Open-source Software Platform Research Articles

OpenSAFELY: A platform for analysing electronic health records designed for reproducible research.

Electronic health records (EHRs) and other administrative health data are increasingly used in research to generate evidence on the effectiveness, safety, and utilisation of medical products and services, and to inform public health guidance and policy. Reproducibility is a fundamental step for research credibility and promotes trust in evidence generated from EHRs. At present, ensuring research using EHRs is reproducible can be challenging for researchers. Research software platforms can provide technical solutions to enhance the reproducibility of research conducted using EHRs. In response to the COVID-19 pandemic, we developed the secure, transparent, analytic open-source software platform OpenSAFELY designed with reproducible research in mind. OpenSAFELY mitigates common barriers to reproducible research by: standardising key workflows around data preparation; removing barriers to code-sharing in secure analysis environments; enforcing public sharing of programming code and codelists; ensuring the same computational environment is used everywhere; integrating new and existing tools that encourage and enable the use of reproducible working practices; and providing an audit trail for all code that is run against the real data to increase transparency. This paper describes OpenSAFELY's reproducibility-by-design approach in detail.

ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package

Abstract. Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across weather forecasting, disaster preparedness, and urban planning, but researchers lack the tools to run these models effectively and efficiently, limiting progress. One of the current challenges is to assimilate observations in highly non-linear dynamical systems, as the practical need is often to detect abrupt changes. We have developed a software platform to improve the use of real-time data in non-linear system representations where non-Gaussianity limits the applicability of data assimilation algorithms such as the ensemble Kalman filter and variational methods. Particle-filter-based data assimilation algorithms have been implemented within a user-friendly open-source software platform in Julia – ParticleDA.jl. To ensure the applicability of the developed platform in realistic scenarios, emphasis has been placed on numerical efficiency and scalability on high-performance computing systems. Furthermore, the platform has been developed to be forward-model agnostic, ensuring that it is applicable to a wide range of modelling settings, for instance unstructured and non-uniform meshes in the spatial domain or even state spaces that are not spatially organized. Applications to tsunami and numerical weather prediction demonstrate the computational benefits and ease of using the high-level Julia interface with the package to perform filtering in a variety of complex models.

A case study of medical image software evolution and its impact in the medical imaging community

Objective: We present the evolution of medical imaging software and its impact on the medical imaging community through the study of four open-source image analysis software platforms: 3D Slicer, FreeSurfer, FSL, and SPM. Materials and methods: We have studied the impact of these software tools over time, measured by the number of scientific citations. Additionally, we have also studied the source code evolution by measuring the lines of code and the tarball size of the stable releases and the changes in programming languages. Results and discussion: The rising number of related scientific publications confirms the popularity of these software tools in the research community, albeit some differences can be observed in the popularity of the tools. Moreover, we demonstrate that source code has evolved to modernize and optimize, at least partially thanks to the collaboration and code sharing with the user community. Furthermore, this evolution reveals an increased use of higher-level programming languages and meta-languages. Conclusions: The study of four open-source packages has revealed certain patterns in the evolution of medical imaging software and their impact on the medical image community. Further analyses and complementary metrics are suggested.

Bibliometric and scientometric analysis on biomarkers and molecular mechanisms for physical frailty and sarcopenia.

The influence of physical frailty and sarcopenia (PFS) on the well-being of older people and continuous pressure on the healthcare systems has prompted a research on the pathophysiology and molecular mechanisms of these conditions. Nonetheless some biomarkers have been suggested as potential markers for PFS none of them have been shown to highlight the complex nature of PFS, which reveals that there is a need for an understanding of the possible biomarker candidates. The aim of this study was to identify the current research hotspots, status, and trends in the field of biomarkers and molecular mechanisms for PFS. The bibliometric and scientometric analyses were performed using VOSviewer (version 1.6.18) and open source software platform Cytoscape v.3.9 (for visualizing and constructing a network of keywords). Data of publications (from 1997 to 2023) related to biomarkers and molecular mechanisms of PFS were obtained (in May 2023) from the database of Science Citation Index Expanded of Web of Science, Scopus, and PubMed. The keywords obtained from the Scopus database were used to perform a meaningful keyword analysis. A network of keyword relationships was build using Cytoscape. In this study, we present biomarker keywords for PFS in relation to other keywords potentially designating processes and mechanisms and reveal the biomarker identities and current contexts in which these biomarker identities are discussed. Over recent years, scientific interest in the field of PFS has increased and focused on the inflammatory process and probably will be concentrated on myokines (such as cytokines and small proteins) that are synthetized and released by skeletal muscles in response to physical activity. Moreover, proteomic and genetic markers are deeply involved in PFS.

