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        <title>Journal of Open Research Software Feed</title>
        <link>https://reference-global.com/journal/UPJJORS</link>
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            <title>Journal of Open Research Software Feed</title>
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            <link>https://reference-global.com/journal/UPJJORS</link>
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        <copyright>All rights reserved 2026, Ubiquity Press</copyright>
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        <item>
            <title><![CDATA[PyGuLP: A Python Package for Weighted Goal Linear Programming]]></title>
            <link>https://reference-global.com/article/10.5334/jors.716</link>
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            <pubDate>Wed, 21 Oct 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Resource allocation is central to day-to-day activities, planning, and policymaking, often involving multiple targets rather than a single objective. In healthcare and public health, planners may need to address competing targets alongside feasibility constraints such as fixed budgets and resource limits. Traditional linear programming frameworks do not directly represent aspiration targets and deviations from them. Goal linear programming (GLP) uses linear programming constructs to model such targets explicitly and minimize deviations from them. PyGuLP is a lightweight Python package that implements Weighted GLP using standard linear programming constructs, making goal-based optimization accessible while preserving control over the underlying optimization model.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[MIRROR: Local Evidence Bundles for Reproducible Research Software Review, with a JANUS Case Study]]></title>
            <link>https://reference-global.com/article/10.5334/jors.753</link>
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            <pubDate>Wed, 21 Oct 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Many statements in a research article depend on code or data, such as ‘the script in the supplement reproduces Table 4’. Such claims are hard to check later, because files change after the text is written. MIRROR is an open-source Python tool and workflow that keeps each claim next to the files that support it. A bundle is a local folder with the work under review, copies of its supporting files, and a claim register that links each claim to its evidence and to the wording the user judges safe to publish. The bundle records a digest (checksum) of each of these files: a short code that changes whenever the file changes. An offline verifier program reports missing or changed files, format errors, and claims that use assurance or compliance phrases such as ‘guarantees’. The worked case is JANUS, a study of how sentences from articles change when machine-translated into other languages and back. The review recorded which files were prepared for release and which stayed private, and it also led to a data availability statement that names the study’s data and code supplement. A bundle that passes the verifier shows what was kept and claimed, not that a claim is true.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[BigVRSP: A Data-Driven Instance Generator for Long-Haul Vehicle Routing and Scheduling Problems]]></title>
            <link>https://reference-global.com/article/10.5334/jors.694</link>
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            <pubDate>Wed, 23 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper introduces BigVRSP, an instance generator tool for long-haul transportation problems, designed to support numerical experiments in real-life pickup and delivery and truck assignment applications. The software automates the process of building realistic scenarios by combining either historical operational data or artificially generated ones with user preferences through command-line interface (CLI) parameters. Implemented as a series of Python scripts with CLI, it generates structured text datasets as CSV files, for both trucks and customer requests. The tool is openly available in a public repository on Gitlab and can be easily integrated into workflows in many different logistics contexts.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Making Soundscape Assessment Accessible: The SoundGraphy Tool]]></title>
            <link>https://reference-global.com/article/10.5334/jors.771</link>
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            <pubDate>Wed, 23 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The shift in acoustic environmental studies from noise mitigation to a soundscape-centric approach, formalized by the ISO 12913 series, has highlighted the requirement for sophisticated analytical tools to map human perception. This paper presents SoundGraphy, an open-source Python-based software designed to enhance the characterization and visualization of soundscape data. The architecture of SoundGraphy is established upon a functional tripod: a user-friendly graphical user interface (GUI) that democratizes complex analysis for researchers without programming expertise, an advanced data-filtering and representation system incorporating bivariate Kernel Density Estimation (KDE) and radar plots, and the novel integration of the Structural Summary Method (SSM). SoundGraphy is distributed as a standalone executable, removing technical barriers such as manual dependency management. Ultimately, this tool provides a robust framework for objectifying subjective acoustic experiences, bridging the gap between raw data and nuanced human perceptual outcomes across diverse international contexts.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[non-gon: A Non-Polygonal Collision Detection Library for Real-Time Queries Between Smooth Convex Shapes]]></title>
