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10,553 results for “measurements”
Controlling long ion strings for quantum simulation and precision measurements
<p>Experimental data for the publication "Controlling long ion strings for quantum simulation and precision measurements" published in Physical Review A, 105, 052426 (2022)</p>
Pollen Images for OLEAtool Manual Measurements Demonstration
<p>This dataset includes the following:</p> <p>Images [JPEG format] of <em>Olea</em> pollen (<em>Olea europaea</em> L.) used to collect Polar axis diameter (P) and Equatorial axis diameter (E) measurements using the Manual Measurements Module in OLEAtool (https://doi.org/10.5281/zenodo.7046771).</p> <p>Measurements (P and E metrics) collected from the included images using OLEAtool Manual Measurements Module [XLXS format].</p>
Dataset for "How Instrument Transformers Influence Power Quality Measurements: A Proposal of Accuracy Verification Tests"
<p>This is dataset for paper published:</p> <p>Crotti, Gabriella, Yeying Chen, Huseyin Çayci, Giovanni D’Avanzo, Carmine Landi, Palma Sara Letizia, Mario Luiso, Enrico Mohns, Fabio Muñoz, Renata Styblikova, and Helko van den Brom. 2022. "How Instrument Transformers Influence Power Quality Measurements: A Proposal of Accuracy Verification Tests" <em>Sensors</em> 22, no. 15: 5847. https://doi.org/10.3390/s22155847</p> <p> </p> <p>Excel file provides data in the time domain for tests performed on the inductive VT</p> <p> </p>
Dataset for the publication "Evaluation of Voltage Transformers' Accuracy in Harmonic and Interharmonic Measurement"
<p>This is dataset for paper published:</p> <p>G. Crotti, G. D’Avanzo, C. Landi, P. S. Letizia and M. Luiso, "Evaluation of Voltage Transformers’ Accuracy in Harmonic and Interharmonic Measurement," in <em>IEEE Open Journal of Instrumentation and Measurement</em>, vol. 1, pp. 1-10, 2022, Art no. 9000310, doi: 10.1109/OJIM.2022.3198473.</p>
Mooring current and temperature and Salinity measurements
<p>The Mooring LCM, a shared infrastructure located along the Levante Canyon of the Eastern Ligurian Sea . The mooring is placed at 600 m depth on the Levante Canyon, offshore the Cinque Terre Cinque Marine Protected Area at 44°05.443’N, 9°29.900’E . It was firstly deployed in October 2019 . LCM is a stand-alone mooring offshore, dedicated to the long term monitoring of hydrological properties of water masses . The first installation of the LCM was made possible thanks to the "Dallaporta" CNR Oceanographic Ship, allowing the observatory to be positioned at about 6.5 nautical miles off the coast. It operates in delayed-mode and is equipped with sensors that measure physical and biogeochemical parameters along the water column from 83 m to 580 m. Starting from the bottom , the offshore monitoring station is equipped with a sediment trap placed at a depth of 582m, which allows to obtain information on the supply of sediments from the surface to the seabed. Further above, CTD probes (SBE37) are placed at three different depths, respectively at 579m, 335m and 85m. The LCM also includes two acoustic doppler current profilers (ADCP), placed respectively at 406m( Nortek Continental) and 325m ( WH Quarter Master) depth, which measures currents throughout the water column, in order to monitor the link of near-surface waters with the deep ones.</p> <p> </p> <p> </p>
Dataset for the publication "The Use of Voltage Transformers for the Measurement of Power System Subharmonics in Compliance With International Standards"
<p>This is dataset for paper published:</p> <p>G. Crotti, G. D’Avanzo, P. S. Letizia and M. Luiso, "The Use of Voltage Transformers for the Measurement of Power System Subharmonics in Compliance With International Standards," in <em>IEEE Transactions on Instrumentation and Measurement</em>, vol. 71, pp. 1-12, 2022, Art no. 9005912, doi: 10.1109/TIM.2022.3204318.</p> <p> </p>
Combined measures of mimetic fidelity explain imperfect mimicry in a brood parasite–host system
