Open Source Software Research Articles

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

Abstract 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.

Team Makes You Better: Evidence from Online Medical Consultation Platforms

Practice- and Policy-Oriented Abstract Our study of online medical consultation platforms shows that, when physicians work in virtual teams, they perform better even in their individual patient consultations. This improvement occurs primarily through peer effects: physicians are motivated to work harder when they can observe their teammates’ performance. The findings from our study offer valuable insights for the design and operation of online medical consultation (OMC) platforms. Specifically, whereas the rise of OMC platforms has significantly improved the connectivity among patients and physicians by reducing or even eliminating certain frictions in patient–physician encounters, there remain major challenges as to how to effectively match patients and physicians and facilitate value creation for both sides given the overall supply shortage of physicians. Our analysis indicates that team consultation may complement rather than cannibalize individual consultation. Whereas our research focuses on the healthcare sector, these insights apply broadly to other contexts (e.g., open-source software development and massive open online course platforms) in which professionals can form voluntary teams. The key is to carefully balance individual work with teamwork, providing the right tools and incentives for collaboration.

3D visualization of human colon tissue using a modified CUBIC-based tissue-clearing technique

Background: Colorectal cancer represents one of the most common neoplastic diseases worldwide, making it a frequent focus in routine pathological analyses. Visualizing complex three-dimensional (3D) structures, such as nerves within tumors, requires thick tissue sections, which necessitates the use of optical tissue-clearing methods to achieve transparency. However, following tissue clearing, samples typically require advanced imaging techniques such as light-sheet and two-photon confocal microscopy, which are usually unavailable in standard histological laboratories. Objective: We aimed to demonstrate how a well-established tissue-clearing approach can be adapted for use in a routine histological laboratory, enabling a robust 3D visualization of nerve fibers in samples of both normal human colon and colon cancer tissues. Methods: We modified the “clear unobstructed brain/body imaging cocktails” method, originally developed for whole-brain imaging in mice, and applied it to human colon tissue samples measuring approximately 10 mm3, a standard size typically processed in pathological laboratories. Results: Our protocol, which integrates a tissue-clearing technique, enabled reliable immunofluorescent visualization of colonic nerve fibers labeled with anti-β3-tubulin antibodies. The labeled nerve fibers could be observed using a standard epifluorescence microscope, and high-quality 3D reconstructions were generated through a simple image analysis approach using the open-source software ilastik, which eliminates the need for confocal microscopy. Conclusion: The proposed steps provide a valuable method for researchers to visualize complex 3D structures, such as neural cells and processes, in both normal and tumor-transformed tissue settings.

CellMAP: an open-source software tool to batch-process cell topography and stiffness maps collected with an atomic force microscope

Atomic force microscopy (AFM) is the gold-standard technique to simultaneously map the morphology and viscoelastic properties of living cells. Although existing software tools, both open-source and from AFM manufacturers, can analyze cells individually, there is a growing need for fast and accessible codes to compile data from multiple cells into a single dataset. To address this, we present CellMAP, a user-friendly software tool that streamlines the batch-processing of AFM-derived topography and stiffness maps of living cells. Our analysis pipeline includes but is not limited to: flattening of the underlying substrate surface, filtering of outlier values, measurement of the cell surface and volume, and measurement of height and stiffness distributions. CellMAP can also generate a composite cell that reflects the height and stiffness properties of an entire cell population.

Deep phenotyping platform for microscopic plant-pathogen interactions

The increasing availability of genetic and genomic resources has underscored the need for automated microscopic phenotyping in plant-pathogen interactions to identify genes involved in disease resistance. Building on accumulated experience and leveraging automated microscopy and software, we developed BluVision Micro, a modular, machine learning-aided system designed for high-throughput microscopic phenotyping. This system is adaptable to various image data types and extendable with modules for additional phenotypes and pathogens. BluVision Micro was applied to screen 196 genetically diverse barley genotypes for interactions with powdery mildew fungi, delivering accurate, sensitive, and reproducible results. This enabled the identification of novel genetic loci and marker-trait associations in the barley genome. The system also facilitated high-throughput studies of labor-intensive phenotypes, such as precise colony area measurement. Additionally, BluVision’s open-source software supports the development of specific modules for various microscopic phenotypes, including high-throughput transfection assays for disease resistance-related genes.

