Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

Synthetic Multimodal Dataset using MuJoCo: UR5 Robot Motion

Using the Mujoco environment, we simulated robot trajectory and transitions from one formation         to another. Mujoco is a 3D simulator, while Gym serves as an interface to the UR5 robot.         The robot has measurement units that allow the acquisition of the angles, positions,         and quaternions of the joints and the position of the end-effector. The robot is located on         a table with 4 cameras all from the same radius to the center of the robot just         rotated by 90° for each of them. Using the described environment, we collected 1999 samples          at a rate of 10 samples per second.                  ################################################################         ################################################################                  camera views:         id: 0   name: 'camera_0'   xmat: array([ 0.70710678,  0.42537261, -0.56485232, -0.70710678,  0.42537261,        -0.56485232,  0.        ,  0.79882181,  0.60156772])   xpos: array([-2., -2.,  3.])         id: 1   name: 'camera_1'   xmat: array([-0.70710678,  0.42537261, -0.56485232, -0.70710678, -0.42537261,         0.56485232,  0.        ,  0.79882181,  0.60156772])   xpos: array([-2.,  2.,  3.])         id: 2   name: 'camera_2'   xmat: array([ 0.70710678, -0.42537261,  0.56485232,  0.70710678,  0.42537261,        -0.56485232, -0.        ,  0.79882181,  0.60156772])   xpos: array([ 2., -2.,  3.])         id: 3   name: 'camera_3'   xmat: array([-0.70710678, -0.42537261,  0.56485232,  0.70710678, -0.42537261,         0.56485232,  0.        ,  0.79882181,  0.60156772])   xpos: array([2., 2., 3.])                               The camera data is stored as .png files with a size of 256x256                   ################################################################         ################################################################                  The files angles.pt, angular_velocity.pt, angular_acceleration.pt contains information about         the motor data of the joints. The angles, velocity, acceleration is the information about the          Motor in each joint in following order:             ['base_to_lik', 'base_to_rik', 'elbow_joint', 'shoulder_lift_joint', 'shoulder_pan_joint', 'wrist_1_joint', 'wrist_2_joint', 'wrist_3_joint']                                   ################################################################         ################################################################                  The files pose.pt and quaternion.pt contains information about         the body data of the robot. The pose of each element is in following order:             ['base', 'base_link', 'box_2_link', 'box_link', 'drop_box', 'ee_link', 'forearm_link', 'left_inner_finger', 'left_inner_knuckle', 'right_inner_finger', 'right_inner_knuckle', 'robotiq_85_base_link', 'shoulder_link', 'upper_arm_link', 'world', 'wrist_1_link', 'wrist_2_link', 'wrist_3_link']                           ################################################################         ################################################################                  The file action.pt contains information about the used action in the corresponding time-step.          The action of each element is in following order:             ['forearm_T', 'gripper_motor', 'shoulder_lift_T', 'shoulder_pan_T', 'wrist_1_T', 'wrist_2_T', 'wrist_3_T']

