Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102014
Błażej Zieliński, Szymon Ściegienny, Hubert Orlicki, Wojciech Książek
The differential evolution algorithm, introduced in 1997, remains one of the most frequently used methods for solving complex optimization problems. The basic version of the algorithm is widely available and implemented in many popular programming languages. However, the algorithm has continued to evolve, with newer, improved variants often achieving superior results over the original. Unfortunately, many of these modifications are not readily accessible as prebuilt programming solutions, creating a need for a comprehensive programming library that includes the most popular and effective variants of the base algorithm. The library we designed, DetPy (Differential Evolution Tools), provides implementations of the standard differential evolution algorithm along with 15 distinct variants. This tool allows researchers working on optimization problems to compare multiple algorithmic approaches, making it easier to select the most effective solution for their specific challenges.
{"title":"DetPy (Differential Evolution Tools): A Python toolbox for solving optimization problems using differential evolution","authors":"Błażej Zieliński, Szymon Ściegienny, Hubert Orlicki, Wojciech Książek","doi":"10.1016/j.softx.2024.102014","DOIUrl":"10.1016/j.softx.2024.102014","url":null,"abstract":"<div><div>The differential evolution algorithm, introduced in 1997, remains one of the most frequently used methods for solving complex optimization problems. The basic version of the algorithm is widely available and implemented in many popular programming languages. However, the algorithm has continued to evolve, with newer, improved variants often achieving superior results over the original. Unfortunately, many of these modifications are not readily accessible as prebuilt programming solutions, creating a need for a comprehensive programming library that includes the most popular and effective variants of the base algorithm. The library we designed, DetPy (Differential Evolution Tools), provides implementations of the standard differential evolution algorithm along with 15 distinct variants. This tool allows researchers working on optimization problems to compare multiple algorithmic approaches, making it easier to select the most effective solution for their specific challenges.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102014"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102007
Anne-Isabelle Graux, Thomas Demarty, Philippe Faverdin
We present CowComfort, an R-shiny app developed to assist researchers, stakeholders and policy makers in the spatialized visualization of the evolution of the thermal comfort of dairy cows under climate change. The application is designed to take into account the uncertainty associated with the climatic projection and the calculation of the thermal indices. This app and its associated data can be used in modelling research and to communicate about the impact of climate change on dairy cows and the required adaptations. An illustration is given for French climatic data and for an evaluation of the thermal stress based on several calculations of the temperature humidity index. It can be easily repeated and extended to other climate situations and thermal stress evaluations.
{"title":"CowComfort: A R-shiny app to visualize the evolution of thermal comfort of dairy cows under climate change and the associated uncertainty","authors":"Anne-Isabelle Graux, Thomas Demarty, Philippe Faverdin","doi":"10.1016/j.softx.2024.102007","DOIUrl":"10.1016/j.softx.2024.102007","url":null,"abstract":"<div><div>We present CowComfort, an R-shiny app developed to assist researchers, stakeholders and policy makers in the spatialized visualization of the evolution of the thermal comfort of dairy cows under climate change. The application is designed to take into account the uncertainty associated with the climatic projection and the calculation of the thermal indices. This app and its associated data can be used in modelling research and to communicate about the impact of climate change on dairy cows and the required adaptations. An illustration is given for French climatic data and for an evaluation of the thermal stress based on several calculations of the temperature humidity index. It can be easily repeated and extended to other climate situations and thermal stress evaluations.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102007"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102001
Sihan Di, Nanjia Yu, Shutao Han, Haodong He
This paper introduces pyMechOpt, an open-source Python package designed for the optimization of chemical reaction mechanisms. The package implements a range of optimization methods, including conventional algorithms such as genetic algorithms (GA) and particle swarm optimization (PSO), as well as introducing novel methods such as coordinate descent (CD) and multi-objective optimization algorithms. The optimization of a reduced GRI-Mech 3.0 mechanism for methane combustion is used to demonstrate the capabilities of pyMechOpt. The SILSCD method demonstrated a notable reduction in the objective functions, exceeding the capabilities of other methods. In the context of multi-objective optimization, NSGA-III demonstrated a balanced Pareto front, outperforming both CTAEA and MOEAD. These results serve to illustrate the efficacy of the novel methods implemented in pyMechOpt. The package provides a versatile platform for researchers to customize optimization algorithms and objective functions, supporting detailed analysis of results. This package makes a contribution to the field by introducing innovative optimization methods and a comprehensive software tool for refining chemical reaction mechanisms.
