Open Source Development Research Articles

"A Lot of Moving Parts": A Case Study of Open-Source Hardware Design Collaboration in the Thingiverse Community

Open-source is a decentralized and collaborative method of development that encourages open contribution from an extensive and undefined network of individuals. Although commonly associated with software development (OSS), the open-source model extends to hardware development, forming the basis of open-source hardware development (OSH). Compared to OSS, OSH is relatively nascent, lacking adequate tooling support from existing platforms and best practices for efficient collaboration. Taking a necessary step towards improving OSH collaboration, we conduct a detailed case study of DrawBot, a successful OSH project that remarkably fostered a long-term collaboration on Thingiverse - a platform not explicitly intended for complex collaborative design. Through analyzing comment threads and design changes over the course of the project, we found how collaboration occurred, the challenges faced, and how the DrawBot community managed to overcome these obstacles. Beyond offering a detailed account of collaboration practices and challenges, our work contributes best practices, design implications, and practical implications for OSH project maintainers, platform builders, and researchers, respectively. With these insights and our publicly available dataset, we aim to foster more effective and efficient collaborative design in OSH projects.

Collaborative development of open-source outbreak detection tools: Needs, concept and implementation

Abstract Issue The early detection of infectious disease outbreaks through surveillance is critical to initiate targeted public health interventions. Automated outbreak detection helps to identify trends and unusual events. Yet, there is no overview of how many EU countries use outbreak detection systems and what factors hindered their implementation. Description of the problem One aim of the Joint Action UNITED4Surveillance (U4S) is to identify EU countries’ requirements for outbreak detection, and based on these, develop and deploy a tool for the timely and accurate detection of outbreaks. Through surveys, interviews and an in-person workshop involving public health experts, we identified uses cases, collected functional and technical requirements for a tool. Subsequently the tool development started in August 2023 as an open-source software process on GitHub. Results Despite high interest, only three of the 21 countries that took part in the survey have implemented outbreak detection tools, even though national surveillance systems offer the potential for their use. Reasons for this are limited resources and low prioritization. Experts identified gastrointestinal diseases as one major potential use case and emphasised the necessity of flexible outputs. Tailored to these requirements, data scientists from five European public health institutes developed an open-source R package for signal detection. The tool generates signals based on national surveillance data and enables the user to analyses these results using a dashboard with flexible exploration and generation of a report. The tool is now being piloted locally by ten European public health authorities and its use will be evaluated. Lessons There is a need to use automated outbreak detection tools for routine surveillance of infectious diseases in EU countries but often lack resources. Projects like U4S can bring together expertise to build sustainable tools and supporting material that can address the identified needs. Key messages • Collaborative and open-source development leads to tools that are well tailored to the needs of the community and foster a high uptake in the usage of these tools. • The tool enhances surveillance and outbreak detection in countries without the capacities to develop and implement outbreak detection methods for their surveillance systems.

Multilayer networks in landscape ecology: a case study to assess changes in aquatic habitat connectivity for flying and non-flying benthic macroinvertebrates in a Danube floodplain

ContextIn efforts to mitigate anthropogenic impacts on floodplain biodiversity, restoration measures that enhance habitat connectivity have been applied. However, these approaches have either neglected the spatial position of water bodies or the dynamic nature of the floodplain ecosystem.ObjectivesThis study focuses on the novel application of the multilayer network framework to assess changes in the aquatic habitat connectivity in floodplains, showcasing its application in the context of aquatic passive dispersal (drift) of two indicator groups of benthic macroinvertebrates (Oligochaetes and Chironomids)MethodsOur case study is located in the Donau-Auen National Park in Austria and follows floodplain restoration measures (side-channel reconnection) applied in the mid-1990s. Multilayer networks were constructed to represent the conditions before, short-term, and long-term after restoration to quantify habitat connectivity across inundation frequencies. Our network analyses involved multilayer correlation, static and dynamic monolayer centralities (centrality profiles), and multilayer centrality assessments. We used a Partial Least Squares Regression analysis as a variable selection tool to identify which centrality measures better explained the variance in α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} diversity and Local Contributions to Beta Diversity (LCBD) of benthic macroinvertebrates.ResultsIn the short-term, our connectivity analysis indicated an increase in habitat connectivity. However, centrality profiles, multilayer correlation, and multilayer centrality techniques identified a long-term decrease in connectivity. Multilayer centralities had higher Variable Importance in the Projection scores (VIP) than their monolayer counterpart in explaining variations in α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} diversity and LCBD for strict aquatic dispersers. Meanwhile, for flying dispersers, monolayer centralities had the highest VIP scores for explaining α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} diversity.ConclusionsThis study underscores the relevance of integrating dynamic aspects of water-mediated transport beyond traditional pairwise distances. Although in this study we apply this tool by showcasing the aquatic passive dispersal mode, the application of this method can be extended to other dispersal modes and representative abilities for diverse groups of aquatic organisms. The expanding cross-disciplinary applications and open-source tool development for multilayer networks offer practical implications for planning and evaluating management measures.

