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.
10,553
datasets available to search
ShareScore release 0.7.1
Dataset results
10,553 results for “measurements”
Temperature data on forest plots recorded with hourly measurements in LandKlif Project
<p><span>Temperature data on forest plots in LandKlif project recorded with hourly measurements (EasyLOG USB, measured accurate to 0.5°C). Thermologgers were attached to the wildlife cameras within the forest. Due to battery leakage, corrosion, and programming errors, only 39 out of 56 loggers collected temperature data.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Two datasets to illustrate quantitative analysis methods for fluorescent calcium measurements
<p>Two datasets in HDF5 formats used for illustrating some quantitative data analysis methods.</p> <p><strong>CCD_calibration.hdf5</strong>: Imago/SensiCam CCD camera (Till Photonics) calibration data set. <br> Fluorescence measurments were made using a fluorescent plastic slide. 10 exposure times from 10 to 100 ms (each making an HDF5 group) were used. For each exposure time 100 exposures were performed (with 200 ms between each). The fluorescence measured in each of the 60 x 80 pixels of the camera are stored in the stack data set of each group. The time data set (a vector) of each group contains the time at which each illumination was done. These recordings were done by Andreas Pippow (Kloppenburg Laboratory Cologne University, http://cecad.uni-koeln.de/Prof-Peter-Kloppenburg.82.0.html). <br> They were used in: Sébastien Joucla, Andreas Pippow, Peter Kloppenburg and Christophe Pouzat (2010) Quantitative estimation of calcium dynamics from ratiometric measurements: A direct, non-ratioing, method. Journal of Neurophysiology 103: 1130-1144.</p> <p><strong>Data_POMC.hdf5</strong>: POMC data set recorded by Andreas Pippow (Kloppenburg Laboratory Cologne University, http://cecad.uni-koeln.de/Prof-Peter-Kloppenburg.82.0.html). 168 measurements performed with a CCD camera recording Fura-2 fluorescence (excitation wavelength: 340 nm). The size of the CCD chip is 60 x 80 pixels. A stimulation (depolarization induced calcium entry) comes at time 527. <br>Details about this data set can be found in: Joucla et al (2013) Estimating background-subtracted fluorescence transients in calcium imaging experiments: A quantitative approach. Cell Calcium. 54 (2): 71-85.</p> <p> </p>
Compilation of parallel measurements comparing the temperatures recorded in Stevenson screens with those recorded in pre-Stevenson screen thermometer exposures
<p>Compilation of parallel measurements comparing the temperatures recorded in Stevenson screens with those recorded in pre-Stevenson screen thermometer exposures. This dataset accompanies Wallis et al. (2024); further details of the dataset and its creation can be found in the attached readme file and Wallis et al. (2024).</p> <p>---</p> <p><strong>References</strong></p> <p>Wallis, E.J., Osborn, T.J., Taylor, M., Jones, P.D., Joshi, M. & Hawkins, E. (2024) Quantifying exposure biases in early instrumental land surface air temperature observations. <em>International Journal of Climatology, </em>https://doi.org/10.1002/joc.8401</p>
Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network [Dataset]
<p>data07B.csv: dataset for the study of the SARS-CoV-2 transmission.</p> <p>CPTNetica.txt: conditional probabilities tables of each variable given through Netica once the BN obtained in R code is loaded.</p> <p>code01.R: code to learn structure and parameters of the SARS-CoV-2 BN model.</p>
Dataset of vehicle emission measurements in real-world subfreezing winter conditions
<p>Dataset of vehicle emission measurements in real-world subfreezing winter conditions. Measured by chasing the measured vehicle. See Info.txt for description of the data.</p>
A Dataset of Outdoor RSS Measurements for Localization
