Find research datasets worth reusing

CAIRT FL2S Results of Case Study Scenario 7 (CSS7) for Southern Hemisphere Winter

<p>Results of the fast level-2 simulator (FL2S) of CAIRT developed within the Earth Explorer 11 Phase 0 Science and Requirements Consolidation Study (SciReC) – CAIRT. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak'), additional application of noise ('_aknoi'), application of systematic uncertainties ('_sys'), and application of all effects ('_aknoisys'). Further information is available from the authors.</p>

CAIRT FL2S Results of Case Study Scenario 3 (CSS3) for Biomass Burning

Survey Results - User Accuracy Effects on Algorithmic Accuracy

<p>The survey was hosted on Qualtrics and participants recruited via Cloud Research. Participants are US-only. The data includes those who did not finish. No PII data was collected. </p><p>The survey included a deception scenario for a mortgage application followed by a battery of questions to assess ratings of the algorithm, assess participant honesty, and assess algorithmic awareness.</p>

CAIRT/IASI-NG/CAIRT+IASI-NG FL2S results of Case Study Scenario 4 for limb-nadir application

<p>Results of the fast level-2 simulator (FL2S) for Case Study Scenarios 4 (only SO2 files) for limb-nadir application. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak') and additional application of noise ('_aknoi') for CAIRT alone, IASI-NG alone, and the combined product CAIRT+IASI-NG.</p>

CAIRT FL2S Results of Case Study Scenario 6 (CSS6) for Auroral NO

<p>Corrected version. Results of the fast level-2 simulator (FL2S) of CAIRT developed within the Earth Explorer 11 Phase 0 Science and Requirements Consolidation Study (SciReC) – CAIRT. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak'), additional application of noise ('_aknoi'), application of systematic uncertainties ('_sys'), and application of all effects ('_aknoisys'). Further information is available from the authors. </p>

