BioIntertidal Mapper 2.0 is a user-friendly tool with a graphical user interface that automates Sentinel-2 processing (since 2017) for intertidal habitat mapping in Google Earth Engine (GEE) using the Normalized Difference Vegetation Index (NDVI). Unlike version 1.0, low-tide scenes are no longer selected using the WorldTides API; instead, images are screened by estimating the proportion of water pixels using the Normalized Difference Water Index. Reflectance inputs were updated to the Sentinel-2 Harmonized dataset. GEE authentication was simplified, the interface refined, and exports expanded to include RGB imagery alongside filtered NDVI products saved to Google Drive. The software enables rapid, reproducible operational mapping for scientists and coastal managers.
{"title":"Updated 2.0 to biointertidal mapper software: A satellite approach for NDVI-based intertidal habitat mapping","authors":"Sara Haro , Ricardo Bermejo , Lara Veylit , Liam Morrison","doi":"10.1016/j.softx.2026.102539","DOIUrl":"10.1016/j.softx.2026.102539","url":null,"abstract":"<div><div>BioIntertidal Mapper 2.0 is a user-friendly tool with a graphical user interface that automates Sentinel-2 processing (since 2017) for intertidal habitat mapping in Google Earth Engine (GEE) using the Normalized Difference Vegetation Index (NDVI). Unlike version 1.0, low-tide scenes are no longer selected using the WorldTides API; instead, images are screened by estimating the proportion of water pixels using the Normalized Difference Water Index. Reflectance inputs were updated to the Sentinel-2 Harmonized dataset. GEE authentication was simplified, the interface refined, and exports expanded to include RGB imagery alongside filtered NDVI products saved to Google Drive. The software enables rapid, reproducible operational mapping for scientists and coastal managers.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102539"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.softx.2026.102517
Marcos Jesús Sequera Fernández, Mohammadhossein Homaei, Óscar Mogollón Gutierrez, José Carlos Sancho Núñez
Foruster is a cross-platform desktop application, developed in Rust, for live-system forensic analysis. Unlike traditional tools that require system shutdown, Foruster is designed to identify and catalog files of interest on active storage volumes. Its user interface, built with the Slint framework, guides the analyst through the selection of devices, the configuration of search profiles, and the real-time visualization of results. The software features heuristic detection of anomalies, such as deceptive file extensions, and ensures the integrity of findings through cryptographic hashing, optimizing the digital forensic investigation process.
{"title":"Foruster: A cross-platform tool for live forensic triage and anomaly detection","authors":"Marcos Jesús Sequera Fernández, Mohammadhossein Homaei, Óscar Mogollón Gutierrez, José Carlos Sancho Núñez","doi":"10.1016/j.softx.2026.102517","DOIUrl":"10.1016/j.softx.2026.102517","url":null,"abstract":"<div><div>Foruster is a cross-platform desktop application, developed in Rust, for live-system forensic analysis. Unlike traditional tools that require system shutdown, Foruster is designed to identify and catalog files of interest on active storage volumes. Its user interface, built with the Slint framework, guides the analyst through the selection of devices, the configuration of search profiles, and the real-time visualization of results. The software features heuristic detection of anomalies, such as deceptive file extensions, and ensures the integrity of findings through cryptographic hashing, optimizing the digital forensic investigation process.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102517"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.softx.2026.102536
Floris den Hengst , Shaad Alaka , Bart A. Kamphorst
The rise of lifestyle-related, non-communicable diseases such as Type II diabetes, cardiovascular diseases, and depression has prompted the development of various behavior change technologies to promote sustained healthy behaviors. User adherence, however, has remained low.
The Collaborative Hybrid Intelligence Platform CHIP is introduced to address adherence challenges by placing the user perspective at the center and facilitating dialogue-based interactions between users and their technical and non-technical support systems—including AI systems, clinicians and caretakers. These interactions aim to uncover barriers to adherence and collaboratively shape personalized lifestyle plans that align with a person’s preferences, values, and context.
CHIP is a microservice-based research platform written in Python with modules implemented as Docker containers. Its modularity allows researchers to replace or adapt specific components, such as natural language reasoners, for technical evaluation and domain-specific adaptation.
