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Installing a EC Sensor on a remote location.
<p><strong>1.Introduction</strong></p> <p>The objective of this document is to provide a description of the dataset entitled “Installing an EC Sensor on a remote location”.</p> <p>The guidelines on how to use the files are included in this document.</p> <p>The dataset is part of the deliverables D9.4 (First data management plan) and D9.5 (Final data management plan).</p> <p><strong>2.Description of the data</strong></p> <p><strong>2.1.Origin</strong></p> <p>This dataset includes data collected from the experiments related to the task “Installing a EC Sensor on a remote location” as part of the WP8 “validation in the industrial scenario”.</p> <p><strong>2.2.Type</strong></p> <p>The data consists of Eddy Current (EC) measurements.</p> <p><strong>2.3.Formats</strong></p> <p>The acquired data are available in several formats.</p> <p>2.3.1.*.sidata files</p> <p>These files are proprietary format that can be opened with the software “UPecView” supplied by Sensima Inspection (http://www.sensimainsp.com).<br> This software provides an interface familiar to what expected by eddy-current inspectors.</p> <p>Each file includes all the relevant information that may be used for analysis: the measurements and the instrument configuration (ex. Excitation frequency of the probe) is contained in this file.</p> <p>2.3.2.*.csv files</p> <p>The csv files contain an export of the measurements only (without instrument settings); a comma separator is used. Each row is composed of the following variables: Time (s), Signal (in-phase), Signal (out-of-phase), Channel/state, Extra signal (ADC), Encoder coordinate 1 (x), Encoder coordinate 2 (y), Encoder coordinate 3 (z), Encoder error status.</p> <p> </p> <p> </p> <p><strong>3.Measurements indexing</strong></p> <p>Folder</p> <p>Filename</p> <p>Creation date</p> <p>Description</p> <p>Target</p> <p>Location</p> <p> </p> <p>EXP001</p> <p>0001A</p> <p>12.03.2019</p> <p>Calibration block scan</p> <p>Calibration block</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0001B</p> <p>12.03.2019</p> <p>Manual reference scan on weld pipe</p> <p>Calibration block</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002A</p> <p>12.03.2019</p> <p>Drone overall scan inspection and sensor deployment</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002B</p> <p>12.03.2019</p> <p>Deployed sensor</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002C</p> <p>12.03.2019</p> <p>Deployed sensor</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002D</p> <p>12.03.2019</p> <p>Permanent sensor removal</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
pH sensor ageing experiment (SoDAN-dataset1)
<p>The data in this repository has been used to produce all results reported in [1]. It includes raw pH sensor potential measurements (in mV) recorded during exposure to nitrified urine and during sensor characterization tests.</p> <p>Modifications since v1.0.0:</p> <p>- Grouped data into zip files</p> <p>- Removed individual files</p> <p>References:<br> [1] https://engrxiv.org/mv6tz/</p>
Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
<p>Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).</p>
STOP-IT Real-Time Sensor Data Protection (RSDP)
<p>Sensors, and other devices, generate large amounts of data, which can be used for different purposes; for example, controlling the proper functioning of a critical infrastructure, performing predictive maintenance actions or making decisions that improve the productivity of an industrial plant. However, the use or analysis of erroneous or corrupt data can cause catastrophic situations. For this reason, it is very important to be able to guarantee the integrity of the data generated by sensors, or other devices, which will be used to perform relevant actions for a critical infrastructure, industrial plant, etc. The RSDP tool provides exactly that service, it checks the integrity of the data that has been previously stored in the system, and this can be guaranteed thanks to the use of Blockchain, or DLT, technologies.</p>
Project STORM: monitoring the masonry of Michelangelo's Cloister, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2018 - 2019
<p>This dataset for monitoring the masonry of Michelangelo's Cloister at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0 and S3);</li> <li>Temperature of masonry (sensors S1, S2 and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2018 to May 2019 and separated by month in 8 sheets. In total more than 2 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Project STORM: monitoring the masonry of Hall I, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2017 - 2019