Preliminary Mitosis Detection Results for TCGA-BRCA Dataset

<p>This dataset provides mitosis detection results employing the "Mitosis Detection, Fast and Slow" (MDFS) algorithm [<a href="https://arxiv.org/abs/2208.12587">[2208.12587] Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures (arxiv.org)</a>] on the TCGA-BRCA dataset. </p> <p>The MDFS algorithm exemplifies a robust and efficient two-stage process for mitosis detection. Initially, potential mitotic figures are identified and later refined. The proposed model for the preliminary identification of candidates, the EUNet, stands out for its swift and accurate performance, largely due to its structural design. EUNet operates by outlining candidate areas at a lower resolution, significantly expediting the detection process. In the second phase, the initially identified candidates undergo further refinement using a more intricate classifier network, namely the EfficientNet-B7. The MDFS algorithm was originally developed for the MIDOG challenges.</p> <p> </p> <h2>Viewing in QuPath</h2> <p>The dataset at hand comprises GeoJSON files in two categories: mitosis and proxy (mimicker -- the candidates that are unlikely to be mitosis based on our algorithm). Users can open and visualize each category overlaid on the Whole Slide Image (WSI) using QuPath. Simply drag and drop the annotation file onto the opened image in the program. Additionally, users can employ the provided Python snippet to read the annotation into a Python dictionary or a Numpy array.</p> <p> </p> <h2>Loading in Python</h2> <p>To load the GeoJSON files in Python, users can use the following code:</p> <blockquote> <p>import json</p> <p>import numpy as np</p> <p>import pandas as pd</p> <p> </p> <p>def load_geojson(filename):</p> <p>  # Load the GeoJSON file</p> <p>  with open(filename, 'r') as f:</p> <p>      data = json.load(f)</p> <p> </p> <p>  # Extract the properties and store in a dictionary</p> <p>  slide_properties = data["properties"]</p> <p> </p> <p>  # Convert the points to a numpy array</p> <p>  points_np = np.array([(feat['geometry']['coordinates'][0], feat['geometry']['coordinates'][1], feat['properties']['score']) for feat in data['features']])</p> <p> </p> <p>  # Convert the points to a pandas DataFrame</p> <p>  points_df = pd.DataFrame(points_np, columns=['x', 'y', 'score'])</p> <p> </p> <p>  return slide_properties, points_np, points_df</p> <p> </p> <p> </p> <p># Use the function to load mitosis data</p> <p>mitosis_properties, mitosis_points_np, mitosis_points_df = load_geojson('mitosis.geojson')</p> <p> </p> <p># Use the function to load mimickers data</p> <p>mimickers_properties, mimickers_points_np, mimickers_points_df = load_geojson('mimickers.geojson')</p> </blockquote> <h2>Properties</h2> <p>Each WSI in the dataset includes the candidate's centroid, bounding box, hotspot location, hotspot mitotic count, and hotspot mitotic score. The structures of the mitosis and mimicker property dictionaries are as follows:</p> <p>Mitosis property dictionary structure:</p> <blockquote> <p>mitosis_properties = {</p> <p>   'slide_id': slide_id,</p> <p>   'slide_height': img_h,</p> <p>   'slide_width': img_w,</p> <p>   'wsi_mitosis_count': num_mitosis,</p> <p>   'mitosis_threshold': 0.5,</p> <p>   'hotspot_rect': {'x1': hotspot[0], 'y1': hotspot[1], 'x2': hotspot[2], 'y2': hotspot[3]},</p> <p>   'hotspot_mitosis_count': mitosis_count,</p> <p>   'hotspot_mitosis_score': mitosis_score,</p> <p>}</p> </blockquote> <p> </p> <p>Proxy figure (mimicker) property dictionary structure:</p> <blockquote> <p>mimicker_properties = {</p> <p>   'slide_id': slide_id,</p> <p>   'slide_height': img_h,</p> <p>   'slide_width': img_w,</p> <p>   'wsi_mimicker_count': num_mimicker,</p> <p>   'mitosis_threshold': 0.5,</p> <p>}</p> </blockquote> <h2><br>Disclaimer:</h2> <p>It should be noted that we did not conduct a comprehensive review of all mitotic figures within each WSI, and we do not purport these to be free of errors. Nonetheless, a pathologist examined the resultant hotspot regions of interest from 757 WSIs within the TCGA-BRCA Mitosis Dataset where we found strong correlations between pathologist and MDFS mitotic counts  (r=0.8, p$<$0.001). Furthermore, MDFS-derived mitosis scores are shown to be as prognostic as pathologist-assigned mitosis scores [1]. This examination was also aimed at verifying the quality of the selections, ensuring excessive false detections or artifacts did not primarily drive them and were in a plausible location in the tumor landscape.</p> <p> </p> <p>[1] Ibrahim, Asmaa, et al. "Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application." <em>Modern Pathology</em> 37.3 (2024): 100416.</p>

opencc-by-4.0Dec 2023View details →

CTD+ hydrographic measurement results from Admiralty Bay, Antarctica from February 2022 to February 2023

<p>The dataset contains CTD+ measurement results from Admiralty Bay on King George Island. It consists of data on conductivity, salinity, temperature, pH, turbidity, optical dissolved oxygen (ODO), fluorescent Dissolved Organic Matter (fDOM), chlorophyll A, and Phycoerythrin, measured from February 2022 to February 2023.</p><p>This dataset is a continuation of a larger measurement campaign described in: </p><p>Osińska, M., Wójcik-Długoborska, K. A., & Bialik, R. J. (2023). Annual hydrographic variability in Antarctic coastal waters infused with glacial inflow. <i>Earth System Science Data</i>, <i>15</i>(2). https://doi.org/10.5194/essd-15-607-2023</p><p>which can be found at:</p><p>Osińska, M., Wójcik-Długoborska, K. A., & Bialik, R. J. (2022). Water conductivity, salinity, temperature, turbidity, pH, fluorescent dissolved organic matter (fDOM), optical dissolved oxygen (ODO), chlorophyll a and phycoerythrin measurements in Admiralty Bay, King George Island, from Dec 2018 to Jan 2022. <i>PANGAEA</i>. https://doi.org/https://doi.org/10.1594/PANGAEA.947909</p>