<p><strong>Update: </strong>New version includes additional samples taken in November 2022.</p> <p><strong>Dataset Description</strong></p> <p>This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters.</p> <p>The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus.</p> <table> <tbody> <tr> <th>Dataset Description</th> <th>Sample Count</th> <th>Receiver Count</th> </tr> </tbody> <tbody> <tr> <td>No-Tx Samples</td> <td>46</td> <td>10 to 25</td> </tr> <tr> <td>1-Tx Samples</td> <td>4822</td> <td>10 to 25</td> </tr> <tr> <td>2-Tx Samples</td> <td>346</td> <td>11 to 12</td> </tr> </tbody> </table> <p>The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows:</p> <p>\(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) \)</p> <table> <tbody> <tr> <th>Measurement Parameters</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>Frequency</td> <td>462.7 MHz</td> </tr> <tr> <td>Radio Gain</td> <td>35 dB</td> </tr> <tr> <td>Receiver Sample Rate</td> <td>2 MHz</td> </tr> <tr> <td>Sample Length</td> <td>N=10,000</td> </tr> <tr> <td>Band-pass Filter</td> <td>6 kHz</td> </tr> <tr> <td>Transmitters</td> <td>0 to 2</td> </tr> <tr> <td>Transmission Power</td> <td>1 W</td> </tr> </tbody> </table> <p>Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device.</p> <p><strong>Usage Instructions</strong></p> <p>Data is provided in <code>.json</code> format, both as one file and as split files.</p> <pre><code>import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f) </code></pre> <p>The <code>json</code> data is a dictionary with the sample timestamp as a key. Within each sample are the following keys:</p> <ul> <li><code>rx_data</code>: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name.</li> <li><code>tx_coords</code>: A list of coordinates for each transmitter. Each entry contains latitude and longitude.</li> <li><code>metadata</code>: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in <code>tx_coords</code></li> </ul> <p><strong>File Separations and Train/Test Splits</strong></p> <p>In the <code>separated_data.zip</code> folder there are several train/test separations of the data.</p> <ul> <li><code>all_data</code> contains all the data in the main JSON file, separated by the number of transmitters.</li> <li><code>stationary</code> consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers.</li> <li><code>train_test_splits</code> contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with <code>unused.json</code>, is equivalent to the file <code>all_data/single_tx.json</code>. <ul> <li>The <code>random</code> split is a random 80/20 split of the data.</li> <li><code>special_test_cases</code> contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus.</li> <li>The <code>grid</code> split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the <code>grid</code> split.</li> <li>The <code>seasonal</code> split contains data separated by the month of collection, in April, July, or November</li> <li>The <code>transportation</code> split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The <code>non-driving.json</code> file contains the union of the walking and cycling data.</li> <li><code>campus.json</code> contains the on-campus data, so is equivalent to the union of each split, not including <code>unused.json</code>.</li> </ul> </li> </ul> <p><strong>Digital Surface Model</strong></p> <p>The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR <a href="https://doi.org/10.5069/G9TH8JNQ">survey</a>. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models.</p> <p>To read the data in python:</p> <pre><code>import rasterio as rio import numpy as np import utm dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T') </code></pre> <p><strong>Dataset Acknowledgement:</strong> This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product.</p> <p><strong>DSM DOI:</strong> <a href="https://doi.org/10.5069/G9TH8JNQ">https://doi.org/10.5069/G9TH8JNQ</a></p>
RMTable Consolidated Catalog of Faraday Rotation Measures of Astronomical Radio Sources
<p>This is a catalog of Faraday rotation measures (and other related properties) of astronomical radio sources, consolidated from many published catalogs in the astronomical literature from 1980 to the present day. These catalogs have been converted to the RMTable standard and stored in 3 formats: FITS binary table, tab-seperated-value ASCII, and VOTable XML.</p> <p>These catalog files can be read by any suitable reader, but we have created a Python module, RMTable (https://github.com/CIRADA-Tools/RMTable), which streamlines the process of interacting with and creating new RMTables.</p>
A Case-Control Study to Measure Behavioral Risks of Malware Encounters in Organizations
<p>The behavior of enterprise users (e.g. browsing at night or visiting gambling sites) is a potential factor that might increase the chances of malware encounters (e.g. coinminers vs ransomware) on the field. This dataset report the aggregated results of a case-control study on telemetry data collected by Trend Micro, a global cybersecurity vendor, to identify users’ behavioral characteristics that can be used to differentiate cybersecurity risks profiles.</p>