            <link>https://reference-global.com/article/10.5334/jors.585</link>
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            <pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Accurate collision detection is fundamental to modern game engines and physics simulators, enabling applications in biomechanics, robotic obstacle avoidance, surgical training, and granular material simulation. While polygonal meshes remain the standard, smooth convex shapes, such as ellipses and ellipsoids, offer significant advantages in geometric accuracy, memory efficiency, and computational performance. Although analytical methods exist, they are scattered across disciplines and lack a unified implementation. To address this, we present non-gon a curated library comprised of 14 analytical functions for real-time distance and proximity queries between 2D and 3D smooth convex shapes, implemented in TypeScript and Three.js. In addition to the core analytic functions, the library features curve and surface rendering, along with several interactive web application examples. Finally, we invite all communities working on physics-based engines to contribute to the library, either by integrating its analytic functions into their codebases or by extending the collection of supported contact pairs.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[microTorch: A Software Package for Fast and Flexible Self-Supervised Diffusion MRI Model Fitting]]></title>
            <link>https://reference-global.com/article/10.5334/jors.736</link>
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            <pubDate>Mon, 31 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The microTorch software package allows for the fast and flexible fitting of a wide range of multi-compartment microstructure models to diffusion magnetic resonance imaging (dMRI) data. It employs a self-supervised learning approach, which enables the speed gains of deep learning whilst minimising estimation bias induced by the training data distribution. Its modular architecture enables users to readily evaluate different combinations of compartment models, apply established multi-compartment models, and interchange neural network architectures without modifying the underlying framework. The package supports custom acquisition schemes and provides configurable training settings, allowing it to be tailored to specific datasets. microTorch is built using PyTorch and is freely available at https://github.com/snigdha-sen/microtorch.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[GravDyn: A Python Framework for Gravitational Modeling of Irregular Celestial Bodies Using Polyhedral, Mascon, and Series Expansion Methods]]></title>
            <link>https://reference-global.com/article/10.5334/jors.743</link>
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            <pubDate>Mon, 31 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[GravDyn is an open-source Python package for computing gravitational potentials and accelerations around irregular celestial bodies. It implements three approaches within the same workflow: the constant-density polyhedral method, a layered mascon approximation based on tetrahedral decomposition, and the Potential Series Expansion Method (PSEM). The package handles shape-model preprocessing, builds the selected gravity representation, and evaluates potentials and accelerations efficiently. The three implemented methods serve different regimes: the polyhedral model provides a reference solution near the surface, the mascon model supports layered internal density structures, and PSEM offers fast evaluation outside the Brillouin sphere once the polynomial coefficients have been generated. Validation tests against the polyhedral solution show that the mascon and PSEM models reproduce the reference potential with small relative errors while reducing evaluation costs. GravDyn is intended for mission design and studies of spacecraft motion, orbital stability, and gravitational modeling around asteroids and other small bodies.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[AgroEcoMetrics: A Python Toolkit for Modeling and Visualizing Agricultural Microclimates]]></title>
            <link>https://reference-global.com/article/10.5334/jors.659</link>
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            <pubDate>Thu, 27 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[AgroEcoMetrics is an open-source Python package for loading, transforming, and modeling agricultural microclimate data. This package includes tools for processing time-series weather data, modeling air and soil temperatures, estimating evapotranspiration and photoperiods, and calculating and visualizing growing degree days and rainfall runoff. The package integrates with existing research pipelines in agricultural science and supports both empirical and mechanistic modeling. It is hosted on GitHub and licensed under the MIT license, with potential applications in other domains, such as pest forecasting or precision agriculture.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[ArcLP: A Matlab Implementation of an 𝒪(√nL) Arc-search Infeasible Interior-Point Algorithm for Linear Programming]]></title>