Generated power forecast of dye-sensitized solar plant with deep neural network

ABSTRACT The enormous amount of solar power and its state-of-the-art capturing technology that produces electricity, increases the grid interconnection rate of photovoltaic (PV) plants. Prior knowledge of the aperiodic characteristics of solar energy received by the Earth’s surface is essential for PV plants to ensure reliable operation and stable, secure grid connections. Power generation under diffused sunlight by PV plants with the most widely used first-generation solar cells has significant limitations. A dye-sensitized solar plant (DSSP), utilizes dye-sensitized solar cell (DSSC) technology, remains active in low light conditions. Though the activeness enables such plants to generate electricity throughout the year while receiving diffuse sunlight, unlike conventional solar technologies the plants have a requirement of knowledge of future power generation. This could be the first paper to report on the generated power forecast for such plants. The study has utilized a machine learning approach with a proposed DSSP model for the forecast. Spyder, an open-source integrated development environment, is the test workbench for the experimentation. The results show the robustness and reliability of the method, regardless of the weather conditions in the test area. The DSSP, along with the prediction model, will moderately overcome the shortcomings of power generation under diffuse daylight with intermittent solar radiation and grid connections.

Decent deepfakes? Professional deepfake developers’ ethical considerations and their governance potential

AbstractPolicymakers and societies are grappling with the question of how to respond to deepfakes, i.e., synthetic audio-visual media which is proliferating in all areas of digital life– from politics to pornography. However, debates and research on deepfakes’ impact and governance largely neglect the technology’s sources, namely the developers of the underlying artificial intelligence (AI), and those who provide code or deepfake creation services to others, making the technology widely accessible. These actors include open-source developers, professionals working in large technology companies and specialized start-ups, and for deepfake apps. They can profoundly impact which underlying AI technologies are developed, whether and how they are made public, and what kind of deepfakes can be created. Therefore, this paper explores which values guide professional deepfake development, how economic and academic pressures and incentives influence developers’ (perception of) agency and ethical views, and how these views do and could impact deepfake design, creation, and dissemination. Thereby, the paper focuses on values derived from debates on AI ethics and on deepfakes’ impact. It is based on ten qualitative in-depth expert interviews with academic and commercial deepfake developers and ethics representatives of synthetic media companies. The paper contributes to a more nuanced understanding of AI ethics in relation to audio-visual generative AI. Besides, it empirically informs and enriches the deepfake governance debate by incorporating developers’ voices and highlighting governance measures which directly address deepfake developers and providers and emphasize the potential of ethics to curb the dangers of deepfakes.

Multifaceted formal methods and their interdisciplinary role — From the cathedral of ‘components as coalgebras’ to the HCI context and the open source software bazaar

