Public Repositories Research Articles

Automated generation of scientific workflow generators with WfChef

Scientific workflow applications have gained significant importance, and their automated and efficient execution on large-scale computing platforms has been the subject of extensive research and development. For these efforts to be successful, a solid experimental methodology is needed to evaluate workflow algorithms and systems. A foundation for this methodology is the availability of realistic workflow instances. Although public repositories provide workflow instances for a few scientific applications, these are limited in scope, and workflow instances are not available for all application scales of interest. To address this limitation, previous work has developed generators of synthetic workflow instances of arbitrary scales. Despite being popular, the implementation of these generators is a manual and labor-intensive process that requires expert application knowledge. As a result, these generators only target a handful of applications, even though there are hundreds of workflow applications in production.We introduce WfChef , a fully automated framework for constructing a synthetic workflow generator for any scientific application. Based on an input set of workflow instances for a particular application, WfChef automatically produces a synthetic workflow generator. To measure the realism of the generated workflows, we define and evaluate several metrics. Using these metrics, we compare the realism of the workflows generated by WfChef generators to that of the workflows generated by the previously available, hand-crafted generators. We find that WfChef generators not only require zero development effort (because they are automatically produced), but also generate workflows that are more realistic than those generated by hand-crafted generators.

CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII

Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine clinical practice. The workflow of radiomics is complex due to several methodological steps and nuances, which often leads to inadequate reporting and evaluation, and poor reproducibility. Available reporting guidelines and checklists for artificial intelligence and predictive modeling include relevant good practices, but they are not tailored to radiomic research. There is a clear need for a complete radiomics checklist for study planning, manuscript writing, and evaluation during the review process to facilitate the repeatability and reproducibility of studies. We here present a documentation standard for radiomic research that can guide authors and reviewers. Our motivation is to improve the quality and reliability and, in turn, the reproducibility of radiomic research. We name the checklist CLEAR (CheckList for EvaluAtion of Radiomics research), to convey the idea of being more transparent. With its 58 items, the CLEAR checklist should be considered a standardization tool providing the minimum requirements for presenting clinical radiomics research. In addition to a dynamic online version of the checklist, a public repository has also been set up to allow the radiomics community to comment on the checklist items and adapt the checklist for future versions. Prepared and revised by an international group of experts using a modified Delphi method, we hope the CLEAR checklist will serve well as a single and complete scientific documentation tool for authors and reviewers to improve the radiomics literature.

ARID1A Mutation from Targeted Next-Generation Sequencing Predicts Primary Resistance to Gemcitabine and Cisplatin Chemotherapy in Advanced Biliary Tract Cancer.

There are clinical unmet needs in predicting therapeutic response and precise strategy for the patient with advanced biliary tract cancer (BTC). We aimed to identify genomic alterations predicting therapeutic response and resistance to gemcitabine and cisplatin (Gem/Cis)-based chemotherapy in advanced BTC. Genomic analysis of advanced BTC multi-institutional cohorts was performed using targeted panel sequencing. Genomic alterations were analyzed integrating patients' clinicopathologic data, including clinical outcomes of Gem/Cis-based therapy. Significance of genetic alterations was validated using clinical next-generation sequencing (NGS) cohorts from public repositories and drug sensitivity data from cancer cell lines. 193 BTC patients from three cancer centers were analyzed. Most frequent genomic alterations were TP53 (55.5%), KRAS (22.8%), ARID1A (10.4%) alterations, and ERBB2 amplification (9.8%). Among 177 patients with BTC receiving Gem/Cis-based chemotherapy, ARID1A alteration was the only independent predictive molecular marker of primary resistance showing disease progression for 1st-line chemotherapy in the multivariate regression model (odds ratio, 3.12; p=0.046). In addition, ARID1A alteration was significantly correlated with inferior progression-free survival on Gem/Cis-based chemotherapy in the overall patient population (p=0.033) and in patients with extrahepatic cholangiocarcinoma (CCA) (p=0.041). External validation using public repository NGS revealed that ARID1A mutation was a significant predictor for poor survival in BTC patients. Investigation of multi-OMICs drug sensitivity data from cancer cell lines revealed that cisplatin-resistance was exclusively observed in ARID1A mutant bile duct cancer cells. Integrative analysis with genomic alterations and clinical outcomes of the first-line Gem/Cis-based chemotherapy in advanced BTC revealed that patients with ARID1Aalterations showed a significant worse clinical outcome, especially in extrahepatic CCA. Well-designed prospective studies are mandatory to validate the predictive role of ARID1Amutation.

