Application Programming Research Articles

API C4E Augmentation: AI-Powered Agent(AIPA) Framework

Abstract: In today’s rapidly evolving digital landscape, organizations increasingly rely on Application Programming Interface known as API for seamless integration and data exchange. API providers comprising consumers, developers, and project teams face several challenges in discovering, developing and mapping APIs in compliance with organizational enterprise architecture principles. This paper aims to address these challenges by proposing an AI-Powered Agent (AIPA) Framework that leverages Generative AI and AI Code Assistant tools to enhance API governance and streamline the API development cycle (API Management, develop API, testing, security, deployment and monitoring). The proposed framework facilitates API discovery through an interactive chat interface that summarizes available APIs based on the use case. Additionally, it aims to automate key aspects of API development, including compliance checks, security protocols, and document generation thereby significantly reducing effort(s), human error(s), and improving productivity and efficiency. Establishing an API Center for Enablement (C4E) ensures consistent adoption of best practices across the organization. By integrating AI-driven solution with API governance, this paper outlines a pathway for organizations to improve API quality, security, and usability while empowering to adapt to digital disruptions. The findings suggest that a comprehensive, AI-enhanced governance framework enhances operational and development efficiency and fosters innovation.

The secondary metabolism collaboratory: a database and web discussion portal for secondary metabolite biosynthetic gene clusters.

Secondary metabolites are small molecules produced by all corners of life, often with specialized bioactive functions with clinical and environmental relevance. Secondary metabolite biosynthetic gene clusters (BGCs) can often be identified within DNA sequences by various sequence similarity tools, but determining the exact functions of genes in the pathway and predicting their chemical products can often only be done by careful, manual comparative analysis. To facilitate this, we report the first release of the secondary metabolism collaboratory (SMC), which aims to provide a comprehensive, tool-agnostic repository of BGC sequence data drawn from all publicly available and user-submitted bacterial and archaeal genome and contig sources. On the website, users are provided a searchable catalog of putative BGCs identified from each source, along with visualizations of gene and domain annotations derived from multiple sequence analysis tools. SMC's data is also available through publicly-accessible application programming interface (API) endpoints to facilitate programmatic access. Users are encouraged to share their findings (and search for others') through comment posts on BGC and source pages. At the time of writing, SMC is the largest repository of BGC information, holding 13.1M BGC regions from 1.3M source sequences and growing, and can be found at https://smc.jgi.doe.gov.

Gender disparity in Canadian Institutes of Health Research funding within neurology

BackgroundDespite efforts to advance equity, diversity and inclusion, women face gender-based barriers in research, including in neurology. Compared with men, women are less likely to hold leadership positions and be...

Evaluation of Soil Texture, EC, pH and Primary Macro Elements in Five Mango Orchard Soils of Kotri, Sindh, Pakistan

Analysis of soil is a very important practice for evaluating the nutrient status and ultimately establishing the fertilizer application program for soil health, fruit quality and production. The aim of present study was to assess the soil texture, electrical conductivity (EC), pH, and primary macronutrients such as N, P, K at 0-15 and 15-30cm depths from five orchards such as Aijaz farm (Orchard 1), Zardari farm (Orchard 2), Shahnawaz farm (Orchard 3), Ismail farm (Orchard 4), and Malik Azad farm (Orchard 5) of Kotri, Sindh Pakistan. The results indicated that most of the soils were sandy clay loam in textural class. The maximum electrical conductivity was found 0.65 dS m-1 in orchard 5, whereas the lowest electrical conductivity was found as 0.42 dS m-1 in orchard 1 at 15-30cm. All the orchards were found alkaline in nature at both 0-15 and 15-30cm depths. The total N content was found 80% low and 20% adequate at 0-15cm depth and 100% orchards were found low in total N content in soil at 15-30cm depth as compared with standard values. The AB-DTPA-P content in orchards were found 60% marginal and 40% adequate at 0-15cm depth, whereas 20% low, 40% marginal and 40% adequate were found at 15-30cm depth. AB-DTPA-K (mg kg-1) was found 20% low and 80% marginal at 0-15 and 15-30cm depths. Future studies should focus on assessing the soil biological properties, plant analysis, soil interaction mechanism, amended with press mud compost, farmyard manure, biochar, modified biochar, nano-material, minerals and low-cost additives.

TranscriptDB: a transcript-centric database to study eukaryotic transcript conservation and evolution.

