Development Of Workflow Research Articles

Designing workflows for materials characterization

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. We discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research.

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners.

A Direct Comparison of rAAV5 Variants Derived from the Baculovirus Expression System Using LC-MS Workflows Demonstrates Key Differences in Overall Production Yield, Product Quality and Vector Efficiency.

Gene therapy holds great promise for the treatment of severe diseases, and adeno-associated virus (AAV) vectors have emerged as valuable tools in this field. However, challenges such as immunogenicity and high production costs complicate the commercial viability of AAV-based therapies. To overcome these barriers, improvements in production yield, driven through the availability of robust and sensitive characterization techniques that allow for the monitoring of critical quality attributes to deepen product and process understanding are crucial. Among the main attributes affecting viral production and performance, the ratio between empty and full capsids along with capsid protein stoichiometry are emerging as potential parameters affecting product quality and safety. This study focused on the production of AAV vectors using the baculovirus expression vector system (BEVS) in Sf9 cells and the complete characterization of AAV5 variants using novel liquid chromatography and mass spectrometry techniques (LC-MS) that, up to this point, had only been applied to reference commercially produced virions. When comparing virions produced using ATG, CTG or ACG start codons of the cap gene, we determined that although ACG was the most productive in terms of virus yield, it was also the least effective in transducing mammalian cells. This correlated with a low VP1/VP2 ratio and a higher percentage of empty capsids. Overall, this study provides insights into the impact of translational start codon modifications during rAAV5 production using the BEVS, the associated relationship with capsid packaging, capsid protein stoichiometry and potency. The developed characterization workflow using LC-MS offers a comprehensive and transferable analysis of AAV-based gene therapies, with the potential to aid in process optimization and facilitate the large-scale commercial manufacturing of these promising treatments.

The status and future of essential geodiversity variables.

Rapid environmental change, natural resource overconsumption and increasing concerns about ecological sustainability have led to the development of 'Essential Variables' (EVs). EVs are harmonized data products to inform policy and to enable effective management of natural resources by monitoring global changes. Recent years have seen the instigation of new EVs beyond those established for climate, oceans and biodiversity (ECVs, EOVs and EBVs), including Essential Geodiversity Variables (EGVs). EGVs aim to consistently quantify and monitor heterogeneity of Earth-surface and subsurface abiotic features, including geology, geomorphology, hydrology and pedology. Here we assess the status and future development of EGVs to better incorporate geodiversity into policy and sustainable management of natural resources. Getting EGVs operational requires better consensus on defining geodiversity, investments into a governance structure and open platform for curating the development of EGVs, advances in harmonizing in situ measurements and linking heterogeneous databases, and development of open and accessible computational workflows for global digital mapping using machine-learning techniques. Cross-disciplinary collaboration and partnerships with governmental and private organizations are needed to ensure the successful development and uptake of EGVs across science and policy. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Protein–protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis

BackgroundIt is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein–protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation.MethodsThe relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria.ResultsThe meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation.ConclusionsThe implemented workflow could be used for other multifactorial diseases.

Advancements in software engineering using AI

The integration of Artificial Intelligence (AI) into the space of software engineering marks a transformative period that reshapes traditional development processes and propels the industry into a new era of innovation. This exploration delves into the multifaceted impact of AI, from its roots in early symbolic AI to the contemporary dominance of machine learning and deep learning. AI’s applications span various domains, but its significance in software engineering lies in its ability to enhance efficiency, improve software quality, and introduce novel approaches to problem-solving. From automating routine tasks to streamlining complex development workflows, AI acts as a virtual collaborator, allowing human developers to focus on higher-order thinking and creativity. This study introduces the application of AI in software engineering. reverse-engineering, and development environments. Moreover, ethical considerations, challenges, and future trends, including explainable AI, reinforcement learning, and human-AI collaboration, are presented.

Predicting success in Cu-catalyzed C-N coupling reactions using data science.

Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C-N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment.

Developing a digital twin for a laboratory ball mill operation – a step towards mining metaverse

Digital twins (DTs) are transforming business operations across industries through accurate replication of physical entities using the Internet of Things and big data analytics. Despite booming progress in the manufacturing, aerospace and buildings sectors, the adoption of DTs in the minerals industry has been slow, and integration with efficient visualisation and user interactions has not been fully optimised to achieve maximum fidelity and usability. One promising avenue for enhancing DT capabilities is the utilisation of extended reality (XR) technologies, which also hold great potential for realising an industrial metaverse where real-world business activities can be conducted in a virtual space. This article proposes a cost-effective and scalable approach to developing a DT with real-time monitoring and control capabilities for a ball mill operation, a widely used processing equipment in the minerals industry. The case study showcases two approaches with different levels of system integration by leveraging serious game development platforms, toolkits and workflows.

