Open Source Resources Research Articles

OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization.

The Observational Health Data Sciences and Informatics (OHDSI) is the largest distributed data network in the world encompassing more than 331 data sources with 2.1 billion patient records across 34 countries. It enables large-scale observational research through standardizing the data into a common data model (CDM) (Observational Medical Outcomes Partnership [OMOP] CDM) and requires a comprehensive, efficient, and reliable ontology system to support data harmonization. We created the OHDSI Standardized Vocabularies-a common reference ontology mandatory to all data sites in the network. It comprises imported and de novo-generated ontologies containing concepts and relationships between them, and the praxis of converting the source data to the OMOP CDM based on these. It enables harmonization through assigned domains according to clinical categories, comprehensive coverage of entities within each domain, support for commonly used international coding schemes, and standardization of semantically equivalent concepts. The OHDSI Standardized Vocabularies comprise over 10 million concepts from 136 vocabularies. They are used by hundreds of groups and several large data networks. More than 8600 users have performed 50 000 downloads of the system. This open-source resource has proven to address an impediment of large-scale observational research-the dependence on the context of source data representation. With that, it has enabled efficient phenotyping, covariate construction, patient-level prediction, population-level estimation, and standard reporting. OHDSI has made available a comprehensive, open vocabulary system that is unmatched in its ability to support global observational research. We encourage researchers to exploit it and contribute their use cases to this dynamic resource.

ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMs

Motivated by the widespread increase in the phenomenon of code-switching between Egyptian Arabic and English in recent times, this paper explores the intricacies of machine translation (MT) and automatic speech recognition (ASR) systems, focusing on translating code-switched Egyptian Arabic-English to either English or Egyptian Arabic. Our goal is to present the methodologies employed in developing these systems, utilizing large language models such as LLama and Gemma. In the field of ASR, we explore the utilization of the Whisper model for code-switched Egyptian Arabic recognition, detailing our experimental procedures including data preprocessing and training techniques. Through the implementation of a consecutive speech-to-text translation system that integrates ASR with MT, we aim to overcome challenges posed by limited resources and the unique characteristics of the Egyptian Arabic dialect. Evaluation against established metrics showcases promising results, with our methodologies yielding a significant improvement of 56% in English translation over the state-of-the-art and 9.3% in Arabic translation. Since code-switching is deeply inherent in spoken languages, it is crucial that ASR systems can effectively handle this phenomenon. This capability is crucial for enabling seamless interaction in various domains, including business negotiations, cultural exchanges, and academic discourse. Our models and code are available as open-source resources.1,2.

ResuméAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models

The increasing reliance on online recruitment platforms coupled with the adoption of AI technologies has highlighted the critical need for efficient resume classification methods. However, challenges such as small datasets, lack of standardized resume templates, and privacy concerns hinder the accuracy and effectiveness of existing classification models. In this work, we address these challenges by presenting a comprehensive approach to resume classification. We curated a large-scale dataset of 13,389 resumes from diverse sources and employed Large Language Models (LLMs) such as BERT and Gemma1.1 2B for classification. Our results demonstrate significant improvements over traditional machine learning approaches, with our best model achieving a top-1 accuracy of 92% and a top-5 accuracy of 97.5%. These findings underscore the importance of dataset quality and advanced model architectures in enhancing the accuracy and robustness of resume classification systems, thus advancing the field of online recruitment practices. Our models, code, and dataset are available as open-source resources.123

Design Science Research for Developing Risk Tourism Mapping Based on Visitor Sentiment Review

Real-time monitoring and assessment procedures at tourism sites are crucial. The lack of it may caused an operational incident. Therefore, this study aims to develop a design science research (DSR) methodology to produce an artifact in the form of a risk mapping system that can display a map of tourism sites in an urban area based on their risk group. The DSR approach has five stages to develop artifacts: explicate problem, define requirements, design and develop artifacts, demonstrate it, and evaluate it. The findings of this study propose suitable research strategies to develop artifacts based on relevant knowledge bases. It also discusses in depth the implementation of the explicate problem stage, which involves observing the root causes of operational accidents at tourism sites. It also finds that real-time monitoring of tourism sites can prevent accidents, and proposes utilizing reviews from visitors on Google Review, which can be categorized through text mining to identify risk factors based on Sapta Pesona's framework. This study provides valuable insights for developing monitoring systems in the field of tourism risk management using visitor review texts as an open-source resource.

