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Dataset for Global Reference Seismological Data Sets: Multimode Surface Wave Dispersion
<ul> <li><strong>How fast do surface waves travel globally after any earthquake?</strong></li> <li><strong>Do we get the same information from various measurement techniques?</strong></li> <li><strong>Which features in the Earth are robust and can be resolved by a reference model?</strong></li> </ul> <p>Reference data with uncertainties are useful for improving existing measurement techniques, validating models of interior structure, calculating teleseismic data corrections in local or multiscale investigations and developing a 3-D reference Earth model. This study was done in collaboration with 18 scientists from 16 institutions in 7 countries who actively participated in the <a href="http://rem3d.org">REM3D</a> project. The project assimilated, archived, reconciled and modeled big (>200 million measurements) and diverse <a href="https://globalseismology.princeton.edu/data/surface-waves">surface-wave datasets</a> for global subsurface structure.</p> <p>The reference data set summarizes measurements of dispersion of fundamental-mode surface waves and up to six overtone branches from 44,871 earthquakes recorded on 12,222 globally distributed seismographic stations. Dispersion curves are specified at a set of reference periods between 25 and 250 s to determine propagation-phase anomalies with respect to a reference Earth model. Empirically determined observational uncertainties (1 sigma) for each wave type, branch number and period can be found in Table 3. </p> <p><strong>Summary:</strong></p> <p><strong>[I]</strong> <strong>Reconciled large and diverse catalogues</strong> of Love-wave (49.65 million) and Rayleigh-wave dispersion (177.66 million) from eight groups worldwide.<br> <strong>[II]</strong> Retrieved missing station and earthquake <strong>metadata</strong> in several legacy compilations and codified <strong>scalable formats</strong> to facilitate reproducibility, easy storage and fast I/O on HPC systems.<br> <strong>[III]</strong> <strong>Systematic discrepancies </strong>between raw phase anomalies can be attributed to discrepant theoretical approximations, reference Earth models and processing schemes.<br> <strong>[IV]</strong> <strong>Phase-velocity variations</strong> yielded by the inversion of the summary data set are <strong>highly correlated</strong> (R ≥ 0.8) with those from the quality-controlled contributing data sets, especially for long-wavelength variations (up to degree ∼25) in fundamental-mode dispersion (50–100 s).<br> <strong>[IV]</strong> <strong>Only 2ζ azimuthal variations</strong> in phase velocity of <strong>fundamental-mode Rayleigh waves</strong> are <strong>required</strong>; maps of 2ζ azimuthal variations are highly consistent between catalogues ( R = 0.6–0.8).</p> <p><strong>Feedback/Questions?</strong> Please contact Raj Moulik (<a href="https://rajmoulik.com">rajmoulik.com</a>) at <a href="mailto:moulik@caa.columbia.edu?subject=Query%20from%20Zenodo">moulik@caa.columbia.edu</a> </p> <p><strong>Reference:</strong></p> <p><em>Please cite the following work if you use this data or software.</em></p> <ul> <li>Moulik, P. <em>et al., </em>(2022) Global reference seismological data sets: multimode surface wave dispersion. <em>Geophys J Int</em> <strong>228</strong>, 1808–1849, doi: <a href="https://doi.org/10.1093/gji/ggab418">10.1093/gji/ggab418</a>. <em><a href="https://rajmoulik.com/Publications/Moulik_Reference_Surface_Waves_GJI2022.pdf">pdf</a></em></li> </ul> <p><em>You can also cite the dataset and software from this Zenodo page (Optional).</em></p> <ul> <li> <p>Moulik, P. (2022) Dataset for Global Reference Seismological Data Sets: Multimode Surface Wave Dispersion. In Geophys. J. Int. (v1.0, Vol. 228, pp. 1808–1849). Zenodo. doi: <a href="https://doi.org/10.5281/zenodo.8371228">10.5281/zenodo.8371228</a></p> </li> </ul> <p><strong>HDF5 Container Format</strong></p> <ul> <li><strong>Reference Love waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Love.data.h5">Download All Periods and Branches as Summary.SW.Love.data.h5</a>)</li> <li><strong>Reference Rayleigh waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Rayl.data.h5">Download All Periods and Branches as Summary.SW.Rayl.data.h5</a>)</li> </ul> <p>Summary (reference) data between pairs of 2562 evenly-spaced knot points with an average spacing of 4.33◦. These files store the data in the RSDF HDF5 container format. These can be read using standard HDF5 modules (e.g. h5py) or using <a href="http://avni.globalseismology.org/">AVNI</a>. For example, to read the reference data for fundamental mode R1 waves at 100s into a Pandas Dataframe containing data (df['data']) and a dictionary with the metadata (df['metadata']), and thereafter write contents to an ASCII text file, enter the following in Python:</p> <ul> <li><em>from avni.data.SW import readSWhdf5,writeSWascii</em></li> <li><em>df=readSWhdf5(query='0/100.0/R1/REM3D',hdffile='Summary.SW.Rayl.data.h5',datatype='summary')</em></li> <li><em>writeSWascii(df,'test.txt')</em></li> </ul> <p><strong>ASCII (text) Format</strong></p> <p>These files contain the same reference data as the HDF5 files above but in gzipped ASCII files. The files are named according to the overtone branch, wave type and period as <em>Summary.