Open-source Tool Research Articles

The Expertise Finder: Using NASA ADS for Panel Reviewer Selection

Astronomical research is often made possible by the utilization of different resources that support members of the community in their research endeavors (e.g., telescopes access, or funds). Often, the entities that provide these resources establish a competitive process, which include the submission, by interested parties, of research proposals describing the research objectives and how the resources will be used to reach them. These proposals are evaluated by “ad hoc” panels of researchers, that provide a ranked list of the proposals based on their scientific relevance and other factors. This is also true for many NASA funding opportunities. In this article we present an open-sourced tool, the Expertise Finder, that simplifies the reviewers identification process. Expertise Finder provides NASA officials ways to scout prospective reviewers at small, less resourced, institutions that may have fewer opportunities to present and publish their research work with respect to colleagues at larger institutions.

Analysis of the Research Status of Traditional Chinese Medicine Pension Industry Based on Bibliometrics

At present, the problem of population aging in China is becoming increasingly prominent, and the combination of traditional Chinese medicine and elderly care is becoming a hot topic. In this paper, VOSviewer was used to statistically and visually analyze the literature related to traditional Chinese medicine pension in CNKI database from 2013 to 2023. Firstly, we collected and sorted out the research literature on the Chinese medicine pension industry in recent years, and then used bibliometric methods to quantitatively analyze the quantity, quality and research fields of the literature. The study found that the number of research literature on the elderly care industry of traditional Chinese medicine has increased year by year, the research quality has been continuously improved, and the research field has gradually expanded. At the same time, we also found that the research hotspots of traditional Chinese medicine for the elderly care industry are mainly focused on elderly care services, traditional Chinese medicine health preservation, rehabilitation treatment, etc (Ge & Xu, 2023). In addition, this paper also puts forward the shortcomings and development directions of the research on the Chinese medicine pension industry in China and hopes that this study can provide useful reference and guidance for the development of the Chinese medicine pension industry. Based on the China National Knowledge Infrastructure (CNKI) database as the main data source, this paper uses the topic search method in advanced search to search the CNKI database, and confirms that “Chinese medicine for the elderly” is used to read a large number of literature and understand the health care of traditional Chinese medicineAs the theme, the document format was set to see the full text only and Chinese and English extensions, the search time range was set from January 1th, 2013 to December 31th, 2023, the document type was set to academic journals, and the journal source category was set to all journals, a total of 149 articles were obtained, excluding news, publicity, interviews, correspondence and other literature that did not meet the research theme, and finally a total of 149 articles were obtained by manual screening 126 articles in Chinese. VOSviewer is an open-source network analysis tool, mainly used for scientific literature analysis, which can help users carry out visual analysis and network construction and is suitable for analyzing the research status of a certain field. In this paper, the VOSviewer visual analysis tool was used to process and analyze the literature data of 126 academic journals included in CNKI from 2013 to 2023, and the knowledge map of Chinese medicine pension in China was drawn, so as to visually analyze the author cooperation, hot topics and cutting-edge trends of Chinese medicine pension research in China.

DeltaLCA

Reducing the environmental footprint of electronics and computing devices requires new tools that empower designers to make informed decisions about sustainability during the design process itself. This is not possible with current tools for life cycle assessment (LCA) which require substantial domain expertise and time to evaluate the numerous chips and other components that make up a device. We observe first that informed decision-making does not require absolute metrics and can instead be done by comparing designs. Second, we can use domain-specific heuristics to perform these comparisons. We combine these insights to develop DeltaLCA, an open-source interactive design tool that addresses the dual challenges of automating life cycle inventory generation and data availability by performing comparative analyses of electronics designs. Users can upload standard design files from Electronic Design Automation (EDA) software and the tool will guide them through determining which one has greater carbon footprints. DeltaLCA leverages electronics-specific LCA datasets and heuristics and tries to automatically rank the two designs, prompting users to provide additional information only when necessary. We show through case studies DeltaLCA achieves the same result as evaluating full LCAs, and that it accelerates LCA comparisons from eight expert-hours to a single click for devices with ~30 components, and 15 minutes for more complex devices with ~100 components.