Aqueous geochemical measurements and speciation calculations with concurrent copper resistance gene counts from sediment metagenomes over a seasonal cycle from 2015 to 2016 on Silver Bow Creek and Blacktail Creek near Butte, MT
<p>This dataset contains information from concurrently gathered geochemical and metagenomic samples collected from Silver Bow Creek and Blacktail Creek near Butte, MT (SBC/BC) during 2015 and 2016. SBC/BC is recovering from metal contamination related to extensive mining in the area. Full geochemical measurements, geochemical speciation calculations, and gene counts of sequences mapping to copper resistance genes using MG-RAST are included. </p>
Citizen science snow measurements
<p>Data set includes citizen science observations of snow collected mainly from Finland and Sweden. Data set is collected in CHARTER project with a simplified protocol which follows the international snow observational standards. Data set includes 47 measurement occasions. The protocol includes background information such as measurement date and time, location, description of surroundings, reindeer pasture type, and visible trampling or digging in snow. Measurements includes snow depth in 1-5 points and definition of ice and crust layers at 1-2 of the points. A hardness hand test is used for layer detection (pushing snow first with fist, then with 4 fingers, 1 finger, pencil and knife blade, until snow is too hard to be pierced). For each ice and crust layer, distance of the layer top and bottom from the ground is measured. In addition, it was optional to measure properties of all layers in snowpack (hardness, grain type and distances from the ground) and the snow water equivalent by using cylindrical tube to extract and weight sample of snow.</p> <p>Data set includes date, time, location, longitude, latitude, air temperature, signs of foraging, description of surroundings, type of reindeer pasture, ground, snow height, description of snow conditions with your own language, layer distances from ground, grain type for layers, hardness for layers, snow water equivalent (tube diameter, snow height, weight), recent rain on snow events, comments and links to photos.</p>
Meteorological Data from Chios: May 2024 Baseline Measurements for the MUSICA Project
<h2><strong>May 2024 – Chios (Chiostown)</strong></h2> <h3>Introduction</h3> <p>The present meteorological data is collected from the weather station in Chiostown, located in Chios, and is published on the Zenodo platform for open access. The station is positioned at an elevation of 32 meters, and the data includes measurements of temperature, rainfall, wind speed, and wind direction, covering the period from May 1st to May 31st, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, which aims to monitor climate changes in the Chiostown area and the broader region of Chios. The data for May 2024 captures the transition from spring to early summer, offering insights into the warming trend and dry conditions typical for the region during this period.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for May 2024</h3> <ul> <li><strong>Highest temperature</strong>: 28.8°C, recorded on May 19th, 2024, at 18:20.</li> <li><strong>Lowest temperature</strong>: 12.3°C, recorded on May 15th, 2024, at 05:00.</li> <li><strong>Total rainfall</strong>: 1.2 mm, with the highest daily rainfall of 1.19 mm recorded on May 11th, 2024.</li> <li><strong>Highest wind speed</strong>: 56.3 km/h, recorded on May 27th, 2024, at 10:40.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena</p>
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
Surface brightness temperatures measured by the HATPRO microwave radiometer onboard the RV Polarstern during the ATWAICE expedition PS144 to the Arctic in summer 2022
<p>The data set contains daily files of raw microwave radiation measurements by the HATPRO microwave radiometer (see Rose et al., 2015) onboard about 22 m height at the top deck (starboard) of RV Polarstern during cruise PS131 (ATWAICE expedition, see Kanzow, 2023). Via a mirror construction the radiometers were observing the surface at a viewing angle of about 53° off-nadir for 15 min each hour. The actual viewing angle could vary by a few degree because of ship motion. The data covers the range July 11, 2022 to August 11, 2022. The radiation measurements are given as brightness temperatures in seven K band channels (22.24 - 31.4 GHz), vertical polarization, and seven V band (51.26 - 58 GHz) channels, horizontal polarization. </p> <p>Version 2 of the uploaded data is quality-controlled (see the flag variable).</p>