$overtone.$wave.$period.REM3D.gz</em> Table A1 from the paper describes the various columns in the surface-wave RSDF ASCII format files.</p> <ul> <li><strong>Love waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Love.data.zip">Download All Periods and Branches as Summary.SW.Love.data.zip</a>) <ul> <li>Fundamental Modes <ul> <li>Minor Arc Arrivals (L1) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.25s.REM3D.gz">25s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.27s.REM3D.gz">27s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.30s.REM3D.gz">30s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.32s.REM3D.gz">32s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.35s.REM3D.gz">35s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.250s.REM3D.gz">250s</a></li> <li>Major Arc Arrivals (L2) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.250s.REM3D.gz">250s</a></li> <li>Higher Obit Arrivals - L3 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.250s.REM3D.gz">250s</a>; L4 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.250s.REM3D.gz">250s</a>; L5 at at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.250s.REM3D.gz">250s</a>.</li> </ul> </li> <li>I<sup>st</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.175s.REM3D.gz">175s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.200s.REM3D.gz">200s</a></li> <li>II<sup>nd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.125s.REM3D.gz">125s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.150s.REM3D.gz">150s</a></li> <li>III<sup>rd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.60s.REM3D.gz">60s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.75s.REM3D.gz">75s</a></li> <li>IV<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.50s.REM3D.gz">50s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.60s.REM3D.gz">60s</a></li> <li>V<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.50s.REM3D.gz">50s</a></li> </ul> </li> <li><strong>Rayleigh waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Rayl.data.zip">Download All Periods and Branches as Summary.SW.Rayl.data.zip</a>) <ul> <li>Fundamental Modes <ul> <li>Minor Arc Arrivals (R1) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.25s.REM3D.gz">25s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.27s.REM3D.gz">27s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.30s.REM3D.gz">30s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.32s.REM3D.gz">32s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.35s.REM3D.gz">35s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.250s.REM3D.gz">250s</a></li> <li>Major Arc Arrivals (R2) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.250s.REM3D.gz">250s</a></li> <li>Higher Obit Arrivals - R3 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.250s.REM3D.gz">250s</a>; R4 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.250s.REM3D.gz">250s</a>; R5 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.250s.REM3D.gz">250s</a>.</li> </ul> </li> <li>I<sup>st</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.175s.REM3D.gz">175s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.200s.REM3D.gz">200s</a></li> <li>II<sup>nd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.125s.REM3D.gz">125s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.150s.REM3D.gz">150s</a></li> <li>III<sup>rd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.60s.REM3D.gz">60s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.75s.REM3D.gz">75s</a></li> <li>IV<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.50s.REM3D.gz">50s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.60s.REM3D.gz">60s</a></li> <li>V<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.50s.REM3D.gz">50s</a></li> <li>VI<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.50s.REM3D.gz">50s</a></li> </ul> </li> </ul> <p><strong>Other Data Products:</strong></p> <ul> </ul> <ul> <li><strong>ReferenceSW_Moulik2022_Figures(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/ReferenceSW_Moulik2022_Figures.zip">.zip</a> or <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/ReferenceSW_Moulik2022_Figures.pdf">.pdf</a>)</strong> - contains all figures from the paper in .png format</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Scatter_Plots.zip"><strong>Scatter_Plots.zip</strong></a> - contains scatter plots similar to Figure 5 in the paper, which compares measurements between two sets of techniques. The files with the suffix *raw.png are comparisons for original raw datasets, while those with the suffix *.clean.png are comparisons after the entire workflow is completed to create the clean datasets (e.g. Figure 13, bottom row).