Making head and neck cancer clinical data Findable-Accessible-Interoperable-Reusable to support multi-institutional collaboration and federated learning

Abstract Objectives Federated learning (FL) is a group of methodologies where statistical modelling can be performed without exchanging identifiable patient data between cooperating institutions. To realize its potential for AI development on clinical data, a number of bottlenecks need to be addressed. One of these is making data Findable-Accessible-Interoperable-Reusable (FAIR). The primary aim of this work is to show that tools making data FAIR allow consortia to collaborate on privacy-aware data exploration, data visualization, and training of models on each other’s original data. Methods We propose a “Schema-on-Read” FAIR-ification method that adapts for different (re)analyses without needing to change the underlying original data. The procedure involves (1) decoupling the contents of the data from its schema and database structure, (2) annotation with semantic ontologies as a metadata layer, and (3) readout using semantic queries. Open-source tools are given as Docker containers to help local investigators prepare their data on-premises. Results We created a federated privacy-preserving visualization dashboard for case mix exploration of 5 distributed datasets with no common schema at the point of origin. We demonstrated robust and flexible prognostication model development and validation, linking together different data sources—clinical risk factors and radiomics. Conclusions Our procedure leads to successful (re)use of data in FL-based consortia without the need to impose a common schema at every point of origin of data. Advances in knowledge This work supports the adoption of FL within the healthcare AI community by sharing means to make data more FAIR.

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

Predicting protein retention in ion-exchange chromatography using an open source QSPR workflow.

Protein-based biopharmaceuticals require high purity before final formulation to ensure product safety, making process development time consuming. Implementation of computational approaches at the initial stages of process development offers a significant reduction in development efforts. By preselecting process conditions, experimental screening can be limited to only a subset. One such computational selection approach is the application of Quantitative Structure Property Relationship (QSPR) models that describe the properties exploited during purification. This work presents a novel open-source Python tool capable of extracting a range of features from protein 3D models on a local computer allowing total transparency of the calculations. As open-source tool, it also impacts initial investments in constructing a QSPR workflow for protein property prediction for third parties, making it widely applicable within the field of bioprocess development. The focus of current calculated molecular features is projection onto the protein surface by constructing surface grid representations. Linear regression models were trained with the calculated features to predict chromatographic retention times/volumes. Model validation shows a high accuracy for anion and cation exchange chromatography data (cross-validated R2 of 0.87 and 0.95). Hence, these models demonstrate the potential of the use of QSPR to accelerate process design.

Advancing crop classification in smallholder agriculture: A multifaceted approach combining frequency-domain image co-registration, transformer-based parcel segmentation, and Bi-LSTM for crop classification.

Agricultural Remote Sensing has the potential to enhance agricultural monitoring in smallholder economies to mitigate losses. However, its widespread adoption faces challenges, such as diminishing farm sizes, lack of reliable data-sets and high cost related to commercial satellite imagery. This research focuses on opportunities, practices and novel approaches for effective utilization of remote sensing in agriculture applications for smallholder economies. The work entails insights from experiments using datasets representative of major crops during different growing seasons. We propose an optimized solution for addressing challenges associated with remote sensing-based crop mapping in smallholder agriculture farms. Open source tools and data are used for inter and intra-sensor image registration, with a root mean square error of 0.3 or less. We also propose and emphasize on the use of delineated vegetation parcels through Segment Anything Model for Geospatial (SAM-GEOs). Furthermore a Bidirectional-Long Short-Term Memory-based (Bi-LSTM) deep learning model is developed and trained for crop classification, achieving results with accuracy of more than 94% and 96% for validation sets of two data sets collected in the field, during 2 growing seasons.