Surface brightness temperatures measured by the MiRAC-P microwave radiometer onboard the RV Polarstern during the ATWAICE expedition PS144 to the Arctic in summer 2022
<p>The data set contains daily files of raw microwave radiation measurements by the MiRAC-P (or LHUMPRO-243-340) microwave radiometer (see Mech et al., 2019) onboard about 22 m height at the top deck (starboard) of RV Polarstern during cruise PS131 (ATWAICE expedition). Via a mirror construction the radiometers were observing the surface at a viewing angle of about 53° off-nadir for 15 min each hour. The actual viewing angle could vary by a few degree because of ship motion. The data covers the range July 11, 2022 to August 11, 2022. The radiation measurements are given as brightness temperatures in six double side band averaged G band (183.31 +/- 0.6 to 183.31 +/- 7.5 GHz), vertical polarization, and one higher frequency (243 GHz) channel, horizontal polarization. The 340 GHz channel was malfunctioning. </p> <p>Version 2 of the uploaded data is quality-controlled (see the flag variable).</p>
Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river"
<h2>Summary</h2> <p>Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river" by Aki Vähä, Timo Vesala, Sofya Guseva, Anders Lindroth, Andreas Lorke, Sally MacIntyre, and Ivan Mammarella (2024), published in Biogeosciences.</p> <h2>Materials and Methods</h2> <h3>Measurement site</h3> <p>The experiment was conducted on a floating platform on the River Kitinen in northern Finland. The measurements took place from 1 June to 2 October, 2018.</p> <p>The River Kitinen is 235 km long and has a catchment area of 7672 km2. The catchment area consists mostly of managed boreal forest with Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) as the main tree species, wetlands of which a large portion is drained, small streams and rivers, some low mountains and a few small settlements. The experiment site (67.37◦ N, 26.62◦ E, 173 m above sea level) was located next to the Finnish Meteorological Institute’s research and weather station in Tähtelä. At the experiment location the river is 180 m wide and forms a straight section extending approximately 600 m upstream and 1000 m downstream from the site. The direction of the river at the site is roughly north-northwest–south-southeast and it flows towards the south. The mean annual discharge, measured at the closest power plant downstream, is 103 m3 s−1. The maximum depth at the site is 7 m. The River Kitinen’s Strahler stream order at the site is 5. The floating platform was located about 70 m from the eastern river bank where the water depth was 4.5 m.</p> <h3>Eddy covariance</h3> <p>The eddy covariance system measuring water-atmosphere turbulent fluxes was mounted on a mast on the southern side of the platform. This installation consisted of an ultrasonic anemometer (uSonic-3 Scientific, METEK Meteorologische Messtechnik GmbH, Elmshorn, Germany) for measuring the wind speed in three Cartesian coordinates and the sonic temperature, an enclosed-path gas analyser (LI-7200RS, LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) for measuring carbon dioxide and water vapour mole fractions, and a closed-path gas analyser (G1301-f, Picarro, Inc., Santa Clara, California, USA) for measuring methane and water vapour mole fractions. The centre of the sonic anemometer was 1.82 m above the water surface. An inclinometer (DOG2 micro-electro-mechanical system, Measurement Specialties, Inc., Hampton, Virginia, USA) was used for measuring the pitch and roll of the platform. Eddy covariance fluxes were calculated using the EddyUH software (Mammarella et al. 2016), following the state of art methodologies (Sabbatini et al. 2018, Nemitz et al. 2018).