Find research datasets worth reusing

opencc-by-4.0Aug 2023View details →

Fig. 5. A data-display network constructed from uncorrected 18S rDNA p in Toxoplasma gondii and related Sarcocystidae parasites in harvested caribou from Nunavik, Canada

Fig. 5. A data-display network constructed from uncorrected 18S rDNA p-distances, using all characters, for tissue dwelling coccidians (mostly Sarcocystis spp.). Group names bear no taxonomic designation but merely assigned for discussion purposes. Bootstrap supports are displayed by the gray curves and associated values imposed on the network. Red dots indicate sequences generated in this study. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3. Haplotype network for E in Prevalence, molecular characterisation and phylogenetic analyses of hydatid cysts and cysticercus tenuicollis isolates and first report of E. canadensis (G6/G7) in wild boars in Bingol province, Türkiye

Fig. 3. Haplotype network for E. canadensis (G6/G7) using cox1 gene (616 bp) sequences of different countries. The E. canadensis (G6/G7) isolate obtained in this investigation (Hap_01) and the sequences identified as G7 in the Genbank database were utilized. Circle size relative to haplotype data set frequency. Each hatch mark is representative of one nucleotide change. Haplotypes formed by the isolates obtained in this study are marked with an asterisk.

Fig. 2. Haplotype network constructed using cox1 in Prevalence, molecular characterisation and phylogenetic analyses of hydatid cysts and cysticercus tenuicollis isolates and first report of E. canadensis (G6/G7) in wild boars in Bingol province, Türkiye

Fig. 2. Haplotype network constructed using cox1 (744 bp) gene sequences of T. hydatigena. Seven haplotypes formed by the T. hydatigena isolates obtained in this study: (Hap 1-Hap 7). Circle size relative to haplotype data set frequency. Each hatch mark is representative of one nucleotide change. Haplotypes formed by the isolates obtained in this study are marked with an asterisk.

opencc-by-4.0Jul 2024View details →

Research data supporting the publication "Hierarchy of Topological Transitions in a Network Liquid"

This dataset contains the data used to produce each of the figures in the manuscript "Hierarchy of Topological Transitions in a Network Liquid" published in PNAS. The dataset includes configuration files for the dendrimer system generated via NPT Monte Carlo simulations and the topological analysis of the networks formed by the dendrimer particles.

Data basis of "Investigation of Railway Network Capacity by Means of Dynamic Flows"

Input data for the article Investigation of Railway Network Capacity by Means of Dynamic Flows (Nikolayzik, Maus and Nießen). The dataset contains two files for each analysed scenario (complete network, upper subnetwork, lower subnetwork). The first file ("input_data_infrastructure_{scenario}.csv") contains information on the investigated infrastructure: For each station the number of available tracks is listed and for the lines information on whether it is a single- or double-track line, the average minimum headway time, hourly capacity limits and travel times for the different train types are included. The information is thereby split into two parts, depending on whether the core network or the linking lines are described. The second file ("input_data_trains_{scenario}.csv") contains the trains that can generally be scheduled in the considered network, including information on the corresponding train type, departure frequencies, their routes and a minimally allowed dwell time.   Further, the file "input_data_route_conflicts_nodes.py" contains the information on which routes inside a station exclude each other as is described in the article.

Results of Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model

This repository contains: <ol> <li>A deep neural network (DNN) based soil moisture (SM) estimates (NN) based on the SMAP TB (Descending, 6 AM) and SMAP SCA-V ancillary data as the input variables. (<a href="../api/records/13309165/draft/files/SMAP_NN_36km_20150331_20220326.nc/content" target="_blank" rel="noopener noreferrer">SMAP_NN_36km_20150331_20220326.nc</a>)</li> <li>Temporally averaged roughness parameter (hNN) and scattering albedo (omegaNN) which are retrieved by inversely tracking the DNN model. (<a href="../api/records/13309165/draft/files/SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc/content" target="_blank" rel="noopener noreferrer">SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc</a>)</li> </ol> Summary: The DNN model has been developed by relating SMAP TB and SMAP SCA-V ancillary data with in-situ SM data from the international soil moisture network (ISMN) using DNN. To minimize scale mismatch between gridded SMAP data and point in-situ data, the triple collocation analysis was conducted. The SM estimated from the DNN algorithm (NN) showed a good agreement with the ISMN data that was not used in the model training. Moreover, for a densely vegetated region located in the Amazon (Tambopata site) the NN showed less bias compared to available SM retrievals.  Two parameters hNN and omegaNN are retrieved by ingesting NN to the modified dual channel algorithm. When the SM retrieval was conducted using the hNN and omegaNN, the result showed good agreement with the NN (DNN-based SM) with R of 0.986, ubRMSD of 0.015 m3/m3, and bias of -0.001 m3/m3. The paper "Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model" published in the Transactions on Geoscience and Remote Sensing. For more details, please contact me (wotp12@unist.ac.kr)

SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data

<div> SDUST2023BCO.nc is the global marine bathymetric model covering 80°S~80°N and 0°~360°E on 1′×1′ grids. The dataset contains geospatial information (latitude, longitude), SDUST2023BCO bathymetric model and an attachment data. </div>

Data from: Cross-sectional personal network analysis of adult smoking in rural areas

This data package, titled Data from: Cross-sectional personal network analysis of adult smoking in rural areas, includes several files. First, there are annonymized raw data files in .rds file format (ego_data.rds & alter_data.rds). Second, there is the R code that allow the replication of various statistical analyses. Interested parts may consult the R code as .pdf file format (Supplementary_Material_R_Code.pdf), .Rmd file format (that can be run to create the .pdf file format) and the .R file format (that can be accesed with R and RStudio). Moreover, the labels files are useful for recreating the Supplementary Material pdf file.  Readers should know that this dataset corresponds to the study (paper) Cross-sectional personal network analysis of adult smoking in rural areas.  The ego_data.rds file includes 20 variables by 76 observations (respondents) while the alter_data.rds file includes 46 variables by 1681 observations (social contacts). We collected this information by deploying a personal network analysis research design. Initially, we interviewed 83 respondents (dubbed egos). Due to missing data, we kept in the analysis 76 egos and dropped seven respondents. We recruited the respondents using a link-tracining sampling framework. We started from a number of six seeds. We interviewed the seeds then we asked them to recommend other people in the study. We continued in a referee-referral fashion until 83 interviews were completed. The study was performed in a small rural Romanian community (4124 residents): Lerești (Argeș county).  Our study was carried out in accordance with the recommendations, relevant guidelines, and regulations (specifically, those provided by the Romanian Sociologists Society, i.e., the professional association of Romanian sociologists). The research was performed in accordance with the Declaration of Helsinki. The research protocol was approved by a named institutional/licensing committee. Specifically, the Ethics Committee of the Center for Innovation in Medicine (InoMed) reviewed and approved all these study procedures (EC-INOMED Decision No. D001/09-06-2023 and No. D001/19-01-2024). All participants gave written informed consent. The privacy rights of the study participants were observed. The authors did not have access to information that could identify participants. Face-to-face interviews were collected between September 13 – 23, 2023, in Lerești, Romania. After each interview, information that could identity the participants were anonymized. Before conducting the interview, we provided each participant with a dossier containing informative materials about the project's objectives, how the data would be analyzed and reported, and their participation rights (e.g., the right to withdraw from the project at any time, even after the interview was completed). All study participants gave their written informed consent prior to enrolment in the study. The variables in the ego_data.rds file are as follows: (1) "networkCanvasEgoUUID" (unique alpha numeric code for each observation);  (2) "ego_age" (the age of each study participant);  (3) "ego_age.cen" (the age of each study participant, centered);  (4) "ego_educ_b" (the education of each ego, binary);  (5) "ego_educ_f" (the education of each ego, educational achievement);  (6) "ego_marital.s_f" (the marital status of each ego); (7) "ego_occupation.cat2_f" (the occupation of each ego);  (8) "ego_occupation_b" (the occupation of each ego, unemployed vs employed);  (9) "ego_relstatus_b" (whether the ego is in a relationship or not);  (10) "ego_sex_f" (the sex of the ego assigned at birth; male & female);  (11) "ego_sex_n" (the sex of the ego assigned at birth; 0 = male & 1 = female);   (12) "ego_smk_status_b1" (smoking status: 1 smoking, 0 others); (13) "ego_smk_status_b2" (smoking status: 1 former smoker, 0 others);   (14) "ego_smk_status_b3" (smoking status: 1 not a smoker, 0 others);  (15) "ego_smkstatus_f"  (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker);  (16) "ego_smoking_3cat"  (smoking status: non-smoker, former smoker, smoker); (17) "net.size" (number of social contacts, alters, that were elicited by an ego); (18) "net.components" (number of strong components in the personal network); (19) "net.deg.centralization" (personal network degree centralization); (20) "net.density" (personal network density).  The variables in the alter_data.rds file are as follows: (1) "alter_age" (the age of the alter);  (2) "alter_age.cen" (the age of the alter - centered);  (3) "alter_btw" (alter's betweenness score);  (4) "alter_btw.cen" (alter's betweenness score - centered);  (5) "alter_deg" (alter's degree score);  (6)  "alter_deg.cen" (alter's degree score - centered);   (7) "alter_educ_b" (alter's education);  (8) "alter_educ_f"  (alter's education);  (9) "alter_marital.s_f" (alter's marital status);  (10) "alter_relstatus_b" (alter's marital status - binary variable); (11) "alter_sex_f" (alter's sex assigned at birth); (12) "alter_sex_n" (alter's sex assigned at birth; 1 - female; 0 - male);  (13) "alter_smk_status_b1" (alter's smoking status; 1 smoker, 0 others); (14) "alter_smk_status_b2" (alter's smoking status; 1 former smoker, 0 others); (15) "alter_smk_status_b3" (alter's smoking status; 1 non-smoker, 0 others); (16) "alter_smoking_3cat" (alter's smoking status: three categories - smoker, non-smoker, former smoker); (17) "assortativity_score_fsmoker" (assortativity score for alter, former smoker); (18) "assortativity_score_nsmoker" (assortativity score for alter, non-smoker); (19) "assortativity_score_smoker" (assortativity score for alter, smoker); (20) "ego.alter_meet_f" (ego's meeting frequency with alter);  (21) "ego_alter_meet_b" (ego's meeting frequency with alter, binary variable); (22) "ego.alter_meet_n" (ego's meeting frequency with alter, numerical codes); (23) "alter_rel.w.ego_f" (type of alters in an ego's network); (24) "networkCanvasUUID" (alpha numeric code for alter); (25) "networkCanvasEgoUUID" (alpha numeric code for ego);  (26) "ego_smkstatus_f" (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker);  (27) "ego_smoking_3cat" (three categories, smoking status: former smoker, non-smoker, smoker); (28) "ego_type_fsmk" (former smoking egos by type of ego-alter relationship); (29) "ego_type_nsmk" (non smoking egos by type of ego-alter relationship); (30) "ego_type_smk" (smoking egos by type of ego-alter relationship); (31) "ego_sex_f" (ego's sex, binary); (32) "ego_sex_n" (ego's sex, numerical code, 1 female, 0 male);  (33) "ego_educ_b" (ego's education, binary variable) (34) "ego_age" (ego's age) (35) "ego_age.cen" (ego's age, centered) (36) "ego_relstatus_b" (ego's marital status, binary variable) (37) "ego_occupation_b" (ego's employment status, binary variable) (38) "net.components" (number of strong components in the personal network) (39) "net.deg.centralization" (degree centralization score in the personal networ) (40) "net.density" (density score in the personal network) (41) "prop_fsmokers" (proportion of former smokers in the personal network - alters) (42) "prop_fsmokers.cen" (proportion of former smokers in the personal network, centered- alters) (43) "prop_nsmokers" (proportion of non-smokers in the personal network- alters) (44) "prop_nsmokers.cen" (proportion of non-smokers in the personal network, centered- alters) (45) "prop_smokers" (proportion of smokers in the personal network- alters) (46) "prop_smokers.cen" (proportion of smokers in the personal network, centered- alters)

Assets (code, scripts and datasets) for the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks"

This dataset contains all relevant software and data related to the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks", submitted to AGU journals.

