Open-source Tool Research Articles

Correlation of dynamic blood dose with clinical outcomes in radiotherapy for head-and-neck cancer

Background and purposeRadiation-induced lymphopenia (RIL) during cancer radiotherapy is receiving growing attention due to its association with adverse clinical outcomes. Correlations between RIL and poorer locoregional control (LRC), distant-metastasis-free survival (DMFS), and overall survival (OS) have been demonstrated across multiple treatment sites. Estimates of radiation delivered to circulating blood or lymphocytes have been shown to be correlated with severe RIL. This study aims to evaluate whether blood dose estimates are equally correlated with patient outcomes directly. Materials and methodsFor 298 head-and-neck cancer patients, blood dose was estimated via the total body dose (Dbody), a static blood dose model considering the mean dose to relevant organs and tissues (Dstatic), and a dynamic model which further included temporal aspects such as blood flow and treatment delivery time (Ddynamic). The latter utilized hematological dose (HEDOS), an open-source computational tool for blood dose simulations. Survival analysis was performed to evaluate potential correlations between blood dose and LRC, DMFS, and OS. ResultsMultivariable Cox regression analysis found a statistically significant (p < 0.05) correlation between various dynamic blood dose metrics and clinical outcomes. Dbody and Dstatic did not correlate with any of the outcomes considered. ConclusionA statistically significant correlation between the dynamic blood dose model and adverse clinical outcomes was observed. During multivariable regression analysis, neither static blood dose model exhibited a statistically significant correlation with any of the outcomes studied.

HeatNetSim: An open-source simulation tool for heating and cooling networks suitable for future energy systems

HeatNetSim: An open-source simulation tool for heating and cooling networks suitable for future energy systems

Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis

Cooking with traditional fuels can lead to severe health issues caused by household air pollution, and can also affect gender equality and drive environmental degradation. In Nepal, despite government efforts to promote electric cooking, more than half of the population still uses traditional fuels, with electric cooking adoption remaining below 1%. Several of the barriers to and enablers of clean cooking vary geographically; however, few studies have considered spatial explicit information in planning national-scale transitions to clean cooking. In this study we provide a spatially explicit roadmap to estimate the required investments and benefits gained from the transition across Nepal. This study uses geospatial modelling methods to evaluate strategies to achieve the Government of Nepal's vision for a national-scale transition to clean cooking. We integrate the open-source clean cooking geospatial assessment tool OnStove and a spatial multicriteria analysis model. With OnStove, we evaluate which cooking technologies and fuels maximise the net benefits of a clean-cooking transition across each km2 of the region. With the multicriteria analysis, we weigh stakeholder preferences and prioritise areas of action where policy should be implemented. We used the most up-to-date geospatial data to the year 2023, such as the High Resolution Settlement Layer, Open Street Maps' road networks, the Global Human Settlement Layer, NASA/USGS forest cover maps, and Facebook's Relative Wealth Index, among others. We also relied on data from the Nepal Oil Corporation, the Nepal Electricity Agency, the Central Bureau of Statistic's 2021 national census, and the Alternative Energy Promotion Center. We evaluate four scenarios capturing advances on clean cooking policy up to the year 2022, current market inefficiencies, and the potential effects of new policies for clean-cooking transition in Nepal. Our results show that transitional and clean cooking technologies provide higher net benefits than traditional options everywhere across Nepal in all scenarios. Our net-benefit analysis shows that around 9563 deaths could be averted yearly if benefits and externalities were perceived and valued correctly. Furthermore, substantial benefits could be achieved in regard to greenhouse gas emissions avoidance, time saved, and health-cost reductions. Our results also show that the current subsidy strategy from the Government of Nepal is well aligned with the benefits achieved under a cost-benefit analysis. In this context, electric cooking can bring the highest benefits to the largest part of the population. The analysis showed how high subsidies for liquefied petroleum gas in Nepal can present trade-offs with energy security and independence, and how this could be avoided by transferring part of the subsidy to cover differentiated electric cooking tariffs. Accounting for stakeholder preferences and sociodemographic and geographical differences to prioritise areas of focus can balance affordability constraints and target the most vulnerable people first, thus achieving integrated and inclusive planning. Using spatially explicit modelling approaches to evaluate strategies for a clean cooking transition can provide more nuanced results that have not been possible before. This approach can enable data-driven and integrated planning to help to understand which locations of a study area should be prioritised for policy application. Integrated planning can help to reduce affordability constraints on the population and design strategies for a sustainable and inclusive transition. These strategies allow financial institutions, donors, impact investors, development organisations, and government agencies to use their resources, funds, and assistance to create a large impact. Clean Cooking Alliance.

