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3 results for “optical metrology”

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zenodo48/100

Hybrid Metrology for Nanostructured Optical Metasurfaces - Dataset

<p>This is the dataset of "Hybrid Metrology for Nanostructured Optical Metasurfaces", https://doi.org/10.1021/acsami.3c13923.&nbsp;</p>

opencc-by-4.0Nov 2023View details →
zenodo44/100

Data set for "Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel"

<p>Here we share the relevant data of the manuscript &ldquo;Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel&rdquo;.</p> <p>Files:</p> <ul> <li>Opt_Fr_stability_part1.txt</li> <li>Opt_Fr_stability_part2.txt</li> </ul> <p>contain the data used for evaluation of optical frequency transfer stability (Fig. 7 in the paper). The measurements were done with 8-channels K+K phase/frequency recorder. Column 1 contains date, col. 2: time, col. 5: in-loop beatnote phase, col. 6: out-of-loop beatnote phase. The phase is recorded in cycles. In case of out-of-loop beatnote it was divided by factor of two before recording, therefore the data from col. 6 should be multiplied by two to obtain true values of the optical phase fluctuations.</p> <p>File:</p> <ul> <li>RF_stability.txt</li> </ul> <p>contains the data used for evaluation of RF frequency transfer stability (Fig. 8 in the paper). Column 1 contains time in hours, and col. 2 RF phase fluctuations in seconds.</p>

opencc-by-4.0Jun 2021View details →
zenodo44/100

Datasets for the article "Embedded Digital Phase Noise Analyzer for Optical Frequency Metrology"

<p>These files contain the datasets related to the figures presented in the article Donadello et al. (2023) &quot;Embedded Digital Phase Noise Analyzer for Optical Frequency Metrology&quot;, IEEE Transactions on Instrumentation and Measurement, 72, pp. 1&ndash;12, Article Sequence Number: 2005412. Available at: https://doi.org/10.1109/TIM.2023.3288255.<br> Each dataset is related to the respective figure number in the article, as indicated in the filename (e.g. &quot;data_fig3a.csv&quot; contains the data used to produce Fig. 3, subplot a).</p> <p>All the files are formatted in the comma-separated format.</p> <p>Description:<br> &quot;data_fig3a.csv&quot;: coefficients for IQ demodulation and filtering, with f_int=200kHz.<br> &quot;data_fig3b.csv&quot;: frequency response of demodulation filtering, with f_int=200kHz and f_int=10kHz.<br> &quot;data_fig4a.csv&quot;: power spectral density (PSD) of frequency signals, at different signal amplitudes.<br> &quot;data_fig4b.csv&quot;: overlapping Allan deviation of frequency signals, with either OCXO and maser references.<br> &quot;data_fig4c.csv&quot;: measured signal frequency and amplitude as a function of nominal frequency deviation.<br> &quot;data_fig4d.csv&quot;: measured signal amplitude as a function of nominal amplitude.<br> &quot;data_fig5.csv&quot;: example signals related to the synchronized acquisition of RF inputs.<br> &quot;data_fig7a.csv&quot;: synchronized time series of frequency deviation acquired over a fiber link in the self-heterodyne interference scheme.<br> &quot;data_fig7b.csv&quot;: PSD of the frequency signals acquired over a fiber link in the self-heterodyne interference scheme.<br> &quot;data_fig8a-without-corr.csv&quot;: synchronized time series of frequency deviation acquired over a fiber link in the heterodyne interference scheme without frequency drift correction.<br> &quot;data_fig8a-with-corr.csv&quot;: synchronized time series of frequency deviation acquired over a fiber link in the heterodyne interference scheme with frequency drift correction.<br> &quot;data_fig8b.csv&quot;: PSD of the frequency signals acquired over a fiber link in the heterodyne interference scheme.</p>

opencc-by-4.0Sep 2023View details →

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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.

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OpenNeuro

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