            <link>https://reference-global.com/article/10.5334/jors.674</link>
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            <pubDate>Wed, 12 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper presents a Matlab implementation of an arc-search infeasible interior-point algorithm for linear programming (LP), which has a proven polynomial bound of 𝒪(√nL), the best among all interior-point algorithms for LP. Software architecture and major functions are discussed. Its ease of use is described by a simple example. Crucial strategies are summarized. Quality of the software is assured because this software has been extensively tested on both PC and Linux for the widely used Netlib benchmark linear programming problems in standard form. Some benchmark test problems involve tens of thousands of constraints and hundreds of thousands of variables. For all tested problems, the code found the optimal solution. The numerical results have been compared to those obtained by the popular Mehrotra’s predictor-corrector algorithm. We conclude that the implemented algorithm not only has the best polynomial bound but also is computationally competitive compared against the popular Mehrotra’s predictor-corrector algorithm.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[BickGraphing: Web-Based Application for Visual Inspection of Audio Recordings]]></title>
            <link>https://reference-global.com/article/10.5334/jors.702</link>
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            <pubDate>Mon, 27 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[BickGraphing is a browser-based research tool that enables visual inspection of acoustic recordings. The tool was built in support of visualising crop-feeding pest sounds in support of the Insect Eavesdropper project; however, it is widely applicable to all audio visualisations in research. It allows multiple uploads of large .wav files, computes waveforms and spectrograms locally, and supports interactive exploration of audio events in time and frequency. The application is implemented as a SvelteKit and TypeScript web app with a client-side signal-processing pipeline using WebAssembly-compiled FFmpeg and custom FFT utilities. The software is released on an open Git repository (https://github.com/bicklabuw/BickGraphing) and archived under a standard MIT License and can be reused for rapid visual quality checks of .wav recordings in insect bioacoustics and related fields. BickGraphing has the potential to be a local, easy-to-use coding-free visualisation platform for audio data in research.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[CodeEntropy: A Python Package for Multiscale Cell Correlation Entropy Estimation from Molecular Dynamics Simulations]]></title>
            <link>https://reference-global.com/article/10.5334/jors.741</link>
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            <pubDate>Mon, 20 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[CodeEntropy is an open-source Python package implementing the multiscale cell correlation (MCC) method for estimating entropy directly from molecular dynamics (MD) simulations. The software provides a modular, configuration-driven framework in which MCC calculations are expressed as a dependency-constrained workflow represented by a directed acyclic graph (DAG). This design maps the theoretical stages of MCC onto explicit computational transformations, enabling transparent and reproducible entropy estimation across molecular hierarchies. Designed as a reference implementation, CodeEntropy emphasises correctness, reproducibility, and transparency through structured outputs, automated testing, and continuous integration. Results are exported in machine-readable formats suitable for downstream analysis and integration with existing molecular simulation workflows. Distributed under the MIT License, CodeEntropy provides a sustainable platform for reproducible entropy estimation and for extending theoretical developments within the molecular simulation community.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[VAP2D: A Program for Quantitative Analysis of 2D Vessel Images]]></title>
            <link>https://reference-global.com/article/10.5334/jors.655</link>
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            <pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Vessel analysis is crucial in fields such as medical imaging and biomedical engineering. This study introduces VAP2D (two-dimensional vessel analysis program), an open-source, cross-platform, user-friendly software available on GitHub for the quantitative analysis of 2D vessel images. VAP2D enables the detection of branch points and endpoints, the calculation of vessel lengths and densities, and the generation of detailed reports.
The software supports both traditional image processing techniques and deep learning-based methods, offering users flexibility based on data type and image quality. In addition, it incorporates essential pre-processing steps, including noise removal, segmentation, and skeletonization, to optimize the analysis of raw images.