Computational results data for the assoziated publication "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics"

This repository contains two Excel tables with computational results for our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics". Each Excel table includes multiple worksheets, each representing different scenarios and models evaluated in our study. Worksheets Overview Each Excel table contains the following worksheets: 1. ML: Results for the "ML" big M values. 2. MH: Results for the "MH" big M values. 3. MF: Results for the "MF" big M values. 4. Path: Results for the path model. 5. FMInstances: Results from applying our models on the original Fontaine and Minner (2018) instances. Columns Description Each worksheet contains the following columns: - Name of Instance: A complex string with the identifier of the used instance. The relevant part is "_RXXX_", where XXX is the random seed used to generate the instance. - Number users: The number of users/commodities in the network. - B: The budget (always set to infinity in our instances). - GUROBI_RUNTIME: The time limit set for the computations. - Modelkind: The type of model used. Possible values are:   - INDICATOR: Compact model.   - FMbenders: Benders model from Fontaine and Minner (2018).   - ICM: Benders-like cuts.   - PathModel: Path enumeration model. - BigM computation: Time required to compute all the big M values used (not included in the time limit). - runtime: Runtime of the selected model. - BBnodes: Number of nodes in the Branch & Bound tree. - gap: Gap reported by Gurobi after reaching the time limit. - Cuts BLC: Number of Benders-like cuts included. - Time BLC: Time required for separating Benders-like cuts. - M improve BLC: Frequency of improvements to a big M when using the improved big M term in Benders-like cuts. - Mcutoff_AVE: Average (non-zero) improvement of a big M when using the improved big M term in Benders-like cuts. - Cuts FMBenders: Number of Benders cuts generated in the Fontaine and Minner (2018) model. - Time FMBenders: Time required to generate the Benders cuts in the Fontaine and Minner (2018) model. - Runtime path enum: Time required to enumerate all paths for the path-based model (not included in the time limit). - Average Num Path: Average number of paths generated for a single commodity/user. Multiply this value by the number of users to obtain the absolute number of paths generated. Note on FCP All the results found for the instances already had integer flow solutions. Additionally, we conducted experiments where we explicitly forced the solutions to be integer for the Benders-like cuts model. We observed that the runtimes remained the same, with only some natural insignificant hardware-induced fluctuations. Therefore, we omit reporting these results again. For further information or questions, please refer to our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics" or contact the authors.

Secondary Data for: Enhanced Modeling of Back-Mixing in Chemical Reactor Networks

Secondary data for the results presented in the preprint "Enhanced Modeling of Back-Mixing in Chemical Reactor Networks" by L. Gossel, M. Fricke and D. Bothe (2023).  https://arxiv.org/abs/2305.11591 Tables containing the secondary data of the results presented in Figure 5, a-d are provided.  The used code is confidential and thus not included in the repository.  Funded by the Hessian Ministry of Higher Education, Research, Science and the Arts - cluster project Clean Circles.

opencc-by-4.0Jun 2024View details →

Complex Lava Tube Networks Developed Within the 1792-93 Lava Flow Field on Mount Etna (Italy): Insights for hazard assessment Supporting Informations: Maps and sections of the lava tubes

<div> <div> <div> This supporting information for the above paper submitted to Frontiers in Earth Science - Volcanology, comprises Table 1, as well as the maps and sections of the 8 lava tubes analyzed in this paper, that are located within the 1792-93 lava flow field at Etna volcano. The methods used for the new surveys of the lava tubes are also explained. </div> </div> </div>

Data Sharing Practices in the MRC Circadian Mental Health Network.