Development and evaluation of a comprehensive workflow for suspect screening of exposome-related xenobiotics and phase II metabolites in diverse human biofluids

Suspect and non-target screening (SNTS) methods are being promoted in order to decode the human exposome since a wide chemical space can be analysed in a diversity of human biofluids. However, SNTS approaches in the exposomics field are infra-studied in comparison to environmental or food monitoring studies. In this work, a comprehensive suspect screening workflow was developed to annotate exposome-related xenobiotics and phase II metabolites in diverse human biofluids. Precisely, human urine, breast milk, saliva and ovarian follicular fluid were employed as samples and analysed by means of ultra-high performance liquid chromatography coupled with high resolution tandem mass spectrometry (UHPLC-HRMS/MS). To automate the workflow, the “peak rating” parameter implemented in Compound Discoverer 3.3.2 was optimized to avoid time-consuming manual revision of chromatographic peaks. In addition, the presence of endogenous molecules that might interfere with the annotation of xenobiotics was carefully studied as the employment of inclusion and exclusion suspect lists. To evaluate the workflow, limits of identification (LOIs) and type I and II errors (i.e., false positives and negatives, respectively) were calculated in both standard solutions and spiked biofluids using 161 xenobiotics and 22 metabolites. For 80.3 % of the suspects, LOIs below 15 ng/mL were achieved. In terms of type I errors, only two cases were identified in standards and spiked samples. Regarding type II errors, the 7.7 % errors accounted in standards increased to 17.4 % in real samples. Lastly, the use of an inclusion list for endogens was favoured since it avoided 18.7 % of potential type I errors, while the exclusion list caused 7.2 % of type II errors despite making the annotation workflow less time-consuming.

Optically stimulated luminescence dosimeters for simultaneous measurement of point dose and dose-weighted LET in an adaptive proton therapy workflow.

To demonstrate the suitability of optically stimulated luminescence detectors (OSLDs) for accurate simultaneous measurement of the absolute point dose and dose-weighted linear energy transfer (LETD) in an anthropomorphic phantom for experimental validation of daily adaptive proton therapy. A clinically realistic intensity-modulated proton therapy (IMPT) treatment plan was created based on a CT of an anthropomorphic head-and-neck phantom made of tissue-equivalent material. The IMPT plan was optimized with three fields to deliver a uniform dose to the target volume covering the OSLDs. Different scenarios representing inter-fractional anatomical changes were created by modifying the phantom. An online adaptive proton therapy workflow was used to recover the daily dose distribution and account for the applied geometry changes. To validate the adaptive workflow, measurements were performed by irradiating Al2O3:C OSLDs inside the phantom. In addition to the measurements, retrospective Monte Carlo simulations were performed to compare the absolute dose and dose-averaged LET (LETD) delivered to the OSLDs. The online adaptive proton therapy workflow was shown to recover significant degradation in dose conformity resulting from large anatomical and positioning deviations from the reference plan. The Monte Carlo simulations were in close agreement with the OSLD measurements, with an average relative error of 1.4% for doses and 3.2% for LETD. The use of OSLDs for LET determination allowed for a correction for the ionization quenched response. The OSLDs appear to be an excellent detector for simultaneously assessing dose and LET distributions in proton irradiation of an anthropomorphic phantom. The OSLDs can be cut to almost any size and shape, making them ideal for in-phantom measurements to probe the radiation quality and dose in a predefined region of interest. Although we have presented the results obtained in the experimental validation of an adaptive proton therapy workflow, the same approach can be generalized and used for a variety of clinical innovations and workflow developments that require accurate assessment of point dose and/or average LET.