</p> <h3>Auxiliary measurements</h3> <p>Ambient air temperature and relative humidity were measured with a Rotronic HC2-S3C03 probe (Rotronic AG, Bassersdorf, Germany), mounted inside a Young model 41003 (R. M. Young Company, Traverse City, Michigan, USA) multi-plate radiation shield on the platform’s north-eastern corner. Air temperature and relative humidity were available only after 15th of June. Before that, the sonic temperature and humidity calculated from χH2O, measured with the LI-7200RS, were used instead. Atmospheric pressure and precipitation were measured at the Tähtelä weather station. Photosynthetically active radiation (PAR) in water was measured with two LI-192 sensors (LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) and one LI-193 sensor (LI-COR). The sensors were hanging from wires at 0.3 m, 0.65 m and 1.0 m depths on a beam on the southern side of the platform. Measurements of water side CO2 partial pressure (pCO2) were done by using an off-axis integrated cavity output spectrometer (Ultraportable Greenhouse Gas Analyzer – UGGA), Los Gatos Research, Inc., Santa Clara, California, USA) that was connected to the headspace of an equilibrator consisting of a floating Plexiglas chamber.</p> <p>A water temperature chain was set up 100 m upstream of the platform. It consisted of five temperature loggers of the type RBR Solo (RBR Ltd. Ottawa, Ontario, Canada). The loggers were placed on a taut line mooring at depths of 0.35 m, 1.35 m, 2.35 m, 3.35 m and 4.35 m (6 June to 17 June) and 0.07 m, 1.05 m, 2.05 m, 3.05 m and 4.05 m (17 June onwards). The topmost measurement was used as the surface temperature. The water flow velocity was measured with a acoustic Doppler velocimeter (Nortek Vector, Nortek AS, Rud, Norway) which was installed on a beam on the north-western corner of the platform, facing down (Guseva et al., 2021). The depth of the measurements was 0.4 m below the surface.</p>
Calibration of non-local damage models from full-field measurements: application to discrete element fields.
<p>The codes, datasets, and results from the manuscript 'Calibration of non-local damage models from full-field measurements: application to discrete element fields' are available here.</p> <p> </p> <p>This repository is organized into four folders:</p> <ol> <li><strong>'Ideal Case' folder</strong>: This corresponds to Section ‘3. Application of the calibration method on a 1D ideal case' of the manuscript. In this folder, you will find the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM+</a> and <a href="https://www.python.org/" target="_blank" rel="noopener">Python</a> scripts and a makefile to run them. These codes reproduce all the figures, as well as the complete dataset associated with Figures 1-7.</li> <li><strong>'Virtual Testing Machine' folder</strong>: This corresponds to Section 4, 'Introduction of a Virtual Testing Machine' of the manuscript. Here, you will find: <ul> <li>The data for Figure 9, contains the force vs. CMOD response for all sizes and geometries.</li> <li>The data for Figure 10, contains the scores associated with this parametric identification.</li> </ul> </li> <li><strong>'Green Functions' folder</strong>: This includes the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM++</a> code to solve the boundary value problem for obtaining Green's function of the Eikonal equation. The code is demonstrated with both a non-damaged case and a polynomial damage case. In the first case, Green’s function corresponds to the weighting functions of the Implicit Gradient method (<a href="https://doi.org/10.1002/(SICI)1097-0207(19961015)39:19<3391::AID-NME7>3.0.CO;2-D" target="_blank" rel="noopener">Peerlings et al., 1996</a>).</li> <li><strong>'Real Case' folder</strong>: This corresponds to Section 5, 'Application of the Proposed Calibration Procedure to Virtual Tests.' Here, you will find: <ul> <li>The complete 'Damage vs. Strain' Dataset was generated with the Virtual Testing Machine. Figures 12-15 correspond to this dataset, processed as explained in the manuscript.</li> <li>The 'damage vs. damage driving variable' dataset for each characteristic length. Figure 16 in the manuscript corresponds to this dataset.</li> <li>The data for Figures 17 and 18, contain the evolution of the error with the characteristic length for different evolution laws.</li> </ul> </li> </ol>
Publication text: code, data, and new measures
<p>This Zenodo page describes data collection, processing, and different open access data files related to the text of scientific publications from OpenAlex. If you use the code or data, please cite the following paper: </p> <p>Sam Arts, Nicola Melluso, Reinhilde Veugelers; Beyond Citations: Measuring Novel Scientific Ideas and their Impact in Publication Text. <em>The Review of Economics and Statistics</em> 2025; 1–33 doi: <a href="https://doi.org/10.1162/rest_a_01561" target="_blank" rel="noopener">https://doi.org/10.1162/rest_a_01561</a></p> <p> </p>
Concentrating solar power (CSP) plants AI-training dataset for flux density measurements.
