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10,554 results for “measurements”
A direct comparison of single grain and multi-grain aliquot measurements of feldspars from colluvial deposits in KwaZulu-Natal, South Africa
<p>This dataset accompanies a manuscript intended for submission to the journal Geochronology. The dataset includes the raw luminescence data (.binx format) generated during equivalent dose measurements of all samples, for single grain and multi-grain aliquot measurements. It also includes the .RDS files, which are the output of bayesian modelling using BayLum. Furthermore, the dataset includes the Rcodes used for bayesian modelling, as well as to generate the standardised growth curves and the application of the LnTn method. The dataset is accompanied by a .txt file, which contains information on file labelling, measurement conditions, and the R codes developed/used as part of the study.</p>
Measurements of Methanethiol (MeSH) and dimethyl sulfide (DMS) in surface seawater digitalised from AMT-7 1998.
<p>Global emissions of methanethiol are highly uncertain and the drivers influencing its seawater concentrations are quasi unexplored. Here we have digitalised measurements from 1998 taken during AMT-7 (Atlantic Meridional Transect), which may be useful for future investigators. The measurements are originally presented in the following reference:</p> <p>Kettle, A. J., Rhee, T. S., von Hobe, M., Poulton, A., Aiken, J., and Andreae, M. O.: Assessing the flux of different volatile sulfur gases from the ocean to the atmosphere, J. Geophys. Res., 106, 12193–12209, https://doi.org/10.1029/2000JD900630, 2001.</p> <p><br>Measurements from AMT-7 were extracted from the graphs in the manuscript using an image extraction technique (https://apps.automeris.io/wpd/, last accessed July 2024). To ensure concurrency of the measurements and deal with the error in the time stamp related to the use of an image extraction technique, data from AMT-7 was binned in 6-hourly bins. Data gaps are listed with an improbable number of -999.</p> <p> </p> <p><span>Definitions of acronyms, site abbreviations, or other project-specific designations:</span></p> <p><span>Lat=latitude (negative indicates south)</span></p> <p><span>Lon=longitude (negative indicates west)</span></p> <p><span>t_Kettle01 = The mean measured concentration during each 6 hour bin is shown, 3 hours either side of the listed time. The timestamp indicates sampling time in local solar time, expressed as DD/MM/YYYY HH:MM</span></p> <p><span>MeSH_nM= methanethiol surface seawater concentration in nM, defined as nmol dm^(-3)</span></p> <p><span>DMS_nM= dimethyl sulfide surface seawater concentration in nM, defined as nmol dm^(-3)</span></p> <p><span>Chla_mg_m3 = surface chlorophyll a concentration in mg m^(-3)</span></p> <p><span>SST_C= sea surface temperature in degrees Celsius </span></p> <p><span>SSS_PSU= sea surface salinity in practical salinity units (PSU)</span></p>
Observed changes in significant wave heights derived from long-term homogenized measurements offshore mainland Portugal
<p>The data set contains statistical estimators of significant height acquired over the last 40 years by wave stations operating on the Portuguese coast; Monthly Mean, Monthly Maximum and the monthly 99, 95, 90, 50 , 10 percentiles</p> <p>The data was calculated based on long-term homogenized 3h series of in-situ significant wave height</p> <p>These buoys have been maintained by the Instituto Hidrografico and constitute one of the longest time series of wave data.</p> <p>Each file correspond to a particular wave station. All files are in ASCII and have the same 9 column format. The heading identify the variables</p> <p>Leixoes_corr_monthly_percentiles.txt - Data acquired by Leixoes buoy: Location 41º 19.00' N 8º 59.00' W, depth app 100 m</p> <p>Sines_corr_monthly_percentiles.txt - Data acquired by Sines buoy: Location 37º 55.27' N 8º 55.73 ' W, depth app 100 m</p> <p>Faro_coor_monthly_percentiles.txt - Data acquired by Faro buoy: Location 36º 54.28' N 7º 53.90' W, depth app 100 m</p> <p> </p>
Measuring the effects of regolith porosity on mid-IR spectra of the Allende meteorite