Mitigating the aggregation challenge in immunocytokine production: Strategies during cell line development and purification optimization

The rapid growth of bispecific antibodies and antibody fusion molecules in biopharmaceuticals necessitates precise process development. The molecular design, the biophysical properties, such as protein hydrophobicity and charge-charge interactions, and the process development workflows are crucial to manage the critical quality attribute (CQA) of development candidates at the development and manufacturing stage. This study focuses on aggregation mitigation for an immunocytokine, HLX101, comprised of an engineered cytokine fused to the knob chain of a Knob-into-Hole IgG. Aggregation and other CQAs were monitored and controlled throughout cell line development (CLD), expression, and purification. Our study effectively identified HLX101 clone #10 as optimal for large-scale manufacturing by demonstrating stable expression and by balancing high yield with low aggregation. This was achieved by using purification process results to guide the selection of the final clone in the cell line development, integrating both processes to optimize overall outcomes in immunocytokine production. Thus, clone #10, with its favorable profile, emerged as more efficient, enhancing overall yield and quality. This underscores the importance of considering both upstream and downstream processes in immunocytokine production.

A qualitative assessment of using ChatGPT as large language model for scientific workflow development.

Scientific workflow systems are increasingly popular for expressing and executing complex data analysis pipelines over large datasets, as they offer reproducibility, dependability, and scalability of analyses by automatic parallelization on large compute clusters. However, implementing workflows is difficult due to the involvement of many black-box tools and the deep infrastructure stack necessary for their execution. Simultaneously, user-supporting tools are rare, and the number of available examples is much lower than in classical programming languages. To address these challenges, we investigate the efficiency of large language models (LLMs), specifically ChatGPT, to support users when dealing with scientific workflows. We performed 3 user studies in 2 scientific domains to evaluate ChatGPT for comprehending, adapting, and extending workflows. Our results indicate that LLMs efficiently interpret workflows but achieve lower performance for exchanging components or purposeful workflow extensions. We characterize their limitations in these challenging scenarios and suggest future research directions. Our results show a high accuracy for comprehending and explaining scientific workflows while achieving a reduced performance for modifying and extending workflow descriptions. These findings clearly illustrate the need for further research in this area.

Multi-Layer Digital Services Increase Developers’ Requirements to Agriculture IoT Platforms

Highlights Digital ecosystems in agriculture enable a new class of digital services that span over several layers of the IoT stack. This work defines digital ecosystems in agriculture based on IoT layers and utilized technologies. An example for a multi-layer digital service is developed, deployed, and tested using the NEVONEX Ecosystem. Surveys reveal software providers require more platform functionalities and early iteration testing on real farms. Abstract. In recent years, digital platforms in agriculture went through a phase of acquisitions and consolidation, where big players like agrochemical companies (Agrichems) and agricultural equipment manufacturers (OEMs) worked on expanding their technology stacks to build strategic portfolios of digital service offerings. To operate farm equipment efficiently, a combination of platforms and technologies is often needed, leading to the emergence of digital Agri-ecosystems. With the rising implementation of IoT technology, digital Agri-ecosystems enable multi-layer digital services and break down established categories (cloud-based and embedded). Content providers and developers creating those digital services face three options: aggregated cloud APIs (application programming interfaces), engineering service providers with proprietary technology stacks, and dedicated IoT ecosystems offering agnostic end-to-end workflows for development and deployment (first known example: NEVONEX). It is not yet fully understood how using an end-to-end IoT ecosystem changes developer requirements in comparison to existing solutions. Therefore, in this study, an example of a multi-layer digital service is developed and tested. Expert interviews (n = 21) explore relevant developer experiences and requirements. A consecutive survey (n = 30; NEVONEX developer conference) validates the findings. Results show that IoT- ecosystems speed up development and deployment cycles, save resources, and improve interoperability in operations. Engineering service providers remain very important players. Partner management, communication, abstraction of interfaces, testing infrastructure, and early end-user input are required by developers. In conclusion, IoT- ecosystems are beneficial for multi-layer digital service development; however, to fully satisfy developers and content providers, adjustments to platform functionalities and better testing opportunities on real farms are needed. Keywords: Automation, Digital Ecosystem, Farm equipment, NEVONEX

Laboratory based correlative cryo-soft X-ray tomography and cryo-fluorescence microscopy.

