Open Source Software Research Articles

Study of Open-source Software Adoption Strategy in E-government: Madinah Development Authority

Open Source Software (OSS) has gained significant traction in the government sector due to its potential to reduce costs, enhance security, and offer diverse benefits. This study focuses on the adoption of OSS within the Madinah Development Authority (MDA), a Saudi Arabian governmental agency. It aims to explore the OSS adoption process, identify challenges, and propose solutions to maximize its benefits. Employing a hybrid approach, data were collected through preliminary interviews with managers and a structured questionnaire survey among MDA employees. A SWOT analysis was conducted to evaluate the organization's IT environment and staff capabilities. The study’s key contribution is the development of a phased strategy tailored for MDA to successfully adopt OSS, addressing identified challenges and optimizing the benefits of open-source solutions for government operations.

Fractional hitting sets for efficient multiset sketching

The exponential increase in publicly available sequencing data and genomic resources necessitates the development of highly efficient methods for data processing and analysis. Locality-sensitive hashing techniques have successfully transformed large datasets into smaller, more manageable sketches while maintaining comparability using metrics such as Jaccard and containment indices. However, fixed-size sketches encounter difficulties when applied to divergent datasets. Scalable sketching methods, such as sourmash, provide valuable solutions but still lack resource-efficient, tailored indexing. Our objective is to create lighter sketches with comparable results while enhancing efficiency. We introduce the concept of Fractional Hitting Sets, a generalization of Universal Hitting Sets, which cover a specified fraction of the k-mer space. In theory and practice, we demonstrate the feasibility of achieving such coverage with simple but highly efficient schemes. By encoding the covered k-mers as super-k-mers, we provide a space-efficient exact representation that also enables optimized comparisons. Our novel tool, supersampler, implements this scheme, and experimental results with real bacterial collections closely match our theoretical findings. In comparison to sourmash, supersampler achieves similar outcomes while utilizing an order of magnitude less space and memory and operating several times faster. This highlights the potential of our approach in addressing the challenges presented by the ever-expanding landscape of genomic data. supersampler is an open-source software and can be accessed at https://github.com/TimRouze/supersampler. The data required to reproduce the results presented in this manuscript is available at https://github.com/TimRouze/supersampler/experiments.

Ascertaining the Educational Efficacy of using Free Open-source Software Research Artificial Intelligence Tools: A Formulative Study at CPGS-AS, CAU(I), Umiam, Ri-Bhoi, Meghalaya

One of the most significant challenges students currently face is the task of writing and publishing research papers. This process requires a systematic and distinctive approach, including comprehensive analysis, critical interpretation, and the ability to synthesize research on a specific topic. In the current academic and professional landscape, proficiency in artificial intelligence (AI) has become an essential prerequisite for students to excel in this domain. Although artificial intelligence (AI) has become increasingly important in academic research, there remains a paucity of studies exploring its application and impact. Therefore, the present study was conducted to examine the awareness and ascertain the efficacy of using Free and Open-Source Software (FOSS) AI tools in academic research among M.Sc. (Ag.) students at CPGS-AS, CAU (I), Umiam, Ri-Bhoi, Meghalaya. The study adopted a formative research design to achieve its objectives. Convenient sampling procedure was carried out in order to select 62 respondents. The scientific inquiry revealed that the majority of respondent (67.74%) were aware of Software Ownership; while 75.80% were knowledgeable about the features of FOSS, particularly its freedom to use FOSS AI tools. Additionally, more than half of the respondents (67.2%) reported learning about FOSS AI tools through social media and the internet. However, majority of respondents (79.03%) demonstrated low awareness of the use of FOSS AI tools in academic research. A statistically significant difference was found in the respondents' scores before and after the intervention of FOSS AI tools in academic research, indicating a marked improvement in efficacy among the students.