The Health Equity Explorer: An open-source resource for distributed health equity visualization and research across common data models.

There is an urgent need to address pervasive inequities in health and healthcare in the USA. Many areas of health inequity are well known, but there remain important unexplored areas, and for many populations in the USA, accessing data to visualize and monitor health equity is difficult. We describe the development and evaluation of an open-source, R-Shiny application, the "Health Equity Explorer (H2E)," designed to enable users to explore health equity data in a way that can be easily shared within and across common data models (CDMs). We have developed a novel, scalable informatics tool to explore a wide variety of drivers of health, including patient-reported Social Determinants of Health (SDoH), using data in an OMOP CDM research data repository in a way that can be easily shared. We describe our development process, data schema, potential use cases, and pilot data for 705,686 people who attended our health system at least once since 2016. For this group, 996,382 unique observations for questions related to food and housing security were available for 324,630 patients (at least one answer for all 46% of patients) with 65,152 (20.1% of patients with at least one visit and answer) reporting food or housing insecurity at least once. H2E can be used to support dynamic and interactive explorations that include rich social and environmental data. The tool can support multiple CDMs and has the potential to support distributed health equity research and intervention on a national scale.

Viability of Open-Source Wind Resource Assessment Software

As the world shifts towards renewable energy sources, newer and larger projects come to fruition every year. Renewable energy projects require a complex analysis before proceeding with development. Part of this analysis procedure is resource assessment. This research aims to determine the viability of using open-source software for industry applications. This is performed through a comparison of the current industry standard software, WAsP, and Continuum. A site with a wide range of land cover conditions is chosen to see how the different software performs under varying conditions. Multiple different measured wind data inputs are used in the comparison, allowing for a comprehensive study. A focus is made on the software’s accuracy, while also comparing the analytics and outputs. The interface, workflow, input data, outputs, computational cost and additional requirements of each software package are discussed. It is found that Continuum is accurate within 8% when compared to WAsP’s annual energy production and capacity factor outputs. Due to the open-source nature of Continuum, it also lends itself to further customised developments that could be advantageous when performing resource assessments. WAsP is found to have preferential reporting outputs, however. This research highlights the opensource resource assessment process, from input to output. Keywords: Wind resource assessment, open-source software, WAsP, Coninuum, AEP, wind energy software

A tiny library with a million volumes: Open-source resource sharing with fulfillment interlibrary loan (ILL)

his paper is based upon a lightning talk given at the NISO Plus conference on February 14, 2023 and it describes interlibrary Loan syste4m, Fulfillment, developed by the Equinox Open Library Initiative. A thriving resource-sharing consortium allows libraries to offer broader and deeper opportunities for individual learning and greater community equity. It unlocks access to additional copies beyond what an owning library has available, along with a broad range of content owned within the network, which provides a diverse collection for library users. Providing access to such a rich network of resources is the goal of Fulfillment, an open-source resource sharing system which breaks down barriers to sharing collections.

Spherical dome: design, digital twin manufacturing, and testing of a glubam structure

ABSTRACT Domes are common architectural elements in real life, with spherical or sphere-like shapes being the most common. Nonetheless, there has been limited research on incorporating modern design and manufacturing technologies into spherical domes, especially those made of bio-based materials. This paper presents a comprehensive study of the design pipeline for spherical domes. In terms of spherical tessellation for dome design, a set of innovative algorithms and programs is made available as open-source resources, comprising both manual and numerical approaches, where the numerical approaches are validated by existing results while also expanding these results, and new manual approaches are proposed and benchmarked as alternatives to conventional methods with similar or better performance, condition-wise. A proof-of-concept prototype is constructed according to the proposed pipeline to demonstrate its feasibility for later experiments. A preliminary loading test was conducted, validating its satisfactory mechanical behavior. In summary, a comprehensive design pipeline for spherical domes is proposed and validated through a prototype project, during which various innovations are made, from spherical tessellation to parametrical design, analysis, and digital twin manufacturing.