EXAMINING THE EFFECT OF MOROCCAN INFORMAL MICROENTERPRISES’ ATTRIBUTES ON THE DECISION TO TRANSITION INTO A FORMAL STATUS

Microenterprises have long been neglected in theoretical debates on the informal sector, receiving limited attention and superficial analysis. The aim of this article is to study the influence of the characteristics of informal microenterprises in Morocco on the decision to move to the formal sector. The article adopts a quantitative approach, focusing on 500 informal microentrepreneurs in northern Morocco. Through descriptive statistics and the use of a binomial logistic regression model, the study focuses on the effects of multiple aspects of these businesses. Data is collected and analyzed using the open-source tool R. The survey results reveal that engagement in a service-providing activity, the availability of business premises, and hiring more than four workers positively affect the propensity to formalize Moroccan informal microenterprises. Similarly, the use of simplified accounting systems, a longer period of existence and recourse to subcontracting are factors that encourage these ventures to opt for formalization. These findings are of key theoretical importance, exploiting the singularity of the Moroccan context and challenging the adoption of the neoliberal approach to explaining informality in Morocco. The conclusions are accompanied by recommendations designed to stimulate the transition to a formal sector. Implications of these findings for policy-makers, practitioners and entrepreneurs are discussed.

An open-source acoustic detector for beluga whales, with evaluations in the Western Canadian Arctic

Belugas (Delphinapterus leucas) face threats from various sources, including noise from oil and gas exploration, vessel traffic, and other human activities. Here we present the development of a deep learning-based acoustic detector to automatically detect the species, and measure its performance when applied to study sites in the western Canadian Arctic. We used over 20,000 individually annotated beluga vocalizations to train deep learning models in the binary task of classifying 3-second audio clips into containing beluga vocalizations or not. Approximately 7,000 annotated vocalizations were reserved for testing, and models were evaluated on their ability to correctly label audio clips of two lengths: 3 s and 60 s. The average F1 score (across 10 models) on 3 s clips was 0.82 with a standard deviation of 0.027, with the best model achieving 0.86. When applied to 60 s clips, the best model achieved an F1 score of 0.96. We used the trained classifier to build a detector that processes longer recordings and will make it available as an open-source tool.

From tomographic imaging to numerical simulations: an open-source workflow for true morphology mesoscale FE meshes

Full-field techniques such as tomography are becoming progressively more central in the study of complex phenomena, in particular where spatiotemporal evolution is crucial, as in moisture transport or crack initiation in porous media. These techniques provide a unique insight in the local process whose quantification allows the improvement of our understanding and of the models describing them. Nevertheless, the model validation can be pushed further by attempting to explicitly represent the heterogeneities and simulate their role in the processes. Once validated, these models can be used to perform “virtual experiments”, and overcome the limitations of the experiments (e.g., sample size and number, fine control of the boundary and initial conditions). This study proposes a connection between tomography images and mesoscale models through a workflow that mainly employs open-source tools. This workflow is illustrated through the digitization of a Portland cement concrete sample, stemming from neutron tomographies and resulting in a numerical finite element mesh. The proposed workflow is flexible, allowing for the conversion of images from various sources, such as x-ray or neutron tomographies, to different numerical representations of the domain, such as finite element meshes or even a discrete domain required by discrete element methods, while preserving real morphologies with an accuracy proportionate to the specific need of the problem. Beside its generalizability, our method also offers automated labelling of the different domains and boundaries in both the volumetric and surface meshes, which is often necessary for assigning material properties and boundary conditions. Finally, the series of image, geometry and mesh processing steps described in this work are made available on a GitHub repository.

CALIBRATION OF MICROSCOPIC TRAFFIC SIMULATION OF URBAN ROAD NETWORK INCLUDING MINI-ROUNDABOUTS AND UNSIGNALIZED INTERSECTION USING OPEN-SOURCE SIMULATION TOOL

Microscopic traffic simulation models offer an effective way to analyze and assess different transportation systems thanks to their efficiency and reliability. As traffic management issues become more prevalent, notably in urban areas, simulation tools enable a significant opportunity to replicate real-world conditions before implementation. Therefore, the calibration of traffic simulation models plays a substantial role in obtaining accurate and confidential results. Nowadays, urban regions are facing the challenge of restricted space for developing traffic solutions. As a consequence of environmental restrictions, the use of mini-roundabouts rather than larger roundabouts is increasing. Based on the given literature review, it is seen that not much attention was given to the complex modeling and calibration of microsimulation models of mini-roundabouts and unsignalized intersections. The objective of this study is to offer the calibration of microscopic traffic simulation of urban road network, including closely located mini-roundabouts and unsignalized intersection. To this end, an open-source tool called SUMO (Simulation of Urban Mobility) was utilized as a simulation environment in this study. The necessary data for developing a microsimulation model in SUMO was gathered using a videography technique. The traffic count data and speed were considered performance measures between field observations and simulation outputs. The routeSampler tool of SUMO, which has recently emerged in the literature, was used to match traffic count data and the corresponding time interval for traffic volume data calibration. The calibration of car-following model parameters using a trial-and-error approach was employed based on mean absolute percent error (MAPE) between simulated speeds and field-measured speeds. According to the findings of the study, the simulation model fulfilled the calibration aims of the FHWA guideline and is suitable for further research.