Computational approaches for circRNAs prediction and in silico characterization.

Circular RNAs (circRNAs) are single-stranded and covalently closed non-coding RNA molecules originated from RNA splicing. Their functions include regulatory potential over other RNA species, such as microRNAs, messenger RNAs and RNA binding proteins. For circRNA identification, several algorithms are available and can be classified in two major types: pseudo-reference-based and split-alignment-based approaches. In general, the data generated from circRNA transcriptome initiatives is deposited on public specific databases, which provide a large amount of information on different species and functional annotations. In this review, we describe the main computational resources for the identification and characterization of circRNAs, covering the algorithms and predictive tools to evaluate its potential role in a particular transcriptomics project, including the public repositories containing relevant data and information for circRNAs, recapitulating their characteristics, reliability and amount of data reported.

DCServCG: A data-centric service code generation using deep learning

Modern software development paradigms, including Service-Oriented Architecture (SOA), tend to make use of available services e.g., web service Application Programming Interfaces (APIs) to generate new software. Thus, for the further advancement of SOA, the development of accurate automatic tasks, such as service discovery and composition, is necessary. Most of these automated tasks rely heavily on web service metadata annotation. The lack of machine-readable documentation and structured metadata reduces the accuracy and volume of automatic data annotation, negatively affecting the performance of automated SOA tasks. This study aims to propose automatic code completion for improving web service-based systems by identifying and capturing service usage collected from public repositories that share Open Source Software (OSS). To this end, a Data-Centric Service Code Generation (DCServCG) model is proposed to improve old-fashioned, general-purpose code generators that neglect essential service-based code characteristics e.g., sequence overlap and bias issues. DCServCG takes advantage of the data-centric concept, i.e., conditional text generation, to overcome the mentioned issues. We have evaluated the approach from the point of view of language modeling metrics. The obtained results indicate that the usage of the data-centric approach reduces perplexity by 1.125. Moreover, the DCServCG model uses de-noising and conditional text generation, which is trained on the transformer by distilling the knowledge, DistilGPT2 (82M parameters) trained faster and its perplexity is 0.363 lower than ServCG (124M parameters) without de-noising and conditional text generation, which lower perplexity value indicates better model generalization performance.

Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.

Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.

Mutual Information-Based Modeling for Services Dependency

Web services composition has drawn a great attention in computing industries to build complex and large systems. However, web services composition modeling has major challenges, including dependency determination, complexity, user requests dependency, handling cycles within a composition, service redundancy and scalability concerns. The service dependency graph (SDG) between services in a repository should be accurate to ensure the quality of composition and the associated user requests' responses. Despite of the crucial importance of accurate dependencies for adequate web services compositions, current modeling approaches do not provide any metric to evaluate the dependency between services for quality estimation. In this paper, the Mutual Information-based Services Dependency (MISD) model is proposed as a graph-based modeling approach, independent to any given user request. It constructs services dependency graphs based on Web Services Mutual Information (WSMI), a proposed modified version of mutual information as the dependency metric, along with other criteria for an accurate, efficient dependency evaluation. It finds the optimum composition representing the structure of the web services composition in a repository rather than the path of given user requests. The experimental dependency analysis emphasizes the efficiency of the generated OC and accuracy of the constructed SDG to be 76% and 86% higher than the state-of-the-art models respectively. The time cost to build the SDG and to find the OC is reduced dramatically up to 99% for different public repositories compared to the state-of-the-art studies as the number of user requests increases.