<p>This dataset contains three types of data: 1) Mid-Infrared (MIR; 5-35 micron) laboratory spectra of the Allende meteorite, 2) Measured parameters of the MIR Allende spectra, and 3) MIR spectra from the Spitzer Space Telescope of asteroids (85989) 1999 JD6, (234) Barbara, (5261) Eureka, and (114) Kassandra as described in Dausend et al., (in rev). </p> <p>Allende meteorite spectra files are labeled accordingly: Allende_[largest particle size]_[Allende ratio]</p> <p>Example: The file labeled Allende_63_70.txt contains spectra of Allende powder with 45-63 micron particle size, and an Allende:KBr ratio of 70:30.</p> <div>Additional information regarding Allende spectra feature parameters: </div> <div>We combined some features together for band parameter analyses to form ‘composite features’. A composite feature is made of two or more individual features that, together, form a larger spectral feature. In the spreadsheet these features are labeled with a ‘c’ prefix. The composite feature definitions are:</div> <div>cP_1 = P_1-2 (primary Christiansen Feature)</div> <div>cP_2 = P_3-4</div> <div>cP_3 = P_5-7</div> <div>cP_4 = P_3-7 (10 μm feature)</div> <div>cD_1 = D_2-6</div> <div> </div> <div>Corrections: </div> <ul> <li> <div>In the ‘Allende_peak_parameter’ and ‘Allende_dip_parameter’ spreadsheets, the ‘Area’ unit should be labeled as ‘μm’, not ‘μm^2’. </div> </li> </ul> <ul> <li>The last column of the ‘Wavenumbers’ sheet in “Allende_dip_parameters.xlsx” is labeled as ‘cP1 error’, but it should be ‘cD1 error’.</li> </ul>
2020 IMPACTS HVPS Individual Particle Measurements, Habits, and Size Distributions
<p>Morphological measurements for particles detected by the vertical channel of the High Volume Precipitation Spectrometer (HVPS) during seven flights of the 2020 IMPACTS field campaign, in netCDF format. Some flights have multiple files. Files ending in V_1.nc are individual particle files, while files ending in V.HVPS.nc are size distribution files. The numbers in all filenames represent the file start times, in format YYMMDDHHMMSS (in UTC). The individual particle data files include habit classifications as were used in Schima et al. 2024 (citation pending publication), and similarly, habit size distributions are available within the size distribution files. These files were created using University of Illinois/Oklahoma Optical Array Probe Processing Software (UIOPS, https://doi.org/10.5281/zenodo.1285968).</p> <p> </p> <p><strong>Some additional notes on habits</strong></p> <p>The size distribution files have a "habit" dimension of length 10. Along this dimension, indices in the files correspond to the following habits:</p> <div>0: sphere</div> <div>1: column/needle</div> <div>2: rejected</div> <div>3: tiny</div> <div>4: plate</div> <div>5: irregular</div> <div>6: graupel</div> <div>7: dendrite</div> <div>8: aggregate</div> <div>9: center-out</div> <div> </div> <div> </div> <div>The individual particle files have numeric codes for various habits. These codes refer to the following habits:</div> <div> </div> <div>77: missing</div> <div>67: center-out</div> <div>116: tiny</div> <div>111: rejected</div> <div>108: column/needle</div> <div>97: aggregate</div> <div>103: graupel</div> <div>115: sphere</div> <div>104: plate<br>105: irregular<br>100: dendrite</div> <div> </div>
2020 IMPACTS 2DS Individual Particle Measurements, Habits, and Size Distributions
<p>Morphological measurements for particles detected by the horizontal channel of the 2-Dimensional Stereo Probe (2DS) during seven flights of the 2020 IMPACTS field campaign, in netCDF format. Some flights have multiple files. Files ending in H_1.nc are individual particle files, while files ending in H.2DS.nc are size distribution files. The numbers in all filenames represent the file start times, in format YYMMDDHHMMSS (in UTC). The individual particle data files include habit classifications as were used in Schima et al. 2024 (citation pending publication), and similarly, habit size distributions are available within the size distribution files. These files were created using University of Illinois/Oklahoma Optical Array Probe Processing Software (UIOPS, https://doi.org/10.5281/zenodo.1285968).</p> <p> </p> <p><strong>Some additional notes on habits</strong></p> <p>The size distribution files have a "habit" dimension of length 10. Along this dimension, indices in the files correspond to the following habits:</p> <div>0: sphere</div> <div>1: column/needle</div> <div>2: rejected</div> <div>3: tiny</div> <div>4: plate</div> <div>5: irregular</div> <div>6: graupel</div> <div>7: dendrite</div> <div>8: aggregate</div> <div>9: center-out</div> <div> </div> <div> </div> <div>The individual particle files have numeric codes for various habits. These codes refer to the following habits:</div> <div> </div> <div>77: missing</div> <div>67: center-out</div> <div>116: tiny</div> <div>111: rejected</div> <div>108: column/needle</div> <div>97: aggregate</div> <div>103: graupel</div> <div>115: sphere</div> <div>104: plate<br>105: irregular<br>100: dendrite</div>