</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Half_cycle.zip"><strong>Half_cycle.zip</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cycle_skips.zip"><strong>Cycle_skips.zip</strong></a> - contains list of source-station paths where discrepancies were found between pairs of techniques. Half (±0.9–1.1 · π ) or full-cycle discrepancies (±0.9–1.1 · 2π ) identified in Section 4.5 are used during outlier analysis (Section 5.3) to create the clean summary dataset. Half- and full-cycle discrepancies identified in these files indicate potential polarity reversals and cycle skips respectively. Note that all of these discrepancies have not been checked for specific causes manually. </li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/vflip-table.REM3D"><strong>vflip-table.REM3D</strong></a> - an ASCII file containing station names and start/end times where polarity reversal issues have been confirmed through manual analysis. This is in contrast to the automated half-cycle discrepancies identified in <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Half_cycle.zip"><strong>Half_cycle.zip</strong></a> above.</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/M1442"><strong>M1442</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/B2562"><strong>B2562</strong></a> - Files containing the knot locations of evenly-spaced points on the surface. B2562 has an average knot spacing of 4.33◦ and is used as the underlying grid for the homogenization process to get summary data (Section 5.1). In order to obtain 2-D variations in local phase slowness or velocity, we use 1442 splines with an average knot spacing of 5.77◦ (Section 6.1)</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Love.data.h5"><strong>Cleanhomo.SW.Love.data.h5</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Rayl.data.h5"><strong>Cleanhomo.SW.Rayl.data.h5</strong></a> - Clean homogenized data for each research group obtained at the end of our workflow (Figure 2). The ASCII files containing the same data are provided in <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Love.data.zip"><strong>Cleanhomo.SW.Love.data.zip</strong></a> and <strong><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Rayl.data.zip">Cleanhomo.SW.Rayl.data.zip</a>. </strong>The summary dataset listed earlier represents the reconciled measurements, and should be preferred over those from individual groups in most applications.</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Inversion_Example.zip"><strong>Inversion_Example.zip</strong></a> - Contains an example of a 2D slowness map inversion with 2ζ azimuthal variations using the reference summary dataset at 100s for fundamental-mode minor-arc Rayleigh waves (R1). Also provided are plots for anistropic variation (<em>Anisotropy_Plots</em>), spline coeffients of 1442 evenly-spaced spherical splines (<em>Spline_Coefficients</em>), and corresponding values at every 1X1 degree pixel in extended pixel format (<em>Maps_epix</em>). The aim of this study is to provide dispersion measurements of surface-wave arrivals, not to provide detailed 2D phase velocity/slowness models. </li> </ul> <p><strong>Note about Data Format</strong></p> <p>The underlying philosophy and format of data files are discussed in the <a href="https://globalseismology.princeton.edu/rsdf">reference seismic data format (RSDF) project</a>. Table A1 from the GJI paper describes the various columns in the surface-wave RSDF format files above.</p>
Prediction of Targeted Therapy Efficacy in EGFR-mutant Lung Cancer Patients Using AI-based Multimodal Data
ClinicalTrials.gov study NCT07287904. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Data from: Mate choice in the eye and ear of the beholder? Female multimodal sensory configuration influences her preferences
Open the record for dataset details and reuse information.
Data from: Wind- and rain-induced vibrations impose different selection pressures on multimodal signaling
Open the record for dataset details and reuse information.
Loneliness and well-being in Finnish immigrants: A multimodal dataset from wearables and passive data collection
Open the record for dataset details and reuse information.
Data from: In situ modeling of multimodal floral cues attracting wild pollinators across environments
Open the record for dataset details and reuse information.
Data from: Simultaneous pulsed flowering in a temperate legume: causes and consequences of multimodality in the shape of floral display schedules
Open the record for dataset details and reuse information.