{"title":"Collaborative hybrid intelligence platform CHIP: A modular architecture for developing and testing personalized lifestyle support interactions","authors":"Floris den Hengst , Shaad Alaka , Bart A. Kamphorst","doi":"10.1016/j.softx.2026.102536","DOIUrl":"10.1016/j.softx.2026.102536","url":null,"abstract":"<div><div>The rise of lifestyle-related, non-communicable diseases such as Type II diabetes, cardiovascular diseases, and depression has prompted the development of various behavior change technologies to promote sustained healthy behaviors. User adherence, however, has remained low.</div><div>The Collaborative Hybrid Intelligence Platform CHIP is introduced to address adherence challenges by placing the user perspective at the center and facilitating dialogue-based interactions between users and their technical and non-technical support systems—including AI systems, clinicians and caretakers. These interactions aim to uncover barriers to adherence and collaboratively shape personalized lifestyle plans that align with a person’s preferences, values, and context.</div><div>CHIP is a microservice-based research platform written in Python with modules implemented as Docker containers. Its modularity allows researchers to replace or adapt specific components, such as natural language reasoners, for technical evaluation and domain-specific adaptation.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102536"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.softx.2026.102526
O. Agost , F. Aran , J. Rius , P. Fraile , I. Barri , J. Vilaplana , J. Mateo
Simet provides a modular framework designed for the rigorous evaluation of synthetic image datasets. The framework integrates data provisioning, preprocessing, feature extraction, and complementary metrics, including Fréchet Inception Distance (FID), generative Precision/Recall, and classifier two-sample area under the receiver operating characteristic curve (ROC-AUC), within a single GPU-accelerated pipeline. A restraint mechanism enables declarative pass or fail gating. YAML- and command-line (CLI)-driven orchestration, shared feature caches, and structured logs facilitate reproducible, continuous-integration (CI)-ready workflows. Extensible abstractions, including providers, transforms, feature extractors, and metrics, allow practitioners to add new data sources or tests with minimal code. Templates support downstream utility evaluations, such as training on synthetic data and testing on real data (TSTR). Simet is positioned relative to existing toolkits, and protocols are outlined to demonstrate scalable, multidimensional evaluation of synthetic image data.
{"title":"Simet: Synthetic image metrics - a synthetic image evaluation framework","authors":"O. Agost , F. Aran , J. Rius , P. Fraile , I. Barri , J. Vilaplana , J. Mateo","doi":"10.1016/j.softx.2026.102526","DOIUrl":"10.1016/j.softx.2026.102526","url":null,"abstract":"<div><div>Simet provides a modular framework designed for the rigorous evaluation of synthetic image datasets. The framework integrates data provisioning, preprocessing, feature extraction, and complementary metrics, including Fréchet Inception Distance (FID), generative Precision/Recall, and classifier two-sample area under the receiver operating characteristic curve (ROC-AUC), within a single GPU-accelerated pipeline. A restraint mechanism enables declarative pass or fail gating. YAML- and command-line (CLI)-driven orchestration, shared feature caches, and structured logs facilitate reproducible, continuous-integration (CI)-ready workflows. Extensible abstractions, including providers, transforms, feature extractors, and metrics, allow practitioners to add new data sources or tests with minimal code. Templates support downstream utility evaluations, such as training on synthetic data and testing on real data (TSTR). Simet is positioned relative to existing toolkits, and protocols are outlined to demonstrate scalable, multidimensional evaluation of synthetic image data.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102526"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.softx.2026.102528
Vadym Chibrikov, Justyna Cybulska, Artur Zdunek
The issue of food quality and its control has become a daily routine for humanity, driven by both health and economic reasons. Among other components, plant cell wall components—such as cellulose, hemicellulose, and pectin—have several pro-health roles in the human organism that are barely discussed in public. To address this, there is a clear need for a portable digital framework built on accurate, accessible, and scientifically-proven data. Here, our commitment was the development of FibreApp, an Android/iOS mobile application that helps users obtain data on the chemical composition of common fruit and vegetable species available on the European market. FibreApp's architecture was designed as a hybrid local/offline system that integrates on-device machine learning for visual identification with a pre-loaded, unified database of fruit and vegetable compositional parameters. A machine learning-powered livestream tool for image classification of fruits and vegetables was included in the app by rigorously following a systematic image acquisition protocol, coupled with a transfer learning approach using pre-trained feature extractors to train the machine learning models. The latter performed well despite significant changes in lighting and diverse polar orientations, as well as during polyclass image classification. FibreApp was released and field-tested, positioning it to capture a niche in improving public awareness of fruits and vegetables as a source of functional polysaccharides.