An Economical Open-Source Lagrangian Drifter Design to Measure Deep Currents in Lakes
<p>An economical, open-source Lagrangian drifter designed to collect current data on lakes<200km2 was evaluated against existing designs. The new design was tested in deep inland lakes in the Finger Lakes region of New York, USA and is effective at tracking deep currents. The ease and low-cost of fabrication and launch/recovery should facilitate use of this design by less-advantaged communities & researchers.</p> <p>This project includes data and code for preparation of graphs and charts to illustrate Lagrangian drifter experiments in Seneca Lake and Keuka Lake, New York, USA.</p>
Data and code for "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. 2024
<p>This data and code is made available to support the article: "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. 2024".</p> <p>Includes: TMOC method output tide correlation, Antarctic Peninsula grounding line, DInSAR data, TMOC Code.</p> <p> </p> <p><strong>For the data:</strong></p> <p>These data are made available to acompany the article "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. (2024)</p> <p>This datset contains:</p> <p>AP_TMOC_tide_correlation_2019_2020.tif - Significance adjusted tide correlation values for the TMOC method for 2019-2020 for the Antarctic Peninsula.</p> <p>AP_GL_TMOC_2019_2020.shp - A continuous grounding line made from TMOC data and British Antarctic Survey Coastline Data. Intended for use by others.</p> <p>AP_GL_TMOC_2019_2020_source.shp - A discontinuous grounding line made from TMOC data and British Antarctic Survey Coastline Data including the source of each line segment.</p> <p>The folder 'Interferograms' contains the DInSAR products used in the manuscript, sorted by Sentinel-1 frame</p> <p> </p> <p><strong>For the code:</strong></p> <p>This code is made available to support the article "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al.</p> <p>The authors take no responsibility for the quality of results derived using this code.</p> <p>This code is licensed under a Creative Commons Attribution 4.0 International Licence: http://creativecommons.org/licenses/by/4.0/</p> <p>external functions required:<br>geoimread - https://uk.mathworks.com/matlabcentral/fileexchange/46904-geoimread<br>polarstreo_inv - https://uk.mathworks.com/matlabcentral/fileexchange/32907-polar-stereographic-coordinate-transformation-map-to-lat-lon<br>CATS208 tide model and TMD 2.5 matlab toolbox - https://www.esr.org/research/polar-tide-models/tmd-software/</p> <p>The function TMOC_GL_v8 implements the TMOC method descibed in Wallis et al. 2024. This is a 'bring your own data' version.</p> <p>The script pp_folder prost-processes the outputs using the functuon LPfilt_cc</p> <p> </p>
Raw data Lipid measurement
<p><span>Variants in </span><em><span>GBA1</span></em><span> result in dysregulated sphingolipids. </span><span>We investigated five CSF d18:1 sphingolipid species in a multicenter cohort comprising people with </span><span>Parkinson’s Disease </span><span>and Dementia with Lewy bodies with and without <em>GBA1</em> variants and healthy controls. We found no increase of d18:1 sphingolipid species in heterozygous <em>GBA1</em> variant participants. Sphingolipid levels had no effect on development of cognitive impairment.We conclude that CSF d18:1 sphingolipids seem not suitable to be used as a state marker in </span><span>Parkinson’s Disease</span><span>.</span></p>
Evaluation of the influence of rain on air surface temperature measurements
