Find research datasets worth reusing

opencc-by-4.0Jul 2022View details →

FUMES III: Ultraviolet and Optical Variability of M Dwarf Chromospheres

We obtained ultraviolet and optical spectra for 9 M dwarfs across a range of rotation periods to determine whether they showed stochastic intrinsic variability distinguishable from flares. The ultraviolet spectra were observed during the Far Ultraviolet M Dwarf Evolution Survey Hubble Space Telescope program using the Space Telescope Imaging Spectrograph. The optical observations were taken from the Apache Point Observatory 3.5-meter telescope using the Dual Imaging Spectrograph and from the Gemini South Observatory using the Gemini Multi-Object Spectrograph. We used the optical spectra to measure multiple chromospheric lines: the Balmer series from H$\alpha$ to H$10$ and the Ca II H and K lines. We find that after excising flares, these lines vary on the order of $1-20\%$ at minute-cadence over the course of an hour. The absolute amplitude of variability was greater for the faster rotating M dwarfs in our sample. Among the 5 stars for which measured the weaker Balmer lines, we note a tentative trend that the fractional amplitude of the variability increases for higher order Balmer lines. We measured the integrated flux of multiple ultraviolet emission features formed in the transition region: the N V, Si IV, and C IV resonance line doublets, and the C II and He II multiplets. The signal-to-noise (S/N) ratio of the UV data was too low for us to detect non-flare variability at the same scale and time cadence as the optical. We consider multiple mechanisms for the observed stochastic variability and propose both observational and theoretical avenues of investigation to determine the physical causes of intrinsic variability in the chromospheres of M dwarfs. All code associated with the analysis and plots for this paper are included in .py scripts. All raw data for the optical spectra are its files compressed into a tar.gzip archive while all reduced spectra are in fits files. All tables are provided in the astropy ASCII text ecsv file format. <ul> <li>Optical Reduction Code: The reduction code is divided into two folders, one labelled "pydis" and another labelled "pygemini".</li> <li>Equivalent Width Measurements: The equivalent widths are measured using a combination of two tables for each exposure: one ending with the suffix "ew_windows.ecsv" that lists the boundaries of the blue and red continua windows and the continuum flux density value, and another ending with the suffix "ew_lines.ecsv" that lists the boundaries of the wavelength window, the integrated line flux with its error, and the equivalent width with its error. These tables are in a subdirectory named "fit_tables".</li> <li>spectralPhoton: The version of spectralPhoton code and the scripts we use to split the Hubble x1d spectra are in a directory named "uv".</li> <li>Line-fitting Tables: The parameter values and associated errors are recorded in tables structured similarly to the equivalent width tables, ending with suffixes "windows.ecsv" and "lines.ecsv".</li> <li>Time Series Tables: The equivalent widths and integrated line fluxes are collated into time series tables in the subdirectory "time_series". Entries with cosmic ray hits in the middle of the line or other spectral defects have been commented out using a # symbol.</li> <li>Posterior Distributions: All posterior samples and their log-likelihood values are recorded in numpy binary .npy files that can be read using the numpy.load() function. These files are divided into two subdirectories named ``abs"  and ``frac" for the absolute and fractional flux fits respectively.</li> </ul>

opencc-by-4.0Sep 2017View details →

All optical mapping data for OMSV

All optical mapping data (both real data and synthetic data) used in OMSV paper. There are three types of data for each sample: <ul> <li>Raw optical mapping data (.cmap format)</li> <li>Normal alignment of optical mapping data (.oma format) </li> <li>Split alignment of optical mapping data (.oma format)</li> </ul> More details could be found in the paper of OMSV (https://doi.org/10.1101/143040).

opencc-by-4.0Jul 2017View details →

Analysis of Optical Losses in a Photoelectrochemical Cell: A Tool for Precise Absorptance Estimation

-optical constants of all layers -SCOUT optical model file

opencc-by-4.0Sep 2017View details →

Data presented in "Characteristics of a magneto-optical trap of molecules"

Data presented in the figures of our paper "Characteristics of a magneto-optical trap of molecules". The data is provided for figures 4a, 4b, 5a, 5b, 6a, 6b, 6c, 6d, 8a, 8b, 8c, 8d, 10a, 10b, 10c, 10d, 11a, 11b, 11c, 11d, 12b, 12c, 13a, 13b, 14a, 14b, 14c, 15, 16a and 16b.

opencc-by-4.0Jan 2018View details →

Data presented in "Blue-detuned magneto-optical trap"

Data presented in figures 1, 2, 3, 4 and 5 of our paper "Blue-detuned magneto-optical trap"

opencc-by-4.0Apr 2024View details →

Global Cloud Biases in Optical Satellite Remote of Rivers - Accompanying Dataset

Open the record for dataset details and reuse information.