opencc-by-4.0Nov 2024View details →

Synthetic Multimodal Dataset using ABB Studio: Single Robot Welding Station

## README ### Overview This repository provides the setup and data collected from simulations conducted in RobotStudio for various ABB robot configurations. The simulation, based on Löppenberg et al. (2024), was designed to capture precise sensory data in a controlled environment that mirrors real-world welding conditions. ### Simulation Setup The simulation tracks essential parameters of the ABB robot and its end-effector, including: - **Joint Angles**: Real-time angles for each joint. - **TCP Position**: 3D positional coordinates of the Tool Center Point. - **TCP Orientation**: Orientation captured as quaternions to represent rotational states. - **Additional Attributes**: Various other robot-specific details relevant to performance in a welding setup. ### Data Collection To generate a high-resolution dataset, data were sampled at intervals between 12 ms and 96 ms. This detailed sampling provides accurate insights into the robot's operation in a simulated welding environment. ### Key Parameters The dataset includes the following parameters: - **Camera View**: One camera view with a resolution of 3 x 256 x 256, normalized to the range [0, 1]. - **Motor Power (P)**: The power consumed by the robot’s motors, constrained within $[0, P_{\text{max}}]$. - **TCP Speed (v_TCP)**: The velocity of the end-effector, with a maximum cap of 5 m/s. - **TCP Acceleration (a_TCP)**: The acceleration of the TCP, bounded within $[a_{\text{min}}, a_{\text{max}}]$. - **TCP Orientation (q_TCP)**: Quaternion representing orientation, with a normalization condition $||\mathbf{q}_{\text{TCP}}(t)|| = 1$. - **TCP Position (p_TCP)**: A 3D vector for positional coordinates, with each coordinate constrained within $[p_{\text{min}}, p_{\text{max}}]$. - **Braking Distance (d_brake)**: The distance each joint travels during braking, within bounds $[d_{\text{min}}, d_{\text{max}}]$. - **Holding Position (θ_hold)**: The maintained angles for each joint, constrained to the interval $[-π, π]$. - **Joint Angles (θ)**: The current angles of each joint, also within the interval $[-π, π]$. Frame '02655_frame' is corrupted. That means the corresponding values of the other modality needs to be removed as well. ### Citation For more details on the simulation methodology, please refer to: - Löppenberg, M., Yuwono, S., Diprasetya, M. R., & Schwung, A. (2024). Dynamic robot routing optimization: State-space decomposition for operations research-informed reinforcement learning. Robotics and Computer-Integrated Manufacturing, 90, 102812.

opencc-by-4.0Nov 2024View details →

Synthetic Multimodal Dataset using ABB Studio: Dual Robot Welding Station

## README ### Overview This repository provides the setup and data collected from simulations conducted in RobotStudio for various ABB robot configurations. The simulation, based on Diprasetya et al. (2023), was designed to capture precise sensory data in a controlled environment that mirrors real-world welding conditions. ### Simulation Setup The simulation tracks essential parameters of the ABB robot and its end-effector, including: - **Joint Angles**: Real-time angles for each joint. - **TCP Position**: 3D positional coordinates of the Tool Center Point. - **TCP Orientation**: Orientation captured as quaternions to represent rotational states. - **Additional Attributes**: Various other robot-specific details relevant to performance in a welding setup. ### Data Collection To generate a high-resolution dataset, data were sampled at intervals between 12 ms and 96 ms. This detailed sampling provides accurate insights into the robot's operation in a simulated welding environment. ### Key Parameters The dataset includes the following parameters for each robot: - **Camera View**: One camera view with a resolution of 3 x 256 x 256, normalized to the range [0, 1]. - **Motor Power (P)**: The power consumed by the robot’s motors, constrained within $[0, P_{\text{max}}]$. - **TCP Speed (v_TCP)**: The velocity of the end-effector, with a maximum cap of 5 m/s. - **TCP Acceleration (a_TCP)**: The acceleration of the TCP, bounded within $[a_{\text{min}}, a_{\text{max}}]$. - **TCP Orientation (q_TCP)**: Quaternion representing orientation, with a normalization condition $||\mathbf{q}_{\text{TCP}}(t)|| = 1$. - **TCP Position (p_TCP)**: A 3D vector for positional coordinates, with each coordinate constrained within $[p_{\text{min}}, p_{\text{max}}]$. - **Braking Distance (d_brake)**: The distance each joint travels during braking, within bounds $[d_{\text{min}}, d_{\text{max}}]$. - **Holding Position (θ_hold)**: The maintained angles for each joint, constrained to the interval $[-π, π]$. - **Joint Angles (θ)**: The current angles of each joint, also within the interval $[-π, π]$. ### Citation For more details on the simulation methodology, please refer to: - Diprasetya, M. R., Yuwono, S., Löppenberg, M., & Schwung, A. (2023, July). Integration of ABB Robot Manipulators and Robot Operating System for Industrial Automation. In 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) (pp. 1-7). IEEE.