<p>This dataset for monitoring the masonry of Hall I at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0, S2 and S3);</li> <li>Temperature of masonry (sensors S1, and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2017 to May 2019 and separated by month in 14 sheets. In total more than 4 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
User stories and xAPI statements for "A mobile campus application as a sensor node for Personal Learning Environments"
<p>This dataset provides the full user stories and xAPI statements as used in the prototype described in the article "A mobile campus application as a sensor node for Personal Learning Environments". It consists of two PDF documents described below. The files were created as part of the master thesis of Hendrik Geßner.</p> <p>"User Stories.pdf" contains a complete list of user stories with required context information, existing portlets and a category. The process that led to this collection is described very briefly in the article mentioned above, a graphical explanation is available in the attached image "Use case process complete.jpg"</p> <p>"xAPI Statements.pdf" contains all xAPI statements used in the prototype described in the article mentioned above. Dynamic elements such as names or IDs are highlighted on color. The statements appear in the following order: attended, used, loggedin, wasat, opened, closed, joined, left.</p>
Atmospheric, hydrodynamic and water quality observations from environmental-quality stations, water level sensors, acoustic Doppler velocimeters, and meteorological stations located at the Guadalquivir river estuary (2008 - 2010)
<p>The dataset included in this repository was obtained during the project entitled “Propuesta metodológica para diagnósticar las consecuencias de las actuaciones humanas en el estuario del Guadalquivir” funded by the Autoridad Portuaria de Sevilla (APS), by the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía), CTM2011-22580, MedEX (CTM2008-04036-E) and PR11-RNM-7722. The data were collected in real time from 2008 until 2010 with a remote monitoring system installed by the Institute of Marine Sciences of Andalusia (ICMAN-CSIC) (Navarro et al., 2011).</p> <p> </p> <p>The environmental quality station recorded turbidity, temperature, conductivity, normalized turbidity, dissolved oxygen, oxygen, oxygen saturation, percentage of oxygen saturation, fluorescence, normalized fluorescence, and salinity every thirty minutes. Current data were measured every 15 minutes by means of acoustic current profilers. The former datasets were obtained at several depths and different locations along the Guadalquivir estuary. Water level sensors recorded the position of the free water surface every 10 minutes at several locations along the Guadalquivir estuary. Wind velocity and direction and solar radiation were measured every 10 minutes in a meteorological station at the mouth of the Guadalquivir estuary.</p> <p>Brief description of dataset.</p> <ul> <li> <p>velocities.csv (in m/s)</p> </li> <li> <p>Turbidity.csv (in Volts), temperature (in Celsius), conductivity (in Siemens/m), normalized turbidity (in FNU), dissolved oxygen (mg/L), oxygen (in Volts), fluorescence (in Volts), normalized fluorescence (in Volts), oxygen saturation (mg/L), percentage of oxygen saturation (%), salinity (in PSU).</p> </li> <li> <p>qual_Salmedina.csv, R_mean (mean radiative flux in W/m²), R_max (max radiative flux in W/m²), Rel_humidity (relative humidity in %), D_mean (wind mean direction in degrees), D_max (wind maximum direction in degrees), D_sig (standard deviation of the wind direction in degrees), V_mean (mean wind velocity in m/s), V_max (maximum wind velocity in m/s), V_sig (standard deviation of the wind velocity in m/s), P_atm_mean (mean atmospheric pressure in mbar), T_mean (mean air temperature in Celsius), T_max (maximum air temperature in Celsius), T_sig (standard deviation of the air temperature in Celsius).</p> </li> <li> <p>Sealevel.csv (in meters)</p> </li> </ul> <p>A wide description of the datasets can be found in Navarro et al (2011).</p> <p>Contact person: infogdfa@ugr.es (or mcobosb@ugr.es)</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>
Sensor-based Pallet Activity Recognition in Logistics (SPARL Version 2) - A multi-modal Dataset