<p>The persistence of imperfect mimicry in nature presents a challenge to mimicry theory. Some hypotheses for the existence of imperfect mimicry make differing predictions depending on how mimetic fidelity is measured. Here, we measure mimetic fidelity in a brood parasite–host system using both trait-based and response-based measures of mimetic fidelity. Cuckoo finches <em>Anomalospiza</em> <em>imberbis</em> lay imperfectly mimetic eggs that lack the fine scribbling characteristic of eggs of the tawny-flanked prinia <em>Prinia</em> <em>subflava</em>, a common host species. A trait-based discriminant analysis based on Minkowski functionals—that use geometric and topological morphometric methods related to egg pattern shape and coverage—reflects this consistent difference between host and parasite eggs. These methods could be applied to quantify other phenotypes including stripes and waved patterns. Furthermore, by painting scribbles onto cuckoo finch eggs and testing their rate of rejection compared to control eggs (i.e. a response-based approach to quantify mimetic fidelity), we show that prinias do not discriminate between eggs based on the absence of scribbles. Overall, our results support relaxed selection on cuckoo finches to mimic scribbles, since prinias do not respond differently to eggs with and without scribbles, despite the existence of this consistent trait difference.</p>
Datasets of sap flow, meteorological, leaf gas measurements in urban green areas in Helsinki
<p>Datasets of sap flow, meteorological, leaf gas measurements used in the manuscript “Sap flow and leaf gas exchange response to drought and heatwave in urban green spaces in a Nordic city”. Data contains cleaned half-hourly sap flow data and half-hourly meteorological datasets (Tair, Tsoil, RH, soil temperature, soil moisture) at four different urban green areas in Helsinki.</p> <p>Manual measurements of leaf gas exchanges using GFS instruments. Datasets contains mainly the Amax parameters derived from curve fitting and instantaneous values of G and E at PAR 1100 W m<sup>-2</sup>.</p> <p>Folders contain:</p> <ul> <li>Leaf gas exchange data <ul> <li>Leaf_gas_data_all_v2.csv</li> <li>Metadata_leaf gas exchange data.csv</li> </ul> </li> <li>Meteo data <ul> <li>Meteo_Forest_data_30min.csv</li> <li>Meteo_Orchard_data_30min.csv</li> <li>Meteo_Park_data_30min.csv</li> <li>Meteo_Street_data_30min.csv</li> <li>Metainfo_meteo.xlsx</li> </ul> </li> <li>Sap flow data <ul> <li>Sap_Forest_30min_cleaned.csv</li> <li>Sap_Orchard_30min_cleaned.csv</li> <li>Sap_Park_30min_cleaned.csv</li> <li>Sap_Street_30min_cleaned.csv</li> <li>Metadata_info_sap.csv</li> </ul> </li> </ul>
Multichannel Displacement measurement via self mixing interferometry and neural network : training and test datasets
<p>Self mixing interferometry is a simple and robust sensing method which can be used (among other things) to measure the displacement of a target along the light propagation axis. While conceptually simple, the actual use of this method is less straightforward than originally envisioned because reconstructing the target displacement from the interferometric signal is often tricky. A small neural network can do this task very well after proper training, as described in [10.1364/OE.419844], with dataset [10.5281/zenodo.7303745]. </p> <p>Here, the dataset is composed by a training set and a test set, in a specific configuration in which 3 self-mixing sensors measure simultaneously the same target displacement. Both datasets contain the displacement itself and the 3 interferometric signals (1 per sensing channel)</p> <p><strong>The training set </strong>relies on two python/numpy data files corresponding to <strong>harmonic displacements</strong> for different frequencies ranging from 53 and 93 Hz and amplitudes from 3.5 to 7.5 µm : </p> <ul> <li>Training_set_2_lostchannel_displacement.npy : 93744-elements long numpy array containing the target's displacement in units of µm/ms with a 