The Doughnut Biotool: A tool to assess life-cycle biodiversity impacts from building projects

Abstract Life-cycle assessment (LCA) is widely used in certifications, regulations, and voluntary declarations covering the climate impact of buildings over their entire value chain. However, biodiversity impacts and other environmental impact categories are often disregarded. Conversely, there is a trend towards more systematic assessments of the biodiversity impacts that occur on-site as part of development projects (e.g. land transformation on the project site). However, off-site biodiversity impacts occurring throughout the rest of the value chain (e.g. from forestry activities, mining, manufacturing, heat and electricity production) are rarely considered. To bridge this gap, this article introduces the Doughnut Biotool, an open-source calculation tool to assess biodiversity impacts throughout the entire life cycle of development projects. The tool was initially developed to help implement Doughnut Economics and absolute sustainability principles in development projects. It constitutes a first step towards a better consideration of biodiversity impacts related to material- and energy use in building and construction projects. This paper presents the design of the tool and the method and data it relies on. The tool is applied to two separate case study projects, and used as a basis to discuss hotspots of biodiversity impacts within the building’s life cycle. Finally, the paper includes a discussion of the main challenges and opportunities for the future development and use of the tool, as well as for biodiversity impact assessments in the building sector as a whole.

Hierarchical deep compartment modeling: A workflow to leverage machine learning and Bayesian inference for hierarchical pharmacometric modeling.

Population pharmacokinetic (PK) modeling serves as the cornerstone for understanding drug behavior within a specific population. It integrates subject covariates to elucidate the variability in PK parameters, thus enhancing predictive accuracy. However, covariate modeling within this framework can be intricate and time-consuming due to the often obscure structural relationship between covariates and PK parameters. Previous attempts, such as deep compartment modeling (DCM), aimed to streamline this process using machine learning techniques. Nonetheless, DCM fell short in assessing residual errors and interindividual variability (IIV), potentially leading to model misspecification and overfitting. Furthermore, DCM lacked the ability to quantify model uncertainty. To address these limitations, we introduce hierarchical deep compartment modeling (HDCM) as an advancement of DCM. HDCM harnesses machine learning to discern the interplay between covariates and PK parameters while simultaneously evaluating diverse levels of random effects and quantifying uncertainty through Bayesian inference. This tutorial provides a comprehensive application of the HDCM workflow using open-source Julia tools.

New Features in the pyCSEP Toolkit for Earthquake Forecast Development and Evaluation

Abstract The Collaboratory for the Study of Earthquake Predictability (CSEP) is a global community dedicated to advancing earthquake predictability research by rigorously testing probabilistic earthquake forecast models and prediction algorithms. At the heart of this mission is the recent introduction of pyCSEP, an open-source software tool designed to evaluate earthquake forecasts. pyCSEP integrates modules to access earthquake catalogs, visualize forecast models, and perform statistical tests. Contributions from the CSEP community have reinforced the role of pyCSEP in offering a comprehensive suite of tools to test earthquake forecast models. This article builds on Savran, Bayona, et al. (2022), in which pyCSEP was originally introduced, by describing new tests and recent updates that have significantly enhanced the functionality and user experience of pyCSEP. It showcases the integration of new features, including access to authoritative earthquake catalogs from Italy (Bolletino Seismico Italiano), New Zealand (GeoNet), and the world (Global Centroid Moment Tensor), the creation of multiresolution spatial forecast grids, the adoption of non-Poissonian testing methods, applying a global seismicity model to specific regions for benchmarking regional models and evaluating alarm-based models. We highlight the application of these recent advances in regional studies, specifically through the New Zealand case study, which showcases the ability of pyCSEP to evaluate detailed, region-specific seismic forecasts using statistical functions. The enhancements in pyCSEP also facilitate the standardization of how the CSEP forecast experiments are conducted, improving the reliability, and comparability of the earthquake forecasting models. As such, pyCSEP exemplifies collaborative research and innovation in earthquake predictability, supporting transparent scientific practices, and community-driven development approaches.