VAP2D has been rigorously tested to accurately detect branch points, endpoints, vessel densities, and lengths. Its modular structure, graphical user interface (GUI), and ability to export results in CSV or PDF formats make it a practical and adaptable tool for researchers and laboratory technicians. By bridging the gap between scientific research and practical applications, VAP2D provides a reliable platform for vessel analysis in medical imaging, biomedical engineering, and beyond.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[KernelPopper: A Scikit-Learn Compatible Package That Calculates Feature Weights From Trained Kernel Ridge Regression Models]]></title>
            <link>https://reference-global.com/article/10.5334/jors.701</link>
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            <pubDate>Mon, 13 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Kernel Ridge Regression (KRR) models extend linear least squares regression using the “kernel trick” to learn non-linear relationships between input and target variables. During ordinary least squares regression training, the model learns by determining a weight for each feature that captures its contribution to predicting the target value. KRR models instead focus on dual weights, which evaluate the importance of each training sample rather than the features. To calculate feature weights from the learned dual weights, we have developed a package for the KernelRidge model class from the widely used scikit-learn Python package. This enables interpretation of the weights learned by trained KRR models. The package is currently restricted to polynomial kernels of degrees two and three.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Isotope Spectrum Builder: An Application for Modeling Gamma-Ray Spectra of Common Isotopes Using Monte Carlo Pools and Experimental Backgrounds]]></title>
            <link>https://reference-global.com/article/10.5334/jors.740</link>
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            <pubDate>Thu, 09 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The Isotope Spectrum Builder is an open-source software framework for generating realistic gamma-ray spectra by combining physics-based Monte Carlo signal event pools with experimentally derived background radiation data. Signal events are generated offline using detailed Geant4 simulations and incorporate detector-response effects via energy smearing, while background events are obtained by resampling measured laboratory background spectra acquired with the same detector technologies.
The software supports interactive and automated spectrum generation through a web interface and command-line batch tools, enabling users to configure the detector type, isotope, energy range, binning from 1024 to 4096 channels, signal and background statistics, and normalization mode, including raw counts, counts per second, or unit-area normalization. Generated spectra can be exported as CSV files and annotated PNG figures, or produced in large batches for machine-learning and algorithm-development workflows. The package is distributed through PyPI, allowing users to install it directly with pip and access the spectrum generation functionality as a reusable Python library.
The Isotope Spectrum Builder is intended for education, algorithm benchmarking, and data-driven research in nuclear spectroscopy, offering a lightweight yet reproducible alternative to on-demand Monte Carlo simulation.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[LASPATED: A Library for the Analysis of Spatio-Temporal Discrete Data]]></title>
            <link>https://reference-global.com/article/10.5334/jors.544</link>
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            <pubDate>Wed, 08 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[In many settings, a user would like to use data of events that occur at points in space and time to estimate the intensity or rate of these events as a function of space and time. A nonhomogeneous Poisson process is a stochastic point process over space and/or time that is entirely specified by its intensity function, and is therefore a simple and popular stochastic model of random events in space and time.
We describe methods, tools, and a software library called LASPATED to fit nonhomogeneous spatio-temporal Poisson process models using spatio-temporal data and space-time discretization. The methods construct an intensity function for the Poisson process by discretizing space and time, and estimating arrival intensity as a function of subregion and time interval. With such methods, it is typical that the dimension of the estimator is large relative to the amount of data, and therefore the software makes provision for the use of additional data. The first method considered uses additional data to add a regularization term to the likelihood function for estimating the intensity of the Poisson process. The second method considered uses additional data to estimate arrival intensity as a function of covariates. We describe how the LASPATED Python and C++ packages perform various types of space and time discretization, and how they calibrate Poisson models, with options to use regularization or covariates. We demonstrate the use of the methods with simulated and real data. LASPATED can be used to estimate nonhomogeneous spatio-temporal Poisson process models for a wide range of applications, such as crime rates, earthquake rates, weather events, medical events, and fires.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[A Python Package for Distance-Based Generalized Sensitivity Analysis (DGSA)]]></title>
            <link>https://reference-global.com/article/10.5334/jors.697</link>