Cryo-soft X-ray tomography is the unique technology that can image whole intact cells in 3D under normal and pathological conditions without labelling or fixation, at high throughput and spatial resolution. The sample preparation is relatively straightforward; requiring just fast freezing of the specimen before transfer to the microscope for imaging. It is also possible to image chemically fixed samples where necessary. The technique can be correlated with cryo fluorescence microscopy to localize fluorescent proteins to organelles within the whole cell volume. Cryo-correlated light and soft X-ray tomography is particularly useful for the study of gross morphological changes brought about by disease or drugs. For example, viral fluorescent tags can be co-localized to sites of viral replication in the soft X-ray volume. In general this approach is extremely useful in the study of complex 3D organelle structure, nanoparticle uptake or in the detection of rare events in the context of whole cell structure. The main challenge of soft X-ray tomography is that the soft X-ray illumination required for imaging has heretofore only been available at a small number of synchrotron labs worldwide. Recently, a compact device with a footprint small enough to fit in a standard laboratory setting has been deployed ("the SXT-100") and is routinely imaging cryo prepared samples addressing a variety of disease and drug research applications. The SXT-100 facilitates greater access to this powerful technique and greatly increases the scope and throughput of potential research projects. Furthermore, the availability of cryo-soft X-ray tomography in the laboratory will accelerate the development of novel correlative and multimodal workflows by integration with light and electron microscope based approaches. It also allows for co-location of this powerful imaging modality at BSL3 labs or other facilities where safety or intellectual property considerations are paramount. Here we describe the compact SXT-100 microscope along with its novel integrated cryo-fluorescence imaging capability.

A constitutional isomer selective chemical proteomic strategy for system-wide profiling of protein lysine 5-hydroxylation.

Lysine 5-hydroxylation (5-Hyl) has been well recognized as an essential protein post-translational modification regulating cellular structural stability, RNA alternative splicing and epigenetic gene expression. System-wide enrichment and quantification of 5-Hyl targets have been challenging due to their chemical inert nature and difficulties in differentiating structural isomers in a complex biological sample. Here, we report the development of an efficient chemical proteomic workflow for affinity enrichment and constitutional isomer specific profiling of endogenous 5-Hyl substrates based on highly selective periodate chemistry. Our study confidently identified over 1600 5-Hyl sites on 630 proteins in human 293T cells, revealing functional significance of the modification in protein structure, transcription and chromatin regulation. Analysis of histone 5-Hyl sites showed that histones H2B and H1 are major targets of the 5-hydroxylysine epigenetic mark. Quantitative proteomic analysis through our chemical enrichment workflow identified specific 5-Hyl substrate proteins mediated by the overexpression of Jumonji-domain containing protein 6 (JMJD6). Our study uncovered two cancer-relevant alternative splice isoforms of JMJD6 that regulate 5-Hyl proteins in distinct cellular pathways, providing unique insights into the functional roles of JMJD6 alternative splicing in transcriptional regulation and cellular development.

Screening potential dye sensitizers for water splitting photocatalysts using a genetic algorithm.

Addressing the global fossil energy crisis necessitates the efficient utilization of sustainable energy sources. Hydrogen, a green fuel, can be generated using sunlight, water, and a photocatalyst. Employing sensitizers holds promise for enhancing photocatalyst performance, enabling high rates of hydrogen evolution through increased visible light absorption. However, sifting through millions of diverse molecules to identify suitable dyes for specific photocatalysts poses a significant challenge. In this study, we integrate genetic algorithm and geometry-frequency-noncovalent extended tight binding methods to efficiently screen 2.6 million potential sensitizers with a D-π-A-π-AA structure within a short timeframe. Subsequently, these optimized sensitizers are rigorously reassessed by using DFT/TDDFT methods, elucidating why they may serve as superior dyes compared to the reference dye WS5F, particularly in terms of light absorption, driving force, binding energy, etc. Additionally, our methodology uncovers molecular motifs of particular interest, including the furan π-bridge and the double cyano anchoring acceptor, which are prevalent in the most promising set of molecules. The developed genetic algorithm workflow and dye design principles can be extended to various compelling projects, such as dye-sensitized solar cells, organic photovoltaics, photo-induced redox reactions, pharmaceuticals, and beyond.

