that this dataset is designed to address, is the severity forecasting before wildfires occur. The dataset is not used to predict wildfire events, but rather to predict the severity (size of area damaged by fire) of a wildfire event, if that happens in a specific place under the current and historical forest status, as recorded from multispectral and SAR images, and meteorological data.</p> <p>Using the data cube for the collected wildfire events, the EO4WildFires dataset is used to realize three (3) different preliminary experiments, in order to evaluate the contributing factors for wildfire severity prediction. The first experiment evaluates wildfire size using only the meteorological parameters, the second one utilizes both the multispectral and SAR parts of the dataset, while the third exploits all dataset parts. In each experiment, machine learning models are developed, and their accuracy is evaluated.</p>
Dataset for 'Zinc hybrid sintering for printed transient sensors and wireless electronics'
<p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “Zinc hybrid sintering for printed transient sensors and wireless electronics”.</p> <p>This work aims to study and develop a method for the efficient sintering of printed zinc metal, with the aim to facilitate the fabrication of biodegradable electronics by additive manufacturing. Biodegradable electronic devices have potential in tackling the increasingly pressing challenge of electronic waste, and present opportunities for the fabrication of novel bioresorbable medical devices that can harmlessly degrade in the body and eliminate the need for re-operation. The method that is presented in this publication combines electrochemical and photonic sintering approaches to enable the fabrication of highly-conductive degradable metal tracks. Several sensors are shown as demonstrators (temperature, strain, pressure). The data that was collected in the frame of this work is present in this repository. It relates to both the study of the process introduced above as well as the characterization of the demonstrators. More information about the contents of the dataset is present in the included README files.</p>
Datasets for "Cryogenic sensor enabling broad-band and traceable power measurements"
<p>Python code and data used to generate the plots in "Cryogenic sensor enabling broad-band and traceable power measurements". </p> <table> <tbody> <tr> <td><strong>File/Folder</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> <p>Article_Plots_25Jan2023.ipynb</p> </td> <td>main code to analyze and plot the data</td> </tr> <tr> <td>Data</td> <td>sub-folder that includes data analyzed by the code</td> </tr> <tr> <td>Figures</td> <td>sub-folder for figures generated by the code</td> </tr> </tbody> </table> <p>The code requires an installation of <a href="https://qcodes.github.io/Qcodes/start/index.html">QCoDeS</a> to run.</p>
An ice-tethered, non-floating Trident Sensors Helix Beacon during SCALE 2019 Spring Cruise
<p><strong>Brief data description</strong></p> <p>A Trident Sensors Helix Beacon identical to the one described in Womack et al. (2022), was deployed on sea ice at latitude 59.47<sup>o</sup> S and longitude 10.89<sup>o</sup> E, on 30 October 2019, as part of the <em>Southern oCean seAsonal Experiment</em> (SCALE; Ryan-Keogh and Vichi, 2022) aboard the SA Agulhas II.</p> <p>The region where the Trident was deployed (Antarctic marginal ice zone) consisted of first-year ice conditions, with an average thickness of 80-90 cm. The device was deployed by hand by three people, lowered by crane from the ship to the ice on a basket cradle.</p> <p>The temporal resolution was approximately four hours. The survival of the Trident depended on staying fixed to the ice floe and its battery life. The Trident recorded GPS position and air temperature, and transmitted data until 2 December 2019, where it sank due to sea-ice melting.</p> <p><strong>Buoy name and raw data:</strong></p> <p>Trident: Unit4.xlsx</p> <p><strong>Related code: </strong>The buoy data has been processed using https://github.com/mvichi/antarctic-buoys/.</p>
Bastrop (TX, USA) forest fire cross-sensor change detection images