Optical coherence tomography of the macular ganglion cell layer in children with neurofibromatosis type 1 is a useful tool in the assessment for optic pathway gliomas

To investigate whether the ganglion cell layer assessed by OCT is a reliable measure to identify and detect relapses of symptomatic OPGs in children with NF1.

Accurate and Rapid Molecular Subgrouping of High-Grade Glioma via Deep Learning-assisted Label-free Fiber-optic Raman Spectroscopy

Dataset for the manuscript "Accurate and Rapid Molecular Subgrouping of High-Grade Glioma via Deep Learning-assisted Label-free Fiber-optic Raman Spectroscopy"

Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion

This repository contains raw data and analysis routines of the publication “Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion” in Optics Express (doi.org/10.1364/OE.521702). The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.11 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 20-30 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails. Also, keep in mind the significant RAM usage. We've conducted measurements using both a custom-built OCT system and the Thorlabs OCT system. The custom setup specifically focused on measuring diffusion in concentrated suspensions, while the Thorlabs OCT system was used to analyze both concentrated and dilute suspensions. To analyze the data from the custom setup, we require an additional dark measurement file. Conversely, analyzing the Thorlabs measurements necessitates a chirp interpolation file. All filenames, whether for raw data or analysis files, are sufficiently descriptive. Files obtained with the Thorlabs OCT system are easily identifiable as they contain “Thorlabs” in their names. To conduct the analysis of Thorlabs measurements, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate. The results are plotted at the end of our analysis routines, with the parameters displayed as a function of depth or wavenumber. Raw measurement files and analysis routines are described below. <div> <table> <tbody> <tr> <td> Name </td> <td> Description </td> <td> Parameters </td> </tr> <tr> <td> 10050, 10 us.mat </td> <td> Interference intensity from the custom setup for the concentrated Kostrosöl 10050 sample. </td> <td> Na=8192, Nb=20, 4.5 kHz </td> </tr> <tr> <td> CS50-28, 10 us.mat </td> <td> Interference intensity from the custom setup for the concentrated Levasil CS50-28 sample. </td> <td> Na=8192, Nb=20, 4.5 kHz </td> </tr> <tr> <td> Mix, 10 us.mat </td> <td> Interference intensity from the custom setup for the concentrated mixed sample. </td> <td> Na=8192, Nb=20, 4.5 kHz </td> </tr> <tr> <td> Dark, 10 us.mat </td> <td> Background interference intensity from a custom setup. </td> <td> Na=2048, Nb=5, 4.5 kHz </td> </tr> <tr> <td> Concentrated 8050, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 8050 sample. </td> <td> Na=65536, Nb=10, 36 Khz </td> </tr> <tr> <td> Concentrated 9550, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 9550 sample. </td> <td> Na=65536, Nb=10, 36 Khz </td> </tr> <tr> <td> Concentrated mix, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the concentrated mixed sample. </td> <td> Na=65536, Nb=10, 36 Khz </td> </tr> <tr> <td> Dilute 8050, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 8050 sample. </td> <td> Na=32768, Nb=20, 36 Khz </td> </tr> <tr> <td> Dilute 9550, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 9550 sample. </td> <td> Na=32768, Nb=20, 36 Khz </td> </tr> <tr> <td> Dilute mix, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the dilute mixed sample. </td> <td> Na=32768, Nb=20, 36 Khz </td> </tr> <tr> <td> Chirp.data </td> <td> File containing k-interpolation data for the Thorlabs OCT measurements. </td> <td>   </td> </tr> <tr> <td> ReadOCTFile.py </td> <td> Written by Jos de Wit, this module reads and imports spectra from raw Thorlabs OCT files. </td> <td>   </td> </tr> <tr> <td> Data_processing.py </td> <td> This module contains all analysis functions. </td> <td>   </td> </tr> <tr> <td> Custom_concentrated.py </td> <td> The script is for analyzing raw concentrated measurement files from the custom setup. </td> <td>   </td> </tr> <tr> <td> Thorlabs_concentrated.py </td> <td> The script is for analyzing raw concentrated measurement files from the Thorlabs setup. </td> <td>   </td> </tr> <tr> <td> Thorlabs_dilute.py </td> <td> The script is for running analysis of raw dilute measurement files from the Thorlabs setup. </td> <td>   </td> </tr> </tbody> </table> </div>

