Find research datasets worth reusing

opencc-by-4.0Feb 2024View details →

HANZE v2.2 flood impact model input data

This dataset provides input data needed to run HANZE v2.2 model. The ZIP files need to be downloaded and unpacked in the same directory, which has to be defined in "get_file.py" of the HANZE model (variable "repo_path" at the beginning of the file).

opencc-zeroFeb 2024View details →

Data from: Dynamic models for impact-initiated stress waves through snow columns

The objective of this research is to model snow's response to dynamic, impact loading. Two constitutive relationships are considered: elastic and Maxwell-viscoelastic. These material models are applied to laboratory experiments consisting of 1000 individual impacts across 22 snow column configurations. The columns are 60 cm tall with a 30 cm by 30 cm cross-section. The snow ranges in density from 135-428 kg m-3 and is loaded with both short-duration (~1 ms) and long-duration (~10 ms) impacts. The Maxwell-viscoelastic model more accurately describes snow's response because it contains a mechanism for energy dissipation, which the elastic model does not. Furthermore, the ascertained model parameters show a clear dependence on impact duration; shorter duration impacts resulted in higher wave speeds and greater damping coefficients. The stress wave's magnitude is amplified when it hits a stiffer material because of the positive interference between incident and reflected waves. This phenomenon is observed in the laboratory and modeled with the governing equations.

opencc-zeroFeb 2024View details →

Data from: Combining mesocosms with models to unravel the effects of global warming and ocean acidification on a temperate marine ecosystem

Ocean warming and species exploitation have already caused large-scale reorganization of biological communities across the world. Accurate projections of future biodiversity change require a comprehensive understanding of how entire communities respond to global change. We combined a time-dynamic integrated food web modelling approach (Ecosim) with previous data from community-level mesocosm experiments to determine the independent and combined effects of ocean warming and acidification, and fisheries exploitation, on a well-managed temperate coastal ecosystem. The mesocosm parameters enabled important physiological and behavioural responses to climate stressors to be projected for trophic levels ranging from primary producers to top predators, including sharks. Through model simulations, we show that under sustainable rates of exploitation, near-future warming or ocean acidification in isolation could benefit species biomass at higher trophic levels (e.g., mammals, birds, and demersal finfish) in their current climate ranges, with the exception of small pelagic fish. However, under warming and acidification combined biomass-increases at higher trophic levels will be lower or absent, whilst in the longer term reduced productivity of prey species is unlikely to support the increased biomass at the top of the food web. We also show that increases in exploitation will suppress any positive effects of human-driven climate change, causing individual species biomass to decrease at higher trophic levels. Nevertheless, total future potential biomass of some fisheries species in temperate areas might remain high, particularly under acidification, because unharvested opportunistic species will likely benefit from decreased competition and show an increase in biomass. Ecological indicators of species composition such as the Shannon diversity index declined under all climate change scenarios, suggesting a trade-off between biomass gain and functional diversity. By coupling parameters from multi-level mesocosm food web experiments with dynamic food web models, we were able to simulate the generative mechanisms that drive complex responses of temperate marine ecosystems to global change. This approach, which blends theory with experimental data, provides new prospects for forecasting climate-driven biodiversity change and its effects on ecosystem processes.

opencc-zeroFeb 2024View details →

Data from: Exploring the multi-level impacts of a youth-led comprehensive sexuality education model in Madagascar using human-centered design methods

Comprehensive sexuality education (CSE) is recognized as a critical tool for addressing sexuality and reproductive health challenges among adolescents. However, little is known about the broader impacts of CSE on populations beyond adolescents, such as schools, families, and communities. This study explores multi-level impacts of an innovative CSE program in Madagascar, which employs young adult CSE educators to teach a three-year curriculum in government middle schools across the country. The two-phased study embraced a participatory approach and qualitative Human-centered Design (HCD) methods. In phase 1, 90 school principals and administrators representing 45 schools participated in HCD workshops, which were held in six regional cities. Phase 2 took place one year later, which included 50 principals from partner schools, and focused on expanding and validating findings from phase 1. From the perspective of school principals and administrators, the results indicate several areas in which CSE programming is having spill-over effects, beyond direct adolescent student sexuality knowledge and behaviors. In the case of this youth-led model in Madagascar, the program has impacted the lives of students (e.g., increased academic motivation and confidence), their parents (e.g., strengthened family relationships and increased parental involvement in schools), their schools (e.g., increased perceived value of schools and teacher effectiveness), their communities (e.g., increased community connections), and impacted broader structural issues (e.g., improved equity and access to resources such as menstrual pads). While not all impacts of the CSE program were perceived as positive (e.g., students start experimenting with sex and love), the findings uncovered opportunities for targeting investments and refining CSE programming to maximize positive impacts at family, school, and community levels.