{"title":"pyMechOpt: A Python toolbox for optimizing of reaction mechanisms","authors":"Sihan Di, Nanjia Yu, Shutao Han, Haodong He","doi":"10.1016/j.softx.2024.102001","DOIUrl":"10.1016/j.softx.2024.102001","url":null,"abstract":"<div><div>This paper introduces pyMechOpt, an open-source Python package designed for the optimization of chemical reaction mechanisms. The package implements a range of optimization methods, including conventional algorithms such as genetic algorithms (GA) and particle swarm optimization (PSO), as well as introducing novel methods such as coordinate descent (CD) and multi-objective optimization algorithms. The optimization of a reduced GRI-Mech 3.0 mechanism for methane combustion is used to demonstrate the capabilities of pyMechOpt. The SILSCD method demonstrated a notable reduction in the objective functions, exceeding the capabilities of other methods. In the context of multi-objective optimization, NSGA-III demonstrated a balanced Pareto front, outperforming both CTAEA and MOEAD. These results serve to illustrate the efficacy of the novel methods implemented in pyMechOpt. The package provides a versatile platform for researchers to customize optimization algorithms and objective functions, supporting detailed analysis of results. This package makes a contribution to the field by introducing innovative optimization methods and a comprehensive software tool for refining chemical reaction mechanisms.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102001"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The X-ray tube is the core component of X-ray imaging, which is extensively utilized in both biomedical imaging and industry applications. Analyzing the spectrum generated by the X-ray tube can significantly contribute to the optimization of parameters pertinent to X-ray applications. However, for most medical practitioners, the absence of engineering expertise poses a challenge in measuring the characteristics of X-ray tubes using specialized equipment or in conducting simulations through programming on their own. Therefore, there is a pressing need for a simple and user-friendly X-ray tube simulation software. The happyXTube application addresses this need by providing a real-time and well-designed tool that utilizes a simplified tube model. It is programmed in C++ and enables real-time simulations to facilitate the research characteristics of X-ray tube. Furthermore, the accuracy of the simulation results is verified by comparing the simulated spectra and calculated first half-value layers with data obtained from Geant4, SpekPy, Xpecgen and Ebel model. This demonstrates that happyXTube is a straightforward, easy-to-use and powerful simulation tool, which is beneficial for the rapid verification and comprehension of tube characteristics.
{"title":"happyXTube: A software for the real-time X-ray tube simulation","authors":"Yichu Chen, Wei Yu, Sanming Hu, Jing Deng, Xiao Chen","doi":"10.1016/j.softx.2025.102043","DOIUrl":"10.1016/j.softx.2025.102043","url":null,"abstract":"<div><div>The X-ray tube is the core component of X-ray imaging, which is extensively utilized in both biomedical imaging and industry applications. Analyzing the spectrum generated by the X-ray tube can significantly contribute to the optimization of parameters pertinent to X-ray applications. However, for most medical practitioners, the absence of engineering expertise poses a challenge in measuring the characteristics of X-ray tubes using specialized equipment or in conducting simulations through programming on their own. Therefore, there is a pressing need for a simple and user-friendly X-ray tube simulation software. The happyXTube application addresses this need by providing a real-time and well-designed tool that utilizes a simplified tube model. It is programmed in C++ and enables real-time simulations to facilitate the research characteristics of X-ray tube. Furthermore, the accuracy of the simulation results is verified by comparing the simulated spectra and calculated first half-value layers with data obtained from Geant4, SpekPy, Xpecgen and Ebel model. This demonstrates that happyXTube is a straightforward, easy-to-use and powerful simulation tool, which is beneficial for the rapid verification and comprehension of tube characteristics.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102043"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2025.102041
Fernando Sola , Daniel Ayala , Marina Pulido , Rafael Ayala , Lorena López-Cerero , Inma Hernández , David Ruiz
Advancements in genomic and proteomic sequencing, along with molecular biology techniques, have led to the generation of vast amounts of sequence data stored in different collaborative databases. The integration of these heterogeneous data sources holds immense potential for advancing our understanding of biological systems and processes, although it presents several challenges due to inconsistencies in formats and annotations. To address this, we introduce Gintegrator, a web application that streamlines the process of translating gene and protein identifiers across major sequence databases such as NCBI, UniProt and KEGG. By introducing the use of identical or similar genes and proteins in the translation workflow, and performing real-time queries to access the most recent data, Gintegrator enhances both the accuracy and success rate of identifier mapping, while also facilitating efficient and reliable biological data integration and analysis for researchers.
