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10,553 results for “measurements”
Measuring coselectional constraint in learner corpora: A graph-based approach
<p>All data from the thesis, plots, scripts, rough draft of annotation guidelines (more is included in thesis). not all svgs are included yet, but can be computed from scripts + data. more data will be added in next version (after defense).</p>
Dataset for: Diurnal patterns in solute concentrations measured with in situ UV-Vis sensors: natural fluctuations or artefacts?
<p>This dataset contains high-resolution (10-minute interval) data for nitrate, dissolved organic carbon, precipitation and discharge at four measurement stations (NF, SHA, TTP, OUT) in the South West Mau, Kenya. This data was used for the analysis of diurnal patterns in nitrate and dissolved organic carbon concentrations. The zipped folder contains the following files and data:</p> <ul> <li>Calibration.csv: <ul> <li>site = name of measuring site</li> <li>date = date and time of grab sample (yyyy-mm-dd hh:mm:ss)</li> <li>DOC = dissolved organic carbon concentration in grab sample (mg C/L)</li> <li>nitrate = nitrate concenctration in grab sample (mg N/L)</li> </ul> </li> <li>Files with suffix ".ts.csv" (time series data from 1-11-2014 to 31-10-2019; prefix indicates measuring site): <ul> <li>date = date and time of measurement (yyyy-mm-dd hh:mm:ss)</li> <li>nit.raw = nitrate concentration measured by sensor (mg N/L)</li> <li>nit.flag = indication of validity of nitrate measurement (if NA, measurement is valid; for explanation of flags, see supplement of <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR021592">Jacobs et al. 2018</a>)</li> <li>nit.proc = processed nitrate concentration (mg N/L)</li> <li>nit.bg = background concentration of nitrate (48-h moving median; mg N/L)</li> <li>nit.patt = deviation from background concentration of nitrate (nit.proc minus nit.bg; mg N/L)</li> <li>doc.raw = dissolved organic carbon concentration measured by sensor (mg C/L)</li> <li>doc.flag = indication of validity of dissolved organic carbon measurement (if NA, measurement is valid)</li> <li>doc.proc = processed dissolved organic carbon concentration (mg C/L)</li> <li>doc.bg = background concentration of dissolved organic carbon (48-h moving median; mg C/L)</li> <li>doc.patt = deviation from background concentration of dissolved organic carbon (doc.proc minus doc.bg; mg C/L)</li> <li>p = precipitation (mm/10 mins)</li> <li>q = discharge (m³/s)</li> <li>sensor = serial number of sensor</li> </ul> </li> <li>Files with suffix ".exp.csv" (data for sensor comparison experiment from 5-9-2017 to 1-12-2017; prefix indicates measuring site): <ul> <li>date = date and time of measurement (yyyy-mm-dd hh:mm:ss)</li> <li>prec = precipitation (mm/10 mins)</li> <li>nit.orig = processed nitrate concentration measured by fixed sensor (mg N/L)</li> <li>nit.bg.orig = background concentration of nitrate measured by fixed sensor (48-h moving median; mg N/L)</li> <li>nit.patt.orig = deviation from background concentration of nitrate measured by fixed sensor (nit.orig minus nit.bg.orig; mg N/L)</li> <li>nit.dup = processed nitrate concentration measured by mobile sensor (mg N/L)</li> <li>nit.bg.dup = background concentration of nitrate measured by mobile sensor (48-h moving median; mg N/L)</li> <li>nit.patt.dup = deviation from background concentration of nitrate measured by mobile sensor (nit.dup minus nit.bg.dup; mg N/L)</li> <li>doc.orig = processed dissolved organic carbon concentration measured by fixed sensor (mg C/L)</li> <li>doc.bg.orig = background concentration of dissolved organic carbonmeasured by fixed sensor (48-h moving median; mg C/L)</li> <li>doc.patt.orig = deviation from background concentration of dissolved organic carbonmeasured by fixed sensor (doc.orig minus doc.bg.orig; mg C/L)</li> <li>doc.dup = processed dissolved organic carbonconcentration measured by mobile sensor (mg C/L)</li> <li>doc.bg.dup = background concentration of dissolved organic carbonmeasured by mobile sensor (48-h moving median; mg C/L)</li> <li>doc.patt.dup = deviation from background concentration of dissolved organic carbon measured by mobile sensor (doc.dup minus doc.bg.dup; mg C/L)</li> <li>set = experimental treatment</li> </ul> </li> </ul>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