Polarity-JaM: an image analysis toolbox for cell polarity, junction and morphology quantification

Cell polarity involves the asymmetric distribution of cellular components such as signalling molecules and organelles within a cell, alterations in cell morphology and cell-cell contacts. Advances in fluorescence microscopy and deep learning algorithms open up a wealth of unprecedented opportunities to characterise various aspects of cell polarity, but also create new challenges for comprehensible and interpretable image data analysis workflows to fully exploit these new opportunities. Here we present Polarity-JaM, an open source package for reproducible exploratory image analysis that provides versatile methods for single cell segmentation, feature extraction and statistical analysis. We demonstrate our analysis using fluorescence image data of endothelial cells and their collective behaviour, which has been shown to be essential for vascular development and disease. The general architecture of the software allows its application to other cell types and imaging modalities, as well as seamless integration into common image analysis workflows, see https://polarityjam.readthedocs.io. We also provide a web application for circular statistics and data visualisation, available at www.polarityjam.com, and a Napari plug-in, each with a graphical user interface to facilitate exploratory analysis. We propose a holistic image analysis workflow that is accessible to the end user in bench science, enabling comprehensive analysis of image data.

SpectroIBIS: Automated Data Processing for Multiconformer Quantum Chemical Spectroscopic Calculations.

Quantum chemical spectroscopic calculations have grown increasingly popular in natural products research for aiding the elucidation of chemical structures, especially their stereochemical configurations. These calculations have become faster with modern computational speeds, but subsequent data handling, inspection, and presentation remain key bottlenecks for many researchers. In this article, we introduce the SpectroIBIS computer program as a user-friendly tool to automate tedious tasks commonly encountered in this workflow. Through a simple graphical user interface, researchers can drag and drop Gaussian or ORCA output files to produce Boltzmann-averaged ECD, VCD, UV-vis and IR data, optical rotations, and/or 1H and 13C NMR chemical shifts in seconds. Also produced are formatted, publication-quality supplementary data tables containing conformer energies and atomic coordinates, saved to a DOCX file compatible with Microsoft Word and LibreOffice. Importantly, SpectroIBIS can assist researchers in finding common calculation issues by automatically checking for redundant conformers and imaginary frequencies. Additional useful features include recognition of conformer energy recalculations at a higher theory level, and automated generation of input files for quantum chemistry programs with optional exclusion of high-energy conformers. Lastly, we demonstrate the applicability of SpectroIBIS with spectroscopic calculations for five natural products. SpectroIBIS is open-source software available as a free desktop application (https://github.com/bbulcock/SpectroIBIS).

AI-driven histologic analysis of human Achilles tendinopathy provides a roadmap to unravel pathogenesis.

Achilles tendinopathy is a common source of pain and dysfunction, yet its pathogenesis remains poorly understood. Research on human tendons is hampered by lack of standardisation in tissue sample validation, making interpretation of results challenging. We sought to develop an automated and operator-independent approach to histologically score human tendons. We assembled a cohort of 15 tendinopathic and 10 normal control Achilles tendon samples. We stained longitudinal sections with haematoxylin and eosin and Alcian blue and developed a low temperature epitope-retrieval protocol for immunostaining of blood vessels. Histologic sections were scored by pathologists using the current gold standard Bonar score. Whole sections were then analysed with open-source software (QuPath). Histologic features were automatically quantified across the entire section and summarised in the BonAIr score. Tissue from the same patients was subsequently analysed by quantitative polymerase chain reaction and flow cytometry to validate elements of the BonAIr score. We observed increased cell roundness, collagen disarrangement, ground substance, and vascularity in tendinopathy using both the Bonar and BonAIr scores. Increased cellularity was only detected by the BonAIr score. Cellular and transcriptomic analyses corroborated tendinopathic shifts in all elements of the BonAIr score and further identified elevated THY1/CD90 expression in tendinopathy. CD90+ cells were found to localise to areas of low collagen alignment. These results align with the concept of stromal cell dysregulation in tendinopathy. Automated analysis of whole tendon sections refines conventional histopathologic scoring and predicts cellular and molecular changes found in tendinopathy. The BonAIr score should be further developed for standardised assessment of tendons samples across other anatomical locations and different research centres.

Exploring the role of organizational identification in the nexus of psychological capital and organizational-oriented citizenship behaviors: evidence from healthcare professionals in India.