<p>SPARL is a freely accessible data set for sensor-based activity recognition of pallets in logistics. The data set consists of 20 recordings from three scenarios. A description of the scenarios can be found in the protocol file.</p> <p>Four different sensors were used simultaneously for all recordings:</p> <ul> <li>MSR Electronics MSR 145 <ul> <li>Sampling rate 50 Hz</li> </ul> </li> <li>MBIENTLAB MetaMotionS <ul> <li>Sampling rate 100 Hz</li> </ul> </li> <li>Kistler KiDaQ Module 5512A <ul> <li>Sampling rate 100 kHz</li> <li>the raw data is also downsampled to 5 kHz and 20 kHz for easier processing </li> </ul> </li> <li>Holybro Flightcontroller PX4FMU <ul> <li>The board uses two accelerometers and two gyroscopes, all with a sampling rate of 1000 Hz <ul> <li>Accelerometer 1: IvenSense MPU6000 </li> <li>Accelerometer 2: STMicroelectronics LSM303D </li> <li>Gyroscope 1: IvenSense MPU6000</li> <li>Gyroscope 2: STMicroelectronics L3GD20</li> </ul> </li> </ul> </li> </ul> <p>The recordings were accompanied by three logitech Mevo Start cameras, of which all recordings are included anonymously in the data set. </p> <p>The videos were annotated by one person in each frame. For this purpose, the annotation tool SARA was used, which can be found <a href="../records/8189341">here</a>. The JSON schema used for annotation is also included in the SPARL dataset. The R code used our evaluation can be found in <a title="https://github.com/bommert/WGTL24" href="https://github.com/bommert/WGTL24">GitHub</a>.</p> <p>If you have any questions about the dataset, please contact: sven.franke@tu-dortmund.de</p> <p><strong>If you use this dataset for research, please cite the following paper: “Data-driven, sensor-based taxonomy for environmental life cycle assessment of pallets”, Nr. 20 (2024): Logistics Journal: Proceedings, DOI: <a href="http://dx.doi.org/10.2195/lj_proc_franke_en_202410_01" target="_blank" rel="noopener">10.2195/lj_proc_franke_en_202410_01</a></strong></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>
Comparative analysis of ADC values between high-cost (US331-000005-030PA) and low-cost (B07YZLCSRP) depth sensors
<p>The dataset provides a comparison between an expensive depth sensor, the "US331-000005-030PA", and a cheaper sensor, the "B07YZLCSRP". The dataset includes the ADC values from both sensors as well as the offset between them.</p> <p>Data were collected using an autonomous underwater profiler called s-Nautilus at the Real Club de Regatas de Cartagena. During this test, the s-Nautilus profiler was moved to various depths, and the time and 12-bit ADC values from both sensors were recorded.</p> <p>The recorded variables include:</p> <ul> <li><strong>timestamp UNIX (s):</strong> the timestamp indicating the date and time of each measurement.</li> <li><strong>hours (hh:mm:ss):</strong> time of recording of each measurement.</li> <li><strong>incr_time (s): </strong>cumulative time increment for each measurement.</li> <li><strong>ADC cheap sensor (unit of ADC of 12 bits): </strong>12-bit ADC values of depth sensor "B07YZLCSRP" at various depths of the s-Nautilus.</li> <li><strong>ADC expensive sensor (unit of ADC of 12 bits):</strong> 12-bit ADC values of depth sensor "US331-000005-030PA" at various depths of the s-Nautilus.</li> <li><strong>ADC difference (unit of ADC of 12 bits):</strong> difference in ADC values between the two sensors.</li> <li><strong>ADC + offset (unit of ADC of 12 bits):</strong> ADC values of depth sensor "B07YZLCSRP" adjusted by calculated offset.</li> <li><strong>average ADC differences (unit of ADC of 12 bits):</strong> average offset ADC for all measurements.</li> </ul>
Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach
<p>Main data used for the scientific paper entitled: "Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach".</p> <ol> <li>"Danta_dem_10cm_px.tif": orthomosaic-derived DEM</li> <li>"Danta_rgb_2.2cm_px.tif": ortophoto </li> <li>"GPS points": list of GPS samples points</li> <li>"Main data": field vegetation data and indexes used for the regressions</li> <li>"Raw PointCloud". Lidar original dataset</li> <li>"Pre-processed PointCloud": Lidar dataset after pre-processing (see paper's methods) </li> <li>"DTM_DantaGround_grid50cm_minimo": Output (TIFF); the LiDAR-derived DTM showed in the paper</li> <li>"LAI": Output (Shapefile); the LiDAR-derived LAI showed in the paper.</li> </ol> <p> </p>
Analog and digital sun sensor thermal test measurements
<p>Internal temperature sensor measurements for analog and digital sun sensor thermal (only thermal no vacuum) cycling tests. An additional external omega temperature logger for reference was used.</p> <p>During the first test the soaking time was wrongly configured in the thermal chamber settings.</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>
Dry matter databases derived by crossing burned areas databases with above ground estimations from SMOS sensor
