SoftwareXPub Date : 2025-07-15DOI: 10.1016/j.softx.2025.102254
Ladislav Bartoš , Peter Pajtinka , Robert Vácha
{"title":"gorder: Comprehensive tool for calculating lipid order parameters from molecular simulations","authors":"Ladislav Bartoš , Peter Pajtinka , Robert Vácha","doi":"10.1016/j.softx.2025.102254","DOIUrl":"10.1016/j.softx.2025.102254","url":null,"abstract":"<div><div>Lipid order parameters are an important metric for quantifying the molecular structure of biological membranes. They can be derived from both molecular simulations and experimental measurements, enabling robust comparisons between the two. Although methods for calculating lipid order parameters from molecular dynamics simulations of membrane systems at various resolutions are well established, a comprehensive and user-friendly package for these calculations is lacking, which has even led some researchers to use tools that are known to perform the calculations incorrectly. To address this, we have developed <span>gorder</span>, an analysis tool capable of calculating lipid order parameters in atomistic, united-atom, and coarse-grained systems, compatible with any force field, and applicable to both planar and curved membrane geometries. <span>gorder</span> is designed to be fast and versatile, providing a unified solution for lipid order calculations. The tool is freely available from <span><span>https://crates.io/crates/gorder</span><svg><path></path></svg></span> and <span><span>https://github.com/Ladme/gorder</span><svg><path></path></svg></span> under the MIT License.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102254"},"PeriodicalIF":2.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632628","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}
SoftwareXPub Date : 2025-07-11DOI: 10.1016/j.softx.2025.102267
Kirk Ming Yeoh, Karthikayen Raju, Vincent Beng Chye Tan
{"title":"A Python script repository for multiscale modelling with Direct FE2 in Abaqus","authors":"Kirk Ming Yeoh, Karthikayen Raju, Vincent Beng Chye Tan","doi":"10.1016/j.softx.2025.102267","DOIUrl":"10.1016/j.softx.2025.102267","url":null,"abstract":"<div><div>While computational homogenization via FE<sup>2</sup> is a useful multiscale modelling tool for many fields, conventional implementations often require an expert level of user involvement. To address this, this work presents a Python script repository to easily set up Direct FE<sup>2</sup> input files for multiscale modelling in Abaqus. The scripts take in user inputs defining the macroscale problem along with its microscale RVE and returns an Abaqus input file that can be readily submitted for analysis. As Direct FE<sup>2</sup> uses only functions which are readily available in Abaqus to perform computational homogenization, no user-defined subroutines are required. This repository seeks to provide researchers in various fields ease of access to multiscale modelling as a tool to enhance their work. Furthermore, it aims to foster collaboration between researchers working on the Direct FE<sup>2</sup> method and further promote its development.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102267"},"PeriodicalIF":2.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604501","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}
SoftwareXPub Date : 2025-07-10DOI: 10.1016/j.softx.2025.102261
Jakub Śledziowski , Andrzej Giza , Paweł Terefenko
{"title":"S-LiNE: An open-source LiDAR toolbox for dune coasts shoreline mapping","authors":"Jakub Śledziowski , Andrzej Giza , Paweł Terefenko","doi":"10.1016/j.softx.2025.102261","DOIUrl":"10.1016/j.softx.2025.102261","url":null,"abstract":"<div><div>This paper presents an open-source toolbox designed to streamline shoreline detection and analysis directly from Light Detection and Ranging (LiDAR) raw point clouds in LAS format. The application is based on Python scripts and supports LiDAR datasets from both unmanned aerial vehicles (UAV) and airborne laser scanning (ALS). It performs key processing steps including elevation correction using a geoid model (for UAV data), shoreline extraction based on point cloud characteristics (intensity, red-green-blue (RGB) values, scan angle – for UAV data, and classification – for ALS data), and statistical comparison of shoreline positions over time. The tool features a graphical user interface built with Streamlit, enabling users to operate it without any programming experience. By eliminating the need for raster generation and external classification, the tool significantly reduces processing time while ensuring reproducibility. Output files are saved in widely used formats compatible with Geographic Information System (GIS), including GeoJSON, SHP, and CSV. The toolbox addresses a key gap in coastal monitoring workflows, offering a scalable, user-friendly solution for researchers and practitioners working with high-resolution coastal data.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102261"},"PeriodicalIF":2.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587489","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}
SoftwareXPub Date : 2025-07-07DOI: 10.1016/j.softx.2025.102247
Gage R. Rowden, Peter A. Larsen