opencc-by-4.0Sep 2023View details →

FoldingDiff generated structures (n=780, main results) and associated metadata

<p>Backbone structures generated by FoldingDiff spanning lengths [50, 128). Each length has 10 randomly sampled structures for a total of 780 backbone structures. These were used to derive all results in our manuscript's main results section. In addition to structures in .pse format, we provide an excel table with the following sheets:</p><ul><li>Table containing metadata for each of the aforementioned generated structures. Metadata includes scTM designability scores using ProteinMPNN + OmegaFold and using ProteinMPNN + AlphaFold2, maximum training set TM score (similarity), structure length, and number of sheets/helices present as annotated by P-SEA.</li><li>Table containing Gauss integral embeddings for each of the 780 backbones generated by FoldingDiff</li><li>Table containing Gauss integral embeddings for select test set structures between 50 and 128 residues in length. These were used to compare and contextualize structures/embeddings from FoldingDiff.</li></ul>

opencc-by-4.0Jun 2023View details →

Source code and simulation results: Poles and zeros of electromagnetic quantities in photonic systems

<h4><strong>Summary</strong></h4> <p>This publication supplements the article "Poles and zeros of electromagnetic quantities in photonic systems" with tabulated data and matlab code that allows to reproduce the results. The article elaborates how evaluating resonances based on contour integrals of scalar electromagnetic quantities extends to computing zeros. Furthermore, direct differentiation of underlying scattering problems is used to compute sensitivities with respect to design parameters.</p> <h4><strong>Structure</strong></h4> <p>The script 'main_text.m' can be used to reproduce the results provided in the paper. In tabulated form the results are contained in the directory <strong>tabulated</strong>. Furthermore, the script 'supplement.m'  can be used to reproduce results presented in the supplement. The directory <strong>RPExpand </strong>contains the software RPExpand v2, which is available on <a href="https://doi.org/10.5281/zenodo.10371002">Zenodo</a> with additional examples. </p> <h4><strong>Compute residues</strong></h4> <p>The modal expansion of the Fourier transform is based on its residues at the dominant resonances. If the poles are simple, which often is the case, the residues can be obtained directly from the eigenvectors of the generalized eigenvalue problem used to obtain the poles or the zeros. Introducing the Vandermonde matrix</p> <p>$V = \begin{bmatrix} 1 & \dots & 1 \\ w_1 & \dots & w_M \\ \vdots & & \vdots \\ w_1^{M-1} &\dots & w_M^{M-1} \end{bmatrix}$,</p> <p>the Hankel matrix $H$ can be written as $H = V A V^T$ with $A$ being the diagonal matrix $\mathrm{diag}(a_1,\dots,a_M)$ containing the residues $a_m $. This decomposition is a consequence of the Cauchy's reisdue theorem if the poles are simple. Furthermore, we now that $V^{-T}$ solves the generalized eigenproblem $H^<X = HX\Omega$ (Eq. 2 in the original paper) and hence the eigenvectors we get from Matlabs eig routine are $X = V^{-T}D$ where $D$ is some scaling. It follows that we obtain the residues using $A = X^T H X (X V^T)^{-2}$</p> <h4><strong>Derivatives</strong></h4> <p>Similarly, our framework provides a straight forward approach to the derivatives of zeros and poles if they are simple. Using direct differentiation we have access to partial derivatives of the quantity $q(\omega)$ and hence the derivatives of the moments $s_k = \frac{1}{2\pi i} \oint_C \omega^k q(\omega) \mathrm{d}\omega$. For the zeros the inverse $1/q(\omega)$ and the respective derivative are considered. Using Cauchy's residue theorem the derivatives $\frac{\partial w_m}{\partial p}$are solutions of the linear system of equations  $\frac{\partial s_k}{\partial p} = \sum_{m = 1}^{M}\left[k\omega_m^{k-1}\frac{\partial w_m}{\partial p} a_m + \omega_m^k\frac{\partial a_m}{\partial p} \right]$.</p> <h4><strong>Higher order singularities</strong></h4> <p>Finding higher order poles and zeros is possible without further adaptation. Computing derivatives and residues requires some special care. The moments are then given by $s_k = \sum_{m=1}^{M} \sum_{n = 1}^{N_m} a_{m,n} \frac{k! \, \omega^{k-n+1}}{(k-n+1)!(n-1)!}$with $a_{m,n}$ being the residue of the pole $\omega_m$ and $n $ refers to the order. Accordingly expressions for the derivatives are available.</p> <h4><strong>Error estimates</strong></h4> <p>The estimated errors in Table 1 refer to the number of integration points, i.e. we are interested in the question how close we get with a given number of integration points to the exact solution of the chosen approximate model of the physical system. Due to propagation of the error the convergence of the derivatives is shifted towards a larger number of integration points.</p> <h4><strong>Requirements</strong></h4> <ul> <li>JCMsuite (version 5.4.3 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holder in 'zeros_poles.m' by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <h4><strong>References</strong></h4> <p>[1] Felix Binkowski, Fridtjof Betz, Rémi Colom, Patrice Genevet, Sven Burger, Poles and zeros of electromagnetic quantities in photonic systems, https://doi.org/10.48550/arXiv.2307.04654</p> <p>[2] Anthony P. Austin, Peter Kravanja, Lloyd N. Trefethen, Numerical algorithms based on analytic function values at roots of unity, SIAM Journal of Numerical Analysis 52, 1795 (2014), https://doi.org/10.1137/130931035</p> <p>[3] Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Philipp-Immanuel Schneider, Lin Zschiedrich, Sven Burger, Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators, Communications Physics <strong>5</strong>, 202 (2022), https://doi.org/10.1038/s42005-022-00977-1</p>