Pulmonary Mycobacterium tuberculosisinfection alters the hepatic immunometabolic landscape and compromises insulin responsiveness early after infection

Abstract Immunity against Mycobacterium tuberculosis(Mtb) is associated with both regional and systemic inflammation as well as with metabolic disruption in tuberculosis (TB) patients. The metabolic and immunological responses are intertwined but the specific impact of localized disease on systemic metabolism is not well defined. We investigated the impact of low dose aerosol infection of the lung with Mtbon the systemic metabolic regulatory organ – the liver. Using an unbiased approach we defined the mRNA and protein dysregulation occurring in the early stage of infection. We found that infection leads to immune pathway activation and reduced metabolic activity in liver. Comparison of the gene expression patterns between of the lung, liver and blood revealed organ specific metabolic gene dysregulation in the context of non-organ specific activation of interferon response pathways. We have also found that infection leads to suppression of carbohydrate and lipid metabolism, without alteration of fasting or random blood glucose levels. Infected mice show reduced sensitivity to exogenous insulin but the glucose tolerance remains intact indicating insulin resistance. Analysis of metabolomics data from public repository reveals a metabolic signature of insulin resistance correlates with TB progression. Taken together, our finding shows that aerosol Mtbinfection influences hepatic metabolism very soon after infection and may contribute to early metabolic disease. Defining the early interplay between localised infection, inflammation and metabolic regulation is key to understanding the impact of latent and subclinical TB on the large proportion of the World’s population who do not exhibit TB symptoms but who are infected with Mtb. Supported by Grants from Royal Society Wolfson Merit Award (AMC) Newton Fellowship (MKD), Wellcome Trust Institutional Support Fund Fellowship (MKD), MRC award MR/P011136/1 (AMC).

Lessons Learned from a National Initiative Promoting Publicly Available Standards-Based Clinical Decision Support.

Clinical decision support (CDS), which provides tools to assist clinical decision-making, can improve adherence to evidence-based practices, prevent medical errors, and support high-quality and patient-centered care delivery. Publicly available CDS that uses standards to express clinical logic (i.e., standards-based CDS) has the potential to reduce duplicative efforts of translating the same clinical evidence into CDS across multiple health care institutions. Yet development of such CDS is relatively new and its potential only partially explored. This study aimed to describe lessons learned from a national initiative promoting publicly available, standards-based CDS resources, discuss challenges, and report suggestions for improvement. Findings were drawn from an evaluation of the Agency for Healthcare Research and Quality Patient-Centered Outcomes Research CDS Initiative, which aimed to advance evidence into practice through standards-based and publicly available CDS. Methods included literature and program material reviews, key informant interviews, and a web-based survey about a public repository of CDS artifacts and tools for authoring standards-based CDS. The evaluation identified important lessons for developing and implementing standards-based CDS through publicly available repositories such as CDS Connect. Trust is a critical factor in uptake and can be bolstered through transparent information on underlying evidence, collaboration with experts, and feedback loops between users and developers to support continuous improvement. Additionally, while adoption of standards among electronic health record developers will make it easier to implement standards-based CDS, lower-resourced health systems will need extra support to ensure successful implementation and use. Finally, although we found the resources developed by the Initiative to offer valuable prototypes for the field, health systems desire more information about patient-centered, clinical, and cost-related outcomes to help them justify the investment required to implement standards-based, publicly available CDS. While the standards and technology to publicly share standards-based CDS have increased, broad dissemination and implementation remain challenging.

Characterization of Deep Learning-Based Speech-Enhancement Techniques in Online Audio Processing Applications.