From development to deployment: introducing MobGap, the open-source tool for mobility assessment with wearable devices by Mobilise-D

From development to deployment: introducing MobGap, the open-source tool for mobility assessment with wearable devices by Mobilise-D

Special Section Guest Editorial: Open-source neurophotonic tools for neuroscience.

The editorial completes the Neurophotonics special series on open-source neurophotonic tools for neuroscience.

An open-source data storage and visualization platform for collaborative qubit control.

Developing collaborative research platforms for quantum bit control is crucial for driving innovation in the field, as they enable the exchange of ideas, data, and implementation to achieve more impactful outcomes. Furthermore, considering the high costs associated with quantum experimental setups, collaborative environments are vital for maximizing resource utilization efficiently. However, the lack of dedicated data management platforms presents a significant obstacle to progress, highlighting the necessity for essential assistive tools tailored for this purpose. Current qubit control systems are unable to handle complicated management of extensive calibration data and do not support effectively visualizing intricate quantum experiment outcomes. In this paper, we introduce Qubit Control Storage and Visualization (QubiCSV), a platform specifically designed to meet the demands of quantum computing research, focusing on the storage and analysis of calibration and characterization data in qubit control systems. As an open-source tool, QubiCSV facilitates efficient data management of quantum computing, providing data versioning capabilities for data storage and allowing researchers and programmers to interact with qubits in real time. The insightful visualization are developed to interpret complex quantum experiments and optimize qubit performance. QubiCSV not only streamlines the handling of qubit control system data but also improves the user experience with intuitive visualization features, making it a valuable asset for researchers in the quantum computing domain.

Assessing wind turbine blade durability longevity utilizing national renewable energy laboratory tools

With the escalating significance of renewable energy, there exists a pressing demand for precise software and tools capable of designing and forecasting wind turbine blade service life. OpenFAST, TurbSim, and MLife are pivotal open-source wind turbine simulation tools that integrate aerodynamics, structural dynamics, wind simulation, and control systems. They enable accurate prediction of wind turbine performance. This study aims to leverage these software resources to model turbulent wind conditions using predefined basic parameters and then calculate fatigue and longevity of the wind turbine blade to determine service life. Particularly, MLife was employed for rose loading, enhancing the fidelity of the loading cycle simulation. Collaboration between the National Renewable Energy Laboratory and the research team played a crucial role in establishing a foundational baseline and implementing realistic input parameters for evaluating the ultimate load, determining damage equivalent loads (DEL), and projecting service life. In all simulations, a constant factor of safety (FOS) of 1.5 was consistently applied. Additionally, Finite Element Analysis (FEA) was employed to validate our numerical experiments, ensuring the accuracy of the obtained results by correlating simulation outcomes with real-world scenarios, further strengthening the credibility of the study.

Lossless Approximate Pattern Matching: Automated Design of Efficient Search Schemes.

This study introduces a pioneering approach to automate the creation of search schemes for lossless approximate pattern matching. Search schemes are combinatorial structures that define a series of searches over a partitioned pattern. Each search specifies the processing order of these parts and the cumulative lower and upper bounds on the number of errors in each part of the pattern. Together, these searches ensure the identification of all approximate occurrences of a search pattern within a predefined limit of k errors. While existing literature offers designed schemes for up to k = 4 errors, designing search schemes for larger k values incurs escalating computational costs. Our method integrates a greedy algorithm and a novel Integer Linear Programming (ILP) formulation to design efficient search schemes for up to k = 7 errors. Comparative analyses demonstrate the superiority of our ILP-optimal schemes over alternative strategies in both theoretical and practical contexts. Additionally, we propose a dynamic scheme selection technique tailored to specific search patterns, further enhancing efficiency. Combined, this yields runtime reductions of up to 53% for higher k values. To facilitate search scheme generation, we present Hato, an open-source software tool (AGPL-3.0 license) employing the greedy algorithm and utilizing CPLEX for ILP solving. Furthermore, we introduce Columba 1.2, an open-source lossless read-mapper (AGPL-3.0 license) implemented in C++. Columba surpasses existing state-of-the-art tools by identifying all approximate occurrences of 100,000 Illumina reads (150 bp) in the human reference genome within 24 seconds (maximum edit distance of 4) and 75 seconds (maximum edit distance of 6) using a single CPU core. Notably, our study showcases Columba's capability to align 100,000 reads of length 50, with high error rates and up to an edit distance of 7, in a mere 2 hours and 15 minutes. This achievement is unmatched by other lossless aligners, which require over 3 hours for edit distance 5 alignments. Moreover, Columba exhibits a mapping rate four times higher than that of a lossy tool for this dataset.