In this article we revisit the history of formal methods with a focus on important aspects that contribute to their interdisciplinary role. We consider: the variability of mathematical representation techniques on which the theoretical foundations of formal methods are based; formal methods multidisciplinarity; their capability to serve at a meta-level in providing the semantics of programming languages, specification and modelling languages as well as higher-level and domain-specific formal notations; and, finally, how some of these higher-level and domain-specific notations may be lifted at an interdisciplinary level.Within this historical review, we are inspired by Luís Barbosa's “components as coalgebras” approach in seeing that the duality data-process is underlying all those aspects of formal methods. We also see that such a duality may not only be expressed in universal terms within category theory, but may also be characterised in practical terms and focused applications by two distinct logic paradigms, equational logic for the data and rewriting logic for the process, by two modelling directions, forward process definitions and backward data-driven process transformations, and by the distinction between syntax, defined by the data structures, and semantics, provided by rewrite rules. We use the Maude modelling language to illustrate the application of the data-process duality. In fact, Maude use equational logic to define data types and rewriting logic to express system evolution. Illustrative examples are from the areas of cognitive science and human-computer interaction (HCI).We then define a data-driven model transformation, which we call elaborative mining, which adopts a backward perspective to recover a behaviour that was observed in real life but was not predicted forward by the original model. Finally, we see how the “open source software bazaar”, which is a metaphor for the apparently chaotic open source development process, offers us a big data context to lift the driving process for model transformation from deterministic to statistical.

A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves

The field of software engineering is advancing at astonishing speed, with packages now available to support many stages of data science pipelines. These packages can support infectious disease modelling to be more robust, efficient and transparent, which has been particularly important during the COVID-19 pandemic. We developed a package for the construction of infectious disease models, integrated it with several open-source libraries and applied this composite pipeline to multiple data sources that provided insights into Australia's 2022 COVID-19 epidemic. We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters.The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. Extending the base model to include mobility effects slightly improved model fit to data, with this approach selected as the model configuration for further epidemiological inference. Under our assumption of widespread immunity against severe outcomes from recent vaccination, incorporating an additional effect of the main vaccination programs rolled out during 2022 on transmission did not further improve model fit. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30–60% of recently acquired natural immunity and that natural immunity lasted only one to eight months on average. We documented our analyses algorithmically and present our methods in conjunction with interactive online code notebooks and plots.We demonstrate the feasibility of integrating a flexible domain-specific syntax library with state-of-the-art packages in high performance computing, calibration, optimisation and visualisation to create an end-to-end pipeline for infectious disease modelling. We used the resulting platform to demonstrate key epidemiological characteristics of the transition from the emergency to the endemic phase of the COVID-19 pandemic.

Automated, Near Real-Time Ground-Motion Processing at the U.S. Geological Survey

Abstract We describe automated ground-motion processing software named gmprocess that has been developed at the U.S. Geological Survey (USGS) in support of near-real-time earthquake hazard products. Because of the open-source development process, this software has benefitted from the involvement and contributions of a broad community and has been used for a wider range of applications than was initially envisioned. Here, we give an overview and introduction to the software, including how it has leveraged other open-source libraries. We highlight some key features that gmprocess provides, compare response spectra calculated with the automated processing approach of gmprocess to the response spectra provided by the Next Generation Attenuation projects, and summarize projects that have utilized gmprocess. These use-cases demonstrate that this software development effort has been successfully leveraged in earthquake research activities both within and outside the USGS.

Low-cost, portable, easy-to-use kiosks to facilitate home-cage testing of nonhuman primates during vision-based behavioral tasks.