Detecting ringed seal vocalizations in multiple environments using deep learning

Deep learning methods have recently been successfully applied to create a variety of automated acoustic detectors in the field of marine bioacoustics. Automated detectors are essential for analyzing large volumes of passive acoustic monitoring (PAM) data since manual analysis is prohibitively time-consuming and costly. PAM is the primary method for obtaining data on species which are endemic to remote regions, such as the Canadian Arctic. Arctic ringed seals are listed as a Species of Special Concern in Canada due to a loss of critical habitat caused by the effects of climate change. Here, ResNet, a convolutional neural network architecture, is trained on thousands of examples of ringed seal vocalizations recorded at various locations within the Canadian Arctic to create the first practical automated ringed seal detector. The network achieves a precision of 0.89, recall of 0.80, and F1 score of 0.85 when tested on 215 five-minute recordings from sites included in the training process. To improve the generalizability of the detector for new locations, fine-tuning is performed using a small subset of annotated data from new sites. The detector will be available as an open-source tool for researchers to use as the basis for further development of new automated detectors.

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets.

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. The procedures are aimed at researchers in the life sciences with modest computational expertise, who use volume electron microscopy and need to generate three-dimensional (3D) segmentation labels for different types of cell organelles while minimizing manual annotation efforts, to analyze the spatial interactions between organelle instances and to visualize the 3D segmentation results. We provide detailed guidelines for choosing well-suited segmentation tools for specific cell organelles, and to bridge compatibility issues between freely available open-source tools, we distribute the critical steps as easily installable Album solutions for deep learning segmentation, spatial analysis and 3D rendering. Our detailed description can serve as a reference for similar projects requiring particular strategies for single- or multiple-organelle analysis, which can be achieved with computational resources commonly available to single-user setups.

Semi-automated approaches for interrogating spatial heterogeneity of tissue samples

Tissues are spatially orchestrated ecosystems composed of heterogeneous cell populations and non-cellular elements. Tissue components’ interactions shape the biological processes that govern homeostasis and disease, thus comprehensive insights into tissues’ composition are crucial for understanding their biology. Recently, advancements in the spatial biology field enabled the in-depth analyses of tissue architecture at single-cell resolution, while preserving the structural context. The increasing number of biomarkers analyzed, together with whole tissue imaging, generate datasets approaching several hundreds of gigabytes in size, which are rich sources of valuable knowledge but require investments in infrastructure and resources for extracting quantitative information. The analysis of multiplex whole-tissue images requires extensive training and experience in data analysis. Here, we showcase how a set of open-source tools can allow semi-automated image data extraction to study the spatial composition of tissues with a focus on tumor microenvironment (TME). With the use of Lunaphore COMET platform, we interrogated lung cancer specimens where we examined the expression of 20 biomarkers. Subsequently, the tissue composition was interrogated using an in-house optimized nuclei detection algorithm followed by a newly developed image artifact exclusion approach. Thereafter, the data was processed using several publicly available tools, highlighting the compatibility of COMET-derived data with currently available image analysis frameworks. In summary, we showcased an innovative semi-automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using a simple slide in, data out approach. Our workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the tissue composition.