Eukaryotic genes can encode multiple distinct transcripts through the alternative splicing (AS) of genes. Interest in the AS mechanism and its evolution across different species has stimulated numerous studies, leading to several databases that provide information on AS and transcriptome data across multiple eukaryotic species. However, existing resources do not offer information on transcript conservation and evolution between genes of multiple species. Similarly to genes, identifying conserved transcripts-those from homologous genes that have retained a similar exon composition-is useful for determining transcript homology relationships, studying transcript functions and reconstructing transcript phylogenies. To address this gap, we have developed TranscriptDB, a database dedicated to studying the conservation and evolution of transcripts within gene families. TranscriptDB offers an extensive catalog of conserved transcripts and phylogenies for 317 annotated eukaryotic species, sourced from Ensembl database version 111. It serves multiple purposes, including the exploration of gene and transcript evolution. Users can access TranscriptDB through various browsing and querying tools, including a user-friendly web interface. The incorporated web servers enable users to retrieve information on transcript evolution using their own data as input. Additionally, a REST application programming interface is available for programmatic data retrieval. A data directory is also available for bulk downloads. TranscriptDB and its resources are freely accessible at https://transcriptdb.cobius.usherbrooke.ca.

Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces

Our long journey on the road of telecommunications is continuously evolving. We have experienced several technological changes, modernizations, optimizations, and various mergers in the past decades. Virtualization and ‘cloudification’ of legacy telecommunication equipment has made communication networks not only more flexible, but also opened new doors. Brand new types of services have become available thanks to the ongoing fusion of the two domains of telecommunications and IT (Information Technology). This overview paper first discusses the evolution of services with an enhanced focus on mobile networks. Then, the possibilities offered by IT are shown. Finally, some examples are given of how Communication Service Providers and end users can benefit from these recent changes.

Teacher experiences and understanding of citizen science in Australian classrooms.

Citizen science represents an important opportunity for school students to make real-world connections with science through context-based learning with the potential to increase their engagement, enjoyment and understanding of science. However, to date, citizen science has not experienced wide uptake in school settings and there is a paucity of information about its implementation in the classroom. Here we present a mixed-method approach investigating teachers' knowledge and use of citizen science in Australian classrooms. We explored teachers' experience and perceptions of citizen science, and opportunities and barriers to incorporate citizen science as an educational approach through an online questionnaire. Among the teachers surveyed, 45% (n = 295) had personally participated in citizen science outside of school and 41% (n = 283) had incorporated citizen science projects in classroom lessons. Teachers (45%, n = 295) reported participating in citizen science initiatives multiple times. Also, most projects that teachers were involved in (77%, n = 292) were related to ecological studies, such as species monitoring. Citizen science was reported to be a relatively new approach; used by teachers for less than a year on average. The main challenges included a lack of knowledge, time, confidence, and clarity regarding citizen science project alignment with the Australian curriculum. Additionally, 92% of respondents said they would be more encouraged to use citizen science in classrooms if projects were aligned to the curriculum. Identifying ways to increase teachers' openness to incorporate citizen science in their classrooms is crucial to its successful widespread, long-term, and meaningful implementation. Encouraging broader participation of teachers in citizen science based on their previous experiences could address their expectations and increase their confidence and feeling of ownership. These research findings suggest that meaningful and applicable citizen science programs could be co-created by addressing resource limitations and curriculum alignment challenges. Implementing solutions to these barriers is likely to contribute to the development of sustainable school-inclusive citizen science projects, with potential to positively impact science education in the long-term.

CMS's Drug Price Negotiation Program 2023 Patient-Focused Listening Sessions: A Descriptive Analysis of Speaker Remarks.

