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10,554 results for “measurements”
LENS: A LEO Satellite Network Measurement Dataset - 202405 - Part 3
<p>LENS dataset 2024-05 Part 3</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset (CSV) - 202404
<p>LENS dataset 2024-04 (CSV format)</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202405 - Part 2
<p>LENS dataset 2024-05 Part 2</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202404 - Part 1
<p>LENS dataset 2024-04 Part 1</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
Liquid water content, ice water content and density measured via the SnowMelt Instrument at Auberge station
<pre><span><span>Liquid water content, ice water content and density measured via the SnowMelt Instrument nearby the Auberge station during 3 consecutive winters, from <span>2021-11-05</span> to <span>2024-03-21, for the bottom snow layer (0-7cm). Data are aggregated at the hourly time step. The instrument was unmounted during no-snow periods. The liquid water content of the bottom snow layer can be used as a proxy for snow melt at the measurement point.</span></span></span></pre>
LENS: A LEO Satellite Network Measurement Dataset - 202405 - Part 1
<p>LENS dataset 2024-05 Part 1</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202404 - Part 3
<p>LENS dataset 2024-04 Part 3</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202404 - Part 4
<p>LENS dataset 2024-04 Part 4</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
Ground surface temperature measurements at grazed and ungrazed plots in Central Mongolia
<p>Ground surface temperature measurements from two sites with different topographic aspect in Central Mongolia. The dataset includes both grazed and ungrazed plots, and covers ca. 14 months from May 2022 to August 2023.</p>
Measuring avian bill size: Comparing and evaluating 3D surface scanning with traditional size estimates in Australian birds
<p>Unidimensional measurements for estimating bill size, like length and width, are commonly used in ecology and evolution, but can be criticised due to issues with repeatability and accuracy. Furthermore, formula-based estimates of bill surface area tend to assume uniform bill shapes across species, which is rarely the case. 3D surface scanning can potentially help overcome some such issues by collecting detailed external morphology and direct measurements of surface area, rather than composite estimates of size. Here, we evaluate the use of 3D surface scanners on avian museum specimens to test the repeatability of 3D-based measurements and compare these to traditional formula-based methods of estimating bill size from unidimensional measurements. Using 28 Australian bird species, we investigate inter-observer repeatability of surface area measurements from 3D surface scans. We then compare 3D-based size estimates to formula-based size estimates to infer the accuracy and precision of formula-based measurements of bill surface area. We find that morphometric measurements from 3D surface scans are highly repeatable between observers, without the need for extensive training, demonstrating an advantage over unidimensional measuring methods, like callipers. When comparing 3D-based measurements to formula-based estimates of bill surface area, most formulae for estimating size consistently underestimate surface area, and with considerable variation between species. Where 3D scanning is not possible, we find that a commonly used cone formula for estimating bill size is most precise across diverse bill shapes, therefore supporting its use in interspecific contexts. However, we find that incorporating an additional unidimensional measure of bill curvature into formulae improves the accuracy of the calculated area. Our results reveal the high potential for 3D surface scanners in avian morphometric research, especially for studies necessitating large sample sizes collected by multiple observers, and gives suggestions for formula-based approaches to estimate bill size.</p>
Fig. 2 in Longshore currents on a meso-tidal beach of Goa, India - Measurements and improved formulae
Fig. 2 — Alongshore varying current speed and current direction at Candolim with water level
Fig. 1 in Longshore currents on a meso-tidal beach of Goa, India - Measurements and improved formulae
Fig. 1 — Map of (a) Study area, and (b) Measurement locations
Fig. 4 in Longshore currents on a meso-tidal beach of Goa, India - Measurements and improved formulae
Fig. 4 — Comparison of estimated longshore current velocity with measurements at C1 \
Comparison of measurement protocols for internal channels of transparent microfluidic devices - datasets
<p>Supplementary material to "Comparison of measurement protocols for internal channels of transparent microfluidic devices" submitted to Micromachines.</p> <p>Data accompanying comparison of measurement protocols.</p> <p> </p> <p>Further readings can be found at <a href="https://mfmet.eu/publications">https://mfmet.eu/publications</a></p> <p>The project (20NRM02 MFMET) have received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
To Trust or Not to Trust: Towards a novel approach to measure trust for XAI systems
<p>Experimental data from unpublished work <em>To Trust or Not to Trust: Towards a novel approach to measure trust for XAI systems</em></p>
Research Compendium for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive is the Reproducible Research Compendium for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques.</p> <p>This compendium includes all files related to MARGE associated with the manuscript.</p> <p>NN model files are split into smaller files for convenience, given their sizes. To recombine the files, do, e.g., <br> cat cnn_weights_NH-LW.h5* > cnn_weights_NH-LW.h5</p> <p>User interested in running MARGE will need to clone the GitHub repo (https://github.com/exosports/MARGE), apply the patch file to checksum fc95b3c, organize the relevant files into directories as listed in the configuration files (Zenodo does not support organizing files into directory structures) and calculate the number of training, validation, and test cases to be stored in the relevant input file specified in the configuration file. MARGE is under the Reproducible Research Software License (https://planets.ucf.edu/resources/reproducible-research/software-license/). For more details on MARGE, see the User Manual on GitHub.</p>
Measured Ti L2,3-edge NEXAFS Spectroscopic Signatures
<p>Measured Ti L2,3-edge NEXAFS of a molecular library as well as mono- and dinuclear peroxo complexes, measured at the X-Treme beamline at the Swiss Light Source (SLS, PSI, Villigen, Switzerland).</p>
Plotting code and data for figures in ''Data Assimilation of Ion Drift Measurements for Estimation of Ionospheric Plasma Drivers''
<h2>2024_Hu_SpaceWeather_Data Assimilation of Ion Drift Measurements for Estimation of Ionospheric Plasma Drivers</h2> <p>This package includes the scripts and files for reproducing the plots (or subplots) in the paper, J. Hu, S. McDonald, A. Chartier, A. L. Rubio, S. Datta-Barua, Data Assimilation of Ion Drift Measurements for Estimation of Ionospheric Plasma Drivers, Space Weather.</p> <p> </p> <p> </p> <p>plotting_code_fig6.m : MATLAB code plotting the figure 6, SAMI3/IDA4D TEC global map</p> <p>---> calc_noon.m : MATLAB function calculating noon time location for the specific UT</p> <p>---> plot_tec_map.m : MATLAB function inside the plotting_code_fig.m</p> <p>plotting_code_fig8.m : MATLAB code plotting the figure 8, validation results compared to Millstone Hill Incoherent Scatter Radar measurements</p> <p>plotting_code_fig9.m : MATLAB code plotting the figure 9, validation results compared to SuperDARN measurements</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Measuring the Total Photon Economy of Molecular Species through Fluorescent Optical Cycling: Raw Data
<p>Data acquired in the the paper titled: <br><br><a name="_Hlk150935558"></a><strong><span>Measuring the Total Photon Economy of Molecular Species through Fluorescent Optical Cycling </span></strong></p>
Galaxy job runtime measurements with Encrypted and plain storage volumes on Cloud deployments.
<p>Storage volume performance measurement on Cloud environment using Galaxy and Mapping tools: Bowtie2, STAR and Salmon. Galaxy job runtime for encrypted and not ecrypted storage volumes are reported.</p> <p>Scripts and Documentation on GitHub.</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.