{"title":"quicR: An R library for streamlined data handling of real-time quaking induced conversion assays","authors":"Gage R. Rowden, Peter A. Larsen","doi":"10.1016/j.softx.2025.102247","DOIUrl":"10.1016/j.softx.2025.102247","url":null,"abstract":"<div><div>Real-time quaking induced conversion (RT-QuIC) has become a valuable diagnostic tool for protein misfolding disorders such as Creutzfeldt–Jakob disease and Parkinson’s disease. Given that the technology is relatively new, academic and industry standards for quality filtering data and high throughput analysis of results have yet to be fully established. The open source R library, <strong>quicR</strong>, was developed to provide a standardized approach to RT-QuIC data analysis. <strong>quicR</strong> provides functions, which can be easily integrated into existing R workflows, for data curation, analysis, and visualization.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102247"},"PeriodicalIF":2.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570028","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}
SoftwareXPub Date : 2025-07-03DOI: 10.1016/j.softx.2025.102251
Christopher J. Jackett , Kevin Barnard , Franziska Althaus , Nicolas Mortimer , David Webb , Candice Untiedt , Aaron Tyndall , Ian Jameson , Bec Gorton , Carlie Devine , Joanna Strzelecki , Peter H. Thrall , Ben Scoulding
{"title":"Marimba: A Python framework for structuring and processing FAIR scientific image datasets","authors":"Christopher J. Jackett , Kevin Barnard , Franziska Althaus , Nicolas Mortimer , David Webb , Candice Untiedt , Aaron Tyndall , Ian Jameson , Bec Gorton , Carlie Devine , Joanna Strzelecki , Peter H. Thrall , Ben Scoulding","doi":"10.1016/j.softx.2025.102251","DOIUrl":"10.1016/j.softx.2025.102251","url":null,"abstract":"<div><div>The rapid advancement of scientific imaging technologies has created significant challenges in managing large-scale image datasets while maintaining compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. We present Marimba, an open-source Python framework for structuring, processing, and packaging scientific image datasets. Marimba enhances data management through unified workflow processing, automated metadata embedding, efficient data handling, and standardized dataset packaging while integrating with the image FAIR Digital Object (iFDO) metadata standard. The framework's capabilities were evaluated through four diverse marine case studies involving multi-instrument microscopy, automated plankton imagery, deep-sea coral surveys, and historical image digitization. Marimba successfully processed datasets ranging from thousands to hundreds of thousands of images and videos, demonstrating robust performance and scalability. Marimba's modular architecture enables customization for specific research requirements while ensuring consistent data management practices. Results demonstrate Marimba's potential to advance scientific image data management by improving workflow efficiency, data quality, and adherence to FAIR principles throughout the research data lifecycle.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102251"},"PeriodicalIF":2.4,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534720","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}
{"title":"An Item Response Theory-based R module for Algorithm Portfolio Analysis","authors":"Brodie Oldfield , Sevvandi Kandanaarachchi , Ziqi Xu , Mario Andrés Muñoz","doi":"10.1016/j.softx.2025.102239","DOIUrl":"10.1016/j.softx.2025.102239","url":null,"abstract":"<div><div>Experimental evaluation is crucial in AI research, especially for assessing algorithms across diverse tasks. Many studies often evaluate a limited set of algorithms, failing to fully understand their strengths and weaknesses within a comprehensive portfolio. This paper introduces an Item Response Theory (IRT) based analysis tool for algorithm portfolio evaluation called AIRT-Module. Traditionally used in educational psychometrics, IRT models test question difficulty and student ability using responses to test questions. Adapting IRT to algorithm evaluation, the AIRT-Module contains a Shiny web application and the R package <span>airt</span>. AIRT-Module uses algorithm performance measures to compute anomalousness, consistency, and difficulty limits for an algorithm and the difficulty of test instances. The strengths and weaknesses of algorithms are visualised using the difficulty spectrum of the test instances. AIRT-Module offers a detailed understanding of algorithm capabilities across varied test instances, thus enhancing comprehensive AI method assessment. It is available at <span><span>https://sevvandi.shinyapps.io/AIRT/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102239"},"PeriodicalIF":2.4,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523048","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}
SoftwareXPub Date : 2025-07-01DOI: 10.1016/j.softx.2025.102227
A. Barki, J. Zghal, L. Gallimard, I. Bruant, L. Davenne
