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10,553 results for “measurements”
Glycomics measurements of the retrospective study of the Pain-Omics project
<p>This deposit contains the QCed glycomics data for the patients in the retrospective study of the Pain-Omics FP7 project.</p>
Variability and bias in measurements of metals mass fractions in automobile shredder residue
<p>Measured mass fractions of various metals in individually digested test samples of automobile shredder light fraction (single_digestions_ppm.csv) and the calculated means and standard deviations of these (mean_sd_ppm.csv). For all metadata see accompanying readme file Loevik2019_metal_mass_fractions_in_automobile_SLF_Readme.txt.</p>
PsPM-RRM3: SCR, ECG and respiration measurement in response to aversive/arousing IAPS pictures, and neutral/aversive sounds
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements for each of 20 healthy unmedicated participants (10 males and 10 females aged 25.3 +/- 5.1 years; male/female numbers were misprinted in Bach et al. 2016) in response to negatively and positively arousing IAPS pictures and neutral (65 dB) and aversive (85 dB) white noise sounds. All stimuli had 1 s duration. ITI was selected randomly on each trial from 40 s, 45 s or 50 s.</p>
PsPM-DoxMem2: Pupil, SCR, ECG, EMG and respiration measurement in a classical pavlovian discriminant delay fear conditioning task, reminder under doxycycline/placebo, retention and re-learning
<p>This dataset includes eyetracker, skin conductance response (SCR), electrocardiogram (ECG), respiration and electromyogram (EMG, only relevant for retention phase) measurements. Also included are CS and US information, keypress responses and keypress response times for 79 healthy participants (40 males and 39 females aged 24.8+/-4.9 years). Participants underwent a classical (Pavlovian) discriminant delay fear conditioning task with 1 CS- and 2 CS+ (50% reinforcement), were reminded of one CS+ one week later under either doxycycline or placebo, and were tested in a retention/extinction and re-learning task another week later. CS were isoluminant coloured triangles. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 500 Hz frequency. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. Before the fear conditioning task, participants completed several questionnaires. During the retention/extinction phase, an auditory startle probe (ST) and no US was delivered 3.5 s after CS onset via headphones (102 dB, 40 ms duration with 2 ms on- and offset ramp). In an immediately following re-learning phase, the ST was omitted and the CS reinforced with the same schedule as during acquisition. The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p> <p> </p>
Terrasar measurement data of "Sar Super-Resolution Using Physics-Aware Adaptive Compressed Sensing"
<p>This data set was used to test of the method described in "Sar Super-Resolution Using Physics-Aware Adaptive Compressed Sensing". It consists of the related Terrasar data and a MATLAB file to import the data into MATLAB.</p>
Attributes: A Curriculum Analytics System for measuring learning outcomes - Overview
<p><span><strong>Link to video </strong><a href="https://vimeo.com/1015456231?share=copy#t=0"><strong>https://vimeo.com/1015456231?share=copy - t=0</strong></a><br><br>The Curriculum Analytics System at the Instituto Tecnológico de Costa Rica, integrated into TEC Digital, supports faculty, coordinators, and students in assessing engineering learning outcomes during accreditation processes. The system offers two key user modules: one for coordinators to map and manage learning outcomes, and another for instructors to conduct assessments through the course portal. Coordinators oversee course and attribute mapping using visual representations of study plans, control points, and outcome visualizations. Instructors configure assignments and evaluate student submissions with standardized rating scales. The system tracks progress in real-time and generates</span> <span>comprehensive reports with performance metrics, facilitating continuous improvement in academic programs.</span></p> <p><strong><span>Key words: </span></strong><span>attributes, learning outcomes, TEC Digital, curriculum analytics, continuous improvement. </span></p>
Measurement of interturn short-circuits emulation on dual three-phase PMS motor
<p>The published data contains measurement records made on a special permanent magnet synchronous motor (PMSM). The motor has a stator with winding taps. The winding taps can be used to emulate various severities of interturn short-circuits.</p> <p>A detailed machine description and modeling can be found in the paper "Interturn short circuit modelling in dual three-phase PMSM" (https://dx.doi.org/10.1109/IECON49645.2022.9968364). </p> <p>The file name specifies the measurement conditions according to</p> <p>spd10-5000rpm_flt<strong>N</strong>z<strong>P</strong>_<strong>XX</strong>NM.mat</p> <p><strong>N</strong> - Number of shorted turns (0-healthy operation)</p> <p><strong>P</strong> - Fault phase ( U or V)</p> <p><strong>XX </strong>- Load torque generated by a dynamometer</p> <p> </p> <p>For example, the file spd10-5000rpm_flt1zu_25NM.mat contains data where one coil turn in phase U was shorted, and the machine was producing 25 Nm torque.</p> <p>Measurement was performed by the microcontroller and synchronised with the control algorithm. The sample rate is 10 kHz for this reason. Each data file contains measured currents in stator coordinates as well as currents and voltages (control value) in rotor (dq) coordinates. These variables are measured for each three-phase sub-system. Measured motor position, speed and required motor speed are also included. </p>
Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2019-01-01 to 2019-12-31 [L2]
<p>Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level.</p> <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2019.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> <p>For more details read `FRCHEM_2019_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>
Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Werthmannstrasse (FRWRTM) from 2019-01-01 to 2019-12-31 [L2]
<p>Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10.</p> <ul> <li>Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2019.</li> <li>Average, minimum and maximum in-canopy air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> <p>For more details read `FRWRTM_2019_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>
Two decades of body length measurements in size-structured larval and juvenile fish populations in English rivers.