{"title":"FibreApp: Mobile machine learning tool for fruit and vegetable fiber content","authors":"Vadym Chibrikov, Justyna Cybulska, Artur Zdunek","doi":"10.1016/j.softx.2026.102528","DOIUrl":"10.1016/j.softx.2026.102528","url":null,"abstract":"<div><div>The issue of food quality and its control has become a daily routine for humanity, driven by both health and economic reasons. Among other components, plant cell wall components—such as cellulose, hemicellulose, and pectin—have several pro-health roles in the human organism that are barely discussed in public. To address this, there is a clear need for a portable digital framework built on accurate, accessible, and scientifically-proven data. Here, our commitment was the development of <em>FibreApp</em>, an Android/iOS mobile application that helps users obtain data on the chemical composition of common fruit and vegetable species available on the European market. <em>FibreApp</em>'s architecture was designed as a hybrid local/offline system that integrates on-device machine learning for visual identification with a pre-loaded, unified database of fruit and vegetable compositional parameters. A machine learning-powered livestream tool for image classification of fruits and vegetables was included in the app by rigorously following a systematic image acquisition protocol, coupled with a transfer learning approach using pre-trained feature extractors to train the machine learning models. The latter performed well despite significant changes in lighting and diverse polar orientations, as well as during polyclass image classification. <em>FibreApp</em> was released and field-tested, positioning it to capture a niche in improving public awareness of fruits and vegetables as a source of functional polysaccharides.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102528"},"PeriodicalIF":2.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.softx.2026.102533
Beyzanur Siyah , Tolga Berber
This paper presents LumiX, an open-source Python library for mathematical optimization designed for data-intensive applications. LumiX employs a data-centric, type-safe modeling paradigm in which problem data, scenario parameters, and optimization models are managed within a unified framework. Key features include Object-Relational Mapping (ORM) integration for automatic variable generation, a solver-agnostic API supporting OR-Tools, Gurobi, CPLEX, and GLPK, automatic linearization of common non-linear expressions, native goal programming, and integrated analysis tools for sensitivity, scenario, and what-if analyses. We present a multi-stage timetabling case study and a quantitative benchmark comparing LumiX against Pyomo and PuLP. The evaluation demonstrates LumiX’s position as a framework for researchers and practitioners developing data-driven optimization solutions, addressing the gap between lightweight procedural libraries and traditional Algebraic Modeling Languages (AMLs). Current limitations, including Big-M parameter sensitivity and McCormick relaxation tightness, are discussed.