Nonhuman primates (NHPs), especially rhesus macaques, have significantly contributed to our understanding of the neural computations underlying human vision. Besides the established homologies in the visual brain areas between these species and our ability to probe detailed neural mechanisms in monkeys at multiple scales, NHPs' ability to perform human-like visual behavior makes them an extremely appealing animal model of human vision. Traditionally, such behavioral studies have been conducted in controlled laboratory settings, offering experimenters tight control over variables like luminance, eye movements, and auditory interference. However, in-lab experiments have several constraints, including limited experimental time, the need for dedicated human experimenters, additional lab space requirements, invasive surgeries for headpost implants, and extra time and training for chairing and head restraints. To overcome these limitations, we propose adopting home-cage behavioral training and testing of NHPs, enabling the administration of many vision-based behavioral tasks simultaneously across multiple monkeys with reduced human personnel requirements, no NHP head restraint, and monkeys' unrestricted access to experiments. In this article, we present a portable, low-cost, easy-to-use kiosk system developed to conduct home-cage vision-based behavioral tasks in NHPs. We provide details of its operation and build to enable more open-source development of this technology. Furthermore, we present validation results using behavioral measurements performed in the lab and in NHP home cages, demonstrating the system's reliability and potential to enhance the efficiency and flexibility of NHP behavioral research.NEW & NOTEWORTHY Training nonhuman primates (NHPs) for vision-based behavioral tasks in a laboratory setting is a time-consuming process and comes with many limitations. To overcome these challenges, we have developed an affordable, open-source, wireless, touchscreen training system that can be placed in the NHPs' housing environment. This system enables NHPs to work at their own pace. It provides a platform to implement continuous behavioral training protocols without major experimenter intervention and eliminates the need for other standard practices like NHP chair training, collar placement, and head restraints. Hence, these kiosks ultimately contribute to animal welfare and therefore better-quality neuroscience in the long run. In addition, NHPs quickly learn complex behavioral tasks using this system, making it a promising tool for wireless electrophysiological research in naturalistic, unrestricted environments to probe the relation between brain and behavior.

Desain dan Implementasi Robot Mobile 4WD dan Aplikasi Smartphone sebagai Media Pembelajaran Robotik

Technological advancements have significantly contributed to the robotics industry, yet mobility remains a challenge that can be addressed with mobile robots. In education, it is crucial to understand the basic principles of robotics and the use of related software and hardware. This research focuses on using a 4WD mobile robot as a learning tool. This robot offers flexibility and is easily controlled via a smartphone with built-in sensors. A smartphone application was developed using App Inventor, an open-source web-based development platform. The HC-05 Bluetooth module was used to connect the smartphone application with the 4WD robot. The goal of this research is to design innovative learning media to aid in understanding the concepts and control of the 4WD robot, as well as to increase students' interest and motivation in learning robotic systems. The results of the research demonstrated success in creating a 4WD robot prototype controllable via an Android smartphone. The distance between the Arduino and smartphone did not significantly affect the robot's movement, although communication was disrupted if the distance exceeded 10 meters. Directional control was achieved through the rotation of the right and left wheels, and sufficient power supply was needed to drive all wheels. Programming with C/C++ and Bluetooth communication functioned as planned. This innovative learning media is expected to help students understand and operate the 4WD mobile robot more effectively

OPEN SOURCE DEVELOPER SUPPORT TOOL

Abstract : This research paper explores the development and implementation of an open-source developer support tool aimed at providing financial and motivational support to open-source developers. By creating a platform where users can post questions with optional bounties, this project seeks to address the longstanding issue of uncompensated efforts within the open-source community, which has been a significant barrier to the sustainability and growth of many open-source projects. The platform leverages cutting-edge technologies such as Next.js, PostgreSQL, and Stripe to create a seamless and secure environment for both users and developers. Next.js is utilized for building the front-end and back-end of the application, providing server-side rendering and static site generation for improved performance and user experience. PostgreSQL serves as the robust and scalable database management system, ensuring data integrity and efficient query handling. Stripe is integrated for secure authentication and payment processing, enabling financial transactions between users and developers. The motivation behind this project stems from the recognition that open-source software is a critical component of modern technology infrastructure, yet the contributors often lack adequate financial and motivational support. This research delves deeply into the problem definition, outlining the specific challenges faced by open-source developers, including financial instability, lack of recognition, and limited feedback. The objectives of the project are clearly defined, aiming to create a sustainable support system that fosters community engagement and rewards contributors for their valuable work. This paper also details the software and hardware specifications required for the development and deployment of the platform, ensuring that the solution is both practical and scalable. Project planning is a crucial element of this research, with a comprehensive roadmap that includes requirement analysis, design, development, testing, and deployment phases. Each phase is meticulously planned to address potential challenges and ensure the successful implementation of the support tool. Finally, the future scope of the project is discussed, emphasizing the importance of continuous improvement and adaptation to meet the evolving needs of the open-source community. This includes potential enhancements such as advanced spam detection, enhanced user feedback mechanisms, and the integration of additional features to further support and motivate open-source contributors. By addressing these critical aspects, this research underscores the significance of supporting open-source contributors and highlights the potential of the proposed platform to make a meaningful impact on the sustainability and growth of open-source projects. IndexTerms – Open Source, Developer Support, Bounty System, Next.js, PostgreSQL, Stripe, Community Engagement.