LEVERAGING THE UBIQUITOUS GPS SENSOR ON THE SMARTPHONES FOR ACCESSIBLE LAND SURVEYING

The Global Positioning System (GPS) is an essential tool in land surveying. GPS has become an alternative method of surveying that requires less manpower and less time. However, GPS devices are still expensive to buy, especially for students. On the other hand, almost every student has a smartphone with a built-in GPS sensor, so this GPS is certainly accessible to everyone with a smartphone. This study used a smartphone's GPS to conduct land surveying at the campus of Bengkulu University. This smartphone’s GPS was used to track various parameters such as coordinates, elevation, and distance between two or more points to calculate the area within the study area. The innovative method of using the built-in GPS sensor in smartphones will provide convenience for users and introduce simplified open-source software for the land measurement process. The measurement was calibrated using a conventional roll meter to verify the linear error by comparing the two measurements between the smartphone’s GPS and roll meter. The smartphone’s GPS reading was logged using GPS Waypoints and My Tracks, free Android applications on the Google Play store. This study's average error in measurements obtained using GPS on smartphones was 3.02%. This value is sufficient for the initial stage of low-cost land surveys and falls within ideal conditions for GPS measurements. Therefore, this article emphasizes the potential of smartphone GPS to optimize techniques in education and scientific investigations.

Sentence-centric modeling of the writing process

Linguistic modeling of the writing process has gained in importance in recent years. Existing models, both from a linguistic perspective focusing on syntactic analyses as used in natural language processing and from writing research, are insufficient to actually linguistically explain what authors do when writing and revising. Writing is linear in time, but writers are free to move to any point in the text produced so far whenever they want, thus producing specific parts (e.g., sentences) in a non-linear fashion. However, the final product is a linear sequence of sentences. We therefore can interpret writing texts as a sentence-driven process. In this new framework, this article proposes a model of the production of sentences during writing. This sentence-centric model builds on existing considerations of transforming sequences, bursts and revisions, and takes into account aspects of linearity and non-linearity on the sentence level. We present a working implementation (available as open source software) and show which information can be gained by the resulting analyses in a small case study.

A novel automated method for comprehensive renal cast quantification from rat kidney sections using QuPath.

The presence of tubular casts within the kidney serves as an important feature when assessing the degree of renal injury. Quantification of renal tubular casts has been historically difficult due to varying cast morphologies, protein composition, and stain uptake properties, even within the same kidney. Color thresholding remains one of the most common methods of quantification in the laboratory when assessing the percentage of renal casting; however, this method is unable to account for tubule casts stained a variety of colors. We have developed a novel method of automated cast quantification using the machine learning pixel classification tool within QuPath, an open-source software designed for digital pathology. We demonstrated the usability of this method in male and female Dahl salt-sensitive rats fed either low or high salt for 2 wk and male Sprague-Dawley rats treated with podotoxin puromycin aminonucleoside (PAN). Briefly, the pixel classifier was trained to identify kidney tissue, various cast color types, and slide backgrounds. Following the development of the pixel classifier, we applied it to the sample population and compared the results with those of other methods of cast quantification, including color thresholding and manual quantification. We found that the automated pixel classifier designed in QuPath was able to comprehensively quantify metachromatic tubular casts compared with color thresholding. This novel method of cast quantification provides researchers with the ability to reliably automate cast quantification that is both comprehensive and efficient.NEW & NOTEWORTHY We developed a method of automated renal tubule cast quantification using a machine learning-based pixel classifier within QuPath, an open-source image analysis software. The advantages of this approach are demonstrated by rigorous comparison of quantification methods on a set of Masson's trichrome-stained kidney sections from high- and low-salt fed salt-sensitive Dahl rats. Researchers are provided with step-by-step instructions for creating and training a pixel classifier in QuPath for application to image analysis.

ECLStat: A robust machine learning based visual imaging tool for electrochemiluminescence biosensing.

Visual electrochemiluminescence (ECL) has emerged as a prominent diagnostic method for accurately quantifying various disease markers even at point of care setting with high sensitivity and accuracy. It does not employ complicated instruments such as potentiostat and expensive imaging microscopy for quantifying trace amounts of molecules. The ECL system offers significant advantages over other detection processes, such as high sensitivity, selectivity, rapid response, multiplexing, and miniaturization capabilities, making it well-suited for future commercialization. However, the current ECL system lacks standardization and accuracy in the resulting output data due to the manual measurement of ECL signal response using open-source image processing software, which often limits the efficiency of the ECL process in real-time applications. To address the shortcomings of the existing approach and advance the ECL detection process, a fully automated machine learning-assisted standalone graphical user interface (GUI) application was developed for dedicated measurement and management of ECL-emitted light signals. The working performance of the developed program is evaluated for its real-time utility by detecting hydrogen peroxide, which is an important reactive oxygen species, and glucose, which is a significant biomarker of diabetes. The obtained results show the detection limit of 0.024mM and 0.035mM for H2O2 and glucose, with a quantification limit of 0.074mM and 0.10mM, respectively. The ultimate objective of the developed application is to improve accuracy by enabling users to apply machine learning algorithms to raw image data seamlessly without deeply comprehending the underlying computational processes and establish a standard protocol for ECL signal measurements. Moreover, the developed application can be used in other optical detection approaches such as chemiluminescence, colorimetric, and fluorescence.