The US Centers for Medicare and Medicaid Services (CMS) held patient-focused listening sessions in Fall 2023 for each of the first ten drugs selected for the Inflation Reduction Act's (IRA) Drug Price Negotiation Program (DPNP). This study aimed to quantitatively describe speaker input at the sessions, including the absolute and relative time allocated to key areas of interest for the DPNP. In this descriptive analysis of speaker remarks from ten CMS-hosted patient-focused listening sessions, speaker demographics were examined using video streams, time-stamped transcripts, public data, contextual clues, validated tools, and visual assessment when necessary. Audio recordings were transcribed using a speech-to-text application programming interface, followed by human review for accuracy. Transcripts included speaker identification and sentence-level timestamping. Two researchers coded each sentence into predefined categories on the basis of CMS's optional discussion topics, DPNP goals, and potential unintended consequences of the IRA: speaker background, evidence about selected drugs or therapeutic alternatives, drug prices, patient access/benefit design, therapeutic advancements, and other. The total and proportion of time spent by category were calculated per speaker. Out of an anticipated 200 speaker slots, 106 total speakers (median per session: 10; range 5-17) and 70 unique speakers participated, most of whom were patients (n = 38, 54.2%), female (n = 40, 57.1%), and below Medicare age (n = 40, 57.1%). The most popular categories discussed included evidence about the selected drug or therapeutic alternatives (median per slot: 36.5% of time; range 0-93.0%) and patient access/benefit design (median per slot: 12.1%; range 0-75.5%). CMS received a median of 12.3 total minutes of evidence about selected drugs or therapeutic alternatives per listening session across all speakers (range per session 3.9-22.6 minutes). The first listening sessions provided an important, but limited, first step toward patient engagement with the DPNP. Speaker demographics suggest the sessions fell short of capturing the voices of diverse and Medicare-representative patient populations. While speakers often focused their time on patient experience and evidence, CMS received less than 15 minutes of patient-focused evidence about therapeutic alternatives for more than half of the drug listening sessions. CMS has indicated its intent to improve upon the design of the patient-focused listening sessions for drugs selected in the second round of the DPNP. Future iterations should prioritize robust, two-way dialogue, transparency surrounding the impact of patient input on CMS's price determination process, and improvements to event logistics and outreach that facilitate the participation of diverse patients and stakeholders.

Optimizing API Project Efficiency: Agile Configurations, Advanced Design Patterns, and Testing Strategies

In the rapidly evolving digital era, Application Programming Interfaces (APIs) have emerged as the cornerstone of modern software development, enabling seamless communication and data exchange between disparate systems. Optimizing API project efficiency is paramount for organizations aiming to stay competitive and agile in response to market demands. This paper explores strategies for enhancing API efficiency through agile configurations, advanced design patterns, and comprehensive testing methodologies. We delve into the significance of decoupling configurations to achieve agile adaptability, discuss various API design paradigms like RESTful APIs and GraphQL, and examine their specific use cases and benefits. Additionally, we emphasize the importance of robust testing and security practices, highlighting industry-standard tools and technologies that facilitate these processes. Real-world case studies are presented to illustrate practical implementations, and the tangible improvements achieved. By integrating these advanced strategies, organizations can enhance their API performance, scalability, and maintainability, thereby delivering superior value to users and stakeholders in an increasingly interconnected world. Keywords: API efficiency, agile adaptability, decoupling configurations, RESTful API, GraphQL, API testing, microservices, API gateway, performance optimization, configuration management, API security, authentication, automated testing, real-world case studies

Digitalization, Management, and Synthesis of Thermal-Hydraulic Legacy Data Using the Dynamical System Scaling

A unique attribute of the nuclear industry is the extent by which designers, developers, operators, and regulators pay attention to demonstrating the safety case. Nuclear installation resiliency or safety takes overriding priority over function and performance. The preparation of the safety case heavily relies on extensive experimental campaigns that challenge the target design under a spectrum of postulated accident scenarios. For safety reasons, these experiments, typically conducted in subscale test apparatuses intended to reproduce the postulated accident scenarios and event sequences realistically, but subject to proven similarity criteria, are often referred to as scaling analyses. Since the dawn of the nuclear industry, significant resources have been committed to the construction and operation of such test facilities, and a massive amount of data has been generated. Such test data have been and are still used to validate the fundamental assumptions of nuclear power plant phenomena. As we move into the digital world, capturing this multiple decades worth of information in a transparent, easily accessible, and usable manner for the public and industry stakeholders is of paramount importance. Over the last few years, FPoliSolutions has been actively developing an enterprise digital data ontology platform (OGMA) intended to do just that. The goal is to help designers use experimental data to assess their safety case evaluation models. The platform or application programming interface (API) was designed to ontologically organize the experimental data, as well guide the user in the complex analytics associated with the interpretation and use of such test results. The OGMA API supports the user in accessing a library of sophisticated mathematical procedures for performing scaling analyses. For this purpose, the dynamical system scaling (DSS) procedure invented by Dr. Jose’ Reyes was formulated as one of the services in the digital platform. These methods have been demonstrated to provide an excelled mathematical apparatus to identify similarity criteria or quantify distortions between measured data and the target prototypical conditions. Moreover, DSS can provide a level of synthesis across separate effect tests and integral effect tests that has the promise of yielding efficiency in the evaluation code assessment activities, as setting up evaluation models that support the safety case of current and future nuclear power plants is often a daunting and expensive task.