The purpose of this study is to explore the mediation of organizational identification (OI) in the relationship between employees' psychological capital (PsyCap) and their engagement in organizational-oriented citizenship behaviors (OCBO). We examine the proposed theoretical model through the lens of the job demand and resources (JD-R) model and social identity theory (SIT), focusing specifically on the healthcare setting. Employing a structured questionnaire, responses were collected from 240 healthcare professionals working in multinational pharmacovigilance firms in select Tier-I Indian cities using purposive sampling. Model testing constituted performing statistical analyses using partial least squares-structural equation modelling (PLS-SEM) in the R environment, an open-source software. Results revealed a significant positive association between PsyCap and OCBO. In addition, we found that OI mediated the relationship between PsyCap and OCBO. This suggests that practitioners can stimulate extra-role performance in employees through enhancing PsyCap and fostering psychological oneness with the organization. The research emphasizes the significant role of employees' personal resources, manifested in the form of PsyCap, in having a bearing on their attitudes and feelings towards the organization. We explore the under-researched latent mechanism of the PsyCap and OCBO relationship through employees' social identification process. In this research, we uncover the underlying mechanism of PsyCap and OCBO association by highlighting the latent role of OI on a unique set of healthcare professionals.

Deviations in evapotranspiration measurements between enclosed- and open-path eddy covariance systems

The evapotranspiration (ET) measured by a closed-path eddy covariance system (CPEC), even with tube attenuation correction considering relative humidity (RH), frequently underestimated the ET measured by an open-path eddy covariance system (OPEC). An enclosed-path eddy covariance system (EPEC), which combines the advantages of both CPEC and OPEC, shows superior performance in many aspects, but doubts remain regarding its ET measurement accuracy. In this technical note, the comparative data between EPEC and OPEC were collected from various ecosystems and gas analyzers in the Korea Flux Network (KoFlux) and analyzed. Despite careful application of time lag compensation and frequency response correction using EddyPro, a widely used open-source data processing software in the flux science community, significant underestimation (6–8%) of ET measured by EPEC compared to OPEC remained consistently in all the three intercomparison experiments. Such deviations in ET measurements must be considered when synthesizing ET data measured by various EC systems.

Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data

Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data

ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use.

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at experimentalist scientists and applicable to process and product optimizations in various fields, this package simplifies the optimization process, offering features such as benchmarking, noise addition/removal, multiobjective optimization, batch-mode operation, and comprehensive plotting features. The present publication focuses on ease of use by presenting an optimization of a chemical reaction to produce a specific color, such as leaf green.

Applications of Open Source Software in Libraries

Applications of Open Source Software in Libraries

Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering

With the rapidly increasing amount of materials data being generated in a variety of projects, efficient and accurate classification of atomistic structures is essential. A current barrier to effective database queries lies in the often ambiguous, inconsistent, or completely missing classification of existing data, highlighting the need for standardized, automated, and verifiable classification methods. This work proposes a robust solution for identifying and classifying a wide spectrum of materials through an iterative technique, called symmetry-based clustering (SBC). Because SBC is not a machine learning-based method, it requires no prior training. Instead, it identifies clusters in atomistic systems by automatically recognizing common unit cells. We demonstrate the potential of SBC to provide automated, reliable classification and to reveal well-known symmetry properties of various materials. Even noisy systems are shown to be classifiable, showing the suitability of our algorithm for real-world data applications. The software implementation is provided in the open-source Python package, MatID, exploiting synergies with popular atomic-structure manipulation libraries and extending the accessibility of those libraries through the NOMAD platform.

Open source software field research: Spanning social and practice networks for re-entering the field

Sociotechnical research increasingly includes the social sub-networks that emerge from large-scale sociotechnical infrastructure, including the infrastructure for building open source software. This paper addresses these numerous sub-networks as advantageous for researchers. It provides a methodological synthesis focusing on how researchers can best span adjacent social sub-networks during engaged field research. Specifically, we describe practices and artifacts that aid movement from one social subsystem within a more extensive technical infrastructure to another. To surface the importance of spanning sub-networks, we incorporate a discussion of social capital and the role of technical infrastructure in its development for sociotechnical researchers. We then characterize a five-step process for spanning social sub-networks during engaged field research: commitment, context mapping, jargon competence, returning value, and bridging. We then present our experience studying corporate open source software projects and the role of that experience in accelerating our work in open source scientific software research as described through the lens of bridging social capital. Based on our analysis, we offer recommendations for engaging in fieldwork in adjacent social sub-networks that share a technical context and discussion of how the relationship between social and technically acquired social capital is a missing but critical methodological dimension for research on large-scale sociotechnical research.