<p><strong>Abstract. </strong></p> <p>This dataset is composed of a set of four images acquired by different sensors over the Bastrop County, Texas (USA). On September 4, 2011, the region has been struck by ‘‘the most destructive wildland-urban interface wildfire in Texas history’’ which caused 2 casualties, more than 1300 destroyed buildings and almost burned entirely the Bastrop county state park.</p> <p>This dataset is composed of a pair of pre- and post-event images from the same sensor, the Landsat 5 TM (L5T1 and L5T2), which are completed by a post-event of another sensor, the Advanced Land Imager (ALI) from the Earth Observing (EO-1) mission, acquired very shortly after the L5T2 (denoted ALIT2). These three scenes are very similar to each other and no apparent changes between L5T2 and ALIT2 are visible, since images were acquired within 1 day. Differences between these pre- and post-event pairs are only due to burned forest since they were acquired at a 16 days interval during summer. We also dispose of a fourth image of the same area acquired one year and 9 months after the forest fire by the Landsat 8 Operational Land Imager (OLI, L8T2 hereafter). The differences between L5T1 and L8T2 are significant, due both to sun/sensor angles and the long temporal interval between acquisitions. A whole new series of building has been constructed in the burn scar and many cultivated crops are at a different stage of growth.</p> <p>Table : Dataset description</p> <pre><code class="language-markdown">| | Pre-event | Post-event 1 | Post-event 2 | Post-event 3 | |--------- |--------------|--------------|--------------|---------------| | Filename | t1_L5 | t2_L5 | t2_ALI | t2_L8 | | Sensor | Landast 5 TM | Landsat 5TM | EO-1 ALI | Landsat 8 OLI | | Channels | 7 | 7 | 9a | 7b | | GSD | 30, 120 | 30, 120 | 30 | 30 | | Sp. Range | [0.45–2.35, | [0.45–2.35, | [0.4–2.4] | [0.43–2.25] | | [\mu m] | 10.40–12.50] | 10.40–12.50] | | |</code></pre> <p>We prepared the ground truth for pairs of change detection problems by photo-interpretation and relying on the maps provided on the emergency response website [1]. In the ground truth involving the L5T1-L8T2 problem we also included changes related to vegetation density and vegetation/bare soil transitions, since these changes are of the same spectral class as those related to the burned scar.</p> <p>This data has been collected from the NASA LP DAAC Program [2], we are free to redistribute the data. For this reason we provide the Bastrop data and the ground truth we defined and used in these experiments (subset of original crops and ground truth).</p> <p><strong>Data Files</strong></p> <p>The archive contains the following files:</p> <pre><code>data ├── t1_L5.tif ├── t2_L5.tif ├── t2_ALI.tif ├── t2_L8.tif ├── ROI_1.tif ├── ROI_2.tif ├── Cross-sensor-Bastrop-data.mat </code></pre> <p>Where:</p> <ul> <li>`t1_L5.tif` is the pre-event image acquired by Landsat 5 TM</li> <li>`t2_L5.tif` is the post-event image acquired by Landsat 5 TM - `t2_ALI.tif` is the post-event image acquired by EO-1 ALI</li> <li>`t2_L8.tif` is the post-event image acquired by Landsat 8 OLI</li> <li>`ROI_1.tif` is the ground truth for the change detection problem between `t1_L5.tif` and `t2_L5.tif`</li> <li>`ROI_2.tif` is the ground truth for the change detection problem between `t1_L5.tif` and `t2_L8.tif`</li> <li>`Cross-sensor-Bastrop-data.mat` is a Matlab file containing the data in a more convenient format for Matlab users</li> </ul> <p><strong>Citation</strong></p> <p>If you are using this dataset, please cite the following paper:</p> <pre><code>@article{volpi2015jisprs, author = {Michele Volpi and Gustau Camps-Valls and Devis Tuia}, title = {Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {107}, pages = {50-63}, year = {2015}, doi = {https://doi.org/10.1016/j.isprsjprs.2015.02.005}, url = {https://www.sciencedirect.com/science/article/pii/S0924271615000404}, issn = {0924-2716}, }</code></pre> <p>Volpi, M., Camps-Valls, G., Tuia, D. (2015). Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 107, 2015, Pages 50-63. https://doi.org/10.1016/j.isprsjprs.2015.02.005</p>
Buoy and Other Sensors Data at the South East Coast of Iceland (Höfn area) on Nov 20 - 24, 2020
<p>This dataset contains the information about sea state and environmental parameters in the area of tidal inlet near Höfn city, South East coast of Iceland. Buoy data is given with original half-hour time spacing. Wind and tidal sensors' data are given with the original 10 min time spacing. All of the given parameters did not undergo any re-interpolation, filtering or any other processing.</p> <p>structure of the "Buoy_data_20201120T010100_to_20201124T125900.csv" file</p> <pre><code>Comma-separated values'(.csv) table (217 rows 3 columns) with the the following fields: 1st column: time in the format dd-Mmm-yyyy HH:MM:SS; 2nd column: Significant wave height (Hs) in meters (m); 3rd column Peak wave period in seconds (s).