This dataset supports the research conducted within the MRC Circadian Mental Health Network which assesses data sharing practices among Principal Investigators' publications in 2023. This work aims to identify trends, challenges, and inform future recommendations and policies based on the findings. The dataset includes various files that detail the methodology, data collected, and analyses performed.   Repository Contents: <ol> <li> Methods and Analysis Report - Data Sharing Practices in the MRC CMHN.pdf <ul> <li>This report provides the methodologies used for selecting and assessing research papers within the network, along with detailed results, tables, and discussions from the evaluation.</li> </ul> </li> <li> CMHN_All_Data.xlsx <ul> <li>An Excel workbook containing: <ul> <li>Sheet 1: All data and variables collected and analysed for this project.</li> <li>Sheet 2: A README file that explains each variable and its values. </li> </ul> </li> </ul> </li> <li> CMHN DataType Scoring.xlsx <ul> <li>An Excel workbook detailing: <ul> <li>Sheet 1: All datatypes, both code and datasets, evaluated in this study.</li> <li>Sheet 2: A README explaining the variables evaluated and their specific values. </li> </ul> </li> </ul> </li> <li> CMHN Data Extraction Survey.pdf <ul> <li>A copy of the Microsoft Form used to systematically evaluate data-sharing practices from selected publications, describing the structured data extraction process used. </li> </ul> </li> <li> CMHN DataType Scoring Survey.pdf <ul> <li>A Microsoft Form used to assess the types of data (code and datasets) shared. </li> </ul> </li> <li> Data_CSV_Code.csv <ul> <li>This file is the original, uncleaned dataset directly extracted from the initial response data of the Microsoft Form used in the project. It served as the primary dataset for all subsequent data analysis and code execution within the study. </li> </ul> </li> <li> CMHN Code.Rmd <ul> <li>An R Markdown file containing the code used for data analysis; predominantly descriptive statistics due to the limited number of papers with shared data.</li> </ul> </li> </ol> Recommended Use: For comparative purposes or further analysis, researchers are encouraged to utilise the cleaned datasets available in "CMHN_All_Data.xlsx" and "CMHN DataType Scoring.xlsx." Contact: For further inquiries, please email us at <a href="mailto:bio_rdm@ed.ac.uk" target="_blank" rel="noopener">bio_rdm@ed.ac.uk</a>.

opencc-by-4.0May 2023View details →

Data archive for 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'

This repository contains the code and databases used in the paper 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'.  The 'Mekong River Barrier Database (MRBD)' folder contains the basin-scale barrier database developed in this study. This database contains more than 13,000 unique barriers, which were identified by using the convolutional neural networks-based object detection method from Google Earth’s satellite imagery. The 'FCOS' folder contains the barrier detection model (FCOS ResNext-101-FPN), trained for detecting river barriers from remotely sensed images within the MMDetection framework. The 'FCOS_x101_v2' folder contains the enhanced FCOS model. The 'R_script' folder contains R files used in the paper. Coordinate.R was used to extract coordinates from bounding boxes in each TIF image. CAFI.R was used to calculate the CAFI index in each sub-catchment. The 'Barrier image training set' folder contains over 10,000 river barrier satellite images and their associated JSON files, forming the 'training, validation, and test datasets' used during the model training process. This dataset is made available to the user community in raw, in the hope that others will contribute to its future development, thereby enhancing its use and utility. For more information on the MMDetection framework, refer to the following GitHub repository: <a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>

Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange: Neural network potentials training data

<h1>Neural network potentials of an excess proton in bulk water, training data</h1> This dataset contains 2188 configurations labeled at two hybrid DFT levels (revPBE0-D3 and B3LYP-D3). The configurations are given as a single XYZ file: configurations.xyz The box dimensions are written in box.txt The energies for all configurations at a given level of theory are written in energies_LEVEL.txt (one configuration per line) The atomic forces for each configuration at a given level of theory are gathered in a XYZ file: forces_LEVEL.xyz The relative displacements of the Wannier centroids, with respect to the closest oxygen atom, for each configuration at a given level of theory, are in the following XYZ file: wannier-centroids-displacements_LEVEL.xyz