{"title":"LumiX: A type-safe, data-centric python library for modern mathematical optimization","authors":"Beyzanur Siyah , Tolga Berber","doi":"10.1016/j.softx.2026.102533","DOIUrl":"10.1016/j.softx.2026.102533","url":null,"abstract":"<div><div>This paper presents LumiX, an open-source Python library for mathematical optimization designed for data-intensive applications. LumiX employs a data-centric, type-safe modeling paradigm in which problem data, scenario parameters, and optimization models are managed within a unified framework. Key features include Object-Relational Mapping (ORM) integration for automatic variable generation, a solver-agnostic API supporting OR-Tools, Gurobi, CPLEX, and GLPK, automatic linearization of common non-linear expressions, native goal programming, and integrated analysis tools for sensitivity, scenario, and what-if analyses. We present a multi-stage timetabling case study and a quantitative benchmark comparing LumiX against Pyomo and PuLP. The evaluation demonstrates LumiX’s position as a framework for researchers and practitioners developing data-driven optimization solutions, addressing the gap between lightweight procedural libraries and traditional Algebraic Modeling Languages (AMLs). Current limitations, including Big-M parameter sensitivity and McCormick relaxation tightness, are discussed.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102533"},"PeriodicalIF":2.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.softx.2026.102516
Samriddha Das, C. Igathinathane, Xin Sun
The growing reliance on AI and deep learning in vision-based applications requires efficient dataset preparation tools, however, existing solutions are often commercially licensed or lack integrated, multi-format workflows. This study presents An-Augmenter, a cross-platform, open-source software that integrates image annotation and augmentation within an offline environment. It supports YOLO, XML, and JSON formats and ensures annotation-consistent augmentation for labeled and unlabeled datasets. Processing 1200 images with all possible augmentation techniques required 50 s on a standard CPU. Validation using YOLO11n object detection model improved [email protected] from 0.905 to 0.941 on a custom egg dataset and from 0.799 to 0.825 on a public apple dataset, demonstrating improved detection performance with augmented data.
{"title":"An-augmenter: A unified platform for efficient image annotation and data augmentation","authors":"Samriddha Das, C. Igathinathane, Xin Sun","doi":"10.1016/j.softx.2026.102516","DOIUrl":"10.1016/j.softx.2026.102516","url":null,"abstract":"<div><div>The growing reliance on AI and deep learning in vision-based applications requires efficient dataset preparation tools, however, existing solutions are often commercially licensed or lack integrated, multi-format workflows. This study presents An-Augmenter, a cross-platform, open-source software that integrates image annotation and augmentation within an offline environment. It supports YOLO, XML, and JSON formats and ensures annotation-consistent augmentation for labeled and unlabeled datasets. Processing 1200 images with all possible augmentation techniques required 50 s on a standard CPU. Validation using YOLO11n object detection model improved [email protected] from 0.905 to 0.941 on a custom egg dataset and from 0.799 to 0.825 on a public apple dataset, demonstrating improved detection performance with augmented data.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102516"},"PeriodicalIF":2.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.softx.2026.102531
Bojan Blažica , Vladimir Kuzmanovski , Marko Bohanec , Tanja Dergan , Aneta Ivanovska , Jurij Marinko , Robert Modic , Matevž Ogrinc , Marko Debeljak
The development of decision support systems (DSS) for agriculture increasingly relies on complex decision models, yet transforming such models into operational, user-friendly software remains challenging. DEXiWare is a software framework designed to support the development of web-based, cooperative DSS based on decision models built with the DEX (Decision EXpert) method. The framework provides a standardized workflow for operationalizing decision models, including automated model import, data handling, assessment, and scenario analysis, within a reusable backend–frontend architecture. DEXiWare integrates backend services, a web-based user interface, and a decision engine supporting top-down (goal-seeking) and bottom-up (what-if) scenario exploration. The framework is evaluated through its application in multiple agricultural DSS and through usability testing with stakeholders, demonstrating its applicability for translating qualitative decision models into operational decision support tools for sustainability assessment in agricultural production systems.