Deep learning-based speech-enhancement techniques have recently been an area of growing interest, since their impressive performance can potentially benefit a wide variety of digital voice communication systems. However, such performance has been evaluated mostly in offline audio-processing scenarios (i.e., feeding the model, in one go, a complete audio recording, which may extend several seconds). It is of significant interest to evaluate and characterize the current state-of-the-art in applications that process audio online (i.e., feeding the model a sequence of segments of audio data, concatenating the results at the output end). Although evaluations and comparisons between speech-enhancement techniques have been carried out before, as far as the author knows, the work presented here is the first that evaluates the performance of such techniques in relation to their online applicability. This means that this work measures how the output signal-to-interference ratio (as a separation metric), the response time, and memory usage (as online metrics) are impacted by the input length (the size of audio segments), in addition to the amount of noise, amount and number of interferences, and amount of reverberation. Three popular models were evaluated, given their availability on public repositories and online viability, MetricGAN+, Spectral Feature Mapping with Mimic Loss, and Demucs-Denoiser. The characterization was carried out using a systematic evaluation protocol based on the Speechbrain framework. Several intuitions are presented and discussed, and some recommendations for future work are proposed.

Researchers propose preregistration in taxonomy to enhance transparency and credibility and reduce subjectivity

A new opinion piece published in Megataxa today (Conix et al. 2023) calls for preregistration in the field of taxonomy and discusses its potential benefits and objections. Preregistration is a practice for researchers to document their hypotheses, design and methods of their proposed studies in a public repository before they start the study and collect the data (Nosek et al. 2018). It is believed that preregistration can help reduce bias, increase transparency, and improve study design (especially replicability) of research (e.g. Nosek et al. 2018; Krypotos et al. 2022), including exploratory research such as some aspects of biodiversity discovery and taxonomy (Dirnagl et al. 2020). While preregistration has been already employed in other fields such as social sciences (e.g. Camerer et al. 2016, 2018) and ecology/evolution (e.g. Kelly 2019), it has yet been adopted in taxonomy. Conix et al. (2023) argue that preregistration could be beneficial for taxonomy, given the challenges it faces, such as subjectivity, disagreement, and credibility: 

COVMOS: A new Monte Carlo approach for galaxy clustering analysis

We validate the COVMOS method introduced in our previous study allowing for the fast simulation of catalogues of different cosmological field tracers (e.g. dark matter particles, halos, galaxies, etc.). The power spectrum and one-point probability distribution function of the underlying tracer’s density field are set as inputs of the method and arbitrarily chosen by the user. To evaluate the validity domain of COVMOS at the level of the produced two-point statistics covariance matrix, we chose to target these two input statistical quantities from realistic N-body simulation outputs. We performed this cloning procedure based on the ΛCDM as well as on a massive neutrino cosmology, for five redshifts in the range of z ∈ [0, 2]. First, we validated the output real-space two-point statistics (both in the configuration and Fourier space) and estimated over 5000 COVMOS realisations per redshift and per cosmology, with a volume of 1 [Gpc/h]3 and 108 particles each. This validation was performed against the corresponding N-body measurements, estimated from 50 simulations. We found the method to be valid up to k ∼ 0.2h Mpc−1 for the power spectrum and down to r ∼ 20 Mpc h−1 for the correlation function. Then, we extended the method by proposing a new modelling of the peculiar velocity distribution, aimed at reproducing the redshift-space distortions both in the linear and mildly non-linear regimes. After validating this prescription, we finally compared and validated the resulting redshift-space two-point statistics covariance matrices in the same range of scales. We released the Python code associated with this method on a public repository, which allows for the production of tens of thousands of realisations in record time. In particular, COVMOS is intended for any study involving large-scale galaxy-survey science that requires a large number of mock realisations.

