Find research datasets worth reusing

opencc-by-4.0Aug 2018View details →

Trained neural network data for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro

This archive contains data representing a trained-up neural network suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. The network generates coefficients that can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>. In this particular dataset, networks were trained on a training set of coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a> that is available as <a href="https://doi.org/10.5281/zenodo.1341154">DOI:10.5281/zenodo.1341154</a>. The data were generated using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, were: <ul> <li>s, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li>theta, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li>p, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> The training set was computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each, yielding about 22 million numbers. Training the networks took about 3 hours on an 8-core laptop. For the purposes of neurosynchro, the formats of the files in this package should be regarded as internal implementation details. The <a href="https://pypi.org/project/neurosynchro/">neurosynchro</a> Python package will load up the files in this archive and use them to predict synchrotron coefficients. For specifics, see <a href="https://neurosynchro.readthedocs.io/en/stable/">the neurosynchro documentation</a>.

opencc-by-4.0Feb 2019View details →

Data and code for: Community structure in co-inventor networks affects time to first citation for patents

This package provides the datasets and programming code needed to reproduce the results reported in the article "Community structure in co-inventor networks affects time to first citation for patents". v2: Added data and code pertaining to randomized-community-association test and updated README file.

opencc-by-4.0Jul 2019View details →

Network data for the paper: Intellectual and social similarity among scholarly journals.

Network data used for the analysis contained in Baccini A, Barabesi L, Gingras Y, Kalfaoui M (2019) Intellectual and social similarity among scholarly journals. An exploratory comparison of the networks of editors, authors and co-citations. Data are in .net format for Pajek software CC indicates co-citation network. IA indicated Interlocking authorship network. IE indicates interlocking editorship network. Stat is for statistics; Econ is for economics; ILS is for information and library science.

opencc-by-4.0Sep 2019View details →

Handschriftencensus data for Network of Shared Manuscript Transmission

List of nodes and edges to create networks of shared manuscripts transmission with the data from Handschriftencensus (http://handschriftencensus.de) The files can be uploaded directly to Gephi and can be easily adapted to use in other softwares for network analysis.  Reading the Gephi documentation should be enough to understand the fields used.

opencc-by-4.0Dec 2018View details →

Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis

This dataset contains the results of 2 related analyses, described in "Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis" (European Heart Journal Supplements, OUP). Link to the article: https://www.hal.inserm.fr/inserm-02310241 1) In the directory "miRNAs_MARTHA_GWAS" : GWAS summary statistics for 162 circulating miRNAs in 344 VTE patients from the MARTHA cohort. Header for each summary file: Trait: miRNA id chr: Chromosome pos.hg19: Position of the variant in hg19/GRCh37 coordinates SNP: rsid A1: Reference allele on the forward strand A2: Alternate allele on the forward strand freq_A1: Frequency of reference allele rsqr: Imputation quality defined by MACH beta_A1: Estimated effect size (beta regression coefficient) of reference allele se_A1: Estimated standard error of beta p: p-value (significance of estimated beta) z.score: Z-score   2) In the directory "meta_analysis": Random effect meta-analysis combining the results of our GWAS on the MARTHA cohort, and the results from a similar analysis conducted by Nikpay et al. (doi: 10.1093/cvr/cvz030). Summary statistics of 142 microRNAs, common to both datasets, were processed (and combine 1054 samples). Header for each summary file: chr: Chromosome pos.hg19: Position of the variant in hg19/GRCh37 coordinates SNP: rsid A1: Reference allele on the forward strand A2: Alternate allele on the forward strand N: Sample size Q: Cochran's heterogeneity statistic Q.p: p-value of Cochran's Q beta_A1: Estimated effect size (beta regression coefficient) of reference allele se_A1: Estimated standard error of beta p: p-value (significance of estimated beta)

opencc-by-4.0Oct 2019View details →

The North American Monsoon GPS Hydrometeorological Network 2017: Flux and Precipitation Data

Water, energy and carbon fluxes and ancillary meteorological measurements and precipitation data taken during The North American Monsoon GPS-Hydrometeorological Network 2017. The experiment was carried out during the summer of 2017 in the state of Sonora in northwestern Mexico.