opencc-by-4.0Jul 2023View details →

Stellar properties of observed stars stripped in binaries in the Magellanic Clouds - Observations and Results

<p>This Zenodo repository is one of three Zenodo repositories related to the article "Stellar properties of observed stars stripped in binaries in the Magellanic Clouds" by Y. Götberg, M.R. Drout, A.P. Ji, J.H. Groh, B.A. Ludwig, P.A. Crowther, N. Smith, A. de Koter, and S.E. de Mink. In the article, we analyze the optical spectra of ten stars and measure their stellar properties using spectral fitting. This repository contains observational data and the resulting measurements for the stellar properties of the stars analyzed in the article, along with best fit spectral models and spectral models used to estimate the wind mass loss rate of the stars. Below, we describe the content in more detail:</p> <ul> <li><strong>0_ReadMe.txt</strong>: A text file where we describe some more details regarding the content.</li> <li><strong>S2_stacked_stellar_spectra.tar.gz (4.6 MB):</strong> The spectra we use for obtaining stellar properties for the observed stars.</li> <li><strong>Table2.txt</strong>: the apparent AB magnitudes with associated errors for the stars we analyze in the paper.</li> <li><strong>Table3.txt</strong>: the stellar properties for the stars we analyze in the paper. These parameters are obtained by spectral fitting.</li> <li><strong>S5_spectra_best_fit_models.tar.gz (14 MB):</strong> The spectral energy distributions and normalized spectra for the best-fit models, recomputed such that the radius and bolometric luminosity also matches. This tarball contains best-fit spectra for all of the stars in text-files labeled with the format SED_StarX_xxx.txt (~70 MB when inflated). The stellar parameters are presented in Section 5.</li> <li><strong>S5_full_best_fit_models.tar.gz (1.9 GB):</strong> The full CMFGEN version of the best-fit models for each individual star. This tarball contains a tarball for each star, which when inflated becomes ~250-550 MB each.  </li> <li><strong>S7_spectra_mdot_models (31 MB): </strong>The spectral energy distributions and normalized spectra for the models used in the mass-loss analysis that we present in Section 7.</li> <li><strong>S7_full_mdot_models (3.5 GB):</strong> The full CMFGEN version of the spectral models used for the mass-loss rate variation presented in section 7 (excluding the best-fit models, which are provided separately).</li> </ul>

opencc-by-4.0Dec 2023View details →

Quantitative results of the analysis of relevant components of artificial bilayered substitutes developed by tissue engineering