opencc-by-4.0Oct 2018View details →

Data and code for "Multimode microwave circuit optomechanics as a platform to study coupled quantum harmonic oscillators"

Components design files, code used to produce figures and measurement scripts for the results in "Multimode microwave circuit optomechanics as a platform to study coupled quantum harmonic oscillators".

opencc-by-4.0Jan 2023View details →

A Multimodal Dataset for Automatic Edge-AI Cough Detection

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. There is a need for wearable devices that employ multimodal sensors to perform accurate, privacy-preserving, automatic cough counting algorithms directly on the device in an edge-AI fashion. To advance this research field, we contribute the first publicly accessible cough counting dataset of multimodal biosignals. The database contains nearly 4 hours of biosignal data, with both acoustic and kinematic modalities, covering 4,300 annotated cough events. Furthermore, several non-cough sounds (i.e. breathing, laughing, and throat clearing), background noises (i.e. music, traffic, bystander coughing) and motion scenarios (i.e. sitting, walking) mimicking daily life activities are also present, which the research community can use to accelerate ML algorithm development. For detailed information about using this dataset to train edge-AI models and example code, please refer to our public Git repository: https://github.com/esl-epfl/edge-ai-cough-count/

opencc-by-4.0Jun 2023View details →

Controllable Light Scattering on Fiber Bragg Gratings in Multimode Fibers: Tailoring Angular Emission for Advanced Fiber-Based Light

The dataset consists of angular scattering patterns and transmission spectra for two fiber Bragg gratings. The scattering data is represented as 2-D arrays (images). The arrays are rectangular, where the longer side corresponds to the phi angle (azimuth) ranging from 0 to 2pi, and the shorter side represents the theta angle (polar angle) ranging from 0 to pi. To get the angle in rad per pixel just divide the corresponding arc length (p, 2pi) by the number of pixels on the axis.

opencc-by-4.0Jun 2023View details →

PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas

Art forms such as movies and television (TV) dramas are reflections of the real world, which have attracted much attention from the multimodal learning community recently. However, existing corpora in this domain share three limitations: i. annotated in a scene-oriented fashion, they ignore the coherence within plots; ii. their text lacks empathy and seldom mentions situational context; iii. their video clips fail to cover long-form relationship due to short duration. To address these fundamental issues, using 1,106 TV drama episodes and 24,875 informative plot-focused sentences written by professionals, with the help of 449 human annotators, we constructed PTVD, the first plot-oriented multimodal dataset in the cinema domain.

opencc-by-4.0Jun 2023View details →

Material Suplementar - Identifying Requirements for a Multimodal User Experience Evaluation Framework for Interactive Web Systems

Esta planilha contém o detalhamento do processo de codificação realizado para a identificação dos requisitos apresentado no artigo "Identifying Requirements for a Multimodal User Experience Evaluation Framework for Interactive Web Systems". Este artigo está submetido no XXIX Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia). A primeira aba da planilha contém os trechos dos artigos que geraram os códigos e os respectivos requisitos. A segunda aba mostra o agrupamento dos códigos similares para definir o conjunto de requisitos.

opencc-by-4.0Dec 2022View details →

The dataset of "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction"

The dataset used in the study "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction" is available upon request. Please contact the authors of the study or the responsible institution for access to the dataset.

opencc-by-4.0Mar 2022View details →

Geolocating and Measuring Offshore Infrastructure with Multimodal Satellite Data

Please cite this when using the dataset and code. The offshore infrastructure is rapidly spreading in the Scottish waters to satisfy the increasing energy demand. The up-to-date knowledge of their distribution and size is critical for the development and management of marine ecosystems. With the development of remote sensing techniques, satellite data have been widely used in offshore infrastructure detection on the vast ocean. However, the automatic and accurate identification on remote sensing data is still challenging that every kind of data have limitations. Here we combine the Sentinel-1 SAR data and Sentinel-2 Multi-Spectral Instrument (MSI) imagery to propose an automatic method for the location detection and size evaluation of offshore infrastructure in Scottish waters. Specifically, three strategies (transformed median composite, 2D-SSA filtering and threshold segmentation) were designed to first extract the contour range on Sentinel-1 data. Then morphological operations were applied on Sentinel-2 true color image to obtain the precise location and size of each offshore infrastructure. All the Sentinel-1 and Sentinel-2 data are downloaded from https://www.sentinel-hub.com/explore/eobrowser/ The file "loc_S1" is used for the contour range detection (guided area) in Sentinel-1; The file "loc_S2" is for the specific location detection and size evaluation of oil/gas platforms and wind turbines;