Application status and prospect of machine learning in the field of enterprise development planning

Abstract. In today's digital information background, data is already an important means of production, various kinds of data continue to grow, and it is difficult to rely on the manpower to deal with such a huge amount of data. Machine learning can learn the law of data change from a large number of data and build a model for analysis, to give accurate and efficient program opinions. With the development of machine learning, deep machine learning in daily life is gradually widespread, and several successful commercial applications such as Google Translate have been born. Based on machine learning technology, this paper studies its application status and prospects in enterprise development planning. The research results show that more and more enterprises now apply machine learning to the planning and summary of the enterprise, at the same time, there are many functions of machine learning to be developed in the enterprise, through more development, enterprises can carry out more reasonable development and planning. The significance of this research is that it can provide specific application programs and solutions for enterprises, and also provide important references and guidance for the future development of enterprises, which is conducive to the healthy development of enterprises. In the future, with the continuous increase in the amount of data, the continuous progress of algorithms and the improvement of computing power, the application of machine learning in enterprise development planning will be more extensive and in-depth.

MODVORTEx: computer vision-driven automation for magnetic domain wall velocity analysis

Abstract This paper presents Magneto Optic Domain Velocity Observation and Real-Time Extraction (MODVORTEx), a comprehensive software solution for automating domain wall (DW) velocity measurements in magnetic materials using magneto-optic Kerr effect microscopy. Building upon our previous work on bubble domain structures, we introduce a versatile graphical user interface (GUI) that accommodates arbitrary domain shapes and employs advanced computer vision techniques. The software provides different methods for DW detection and velocity calculation, catering to various domain structures of arbitrary shape. Our approach significantly reduces the time and effort required for data extraction, transforming a process that previously took days of manual work into a task completable within minutes. We provide the details of the algorithmic implementation which is organized into pre-processing, DW detection, displacement measurement, and velocity extraction. This tool is applicable to a wide range of scenarios, including bubble domain dynamics, Dzyaloshinskii–Moriya interaction studies in perpendicular magnetic anisotropy systems, current-driven DW motion in patterned strips etc. By providing both GUI and Application Programing Interface, our software offers flexibility for integration into existing measurement systems and adaptability for specific research needs. This automation promises to accelerate research in spintronics and magnetic materials, enabling more comprehensive and accurate studies of DW dynamics.

Identifying Topics Around Nicotine Gum: A Machine Learning Approach with Twitter Data

Background Nicotine gum products from brands like Lucy and Rogue are relatively new arrivals to the tobacco marketplace. While studies of correlates of nicotine gum use are in their nascent stage, data from social media can be used to stay abreast of user experiences with novel tobacco products. This study leveraged machine learning to identify topics of Twitter posts about nicotine gum from the year 2022. Methods Twitter data was collected using the Twitter Application Programming Interface (API), with search terms “nicotine” AND “gum” OR “nic” AND “gum”. 16,940 tweets from 10,353 unique users were included in the analysis. Topic modeling with Top2Vec was used to identify topics and a string search of popular brands and flavors was also conducted. Results Eight distinct topics were identified. Smoking and vaping cessation was the most common topic, followed by promotion, pricing, and marketing; appeal; product comparisons; perceived benefits; distrust in institutions; health concerns; and COVID-19 misinformation. The most mentioned brand was Nicorette. A tenth of the tweets contained a reference to a flavor. Conclusion The goal of identifying topics in nicotine gum-related conversations is to better understand the public’s perceptions and experiences with nicotine gum. These findings may be used to inform survey-based research, policy targets, and health communication campaigns.

An Application Programming Interface (API) Sensitive Data Identification Method Based on the Federated Large Language Model

The traditional methods for identifying sensitive data in APIs mainly encompass rule-based and machine learning-based approaches. However, these methods suffer from inadequacies in terms of security and robustness, exhibit high false positive rates, and struggle to cope with evolving threat landscapes. This paper proposes a method for detecting sensitive data in APIs based on the Federated Large Language Model (FedAPILLM). This method applies the large language model Qwen2.5 and the LoRA instruction tuning technique within the framework of federated learning (FL) to the field of data security. Under the premise of protecting data privacy, a domain-specific corpus and knowledge base are constructed for pre-training and fine-tuning, resulting in a large language model specifically designed for identifying sensitive data in APIs. This paper conducts comparative experiments involving Llama3 8B, Llama3.1 8B, and Qwen2.5 14B. The results demonstrate that Qwen2.5 14B can achieve similar or better performance levels compared to the Llama3.1 8B model with fewer training iterations.