<p>This dataset corresponds to the quantification results carried out for artificial bilayered substitutes developed by tissue engineering and control tissues analyzed in the manuscript entitled "<span>Spatiotemporal characterization of extracellular matrix maturation in human artificial stromal-epithelial tissue substitutes</span>". Tissue engineering techniques offer new strategies to understand complex processes in a controlled and reproducible system. In this study, we generated bilayered human tissue substitutes consisting of a cellular connective tissue with a suprajacent epithelium (full-thickness stromal-epithelial substitutes or SESS), and human tissue substitutes with an epithelial layer generated on top of an acellular biomaterial (epithelial substitutes or ESS). Both types of artificial tissues were studied at sequential time periods to analyze the maturation process of the extracellular matrix (ECM) using histochemical and immunohistochemical techniques. Results showed that both models were able to exhibit a partial development of the epithelial layer. ESS cells showed active proliferation, positive expression of KRT5 and low expression of differentiation markers, whereas SESS epithelium showed higher differentiation levels, with a progressive positive expression of KRT10 and claudin, although the differentiation levels of control native tissues were not reached. Despite the typical rete-ridges and papillae of native tissues were not found, stromal cells in SESS tended to accumulate and actively synthetize ECM components such as collagens and proteoglycans in the stromal area in direct contact with the epithelium (Z1 zone), whereas these components were very scarce in ESS. Regarding the basement membrane (BM), ESS showed a partially-differentiated structure containing fibronectin-1 (FN1) and perlecan (HSPG2), although the PAS staining signal was significantly lower than control native tissues. However, SESS showed higher BM differentiation, with positive expression of FN1, HSPG2, nidogen 1 (NID1), chondroitin-6-sulfate proteoglycans (CH6S), agrin (AGRN), and collagens types IV (COL-IV) and VII (COL-VII), although this structure was negative for lumican (LUM). These results confirm the relevance of epithelial-stromal interaction for ECM development and differentiation, especially regarding BM components, and suggest the usefulness of bilayered artificial tissue substitutes to reproduce ex vivo the ECM maturation and development process of human tissues. The original data obtained for the quantitative analyses of each component are shown in this dataset.</p> <p> </p>

opencc-by-4.0Dec 2023View details →

Wrist and Tibia/Shoe Mounted IMU Measurement Results for Gait Analysis

<p>This document describes a dataset of measurement results collected using wrist, tibia and shoe mounted inertial sensors. The main purpose of the dataset was to test signal translation algorithms converting signals registered using the wrist-worn sensor e.g. a smartwatch to signals which would be measured with a shoe or tibia - mounted device. The dataset includes tri-axial acceleration and angular velocity registered during several walks.</p> <p>More details are included in the dataset_description pdf file.</p> <p>When using the data, please consider also citing the original paper, for which it was collected:</p> <p>Kolakowski, M.; Djaja-Josko, V.; Kolakowski, J.; Cichocki, J. Wrist-to-Tibia/Shoe Inertial Measurement Results Translation Using Neural Networks. <em>Sensors</em> <strong>2024</strong>, <em>24</em>, 293. <a href="https://doi.org/10.3390/s24010293" target="_blank" rel="noopener">https://doi.org/10.3390/s24010293</a></p> <p><strong>The dataset will be gradually updated as new data are gathered.</strong></p>

dryad40/100

Subject-specific knee models, data, and results for specimen S192803

<p>This dataset is part of an ongoing manuscript to validate that sources of data from currently available in vivo methods are sufficient to create computational models of the knee compared with existing in vitro techniques. The data included in this repository is for the S192803 specimen of that dataset and includes experimental data, working models, code, and results obtained for that model and used in that manuscript.</p>

opencc-zeroJan 2024View details →

opencc-by-4.0May 2022View details →

Duferco forecast results using Kernel Ridge

<p>The data uploaded represent thirteen months of Duferco forecast (June 2021 - June 2022) using Kernel Ridge regression with hourly granularity for the Calabria wind farm.</p> <p>The three csv files represents the three different methods developed and tested during the project:</p> <ol> <li><strong>BL:</strong> Kernel Ridge regression with gaussian kernel (Baseline).</li> <li><strong>PC:</strong> The Baseline method with the pre-processing of the data using the power curve.</li> <li><strong>PC+K: </strong>The Baseline method with the pre-processing of the data using the power curve, and the post-processing of the forecast using Kalman smoothing filter.</li> </ol> <p> </p>