Securing Federated Platforms

As the social media landscape undergoes broad transformation for the first time in over a decade, with alternative platforms like Mastodon, Bluesky, and Threads emerging where X has receded, many users and observers have celebrated the promise of these new services and their visions of alternative governance structures that empower consumers. Drawing on a large-scale textual analysis of platform moderation policies, capabilities, and transparency mechanisms, as well as semistructured group interviews with developers, administrators, and moderators of federated platforms, we found that federated platforms face considerable obstacles to robust and scalable governance, particularly with regard to persistent threats such as coordinated behavior and spam. Key barriers identified include underdeveloped moderation technologies and a lack of sustainable financial models for trust and safety work. We offer four solutions to the collective safety and security risks identified: (1) institutionalize shared responses to critical harms, (2) build transparent governance into the system, (3) invest in open-source tooling, and (4) enable data sharing across instances.

Numerical investigation of three-dimensional natural convection heat transfer on corrugated plates of variable height

PurposeThe purpose of this study is to numerically investigate the geometric influence of different corrugation profiles (rectangular, trapezoidal and triangular) of varying heights on the flow and the natural convection heat transfer process over isothermal plates.Design/methodology/approachThis work is an extension and finalization of previous studies of the leading author. The numerical methodology was proposed and experimentally validated in previous studies. Using OpenFOAM® and other free and open-source numerical-computational tools, three-dimensional numerical models were built to simulate the flow and the natural convection heat transfer process over isothermal corrugation plates with variable and constant heights.FindingsThe influence of different geometric arrangements of corrugated plates on the flow and natural convection heat transfer over isothermal plates is investigated. The influence of the height ratio parameter, as well as the resulting concave and convex profiles, on the parameters average Nusselt number, corrected average Nusselt number and convective thermal efficiency gain, is analyzed. It is shown that the total convective heat transfer and the convective thermal efficiency gain increase with the increase of the height ratio. The numerical results confirm previous findings about the predominant effects on the predominant impact of increasing the heat transfer area on the thermal efficiency gain in corrugated surfaces, in contrast to the adverse effects caused on the flow. In corrugations with heights resulting in concave profiles, the geometry with triangular corrugations presented the highest total convection heat transfer, followed by trapezoidal and rectangular. For arrangements with the same area, it was demonstrated that corrugations of constant and variable height are approximately equivalent in terms of natural convection heat transfer.Practical implicationsThe results allowed a better understanding of the flow characteristics and the natural convection heat transfer process over isothermal plates with corrugations of variable height. The advantages of the surfaces studied in terms of increasing convective thermal efficiency were demonstrated, with the potential to be used in cooling systems exclusively by natural convection (or with reduced dependence on forced convection cooling systems), including in technological applications of microelectronics, robotics, internet of things (IoT), artificial intelligence, information technology, industry 4.0, etc.Originality/valueTo the best of the authors’ knowledge, the results presented are new in the scientific literature. Unlike previous studies conducted by the leading author, this analysis specifically analyzed the natural convection phenomenon over plates with variable-height corrugations. The obtained results will contribute to projects to improve and optimize natural convection cooling systems.

The Design and Development of Hands-on Activities for Digital Forensics Education

It has been widely admitted by researchers and educators that hands-on activities are a core component in digital forensics education to help students gain practical skills that are needed in real-world forensic investigations. However, it is not clear in existing works about what kinds of hands-on activities are recommended to be integrated into a digital forensics course and how to design and develop them. In our teaching practice, hands-on activities for a digital forensics course are designed in three categories: 1) activities that assist students in learning how to use common digital forensics tools; 2) activities that help students gain in-depth understanding of the basic concepts and fundamental knowledge that are presented in class lectures; 3) activities that promote students the development of mindsets and data analytical skills that are needed for a digital forensic investigator. Various formats are employed to develop these hands-on exercises in different categories. The educational objectives and student learning outcomes map well to the CAE-CD (Centers of Academic Excellence - Cyber Defense) outcomes by completing their forensic knowledge units. In this paper, we share our idea and experience to design and implement such hands-on assignments in each category for meeting specific educational objectives. Sample exercises are briefly described to explain our idea in each category. Open source tools and data sets are introduced for references. Experiences, lessons, and sample feedback from students are discussed. Our results will provide a point of reference for those who teach digital forensics courses at a college or university, or are developing a digital forensic curriculum.

DEIMoS GUI: An Open-Source User Interface for a High-Dimensional Mass Spectrometry Data Processing Tool.