Online Interest in Elf Bar in the United States: Google Health Trends Analysis.

Despite the popularity of JUUL e-cigarettes, other brands (eg, Elf Bar) may be gaining digital attention. This study compared Google searches for Elf Bar and JUUL from 2022 to 2023 using Google Health Trends Application Programming Interface data. Using an infodemiology approach, we examined weekly trends in Google searches (per 10 million) for "Elf Bar" and "JUUL" at the US national and state levels from January 1, 2022, to December 31, 2023. Joinpoint regression was used to assess statistically significant trends in the search probabilities for "Elf Bar" and "JUUL" during the study period. Elf Bar had less online interest than JUUL at the beginning of 2022. When the US Food and Drug Administration denied JUUL marketing authority on June 23, 2022, JUUL searches peaked at 2609.3 × 107 and fell to 83.9 × 107 on September 3, 2023. Elf Bar searches surpassed JUUL on July 10, 2022, and steadily increased, reaching 523.2 × 107 on December 4, 2022. Overall, Elf Bar's weekly search probability increased by 1.6% (95% CI 1.5%-1.7%; P=.05) from January 2022 to December 2023, with the greatest increase between May 29 and June 19, 2022 (87.7%, 95% CI 35.9%-123.9%; P=.001). Elf Bar searches increased after JUUL's suspension in Pennsylvania (1010%), Minnesota (872.5%), Connecticut (803.5%), New York (738.1%), and New Jersey (702.9%). Increasing trends in Google searches for Elf Bar indicate that there was a growing online interest in this brand in the United States in 2022.

EXPRESSO: a multi-omics database to explore multi-layered 3D genomic organization.

The three-dimensional (3D) organization of the human genome plays a crucial role in gene regulation. EXPloration of Regulatory Epigenome with Spatial and Sequence Observations (EXPRESSO) is a novel multi-omics database for exploration and visualization of multi-layered 3D genomic features across 46 different human tissues. Integrating 1360 3D genomic datasets (Hi-C, HiChIP, ChIA-PET) and 842 1D genomic and transcriptomic datasets (ChIP-seq, ATAC-seq, RNA-seq) from the same biosample, EXPRESSO provides a comprehensive resource for studying the interplay between 3D genome architecture and transcription regulation. This database offers diverse 3D genomic feature types (compartments, contact matrix, contact domains, stripes as diagonal lines extending from a genomic locus in contact matrix, chromatin loops, etc.) and user-friendly interface for both data exploration and download. Other key features include REpresentational State Transfer application programming interfaces for programmatic access, advanced visualization tools for 3D genomic features and web-based applications that correlate 3D genomic features with gene expression and epigenomic modifications. By providing extensive datasets and tools, EXPRESSO aims to deepen our understanding of 3D genomic architecture and its implications for human health and disease, serving as a vital resource for the research community. EXPRESSO is freely available at https://expresso.sustech.edu.cn.

Minimized‐rate topramezone programs for goosegrass [Eleusine indica (L.) Gaertn.] control in Florida

AbstractGoosegrass [Eleusine indica (L.) Gaertn.] is one of the most troublesome warm‐season grassy weeds in subtropical climates of the southern United States. Currently, postemergence options are highly limited, and goosegrass control (GC) becomes more difficult as plants mature. Among current options, topramezone can control mature goosegrass. Research conducted in Florida suggests that despite seemingly effective control, recovery may be occurring from surviving roots. Therefore, this study assessed strategies aimed to ensure the persistent removal of mature goosegrass populations of Florida ecotypes. Two greenhouse studies were conducted to evaluate the response of goosegrass at one to three tillers and 4–10 tillers to topramezone applied once at rates from 0.8 to 12.2 g a.i. ha−1 (study 1) and at total rate reduced to 6.1 g a.i. ha−1 applied once or equally divided into two, four, or eight biweekly applications (study 2). Single topramezone applications at 6.1 g a.i. ha−1 provided comparable GC and biomass reduction to single applications at 12.2 g a.i. ha−1. Reduced rate‐based split‐programs of two and four biweekly applications improved GC and its persistence over a single application as evidenced by a >250 increase in GC area under the progress curve and a >20 days increase in days over 80% GC. Among the proposed extended application programs based on the reduced effective rate of topramezone, the most promising consists of four biweekly applications at a 0.125× rate, equivalent to 1.53 g a.i. ha−1.

The Effect of Mobile Application Follow-Up on Treatment Compliance and Self-Care Management in Patients With Hypertension: Randomized Controlled Trial.