MBI-KG: A knowledge graph of structured and linked economic research data extracted from the 1937 book "Die Maschinen-Industrie im Deutschen Reich".

The MaschinenBauIndustrie Knowledge Graph (MBI-KG) is a structured and semantically enriched dataset extracted from the 1937 publication "Die Maschinen-Industrie im Deutschen Reich" (The Machinery Industry in the German Reich), published by the "Wirtschaftsgruppe Maschinenbau" and edited by Herbert Patschan. This historical source offers data on German companies within the mechanical engineering industry during the pre-World War II era. The book was digitized, and Optical Character Recognition (OCR) was applied to extract text. The unstructured extracted data was then structured and semantically enriched to enable data integration and reuse. The semantically enriched data was uploaded into an open-source knowledge-graph software. The resulting knowledge graph includes detailed information about companies, individuals, and administrative entities relevant to the German mechanical engineering industry. The data is accessible through various means, including a SPARQL endpoint, an API, advanced search functionalities, a reconciliation API, and bulk files. Each entity in the knowledge graph can be exported in multiple formats, such as CSV, RDF (ttl), JSON, and NDJSON, ensuring compatibility with diverse research tools and platforms. This dataset can be reused in various research domains, including economic history, data science, and digital humanities. By providing machine-readable, structured data from a crucial historical period, the MBI-KG facilitates novel analyses and insights into the economic and industrial landscape of early 20th-century Germany. The dataset's interoperability with other data sources and its alignment with FAIR principles further enhance its value for interdisciplinary research and long-term preservation.

Evaluating the Probability of Rainwater Collection as part of Green Infrastructure using GIS and RS Technologies in Industrial Regions, Eskişehir, Türkiye

The study aims to identify the possibility of rainwater harvesting in industrial zones (Eskisehir Organized Industrial Zone (EOIZ), Baksan Industrial Site, Matbaacılar Site, ESTIM Wholesalers Site, Auto Gallery, EMKO Furniture and Woodworks, Teksan Industrial Site, Auto Industrialists and Small Industry, Craft Industrial Site, New Organized Industrial Zone Development Area, and Small and Medium-sized Enterprises (SMEs)) with high water use and concentrated impermeable areas. In this case, the amount of rainwater accumulated in impermeable regions was calculated using GIS and RS to reduce the rainwater load that accumulated or reached the wastewater treatment plant. The study will directly contribute to the displaying of the green water footprint resulting from industrial impermeable zones. This work for industrial areas will be pioneering. This study was analyzed using open-source GIS software and Google Earth software, a free application that allows experts in various trade branches of rainwater harvesting in the industrial region, industrialists, and researchers who want to conduct research in this area to do so quickly and easily. When viewed from this perspective, it is clear that the work has original value in the subject and makes significant contributions to the literature. Furthermore, this work directly contributes to the Sustainable Development Goals of "Clean Water and Sanitation" and "Climate Action".

FSUDAQ - A general purpose GUI data acquisition program for the CAEN x725, x730, x740 digitizers

FSUDAQ is a versatile, multi-threaded, lightweight, and open-source data acquisition software with a graphical user interface, designed to fully utilize the capabilities of first-generation CAEN x725, x730, and x740 series digitizers equipped with various Digital Pulse Processing (DPP) firmware, including Pulse-Height Analysis (PHA), Pulse-Shape Discrimination (PSD), and Charge-Digital Conversion (QDC). It emphasizes user-friendliness, stability, scalability, high throughput, and low latency. The software includes features such as an online waveform scope, scalar panel, and real-time single spectrum display for each input channel, along with an online event builder and analyzer capable of generating 1D and 2D histograms and applying graphical cuts. Users can also create and integrate custom online analyzers to meet specific experimental requirements. FSUDAQ has been successfully tested at the John D. Fox laboratory at Florida State University (FSU) with a diverse set of experiments using the Encore and ANASEN active target detectors, the Super-Enge Split-Pole Spectrograph, and the CATRiNA neutron detectors. In terms of performance, FSUDAQ can handle up to approximately 500k triggers per second per channel without waveform recording, or data rates of around 65 MB/s per optical fiber, with or without waveform recording.