<h2>Description</h2> <p>The dataset is constituted by three .csv files, which contain the measurements performed in an experiment aiming to evaluate the influence of rain on temperature readings. Two devices under tests (DUTs), one naturally ventilated and one artificially ventilated, are compared with a reference system. A .csv file is produced for DUT1, DUT2 and the reference system. Here below the content of each file is briefly described:</p> <ul> <li>Dataset_reference: accurate air temperature measurements obtained using the reference system, which is not affected by rain. The system is constituted by four aspirated thermometers (called Meteo1, Meteo2, Meteo 3, Meteo 4) manufactured at the Danish Technology Institute. The column "PT500" contains instead the rain temperature measurements. The readings are produced using a Fluke Super-DAQ (1586A). </li> <li>Dataset_DUT1: measurements of the naturally ventilated thermometer under an artificially generated rainfall. The readings are produced using the manufacturer datalogger.</li> <li>Dataset_DUT2: measurements of the artificially ventilated thermometer under an artificially generated rainfall. The readings are produced using the manufacturer datalogger.</li> </ul>
5G NR Full Grid Over-the-Air Measurements
<p>This data set is collected for the full resource grid over-the-air measurements of 5G NR downlink tranmission. Some different scenarios including different numerologies, center frequencies, bandwidths and fading are included. Please refer to the PDF file in the folder for the detailed description of the scenarios</p>
Granular Aluminum Parametric Amplifier for Low-Noise Measurements in Tesla Fields
<p>Raw and processed data as well as a jupyter notebooks for the evaluation associated with the paper "Granular Aluminum Parametric Amplifier for Low-Noise Measurements in Tesla Fields" (publicly available on arXiv: <a href="https://arxiv.org/abs/2403.10669">arXiv:2403.10669</a>) by N. Zapata et al. acquired and prepared at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.</p> <p>The notebooks show how to analyze the main results of the manuscript.</p> <p>For additional information please contact: nicolas.gonzalez2@kit.edu or ioan.pop@kit.edu.</p>
Water and energy fluxes measurements over a riparian Tamarix spp. stand in the lower Tarim River basin, northwestern China
<p>This dataset includes water and energy fluxes measurements over a riparian <em>Tamarix spp.</em> stand in the lower Tarim River basin, northwestern China. Details of field site and measurements can be found in the paper: Yuan, G., P. Zhang, M.-a. Shao, Y. Luo, and X. Zhu (2014), Energy and water exchanges over a riparian Tamarix spp. stand in the lower Tarim River basin under a hyper-arid climate, Agricultural and Forest Meteorology, 194(0), 144-154.</p> <p>This dataset also accompanies the published paper in the Water Resources Research: Implementing Dynamic Root Optimization in Noah‐MP for Simulating Phreatophytic Root Water Uptake. Water Resources Research 54(3), 1560-1575. With this dataset, we tested the Noah-MP land surface model with implementation of a soil moisture-responsive root dynamics scheme (VOM-ROOT). </p>
Data presented in "Laser cooled YbF molecules for measuring the electron's electric dipole moment"
<p>Data underlying figure 2, 3 and 4 of the paper</p>
EVN measurement set of experiment N14C2
<p>EVN measurement set of experiment N14C2 (n14c2.ms) and calibration tables for Tsys (n14c2.tsys) and gain curve (n14c2.gcal).</p> <p>IDI files were downloaded from the EVN archive <a href="http://www.jive.nl/fitsfiles?experiment=N14C2_140610">here</a> and the associated EVN User Experiment Pipeline Feedback of N14C2 were downloaded from <a href="http://www.jive.nl/pipeline?experiment=N14C2_140610&pass=n14c2">here</a>. They were converted to a measurement set and the calibration tables were prepared following <a href="https://docs.google.com/document/d/1BbDsOTht7h7wgSCLweoycgB2Kei6qlwSp6ocOkgwG9Y/edit?usp=sharing">this tutorial</a>.</p> <p>The script fringe.n14c2_test_casa4.7_evn.py details how to run the <a href="https://github.com/as595/RadioNetRINGS/tree/master/PythonFringeFitter">fringe fitting python prototype</a> on these data.<br> </p>
Estimating Power without Measuring it: a Machine Learning Approach
<p>Data used for estimating power in real condition in cycling</p> <p>https://github.com/scikit-cycling/research/blob/master/power_regression/abstract.pdf</p>
Additional data for the "In-situ full field measurement during inter-facial debonding in single fiber composite under transverse load"