Data from: A multimodal image guiding system for Navigated Ultrasound Bronchoscopy (EBUS): a human feasibility study
Background: Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) is the endoscopic method of choice for confirming lung cancer metastasis to mediastinal lymph nodes. Precision is crucial for correct staging and clinical decision-making. Navigation and multimodal imaging can potentially improve EBUS-TBNA efficiency. Aims: To demonstrate the feasibility of a multimodal image guiding system using electromagnetic navigation for ultrasound bronchoschopy in humans. Methods: Four patients referred for lung cancer diagnosis and staging with EBUS-TBNA were enrolled in the study. Target lymph nodes were predefined from the preoperative computed tomography (CT) images. A prototype convex probe ultrasound bronchoscope with an attached sensor for position tracking was used for EBUS-TBNA. Electromagnetic tracking of the ultrasound bronchoscope and ultrasound images allowed fusion of preoperative CT and intraoperative ultrasound in the navigation software. Navigated EBUS-TBNA was used to guide target lymph node localization and sampling. Navigation system accuracy was calculated, measured by the deviation between lymph node position in ultrasound and CT in three planes. Procedure time, diagnostic yield and adverse events were recorded. Results: Preoperative CT and real-time ultrasound images were successfully fused and displayed in the navigation software during the procedures. Overall navigation accuracy (11 measurements) was 10.0 ± 3.8 mm, maximum 17.6 mm, minimum 4.5 mm. An adequate sample was obtained in 6/6 (100%) of targeted lymph nodes. No adverse events were registered. Conclusions: Electromagnetic navigated EBUS-TBNA was feasible, safe and easy in this human pilot study. The clinical usefulness was clearly demonstrated. Fusion of real-time ultrasound, preoperative CT and electromagnetic navigational bronchoscopy provided a controlled guiding to level of target, intraoperative overview and procedure documentation.
Data from: Multimodal signals: ultraviolet reflectance and chemical cues in stomatopod agonistic encounters
Complex signals are commonly used during intraspecific contests over resources to assess an opponent's fighting ability and/or aggressive state. Stomatopod crustaceans may use complex signals when competing aggressively for refuges. Before physical attacks, stomatopods assess their opponents using chemical cues and perform threat displays showing a coloured patch, the meral spot. In some species, this spot reflects UV. However, despite their complex visual system with up to 20 photoreceptor classes, we do not know if stomatopods use chromatic or achromatic signals in contests. In a field study, we found that Neogonodactylus oerstedii meral spot luminance varies with sex, habitat and, more weakly, body length. Next, we conducted an experimental manipulation which demonstrated that both chemical cues and chromatic signals are used during contests. In the absence of chemical cues, stomatopods approached an occupied refuge more quickly and performed offensive behaviours at a lower rate. When UV reflectance was absent, stomatopods performed offensive behaviours more frequently and contest duration trended towards shorter fights. These results provide new evidence that UV reflectance and/or visible spectrum luminance is used to amplify threat displays. Our results are the first to demonstrate that chemical and chromatic cues comprise a multimodal signal in stomatopod contests.
Data from: Beyond magic traits: multimodal mating cues in Heliconius butterflies
Species coexistence involves the evolution of reproductive barriers opposing gene flow. Heliconius butterflies display colorful patterns affecting mate choice and survival through warning signaling and mimicry. These patterns are called "magic traits" for speciation because divergent natural selection may promote mimicry shifts in pattern whose role as mating cue facilitates reproductive isolation. By contrast, between comimetic species, natural selection promotes pattern convergence. We addressed whether visual convergence interferes with reproductive isolation by testing for sexual isolation between two closely related species with similar patterns, H. timareta thelxinoe and H. melpomene amaryllis. Experiments with models confirmed visual attraction based on wing phenotype, leading to indiscriminate approach. Nevertheless, mate choice experiments showed assortative mating. Monitoring male behavior toward live females revealed asymmetry in male preference, H. melpomene males courting both species equally while H. timareta males strongly preferred conspecifics. Experiments with hybrid males suggested an important genetic component for such asymmetry. Behavioral observations support a key role for short-distance cues in determining male choice in H. timareta. Scents extracts from wings and genitalia revealed interspecific divergence in chemical signatures, and hybrid female scent composition was significantly associated with courtship intensity by H. timareta males, providing candidate chemical mating cues involved in sexual isolation.