Open Source Tools for Digital Forensic Investigation: Capability, Reliability, Transparency and Legal Requirements

Open Source Tools for Digital Forensic Investigation: Capability, Reliability, Transparency and Legal Requirements

Raw Photoplethysmography as an Enhancement for Research-Grade Wearable Activity Monitors.

Wearable monitors continue to play a critical role in scientific assessments of physical activity. Recently, research-grade monitors have begun providing raw data from photoplethysmography (PPG) alongside standard raw data from inertial sensors (accelerometers and gyroscopes). Raw PPG enables granular and transparent estimation of cardiovascular parameters such as heart rate, thus presenting a valuable alternative to standard PPG methodologies (most of which rely on consumer-grade monitors that provide only coarse output from proprietary algorithms). The implications for physical activity assessment are tremendous, since it is now feasible to monitor granular and concurrent trends in both movement and cardiovascular physiology using a single noninvasive device. However, new users must also be aware of challenges and limitations that accompany the use of raw PPG data. This viewpoint paper therefore orients new users to the opportunities and challenges of raw PPG data by presenting its mechanics, pitfalls, and availability, as well as its parallels and synergies with inertial sensors. This includes discussion of specific applications to the prediction of energy expenditure, activity type, and 24-hour movement behaviors, with an emphasis on areas in which raw PPG data may help resolve known issues with inertial sensing (eg, measurement during cycling activities). We also discuss how the impact of raw PPG data can be maximized through the use of open-source tools when developing and disseminating new methods, similar to current standards for raw accelerometer and gyroscope data. Collectively, our comments show the strong potential of raw PPG data to enhance the use of research-grade wearable activity monitors in science over the coming years.

AngioPy Segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation

BackgroundQuantitative coronary angiography (QCA) typically employs traditional edge detection algorithms that often require manual correction. This has important implications for the accuracy of downstream 3D coronary reconstructions and computed haemodynamic indices (e.g. angiography-derived fractional flow reserve).We developed AngioPy, a deep-learning model for coronary segmentation that employs user-defined ground-truth points to boost performance and minimise manual correction. We compared its performance without correction with an established QCA system. MethodsDeep learning models integrating user-defined ground-truth points were developed using 2455 images from the Fractional Flow Reserve versus Angiography for Multivessel Evaluation 2 (FAME 2) study. External validation was performed on a dataset of 580 images. Vessel dimensions from 203 images with mild/moderate stenoses segmented by AngioPy (without correction) and an established QCA system (Medis QFR®) were compared (609 diameters). ResultsThe top-performing model had an average F1 score of 0.927 (pixel accuracy 0.998, precision 0.925, sensitivity 0.930, specificity 0.999) with 99.2 % of masks exhibiting an F1 score > 0.8. Similar results were seen with external validation (F1 score 0.924, pixel accuracy 0.997, precision 0.921, sensitivity 0.929, specificity 0.999). Vessel dimensions from AngioPy exhibited excellent agreement with QCA (r = 0.96 [95 % CI 0.95–0.96], p < 0.001; mean difference − 0.18 mm [limits of agreement (LOA): −0.84 to 0.49]), including the minimal luminal diameter (r = 0.93 [95 % CI 0.91–0.95], p < 0.001; mean difference − 0.06 mm [LOA: −0.70 to 0.59]). ConclusionAngioPy, an open-source tool, performs rapid and accurate coronary segmentation without the need for manual correction. It has the potential to increase the accuracy and efficiency of QCA.

Seasonality in mood disorders: Probing association of accelerometer-derived physical activity with daylength and solar insolation

Mood disorders are the leading cause of disability worldwide. Up to 30 percent of individuals with major depressive disorder (MDD) and bipolar disorder (BD) display a seasonal pattern of onset, a phenomenon now recognized in the official diagnostic manuals (DSM-5 and ICD-11). Very little is known about the influence of day length (photoperiod) and sunlight intensity (solar insolation) on seasonal patterns in MDD and BD. Here we report a quantitative approach to examine the relationship between sunlight measures and objectively measured motor activity patterns to understand environmental factors driving seasonality in MDD and BD. Our generalized linear model (GLM) assessment of the Depresjon dataset, which includes short-term (up to two weeks) motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls recruited to the study at the University of Bergen Norway (60.4° N latitude, 5.3° E longitude), revealed significant association of accelerometer-derived daytime physical activity with participant’s depressed state (p&lt;0.001), photoperiod (p&lt;0.001), and solar insolation (p&lt;0.001). Our study presents a generalizable strategy to decipher the complex interplay between sunlight, physical activity, and depressed state using open-source digital tools. The ability to identify mood disturbances, particularly in seasonally susceptible individuals, using passive digital biomarker data offers great promise in informing next-generation predictive, personalized diagnostics in mental health.