<p>The following document is an extension of the <em>In-situ full field measurement during inter-facial debonding in single fiber composite under transverse load</em> publication. It contains guidelines for the experimental results for the single fiber experiment of epoxy matrix and PTFE fiber, epoxy matrix and galvanized steel matrix, modified epoxy matrix and PTFE fiber and modified epoxy matrix and galvanized steel matrix. The detailed data from the experiments is provided with this document as <em>CSV </em>files.</p>
Rock-temperature, fracture displacement and acoustic/micro-seismic data measured at Matterhorn Hörnligrat, Switzerland
<p>This repository contains data, which were acquired in the context of project X-Sense2 (financed by nano-tera.ch, ref. no. 530659) at the Matterhorn Hörnligrat fieldsite on 3500 m a.s.l. from 2015 until 1 April 2018. These data were used in the following publication:</p> <p>Weber, S., Faillettaz, J., Meyer, M., Beutel, J., and Vieli, A.: Acoustic and micro-seismic characterization in steep bedrock permafrost on Matterhorn (CH), Journal of Geophysical Research: Earth Surface, 123(6), 1363-1385, doi: 10.1029/2018JF004615, 2018.</p> <p><strong>AM-DATA</strong> This repository contains selected accelerometer data with SI unit m/s<sup>2</sup> (hourly .miniseed-files, MH40 refers to AM<sub>scarp</sub>). These data were measured continuously using an accelerometer based on a Wilcoxon 728A/T (10 − 10000 Hz, 24 kHz resonance frequency), netADC data acquisition system and netSP+ seismological processor of Institute of Mine Seismology. Data were synchronized to a global time reference using GPS (<1 μs). The data is stored in .miniseed-format and splitted in hourly files.</p> <p><strong>SM-DATA</strong> This repository contains selected raw seismometer data in counts (hourly .miniseed-files, MHDL refers to SM<sub>scarp</sub> and MHDT refers to SM<sub>ridge</sub>). These data were measured using a Lennartz electronic low-noise seismometer LE-3Dlite MKIII (1−100 Hz) and Nanometrics Centaur digital recorder, a 24-bit high-resolution seismic data acquisition system disciplined by GPS (<100 μs) with a sampling rate of 1000 sps. The data is stored in .miniseed-format and splitted in hourly files.</p> <p><strong>TIMESERIES</strong> This repository contains 8 timeseries:</p> <ul> <li> <p><em>AS_scarp_high.csv</em> describes the threshold triggeres acoustic emission hits acquired with a piezoelectric sensor Mistras Physical Acoustics Corporation R6α, 35−100 kHz, 55 kHz resonance frequency.</p> </li> <li> <p><em>AS_scarp_low.csv</em> describes the threshold triggeres acoustic emission hits acquired with a piezoelectric sensor Mistras Physical Acoustics Corporation R.45, 5−30 kHz, 20 kHz resonance frequency.</p> </li> <li> <p><em>CR_old.csv</em> described the measured fracture displacement in mm.</p> </li> <li> <p><em>SMridge_nofilter.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm without filtering. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>SMridge_filtered.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm in the frequency band 33-67 Hz. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>SMscarp_filtered.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm without filtering. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>SMscarp_nofilter.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm in the frequency band 33-67 Hz. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>temperature.csv</em> describes the rock temperature (in °C) at different depths: 5, 10, 20, 30, 50 and 100 cm.</p> </li> </ul> <p>All time stamps are in UTC.</p>
Measuring Latency Variation in the Internet
<p>This is the companion web site and dataset to the paper "<a href="https://doi.org/10.1145/2999572.2999603">Measuring Latency Variation in the Internet</a>" which was published at ACM CoNEXT '16.</p> <p>Also published at <a href="https://www.cs.kau.se/tohojo/measuring-latency-variation/">https://www.cs.kau.se/tohojo/measuring-latency-variation/</a></p>
Microscopy images and movies supporting the publication: "tRNA tracking for direct measurements of protein synthesis kinetics in live cells"
<p>This repository contains experimental and simulated microscopy movies and images supporting the publication: Volkov et al. (2018) tRNA tracking for direct measurements of protein synthesis kinetics in live cells. <em>Nat Chem Biol, </em>DOI: 10.1038/s41589-018-0063-y</p> <p>A detailed list of files and file organisation can be found in Repository_content.pdf.</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.