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            <pubDate>Mon, 06 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Distance-based Generalized Sensitivity Analysis (DGSA) is a global sensitivity analysis method that quantifies single parameter sensitivities as well as two-way parameter interactions. One of the main advantages is that it can provide sensitivity analyses where the amount of model evaluation needed is limited, compared to other methods. A Python package is implemented to compute both single parameter and conditional parameter sensitivities using two types of sensitivity measures. Sensitivity results can be visualized using Pareto plots and heatmaps with a perceptually uniform colormap. The source code is stored in a GitHub repository and includes an example dataset to illustrate its usage. This tool helps provide more complete and deeper insights into parameter sensitivity analysis.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[MIFCSheet: An Open-Source Progressive Web Application for Vegetation Inventory Data Collection]]></title>
            <link>https://reference-global.com/article/10.5334/jors.692</link>
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            <pubDate>Fri, 03 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Vegetation inventories are commonly recorded on paper in the field and digitized later, adding time and introducing transcription errors. This paper presents MIFCSheet, an open-source progressive web application for in-situ forest inventory data collection. The software enables offline data capture, structured field measurements, and automatic data validation, improving efficiency and reducing transcription mistakes. MIFCSheet is designed to be flexible, platform-independent, and easily deployable in diverse forestry and ecological monitoring contexts. The application is stored on a public website hosted by the University of Zaragoza at https://mifcsheet.unizar.es/, and its source code is available in a GitHub repository at https://github.com/CristianICS/MIFCSheet.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[pyCAFE: A Finite Element Framework for Solving Acoustic Problems in Python]]></title>
            <link>https://reference-global.com/article/10.5334/jors.677</link>
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            <pubDate>Mon, 22 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[pyCAFE is an open-source Python framework for the numerical solution of two-dimensional, frequency domain acoustic problems governed by the Helmholtz equation using the Finite Element Method (FEM). The software implements an explicit assembly of global acoustic stiffness and mass matrices from element-level contributions using four- and eight-node (CQUAD4 - CQUAD8) quadrilateral elements. It supports prescribed pressure, prescribed normal velocity, impedance, and rigid boundary conditions, and provides both direct frequency domain solvers and a modal solver based on a sparse generalised eigenvalue problem. pyCAFE operates on two-dimensional meshes generated with Gmsh and follows a matrix-based workflow conceptually aligned with commercial finite element software, exposing the discretised system operators directly to the user. The framework includes an automated test suite covering element-level properties, boundary condition enforcement, and solver accuracy, together with a GitHub Actions continuous integration pipeline verified on Python 3.10-3.12. Analytical validation against closed-form eigenfrequencies for a rigid rectangular cavity demonstrates extremely good agreement with the CQUAD8 discretisation. Originally developed as a MATLAB code, the framework has been ported to Python to improve accessibility and integration with open scientific libraries for research and teaching in computational acoustics.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[&lt;tt&gt;swxg&lt;/tt&gt;: A Python Library for Generalized Multivariate, Multisite, Copula-Based Stochastic Weather Generation]]></title>
            <link>https://reference-global.com/article/10.5334/jors.651</link>
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            <pubDate>Fri, 19 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[swxg is a Python library for generalized multivariate, multisite, copula-based stochastic weather generation. It addresses the challenge of generating realistic, spatially and temporally correlated precipitation and temperature data for water resources planning and climate vulnerability assessments. The library uses semiparametric fitting for precipitation via Gaussian mixture hidden Markov models, followed by conditional copula-based temperature generation. swxg includes comprehensive validation tools to assess goodness of fit and ensure generated weather is statistically indistinguishable from observations. The software is openly available on GitHub under an MIT license and is designed for flexible integration into exploratory modeling frameworks, making it suitable for diverse hydroclimatic applications requiring synthetic weather data across multiple sites.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Open-Source Pipeline of Cursor-Tracking for Likert-Scale Data Collected in Qualtrics]]></title>
            <link>https://reference-global.com/article/10.5334/jors.690</link>
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            <pubDate>Mon, 08 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The software was developed to provide Likert-scale cursor tracking that includes item-level correction of self-report responses collected in Qualtrics. The software was first introduced in a feasibility study by Siemers et al. [1], and the present paper provides its full technical details and implementation. The pipeline includes a ready-to-use Qualtrics template with embedded JavaScript, Python scripts for cleaning and processing raw cursor coordinates, and R scripts for visualization and statistical analysis. Together, these components form a complete workflow from survey administration to the analysis of cursor trajectories and cursor-corrected response scores. All materials, code, and documentation are openly available on GitHub and OSF, making Likert-scale cursor tracking reusable and accessible to a wide range of researchers.]]></description>
            <category>ARTICLE</category>
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