</code></pre> <p>structure of the "Sea_level_tide_wind_data_20201120T010100_to_20201124T125900.csv" file (originally data was obtained from the <a href="http://vedur.mogt.is/harbor/index.php?action=ReadingsDrillDown&harborid=4&stationid=1011">web page</a> of Weather information system MogT ehf. Hornafjörður, Iceland - the corresponding table fields' names are doubled in Icelandic for convenience) </p> <pre><code>Comma-separated values'(.csv) table (649 rows 5 columns) with the the following fields: 1st column: time in the format dd-mm-yyyy HH:MM:SS; 2nd column: observed sea level (Sjávarhæð) in meters (m); 3rd column astronomical tide (Flóðatafla) in meters (m); 4th column wind speed (Vindur) in meters per second (m/s); 5th column wind direction (Vindátt) in degrees (deg).</code></pre> <p>The corresponding data chunks are published after the kind allowance of the data owners: Vignir Júlíusson and Greipur Sigurðsson.</p> <p> </p>
Dataset 3 - Connectorisation of POFBG sensors
<p>The data demonstrate a methodology to connectorise the polymer optical fibre with the silica optical fibre after the fabrication of POFBG sensors. The polymer optical fibre was butt-coupled and UV glued (NOA adhesive) with a silica pigtail (SMF-28) for interrogation purposes.</p>
Dataset 1 - Fabrication of POFBG sensors
<p>The data demonstrate the methodology to fabricate Polymer Optical Fiber Bragg Grating sensors using the plane-by-plane femtosecond laser inscription method.</p>
Dataset 2 - Characterisation of POFBG sensors
<p>The data are related to the characterization of Polymer Optical Fiber Bragg Grating sensors in<br> order to define their sensitivity to strain, temperature, and humidity.</p>
Satellite-based measurements of brightness temperatures (AMSR2 sensor) colocated to MOSAiC ground measurements
<p>The file contains measurements of brightness temperatures of satellite overpasses of the research vessel Polarstern during the MOSAiC expedition from October 26, 2019 - May 26, 2020 as well as co-located measurements of different parameters. For every overpass of Polarstern, the satellite measurement closest to the hourly position of Polarstern is taken.</p> <p>The satellite sensor is AMSR2 (six frequencies between 6.9 and 89 GHz and both polarizations) and we use the Level 1R (<em>Madea et al., 2016)</em> product available at JAXA <a href="https://gportal.jaxa.jp/gpr/">https://gportal.jaxa.jp/gpr/</a></p> <p>The co-located parameters are liquid water path, total water vapor, sea ice concentration, multi-year ice fraction, snow depth, snow-air interface temperature, snow-ice interface temperature, wind speed and sea surface temperature. In addition to the co-located parameters as ground truth, the dataset also contains their “uncertainties” given as temporal and/or spatial variability.</p> <p>Note: The dataset contains <strong>only</strong> satellite overpasses where co-located data is available.</p> <p>More information on the parameters are found below and they are described in more detail in <em>Rückert et al., 2023</em><em> </em>and the references given therein.</p> <ul> <li> <p><strong>scantime</strong>: time of satellite observation as included in the satellite data from JAXA</p> </li> <li> <p><strong>lon</strong>: longitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA</p> </li> <li> <p><strong>lat</strong>: latitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA</p> </li> <li> <p><strong>distance</strong>: distance to the hourly Polarstern position</p> </li> <li> <p><strong>TB6.9V, TB6.9H, TB10.7V, TB10.7H, TB18.7V, TB18.7H, TB23.8V, </strong><strong>T</strong><strong>B23.8H, TB36.5V, TB36.5H, TB89V, TB89H</strong>: Brightness temperatures (TB) measured by AMSRE2, the name includes the frequency in GHz and the polarization (either H for horizontal or V for vertical polarization), e.g, TB6.9V is the brightness temperature at 6.9 GHz and vertical polarization</p> </li> <li> <p><strong>LWP</strong>: liquid water path in kg/m² measured by a radiometer onboard the ship (<em>Walbröl et al., 2022</em>), averaged within +/- 10 minutes of the satellite observations</p> </li> <li> <p><strong>sigma_LWP</strong>: temporal variability of liquid water path (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>TWV</strong>: total water vapor (integrated water vapor) in kg/m² measured by a radiometer onboard the ship (<em>Walbröl et al. (2022)</em>), averaged within +/- 10 minutes satellite observation time</p> </li> <li> <p><strong>sigma_TWV</strong>: temporal variability of total water vapor (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>WSP</strong>: wind speed in m/s from the vessel’s meteorological observatory (<em>Schmithüsen et al., 