Codebase release 1.0 for DanceQ

The complexity of quantum many-body problems scales exponentially with the size of the system, rendering any finite-size scaling analysis a formidable challenge. This is particularly true for methods based on the full representation of the wave function, where one simply accepts the enormous Hilbert space dimensions and performs linear algebra operations, e.g., for finding the ground state of the Hamiltonian. If the system satisfies an underlying symmetry where an operator with a degenerate spectrum commutes with the Hamiltonian, it can be block-diagonalized, thus reducing the complexity at the expense of additional bookkeeping. At the most basic level required for Krylov space techniques (like the Lanczos algorithm), it is necessary to implement a matrix-vector product of a block of the Hamiltonian with arbitrary block-wavefunctions, potentially without holding the Hamiltonian block in memory. An efficient implementation of this operation requires the calculation of the position of an arbitrary basis vector in the canonical ordering of the basis of the block. We present here an elegant and powerful, multi-dimensional approach to this problem for the U(1)U(1) symmetry appearing in problems with particle number conservation. Our divide-and-conquer algorithm uses multiple subsystems and hence generalizes previous approaches to make them scalable. In addition to the theoretical presentation of our algorithm, we provide DanceQ, a flexible and modern – header only – C++20 implementation to manipulate, enumerate, and map to its index any basis state in a given particle number sector as open source software under https://DanceQ.gitlab.io/danceq.

DanceQ: High-performance library for number conserving bases

The complexity of quantum many-body problems scales exponentially with the size of the system, rendering any finite-size scaling analysis a formidable challenge. This is particularly true for methods based on the full representation of the wave function, where one simply accepts the enormous Hilbert space dimensions and performs linear algebra operations, e.g., for finding the ground state of the Hamiltonian. If the system satisfies an underlying symmetry where an operator with a degenerate spectrum commutes with the Hamiltonian, it can be block-diagonalized, thus reducing the complexity at the expense of additional bookkeeping. At the most basic level required for Krylov space techniques (like the Lanczos algorithm), it is necessary to implement a matrix-vector product of a block of the Hamiltonian with arbitrary block-wavefunctions, potentially without holding the Hamiltonian block in memory. An efficient implementation of this operation requires the calculation of the position of an arbitrary basis vector in the canonical ordering of the basis of the block. We present here an elegant and powerful, multi-dimensional approach to this problem for the U(1)U(1) symmetry appearing in problems with particle number conservation. Our divide-and-conquer algorithm uses multiple subsystems and hence generalizes previous approaches to make them scalable. In addition to the theoretical presentation of our algorithm, we provide DanceQ, a flexible and modern – header only – C++20 implementation to manipulate, enumerate, and map to its index any basis state in a given particle number sector as open source software under https://DanceQ.gitlab.io/danceq.

Rapid and sensitive protein complex alignment with Foldseek-Multimer.

Advances in computational structure prediction will vastly augment the hundreds of thousands of currently available protein complex structures. Translating these into discoveries requires aligning them, which is computationally prohibitive. Foldseek-Multimer computes complex alignments from compatible chain-to-chain alignments, identified by efficiently clustering their superposition vectors. Foldseek-Multimer is 3-4 orders of magnitudes faster than the gold standard, while producing comparable alignments; this allows it to compare billions of complex pairs in 11 h. Foldseek-Multimer is open-source software available at GitHub via https://github.com/steineggerlab/foldseek/ , https://search.foldseek.com/search/ and the BFMD database.

The Journal of Open Source Software (JOSS): Bringing Open-Source Software Practices to the Scholarly Publishing Communityfor Authors, Reviewers, Editors, and Publishers

Introduction: Open-source software (OSS) is a critical component of open science, but contributions to the OSS ecosystem are systematically undervalued in the current academic system. The Journal of Open Source Software (JOSS) contributes to addressing this by providing a venue (that is itself free, diamond open access, and all open-source, built in a layered structure using widely available elements/services of the scholarly publishing ecosystem) for publishing OSS, run in the style of OSS itself. A particularly distinctive element of JOSS is that it uses open peer review in a collaborative, iterative format, unlike most publishers. Additionally, all the components of the process—from the reviews to the papers to the software that is the subject of the papers to the software that the journal runs—are open. Background: We describe JOSS’s history and its peer review process using an editorial bot, and we present statistics gathered from JOSS’s public review history on GitHub showing an increasing number of peer reviewed papers each year. We discuss the new JOSSCast and use it as a data source to understand reasons why interviewed authors decided to publish in JOSS. Discussion and Outlook: JOSS’s process differs significantly from traditional journals, which has impeded JOSS’s inclusion in indexing services such as Web of Science. In turn, this discourages researchers within certain academic systems, such as Italy’s, which emphasize the importance of Web of Science and/or Scopus indexing for grant applications and promotions. JOSS is a fully diamond open-access journal with a cost of around US$5 per paper for the 401 papers published in 2023. The scalability of running JOSS with volunteers and financing JOSS with grants and donations is discussed.