PsPM-trSP4: SCR measurement in response to face photographs withangry, neutral, and fearful expression while subjected to auditory distractors
<p>This dataset includes skin conductance response (SCR) measurements for each of 42 healthy unmedicated participants (21 males and 21 females aged 25.2 +/- 4.0 years) in response to 38 face photographs (modified from the Karolinska Directed Emotional Faces set, KDEF), each presented once with angry, neutral, and fearful expression for 1 s each. Meanwhile, participants were listening to regular or random distractor sounds, as described in Bach et al. (2015). ITI was selected randomly on each trial from 7.5 s, 9.0 s, or 10.5 s (misprinted in the publications), plus a variable delay of around 0.1 s for image loading. The experiment was preceded by a 2-minute resting period and divided into 3 blocks, separated by resting periods. Each resting period begins and ends with an event marker in the SCR recordings.</p>
PsPM-trSP3: SCR measurement in response to aversive/arousing/neutral IAPS pictures while subjected to auditory distractors
<p>This dataset includes skin conductance response (SCR) measurements for each of 40 healthy unmedicated participants (20 males and 20 females aged 21.9 +/- 3.8 years) in response to the 16 most arousing negative, and most arousing positive (excluding explicit nude) and 16 least arousing neutral IAPS pictures, presented for 1 s each in 1 block, while listening to regular or random distractor sounds, as described in Bach et al. (2015). ITI was 4 s, plus a variable delay of around 0.4 s for image loading.</p>
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
Fig. 6. Prochilodus costatus swimming speeds measured a in Upstream and downstream migration speed of Prochilodus costatus (Characiformes: Prochilodontidae) in upper São Francisco basin, Brazil
Fig. 6. Prochilodus costatus swimming speeds measured a. in this study in stretch 2 and b. in the laboratory by Santos et al. (2012). Central points are medians, boxes represent percentiles 25 and 75 and whiskers represent amplitude. Dashed lines separate different kinds of fish movements. BL/s = swimming velocity in standard length of fish per second.
Precise Lifetime Measurement of the Cesium 5²D₅⸝₂ State
<p>This repository contains data and software related to an experiment in which we determine the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state using atoms in a vapor cell. More information is available in the following paper:</p> <ul> <li>arXiv:1912.10089</li> </ul> <p>We provide the data and Python scripts for data evaluation in six folders. We zipped these folders with Windows 10 Enterprise, Version 1903. In the following, we describe how to use data and scripts to get the lifetime results published in our paper.</p> <p> </p> <p><strong>Raw Time-Tags</strong></p> <p>Here, we provide the raw measurement data. We perform several experiment cycles. An excitation laser is switched on at the beginning of each cycle. In the middle of the cycle, it is switched off. We use two single-photon counting modules (SPCM): one detects fluorescence photons emitted by the atoms, the other reference light from the excitation laser beam. We record the arrival times of those photons with respect to the beginning of the cycle. These time delays can be used to create a histogram and to determine the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state.</p> <p>For each measurement, we provide two data files which are encoded in ‘UTF-8’:</p> <ul> <li>‘figx_xxx_reference_time_tags.dat’</li> <li>‘figx_xxx_fluorescence_time_tags.dat’</li> </ul> <p>where ‘figx_xxx’ is a unique tag indicating the figure and point to which this data corresponds in our paper. The ‘figx_xxx_fluorescence_raw_data.dat’ and ‘figx_xxx_reference_raw_data.dat’ files contain the raw time delays in picoseconds of the fluorescence and the reference photons, respectively.</p> <p>We provide raw time delays in the following folders:</p> <ul> <li>‘fig3_time_tags’: The data used in figure 3.</li> <li>‘fig4_time_tags’: The data used in figure 4. This folder has six subfolders, named ‘point_x’, where x indicates to which point of figure 4 the data belongs. The data of the subfolders ‘point_x_y’ was used for points x and y of figure 4 (the time-tags of the fluorescence photons were split into two sub-datasets with equal size).