1.024 ms time step. </li> <li>Training_set_2_lostchannel_signal.npy : numpy array of shape (3, 93744, 256, 1) containing the interferometric signals. The first dimension refers to the channel (1, 2 or 3), the second dimension is the number of segments of 256 points. Each segment of 256 points correspond to a 1.024 ms window of signal, matching one element of the displacement. For instance, the displacement value in `displacement[416]` corresponds to the interferometric signal segment `signal[0,416,:,0]` for channel 1, `signal[1,416,:,0]` for channel 2 and `signal[2,416,:,0]` for channel 3. </li> </ul> <p><strong>The test </strong>set follows the same architecture and format as the training set, but contains only <strong>random displacements </strong>generated by a delta-correlated signal, which we Fourier filter with a fifth order Butterworth filter between 10 and 100 Hz :</p> <ul> <li>"Displacement_test.npy" : with shape (246078, 1)</li> <li>"Signal_test.npy" : with shape (3, 246078, 256, 1)</li> <li>Only the displacement type has changed from harmonic to random, from the training to the test datasets.</li> </ul> <p>These datasets have been used to train and test a 3 channel neural network (after data augmentation) in order to emphasize the high availability potential of multichannel schemes, against backscattered power fluctuations. </p>
Soil temperature, moisture, and ground heat flux measurements at LPTEG-TREES-1 site, 2019/07/01-2019/09/09
<p>This dataset includes the original measurements of soil temperature, moisture, and surface ground heat flux reconstructed from heat flux plate measurements at the LPTEG-TREES-1 site (N66°53’55’’, E66°45’27’’). Soil temperature (T_soil, °C) was measured at 2 cm below the peat layer surface. Soil liquid water content (theta_liq, m<sup>3</sup>/m<sup>3</sup>) was measured 2 cm below the mineral soil layer surface. Observation for ground heat flux at the soil surface (G_obs, W/m<sup>2</sup>) was reconstructed from the heat flux plate (buried 6 cm below the mineral soil surface) measurement plus the energy storage above the heat flux plate calculated based on soil temperature and soil heat capacity.</p>
Infection safe workplace IoT sensor measurements
<p>This dataset has been collected and created by "Datu Tehnoloģiju Grupa" and Riga Technical University. This dataset contains data collected from IoT devices scattered in the office workplace. The measurements have been collected in a .csv file, with 150000 records. Project “Platform for the Covid-19 safe work environment” (ID. 1.1.1.1/21/A/011) is founded by European Regional Development Fund specific objective 1.1.1 «Improve research and innovation capacity and the ability of Latvian research institutions to attract external funding, by investing in human capital and infra-structure». The project is co-financed by REACT-EU funding for mitigating the consequences of the pan-demic crisis.</p>
NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies
<p><strong>NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies</strong></p> <p>This dataset contains the raw energy measurements as well as R scripts to reproduce the energy consumption plot for the corresponding paper.</p> <p>Each .csv file contains a specific set of measurements and we provide a script to read, process and plot the contained data.</p> <p><strong>Figure 3</strong></p> <p>Mean energy consumption of the different phases for Authentication for NB-IoT and LTE-M.</p> <p>Due to the fact that the duration of <em>Idle Connected</em> in the measurement scripts was 30 seconds and 60 seconds for <em>Idle Not Connected</em>, the D-value and the mean power consumption are divided by 2.</p> <ul> <li>Data – energy_measurements_fig3.csv</li> <li>Code – fig3.R</li> </ul> <p><strong>Figure 4</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for NB-IoT and LTE-M for 1KB of data in HTTP.