Solar Irradiance Forecasting using a Hybrid Quantum Neural Network: A Comparison on GPU-based Workflow Development Platforms

Solar Irradiance Forecasting using a Hybrid Quantum Neural Network: A Comparison on GPU-based Workflow Development Platforms

DIFFERENCE BETWEEN OPENHPC AND HTCONDOR CLUSTER SYSTEMS: IN-DEPTH ANALYSIS

The rapidly developing field of high-performance computing (HPC) requires efficient and scalable solutions to manage extensive computing loads. Although they use different approaches and architectures, OpenHPC and HTCondor are well-known platforms that meet these needs. This article thoroughly analyzes OpenHPC and HTCondor to identify their fundamental differences and work paradigms. OpenHPC is a comprehensive modular structure designed to facilitate the deployment, management, and maintenance of HPC clusters, offering a rich set of pre-integrated HPC software components. Conversely, HTCondor specializes in efficiently planning and managing resource-intensive tasks, using a unique partner selection system for dynamic resource allocation based on job requirements and resource availability. By examining aspects such as system architecture, resource management efficiency, scalability, flexibility, and the user ecosystem, this analysis sheds light on the strengths and weaknesses of each structure. The research aims to provide stakeholders in high-performance computing with the knowledge necessary to make informed decisions regarding the selection and implementation of high-performance computing management systems, ultimately aimed at optimizing the use of computing resources and optimizing research and development workflows.

Automated Bioanalytical Workflow for Ligand Binding-Based Pharmacokinetic Assay Development.

The growth of therapeutic monoclonal antibodies (mAbs) continues to accelerate due to their success as treatments for many diseases. As new therapeutics are developed, it is increasingly important to have robust bioanalytical methods to measure the pharmacokinetics (PK) of circulating therapeutic mAbs in serum. Ligand-binding assays such as enzyme-linked immunosorbent assays (ELISAs) with anti-idiotypic antibodies (anti-IDs) targeting the variable regions of the therapeutic antibody are sensitive and specific bioanalytical methods to measure levels of therapeutic antibodies in a biological matrix. However, soluble circulating drug mAb targets can interfere with the anti-IDs binding to the therapeutic mAb, thereby resulting in an underestimation of total drug concentration. Therefore, in addition to a high binding affinity for the mAb, the selection of anti-IDs and the assay format that are not impacted by soluble antigens and have low matrix interference is essential for developing a robust PK assay. Standardized automated approaches to screen and select optimal reagents and assay formats are critical to increase efficiency, quality, and PK assay robustness. However, there does not exist an integrated screening and analysis platform to develop robust PK assays across multiple formats. We have developed an automated workflow and scoring platform with multiple bioanalytical assay parameters that allow for ranking of candidate anti-IDs. A primary automated indirect electrochemiluminescence (ECL) was utilized to shortlist the anti-IDs that were selected for labeling and screening in pairs. A secondary screen using an ECL sandwich assay with labeled-anti-ID pairings was used to test multiple PK assay formats to identify the best anti-ID pairing/PK assay format. We developed an automated assay using fixed plate maps combined with a human-guided graphical user interface-based scoring system and compared it to a data-dependent scoring system using Gaussian mixture models for automated scoring and selection. Our approach allowed for screening of anti-IDs and identification of the most robust PK assay format with significantly reduced time and resources compared with traditional approaches. We believe that such standardized, automated, and integrated platforms that accelerate the development of PK assays will become increasingly important for supporting future human clinical trials.

Process mining software engineering practices: A case study for deployment pipelines

Context:In mature software development organizations the ci/cd pipeline is the only route to deploy software into production. While the workflow of this process seems straightforward, the reality is different since exceptions and deviations are the norm in actual industry practice. In this context, Process Mining appears as a promising technique to uncover deviations and check compliance with standardized DevOps processes, and highlight bottlenecks and potential improvement areas. Objective:This paper presents a case study designed to assess the potential of using Process Mining techniques to provide visibility into the deployment pipeline. Method:This research uses raw event data extracted from the continuous practices toolchain, which is then used to compute a comprehensive set of DevOps-specific metrics, thus supporting objective monitoring of the quality and efficiency of the deployment workflow. The study focuses on different development units in the Engineering team, each working in a distinct business context but sharing standard practices. Results:We verified that even though there are standards for the deployment pipelines, each team’s workflow denotes local variations with unique points for improvement that are highly coupled to their business unit context. We observed that each team’s pipeline has different temporal profiles that reflect their context and work practices. Additionally, we identified a set of deployment pipeline metrics focusing on process compliance, efficiency, and deployment stability. Conclusion:The main contributions of this paper include (1) the description of an actual application of Process Mining to the deployment pipeline of a highly complex e-commerce platform, (2) how this approach provided an objective understanding of the efficiency and quality of the development workflow, (3) how this process-centric view, combined with domain-specific DevOps metrics, supports continuous practices, and (4) how Developers can analyse their workflows by applying Process Mining while using standard tools like GitLab and PM4Py.