{"title":"PhaFiDyn: An explicit dynamic phase field damage model implementation","authors":"A. Barki, J. Zghal, L. Gallimard, I. Bruant, L. Davenne","doi":"10.1016/j.softx.2025.102227","DOIUrl":"10.1016/j.softx.2025.102227","url":null,"abstract":"<div><div>Predicting the critical load that structures can sustain and the right crack path is an important scientific and practical issue. This reason is the trigger for the appearance of the damage mechanics. During these last decades, many models have shown their ability to predict both critical loads and crack paths. We can cite thick-level set models (TLS), phase field damage models, peridynamics, and more recently Lip-Field damage model. This work aims to show the capability of the PhaFiDyn software to predict crack paths with the unified formulation of the phase field damage model. PhaFiDyn is implemented on <span>FeniCS</span>, an open-source finite element library. Validation test is realized with numerical and experimental results from the literature. PhaFiDyn is easily modular, allowing for expansion of this implementation.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102227"},"PeriodicalIF":2.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517948","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}
SoftwareXPub Date : 2025-06-30DOI: 10.1016/j.softx.2025.102236
Mingi Kang, Kyoungjae Lee
{"title":"Mhorseshoe package in R: Approximate algorithm for the horseshoe prior in Bayesian linear model","authors":"Mingi Kang, Kyoungjae Lee","doi":"10.1016/j.softx.2025.102236","DOIUrl":"10.1016/j.softx.2025.102236","url":null,"abstract":"<div><div>The horseshoe prior is a continuous shrinkage prior frequently used in high-dimensional Bayesian sparse linear regression models. Although the horseshoe prior theoretically guarantees excellent shrinkage properties, performing a Markov Chain Monte Carlo (MCMC) algorithm incurs high computational costs per iteration. We introduce the <span>Mhorseshoe</span> package in R, which implements posterior inference under the horseshoe prior, based on the exact and approximate algorithms proposed in Johndrow et al. (2020). Furthermore, this package incorporates a novel adaptive selection method, which we developed and implemented to determine the tuning parameter in the approximate algorithm. We conducted a simulation study and confirmed that the algorithm can be effectively applied to large datasets.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102236"},"PeriodicalIF":2.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514114","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}
SoftwareXPub Date : 2025-06-28DOI: 10.1016/j.softx.2025.102245
Joan Perez, Giovanni Fusco
{"title":"Population potential on catchment area (PPCA): A Python-based tool for worldwide geospatial population analysis","authors":"Joan Perez, Giovanni Fusco","doi":"10.1016/j.softx.2025.102245","DOIUrl":"10.1016/j.softx.2025.102245","url":null,"abstract":"<div><div>The Population Potential in Catchment Area (PPCA) protocol is a Python-based methodology designed to evaluate and analyze population distributions within specified pedestrian catchment areas globally. PPCA utilizes OpenStreetMap (OSM) and Global Human Settlement (GHS) data and employs Google Earth Engine for data acquisition and morphometric analysis. Through a series of four automated steps, the protocol cleans, processes, and classifies geospatial data, ultimately yielding refined population estimations within defined catchment regions. This protocol enables researchers and urban planners to assess the population that can be potentially accessed on foot using the street network, within given distances. The protocol allows this assessment globally with minimal input requirements, focusing mostly on bounding box coordinates.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102245"},"PeriodicalIF":2.4,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502451","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}
SoftwareXPub Date : 2025-06-28DOI: 10.1016/j.softx.2025.102249
Adam Kuzdraliński
{"title":"PrimeSpecPCR: Python toolkit for species-specific DNA primer design and specificity testing","authors":"Adam Kuzdraliński","doi":"10.1016/j.softx.2025.102249","DOIUrl":"10.1016/j.softx.2025.102249","url":null,"abstract":"<div><div>PrimeSpecPCR is an open-source Python toolkit that automates the workflow of species-specific primer design (comprising forward primer, reverse primer, and probe) and validation. The software implements a modular architecture comprising four main components: (1) automated retrieval of genetic sequences from NCBI databases based on taxonomy identifiers; (2) multiple sequence alignment using MAFFT to generate consensus sequences; (3) thermodynamically optimized primer and probe design via Primer3-py; and (4) multi-tiered specificity testing against the NCBI GenBank database. The toolkit features a user-friendly graphical interface and customizable parameters for quantitative PCR (qPCR) applications. PrimeSpecPCR accelerates primer development through parallel processing, automatic caching of intermediate results, and production of interactive HTML reports that visualize specificity profiles across taxonomic groups, while minimizing human errors and ensuring reproducibility of results. This toolkit reduces the time-intensive, labour-demanding processes conventionally required for designing species-specific molecular assays.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102249"},"PeriodicalIF":2.4,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511037","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}