Data for: Meningioangiomatosis- Multimodal analysis and insights from a systematic review
<p class="CxSpFirst"><span><span><span><span><span><span><span><span><span><span><span><b>Background:</b>Meningioangiomatosis is a poorly studied, rare, benign, and epileptogenic brain lesion.</span></span></span></span></span></span></span></span></span></span></span></p> <p class="CxSpMiddle"><span><span><span><span><span><span><span><span><span><span><span><b>Objective: </b><span>To demonstrate that surgical resection and a short time interval to surgery improves epileptic seizure control, we </span>performed a systematic review and meta-analysis of meningioangiomatosis cases<span>.</span></span></span></span></span></span></span></span></span></span></span></span></p> <p class="CxSpMiddle"><span><span><span><span><span><span><span><span><span><span><span><b>Methods: </b>Using PRISMA-IPD guidelines, the authors performed a systematic review and meta-analysis of histopathologically proven meningioangiomatosis cases.Literature search in the French and English languages (PubMed, Embase, the Cochrane Library and the Science Citation Index) including all studies (January 1981 to June 2020)dealing with histopathologically proven meningioangiomatosis, without age restriction.We assessed clinical, imaging, histomolecular, management, and outcome findings of meningioangiomatosis patients.</span></span></span></span></span></span></span></span></span></span></span></p> <p class="CxSpMiddle"><span><span><span><span><span><span><span><span><span><span><span><b>Results: </b>Two-hundred and seven cases of meningioangiomatosis from 78 studies were included.The majority of meningioangiomatosis was sporadic, preferentially concerned male patients, younger than 20-year-old, and allowed a functionally independent status. Epileptic seizure was the main symptom, with 81.4%of patients having uncontrolled seizures at the time of surgery. Meningioangiomatosis mainly had frontal (32.3%) or temporal (30.7%) locations. Imaging presentation was heterogeneous, and the diagnosis was often missed pre-operatively. The histopathologic pattern was similar whatever the clinical presentation, and immunohistochemistry had limited diagnostic value. On molecular analysis, allelic loss at 22q12 was more frequent in samples of meningioangiomatosis-associated meningioma (37.5%) than in isolated meningioangiomatosis (23.1%). Time interval from diagnosis to surgery (p=0.011) and lack of surgical resection of the meningioangiomatosis (p=0.009) were independent predictors of post-operative seizure control.</span></span></span></span></span></span></span></span></span></span></span></p> <p class="CxSpMiddle"><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions: </b>Due to low scientific evidence, a multicentric prospective study should help refining the management of meningioangiomatosis.</span></span></span></span></span></span></span></span></span></span></span></p> <p><br> </p>
MOJITOO: a fast and universal method for integration of multimodal single cell data
<p>MOJITOO benchmarking seurat Robjects.</p>
Automated electrosynthesis reaction mining with multimodal large language models - raw data and prompts
<p>Compilation of the prompts used, together with the raw response files from the tested MLMM models. </p>
Raw data for "Multimodal imaging of cubic Cu2O@Au nanocage formation via galvanic replacement using X-ray ptychography and nano diffraction"