<p>Long term ecological datasets are valuable in providing context and understanding to complex ecological processes that occur over broad temporal scales, and provide a baseline for analysing change. Monitoring of fish populations in UK waterbodies and elsewhere is typically through measuring the length of individual fish caught in surveys. Through this method, the age structure of fish populations can be determined, as well as over winer survival rates and future recruitment success and cohort sizes can be predicted. The larval and juvenile period are when fish are considered most vulnerable to predation, competition, disease and environmental perturbations. </p> <p><br>This study presents the first long-term larval and juvenile fish lengths dataset for 67 survey sites over two decades (1999-2018) from the rivers Ancholme, Warwickshire Avon, Don, Trent, and Yorkshire Ouse (including the Swale, Ure, Nidd and Wharfe) in the United Kingdom. These rivers represent a range of topographical and biotopical characteristics. For the majority of this study, surveys were conducted on a monthly or fortnightly basis making both annual and seasonal analyses of size structure, growth and body length possible. Although there is some variation in the sampling frequency and some locations varied throughout the study according to requirements. In total, more than 380,000 larval or juvenile fish of 30 species were measured, likely representing one of the most comprehensive datasets of its type.</p> <p>Surveys were conducted in river margins, where the velocity was slowest and larval and juvenile fish tend to aggregate. Fish were captured using a 25 x 3 m micromesh (3 mm mesh size) seine net that was set in a rectangle parallel to the bank. This net capture fish as small as 5 mm and is the most appropriate method of catching larvae and juvenile fish, although occasionally some larger adult fish may have also been captured and measured as part of this dataset for completeness. All fish were identified to species and measured to standard length (mm) and released at the point of capture. The exception was the smallest larvae, which were euthanised with an overdose of methanesulphonate (MS-222) and preserved in 4% formalin solution for microscopic examination.</p> <p><br>The dataset contains 384,090 rows and 13 columns. Each row corresponds to a single fish that was measured at each site and date. Associated site information (site name, location, area fished (m<sup>2</sup>) and survey date) is reported for each row. When only a fraction of the catch was processed, the sub-sample size was reflected in the Count column (e.g. when half the sample was processed, the numbers of fish measured or only counted were multiplied by two). This enables accurate densities to be calculated as the total number of both measured and unmeasured fish is recorded.</p> <p>Description of columns found in the dataset:</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>Column heading</strong></p> </td> <td> <p><strong>Column description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Units</strong></p> </td> </tr> <tr> <td> <p>Fish _Catchment</p> </td> <td> <p>The river catchment/basin location of each fish site</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_River</p> </td> <td> <p>The river/watercourse location of each fish site.</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_SiteName</p> </td> <td> <p>The name of each fish site</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_Latitude</p> </td> <td> <p>The latitude of each fish site (WGS 1984)</p> </td> <td> <p>Integer</p> </td> <td> <p>Decimal degrees</p> </td> </tr> <tr> <td> <p>Fish_Longitude</p> </td> <td> <p>The longitude of each fish site (WGS 1984)</p> </td> <td> <p>Integer</p> </td> <td> <p>Decimal degrees</p> </td> </tr> <tr> <td> <p>Fish_Area</p> </td> <td> <p>Area of fish site surveyed</p> </td> <td> <p>Integer</p> </td> <td> <p>m<sup>-2</sup></p> </td> </tr> <tr> <td> <p>Fish_SurveyDate</p> </td> <td> <p>Date fish survey was carried out</p> </td> <td> <p>Integer</p> </td> <td> <p>dd/mm/yyyy</p> </td> </tr> <tr> <td> <p>Fish_Year</p> </td> <td> <p>Year fish survey was carried out</p> </td> <td> <p>Integer</p> </td> <td> <p>yyyy</p> </td> </tr> <tr> <td> <p>Common_Name</p> </td> <td> <p>The common/vernacular name of each fish taxon recorded in the dataset.