</li> <li>‘fig5_time_tags’: The data underlying figure 5. This folder has subfolders from ‘23C’ to ‘116C’ where the name indicates the temperature in units of °C of the vapor cell during the measurement. Note that the various measurements have different cycle lengths because reabsorption makes the decay of the fluorescence signal longer. For the lifetime value at a temperature of 23 °C, we used the lifetime which we found in figure 4. For some measurements, the time-tags of the reference SPCM are missing because only one SPCM was available for these measurements.</li> </ul> <p> </p> <p><strong>Histograms</strong></p> <p>Since the files of the raw measurement data are large, we also provide histograms of the time tags. For all datasets discussed above, we generated a histogram with a bin length of 5 ns. We save these histograms with the same file name as the files with the raw time tags but with the ending ‘_histo’ instead of ‘_time_tags’, e.g., ‘fig3_fluorescence_histo.dat’ and ‘fig3_reference_histo.dat’.</p> <p>We always provide two file formats:</p> <ul> <li>a data file (.dat), containing rows with the start time of a bin in microseconds, and the number of SPCM counts due to the fluorescence signal until the start of the next bin, separated by a comma. These files are encoded in ‘UTF-8’.</li> <li>a NumPy compressed array format file (.npz), which includes two arrays: The first array is called ‘time’ and contains the starting times of the bins. The second array is called ‘counts’ and includes the corresponding measured number of fluorescence photons per bin. It is possible to load the arrays into a Python script with numpy.load (tested with NumPy version 1.18.1).</li> </ul> <p> </p> <p><strong>Additional Information on the Measurements</strong></p> <p>We provide a JavaScript Object Notation file (.json) for each measurement. These files provide the following information about every measurement: temperature of the cell, number of detected photons, photons per cycle, and the total measurement duration. They are named ‘figx_xxx_info.json’, where ‘figx_xxx’ is the same indicator as discussed in section ‘Raw Time-Tags’.</p> <p> </p> <p><strong>Scripts</strong></p> <p>This folder contains two sample scripts to illustrate how our data can be processed with Python. The first Python script (generate_histograms.py) generates a histogram of the photon arrival times. The second Python script performs a fit in order to determine the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state. We wrote these scripts with Python 3.6.5. To avoid errors, one should download all zipped folders and extract them to the same folder.</p> <ul> <li>The script ‘generate_histograms.py’ processes the fluorescence photon detection events stored in the folder ‘fig3_time_tags’. The file ‘fig3 _fluorescence_time_tags.dat’ is read into the script, and a histogram is generated. To run the script, the following Python libraries are required: NumPy (version 1.18.1), os (version 0.1.4), and json (version 2.0.9).</li> <li>The script ‘fit_data.py’ loads the file ‘fig3_fluorescence_histogram.npz’ from the folder ‘histograms\ fig3_time_tags’ in NumPy arrays. We perform a least-square fit on the histogram of the fluorescence decay. From the fit, we get the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state. Optionally, it is possible to print a fit report and to plot the fit with its residuals. The following Python libraries are required to run the script: NumPy (version 1.18.1), os (version 0.1.4), json (version 2.0.9), pyplot from matplotlib (version 3.1.1), and Parameters, ExponentialModel, and ConstantModel from LmFit (version 1.0.0).</li> </ul> <p> </p> <p><strong>Figures</strong></p> <p>In the folder ‘figures’, we provide the values of the points which we used to generate figure 4 and figure 5. For both figures, we made a JavaScript Object Notation file (.json) where the data of every point is stored in a dictionary. This data contains the fit result of the lifetime and the temperature of the measurement. Additionally, it contains the corresponding errors and the units of every value.</p>
Constraining the dense matter equation of state with joint analysis of NICERand LIGO/Virgo measurements: Data for generating plots