DeepAngle: Fast calculation of contact angles in tomography images using deep learning

DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3-D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intensive solution is to correlate and vectorize all surfaces using an adaptable grid, and then measure the angles within the desired planes. On the contrary, the present study provides a rapid and low-cost technique powered by deep learning to estimate the interfacial angles directly from images. DeepAngle is tested on both synthetic and realistic images against the direct measurement technique and found to improve the r-squared 5 to 16%, while lowering the computational cost 20 times. This rapid method is especially applicable for processing large tomography data and time-resolved images, which is computationally intensive. The developed code and the dataset are available in a public repository on GitHub (https://www.github.com/ArashRabbani/DeepAngle).

Doubling down on best practices: reflecting on teaching physiology during the COVID-19 pandemic

As we pass the third anniversary of the World Health Organization's declaration of the SARS-CoV-2 global pandemic, it is beneficial to reflect upon how physiology education adapted to the challenges of the pandemic. At the initial stages of the pandemic, many educators were faced with the challenge of quickly transitioning to emergency remote teaching (ERT), requiring shifts in teaching methodology and laboratory structure to adapt to the pandemic normal. In this review, we provide a broad overview of the efforts made by the community of educators associated with the American Physiological Society during the pandemic to encourage best practices in teaching, maintain course and program goals during ERT, and innovate in physiology education. We also highlight diversity, equality, and inclusion work that was produced as the scientific community recommitted to tackling systemic and structural inequalities exacerbated by the pandemic and brought to the forefront by the Black Lives Matter movement. Finally, we examine the potentially long-lasting effects of the pandemic on education from both the student and faculty standpoints and how doubling down on what we learned can be beneficial to the future of physiology education.NEW & NOTEWORTHY This literature review examines and categorizes the repository of publications in physiology education that occurred during and related to the COVID-19 pandemic.

MANSOR: A module alignment method based on neighbor information for scientific workflow

SummaryFinding similar scientific workflow modules that can substitute essential components of the privacy workflows from public repositories to create a personalized workflow is growing in popularity. This is a cost‐effective and error‐free strategy for the scientific community. Currently, module alignment approaches heavily depend on syntactic information such as modules' names and types. However, the contextual semantic information of modules, which encompasses inputs, outputs, and datalinks connecting modules, can also convey their functions. Unfortunately, this information is scarcely utilized during the module alignment process. In this work, we propose a module alignment method based on neighbor information for scientific workflow (MANSOR). Specifically, we present a rule‐based attribute similarity computation approach for calculating initial module‐pair similarity in two workflows. The relation similarity is then employed to iteratively fine‐tune the matching degree with uncertainty of module pairs by considering the contextual semantics until reaching a steady state. After pairwise comparison of workflows, the module alignment results are determined based on their module‐pair similarity. Experimental results on the human‐curated corpus of ratings indicate that MANSOR outperforms the existing state‐of‐the‐art approaches with statistical significance.

Non-contact Storage Classification Based on Mechanical Arm in the Context of Prevention and Treatment of COVID-19

COVID-19 has become a global infectious disease, which has a high transmission rate, a wide transmission range, and a variety of transmission modes. Therefore, to reduce the transmission efficiency of COVID-19, human beings try to reduce the manpower ratio to create a non-contact environment in the industry. Therefore, a new model is proposed in this paper. This new model aims to be applied to storage classification in the context of the prevention and treatment of COVID-19 by using the mechanical robot arm. This model will use the UR3 robot arm, Logitech C300 web camera, and OpenCV public repository to implement the computer vision algorithm, and forward and inverse kinematics analysis. This model can successfully implement non-contact storage classification. Through the real experiment, this paper has a good application prospect. It gives a new solution for future research and experiment. In addition, in the summary and outlook part of this paper, new research directions in the future are given.

Celiac Disease Is a Risk Factor for Mature T and NK Cell Lymphoma: A Mendelian Randomization Study.