{"title":"Gintegrator: Enhancing biological sequences data integration with real-time identifier translation","authors":"Fernando Sola , Daniel Ayala , Marina Pulido , Rafael Ayala , Lorena López-Cerero , Inma Hernández , David Ruiz","doi":"10.1016/j.softx.2025.102041","DOIUrl":"10.1016/j.softx.2025.102041","url":null,"abstract":"<div><div>Advancements in genomic and proteomic sequencing, along with molecular biology techniques, have led to the generation of vast amounts of sequence data stored in different collaborative databases. The integration of these heterogeneous data sources holds immense potential for advancing our understanding of biological systems and processes, although it presents several challenges due to inconsistencies in formats and annotations. To address this, we introduce Gintegrator, a web application that streamlines the process of translating gene and protein identifiers across major sequence databases such as NCBI, UniProt and KEGG. By introducing the use of identical or similar genes and proteins in the translation workflow, and performing real-time queries to access the most recent data, Gintegrator enhances both the accuracy and success rate of identifier mapping, while also facilitating efficient and reliable biological data integration and analysis for researchers.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102041"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2025.102035
Wanja Timm Schulze , Sebastian Schwalbe , Kai Trepte , Stefanie Gräfe
In current electronic structure research endeavors such as warm dense matter or machine learning applications, efficient development necessitates non-monolithic software, providing an extendable and flexible interface. The open-source idea offers the advantage of having a source code base that can be reviewed and modified by the community. However, practical implementations can often diverge significantly from their theoretical counterpart. Leveraging the efforts of recent theoretical formulations and the features of Python, we try to mitigate these problems. We present eminus, an education- and development-friendly electronic structure package designed for convenient and customizable workflows, yet built with intelligible and modular implementations.
{"title":"eminus — Pythonic electronic structure theory","authors":"Wanja Timm Schulze , Sebastian Schwalbe , Kai Trepte , Stefanie Gräfe","doi":"10.1016/j.softx.2025.102035","DOIUrl":"10.1016/j.softx.2025.102035","url":null,"abstract":"<div><div>In current electronic structure research endeavors such as warm dense matter or machine learning applications, efficient development necessitates non-monolithic software, providing an extendable and flexible interface. The open-source idea offers the advantage of having a source code base that can be reviewed and modified by the community. However, practical implementations can often diverge significantly from their theoretical counterpart. Leveraging the efforts of recent theoretical formulations and the features of Python, we try to mitigate these problems. We present <span>eminus</span>, an education- and development-friendly electronic structure package designed for convenient and customizable workflows, yet built with intelligible and modular implementations.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102035"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102012
Andrea Micheli , Arthur Bit-Monnot , Gabriele Röger , Enrico Scala , Alessandro Valentini , Luca Framba , Alberto Rovetta , Alessandro Trapasso , Luigi Bonassi , Alfonso Emilio Gerevini , Luca Iocchi , Felix Ingrand , Uwe Köckemann , Fabio Patrizi , Alessandro Saetti , Ivan Serina , Sebastian Stock
Automated planning is a branch of artificial intelligence aiming at finding a course of action that achieves specified goals, given a description of the initial state of a system and a model of possible actions. There are plenty of planning approaches working under different assumptions and with different features (e.g. classical, temporal, and numeric planning). When automated planning is used in practice, however, the set of required features is often initially unclear. The Unified Planning (UP) library addresses this issue by providing a feature-rich Python API for modeling automated planning problems, which are solved seamlessly by planning engines that specify the set of features they support. Once a problem is modeled, UP can automatically find engines that can solve it, based on the features used in the model. This greatly reduces the commitment to specific planning approaches and bridges the gap between planning technology and its users.