Development and Application of a New Open-Source Integrated Surface–Subsurface Flow Model in Plain Farmland

Accurately characterizing rainfall runoff processes in plain farmland, especially at the plot scale with significant micro-topographic features, has presented challenges. Integrated surface–subsurface flow models with high-precision surface flow modules are appropriate tools, yet open-source versions are rare. To address this gap, we proposed an open-source integrated surface–subsurface flow model called the FullSWOF-Plain model, in which the one-dimensional subsurface module Hydrus-1D was integrated with a modified two-dimensional surface water flow module (FullSWOF-2D) using the sequential head method. Various experimental scenarios were simulated to validate the model’s performance, including two outflow cases (i.e., 1D and 2D) without infiltration, a classical one-dimensional infiltration case, and two typical rainfall events at the experimental field. The results demonstrate the accuracy of this proposed model, with the Nash–Sutcliffe efficiency (NSE) of the simulated discharge exceeding 0.90 in the experimental field case and the root mean squared error (RMSE) values for soil moisture at five depths consistently below 0.03 cm3/cm3. However, we observed a lag in the simulated response time of soil moisture due to the neglect of preferential flow. The micro-topography significantly influenced ponding time and ponding areas. Lower local terrain normally experienced earlier surface ponding. Scattered surface ponding water first occurred in the ditch and followed in the relatively low areas in the main field. The concentration process of surface runoff exhibited hierarchical characteristics, with the drainage ditch contributing the most discharge initially, followed by the connection of scattered puddles in the main field, draining excess surface water to the ditch through rills. This quantitative study sheds light on the impact of micro-topography on surface runoff in plain farmland areas.

FAIR compliant database development for human microbiome data samples.

Sharing microbiome data among researchers fosters new innovations and reduces cost for research. Practically, this means that the (meta)data will have to be standardized, transparent and readily available for researchers. The microbiome data and associated metadata will then be described with regards to composition and origin, in order to maximize the possibilities for application in various contexts of research. Here, we propose a set of tools and protocols to develop a real-time FAIR (Findable. Accessible, Interoperable and Reusable) compliant database for the handling and storage of human microbiome and host-associated data. The conflicts arising from privacy laws with respect to metadata, possible human genome sequences in the metagenome shotgun data and FAIR implementations are discussed. Alternate pathways for achieving compliance in such conflicts are analyzed. Sample traceable and sensitive microbiome data, such as DNA sequences or geolocalized metadata are identified, and the role of the GDPR (General Data Protection Regulation) data regulations are considered. For the construction of the database, procedures have been realized to make data FAIR compliant, while preserving privacy of the participants providing the data. An open-source development platform, Supabase, was used to implement the microbiome database. Researchers can deploy this real-time database to access, upload, download and interact with human microbiome data in a FAIR complaint manner. In addition, a large language model (LLM) powered by ChatGPT is developed and deployed to enable knowledge dissemination and non-expert usage of the database.

Characterizing Developers' Linguistic Behaviors in Open Source Development across Their Social Statuses

Open Source Software (OSS) development has attracted numerous developers. As a typical complex sociotechnical system, an OSS project often forms a hierarchical social structure where a few developers are elite while the rest are non-elite. Differences in social status may result in distinct language use behaviors in interpersonal communication. Characterizing such behaviors is critical for supporting efficient and effective communication among developers with different social statuses. This study empirically compared elite and non-elite developers' language behaviors in their communication. We compiled a corpus of - 216,000 discourses collected from 20 large projects on GitHub. We investigated the linguistic differences in three aspects, namely, linguistic styles and characters, main concerns, and sentence patterns. Our findings reveal that elite and non-elite developers showed different linguistic patterns and had different concerns in their discourses. Their discourses also reflect the variation of the main focuses in the development process. Furthermore, elite and non-elite developers exhibited noticeable patterns in their linguistic behaviors in accordance with their roles and corresponding divisions of labor in the production process, no matter which semantic contexts. These findings provide implications for supporting communication that crosses social statuses in OSS development.