opencc-by-4.0Aug 2022View details →

Data and code for the article "Advancing Fine Branch Biomass Estimation with LiDAR and Structural Models"

This is the repository for the data and code to reproduce the article "Advancing Fine Branch Biomass Estimation with LiDAR and Structural Models". Summary: ·  Background and Aims Lidar is a promising tool for fast and accurate measurements of trees. There are several approaches to estimate aboveground woody biomass using lidar point clouds. One of the most widely used methods involves fitting geometric primitives (e.g. cylinders) to the point cloud, thereby reconstructing both the geometry and topology of the tree. However, current algorithms are not suited for accurate estimation of the volume of finer branches, because of the unreliable point dispersions from e.g. beam footprint compared to the structure diameter. ·  Methods We propose a new method that couples point cloud-based skeletonization and multi-linear statistical modelling based on structural data to make a model (structural model) that accurately estimates the aboveground woody biomass of trees from high-quality lidar point clouds, including finer branches. The structural model was tested at segment, axis, and branch level, and compared to a cylinder fitting algorithm and to the pipe model theory. ·  Key Results The model accurately predicted the biomass with 1.6% nRMSE at the segment scale from a k-fold cross-validation. It also gave satisfactory results when up-scaled to the branch level with a significantly lower error (13% nRMSE) and bias (-5%) compared to conventional cylinder fitting to the point cloud (nRMSE: 92%, bias: 82%), or using the pipe model theory (nRMSE: 31%, bias: -27%). The model was then applied to the whole-tree scale and showed that the sampled trees had more than 1.7km of structures on average and that 96% of that length was coming from the twigs (i.e. <5 cm diameter). Our results showed that neglecting twigs can lead to a significant underestimation of tree aboveground woody biomass (-21%). ·  Conclusions The structural model approach is an effective method that allows a more accurate estimation of the volumes of smaller branches from lidar point clouds. This method is versatile but requires manual measurements on branches for calibration. Nevertheless, once the model is calibrated, it can provide unbiased and large-scale estimations of tree structure volumes, making it an excellent choice for accurate 3D reconstruction of trees and estimating standing biomass.

Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters

Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the data collection and statistical methods, sampling and ecological variation, and sample size. N-mixture models are a common method which has been applied to a wide range of taxa for estimating population abundance from non-invasive data representing the distribution of the species. We used population estimates from an aerial survey of moose and videos from camera traps to assess the sensitivity of N-mixture models to ecological conditions, the spatial scale at which they were measured, the criteria used to define independent detections, and model choice based on the common statistical criterion of parsimony. The most parsimonious N-mixture models were considerably biased, producing implausibly large and considerably imprecise estimates of the abundance of moose. Most of the other models produced estimates of abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N-mixture models were not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggest that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N-mixture model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps can provide valuable information on behavior of the target species as well as insights into multiple species in the community.

The magnetic recording stability of vortex state irregularly shaped natural iron oxides: raw data, processing and micromagnetic modelling results

Magnetic minerals, especially those existing within the vortex domain state, serve as the primary natural archives of ancient magnetic fields. In this investigation, we introduce an innovative method to examine the magnetic stability of remanence-bearing minerals. This method involves integrating Synchrotron-based Ptychographic X-ray Computed Nano-tomography (PXCT) with micromagnetic modelling. PXCT, a tomographic technique, is a non-destructive resource, which enables its application to valuable (unique) samples. When applied to a microscopic sample of weakly magnetic carbonate rock, PXCT revealed numerous nanoscopic grains of magnetite/maghemite, each exhibiting diverse morphologies, alongside various non-magnetic minerals present in the rock matrix. Subsequently, micromagnetic models were employed to predict the properties of these grains and investigate the potential impacts of irregular morphologies.

openmit-licenseMar 2024View details →

Data from: Genome-scale annotation of protein binding sites via language model and geometric deep learning

The dataset contains the training and test sets of protein binding sites with DNA, RNA, peptide, protein, ATP, HEM, Zn2+, Ca2+, Mg2+ and Mn2+. Each protein is associated with 3 lines indicating the protein name (PDB accession code and chain), sequence and residue labels (0 for non-binding and 1 for binding), respectively. The ESMFold-predicted structures are also provided.