</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Latin_Name</p> </td> <td> <p>The scientific name of each fish taxon recorded in the dataset</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Net_Number</p> </td> <td> <p>The net number the fish in a given survey were caught on</p> </td> <td> <p>Integer</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Length_mm</p> </td> <td> <p>Length of individual fish caught</p> </td> <td> <p>Integer</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>Count</p> </td> <td> <p>Count of fish caught accounting for sub- sampling</p> </td> <td> <p>Integer</p> </td> <td> <p>Number of fish</p> </td> </tr> </tbody> </table> <p> </p>
Combined data online measurements_WIDER UPTAKE
<p>Raw data from daily online measurements at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.</p>
Data for predicting piglet survival until weaning using birth weight and within-litter birth weight variation as easily measured proxy predictors
<p>The data was used in the analysis presented in the manuscript: Predicting piglet survival until weaning using birth weight and within-litter birth weight variation as easily measured proxy predictors. The manuscript is published in <em>Animal</em> journal. The data is for piglet survival survival at different time-points from birth to weaning from two research farms.</p>
TDA4ContextualEmbeddings - Public - Debug Data for the codebase of the publication "Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction"
<p>Debug dataset for testing the <a href="https://gitlab.cs.uni-duesseldorf.de/general/dsml/tda4contextualembeddings-public">codebase</a> of the paper <a href="https://doi.org/10.18653/v1/2024.sigdial-1.31">“Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction”</a> published at the 25th Meeting of the Special Interest Group on Discourse and Dialogue, Kyoto, Japan (SIGDIAL 2024).</p>
Dataset: Environmental Gamma Dose Rate Measurements using CZT Detectors
<h1>Scope</h1> <p>This dataset compiles the raw and partely processed data for the manuscript <em>Environmental Gamma Dose Rate Measurements using CZT</em><br><em>Detectors </em>by Sebastian Kreutzer, Loïc Martin, Didier Miallier, and Norbert Mercier. </p> <h1>Dataset structure</h1> <ul> <li>00_Measurement_Data: All original spectra recorded with a Kromek GR1+ and Kromek RayMon10 GR1 detector. All files have the file ending .spe</li> <li>10_GEANT4 modelling results as .xlsx and .ods + simulation code in ZIP file</li> <li>20_System_Calibrations: The final detector calibration results as .rda (external representation of R objects)</li> <li>30_R_Scripts: A compact version of R scripts used to derive the results in the manuscript as HTML and Quatro file</li> <li>40_RadionuclideComposition_WH2024: Raw data from the radionuclide measurements of sample WH2024 (.ud, .pdf)</li> <li>60_RadionuclideComposition_Flossi: Radionuclide concentrations of the granute block Flossi extracted from an unpublished Diplom thesis by Uwe Rieser (1991)</li> <li>70_Heidelberg_Dataset4gamma: Dataset for the R package 'gamma' with nuclide concentration results for Weiße-Hohl, Flossi and calculated dose rates </li> <li>80_Strain_Relief_3D_printing: Construction files for printing the strain relief adapter for the GR1. Please note that all files in this subfolder are subject to CC BY-NC licence conditions, excluding commercial use. </li> </ul> <p> </p>
Multi-year measurements of tree motion from an accelerometer on a spruce tree near Niwot Ridge, Colorado
<p>This repository includes 12 Hz three-axis acceleration data from an accelerometer mounted to the bole of a <em>Picea engelmannii</em> (engelmann spruce) next to the C-1 Ameriflux tower at Niwot Ridge LTER, Colorado, USA. The data were recorded from November 2014 through August 2020. More information on the installation can be found in Raleigh et al. (in review, Water Resources Research).</p> <p>The data are stored in netCDF files, chunked based on the collection date when the data were downloaded from the accelerometer.</p> <p><strong>File metadata:</strong></p> <p>Filename</p> <p>GCDC_L01_Raw_Data_Niwot_TreeXX_collection_YYYYMMDD.nc</p> <p>where</p> <p>XX = tree number (01 = spruce, 02 = fir)</p> <p>YYYYMMDD = year (YYYY), month (MM), and day (DD) of data collection</p> <p> </p> <p>Each netCDF includes four variables:</p> <p>1. serial_date = time increment (fractional days), as defined by Matlab: "A serial date number represents the whole and fractional number of days from a fixed, preset date (January 0, 0000) in the proleptic ISO calendar." The serial dates are in mountain standard time (MST) with no adjustments for daylight savings.</p> <p>2. Ax = acceleration in the vertical direction (counts)</p> <p>3. Ay = acceleration in the east-west direction (counts)</p> <p>4. Az = acceleration in the north-south direction (counts)</p> <p>To convert the "counts" unit to gravitational units (g), divide Ax, Ay, and Az each by 2048, as explained in the manufacturer's user manual.</p> <p> </p> <p> </p>