{"title":"DEXiWare: a software development framework for building cooperative decision support systems","authors":"Bojan Blažica , Vladimir Kuzmanovski , Marko Bohanec , Tanja Dergan , Aneta Ivanovska , Jurij Marinko , Robert Modic , Matevž Ogrinc , Marko Debeljak","doi":"10.1016/j.softx.2026.102531","DOIUrl":"10.1016/j.softx.2026.102531","url":null,"abstract":"<div><div>The development of decision support systems (DSS) for agriculture increasingly relies on complex decision models, yet transforming such models into operational, user-friendly software remains challenging. DEXiWare is a software framework designed to support the development of web-based, cooperative DSS based on decision models built with the DEX (Decision EXpert) method. The framework provides a standardized workflow for operationalizing decision models, including automated model import, data handling, assessment, and scenario analysis, within a reusable backend–frontend architecture. DEXiWare integrates backend services, a web-based user interface, and a decision engine supporting top-down (goal-seeking) and bottom-up (what-if) scenario exploration. The framework is evaluated through its application in multiple agricultural DSS and through usability testing with stakeholders, demonstrating its applicability for translating qualitative decision models into operational decision support tools for sustainability assessment in agricultural production systems.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102531"},"PeriodicalIF":2.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.softx.2026.102518
Wojciech Sałabun , Damian Kedziora , Andrii Shekhovtsov
In this paper, we present an extension of the AsymIntervals library, designed to enhance the modelling and processing of uncertainty using Asymmetric Interval Numbers (AINs). In response to the growing demand for expressive and mathematically consistent tools for interval-based uncertainty representation, the library has been extended with a comprehensive set of interval characteristics, logical predicates, relational operators, and mathematical transformations implemented within a unified core class. The extension introduces support for advanced algebraic, trigonometric, as well as exponential and logarithmic operations, flexible construction of AIN objects from multiple input formats, sampling-based data generation, and normalization of AIN collections. Additionally, enhanced export and serialisation mechanisms enable seamless integration with numerical workflows and scientific applications. These improvements substantially broaden the applicability of AsymIntervals in decision analysis, uncertainty modelling, and computational research.
{"title":"Version [1.2]-[AsymIntervals: A Python library for uncertainty modeling with asymmetric interval numbers]","authors":"Wojciech Sałabun , Damian Kedziora , Andrii Shekhovtsov","doi":"10.1016/j.softx.2026.102518","DOIUrl":"10.1016/j.softx.2026.102518","url":null,"abstract":"<div><div>In this paper, we present an extension of the AsymIntervals library, designed to enhance the modelling and processing of uncertainty using Asymmetric Interval Numbers (AINs). In response to the growing demand for expressive and mathematically consistent tools for interval-based uncertainty representation, the library has been extended with a comprehensive set of interval characteristics, logical predicates, relational operators, and mathematical transformations implemented within a unified core class. The extension introduces support for advanced algebraic, trigonometric, as well as exponential and logarithmic operations, flexible construction of AIN objects from multiple input formats, sampling-based data generation, and normalization of AIN collections. Additionally, enhanced export and serialisation mechanisms enable seamless integration with numerical workflows and scientific applications. These improvements substantially broaden the applicability of AsymIntervals in decision analysis, uncertainty modelling, and computational research.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102518"},"PeriodicalIF":2.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.softx.2026.102523
Michael Herbert Ziegler , Mariusz Nowostawski , Basel Katt
The tension between blockchain transparency and user privacy has driven innovation in mixing protocols creating a need for comprehensive analytical frameworks that can rigorously evaluate privacy properties across different implementations. Dakar is an open-source framework that unifies ingestion and provides reproducible classification and analysis of CoinJoin transactions on UTXO blockchains. Its graph database captures the relationships between mixing transactions while a web interface enables experimentation with built-in privacy tools such as CoinJoin transaction heuristics and similarity measures. By enabling researchers to compare and quantify CoinJoin activity across multiple protocols Dakar facilitates studies on privacy-enhancing techniques and supports the discovery and analysis of differences in CoinJoin implementations.
{"title":"Dakar: A CoinJoin forensic software","authors":"Michael Herbert Ziegler , Mariusz Nowostawski , Basel Katt","doi":"10.1016/j.softx.2026.102523","DOIUrl":"10.1016/j.softx.2026.102523","url":null,"abstract":"<div><div>The tension between blockchain transparency and user privacy has driven innovation in mixing protocols creating a need for comprehensive analytical frameworks that can rigorously evaluate privacy properties across different implementations. Dakar is an open-source framework that unifies ingestion and provides reproducible classification and analysis of CoinJoin transactions on UTXO blockchains. Its graph database captures the relationships between mixing transactions while a web interface enables experimentation with built-in privacy tools such as CoinJoin transaction heuristics and similarity measures. By enabling researchers to compare and quantify CoinJoin activity across multiple protocols Dakar facilitates studies on privacy-enhancing techniques and supports the discovery and analysis of differences in CoinJoin implementations.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102523"},"PeriodicalIF":2.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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