Foot kinematics and kinetics data for different static foot posture collected using a multi-segment foot model

Dataset presented in the paper "Foot kinematics and kinetics data for different static foot posture collected using a multi-segment foot model". This dataset contains human foot joints kinematics and kinetics data collected during walking, classified depending on their static foot posture. The kinematics data were recorded using a three-dimensional motion analysis system, and kinetics data were recorded through a pressure platform. The data was collected considering a multi-segment foot model that considers the ankle, midtarsal and first metatarsophalangeal joint. A total of 70 healthy subjects with different static posture (highly pronated, highly supinated and normal, as classified by the foot posture index) participated in the experiments. This dataset contains a total of 350 continuous recordings of anatomical angles and joint moments of the ankle, midtarsal, and first metatarsophalangeal joints of the right foot during walking, as well as the right foot contact pressures recorded. The recordings were collected at 100 Hz, and the resulting data are provided filtered and resampled to 100 frames evenly distributed along the stance phase. Participants’ descriptive data are also provided: age, weight, height, and foot anthropometric data and foot posture index for both feet. The data are presented as a spreadsheet file (.xlsx) and a Matlab structure file (.mat), with contact pressures provided only in the .mat file. Further details and data validation are provided in the main paper.

Data for: A new threshold selection method for species distribution models with presence-only data: extracting the mutation point of the P/E curve by threshold regression

Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real-world applications and model assessments. Prevalent threshold selection methods for presence-only data require unproven pseudo-absence data or subjective researchers' decisions. This study proposes a new method, Boyce-Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo-absence data. We summarize that the mutation point is a typical shape feature of the predicted-to-expected (P/E) curve after reviewing relevant articles. Analysis based on source-sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point. To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods.

Data from: Genetic susceptibility, Mendelian randomization and nomogram model construction of gestational diabetes mellitus

The dataset contains subjects' basic information, including the Identification number of the test sample, fasting plasma glucose (FPG), oral glucose tolerance test 1h plasma glucose (1hPG), oral glucose tolerance test 2h plasma glucose (2hPG), glycated hemoglobin (HbA1c), Systolic blood pressure (SBP), Diastolic blood pressure (DBP), triglyceride (TG), total cholesterol (TC), High-density lipoprotein cholesterol (HDL-c), Low-density lipoprotein cholesterol (LDL-c), and also involves subjects' genetic variant information used for analysis of the association of functional polymorphisms and GDM. The variables including SBP_M, DBP_M, FPG_M, 1hPG_M, 2hPG_M, HbA1c_M, TG_M represent the mean value of SBP, DBP, FPG, 1hPG, 2hPG, HbA1c, TG, which are used for the stratification analysis. This study has obtained the support from the Ethics Committee of Guilin Medical University. All included subjects signed the informed consent.

Data from: A shared pattern of midfacial bone modelling in hominids suggests deep evolutionary roots for human facial morphogenesis

Midfacial morphology varies between hominoids, in particular between great apes and humans for which the face is small and retracted. The underlying developmental processes for these morphological differences are still largely unknown. Here we investigate the cellular mechanism of maxillary development (bone modelling), and how potential changes in this process may have shaped facial evolution. We analysed cross-sectional developmental series of gibbons, orangutans, gorillas, chimpanzees and present-day humans (N=183). Individuals were organized into five age groups according to their dental development. To visualize each species' bone modelling pattern and corresponding morphology during ontogeny, maps based on microscopic data were mapped onto species-specific age group average shapes obtained using geometric morphometrics. The amount of bone resorption was quantified and compared between species. Great apes share a highly similar bone modelling pattern, whereas gibbons have a distinctive resorption pattern. This suggests a change in cellular activity on the hominid branch. Humans possess most of the great ape pattern, but bone resorption is high in the canine area from birth on, suggesting a key role of canine reduction in facial evolution. We also observed that humans have high levels of bone resorption during childhood, a feature not shared with other apes.