2021</em>), averaged within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>sigma_WSP</strong>: temporal variability of wind speed (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>SST</strong>: sea water temperature in K from the vessel’s meteorological observatory (<em>Schmithüsen et al., 2021</em>), averaged within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>sigma_SST</strong>: temporal variability of sea water temperature (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>SND</strong>: snow depth in m obtained from the median of daily snow depth from available Snow and Ice Mass Balance Apparatus (SIMBA) buoys (<em>Lei et al., 2021</em><em>a</em>) in the proximity of Polarstern.</p> </li> <li> <p><strong>sigma_SND</strong>: spatial variability of snow depth (see previous point) given as standard deviation of all buoys available on that day.</p> </li> <li> <p><strong>Tsi:</strong> Snow-ice interface temperature in K obtained from the median of daily measurements from available Snow and Ice Mass Balance Apparatus (SIMBA) buoys (e.g. <em>Lei et al., 2021b</em>, for references of all buoys the reader is referred to the references given in <em>Rückert et al., 2023</em>) in the proximity of Polarstern.</p> </li> <li> <p><strong>sigma_Tsi:</strong> spatial variability of snow-ice interface temperature (see previous point) given as standard deviation of all buoys available on that day.</p> </li> <li> <p><strong>MYIF:</strong> multi-year ice fraction (from 0 to 1) based on classified TerraSAR-X scenes (<em>Guo et al., 2023</em>) in the proximity of Polarstern, interpolated to daily values.</p> </li> <li> <p><strong>sigma_MYIF:</strong> estimated (constant) uncertainty of multi-year ice fraction (see previous point).</p> </li> <li> <p><strong>SIC</strong>: sea ice concentration (from 0 to 1) based on classified TerraSAR-X scenes (<em>Guo et al., 2023</em>) in the proximity of Polarstern, interpolated to daily values.</p> </li> <li> <p><strong>sigma_SIC:</strong> estimated (constant) uncertainty of sea ice concentration (see previous point).</p> </li> <li> <p><strong>Tsa</strong>: Snow-air interface temperature in K based on infrared thermometer data (<em>Cox et al., 2023 a)-d)</em>) installed at four positions in the proximity of Polarstern, averaged within +/- 20 minutes of the satellite observation time.</p> </li> <li> <p><strong>sigma_Tsa:</strong> spatial variability of snow-ice interface temperature (see previous point), given as spatial (4 sites) and temporal (within +/- 20 minutes of the satellite observation time) standard deviation.</p> </li> </ul>
Dataset for 'Printed ecoresorbable temperature sensors for environmental monitoring'
<p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “Printed ecoresorbable temperature sensors for environmental monitoring”.</p> <p>This work aims to study the effect of photonic sintering parameters on the temperature behavior of printed zinc resistors, with the aim to fabricate eco-friendly and ecoresorbable temperature sensors on paper. Biodegradable electronic devices have potential in tackling the increasingly pressing challenge of electronic waste and printing methods allow to reduce toxic byproducts of fabrication and wasted material. The sintering method that is optimized here is based on our previous work combining electrochemical and photonic sintering approaches to enable the fabrication of highly-conductive degradable metal tracks. We optimize the sintering parameters to obtain zinc resistors with a high temperature coefficient of resistance and a linear temperature response curve. The data that was collected in the frame of this work is present in this repository. More information about the contents of the dataset is present in the included README files.</p>
Dataset for 'Organic Electrochemical Transistors Printed from Degradable Materials as Disposable Biochemical Sensors'
<p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the publication entitled “Organic Electrochemical Transistors Printed from Degradable Materials as Disposable Biochemical Sensors”.</p> <p>This work aims to study the fabrication of organic electrochemical transistors using more environmentally-friendly materials, in particular carbon contacts and polylactic acid (PLA) as substrate. Organic electrochemical transistors (or OECTs) offer applications in biosensing, for example for point-of-care devices. We use a combination of additive manufacturing methods (screen printing and inkjet printing) to manufacture these transistors and solve the issues with fabricating them on a low-temperature substrate such as PLA. We also assess these transistors as disposable sensors for the detection of various ion concentrations as well as glucose. The data that was collected in the frame of this work is present in this repository. More information about the contents of the dataset is present in the included README files.</p>
Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"
<p>These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets:</p> <ol> <li><strong>"Sampling location names and coordinates.csv"</strong>: locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work.<strong> [NOTE: </strong>latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li><strong>"Dissemination Block Populations.csv"</strong>: These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li><strong>"Daily average concentrations by site and pollutant.csv"</strong>: This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to "Sampling location names and coordinates.csv" to parse the labels in this data set.</li> </ol> <p>There is also a sample code in Python to construct the kriging maps provided in 2 formats. <strong>[NOTE: </strong>we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.]</p> <ol> <li><strong>"Jain et al - GeoHealth - Kriging Script.ipynb"</strong>: A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li><strong>" Jain et al - GeoHealth - Kriging Script.pdf"</strong>: A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>
Multi-Sensor Dataset From Android Smart Devices
<p>This dataset contains data acquired on various Android devices, using an Android app called ''Mimir'', developed by the authors. Focus is given on raw GNSS measurements, but other sensors are also logged in the surveys. The dataset is provided under the CC-BY 4.0 license. More information are provided inside the ''readme.md'' provided along the dataset, as well as in our related publication.</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>
PDST sensor files European eel (Anguilla anguilla) Belgium
<p><strong>Brief data description</strong></p> <p>This data consists of the raw sensor data from a tagging study on European eel (<em>Anguilla anguilla</em> L.) caught and released in Nieuwpoort Belgium. The applied tags were pop-off G5 data storage tags (CEFAS Technology, Lowestoft, UK). Temperature was measured every 10 seconds and pressure (i.e. depth) every 2 seconds. The sensor data contains the raw data per eel with the tag ID as an individual eel. The tags were externally attached to eels and came off before or at a preprogrammed time, drifted to the surface, washed ashore and when found, the data could be downloaded when the tag was retrieved. Note that some recovered tags were reused hence a tag ID can occur more than once.</p> <p>This data is part of the eel-pdst-analysis GitHub repository: https://github.com/PieterjanVerhelst/eel-pdst-analysis. The sensor files have the following destination: eel-pdst-analysis\data\interim\sensorlogs</p> <p> </p> <p><strong>Files</strong></p> <p>Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/record/8398240/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <p> </p> <p><strong>More Info</strong></p> <p>For more information on the data collection and analysis, see the following two research papers:</p> <p>Scientific Reports: https://doi.org/10.1038/s41598-021-04052-7</p> <p>Science of The Total Environment: <a href="https://doi.org/10.1016/j.scitotenv.2023.167341">https://doi.org/10.1016/j.scitotenv.2023.167341</a></p>
WSD4FEDSRM (Wearable sensor data for fatigue estimation during shoulder rotation movements)
<p>The dataset comprises a collection of many data types during shoulder internal rotation, and external rotation exercises from 34 participants, including demographic information, anthropometric measurements, maximum voluntary isometric contraction force measurements, inertial measuring unit data, surface electromyography recordings, photoplethysmogram data from wearable sensors, as well as measurements from the Borg rating of perceived exertion scale and the Karolinska sleepiness scale.</p>
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.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from February 2016 through December 2016
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 February through December 2016. 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.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2017 through December 2017
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 2017 through December 2017. 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.
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.