Lessons learned from designing an open-source automated feedback system for STEM education

AbstractAs distance learning becomes increasingly important and artificial intelligence tools continue to advance, automated systems for individual learning have attracted significant attention. However, the scarcity of open-source online tools that are capable of providing personalized feedback has restricted the widespread implementation of research-based feedback systems. In this work, we present RATsApp, an open-source automated feedback system (AFS) that incorporates research-based features such as formative feedback. The system focuses on core STEM competencies such as mathematical competence, representational competence, and data literacy. It also allows lecturers to monitor students’ progress. RATsApp can be used at different levels of STEM education or research, as it allows the creation and customization of the educational content. We present a specific case of its implementation in higher education, where we report the results of a usability survey (N=64), using the technology acceptance model 2 (TAM2), to evaluate the user experience of undergraduate students. Our findings confirm the applicability of the TAM2 framework, revealing that factors such as the relevance to the course of study, output quality, and ease of use significantly influence the perceived usefulness. We also found a linear relation between the perceived usefulness and the intention to use, which in turn is a significant predictor of the frequency of use. Moreover, the formative feedback feature of RATsApp received positive feedback, indicating its potential as an educational tool. Furthermore, as an open-source platform, RATsApp encourages public contributions to its ongoing development, fostering a collaborative approach to improve educational tools.

EstuarySAT Database Development of Harmonized Remote Sensing and Water Quality Data for Tidal and Estuarine Systems.

Researchers and environmental managers need big datasets spanning long time periods to accurately assess current and historical water quality conditions in fresh and estuarine waters. Using remote sensing data, we can survey many water bodies simultaneously and evaluate water quality conditions with greater frequency. The combination of existing and historical water quality data with remote sensing imagery into a unified database allows researchers to improve remote sensing algorithms and improves understanding of mechanisms causing blooms. We report on the development of a water quality database "EstuarySAT" which combines data from the Sentinel-2 multi-spectral instrument (MSI) remote sensing platform and water quality data throughout the coastal USA. EstuarySAT builds upon an existing database and set of methods developed by the creators of AquaSat, whose region of interest is primarily larger freshwater lakes in the USA. Following the same basic methods, EstuarySAT utilizes open-source tools: R v. 3.24+ (statistical software), Python (dynamic programming environment), and Google Earth Engine (GEE) to develop a combined water quality data and remote sensing imagery database (EstuarySAT) for smaller coastal estuarine and freshwater tidal riverine systems. EstuarySAT fills a data gap that exists between freshwater and estuarine water bodies. We are able to evaluate smaller systems due to the higher spatial resolution of Sentinel-2 (10 m pixel image resolution) vs. the Landsat platform used by AquaSat (30 m pixel resolution). Sentinel-2 also has a more frequent revisit (overpass) schedule of every 5 to 10 days vs. Landsat 7 which is every 17 days. EstuarySAT incorporates publicly available water quality data from 23 individual water quality data sources spanning 1984-2021 and spatially matches them with Sentinel-2 imagery from 2015-2021. EstuarySAT currently contains 299,851 matched observations distributed across the coastal USA. EstuarySAT's primary focus is on collecting chlorophyll data; however, it also contains other ancillary water quality data, including temperature, salinity, pH, dissolved oxygen, dissolved organic carbon, and turbidity (where available). As compared to other ocean color databases used for developing predictive chlorophyll algorithms, this coastal database contains spectral profiles more typical of CDOM-dominated systems. This database can assist researchers and managers in evaluating algal bloom causes and predicting the occurrence of future blooms.

A Novel Mouse Home Cage Lickometer System Reveals Sex- and Housing-Based Influences on Alcohol Drinking.