5G Campus Network QoS Dataset for Open-Source gNB Implementations

This 5G campus network dataset contains 6 distinct CSV files. Three for each of the testbeds located at either the Norwegian University of Science and Technology(NTNU) or the University of Wuerzburg(WUE). all_Packets: contains all One-Way-Delay measurement packets. Columns:  src, Timestamp, SourceIPOuter, DestinationIPOuter, SourceIPInner, DestinationIPInner, PacketSize, SeqNum, iat, trel, gnb, sdr, bw, slots, ratio, pdist, piat, psize, pnpak, rep, scenario, direction Packets_with_IATs: contains all One-Way-Delay measurement packets, with the Interarrival-times already calculated and added as column. Columns: src, Timestamp, SourceIPOuter, DestinationIPOuter, SourceIPInner, DestinationIPInner, PacketSize, SeqNum, iat, trel, gnb, sdr, bw, slots, ratio, pdist, piat, psize, pnpak, rep, scenario, direction all_Throughput: contains all output information from the throughput measurements conducted with iperf3. Columns:  mbpsactual_uplink, mbpsactual_downlink, meanjitterms_uplink, meanjitterms_downlink, meanloss_uplink, meanloss_downlink, gnb, sdr, bw, slots, ratio, mbpsoffered_uplink, mbpsoffered_downlink, rep, scenario src: Source Device Timestamp: Timestamp from wireshark SourceIPOuter: Source IP of the outer GTP packet DestinationIPOuter: Destination IP of the outer GTP packet SourceIPInner: Source IP of the inner GTP packet DestinationIPInner: Destination IP of the inner GTP packet PacketSize: Size of the packet SeqNum: Sequence number injected during traffic generation iat: Interarrival-time of the packet trel: relative timestamp gnb: gNB implementation used sdr: SDR used bw: configured bandwidth slots: configured DL slots ratio: configured DL:UL ratio pdist: chosen distribution of the packet generation (deterministic/negative-exponential time between transmission of the generated packets) piat: chosen interarrival -time during packet generation psize: chosen packet size mode pnpak: total amount of packets generated for this measurement run rep: repetition of measurement scenario: measurement scenario direction: direction of traffic mbpsactual_uplink: actual uplink throughput in MBps mbpsactual_downlink: actual downlink throughput in MBps meanjitterms_uplink: actual uplink jitter in ms meanjitterms_downlink: actual downlink jitter in ms meanloss_uplink: mean loss in uplink meanloss_downlink: mean loss in downlink mbpsoffered_uplink: configured uplink throughput in MBps mbpsoffered_downlink: configured downlink throughput in MBps This dataset stems from the Paper "Parameterizing 5G New Radio: A Comparative Measurement Study on Throughput and Delay"

Adjacency matrices for dApps contracts and functions network

This dataset encompasses the network structure of decentralized applications (dApps) mainly deployed in the Ethereum blockchain and other platforms such as Binance, Optimism, Polygon, Astar, Shiden, and Thundercore.

opencc-by-4.0May 2022View details →

Dataset of Cellular Network Measurements

This dataset contains 326,157 measurements in cellular networks, collected from August 2021 until Juni 2023 in Germany. The measurement focus on a high density in rural areas and were performed by car, bicycle and by foot. The measurements cover primary the Telefonica/o2 network.

Training data set for: Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometries

<h2>Overview</h2> This is a synthetic volcano deformation dataset accompanying the publication of Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometries, on the journal Volcanica. Synthetic, quasi-static deformation is computed for magma chambers of various geometries, parameterized as spheroids or superpositions of spherical harmonics. Surface deformation is computed using the boundary element method (BEM) of Nikkhoo & Walter (2015). Please reference our paper for details of computational methods. The dataset contains 50,000 realizations of magma chamber geometries/orientations/centroid depths and associated deformation fields. Surface deformation fields are sampled at discrete locations, with a uniform random distribution within [Lh x Lh], and a distribution that concentrates near the chamber (at radial distances, r = 10^(-3  random number) * Lh/2). Note this dataset contains only a small fraction of the total dataset. In total, 824,393 realizations of magma chambers were used to train our emulators. For accessing the complete training data set, please contact the authors.  Each .mat file contains the deformation field associated with a single chamber geometry. Use visData.m to visualize chamber geometry and associated surface displacement. Each file contains two MATLAB structures, "input" and "output".  <h2>Naming of each zip file</h2> The numbers after the underscore, N:M, indicate that this file contains N of the M total chamber realizations for this particular setup.  <a href="../api/records/13800065/draft/files/sph_20AspRatios_1e4:151211.zip.zip/content" target="_blank" rel="noopener noreferrer">sph_20AspRatios_1e4:151211.zip</a>: deformation corresponding to spheroidal magma chambers parameterized by aspect ratios.  <a href="../api/records/13800065/draft/files/sh_complex_1e4:152283.zip/content" target="_blank" rel="noopener noreferrer">sh_complex_1e4:152283.zip</a>: deformation corresponding to chamber geometry produced by superposition of spherical harmonic modes.  <a href="../api/records/13800065/draft/files/sh_mode_approx_1e4:138380.zip/content" target="_blank" rel="noopener noreferrer">sh_mode_approx_1e4:138380.zip</a>: deformation corresponding to chamber geometries corresponding to individual spherical harmonic modes, combined with a spherical mode (the spherical mode prevents chamber surfaces from having zero radii locally) <a href="../api/records/13800065/draft/files/sh_spheroid_approx1e4:202272.zip/content" target="_blank" rel="noopener noreferrer">sh_spheroid_approx1e4:202272.zip</a>: deformation corresponding to chambers approximating spheroids, but parameterized by spherical harmonics. <a href="../api/records/13800065/draft/files/sh_spheroid_perturb_1e4:180247.zip/content" target="_blank" rel="noopener noreferrer">sh_spheroid_perturb_1e4:180247.zip</a>: same as above, but with additional random perturbations parameterized in spherical harmonics. <h2>Variables in each file</h2> Input contains the following fields: dp2mu: pressure change to shear modulus ratio. dx, dy, dz: the coordinates of chamber centroid [meters] mu: dimensionless crustal shear modulus (always set to 1) nu: crustal Poisson's ratio (always set to 0.25) Ns: number of points on the surface where displacements are computed Lh, Lv: horizontal and vertical dimensions of the model domain [meters]. Lh is determined such that at the edge of the model domain, the displacement magnitude is below 10 percent of the maximum. Lv = Lh/2 + abs(dz) for the spheroids ----------------------------------------------------------------------------------------------------------- the input files contain asp: aspect ratio of chamber (length of the semi-major axis divided by that of the semi-minor axis) ra, rb: semi-major, -minor, axis length [meters] thetax, thetay, thetaz: counterclockwise rotation angles with regard to x, y, z axis [degrees]. thetax = [0, 90] degrees, thetay = 0 degrees, thetaz = 360 degrees. for the general geometries-------------------------------------------------------------------------------------------------- the input files contain ls, ms, fs: degree, order, coefficients of spherical harmonic modes. Spherical harmonics are sampled up to degree 5. fs is a complex vector of coefficients such that the resulting shape is real.  normF: normalization factor applied to the shape parameterized by ls, ms, fs, such that the shape as a maximum radius of unity. rmax: scale factor to scale the spherical harmonics parameterized shape to real dimensions [meters]. ============================================================================================= Output contains the following fields, X, Y, Z: coordinates of points where displacement vectors are computed [meters] Ux, Uy, Uz: displacements in x, y, z directions [meters] P, T: coordinates [meters] of vertices for the triangular mesh used in BEM calculation, and the connectivity matrix  C: coordinates [meters] of the center of each triangular element that, dhat, nhat: unit vectors for orthogonal coordinate systems local to each triangular element. that ("t-hat") extends from vertex one to vertex two, nhat is outward normal, and dhat = cross (nhat, that). Reference: 1. Nikkhoo, M., & Walter, T. R. (2015). Triangular dislocation: an analytical, artefact-free solution. Geophysical Journal International, 201(2), 1119-1141.

Data and models for: Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks

Data (ver 1.1) and trained models for our paper "<a href="https://arxiv.org/abs/2409.13851">Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks</a>". If you use such data or models, please cite our paper. These three directories need to be downloaded and copied into our source codes in order to reproduce our paper: <a href="https://github.com/learningmatter-mit/PerovskiteOrderingGCNNs">https://github.com/learningmatter-mit/PerovskiteOrderingGCNNs</a> <ul> <li>data: All data files for training and evaluating GCNNs, with a copy archived on the Materials Data Facility (<a href="https://doi.org/10.18126/ncqt-rh18">DOI: 10.18126/ncqt-rh18</a>)</li> <li>saved_models: All saved model files for evaluating GCNNs</li> <li>best_models: All best model files for evaluating GCNNs</li> </ul>