Artificial Intelligence Approaches in Healthcare Informatics Toward Advanced Computation and Analysis

Introduction Automated Machine Learning or AutoML is a set of approaches and processes to make machine learning accessible for non-experts. AutoML can exhibit optimized enhancement of an existing model or suggest the best models for precise datasets. In the field of computerized Artificial Intelligence (AI), medical experts better utilize AI models with available encrypted information science ability. Methods This paper aims to characterize and summarize the stage-wise design of Automated Machine Learning (AutoML) analysis e-healthcare platform starting from the sensing layer and transmission to the cloud using IoT (Internet of Things). To support the AutoML concept, the Auto Weka2.0 package, which serves as the open-source software platform, holds the predominant priority for experimental analysis to generate statistical reports. Results To validate the entire framework, a case study on Glaucoma diagnosis using the AutoML concept is carried out, and its identification of best-fit model configuration rates is also presented. The Auto-ML built-in model possesses a higher influence factor to generate population-level statistics from the available individual patient histories. Conclusion Further, AutoML is integrated with the Closed-loop Healthcare Feature Store (CHFS) to support data analysts with an automated end-to-end ML pipeline to help clinical experts provide better medical examination through automated mode.

Increasing the accuracy of single-molecule data analysis using tMAVEN

Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physicochemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule data set and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series modeling, analysis, and visualization environment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from preprocessing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule data set with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule data sets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physicochemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule data sets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses.

Реалізація алгоритму пошуку кореляційного інтерферометричного пеленгатора засобами програмної платформи GNU радіо

In the realm of modern radio equipment development, the integration of Software Defined Radio (SDR) systems, encompassing both software and hardware components, has become widespread. SDR systems, particularly, find applications in the creation of direction finders for radio signal sources. The effectiveness of direction finding and the identification of radio radiation sources largely hinge on the technical specifications of the equipment. For contemporary systems aimed at detecting the location of radio radiation sources, direction finders based on a correlation interferometer prove to be highly suitable. These direction finders excel in performing direction finding for a wide array of radio signals, capable of recognizing broadband modulated signals. They can simultaneously process and identify several signals within a single frequency channel, originating either from a singular source of radio signals (coherent) or from multiple sources (incoherent). SDR technology enables programmable reconfiguration of a radio receiver through software. Such receivers boast a broadband radio frequency component with a significant dynamic range, a high-speed Analog-to-Digital Converter (ADC) path, a robust signal processor, and a specialized digital filtering path. SDR receivers are adaptable to signals of various standards and frequencies, depending on the specific tasks at hand. The GNU RADIO software platform stands out as the most flexible open-source software platform for SDR systems. This article delves into the utilization of the GNU RADIO software platform to implement the search algorithm for the direction finder of the correlation interferometer. The Ettus USRP N210 SDR platform was selected for implementing the direction finder using the correlation interferometer algorithm. This study introduces a two-channel software-controlled model of the direction finder, operating according to the correlation interferometer algorithm. The measurement results for the placement angle of radio signal sources yielded an individual absolute error ranging from 2 to 4 degrees, with an average absolute error of about 3 degrees. These results are quite commendable for such mobile two-channel systems. While systems with four or more channels boast higher accuracy, they tend to have lower economic feasibility.

MetaBCI: An open-source platform for brain–computer interfaces

Background:Recently, brain–computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all links of the BCI chain. Method:This study developed a one-stop open-source BCI software, namely MetaBCI, to facilitate the construction of a BCI system. MetaBCI is written in Python, and has the functions of stimulus presentation (Brainstim), data loading and processing (Brainda), and online information flow (Brainflow). This paper introduces the detailed information of MetaBCI and presents four typical application cases. Results:The results showed that MetaBCI was an extensible and feature-rich software platform for BCI research and application, which could effectively encode, decode, and feedback brain activities. Conclusions:MetaBCI can greatly lower the BCI’s technical threshold for BCI beginners and can save time and cost to build up a practical BCI system. The source code is available at https://github.com/TBC-TJU/MetaBCI, expecting new contributions from the BCI community.

Enhancing blockchain security through natural language processing and real-time monitoring

We describe implementing and testing a security monitoring system for Blockchain-based applications by performing Log Analysis through natural language processing (NLP) techniques and enabling queries via REST API. The study focuses on the Hyperledger Fabric framework, a highly reliable and scalable open-source software platform for private blockchains. The real-time monitoring system aims to detect and prevent attack scenarios on the network structure and smart contracts, such as DDOS, Sybil, and Eclipse. The vulnerability analysis is extended to inspecting Docker containers to detect network-based attacks. Although the case study focuses on Hyperledger Fabric, the techniques used are general and applicable to all Blockchain-based systems and fulfill the need to provide adequate monitoring capabilities for blockchain implementations.