<p>In this repository you will find a Jupyter notebook with code to generate the plots from the paper <em>Constraining the dense matter equation of state with joint analysis of NICER and LIGO/Virgo measurements</em> by Raaijmakers et al. (2020).</p>
Dataset - Learning to Measure Static Friction Coefficient in Cloth Contact
<p>Dataset for: Learning to Measure the Static Friction Coefficient in Cloth Contact</p>
Data Set - Laboratory measurement of the wave–induced plastic particles motion: The influence of wave period, plastic size and plastic density
<p><strong>Data set - Laboratory measurement of the wave–induced plastic particles motion: The influence of wave period, plastic size and plastic density</strong></p> <p>This data set describes the wave flume experimental data on the wave-induced plastic particles motion induced by different wave conditions and different plastic particles density and size. A manuscript is currently under review describing the analysis of the data.</p> <p>The data set is divided in two parts:</p> <p>- <strong>Wave flume hydrodynamics</strong>. With measured water surface elevation at different locations within the wave flume. These data are stored in txt files with headings describing the type of measurement, i.e. wave paddle motion, water surface elevation at different sensors, synchronization signal for the video-cameras.</p> <p>- <strong>Lagrangian trajectories. </strong>hdf5 files with information of the particles position, velocity and time (with respect to the synchronization signal in the respective hydrodynamic file) for each experiment. Two tar.gx files have been uploaded with trajectories information:</p> <p> - TOPIOS_Trajectories_FloatingParticles.tar.xz, with information of floating plastic particles and,</p> <p> - TOPIOS_Trajectories_NonfloatingParticles.tar.xz, with information of non-floating plastc particles.</p> <p>An excel file with information of filenames, cross-shore locations of sensors, plastic particles and wave conditions is also uploaded (TOPIOS_Control_exp.xlsx).</p> <p>Any question regarding the data can be addressed at jose.alsina@upc.edu</p>
Dielectric Measurements of Ovine Heart
<p>This file includes datasets from ex vivo dielectric properties measurements performed on four ovine hearts at National University of Ireland Galway in July 2019. </p> <p>The preliminary results of this experiment were to be presented at EuCAP 2020 under the title "Detailed Dielectric Characterisation of the Heart and Great Vessels."</p>
Early Instrumental Meteorological Measurements in Switzerland
<p>This dataset encompasses early instrumental measurement series that have been gathered and digitised in the context of the Swiss National Science Foundation project Nr. 169676 “Swiss Early Instrumental Measurements for Studying Decadal Climate Variability (CHIMES)”. The newest version of the dataset (v2_2020-04) contains an update of certain URLs indicated on the description (first page) of each PDF-Document. It does not contain any new measurement series compared to the original version.</p> <p>An overview on the individual series on ZENODO is given in the summary table in the description PDF-file.</p> <p>The dataset, organised alphabetically by location name is provided as a .zip-file. General information and terms of use are indicated on the first page of each file.</p> <p>To find further information about all Swiss measurement series (also the ones not published on ZENODO) consider the full inventory provided separately as CSV-file.</p> <p> </p>
Data of "Single-Photon Distillation via a Photonic Parity Measurement Using Cavity QED"
<p>Data published in "<em>Single-Photon Distillation via a Photonic Parity Measurement Using Cavity QED</em>"</p> <p>Phys. Rev. Lett. <strong>122</strong>, 133603</p>
Measurement of Absolute Retinal Blood Flow Using a Laser Doppler Velocimeter Combined with Adaptive Optics