</p> <p>The delay between the measurements for Figure 4 were all 30 seconds long, but the identified <em>Standby</em> and <em>Idle</em> phases have different lengths. Therefore, the <em>Idle</em> phase values for both access technologies have been normalized and calculated for 20 seconds each.</p> <ul> <li>Data – energy_measurements_fig4.csv</li> <li>Code – fig4.R</li> </ul> <p><strong>Figure 5</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for HTTP and MQTT for 1KB of data in NB-IoT.</p> <p>In this scenario the delay between the measurements were different again. For <em>MQTT</em> the delay was 150 seconds and for <em>HTTP</em> 30 seconds. Therefore, the data during the <em>Idle</em> and <em>Standby</em> (only for <em>MQTT</em>) phase is normalized and calculated for 20 seconds and 10 seconds, respectively. During the <em>MQTT</em> <em>Idle</em> phase measurements, the device disconnects. This is not taken into account for the evaluation, which is why these energy values are discarded for this figure.</p> <ul> <li>Data – energy_measurements_fig5.csv</li> <li>Code – fig5.R</li> </ul> <p><strong>Contact</strong></p> <p>For questions or issues with this code, please contact Viktoria Vomhoff (viktoria.vomhoff@uni-wuerzburg.de) or any of the authors of the related publication.</p>
Dataset related to publication "Non-contact thermometer for improved air temperature measurements"
<p>These datasets were used to produce Fig. 6, 8 and 9 in paper "Non-contact thermometer for improved air temperature measurements" to be published in <em>Sensors </em><strong>2023</strong><em>, 23. </em>The data were produced within the EMPIR project 18SIB01 GeoMetre.</p>
Bubble measurement results for boiling bubbles in microgravity
<p>This dataset is supplementary material to the submission “Contour boundary detection and measurement of boiling bubbles in microgravity”, by Xenophon Zabulis, Polykarpos Karamaounas, Ourania Oikonomidou, Sotiris Evgenidis, Margaritis Kostoglou, Axel Sielaff, Peter Stephan, Thodoris Karapantsios. The dataset contains videos that show the results of contour tracing and contact angle estimation of the proposed method on the reference data sets provided in [1]. The results are provided in a lossless video format.</p> <p>The reference datasets are 15 and denoted as D1 to D15. Data sets, D6, D7, D8, D9 and D15 record one bubble. The rest record multiple bubbles.</p> <p>All videos show the estimated contact points, the traced bubble contour, and the estimated contact angles. Each video frame shows also the dataset, bubble number, and frame number, in the center of the found bubble. Below this reading, the estimates of the contact angles are printed.</p> <p>Ground truth annotations for the bubbles shown in the first part can be found in [2].</p> <p>[1] A. Sielaff, D. Mangini, O. Kabov, M. Raza, A. Garivalis, M. Zupančič, S. Dehaeck, S. Evgenidis, C. Jacobs, D. Van Hoof, O. Oikonomidou, X. Zabulis, P. Karamaounas, A. Bender, F. Ronshin, M. Schinnerl, J. Sebilleau, C. Colin, P. Di Marco, T. Karapantsios, I. Golobič, A. Rednikov, P. Colinet, P. Stephan, L. Tadrist, The multiscale boiling investigation on-board the international space station: An overview, Applied Thermal Engineering 205 (2022) 117932. doi:10.1016/j.applthermaleng.2021.117932</p> <p>[2] X. Zabulis, P. Karamaounas, O. Oikonomidou, S. Evgenidis, M. Kostoglou, A. Sielaff, P. Stephan, T. Karapantsios, Ground truth annotations for boiling bubble detection and measurement in microgravity (Jan. 2023). doi:10.5281/zenodo.7553797. URL https://doi.org/10.5281/zenodo.7553797</p>
Measuring Space-Time Accessibility: Hansen's Model Vs. Machine Learning
<p>This dataset consists of Hansen accessibility data in 2011 and 2020 in the municipalities of Lombardia and Emilia Romagna (Italy). Neural Network (NN) learns to predict Hansen accessibility based on the number of residents, employed people and travel time.</p>
Data for Measurement report: Air pollution emission factors of inland river ships under compliance with the 10 parts per million limit for sulfur content in fuel