Hypertension (HT) is a global health problem. Although there are effective treatment protocols, patients have difficulty in adapting to regular use of drugs, diet, and lifestyle changes. Mobile apps can be a potential alternative for managing HT and improving self-care behavior. This study aimed to determine the effect of mobile application follow-up on treatment compliance and self-care management in patients with HT. This randomized controlled trial was conducted in November 2021 and March 2022 with 40 experimental and 40 control group patients with HT. Research data were collected using a patient information form, "Hill-Bone Hypertension Treatment Compliance Scale (HBHTTCS)," "Hypertension Self-Care Profile-Motivation Scale (HTSPMS)," a mobile application program (daily blood pressure, pulse, medication, diet, exercise tracking) uploaded to the mobile phones of the patients, and individual motivational messages. The mean total score of the HBHTTCS at Week 6 was 14.57±1.89 and the mean total score of the HTSPMS was 78.15±2.05.There was a statistically significant difference in mean total HTSPMS (p = 0.000) and HBHTTCS (p = 0.000) scores and mean scores on medical (p = 0.002) and nutrition (p = 0.002) subscales of HBHTTCS between the first interview (1st-week follow-up) and the last interview (6th-week follow-up) of patients in the intervention group. This study showed that mobile application follow-up increased motivation levels and treatment compliance in patients with HT and resulted in better self-care. These positive findings indicate importance of integrating mobile applications in the disease management of HT patients. ClinicalTrials.gov ID: NCT05334446, https://clinicaltrials.gov/ct2/show/NCT05334446.

CTR-DB 2.0: an updated cancer clinical transcriptome resource, expanding primary drug resistance and newly adding acquired resistance datasets and enhancing the discovery and validation of predictive biomarkers.

Drug resistance is a principal limiting factor in cancer treatment. CTR-DB, the Cancer Treatment Response gene signature DataBase, is the first data resource for clinical transcriptomes with cancer treatment response, and meanwhile supports various data analysis functions, providing insights into the molecular determinants of drug resistance. Here we proposed an upgraded version, CTR-DB 2.0 (http://ctrdb.ncpsb.org.cn). Around 190 up-to-date source datasets with primary resistance information (129% increase compared to version 1.0) and 13 acquired-resistant datasets (a new dataset type), covering 10 856 patient samples (111% increase), 39 cancer types (39% increase) and 346 therapeutic regimens (26% increase), have been collected. In terms of function, for the single dataset analysis and multiple-dataset comparison modules, CTR-DB 2.0 added new gene set enrichment, tumor microenvironment (TME) and signature connectivity analysis functions to help elucidate drug resistance mechanisms and their homogeneity/heterogeneity and discover candidate combinational therapies. Furthermore, biomarker-related functions were greatly extended. CTR-DB 2.0 newly supported the validation of cell types in the TME as predictive biomarkers of treatment response, especially the validation of a combinational biomarker panel and even the direct discovery of the optimal biomarker panel using user-customized CTR-DB patient samples. In addition, the analysis of users' own datasets, application programming interface and data crowdfunding were also added.

Tracking provenance in clinical data warehouses for quality management

IntroductionData provenance, which documents the origin, history, and transformations of data, can enhance the reproducibility of processing workflows and help to address errors and quality issues. In this work, we focus on tracking and utilizing provenance information as part of quality management in Extract-Transform-Load (ETL) processes used to build clinical data warehouses. MethodsWe designed and implemented a framework that automatically tracks how data flows through an ETL process and detects errors and quality problems during processing. This information is then reported against an Application Programming Interface (API) that stores the issues along with contextual information on their location within the data being transformed and the overall workflow. We further designed a dashboard that supports health data engineers with inspecting the encountered issues and tracing them back to their root causes. ResultsThe framework was implemented in Java using the Spring Framework and integrated into ETL processes for Informatics for Integrating Biology and the Bedside (i2b2). The dashboard was realized using Grafana. We evaluated our approach on three different ETL processes for real-world datasets used to integrate them into our i2b2 clinical data warehouse. Using the provenance dashboard, we were able to identify frequent error patterns and link them to specific data points from the sources as well as ETL process steps. Provenance tracking increased the execution times of loading processes with an impact depending on the number of identified issues. ConclusionsProvenance tracking can be a valuable tool for implementing continuous quality management for ETL processes. Relevant information can be collected from existing ETL workloads using dedicated APIs and visualized through dashboards, which support the identification of frequent patterns of problems together with their root causes, providing valuable information for improvements.