{"title":"Unified Planning: Modeling, manipulating and solving AI planning problems in Python","authors":"Andrea Micheli , Arthur Bit-Monnot , Gabriele Röger , Enrico Scala , Alessandro Valentini , Luca Framba , Alberto Rovetta , Alessandro Trapasso , Luigi Bonassi , Alfonso Emilio Gerevini , Luca Iocchi , Felix Ingrand , Uwe Köckemann , Fabio Patrizi , Alessandro Saetti , Ivan Serina , Sebastian Stock","doi":"10.1016/j.softx.2024.102012","DOIUrl":"10.1016/j.softx.2024.102012","url":null,"abstract":"<div><div>Automated planning is a branch of artificial intelligence aiming at finding a course of action that achieves specified goals, given a description of the initial state of a system and a model of possible actions. There are plenty of planning approaches working under different assumptions and with different features (e.g. classical, temporal, and numeric planning). When automated planning is used in practice, however, the set of required features is often initially unclear. The Unified Planning (UP) library addresses this issue by providing a feature-rich Python API for modeling automated planning problems, which are solved seamlessly by planning engines that specify the set of features they support. Once a problem is modeled, UP can automatically find engines that can solve it, based on the features used in the model. This greatly reduces the commitment to specific planning approaches and bridges the gap between planning technology and its users.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102012"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2025.102059
Jacob Mannhardt , Alissa Ganter , Johannes Burger , Francesco De Marco , Lukas Kunz , Lukas Schmidt-Engelbertz , Paolo Gabrielli , Giovanni Sansavini
Welcome to the ZEN-garden: ZEN-garden is an open-source optimization software to model multi-year energy system transition pathways. To support research focused on the transition of sector-coupled energy systems toward net-zero emissions, ZEN-garden is built upon two principles: Optimizing highly complex sector-coupled energy transition pathways and supporting user-friendly data handling through small, flexible, and robust input datasets. ZEN-garden separates the codebase from the input data to allow for very diverse case studies. Lightweight and intuitive input datasets and unit consistency checks reduce user errors and facilitate using ZEN-garden for both novice and experienced energy system modelers.