Community-Led Development and Participatory Design in Open Source: Empowering Collaboration for Sustainable Solutions

This whitepaper delves into the active role of community-led development (CLD) and participatory design (PD) in open source software, highlighting how these complementary approaches bring stakeholders from various backgrounds together to create a cooperative atmosphere for developing stable solutions. It emphasizes the importance of these methodologies in enabling communities to tackle real-world issues effectively and robustly, thus influencing the expansion of open-source development. Integrating CLD and PD within open-source projects fosters a more inclusive collaborative development environment, driving innovation and user-centric solutions. Through case studies like Kubernetes and Konveyor, it is evident that these methodologies significantly contribute to project success by enhancing adaptability, ensuring broad community engagement, and addressing diverse user needs. The findings underscore the vital role of these strategies in creating sustainable and resilient software solutions, highlighting their potential to transform the technology development landscape.

Development of a microfluidic-assisted open-source 3D bioprinting system (MOS3S) for the engineering of hierarchical tissues

The engineering of new 3D bioprinting approaches has shown great promise in the field of tissue engineering and disease modelling. However, the high cost of commercial 3D bioprinters has limited their accessibility, especially to those laboratories in resource-limited settings. Moreover, the need for a 3D bioprinting system capable of dispensing multiple materials is growing apace. Therefore, the development of a Microfluidic-assisted Open Source 3D bioprinting System (MOS3S) for the engineering of hierarchical tissues is needed to progress in fabricating functional tissues, but with a technology accessible to a wider range of researchers. The MOS3S platform is designed to allow the deposition of biomaterial inks using microfluidic printheads or coaxial nozzles for the in-situ crosslinking and scaffolds fabrication. The coupling of 3D printed syringe pumps with the motion control system is used for driving the tunable extrusion of inks for the fabrication of centimeter scale hierarchical lattice constructs for tissue engineering purposes. MOS3S performance have been validated to fabricate high-resolution structures with coaxial microfluidic technology, opening to new frontiers for seminal studies in pre-clinical disease modelling and tissue regeneration.

Abstract 3892: Systematic generation of a clinicogenomic harmonized oncologic real-world dataset (MSK-CHORD)

Abstract Clinical data storage in unstructured notes and siloed datasets present a major challenge for large-scale cancer informatics. Whether natural language processing (NLP) combined with multimodal integration across datasets can produce a mineable resource and improve discovery of relationships between tumor genomics and clinical phenotypes is unknown. We hypothesized that NLP could automatically annotate a pan-cancer corpus of 82,464 patients with tumor genomic sequencing. To develop algorithms to annotate free-text reports, we leveraged the AACR Project GENIE Biopharma Collaborative (BPC), a structured curation of EMR from five cancer types (non-small cell lung (NSCLC), breast, colorectal, prostate, and pancreatic cancer), to train and validate several Transformer and rule based-based NLP models. After automating the generation of NLP annotations alongside medication, demographic, tumor registry, survival, and tumor genomic sequencing data, we tested whether clinicogenomic relationships not apparent in the smaller BPC cohort might be discoverable in the larger cohort. In 5-fold cross-validation, NLP Transformers accurately annotated the presence of cancer (AUC=0.99), cancer progression (AUC=0.97), and sites of disease (AUC=0.99) from radiology reports, and presence of prior outside treatment (AUC=0.98) and hormone receptor (HR) and HER2 receptor status (AUC=0.98, 0.98) from clinician notes. In addition, rule-based models, trained on non-BPC data and validated on the whole BPC cohort, annotated smoking status from clinician notes (ACC=0.95), and Gleason score (ACC=1.0), PD-L1 status (ACC=0.98), and mismatch repair deficiency (ACC=0.98) from histopathology reports. NLP annotations were merged with genomic and other structured clinical data to create a Clinicogenomic, Harmonized Oncologic Real-world Dataset (MSK-CHORD). Finally, we tested if associations not apparent in the BPC might be discoverable in MSK-CHORD. We found positive associations between Gleason score and gene-level alterations in prostate cancer including TP53, PTEN and BRCA2 (q<0.1), none of which were adequately powered for detection in the BPC. We found PD-L1 status was associated with better survival following immunotherapy treatment in NSCLC, but only in the larger MSK-CHORD was this association statistically significant. In breast cancer, NF1 mutations were associated with prior therapy in both cohorts, but this association was only significant in MSK-CHORD. The infrastructure generating MSK-CHORD uses a combination of on-premise and cloud computing resources and open-source development operation applications to automate processes. Once annotations are created, data is imported into a local instance of cBioPortal, where researchers can visualize data and perform analyses. The system generating MSK-CHORD demonstrates how large-scale data delivery and integration can fuel cancer research. Citation Format: Christopher J. Fong, Karl Pichotta, Thinh Tran, Michele Waters, Tom Fu, Mono Pirun, Mirella Altoe, Brooke Mastrogiacomo, Anisha Luthra, Mehnaj Ahmed, Arfath Pasha, Armaan Kohli, Raymond Lim, Tom Pollard, Darin Moore, Benjamin Gross, Avery Wang, Calla Chennault, Ritika Kundra, Ramyasree Madupuri, Ino de Bruijn, Aaron Lisman, Walid K. Chatila, Subhiksha Nandakumar, Anika Begum, Doori Rose, Kenneth L. Kehl, Deborah Schrag, Michael Berger, Jian Carrot-Zhang, Pedram Razavi, Bob Li, Peter Stetson, Nikolaus Schultz, Justin Jee. Systematic generation of a clinicogenomic harmonized oncologic real-world dataset (MSK-CHORD) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3892.