We report here the creation of a graphical user interface (GUI) for the Data Extraction for Integrated Multidimensional Spectrometry (DEIMoS) tool. DEIMoS is a Python package that processes data from high-dimensional mass spectrometry measurements. It is divided into several modules, each representing a data processing step such as peak detection, alignment, and tandem mass spectra extraction and deconvolution. The inputs for and outputs from DEIMoS can include millions of N-dimensional data points, which can be challenging to visualize in a way that is interactive, informative, and responsive. Here, we used the HoloViz Python data visualization stack, including DataShader and Param, to create an interactive visualization of the mass spectrometry data. We believe the GUI will increase the accessibility of DEIMoS and that the visualization methods could be useful for other open-source mass spectrometry tools.

Can lateral tenodesis improve the rotational stability of the ACL reconstruction? A finite element analysis.

One of the most common knee injuries is the Anterior Cruciate Ligament (ACL) rupture with severe implications on knee stability. The usual treatment is the ACL Reconstruction (ACLR) surgery where the surgeon replaces the torn ligament with a graft in an effort to restore knee kinematics. In case of excessive rotatory instability, Lateral Extra-Articular Tenodesis (LET) can be performed in combination with ACLR. Additionally, LET appears to reduce ACLR graft forces minimizing graft failure chances. However, there are concerns about overconstraining physiological rotation. To gain insight in this controversial topic, we developed an automatic, open-source tool to create a series of Finite Element (FE) models attempting to investigate the interactions of ACLR and LET through simulation. We started by creating a validated model of the healthy knee joint that served as reference for subsequent FE simulations. Then, we created FE models of standalone ACLR and combined ACLR-LET. Each model was assessed by applying a loading profile that resembles the reduction phase of the Pivot-Shift clinical exam. We measured the External Tibia Rotation (ETR), the Posterior Tibia Translation (PTT) of the lateral tibial compartment, and the ACLR graft stress developed around the femoral tunnel insertion site. We observed the following: a) LET reduces ETR and PTT compared to isolated ACLR, b) combined ACLR-LET is more sensitive to LET graft pretension with lower values showcasing performance closer to the healthy joint, c) LET reduces ACLR graft forces for the same pretension values, d) LET exhibits significant overconstraint for higher pretension values. In general, these findings are in agreement with relevant clinical studies and accentuate the potential of the developed framework as a tool that can assist orthopaedists during surgery planning. We provide open access for the FE models of this study to enhance research transparency, reproducibility and extensibility.

Cyber5Gym: An Integrated Framework for 5G Cybersecurity Training

The rapid evolution of 5G technology, while offering substantial benefits, concurrently presents complex cybersecurity challenges. Current cybersecurity systems often fall short in addressing challenges such as the lack of realism of the 5G network, the limited scope of attack scenarios, the absence of countermeasures, the lack of reproducible, and open-sourced cybersecurity training environments. Addressing these challenges necessitates innovative cybersecurity training systems, referred to as “cyber ranges”. In response to filling these gaps, we propose the Cyber5Gym, an integrated cyber range that enhances the automation of virtualized cybersecurity training in 5G networks with cloud-based deployment. Our framework leverages open-source tools (i) Open5GS and UERANSIM for realistic emulation of 5G networks, (ii) Docker for efficient virtualization of the training infrastructure, (iii) 5Greply for emulating attack scenarios, and (iv) Shell scripts for automating complex training operations. This integration facilitates a dynamic learning environment where cybersecurity professionals can engage in real-time attack and countermeasure exercises, thus significantly improving their readiness against 5G-specific cyber threats. We evaluated it by deploying our framework on Naver Cloud with 20 trainees, each accessing an emulated 5G network and managing 100 user equipments (UEs), emulating three distinct attack scenarios (SMC-Reply, DoS, and DDoS attacks), and exercising countermeasures, to demonstrate the cybersecurity training. We assessed the effectiveness of our framework through specific metrics such as successfully establishing the 5G network for all trainees, accurate execution of attack scenarios, and their countermeasure implementation via centralized control of the master using automated shell scripts. The open-source foundation of our framework ensures replicability and adaptability, addressing a critical gap in current cybersecurity training methodologies and contributing significantly to the resilience and security of 5G infrastructures.