opencc-by-4.0Apr 2019View details →

Plant health data from Belgian Plant Sentinel Network

The enclosed files contain the questions (in Dutch, English & French) and the responses from tree surveys conducted under the Belgian Plant Sentinel Network. Background to the project Botanic gardens and arboreta possess outstanding scientific collections of a wide diversity of plants, frequently growing outside their natural geographical range. These can be used as sentinels for both early detection of emerging pests and for potentially invasive pests. This is why the International Plant Sentinel Network was launched by EUPHRESCO (EUropean PHytosanitary RESearch COordination), an international network of organisations funding research projects and coordinating national research programmes in the phytosanitary area. International Network developed a transnational network consisting of gardens, diagnostic laboratories and National Plant Protection Organisations, working together in order to provide an early warning system for new and emerging plant pests and diseases. It focused on developing tools and took place in a limited number of countries. Belgium did not participate in the first phase of International Plant Sentinel Network, even though there are many botanic gardens and arboreta in the country. The largest of them is Meise Botanic Garden, which is one of the largest botanic gardens in the world (92 ha).  Besides Meise there are several gardens with diverse and unique collections spread all over the country. Among these gardens many have staff members with good knowledge of plant protection, which assure the control of pests and diseases in the collections of the gardens. On the other hand, up to now there have been only limited interactions between the gardens and the National Reference Laboratories for plant pests and diseases. It would be interesting to strengthen these interactions, both for the gardens and arboreta, for a better management and protection of their living collections, and for the Reference Laboratories, in order to gather more data on the presence of pests and diseases in the country. That is why we have created a Belgian network similar to international network, aiming at supporting national plant health policy by early warning of new pest threats, and meanwhile operating in the new transnational initiative of the international network. Together they can form a dense Belgian network to gather data and expand the surveillance of emerging pests over the entire national territory. They also hold most of the plant diversity present in the whole country. Standardized methods and tools for making the plant health surveys have been developed by the International Plant Sentinel Network during its first phase from 2013 to 2016. One of these tools is the Plant Health Checker, a form for making standardized surveys of trees. Two versions are available, one for surveying deciduous trees and one for conifers, as the symptoms to watch for differ between these two groups in some cases. Each form is available in English as a paper form with two sides. Moreover, a reference guide with instructions on how to use the checker had also been made with it, as well as a guide to taking photographs for diagnostic purposes. For the Belgian project we adapt these forms in order to facilitate their use. We translated them to French and Dutch, the two main languages of the country, so that all gardeners could use them. A second goal was to adapt them to the test cases selected for the project. Indeed, some symptoms that are important for these organisms are not included in the original plant heath checker, mainly concerning the symptoms on roots for the root-knot nematode and Phytoplasma case. A third aim was to investigate whether the forms could be simplified for making the surveys in the field. For the latter, however, it was decided to await the user experience of the first year, so as to make an evaluation and suggest eventual amendments. It is also planned to develop an electronic version of the checker form, so as to be able to input the survey data directly in digital format and thus skipping a second and tedious step of entering the data from paper forms into the computer. Moreover, it is preferable to have a system which can easily make the data available to a central data system. That is why we chose to use Google Forms within Google Drive, because it can be utilized as a central data system and flexible, and has many functionalities such as sharing the data. This system also allows the direct input of data with an internet connection.

opencc-by-4.0Dec 2019View details →

Fig. 5. Parsimony splits network constructed from a per and ITS2 concatenated sequence data set. Heterozygous specimens are indicated with A and B in Ecological and geographical speciation in Lucilia bufonivora: The evolution of amphibian obligate parasitism

Fig. 5. Parsimony splits network constructed from a per and ITS2 concatenated sequence data set. Heterozygous specimens are indicated with A and B. 'bufonivora_EUROPE_A' represents a consistent haplotype present in all 12 samples from Europe (Table 1), of which just two were heterozygous ('bufonivora_frog' and 'bufonivora_NLWi'). 'bufonivora_CAN' and 'elongata_CAN' are represented by two samples each, none of which were heterozygous. Scale bar represents expected changes per site.

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.)

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.

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)

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.

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

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>