<p>Since July 1, 2019, China’s domestic diesel fuel has been limited to 10 ppm of sulfur. Hence, to explore the applicability of the “sniffer” method and the distribution and level of inland river ships (IRSs) emission factors (EFs) under this limitation, we installed “sniffer” monitoring equipment, from August 2020 to June 2022, at the Gezhou Dam of the Yangtze River in China and monitored emissions from 8,238 IRSs in total passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oilsamples for direct fuel sulfur content detection, which determined the true fuel sulfur content of the passing ships. fuel sulfur content.</p> <p>The “sniffer” monitoring equipment included SO<sub>2</sub>, CO<sub>2</sub>, NO, and NO<sub>2</sub> gas sensors, PM<sub>2.5</sub> and PM<sub>10</sub> particulate matter sensors, as well as wind speed, wind direction, temperature, humidity, and pressure sensors.</p>
Degenerate four wave mixing measurements
<p>Transmission from a photonic molecule, measured with photodetector attached to a DAQ and OSA, as a function of detuning, for various input powers.</p>
Measured discharge data for region 1 and region 2
<p>About data:</p> <p>1. Measured proglacial discharge data for region 1 and region 2</p> <p>2. For region 1, the data spans over the period 2015 till 2019</p> <p>3. For region 2, the data is for the year 2019</p> <p>4. The temporal resolution of the discharge data for region 1 is 3 hours and this was converted to daily estimates for analyses in the paper.</p> <p>5. The temporal resolution of the discharge data for region 2 is one day.</p>
Pre-seismic Sentinel-1 PSI surface motion measurements for the area affected by the February 2023 Türkiye–Syria earthquakes
<p>We have processed Copernicus Sentinel-1A data from 01/2019 to 01/2023 (descending track 21) over the broader area (approx. 48400 sq. km) affected by February 6, 2023, M7.8 and M7.5 earthquakes in Türkiye and Syria, utilizing the SNAPPING Persistent Scatterers Interferometry (PSI) medium resolution service of the Geohazards Exploitation Platform (GEP; <a href="https://geohazards-tep.eu">https://geohazards-tep.eu</a>).</p> <p>Measurements contain average Line-of-Sight (LoS) velocities, corresponding uncertainties, and the complete displacement time series. Please note that the original dataset of about 2M point measurements was split into parts, each containing 200k points, to facilitate easier manipulation and visualization.</p> <p>References</p> <p>[1] Foumelis, M.; Delgado Blasco, J.M.; Brito, F.; Pacini, F.; Papageorgiou, E.; Pishehvar, P.; Bally, P. SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping. Remote Sens. 2022, 14, 6075. <a href="https://doi.org/10.3390/rs14236075">https://doi.org/10.3390/rs14236075</a></p> <p>[2] SNAPPING – Surface motioN mAPPING Sentinel-1 on-demand processing service, Online tutorial, <a href="https://docs.terradue.com/geohazards-tep/tutorials/Snapping.html">https://docs.terradue.com/geohazards-tep/tutorials/Snapping.html</a>.</p>
Dataset Related to "Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller"
<p>Dataset (and programs used to create it) for the publication "Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller":</p> <p>Hannes Tröpgen, Mario Bielert, and Thomas Ilsche. 2023. Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller. In Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering (ICPE ’23), April 15–19, 2023, Coimbra, Portugal. ACM, New York, NY, USA, 10 pages. <a href="https://doi.org/10.1145/3578244.3583729">https://doi.org/10.1145/3578244.3583729</a></p> <p>Find additional descriptions of the data in the included readme files.</p> <p> </p> <p>This work is supported in part by the German National High Performance Computing (NHR@TUD).<br> The authors are grateful to the Center for Information Services and High Performance Computing at TU Dresden for providing the Power9 Systems used in the measurements and the support during them.</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.