Phybers: a package for brain tractography analysis.

We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods. The manipulation of tractography data is not straightforward due to the geometrical complexity of the streamlines, the file format, and the size of the datasets, which may contain millions of fibers. Hence, we collected and structured state-of-the-art methods for the analysis of tractography and packed them into a Python library, to integrate and share tools for tractography analysis. Due to the high computational requirements, the most demanding modules were implemented in C/C++. Available functions include brain Bundle Segmentation (FiberSeg), Hierarchical Fiber Clustering (HClust), Fast Fiber Clustering (FFClust), normalization to a reference coordinate system, fiber sampling, calculation of intersection between sets of brain fibers, tools for cluster filtering, calculation of measures from clusters, and fiber visualization. The library tools were structured into four principal modules: Segmentation, Clustering, Utils, and Visualization (Fibervis). Phybers is freely available on a GitHub repository under the GNU public license for non-commercial use and open-source development, which provides sample data and extensive documentation. In addition, the library can be easily installed on both Windows and Ubuntu operating systems through the pip library.

A Novel Design of a Low-Cost SCADA System for Monitoring Standalone Photovoltaic Systems

Standalone photovoltaic (PV) systems are pivotal in the global transition towards sustainable energy, offering reductions in fossil fuel dependence and helping homes and businesses lower electricity costs. Key to optimizing the performance and efficiency of standalone systems are supervisory control and data logging (SCADA) systems. They monitor and record operational data such as power output, facilitating early detection of potential issues. This paper introduced a novel design for both the Human-Machine Interface (HMI) and data storage in a SCADA system for standalone PV systems, addressing two crucial aspects: real-time monitoring and efficient data retrieval, both at very low cost. The proposed design utilized Bluetooth Low Energy technology to transmit voltage and current data from the PV panel to a mobile application, marking a departure from traditional HMI approaches. This method enabled historical data analysis for trend identification. Additionally, the system intermittently transferred collected data to a cost-effective cloud storage service via Wi-Fi, allowing for substantial data storage at no cost. Remote data storage, another key feature of this design, simplifies data retrieval, which is particularly beneficial for systems in rural areas. Emphasizing open-source development, this design ensured flexibility and customization options. To demonstrate its practical effectiveness of the design, a one-day power curve of the PV system and the battery voltage data are presented, showcasing the design's capability in handling extensive and remote data storage.

How to Find the Right Scope for Open Source Developments?

How to Find the Right Scope for Open Source Developments?