<p>In this dataset, the tools required for the training of a neural net in the context of flux density measurements in concentrating solar power (CSP) plants are included. An Excel file with 931 meteorological conditions and the positions of the power plant and the receiver is included, as well as 15928 pairs of images resulting from ray-tracing in Solarturm Juelich (STJ) each of these conditions with 17 different combinations of heliostats. <br> <br>This dataset is part of the WP1 of TOPCSP european project (funded by HORIZON MSCA Doctoral Network, Project number 101072537).</p>
profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020 interpolated on regular 2 m grid for the World Ocean
<p>The dataset includes profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020 interpolated on regular 2 m grid for the World Ocean </p> <p>Data was collected from open archive (<em>Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)) </em><a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a></p> <p>Global array of Bio-Argo floats equipped with Chl (mg m−3) and PAR(μmol photons m-2 s-1) sensors at -60°S..60°N was used in this study. Data for 2013-2020 was downloaded from the IFREMER data archive (ftp://ftp.ifremer.fr/, <a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a>). It includes 464 floats measuring Chl (~ 70000 profiles), and 167 floats measuring both PAR (~26000 profiles) and Chl. Before the analysis, the measurements of each Bio-Argo buoy were visually checked to filter the outliers in Chl or PAR data. After visual analysis about 1600 profiles of PAR and 2800 profiles of Chl were excluded from the dataset.</p> <p>Chl (mg m−3) was retrieved from a Chl fluorometer (excitation at 470 nm; emission at 695 nm) sensors of three types (FLBB, ECO-Triplet, or MCOMS). We use the raw fluorescence-based estimates of Chl (product “non-adjusted Chl”) derived directly from the measurements of fluorescence with factory calibration coefficients without the corrections on non-photochemical quenching, CDOM fluorescence, and other effects (see (<a href="http://www.argodatamgt.org/Documentation">http://www.argodatamgt.org/Documentation</a>)).</p> <p>A multispectral ocean color radiometer (OCR-504, SATLANTIC Inc.) was used to measure PAR. Only instantaneous PAR measurements made within ± 1.5 hours from noon (10:30-13:30 hours) were used.</p> <p>Then the data from all buoys were interpolated on regular 2-m grid and included in one dataset.</p>
MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 (Greenland Sample Products)
<p>We provide 21 sample products of MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 in three test regions of Greenland Ice Sheet. The full archive of version 2 ITS_LIVE products (including image pair maps, data cubes and mosaics) from Sentinel-1 as well as other optical sensors (Landsat-4/5/6/7/8 and Sentinel-2) can be found at the ITS_LIVE project website: <a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov</a>.</p> <p><strong>Sensor</strong>: Sentinel-1A/B</p> <p><strong>Processor</strong>: <a href="https://github.com/isce-framework/isce2">ISCE</a>v2.4.1 (topsApp -> <a href="https://github.com/leiyangleon/Geogrid">Geogrid</a>v1.4.0 -> <a href="https://github.com/nasa-jpl/autoRIFT">autoRIFT</a>v1.4.0)</p> <p><strong>Project</strong>: NASA MEaSUREs project <a href="https://its-live.jpl.nasa.gov">ITS_LIVE</a></p> <p><strong>Region 1</strong> (69.13N, 50.88W; Jakobshavn Isbræ Glacier): 7 ascending image pairs</p> <p><strong>Region 2</strong> (77.61N, 42.79W; central north of interior Greenland): 3 ascending image pairs</p> <p><strong>Region 3</strong> (72.48N, 35.87W; central south of interior Greenland): 10 descending image pairs and 1 ascending image pair</p> <p>This serves as a supplementary dataset for the companion journal article submitted to Earth System Science Data (to appear).</p> <p> </p> <p><strong>Acknowledgement</strong>: This effort was funded by the NASA MEaSUREs program in contribution to the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project (<a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov/</a>) and through Alex Gardner’s participation in the NASA NISAR Science Team.</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.