<p><strong>Raw data for "Multimodal imaging of cubic Cu2O@Au nanocage formation via galvanic replacement using X-ray ptychography and nano diffraction"</strong></p> <p>The file "raw_data_ptychography_waxs.zip" contains one HDF5 archive for each scan. The archives are structured as follows:</p> <ul> <li>section experiment: <ul> <li>identifiers of the lightsource, beamline, beamtime, session number, and scan number</li> </ul> </li> <li>section measured: <ul> <li>N diffraction patterns of size 512x512 px used for ptychography</li> <li>N WAXS patterns of size 514x1030 px</li> <li>N scan positions in mm</li> <li>one detector mask of size 512x512 px used for ptychography</li> <li>one detector mask of size 514x1030 px used for WAXS</li> <li>slice separation in mm for multi slice reconstruction</li> </ul> </li> <li>section parameters: <ul> <li>distance between sample and forward detector (ptychography) in mm</li> <li>pixel size of forward detector (ptychography) in mm</li> <li>photon energy in keV</li> <li>cropping of diffraction patterns in px used for ptychographic reconstruction</li> </ul> </li> </ul> <p>The following lists show the scan numbers with their corresponding reaction times and slice separations for the in situ series recorded during growth of Cu<sub>2</sub>O nanocubes, as well as galvanic replacement with Au measured out of focus and in focus.</p> <p>Growth of Cu<sub>2</sub>O nanocubes:</p> <table> <tbody> <tr> <td><strong>scan number</strong></td> <td><strong>slice distance, mm</strong></td> <td><strong>reaction time, h</strong></td> </tr> <tr> <td>179</td> <td>1</td> <td>1.58</td> </tr> <tr> <td>185</td> <td>1</td> <td>3.59</td> </tr> <tr> <td>191</td> <td>1</td> <td>4.78</td> </tr> <tr> <td>192</td> <td>1</td> <td>5.21</td> </tr> <tr> <td>193</td> <td>1</td> <td>5.64</td> </tr> <tr> <td>194</td> <td>1</td> <td>6.08</td> </tr> <tr> <td>195</td> <td>1</td> <td>6.51</td> </tr> <tr> <td>196</td> <td>1</td> <td>6.94</td> </tr> <tr> <td>197</td> <td>1</td> <td>7.37</td> </tr> <tr> <td>198</td> <td>1</td> <td>7.81</td> </tr> <tr> <td>199</td> <td>1</td> <td>8.24</td> </tr> <tr> <td>200</td> <td>1</td> <td>8.67</td> </tr> <tr> <td>201</td> <td>1</td> <td>9.10</td> </tr> <tr> <td>202</td> <td>1</td> <td>9.53</td> </tr> <tr> <td>203</td> <td>1</td> <td>9.97</td> </tr> <tr> <td>204</td> <td>1</td> <td>10.41</td> </tr> <tr> <td>205</td> <td>1</td> <td>10.86</td> </tr> <tr> <td>207</td> <td>0.96</td> <td>11.53</td> </tr> <tr> <td>208</td> <td>0.94</td> <td>11.96</td> </tr> <tr> <td>209</td> <td>0.92</td> <td>12.41</td> </tr> <tr> <td>210</td> <td>0.9</td> <td>12.85</td> </tr> <tr> <td>211</td> <td>0.88</td> <td>13.29</td> </tr> <tr> <td>212</td> <td>0.86</td> <td>13.74</td> </tr> <tr> <td>213</td> <td>0.84</td> <td>14.19</td> </tr> <tr> <td>215</td> <td>0.8</td> <td>15.07</td> </tr> <tr> <td>216</td> <td>0.78</td> <td>15.50</td> </tr> <tr> <td>218</td> <td>0.74</td> <td>16.06</td> </tr> <tr> <td>219</td> <td>0.72</td> <td>16.50</td> </tr> <tr> <td>220</td> <td>0.7</td> <td>16.82</td> </tr> <tr> <td>221</td> <td>0.68</td> <td>17.08</td> </tr> <tr> <td>223</td> <td>0.64</td> <td>17.79</td> </tr> <tr> <td>225</td> <td>0.6</td> <td>18.53</td> </tr> </tbody> </table> <p>Galvanic replacement with Au measured out of focus:</p> <table> <tbody> <tr> <td><strong>scan number</strong></td> <td><strong>slice distance, mm</strong></td> <td><strong>reaction