Data from the micrometeorological tower of the Antarctic Modeling Observation System (ATMOS) project of the 40th Brazilian Antarctic Operation (OPERANTAR XL) to calculate the CO2 flux (FCO2)

Micrometeorological tower data obtained by the "Antarctic Modeling Observation System" (ATMOS) project. These data were collected in the southern summer of 2021/2022 during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate CO2 fluxes (FCO2). A 9 m high metal micrometeorological tower was installed on the bow of the Polar Ship Almirante Maximiano, 8.75 m above sea level, for sampling atmospheric variables. In the tower, the Motion Pack II was installed, on the main shaft of the tower, to determine the ship's movement in the orthogonal directions xp, yp and zp of the platform's coordinate system, at a frequency of 20 Hz. As well as, a GPS (Global Positioning System) and an electronic compass, to determine the ship's geographic position and speed. The movement of the ship influences wind speed measurements, and as a solution, a wind speed correction was carried out. An IRGASON sensor (Campell Scientific®) was placed on the secondary shaft of the tower, configured to perform measurements at 20 Hz. The IRGASON has an open path infrared gas analyzer that measures the concentrations of CO2 and water vapor (H2O), a three-dimensional sonic anemometer, which measures the 3 vector components of the wind, and a thermohygrometer that measures air temperature and humidity. From the IRGASON collections, it is possible to calculate the CO2 flux using the Vortex Covariance method.

LI-COR (LI-850) sensor data obtained by the Antarctic Modeling Observation System (ATMOS) project during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate the partial pressure of CO2 (pCO2)

LI-COR (LI 850) sensor data obtained by the "Antarctic Modeling Observation System" (ATMOS) project. These data were collected in the southern summer of 2021/2022 during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate the partial pressure of seawater CO2 (pCO2sea) The LI-850 carbon dioxide analyzer was installed in the laboratory aft of H41 together with a balancer to measure the CO2 concentration of the water. The collection system occurs as follows: the ship's saltwater piping system collects seawater, when this water enters the balancer it generates turbulence. The turbulence generated causes the CO2 present in the water to come into balance with the air. The air that comes out of the balancer is pumped into the LI-850, by its internal pump, and thus, the equipment measures the concentration of CO2 present in the water. To ensure that the air inside the balancer is actually balanced with the seawater, the air leaving the LI-850 is pumped back into the balancer, closing the circuit. From these data it is possible to calculate pCO2sea.

Empirical data and model simulations of the effect of repeated hurricanes on soil carbon dynamics in a humid tropical forest

Increasing hurricane frequency and intensity with climate change is likely to affect soil organic carbon (C) stocks in tropical forests. We examined the cycling of C between soil pools and with depth at the Luquillo Experimental Forest in Puerto Rico in soils over a 30-year period that spanned repeated hurricanes. We used a non-linear matrix model of soil C pools and fluxes ("soilR") and constrained the parameters with soil and litter survey data. Soil chemistry and stable and radiocarbon isotopes were measured from three soil depths across a topographic gradient in 1988 and 2018. Our results suggest that pulses and subsequent reduction of inputs caused by severe hurricanes in 1989, 1998, and two in 2017 led to faster mean transit times and younger mean ages of soil C in the particulate, occluded, and mineral-associated soil organic matter pools at 0–10 cm and 35–60 cm depths relative to a modeled control soil with constant inputs over the thirty years. Between 1988 and 2018, the occluded C stock increased, and d13C in all pools decreased, while changes in particulate and mineral-associated C were undetectable. The differences between 1988 and 2018 suggest that hurricane disturbance results in a dilution of the occluded light C pool with an influx of young, debris-deposited C, and possible microbial scavenging of old and young C in the particulate and mineral-associated pools. These effects led to a younger total soil C pool with faster mean transit times. Our results suggest that increasing frequency of intense hurricanes will speed up rates of C cycling in tropical forests, and eventually lead to net losses of C from tropical forest soils.

opencc-by-4.0Nov 2021View details →

Data for "A stochastic model of geomorphic risk due to episodic river aggradation and degradation"