{"title":"ZEN-garden: Optimizing energy transition pathways with user-oriented data handling","authors":"Jacob Mannhardt , Alissa Ganter , Johannes Burger , Francesco De Marco , Lukas Kunz , Lukas Schmidt-Engelbertz , Paolo Gabrielli , Giovanni Sansavini","doi":"10.1016/j.softx.2025.102059","DOIUrl":"10.1016/j.softx.2025.102059","url":null,"abstract":"<div><div>Welcome to the ZEN-garden: ZEN-garden is an open-source optimization software to model multi-year energy system transition pathways. To support research focused on the transition of sector-coupled energy systems toward net-zero emissions, ZEN-garden is built upon two principles: Optimizing highly complex sector-coupled energy transition pathways and supporting user-friendly data handling through small, flexible, and robust input datasets. ZEN-garden separates the codebase from the input data to allow for very diverse case studies. Lightweight and intuitive input datasets and unit consistency checks reduce user errors and facilitate using ZEN-garden for both novice and experienced energy system modelers.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102059"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2025.102045
Jeroen Van Der Donckt, Jonas Van Der Donckt, Sofie Van Hoecke
Interactive line chart visualizations greatly enhance the effective exploration of large time series. Although downsampling has emerged as a well-established approach to enable efficient interactive visualization of large datasets, it is not an inherent feature in most visualization tools. Furthermore, there is no library offering a convenient interface for high-performance implementations of prominent downsampling algorithms. To address these shortcomings, we present tsdownsample, an open-source Python package specifically designed for CPU-based, in-memory time series downsampling. Our library focuses on performance and convenient integration, offering optimized implementations of leading downsampling algorithms. We achieve this optimization by leveraging low-level Single Instruction, Multiple Data (SIMD) instructions and multithreading capabilities in Rust. In particular, SIMD instructions were employed to optimize the argmin and argmax operations. This SIMD optimization, along with some algorithmic tricks, proved crucial in enhancing the performance of various downsampling algorithms. We evaluate the performance of tsdownsample and demonstrate its interoperability with an established visualization framework. Our performance benchmarks indicate that the algorithmic runtime of tsdownsample approximates the CPU’s memory bandwidth. This work marks a significant advancement in bringing high-performance time series downsampling to the Python ecosystem, enabling scalable visualization.
{"title":"tsdownsample: High-performance time series downsampling for scalable visualization","authors":"Jeroen Van Der Donckt, Jonas Van Der Donckt, Sofie Van Hoecke","doi":"10.1016/j.softx.2025.102045","DOIUrl":"10.1016/j.softx.2025.102045","url":null,"abstract":"<div><div>Interactive line chart visualizations greatly enhance the effective exploration of large time series. Although downsampling has emerged as a well-established approach to enable efficient interactive visualization of large datasets, it is not an inherent feature in most visualization tools. Furthermore, there is no library offering a convenient interface for high-performance implementations of prominent downsampling algorithms. To address these shortcomings, we present <span>tsdownsample</span>, an open-source Python package specifically designed for CPU-based, in-memory time series downsampling. Our library focuses on performance and convenient integration, offering optimized implementations of leading downsampling algorithms. We achieve this optimization by leveraging low-level Single Instruction, Multiple Data (SIMD) instructions and multithreading capabilities in Rust. In particular, SIMD instructions were employed to optimize the argmin and argmax operations. This SIMD optimization, along with some algorithmic tricks, proved crucial in enhancing the performance of various downsampling algorithms. We evaluate the performance of <span>tsdownsample</span> and demonstrate its interoperability with an established visualization framework. Our performance benchmarks indicate that the algorithmic runtime of <span>tsdownsample</span> approximates the CPU’s memory bandwidth. This work marks a significant advancement in bringing high-performance time series downsampling to the Python ecosystem, enabling scalable visualization.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102045"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102008
Eduardo Graells-Garrido , Nicolás García , Andrés Carvallo
Tsundoku is a Python toolkit for analyzing social media data, focusing on text and network analysis. It offers user classification, bot detection, community identification, and topic modeling, with an active learning component to improve model accuracy. Tsundoku generates detailed reports with visualizations, making it accessible to researchers across disciplines. By streamlining the analysis pipeline from data collection to insight generation, Tsundoku helps researchers tackle the challenges of large-scale social media data analysis.
{"title":"Tsundoku: A Python toolkit for social network analysis","authors":"Eduardo Graells-Garrido , Nicolás García , Andrés Carvallo","doi":"10.1016/j.softx.2024.102008","DOIUrl":"10.1016/j.softx.2024.102008","url":null,"abstract":"<div><div><em>Tsundoku</em> is a Python toolkit for analyzing social media data, focusing on text and network analysis. It offers user classification, bot detection, community identification, and topic modeling, with an active learning component to improve model accuracy. Tsundoku generates detailed reports with visualizations, making it accessible to researchers across disciplines. By streamlining the analysis pipeline from data collection to insight generation, Tsundoku helps researchers tackle the challenges of large-scale social media data analysis.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102008"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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