Celiac disease (CeD) is an immune-mediated disorder triggered by gluten ingestion that damages the small intestine. Although CeD has been associated with a higher risk for cancer, the role of CeD as a risk factor for specific malignancies, such as enteropathy-associated T-cell lymphoma (EATL), remains controversial. Using two-sample Mendelian randomization (2SMR) methods and the summarized results of large genome-wide association studies from public repositories, we addressed the causal relationship between CeD and eight different malignancies. Eleven non-HLA SNPs were selected as instrumental variables (IVs), and causality estimates were obtained using four 2SMR methods: random-effects inverse variance-weighted, weighted median estimation, MR-Egger regression, and MR pleiotropy residual sum and outlier (MR-PRESSO). We identified a significant causal relationship between CeD and mature T/NK cell lymphomas. Under a multivariate Mendelian randomization model, we observed that the causal effect of CeD was not dependent on other known lymphoma risk factors. We found that the most instrumental IV was located in the TAGAP locus, suggesting that aberrant T cell activation might be relevant in the T/NK cell malignization process. Our findings provide new insights into the connection between immune imbalance and the development of severe comorbidities, such as EATL, in patients with CeD.

AnimalAccML: An open-source graphical user interface for automated behavior analytics of individual animals using triaxial accelerometers and machine learning

Automated collection of accelerometer data and subsequent machine learning modeling are prevalent combined methods for animal behavior recognition. However, there is a lack of customized tools for user-friendly machine learning model development. Meanwhile, existing models in previous research could not be directly used for behavior interpretation. The objective of this study was to design and develop a tool for customized machine learning model development and animal behavior analysis using triaxial accelerometer data. A graphical user interface was programmed with Python and saved in a public repository for open access. The interface mainly consists of pages of ‘Manage Project’, ‘Preprocess Data’, ‘Develop Models’, and ‘Analyze Behavior’. An open dataset containing triaxial accelerometer data of six beef cattle was used to test the developed interface. The main results show that users can customize appropriate machine learning models for behavior analytics through several mouse clicks on the interface. A total of 15 models can be selected and trained to determine an optimal one, and model performance can be optimized by adjusting parameters of window size, step size, and training-to-validation ratio. Data imbalance can be solved by merging minority classes into one. The newly developed model has the capacity to analyze overall behavior time budget, statistics (e.g., mean, minimum, maximum, and standard deviation) of each behavior duration, and frequency of behavior sequences. The tool is supportive for automated animal behavior analytics critical to enhancing animal welfare, housing environment, genetics selection, and flock management.

A systematic literature review on long‐term localization and mapping for mobile robots

AbstractLong‐term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long‐term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta‐Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long‐term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co‐occurrence to identify the terms more used in long‐term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

Cross-verification and benchmarking analysis of electrodynamic tether simulators

Five electrodynamic tether simulators (BETsMA v2.0, DYNATETHER, EDTSim, FLEX, and TeMPEST) have been cross-verified by running and analysing simulations of increasing complexity. A set of ten simulations without any tether was run to test the orbital propagators and the implementation of the perturbation force due to the non-sphericity of the Earth and its non-homogeneous mass distribution. The environmental models of the five codes and their implementation were then cross-verified by analysing the evolution of the magnetic field and the plasma and atmospheric densities. The electric modules of the simulators for electrodynamic tethers working in the passive mode, i.e., the routines in charge of computing the current and the voltage profiles along the tethers as well as the Lorentz force, were compared by running simulations with bare tethers. Configurations with an ideal electron emitter (zero potential drop), a real emitter, and a resistor and an ideal emitter were considered, as well as round and tape tethers. The electric models of BETsMA, EDTSim, FLEX, and TeMPEST, which assumed a straight tether, were also compared with the result of DYNATETHER for curved tethers. The consistency of the five simulators as a whole was tested by preparing performance maps with the deorbit time versus orbit inclination for a reference scenario and considering tape and round electrodynamic tethers. Although they implement different models and make different assumptions, the results of the five codes are consistent for the full simulation campaign. The simulation data and the software to visualize them are available in a public repository.