Alcohol use disorder (AUD) is a significant global health issue. Despite historically higher rates among men, AUD prevalence and negative alcohol-related outcomes in women are rising. Loneliness in humans has been associated with increased alcohol use, and traditional rodent drinking models involve single housing, presenting challenges for studying social enrichment. We developed LIQ PARTI (Lick Instance Quantifier with Poly-Animal RFID Tracking Integration), an open-source tool to examine home cage continuous access two-bottle choice drinking behavior in a group-housed setting, investigating the influence of sex and social isolation on ethanol consumption and bout microstructure in C57Bl/6J mice. LIQ PARTI, based on our previously developed single-housed LIQ HD system, accurately tracks drinking behavior using capacitive-based sensors and RFID technology. Group-housed female mice exhibited higher ethanol preference than males, while males displayed a unique undulating pattern of ethanol preference linked to cage changes, suggesting a potential stress or novelty-related response. Chronic ethanol intake distinctly altered bout microstructure between male and female mice, highlighting sex and social environmental influences on drinking behavior. Social isolation with the LIQ HD system amplified fluid intake and ethanol preference in both sexes, accompanied by sex- and fluid-dependent changes in bout microstructure. However, these effects largely reversed upon resocialization, indicating the plasticity of these behaviors in response to social context. Utilizing a novel group-housed home cage lickometer device, our findings illustrate the critical interplay of sex and housing conditions in voluntary alcohol drinking behaviors in C57Bl/6J mice, facilitating nuanced insights into the potential contributions to AUD etiology.

Automating COVID-19 epidemiological situation reports based on multiple data sources, the Netherlands, 2020 to 2023

BackgroundDuring the COVID-19 pandemic, the National Institute for Public Health and the Environment in the Netherlands developed a pipeline of scripts to automate and streamline the production of epidemiological situation reports (epi‑sitrep). The pipeline was developed for the Automation of Data Import, Summarization, and Communication (hereafter called the A-DISC pipeline). ObjectiveThis paper describes the A-DISC pipeline and provides a customizable scripts template that may be useful for other countries wanting to automate their infectious disease surveillance processes. MethodsThe A-DISC pipeline was developed using the open-source statistical software R. It is organized in four modules: Prepare, Process data, Produce report, and Communicate. The Prepare scripts set the working environment (e.g., load packages). The (data-specific) Process data scripts import, validate, verify, transform, save, analyze, and summarize data as tables and figures and store these data summaries. The Produce report scripts gather summaries from multiple data sources and integrate them into a RMarkdown document – the epi‑sitrep. The Communicate scripts send e-mails to stakeholders with the epi‑sitrep. ResultsAs of March 2023, up to ten data sources were automatically summarized into tables and figures by A-DISC. These data summaries were featured in routine extensive COVID-19 epi‑sitreps, shared as open data, plotted on RIVM's website, sent to stakeholders and submitted to European Centre for Disease Prevention and Control via the European Surveillance System -TESSy [38]. DiscussionIn the face of an unprecedented high number of cases being reported during the COVID-19 pandemic, the A-DISC pipeline was essential to produce frequent and comprehensive epi‑sitreps. A-DISC's modular and intuitive structure allowed for the integration of data sources of varying complexities, encouraged collaboration among people with various R-scripting capabilities, and improved data lineage. The A-DISC pipeline remains under active development and is currently being used in modified form for the automatization and professionalization of various other disease surveillance processes at the RIVM, with high acceptance from the participant epidemiologists. ConclusionThe A-DISC pipeline is an open-source, robust, and customizable tool for automating epi‑sitreps based on multiple data sources.

Towards the definition of Transformation Stratigraphic Unit (TSU) as new section of the extended matrix methodology

This study explores the interpretation of degradation and alteration phenomena as stratigraphic units, showing the preliminary results of ongoing research concerning new investigative methods and tools useful to formalize transformative processes on built heritage, in a chronological perspective. Merging conservation sciences with archaeology, the study introduces a new section of the Extended Matrix (EM) method: the Transformation Stratigraphic Unit (TSU). The aim of this work is to assess a new method for documenting and investigating cultural heritage, by generating scientifically reliable reconstructive hypotheses, and by analysing and comparing the mutual interaction of different degenerative phenomena and their relationship. The current workflow, still tentative, utilizes open-source tools and is designed to be replicable and it has been applied to the case study of the architectural complex of S. Pietro in Segni (RM). The current workflow, still tentative, utilizes open-source tools and is designed to be replicable and it has been applied to the case study of the architectural complex of S. Pietro in Segni (RM).