dryad32/100

Multimodal immune profiling of SARS-CoV-2 in Uganda – soluble immune mediators

Little is known about the pathobiology of SARS-CoV-2 infection in sub-Saharan Africa, where severe COVID-19 fatality rates are among the highest in the world and the immunological landscape is unique. In a prospective cohort study of 306 adults encompassing the entire clinical spectrum of SARS-CoV-2 infection in Uganda, we integrated profiling of the peripheral blood proteome and transcriptome to dissect the immunopathology of COVID-19 across multiple phases of the pandemic. Beyond the prognostic importance of myeloid cell-driven immune activation and lymphopenia, we show that multifaceted impairment of host protein synthesis and extensive redox imbalance define core biological signatures of severe COVID-19, with central roles for IL-7, IL-15, and lymphotoxin-α in COVID-19 respiratory failure. While prognostic signatures were generally consistent in SARS-CoV-2/HIV-coinfection, type I interferon responses uniquely scaled with COVID-19 severity in persons living with HIV. Throughout the pandemic, COVID-19 severity peaked during phases dominated by A.23/A.23.1 and Delta B.1.617.2/AY variants. Independent of clinical severity, Delta phase COVID-19 was distinguished by exaggerated pro-inflammatory myeloid cell and inflammasome activation, NK and CD8+ T-cell depletion, and impaired host protein synthesis. Combining these analyses with a contemporary Ugandan cohort of adults hospitalized with influenza and other severe respiratory infections, we found activation of epidermal and platelet-derived growth factor pathways to be distinct features of COVID-19, deepening translational understanding of mechanisms potentially underlying SARS-CoV-2-associated pulmonary fibrosis. Collectively, our findings provide biological rationale for use of broad and targeted immunotherapies for severe COVID-19 in sub-Saharan Africa, illustrate the relevance of local viral and host factors to SARS-CoV-2 immunopathology, and highlight underemphasized yet therapeutically exploitable immune pathways driving COVID-19 severity.

opencc-zeroAug 2023View details →

opencc-by-4.0Aug 2023View details →

Experimental Data for "Is a Picture Worth a Thousand Words? Effects of Foregrounded Multimodal and Narrative Features"

This dataset contains data of the publication "Is a Picture Worth a Thousand Words? Effects of Foregrounded Multimodal and Narrative Features". It includes measures for the Story World Absorption Scale, the Transformative Reading Scale, and the Empathy subscale of the Literary Response Questionnaire used to test the effect of multimodal features of multimodal novels and narrative perspectives and their interactive effects on reading-induced imagery and emotional reactions in the process of reading.

opencc-by-4.0Sep 2023View details →

Blast Furnace Raw Material Granularity Recognition Model Based on Deep Learning and Multimodal Fusion of 3D Point Cloud

Provide data code

opencc-by-4.0Aug 2022View details →

Inverted S-wave velocity model for "Anomalous radial anisotropy and its implications for upper mantle dynamics beneath South China from multimode surface wave tomography"

# Instructions for the South China velocity model dataset from multi-mode surface-wave inversion.   1. The detailas can be found at the paper: Tang Q, Sun W, Yoshizawa K, et al. Anomalous radial anisotropy and its implications for upper mantle dynamics beneath South China from multimode surface wave tomography. Journal of Geophysical Research: Solid Earth, 2022, 127(8): e2021JB023485.   2. The relative SV-wave and SH-wave velocity models at different depths (from 0 to 300 km) are stored in the "data" foder. Each file follows the name convention: shear_{SV/SH}.{depth}.dat representing SV or SH velocity at a given depth.   3. In each velocity file, each line have three columns: longitude (deg) latitude (deg) relative_velocity (%)   4. The reference velocity can be found in the two files "SV_velocity" and "SH_velocity", depth and velocity.   5. The relative velocity is calculated from (absolute-reference)/reference*100%. Thus one can have the absolute velocity via absolute = relative * (1+relative/100).