Multi-year measurements of tree motion from an accelerometer on a fir tree near Niwot Ridge, Colorado
<p>This repository includes 12 Hz three-axis acceleration data from an accelerometer mounted to the bole of an <em>Abies lasiocarpa</em> (subalpine fir) next to the C-1 Ameriflux tower at Niwot Ridge LTER, Colorado, USA. The data were recorded from November 2014 through August 2020. More information on the installation can be found in Raleigh et al. (in review, Water Resources Research).</p> <p>The data are stored in netCDF files, chunked based on the collection date when the data were downloaded from the accelerometer.</p> <p><strong>File metadata:</strong></p> <p>Filename</p> <p>GCDC_L01_Raw_Data_Niwot_TreeXX_collection_YYYYMMDD.nc</p> <p>where</p> <p>XX = tree number (01 = spruce, 02 = fir)</p> <p>YYYYMMDD = year (YYYY), month (MM), and day (DD) of data collection</p> <p> </p> <p>Each netCDF includes four variables:</p> <p>1. serial_date = time increment (fractional days), as defined by Matlab: "A serial date number represents the whole and fractional number of days from a fixed, preset date (January 0, 0000) in the proleptic ISO calendar." The serial dates are in mountain standard time (MST) with no adjustments for daylight savings.</p> <p>2. Ax = acceleration in the vertical direction (counts)</p> <p>3. Ay = acceleration in the east-west direction (counts)</p> <p>4. Az = acceleration in the north-south direction (counts)</p> <p>To convert the "counts" unit to gravitational units (g), divide Ax, Ay, and Az each by 2048, as explained in the manufacturer's user manual.</p> <p> </p>
Spectral dataset of daylights and surface properties of natural objects measured in Japan
<p>This is a spectral dataset of natural objects and daylights collected in Japan. </p> <p>We collected 359 natural objects and measured the reflectance of all objects and the transmittance of 75 leaves. We also measured daylights from dawn till dusk on four different days using a white plate placed (i) under the direct sun and (ii) under the casted shadow (in total 359 measurements). We also separately measured daylights at five different locations (including a sports ground, a space between tall buildings and a forest) with minimum time intervals to reveal the influence of surrounding environments on the spectral composition of daylights reaching the ground (in total 118 measurements).</p> <div> <div> <div> <p>If you use this dataset in your research, please cite the following publication.</p> </div> </div> </div> <div> <div> <div> </div> </div> </div> <div>Morimoto, T., Zhang, C., Fukuda, K., & Uchikawa, K. (2022). Spectral measurement of daylights and surface properties of natural objects in Japan. <em>Optics express</em>, <em>30</em>(3), 3183. https://doi.org/10.1364/OE.441063</div> <p> </p> <p>Dataset contains following Excel spread sheets and csv files:</p> <p><strong>(A) Surface properties of natural objects</strong></p> <p><strong> (A-1) Reflectance_ver1-2.xlsx and .csv</strong></p> <p><strong> (A-2) Transmittance_FrontSideUp_ver1-2.xlsx and .csv</strong></p> <p><strong> (A-2) Transmittance_BackSideUp_ver1-2.xlsx and .csv</strong></p> <p><strong>(B) Daylight measurements</strong></p> <p> <strong>(B-1) Daylight_TimeLapse_v1-2.xlsx and .csv</strong></p> <p> <strong>(B-2) Daylight_DifferentLocations_v1-2.xlsx and .csv</strong></p> <p> </p> <p>Data description</p> <p><strong>(A) Surface properties</strong></p> <p><strong>(A-1) Reflectance_ver1-2.xlsx and .csv</strong></p> <p>This file contains surface spectral reflectance data (380 - 780 nm, 5 nm step) of 359 natural objects, including 200 flowers, 113 leaves, 23 fruits, 6 vegetables, 8 barks, and 9 stones measured by a spectrophotometer (SR-2A, Topcon, Tokyo, Japan). Photos of all samples are included in the .xlsx file.