Sông Sài Gòn: Extreme Plastic Pollution Pathways in Riparian Waterways

Plastic pollution in waterways poses a significant global challenge, largely stemming from land-based sources and subsequently transported by rivers to marine environments. With a substantial percentage of marine plastic waste originating from land-based sources, comprehending the trajectory and temporal experience of single-use plastic bottles assumes paramount importance. This project designed, developed, and released a plastic pollution tracking device, coinciding with Vietnam’s annual Plastic Awareness Month. By mapping the plastic tracker’s journey through the Saigon River, this study generated high-fidelity data for comprehensive analysis and bolstered public awareness through regular updates on the Re-Think Plastics Vietnam website. The device, equipped with technologies such as drone flight controller, open-source software, embedded computing, and cellular networking effectively captured GPS position, track, and localized conditions experienced by the plastic bottle tracker on its journey. This amalgamation of data contributes to the understanding of plastic pollution behaviors and serves as a data set for future initiatives aimed at plastic prevention in the ecologically sensitive Mekong Delta. By illuminating the transportation of single-use plastic bottles in the riparian waterways of Ho Chi Minh City and beyond, this study plays a role in collective efforts to understand plastic pollution and preserve aquatic ecosystems. By deploying a GPS-enabled plastic tracker, this study provides novel, high-resolution empirical data on plastic transport in urban tidal systems. These findings contribute to improving waste interception strategies and informing environmental policies aimed at reducing plastic accumulation in critical retention zones.

Optimizing fully-efficient two-stage models for genomic selection using open-source software.

Genomic-assisted breeding has transitioned from theoretical concepts to practical applications in breeding. Genomic selection (GS) predicts genomic breeding values (GEBV) using dense genetic markers. Single-stage models predict GEBVs from phenotypic observations in one step, fully accounting for the entire variance-covariance structure among genotypes, but face computational challenges. Two-stage models, preferred for their simplicity and efficiency, first calculate adjusted genotypic means accounting for spatial variation within each environment, then use these means to predict GEBVs. However, unweighted (UNW) two-stage models assume independent errors among adjusted means, neglecting correlations among estimation errors. Here, we show that fully-efficient two-stage models perform similarly to UNW models for randomized complete block designs but substantially better for augmented designs. Our simulation studies demonstrate the impact of the fully-efficient methodology on prediction accuracy across different implementations and scenarios. Incorporating non-additive effects and augmented designs significantly improved accuracy, emphasizing the synergy between design and model strategy. Consistent performance requires the estimation error covariance to be incorporated into a random effect (Full_R model) rather than into the residuals. Our results suggest that the fully-efficient methodology, particularly the Full_R model, should be more prevalent, especially as GS increases the appeal of sparse designs. We also provide a comprehensive theoretical background and open-source R code, enhancing understanding and facilitating broader adoption of fully-efficient two-stage models in GS. Here, we offer insights into the practical applications of fully-efficient models and their potential to increase genetic gain, demonstrating a improvement after five selection cycles when moving from UNW to Full_R models.

A model of elastic softening and displacive phase transitions in anisotropic phases, with application to stishovite and post-stishovite

Summary This paper introduces a comprehensive framework for modelling both instantaneous and time-dependent elastic softening in anisotropic materials at high pressure and temperature. This framework employs Landau Theory, minimizing the Helmholtz energy by varying isochemical parameters (q) that capture structural changes, atomic ordering, and/or electronic spin states. This allows for internally consistent predictions of volume, unit cell parameters, the elastic tensor, and other thermodynamic properties, while allowing large symmetry-breaking strains. The formulation is validated using the stishovite-to-post-stishovite transition. It is demonstrated that, near this transition, both stishovite and post-stishovite exhibit auxetic behaviour in several directions, with post-stishovite also displaying negative linear compressibility along the long axis of its unit cell (either the a or b axis). The new formulation is implemented in the open-source BurnMan software package.