<p>Estimates of the the fuel consumed during a fire (dry-matter, DM) is derived by combining burned areas (from two different fire inventories) with above ground biomass (derived from SMOS L Band Vegetation Optical Depth). Six new datasets are provided on a 25 km grid for the years 2010-2017.</p> <p>The portion of vegetation that is consumed during biomass burning is expressed as:<br> <strong><span class="math-tex">\(DM= AGB * BA*\beta\)</span></strong></p> <p>where BA is the burned area; AGB is the fuel load, the amount of biomass or organic matter an ecosystem contains per unit area; <span class="math-tex">\(\beta\)</span> is the combustion completeness or burning efficiency, which is the fraction of fuel actually consumed during the fire.</p> <p>Monthly maps of AGB are converted from SMOS L-band vegetation optical depth (VOD) through an algorithm described in [1] and obtained looking at the relationship between three static AGB benchmark maps from the works of [2],[3] and [4]. Each static map provide three L-VOD to AGB conversion curves fitting the 5th, 50th and 95th percentiles of the data using a logistic regression([1]) In the following, the three different databases are named AGB-BA (Baccini), AGB-SA (Saatchi) and AGB-AV (Avitabile) </p> <p>Monthly total of burned areas are available from two sources. The first one, called hereinafter BA-GFED, is available though GEFD4.1s [5] and is based on MODIS MCD64A1 product. It has a 500m pixel resolution and is also available on a regular grid of 0.25 deg. The second BA product, named hereinafter BA-CCI, is a multi-sensors product provided by the European Space Agency Climate Change Initiative (ESA-CCI) [6] We use version FireCCI5.1 which is calculated using a two-phase algorithm, where MODIS active fire locations are used to identify seed pixels corresponding to high confidence burned areas. These areas are then grown using Medium Resolution Imaging Spectrometer (MERIS) vegetation input data.</p> <p>Combustion completeness, is a taken from table 4 of [7]. </p> <p>By selectively combining any AGB estimations with the two available BA datasets, a total of 6 products are generated.</p> <p> </p> <p><strong>References </strong></p> <p>[1] https://doi.org/10.5194/bg-15-4627-2018</p> <p>[2] <a href="https://doi.org/10.1126/science.aam5962">DOI: 10.1126/science.aam5962</a></p> <p>[3]<a href="https://cce.nasa.gov/veg3dbiomass/saatchi_tgrs07.pdf">https://cce.nasa.gov/veg3dbiomass/saatchi_tgrs07.pdf</a></p> <p>[4] <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.13139">https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.13139</a></p> <p>[5] <a href="https://daac.ornl.gov/VEGETATION/guides/fire_emissions_v4_R1.html">https://daac.ornl.gov/VEGETATION/guides/fire_emissions_v4_R1.html</a></p> <p>[6] <a href="https://geogra.uah.es/fire_cci/firecci51.php">https://geogra.uah.es/fire_cci/firecci51.php</a></p> <p>[7] <a href="https://acp.copernicus.org/articles/6/3423/2006/acp-6-3423-2006.pdf">https://acp.copernicus.org/articles/6/3423/2006/acp-6-3423-2006.pdf</a></p> <p> </p>
Datasets of the work named Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform
<pre>- 1_Motion_Simulator/ - IMU_results/ - 20211028101756.csv - 20220114101543.csv - 20220117000000.csv - Rotary_Table/ - 15/ - rover_20220105.nav - rover_20220105.obs - solution_20220105_CAS.log - 360/ - rover_20211221.nav - rover_20211221.obs - solution_20211221_CAS.log - 360-15/ - rover_20220202_CAS.nav - rover_20220202_CAS.obs - solution_20220202_CAS.log - Static_Tests/ - solution_SSRA00CAS0 - solution_SSRA00WHU0 - 2_GNSS_Signal_Simulator/ - platformmov_C1.xtd - platformmov_C2.xtd - platformmov_C3.xtd - TestBetaNoneMov_C1 - TestBetaNoneMov_C1.nav - TestBetaNoneMov_C1.obs - TestBetaNoneMov_C1.ubx - TestBetaNoneMov_C2 - TestBetaNoneMov_C2.nav - TestBetaNoneMov_C2.obs - TestBetaNoneMov_C2.ubx - TestBetaNoneMov_C3 - TestBetaNoneMov_C3.nav - TestBetaNoneMov_C3.obs - TestBetaNoneMov_C3.ubx - 3_Test_Sea/ - 20220503000000.xlsx - solution_28.nav - solution_28.obs - solution_28.ubx Background: {Journal Article using this dataset} 'Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform' Paper DOI: <a href="https://doi.org/10.3390/s23020925">https://doi.org/10.3390/s23020925</a> Abstract: a low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved. </pre>
Sedimentation Event Sensor images (26 October 2015–18 June 2015, 3900 m deep at Station M, NE Pacific)
<p>Images taken by the Sedimentation Event Sensor (26 October 2015–18 June 2015, 3900 m deep at Station M, NE Pacific) . See <a href="https://doi.org/10.1016/j.dsr2.2020.104763">https://doi.org/10.1016/j.dsr2.2020.104763</a> for details</p> <p> </p> <p>Huffard, C. L., Durkin, C. A., Wilson, S. E., McGill, P. R., Henthorn, R., & Smith Jr, K. L. (2020). Temporally-resolved mechanisms of deep-ocean particle flux and impact on the seafloor carbon cycle in the northeast Pacific. <em>Deep Sea Research Part II: Topical Studies in Oceanography</em>, <em>173</em>, 104763.</p>