Testing a new optical strain gage for full-field strain measurement: Raw images

<pre>This dataset contains images obtained during two different tests performed to assess the response of a new optical strain gage developped for full-field strain measurement.</pre> <pre>File contents:   - SMA: Folder containing images obtained with a SMA specimen         - SMA_Paint_Ref: contains 100 images in the reference state. These were taken with the optimal parameters for the painted and engraved half (lower half in the images).         - SMA_Paint_Def: contains 100 images in the deformed state.          - SMA_Gage_Ref: contains 100 images in the reference state. These were taken with the optimal parameters for the gage (upper half in the images).         - SMA_Gage_Def: contains 100 images in the deformed state.    - Wood: Folder containing images obtained with a wood specimen         - Wood_Paint_Ref: contains 100 images in the reference state.         - Wood_Paint_Def: contains 100 images in the deformed state.          - Wood_Gage_Ref: contains 100 images in the reference state.         - Wood_Gage_Def: contains 100 images in the deformed state.  These images can be processed with the Python code available in the OpenLSA library: https://gitlab.ip.uca.fr/expmech/openlsa .</pre> An example is provided in this library.

Lower-body Inertial Sensor and Optical Motion Capture Recordings of Walking and Running

<pre>This dataset contains lower-body inertial sensor (IMU) data and optical motion capture (OMC) data from ten participants walking and running overground at different speeds. The data recording is described in this publication: Dorschky, E., Nitschke, M., Seifer, A. K., van den Bogert, A. J., & Eskofier, B. M. (2019). Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models. Journal of biomechanics, 95, 109278. (https://doi.org/10.1016/j.jbiomech.2019.07.022) Please look at the README.txt file for further information.</pre>

Dataset for "Optical Monitoring of Supramolecular Interactions in Polymers"

This dataset contains the raw data for the open-access, peer-reviewed article “Optical Monitoring of Supramolecular Interactions in Polymers” (<a href="https://doi.org/10.1002/anie.202405922">https://doi.org/10.1002/anie.202405922</a>). The accepted version of the article was first published online on June 11, 2024 in Angewandte Chemie International Edition (Wiley).

Measuring the Total Photon Economy of Molecular Species through Fluorescent Optical Cycling: Raw Data

Data acquired in the the paper titled:  <a name="_Hlk150935558"></a>Measuring the Total Photon Economy of Molecular Species through Fluorescent Optical Cycling

Data for "Overcoming Laser Phase Noise for Low-cost Coherent Optical Communication"

The files contain the data for the paper "Overcoming laser phase noise for low-cost coherent optical communication".

Optical Recognition of the English Alphabet Using Proteinoids

This data set contains the potential in mV vs time in seconds for each letter of the English alphabet detected by proteinoids.

Data for "High-fidelity sub-petabit-per-second self-homodyne fronthaul using broadband electro-optic combs"

The files contain the data and codes for the paper "High-fidelity sub-petabit-per-second self-homodyne fronthaul using broadband electro-optic combs".

PL maps, optical images and PLQY analyses

Large data set of PL maps, optical images and PLQY analyses

opencc-by-4.0Jul 2024View details →

Fiber-optic seismic sensing of vadose zone soil moisture dynamics data sets

CC_daily.h5: Daily cross-correlation functions for common-offset DAS channels. RCC_dv_v_full.csv: Summary of all the measured dv/v from the ballistic surface waves in daily cross-correlation functions.

opencc-by-4.0Dec 2023View details →

Monitoring of neoadjuvant chemotherapy through time domain diffuse optics: Breast tissue composition changes and collagen discriminative potential

This dataset serves as a comprehensive resource containing the raw data, analyses, and various tools utilized to produce the findings that are presented in our paper titled ‘Monitoring of neoadjuvant chemotherapy through time domain diffuse optics: breast tissue composition changes and collagen discriminative potential’.  The aim of this clinical study is to test broad spectral range (635-1060 nm) time domain diffuse optical spectroscopy to monitor the response of breast cancer patients to neoadjuvant chemotherapy. Preliminary results from patients on changes in hemoglobin, water, lipids, collagen concentrations, and scattering parameters due to the therapy are presented. In these initial results, we hypothesize that collagen could be a possible biomarker to distinguish complete and partial responders.