Deep learning based landslide detection using open-source resources: Opportunities and challenges

Deep learning based landslide detection using open-source resources: Opportunities and challenges

Omics Analysis and Quality Control Pipelines in a High-Performance Computing Environment.

Data quality is often an overlooked feature in the analysis of omics data. This is particularly relevant in studies of chemical and pathogen exposures that can modify an individual's epigenome and transcriptome with persistence over time. Portable, quality control (QC) pipelines for multiple different omics datasets are therefore needed. To meet these goals, portable quality assurance (QA) metrics, metric acceptability criterion, and pipelines to compute these metrics were developed and consolidated into one framework for 12 different omics assays. Performance of these QA metrics and pipelines were evaluated on human data generated by the Defense Advanced Research Projects Agency (DARPA) Epigenetic CHaracterization and Observation (ECHO) program. Twelve analytical pipelines were developed leveraging standard tools when possible. These QC pipelines were containerized using Singularity to ensure portability and scalability. Datasets for these 12 omics assays were analyzed and results were summarized. The quality thresholds and metrics used were described. We found that these pipelines enabled early identification of lower quality datasets, datasets with insufficient reads for additional sequencing, and experimental protocols needing refinements. These omics data analysis and QC pipelines are available as open-source resources as reported and discussed in this article for the omics and life sciences communities.

Democratizing insights into hospital cost reports

The Centers for Medicare and Medicaid Services (CMS) provides annual reports of costs, charges, utilization, payment, penalty, payroll, and general institutional characteristics for thousands of Medicare-certified hospitals. However, beyond the small fraction of features offered in dated finalized public use files, the size and complexity of cost report data can make it difficult to use. To gain a greater breadth of up-to-date insights, hospitals and researchers must either pay for third party services or acquire the appropriate expertise. To democratize insights into cost report data, we first developed an open-source public repository of 6908 hospital-specific dataset, each containing 2843 labeled features and spanning years between 2010 and 2023. We then developed an open-source application for analyzing and downloading these data. Users can download and run the application locally or access it online (https://hcris-app.herokuapp.com/), and compare cost report features among hospitals and across time, explore relationships between features, and design new cost report variables. As examples of insights gained from our application, we present results from comparing Rush University Medical Center to 66 non-governmental acute care Illinois hospitals. We look forward to developing our open-source resources according to feedback from the healthcare community.

Seeing the fields through the weeds: introducing the WeedEco R package for comparing past and present arable farming systems using functional weed ecology

The functional ecology of arable weeds provides a way of comparing present-day and past farming regimes. This paper presents the R package WeedEco, an open-source resource which allows users to compare their archaeobotanical dataset against three previously published arable weed models to understand fertility, disturbance or a combination of both. The package provides functions for data organisation, classification and visualisation, allowing users to enter raw archaeobotanical data, obtain trait values from the functional trait dataset, conduct discriminant analysis and plot the results against the relevant present-day model. Using data from the early medieval site of Stafford in the UK, the paper provides a detailed example of the use of the package, demonstrating its different functions, as well as how the results can be interpreted.

OSM: Leveraging model checking for observing dynamic behaviors in aspect-oriented applications

In the intricate domain of software systems verification, dynamically model checking multifaceted system characteristics remains paramount, yet challenging. This research proposes the advanced observe-based statistical model-checking (OSM) framework, devised to craft executable formal models directly from foundational system code. Leveraging model checking predicates, the framework melds seamlessly with aspect-oriented programming paradigms, yielding a potent method for the analytical verification of varied behavioral attributes. Exploiting the transformative capacity of OSM framework, primary system code undergoes a systematic metamorphosis into multifaceted analysis constructs. This not only simplifies the model verification process but also orchestrates feature interactions using an innovative observing join point abstraction mechanism. Within this framework, components encompassing parsing, formal verification, computational analytics, and rigorous validation are intrinsically interwoven. Marrying the principles of model checking with aspect-oriented (AO) modularization, OSM framework stands as a paragon, proficiently scrutinizing and affirming system specifications. This ensures the unyielding performance of electronic health record systems amidst shifting preconditions. OSM framework offers runtime verification of both object-oriented and AO deployments, positioning itself as an indispensable open-source resource, poised to automate the enhancement of system performance and scalability.