I-MSV 2022: Indic-Multilingual and Multi-sensor Speaker Verification Challenge
<p><strong>Dear Users,</strong></p> <p><strong>Data is password protected, to get password all you need to do is register using below link. Note that data is free of Cost </strong></p> <p><a href="https://forms.gle/1gsVhJaJYT4mBp83A">Click here for Registration</a></p> <p>Speaker Verification (SV) is a task to verify the claimed identity of the claimant using his/her voice sample. Though there exists an ample amount of research in SV technologies, the development concerning a multilingual conversation is limited. In a country like India, almost all the speakers are polyglot in nature. Consequently, the development of a Multilingual SV (MSV) system on the data collected in the Indian scenario is more challenging. With this motivation, the Indic- Multilingual Speaker Verification (I-MSV) Challenge 2022 has been designed for understanding and comparing the state of-the-art SV techniques. For the challenge, approximately 100 hours of data spoken by 100 speakers has been collected using 5 different sensors in 13 Indian languages. The data is divided into development, training, and testing sets and has been made publicly available for further research. The goal of this challenge is to make the SV system robust to language and sensor variations between enrollment and testing. In the challenge, participants were asked to develop the SV system in two scenarios, viz. constrained and unconstrained. The best system in the constrained and unconstrained scenario achieved a performance of 2.12% and 0.26% in terms of Equal Error Rate (EER), respectively.</p>
Vibration Sensor and Process Data from Ferrosilicon Production
<p><strong>Elkem Facility</strong></p> <p>The facility specializes in producing ferrosilicon (FeSi) and ferrosilicon magnesium (FSM) master alloys. Elkem Bjølvefossen is among the world’s largest producers of FSM. Three reduction furnaces deliver the base metal which is then alloyed and refined to the right quality of FeSi or FSM. These alloys are important additives in the manufacturing of steel products. Silicon in the form of FeSi is used to remove oxygen from the steel and as an alloying element to improve the final quality of the steel. Silicon increases strength and wear resistance, elasticity, i.e., spring steels, scale resistance, and heat resistant steels and lowers electrical conductivity and magnetostriction.</p> <p>After tapping and refining, the ferro-alloys are crushed to grains ranging from 1 mm to 25 mm in size. Consumers of FeSi and FSM have strict requirements for particle size, related mainly to the chemical kinetics of their refining and alloying processes. For this reason, the crushed material is separated in sieves and packaged by particle size before shipment. Two lattice gratings inside the Mogensen shaker separate the material according to required particle size. </p> <p>The subject of the present study is a mechanical shaker platform containing one or more such sieves. The shaker is a <a href="https://www.mogensen.se/">Mogensen S0556</a> that was installed in 1996 in Bjølvefossen and is no longer produced in this type. This device is powered by two counter-rotating 1.2-horsepower AC motors operating at 960 RPM. Together with the spring suspension, these cause an elliptical motion that both transports and scatters the incoming material across the sieve. The shaker is engineered so that the motion transitions from a slanted ellipse at the in-feed to nearly linear at the output.</p> <p><strong>Vibration Data</strong></p> <p>Vibration data from two sensors. Each sensor measures acceleration in three axes with three different ADCs. </p> <p><strong>ERP and MES data</strong></p> <p>Manufacturing Execution System (MES) data as well as process data from the Enterprise Resource Planning (ERP) is given. It is providing information about the material that is currently being produced as well as the data from the scales from the material packing station where the bags with completed production were packed. MES data has a resolution of 5 seconds and the process data from ERP has a resolution of roughly 10 minutes. This operational data is meant to provide insight into the current state and throughput of the facility and will serve as labels for the correlation analysis with the vibration data. </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.