<p><strong>Purpose</strong>: Development and validation of an absolute laser Doppler velocimeter (LDV) based on an adaptive optical fundus camera which provides simultaneously high definition images of the fundus vessels and absolute maximal red blood cells (RBCs) velocity in order to calculate the absolute retinal blood flow.\newline<br> <strong>Methods</strong>: This new absolute laser Doppler velocimeter is combined with the adaptive optics fundus camera (rtx1, Imagine Eyes$^\copyright$,Orsay, France) outside its optical wavefront correction path. A 4 seconds recording includes 40 images, each synchronized with two Doppler shift power spectra. Image analysis provides the vessel diameter close to the probing beam and the velocity of the RBCs in the vessels are extracted from the Doppler spectral analysis. Combination of those values gives an average of the absolute retinal blood flow. An in vitro experiment consisting of latex microspheres flowing in water through a glass-capillary to simulate a blood vessel and in vivo measurements on six healthy humans were done to assess the device.\newline<br> <strong>Results</strong>: In the in vitro experiment, the calculated flow varied between 1.75µl/min and 25.9µl/min and was highly correlated (r<sup>2</sup>= 0.995) with the imposed flow by a syringe pump.<br> In the in vivo experiment, the error between the flow in the parent vessel and the sum of the flow in the daughter vessels was between -11% and 36% (mean±sd 5.7±18.5%). Retinal blood flow in the main temporal retinal veins of healthy subjects varied between 0.9 µL/min and 13.2µL/min.</p> <p><strong>Conclusion</strong>: This adaptive optics LDV prototype (aoLDV) allows the measurement of absolute retinal blood flow derived from the retinal vessel diameter and the maximum RBCs velocity in that vessel.</p>
The age distribution of global soil carbon inferred from radiocarbon measurements
<p>We use 789 radiocarbon (∆<sup>14</sup>C) profiles, along with other geospatial information, to create globally-gridded datasets of mineral soil ∆<sup>14</sup>C and mean age. The spatial resolution is 0.5 degree by 0.5 degree and the vertical resolution is at each 1 cm increment to a soil depth of 1 meter.</p>
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized. in A new species of freshwater Chaetonotidae (Gastrotricha, Chaetonotida) from Obodska Cave (Montenegro) based on morphological and molecular characters
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized.
PsPM-trSP2: SCR measurement in response to neutral IAPS pictures while subjected to auditory distractors
<p>This dataset includes skin conductance response (SCR) measurements for each of 61 healthy unmedicated participants (30 males and 31 females, misprinted in Bach et al. 2015, aged 25.7 +/- 4.5 years) in response to the 45 least arousing neutral IAPS pictures, presented for 1 s each in 3 blocks, while listening to regular or random distractor sounds, as described in Bach et al. (2015). Inter stimulus interval was randomly determined as 7.65 s, 9 s, or 10.35 s. Each recording starts with a 2-minute baseline interval.</p>
Real-time measurement and source apportionment of elements in Delhi's atmosphere
<p>Here we present semi-continuous and real-time measurements of elemental composition for PM2.5 and PM10 aerosols in Delhi, India, at a time resolution of 30 min to 1 h during two consecutive winters in 2018 and 2019, to identify the prevailing sources. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, brake wear, a S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-30 Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the pollutants enter Delhi via three distinct air corridors during winters including Nepal and Uttar Pradesh (India) in the east, and Pakistan, Punjab (India) and Haryana (India) in the north-west, during winter, when the national capital’s air quality is at its worst.</p> <p>This repository has excel file corresponding to each figures presented in main text published version.</p>
PsPM-CogSF: SCR, ECG and respiration measurements during mental arithmetic, attention, and rest
<p>This dataset includes skin conductance, ECG and respiration measurements for 20 healthy unmedicated participants (9 males and 11 females aged 23.68 +/- 2.94 years, gender information misprinted in Bach & Staib 2015) undergoing two 120-s periods of resting, attention, or mental arithmetic (adding numbers). Selection and order of the two periods is contained as group information. An event marker is recorded at the beginning and the end of each period.</p>
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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.