The code and the dataset can be read/run by using Matlab. The description as follows: 1. Dataset of riverbed measurement (long profile and water level gauge data), carbon dating data, and rainfall record in the Laonong River (Taiwan). The dataset are used for the model calibration and the model application.  2. The developed riverbed stochastic processing model and the maximum likelihood calibration model.  Note: this new version includes the corrected Monte Carlo simulation code and a required Matlab function (fminsearchbnd.m) that was missing in the first version.

opencc-by-4.0Apr 2024View details →

Data and R code for machine learning modelling of favourite places and routes of outdoor recreation

This is a script showing the analysis used in a paper submitted for review in Landscape and Urban Planning, titled "Seeing through their eyes: Revealing recreationists’ landscape preferences through viewshed analysis and machine learning", by Carl Lehto, Marcus Hedblom, Anna Filyushkina and Thomas Ranius.  The zip file contains an R script, data saved in .rds format and a R workspace.

opencc-by-4.0Apr 2024View details →

Data for: A protocol for model intercomparison of impacts of Marine Cloud Brightening Climate Intervention

Replication data for "A protocol for model intercomparison of impacts of Marine Cloud Brightening Climate Intervention" submitted to Geophysical Model Development.

opencc-by-4.0Apr 2024View details →

HANZE v2.3 flood impact model input data

This dataset provides input data needed to run HANZE v2.3 model. The ZIP files need to be downloaded and unpacked in the same directory, which has to be defined in "get_file.py" of the HANZE model (variable "repo_path" at the beginning of the file).

Data and Software of "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids"