opencc-by-4.0Feb 2024View details →

Metadata Quality Assessment Results of data.europa.eu in November 2023

<h2>Description</h2> <p>Metadata Quality Assessment Results of data.europa.eu in November 2023 to evaluate the quality of the datasets harvested by the European portal in terms of FAIR principles</p> <h2>Paper</h2> <ul> <li>Paper: <a href="https://arxiv.org/abs/2402.06693">Fostering the integration of European Open Data into Data Spaces through High-Quality Metadata</a></li> <li>Cite:</li> </ul> <p><code>@misc{conde2024fostering,</code><br><code>      title={Fostering the integration of European Open Data into Data Spaces through High-Quality Metadata}, </code><br><code>      author={Javier Conde and Alejandro Pozo and Andrés Munoz-Arcentales and Johnny Choque and Álvaro Alonso},</code><br><code>      year={2024},</code><br><code>      eprint={2402.06693},</code><br><code>      archivePrefix={arXiv},</code><br><code>      primaryClass={cs.DB}</code><br><code>}</code></p> <p> </p>

Dynamical Parameters and Clustering Results for Four-hundred Very Metal-Poor Stars Studied with LAMOST and Subaru

<p>This is the data associated with the paper "Four-hundred Very Metal-Poor Stars Studied with LAMOST and Subaru. III. Dynamically Tagged Groups and Chemodynamical Properties" by Zhang, Matsuno, Li et al. 2024. Table "LSVMP_HRdata_dynamics.csv" contains the dynamical parameters and clustering results of the HR sample in this paper. File "readme.txt" describes the meaning of each column in the table and the notes for the flag of stars. </p> <p>This sample is obtained by the LAMOST/Subaru joint project. See our paper I (DOI: 10.3847/1538-4357/ac6515) for detailed descriptions of target selection and observations, paper II (DOI: 10.3847/1538-4357/ac6514) for the chemical abundance analysis, and paper III (DOI: 10.3847/1538-4357/ad31a6) for clustering and chemodynamical analysis.</p> <p>If you have any questions about this data, please contact lhn@nao.cas.cn or rz.richie.zhang@gmail.com for more information.</p>

Initial Simulation Results Using ModularBuildingPy on a High-Rise Modular Steel Building

<p>These are output files from 'ModularBuildingPy', a Python-based tool designed for numerical modeling and analysis of volumetric modular steel buildings. Refer to <a href="https://mbbatukan.github.io/ModularBuildingPy/">the documentation</a> for more information. </p>

EMMA-X GWAS results on S. pimpinellifolium RSA traits - part 2

<p>The results of GWAS run through EMMA-X pipeline, on root system architecture traits of S. pimpinellifolim (wild tomato) exposed to control and salt stress conditions on agar plates. Traits are abbreviated as: aLRG for average lateral root growth rate, aLRL for average lateral root length, aLRLpTRS for the ratio between average lateral root length and total root size. The treatments are abbreviated with C or S for control or 100 mM NaCl treatments. The numbers in file name (0-4) indicate the days post transfer to agar plates containing treatment. </p>

EMMA-X GWAS results on S. pimpinellifolium RSA traits - part 1

Data of resulting velocity and anisotropic models, and Moho depth of the Tanlu fault zone

<div>Our dataset includes velocity model, anisotropic model, Moho depth data, core drawing codes, and color files.</div> <div> </div> <div>1. ani_vel_model.txt  </div> <div>Data of our resulting velocity and anisotropic models.</div> <div>         Format: longitude, latitude, vel_value (km/s), ani_value (km/s), azimuth_anisotropy (degree),</div> <div> </div> <div>2. moho.txt  </div> <div>Data of our resulting Moho depth.</div> <div>         Format:  longitude, latitude, moho_depth (km)</div> <div> </div> <div>3. core_code_plot_picture.sh</div> <div>The core codes for plotting velocity model, anisotropy model, and Moho depth map.</div> <div> </div> <div>3. moho.cpt</div> <div>Color files required for plotting Moho depth.</div> <div> </div> <div>4. vel.cpt</div> <div> Color files required for plotting velocity model.</div>