opengpl-2.0-or-laterDec 2021View details →

Dataset for Global Reference Seismological Data Sets: Multimode Surface Wave Dispersion

<ul> <li>How fast do surface waves travel globally after any earthquake?</li> <li>Do we get the same information from various measurement techniques?</li> <li>Which features in the Earth are robust and can be resolved by a reference model?</li> </ul> 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. 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.  Summary: [I] Reconciled large and diverse catalogues of Love-wave (49.65 million) and Rayleigh-wave dispersion (177.66 million) from eight groups worldwide. [II] Retrieved missing station and earthquake metadata in several legacy compilations and codified scalable formats to facilitate reproducibility, easy storage and fast I/O on HPC systems. [III] Systematic discrepancies between raw phase anomalies can be attributed to discrepant theoretical approximations, reference Earth models and processing schemes. [IV] Phase-velocity variations yielded by the inversion of the summary data set are highly correlated (R ≥ 0.8) with those from the quality-controlled contributing data sets, especially for long-wavelength variations (up to degree &sim;25) in fundamental-mode dispersion (50–100 s). [IV] Only 2ζ azimuthal variations in phase velocity of fundamental-mode Rayleigh waves are required; maps of 2ζ azimuthal variations are highly consistent between catalogues ( R = 0.6–0.8). Feedback/Questions? 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>  Reference: Please cite the following work if you use this data or software. <ul> <li>Moulik, P. et al., (2022) Global reference seismological data sets: multimode surface wave dispersion. Geophys J Int 228, 1808–1849, doi: <a href="https://doi.org/10.1093/gji/ggab418">10.1093/gji/ggab418</a>. <a href="https://rajmoulik.com/Publications/Moulik_Reference_Surface_Waves_GJI2022.pdf">pdf</a></li> </ul> You can also cite the dataset and software from this Zenodo page (Optional). <ul> <li> 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> </li> </ul> HDF5 Container Format <ul> <li>Reference Love waves (<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>Reference Rayleigh waves (<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> 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: <ul> <li>from avni.data.SW import readSWhdf5,writeSWascii</li> <li>df=readSWhdf5(query='0/100.0/R1/REM3D',hdffile='Summary.SW.Rayl.data.h5',datatype='summary')</li> <li>writeSWascii(df,'test.txt')</li> </ul> ASCII (text) Format 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 Summary.$overtone.$wave.$period.REM3D.gz Table A1 from the paper describes the various columns in the surface-wave RSDF ASCII format files. <ul> <li>Love waves (<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>Ist 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>IInd 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>IIIrd 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>IVth 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>Vth 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>Rayleigh waves (<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>Ist 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>IInd 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>IIIrd 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>IVth 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>Vth 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>VIth 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> Other Data Products: <ul> </ul> <ul> <li>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>) - 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">Scatter_Plots.zip</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">Half_cycle.zip</a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cycle_skips.zip">Cycle_skips.zip</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">vflip-table.REM3D</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">Half_cycle.zip</a> above.</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/M1442">M1442</a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/B2562">B2562</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">Cleanhomo.SW.Love.data.h5</a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Rayl.data.h5">Cleanhomo.SW.Rayl.data.h5</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">Cleanhomo.SW.Love.data.zip</a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Rayl.data.zip">Cleanhomo.SW.Rayl.data.zip</a>. 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">Inversion_Example.zip</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 (Anisotropy_Plots), spline coeffients of 1442 evenly-spaced spherical splines (Spline_Coefficients), and corresponding values at every 1X1 degree pixel in extended pixel format (Maps_epix). 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> Note about Data Format 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.

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