Toward dynamic stability assessment of power grid topologies using graph neural networks.

To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. Since dynamic stability simulations are intractable and exceedingly expensive for large grids, graph neural networks (GNNs) are a promising method to reduce the computational effort of analyzing the dynamic stability of power grids. As a testbed for GNN models, we generate new, large datasets of dynamic stability of synthetic power grids and provide them as an open-source resource to the research community. We find that GNNs are surprisingly effective at predicting the highly non-linear targets from topological information only. For the first time, performance that is suitable for practical use cases is achieved. Furthermore, we demonstrate the ability of these models to accurately identify particular vulnerable nodes in power grids, so-called troublemakers. Last, we find that GNNs trained on small grids generate accurate predictions on a large synthetic model of the Texan power grid, which illustrates the potential for real-world applications.

Onkopipe: A Snakemake Based DNA-Sequencing Pipeline for Clinical Variant Analysis in Precision Medicine.

NGS is increasingly used in precision medicine, but an automated sequencing pipeline that can detect different types of variants (single nucleotide - SNV, copy number - CNV, structural - SV) and does not rely on normal samples as germline comparison is needed. To address this, we developed Onkopipe, a Snakemake-based pipeline that integrates quality control, read alignments, BAM pre-processing, and variant calling tools to detect SNV, CNV, and SV in a unified VCF format without matched normal samples. Onkopipe is containerized and provides features such as reproducibility, parallelization, and easy customization, enabling the analysis of genomic data in precision medicine. Our validation and evaluation demonstrate high accuracy and concordance, making Onkopipe a valuable open-source resource for molecular tumor boards. Onkopipe is being shared as an open source project and is available at https://gitlab.gwdg.de/MedBioinf/mtb/onkopipe.

KLASIFIKASI PEMANFAATAN OPEN SOURCE RESOURCE PADA MULTI-EDGE COMPUTING DENGAN JARINGAN PRIVATE 5G YANG BERKUALITAS DAN LOW LATENCY MENGGUNAKAN TEKNOLOGI NETWORK SLICING

Multi-edge computing is one of the technologies that allows data processing to be performed at the edge of the network, thereby reducing latency and speeding up overall data processing. On the other hand, 5G private networks with high quality and low latency enable fast and stable data transmission. However, in implementing multi-edge computing technology with 5G private networks, it is necessary to use the right open source resources to support its success at a lower investment cost. This thesis aims to classify the utilization of open source resources in multi-edge computing technology with quality and low latency 5G private networks using network slicing technology. The development methodology includes literature study, system development, data collection, data analysis, and system performance evaluation implemented on top of type 2 hypervisor virtualization on Virtualbox. The results of this thesis are expected to show that there are several open source resources that can be used in multi-edge computing technology with 5G private networks, such as Open5gs, Kubernetes, Virtualbox, so as to form a network with low latency and low cost.

A Generic Interactive Membership Function for Categorization of Quantities.

The membership function is to categorize quantities along with a confidence degree. This article investigates a generic user interaction based on this function for categorizing various types of quantities without modification, which empowers users to articulate uncertainty categorization and enhance their visual data analysis significantly. We present the technique design and an online prototype, supplementing with insights from three case studies that highlight the technique's efficacy among different types of quantities. Furthermore, we conduct a formal user study to scrutinize the process and reasoning users employ while utilizing our technique. The findings indicate that our technique can help users create customized categories. Both our code and the interactive prototype are made available as open-source resources, intended for application across varied domains as a generic tool.