<h1>Id: Data of following publication</h1> title = "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids", journal = "Composite Structures", volume =" 348", pages = "118503", year = "2025", doi = "<a href="https://doi.org/10.1016/j.compstruct.2024.118503" target="_blank" rel="noopener">10.1016/j.compstruct.2024.118503</a>", author = "José Rothkegel, Benjamin Renson, Michael Bruyneel, Ludovic Noels" Data doi on 10.5281/zenodo.10829042 <h1>pyRVE</h1> <h2>Python Code for Geometrical Generator for Braided Composites RVE</h2> pyRVE is a code written in Python using the GMSH API that generates the Representative Unit Cell (RUC) of braided composites. It allows the generation of the RUC of triaxial braided for Diamond and Regular patterns. <h2>Requirements</h2> To run, it requires: <ul> <li>The GMSH Python API, which must be built with OpenCascade support. <ul> <li>Choose a local installation directory; <code>CMAKE_INSTALL_PREFIX=$HOME/local/gmsh</code>, and <code>GMSHPY_INSTALL_DIRECTORY=$HOME/local/gmsh</code> e.g.;</li> <li>Make that directory part of your <code>export PYTHONPATH=$HOME/local/gmsh/lib:$PYTHONPATH</code>.</li> <li>After compiling use <code>make install</code>.</li> </ul> </li> <li>The CM3 app dG3D if the final RVE homogenized solution is needed (<a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a>).</li> <li>Make sure that the latest version of OpenCascade (OCCT) is used. Current used version in occt-V7.8.0.</li> </ul> <h2>Usage</h2> <h3>File Structure</h3> A typical run case must have a file structure, where: <ul> <li><code>brd</code>: the files <code>.brd</code> and <code>.brep</code> are located here. The <code>.brd</code> is a backup of the <code>braidClass</code> instance used in the model saved using <code>pickle</code>, the <code>.brep</code> is the Boundary Representation file that can be opened with GMSH.</li> <li><code>csv</code>: the <code>.csv</code> file saved here is the initial output of the code. It contains the actually used dimensions and the final cover factor of the braid.</li> <li><code>data</code>: It contains <code>.csv</code> files with the material properties and the dimensions of the tows. The original model dimensions are read from here.</li> <li><code>dir</code>: In the case of running the RVE homogenization, the directions of the tow fibers are stored here. They are saved for post processing.</li> <li><code>msh</code>: the mesh file <code>.msh</code> obtained after the geometry geneartion is stores here.</li> <li><code>png</code>: in the case of automatic post processing, png files are stored here.</li> <li><code>res</code>: this folder is used to store the homogenization results. They have to be moved here.</li> <li><code>stp</code>: if acitvated, a <code>.stp</code> file of the geometry is stored here</li> <li><code>svg</code>: the projection of the geometry on the x-y plane is stored here.</li> <li><code>vtk</code>: A copy of the mesh file without the matrix mesh is sotred here as a `.vtk`` file.</li> </ul> <h3>How to Run</h3> We will consider the current file structure to run the example in 000_Base. To run the code, it can be called from the command prompt as <div> <pre><code>python3 ../../source/mainRVE.py --name --pattern <pattern></code></pre> </div> In this case, the <code>--name</code> refers to the index that will be given to the model, where <code></code> must be changed to an integer and <code>--pattern</code> refers to the wanted pattern to be used, where <code><pattern></code> must be changed to either <code>dia</code> or <code>reg</code>. <blockquote> Note: <code><code>--name</code>cat</code> can also be used to reproduce the regular pattern benchmark of the paper. In that case, the volume fraction of fiber in the tows is hard coded as the provided value in the reference (i.e. 0.86). For other cases, the volume fraction is evaluated from the tow cross-sections. Note: <code>mainRVE.py</code> must be accesible from the directory where the case is being run. This example shows the usage of the current file structure. </blockquote> <h3>All Command Line Options</h3> The code can be run using further options that serve different purpouses, some serving pre processing needs and other serving run administration. The different command line options are: <ul> <li>Required: <ul> <li><code>--name</code> : it gives a suffix to the run model. It is usually an integer.</li> <li><code>--pattern</code> : indicates the type of pattern to be used to build the geometry. The two current options are <code>dia</code> for diamond and <code>reg</code> for regular.</li> </ul> </li> <li>Optional <ul> <li><code>-dG3D</code>: it indicates that the homogenization of the generated RUC is to be perfomed.</li> <li><code>-GMSH</code> : it indicates that GMSH must be open upon competion of the generation of the mesh.</li> <li><code>-loadModel</code> : it will try to load a premade model. It will ignore <code>--pattern</code>.</li> <li><code>--rndPrm</code> : it will generate randomized geometrical parameters. It can be used to generate batches of results. It takes an argument that can be <code>2</code>, <code>4</code> or <code>6</code>. Currently, <code>2</code> gives a random value for <code>s_axial</code> and <code>theta</code>, <code>4</code> randomizes the same as <code>2</code> and adds <code>h_axial</code> and <code>h_bias</code>, and <code>6</code> randomizes the same as <code>4</code> and adds <code>w_axial</code> and <code>w_bias</code>.</li> </ul> </li> <li>Pre-Processing <ul> <li><code>-refCF</code>: it tells the code to generate a grid of values for <code>s_axial</code> and <code>theta</code> where only the cover factor is obtained. It is meant for posterior graphing purposes.</li> </ul> </li> </ul> <h3>Examples</h3> Following the run options, a few examples are indicated <ul> <li>A basic mesh generation run for the basic data, considering a regular pattern, for a model named 2:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg</code></pre> </div> <ul> <li>The generation of the cover factor data and export, considering a regular pattern:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --pattern reg -refCF</code></pre> </div> <ul> <li>A run for the modified basic data, where the 2 parameters are modified randomly, considering a regular pattern, for a model named 2:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg --rndPrm 2</code></pre> </div> <ul> <li>A run, where model 2 already exists in <code>brd</code> folder but not the <code>.msh</code> and <code>.vtk</code> files:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 -loadModel </code></pre> </div> <h2>Code Structure</h2> The code is implemented into Python files, where <code>mainRVE.py</code> runs the whole code. The files are: <ul> <li>Braid: <ul> <li><code>braidClass.py</code> :</li> <li><code>bzrPairClass.py</code> :</li> </ul> </li> <li>Geometry <ul> <li><code>bezrClass.py</code> :</li> <li><code>bilnClass.py</code> :</li> <li><code>patchClass.py</code> :</li> <li><code>pntSetClass.py</code> :</li> <li><code>pointClass.py</code> :</li> <li><code>sctnClass.py</code> :</li> <li><code>stripeClass.py</code> :</li> <li><code>surfClass.py</code> :</li> <li><code>surfOffClass.py</code> :</li> </ul> </li> <li>Material: <ul> <li><code>chamis.py</code> :</li> </ul> </li> <li>Tools: <ul> <li><code>dataIO.py</code> :</li> <li><code>postDirection.py</code> :</li> <li><code>tool.py</code> :</li> <li><code>toolData.py</code> :</li> </ul> </li> <li><code>curveClass.py</code> :*</li> </ul>