time, h</strong></td> </tr> <tr> <td>263</td> <td>1</td> <td>-0.53</td> </tr> <tr> <td>265</td> <td>1</td> <td>0.13</td> </tr> <tr> <td>266</td> <td>1</td> <td>0.38</td> </tr> <tr> <td>267</td> <td>1</td> <td>0.63</td> </tr> <tr> <td>268</td> <td>1</td> <td>0.89</td> </tr> <tr> <td>269</td> <td>1</td> <td>1.14</td> </tr> <tr> <td>270</td> <td>1</td> <td>1.40</td> </tr> <tr> <td>271</td> <td>1</td> <td>1.64</td> </tr> <tr> <td>272</td> <td>1</td> <td>1.90</td> </tr> <tr> <td>273</td> <td>1</td> <td>2.14</td> </tr> <tr> <td>274</td> <td>1</td> <td>2.39</td> </tr> <tr> <td>275</td> <td>1</td> <td>2.63</td> </tr> <tr> <td>276</td> <td>1</td> <td>2.87</td> </tr> <tr> <td>277</td> <td>1</td> <td>3.11</td> </tr> <tr> <td>278</td> <td>1</td> <td>3.35</td> </tr> <tr> <td>279</td> <td>1</td> <td>3.60</td> </tr> <tr> <td>280</td> <td>1</td> <td>3.84</td> </tr> <tr> <td>281</td> <td>1</td> <td>4.08</td> </tr> <tr> <td>282</td> <td>1</td> <td>4.32</td> </tr> <tr> <td>283</td> <td>1</td> <td>4.74</td> </tr> <tr> <td>284</td> <td>1</td> <td>5.15</td> </tr> <tr> <td>286</td> <td>1</td> <td>5.59</td> </tr> <tr> <td>287</td> <td>1</td> <td>6.01</td> </tr> <tr> <td>288</td> <td>1</td> <td>6.35</td> </tr> <tr> <td>289</td> <td>1</td> <td>6.74</td> </tr> <tr> <td>290</td> <td>1</td> <td>7.15</td> </tr> <tr> <td>291</td> <td>1</td> <td>7.55</td> </tr> <tr> <td>292</td> <td>1</td> <td>7.94</td> </tr> <tr> <td>293</td> <td>1</td> <td>8.35</td> </tr> <tr> <td>294</td> <td>1</td> <td>8.75</td> </tr> <tr> <td>295</td> <td>1</td> <td>9.16</td> </tr> <tr> <td>296</td> <td>1</td> <td>9.56</td> </tr> <tr> <td>297</td> <td>1</td> <td>9.96</td> </tr> </tbody> </table> <p>Galvanic replacement with Au measured in focus:</p> <table> <tbody> <tr> <td><strong>scan number</strong></td> <td><strong>slice distance, mm</strong></td> <td><strong>reaction time, h</strong></td> </tr> <tr> <td>117</td> <td>1</td> <td>0.33</td> </tr> <tr> <td>118</td> <td>1</td> <td>0.93</td> </tr> <tr> <td>119</td> <td>1</td> <td>1.51</td> </tr> <tr> <td>120</td> <td>1</td> <td>2.08</td> </tr> <tr> <td>121</td> <td>1</td> <td>2.66</td> </tr> <tr> <td>122</td> <td>1</td> <td>3.24</td> </tr> <tr> <td>123</td> <td>1</td> <td>3.87</td> </tr> <tr> <td>124</td> <td>1</td> <td>4.44</td> </tr> <tr> <td>125</td> <td>1</td> <td>5.02</td> </tr> <tr> <td>126</td> <td>1</td> <td>5.61</td> </tr> <tr> <td>127</td> <td>1</td> <td>6.19</td> </tr> <tr> <td>128</td> <td>1</td> <td>6.77</td> </tr> <tr> <td>129</td> <td>1</td> <td>7.35</td> </tr> <tr> <td>130</td> <td>1</td> <td>7.93</td> </tr> <tr> <td>131</td> <td>1</td> <td>8.50</td> </tr> </tbody> </table> <p>The files "waxs_detector_calibration_cu2o_growth.poni" and "waxs_detector_calibration_au_galvanic_replacement.poni" contain the PONI data to be used for azimuthal integration of WAXS patterns using the pyFAI library.</p> <p><strong>Ptychographic reconstructions</strong></p> <p>The file "ptychographic_reconstructions.zip" contains the ptychographic reconstructions shown in the article and supplementary information in tiff format.</p> <p>Stacks of images corresponding to time series:</p> <ul> <li>Figure 1b, 2: P06_Cu2O_growth_scans_00179-00225_entrance_window.tif</li> <li>Figure 1b, 2: P06_Cu2O_growth_scans_00179-00225_exit_window.tif</li> <li>Figure 1d, 4, 5: P06_Au_galvanic_replacement_de-focus_scans_00263-00297_exit_window.tif</li> <li>Figure 5c: P06_Au_galvanic_replacement_in-focus_scans_00117-00131_exit_window.tif</li> </ul> <p><strong>SEM and EDX</strong></p> <p>The file "SEM_EDX.zip" contains the SEM images and EDX maps shown in Figure 3 in png format. Subfolders indicate the reaction time.</p>
Deep Learning With MRI-based Multimodal-data Fusion Enhanced Postoperative Risk Stratification of Breast Cancer
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