Stack bricolage and infrastructural impermanence in financial machine-learning modelling

ABSTRACT Hoping that the promises of machine-learning can be realised in financial markets, investment management and trading firms increasingly employ machine-learning techniques to extract exploitable informational edge from large datasets. In addition to heavy investments in technology and the human resources capable of manipulating it, this development has led to increased use of open-source machine-learning and data-management resources. Drawing on 44 interviews with developers and users of machine-learning techniques in finance, we explore how such platforms and other open-source resources are understood and used by said practitioners. Building on work in the Social Studies of Finance (SSF) on financial modelling and platformisation, we argue that these users of machine learning in finance engage in what we term stack bricolage activities, when they reuse disparate open-source resources in their modelling work. We argue that stack bricolage creates dependencies on open-source cloud resources characterised by infrastructural impermanence, which is a result of their substitutability and maintenance sensitivity. Our study contributes to the emerging SSF literature on machine-learning modelling cultures and debates in Science and Technology Studies and adjacent fields on the reuse of data and software in platformised cloud infrastructures.

Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response.

In this study, the methodology of cyber-resilience in small and medium-sized organizations (SMEs) is investigated, and a comprehensive solution utilizing prescriptive malware analysis, detection and response using open-source solutions is proposed for detecting new emerging threats. By leveraging open-source solutions and software, a system specifically designed for SMEs with up to 250 employees is developed, focusing on the detection of new threats. Through extensive testing and validation, as well as efficient algorithms and techniques for anomaly detection, safety, and security, the effectiveness of the approach in enhancing SMEs' cyber-defense capabilities and bolstering their overall cyber-resilience is demonstrated. The findings highlight the practicality and scalability of utilizing open-source resources to address the unique cybersecurity challenges faced by SMEs. The proposed system combines advanced malware analysis techniques with real-time threat intelligence feeds to identify and analyze malicious activities within SME networks. By employing machine-learning algorithms and behavior-based analysis, the system can effectively detect and classify sophisticated malware strains, including those previously unseen. To evaluate the system's effectiveness, extensive testing and validation were conducted using real-world datasets and scenarios. The results demonstrate significant improvements in malware detection rates, with the system successfully identifying emerging threats that traditional security measures often miss. The proposed system represents a practical and scalable solution using containerized applications that can be readily deployed by SMEs seeking to enhance their cyber-defense capabilities.

Accelerometer-Measured Physical Activity Data Sets (Global Physical Activity Data Set Catalogue) That Include Markers of Cardiometabolic Health: Systematic Scoping Review

Background Cardiovascular disease accounts for 17.9 million deaths globally each year. Many research study data sets have been collected to answer questions regarding the relationship between cardiometabolic health and accelerometer-measured physical activity. This scoping review aimed to map the available data sets that have collected accelerometer-measured physical activity and cardiometabolic health markers. These data were then used to inform the development of a publicly available resource, the Global Physical Activity Data set (GPAD) catalogue. Objective This review aimed to systematically identify data sets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design. Methods Databases, trial registries, and gray literature (inception until February 2021; updated search from February 2021 to September 2022) were systematically searched to identify studies that analyzed data sets of physical activity and cardiometabolic health outcomes. To be eligible for inclusion, data sets must have measured physical activity using an accelerometric device in adults aged ≥18 years; a sample size >400 participants (unless recruited participants in a low- and middle-income country where a sample size threshold was reduced to 100); used an observational, longitudinal, or trial-based study design; and collected at least 1 cardiometabolic health marker (unless only body mass was measured). Two reviewers screened the search results to identify eligible studies, and from these, the unique names of each data set were recorded, and characteristics about each data set were extracted from several sources. Results A total of 17,391 study reports were identified, and after screening, 319 were eligible, with 122 unique data sets in these study reports meeting the review inclusion criteria. Data sets were found in 49 countries across 5 continents, with the most developed in Europe (n=53) and the least in Africa and Oceania (n=4 and n=3, respectively). The most common accelerometric brand and device wear location was Actigraph and the waist, respectively. Height and body mass were the most frequently measured cardiometabolic health markers in the data sets (119/122, 97.5% data sets), followed by blood pressure (82/122, 67.2% data sets). The number of participants in the included data sets ranged from 103,712 to 120. Once the review processes had been completed, the GPAD catalogue was developed to house all the identified data sets. Conclusions This review identified and mapped the contents of data sets from around the world that have collected potentially harmonizable accelerometer-measured physical activity and cardiometabolic health markers. The GPAD catalogue is a web-based open-source resource developed from the results of this review, which aims to facilitate the harmonization of data sets to produce evidence that will reduce the burden of disease from physical inactivity.