</p> <p>For the analysis presented in the paper, we identified reflectance pairs that have a Pearson’s correlation coefficient across 401 spectral channels of more than 0.999 and removed one of reflectances from each pair. The column 'Used in analysis' indicates whether or not each sample is used for the analysis (TRUE indicates used and FALSE indicate not used).</p> <p>At the time of collection, we noted the scientific names of flowers, leaves and barks from a name board provided by the Tokyo Institute of Technology in which samples are collected. If not available, we used a smartphone software which automatically identifies the scientific name from an input image (<em>PictureThis - Plant Identifier</em> developed by Glority Global Group Ltd.). The names of 2 flowers and 9 stones whose name could not be identified through either method were left blank.</p> <p><strong>(A-2) Transmittance_FrontSideUp_v1-2.xlsx and .csv</strong></p> <p>This file contains surface spectral transmittance data (380 - 780 nm, 5 nm step) for 75 leaves measured by a spectrophotometer (SR-2A, Topcon, Tokyo, Japan). Photos of all samples are included in the .xlsx file.</p> <p>For this data, the transmittance was measured with the front-side of leaves up (the light was transmitted from the back side of the leaves). This is the data presented in the associated article.</p> <p><strong>(A-3) Transmittance_BackSideUp_v1-2.xlsx and .csv</strong></p> <p>Spectral transmittance data of the same leaves presented in (A-2).</p> <p>For this data, the transmittance was measured with the back-side of leaves up (the light was transmitted from the front side of the leaves).</p> <p> </p> <p><strong>(B) Daylight measurements</strong></p> <p><strong>(B-1) Daylight_TimeLapse_ver1-2.xlsx and .csv</strong></p> <p>This file contains daylight spectra from sunrise to sunset on four different days (2013/11/20, 2013/12/24, 2014/07/03 and 2014/10/27) measured by a spectrophotometer (SR-LEDW, Topcon, Tokyo, Japan) with a wavelength range from 380 nm to 780 nm with 1 nm step. We measured the reflected light from the white calibration plate placed either under a direct sunlight or under a casted shadow.</p> <p>The column 'Cloud cover' provides visual estimate of percentage of cloud cover across the sky at the time of each measurement. The column 'Red lamp' indicates whether an aircraft warning lamp at the measurement site was on (circle) or off (blank).</p> <p><strong>(B-2) Daylight_DifferentLocations_ver1-2.xlsx and .csv</strong></p> <p>This file includes daylight spectra measured at five different sites within the Suzukakedai Campus of Tokyo Institute of Technology with minimum time gap on 2014/07/08, using a spectroradiometer (IM-1000, Topcon) from 380 nm to 780 nm with 1 nm step. The instrument was oriented either towards the sun or towards the zenith sky. When the instrument was oriented to the sun, we measured spectra in two ways: (i) one using a black cylinder covering the photodetector and (ii) the other without using a cylinder.</p> <p>The column 'Cylinder' indicates whether the black cylinder was used (circle) or not (cross). The column 'Cloud cover' shows the visual estimate of percentage of cloud cover at the time of each measurement. The column 'Sun hidden in clouds' denotes whether the measurement was taken when the sun was covered by clouds (circle) or not (blank).</p>
Simultaneous lidar and radar measurements for aeroso-cloud interaction studies
<p>Optical and microphysical aerosol and cloud properties derived from lidar and radar for aeroso-cloud interaction studies.</p>
Moored current and temperature measurements in the Southern Adriatic Sea at mooring site BB and FF, March 2012-June 2020
<p>This data set includes n.4 files (NetCDF format) containing observational data and related metadata from two mooring sites, sites BB and FF, located in the Southern Adriatic Sea from the period from March 2012 to June 2020. The stand-alone moorings are equipped with an ADCP-RDI system which measures currents along the last 100 meters of the water column and a CTD probe located approximatively 10 m above the bottom. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic submarine observatory of EMSO-ERIC European Consortium. The data are described in data paper Paladini et al., (In prep): Deep water hydrodynamic observations of two moorings sites on the continental slope of the Southern Adriatic Sea (Mediterranean Sea). </p>
Displacement measurements of the open-hardware sandbox using the AS5311 high-resolution magnetic sensor
<p>This dataset includes the experimental data from the AS5311 sensor for measuring the displacement of the Open-Hardware Geological Sandbox.</p> <p>These experiments are explained in the journal article: <a href="https://doi.org/10.1109/ACCESS.2023.3262617">Designing low-cost open-hardware electromechanical scientific equipment: A geological analogue modeling sandbox</a></p> <p>To understand this dataset, go to the Tectonic Open Hardware (TectOH) Sandbox project: <a href="https://github.com/URJCMakerGroup/TectOH">https://github.com/URJCMakerGroup/TectOH</a>. Then go to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional">optional</a> folder and to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional/as5311_magn_sens">magnetic sensor</a> folder.</p> <p>This data set contains two kind of files:</p> <ul> <li><strong>bin</strong>: raw binary files received from the AS5311 high resolution sensor. Although this sensor sends 12 bit data, we have truncated the most significant bits and receive only 8 bits (one byte). Therefore, each byte of these binary files is a measurement of the distance. Each distance increment corresponds to ~0.488nm (2mm/2048)</li> <li><strong>csv</strong>: csv files that can be opened with any spreadsheet app, such as Libreoffice Calc or Microsoft Excel, or even with a text editor. This file contains the processed data from the binary files. These files have been generated with the proc_magn_sensor.py Python script located in the <a href="https://github.com/URJCMakerGroup/TectOH">project repository</a>. There are some columns, which are: <ul> <li>index: measurement number</li> <li>time in milliseconds: each measurement is taken every 250 us</li> <li>median2: in micrometers, since the sensor may jitter, we have applied the median filter twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>median1: in micrometers, median filter only applied once. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean: in micrometers, mean filter. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean int: in micrometers, mean filter rounded to an integer value. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>orig_base: this is not in micrometers, but in the units of the sensor (~0.488nm). The only processing done is that when there is an overflow of 255 to 0, or from 0 to 255, it adds the overflow to continue the trend. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>original: this is the data received from the sensor with no processing, each value is ~0.488nm</li> <li>mean2: in micrometers, mean filter applied twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> </ul> </li> </ul> <p>There are two set of experiments:</p> <ul> <li><strong>Experiments with no load</strong>. These files start with <em>noload_</em><br> In these experiments the gantry is moved 1 mm alternatively to the front and then reversing direction. Moving in this alternate way a few times. There are five experiments each of them with a different speed: v= 10 mm/h; 25 mm/h; 50 mm/h; 82 mm/h and 100 mm/h. The name of the file indicates the speed: <ol> <li>noload_100mmh_1mm: FBFBF: 1mm forth, 1mm back, 1mm forth, 1mm back, 1 mm forth</li> <li>noload_25mmh_1mm: FBFFBBFB</li> <li>noload_50mmh_1mm: FBFBFB</li> <li>noload_82mmh_1mm: FBFBFB</li> <li>noload_100mmh_1mm: FBFBFB</li> </ol> </li> <li><strong>Experiments pushing a 5kg sand load</strong>. These files start with <em>load5kg_</em> <ol> <li>load5kg_25mmh_5mm: moving 5kg at 25mm/h a distance of 5mm</li> <li>load5kg_25mmh_10mm: moving 5kg at 25mm/h a distance of 10mm</li> <li>load5kg_25mmh_20mm: moving 5kg at 25mm/h a distance of 20mm</li> <li>load5kg_75mmh_20mm: moving 5kg at 75mm/h a distance of 20mm</li> <li>load5kg_75mmh_50mm: moving 5kg at 75mm/h a distance of 50mm</li> <li>load5kg_100mmh_25mm: moving 5kg at 100mm/h a distance of 20mm</li> <li>load5kg_100mmh_50mm: moving 5kg at 100mm/h a distance of 50mm</li> </ol> </li> </ul> <p> </p> <p> </p> <p> </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
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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