Применение технологии SDR для повышения помехозащищенности: экспериментальное исследование канала с OFDM модуляцией в GNU RADIO

The article presents a comprehensive analysis of multiplexing techniques, with a specific focus on OFDM. OFDM is a transmission method that employs multiple carriers to achieve high data transfer rates. The article provides a thorough explanation of the fundamental concepts, as well as an in-depth discussion on the various applications, technologies, and modulations associated with OFDM. The primary objective of the study is to investigate the implementation of an OFDM communication system using SDR technology. This involves simulating a radio channel to emulate real-world conditions and evaluating the system's performance in terms of packet reception over an actual radio channel. The use of SDR offers several advantages, such as flexibility, adaptability, and cost-effectiveness. Throughout the research, a detailed analysis of the benefits and limitations of OFDM and SDR is conducted, and potential solutions to overcome the identified drawbacks are proposed. The selection of this research direction is motivated by the significant advantages offered by OFDM and SDR, particularly in terms of channel capacity and cost-efficiency. To validate the theoretical concepts and evaluate the practical implementation, a prototype of the OFDM communication system is developed using GNU Radio, an open-source software platform known for its versatility and extensive capabilities. By examining the performance and feasibility of the OFDM communication system implemented with SDR technology, the study aims to provide valuable insights into the advantages, challenges, and potential enhancements in utilizing OFDM and SDR for multiplexing applications.

Decision support in engineering design: the ELIGERE open source software platform

In engineering design, the selection of the optimal design solution represents a critical phase for the development of successful products. In this paper, we present ELIGERE, an open source decision support system targeted at engineering design applications. It allows to rank multiple design solutions with respect to different evaluation criteria according to the evaluations provided by a group of experts. ELIGERE is composed by three main modules: (1) a distributed web application, for generation and participation to the decision making session; (2) a mathematical engine, based on the fuzzy analytical hierarchy process, to quantify the results of the decision making session according to the evaluation of the experts; (3) a relational database, to collect and store data. The most important contribution of this paper is introducing a practical and effective software tool that facilitates decision-making analysis based on the fuzzy analytical hierarchy process, thereby allowing better-informed choices on concept selection, as it has been designed with a specific focus on the engineering field. In this paper we describe the key concepts of ELIGERE and its modalities of use in several real use cases. Finally, we compare ELIGERE with the widely used general purpose decision support software based on the fuzzy analytical hierarchy process.

Impact of Open-Source Community on Cryptocurrency Market Price: An Empirical Investigation

ABSTRACT Although the prices of cryptocurrencies remained volatile for the past decade, the factors that impact the price dynamics of the new type of investment instrument have not been fully identified yet. In this study, we recognize the dual nature of cryptocurrencies, that is, being a software program and a financial instrument, and examine the impact of software advancement on the price dynamics of cryptocurrencies. The open-source software (OSS) platform functionality enables social behaviors that we use as signals. Using data from the largest OSS platform, we establish the connection between open-source activities and the price movement of cryptocurrency. In particular, as project popularity (forks and watches) and users’ feedback (issues) increase, the market price increases by 4.3 percent, 2.4 percent, and 4.4 percent per annum, respectively. On the contrary, the number of code corrections (pull requests) is negatively related to prices leading to a 5 percent annual price decrease. Our results suggest that OSS contributions create a perfect selection mechanism, where higher quality projects receive more developers’ attention and user feedback, whereas lower quality projects do not, thus creating the separating equilibrium.

A Review on Digital Twins Technology: A New Frontier in Agriculture

Farming is crucial for various aspects of daily life, including food, the economy, environment, culture, and community. It provides employment opportunities, generates income, and increases the export of agricultural products, particularly in rural areas. Sustainable farming practices promote soil health, biodiversity, and ecosystem services, and are essential in many parts of the world. Farming is deeply rooted in cultures and traditions and is a way of life for many communities, passed down from generation to generation. Without farming, we would not have the abundance and variety of food that we enjoy today. Advancements in technology, such as artificial intelligence, machine learning, and the Internet of Things, have greatly impacted agriculture by producing vast amounts of digital data on crops, soil, and weather conditions. However, managing and analyzing this data can be challenging for farmers, especially those in developing nations. To address this issue, affordable digital farming solutions, including open-source software platforms, sensor networks, and mobile apps, are being developed to help farmers optimize their resources, increase yields, and profits. Digital twin technology can play a crucial role in digital farming by providing farmers with a virtual replica of their physical farm. It is a digital depiction of a real-world asset, such a farm or a particular crop field, that gathers information from sensors, weather stations, and satellite pictures. This technology has arisen that has been hailed as revolutionary in a number of fields, including manufacturing machines, construction, agriculture, healthcare, and the automotive and aerospace industries. However, the technology is still in its early stages in agriculture, and it can be challenging to handle the interactions between different farming-related digital twin components. Additionally, digital twinning can require significant investment in technology and infrastructure, which may be a barrier for small-scale farmers.

The impacts of lockdown on open source software contributions during the COVID-19 pandemic

The COVID-19 pandemic instigated widespread lockdowns, compelling millions to transition to work-from-home (WFH) arrangements and rely heavily on computer-mediated communications (CMC) for collaboration. This study examines the impacts of lockdown on innovation-driven work productivity, focusing on contributions to open source software (OSS) projects on GitHub, the world's largest OSS platform. By leveraging two lockdowns in China as natural experiments, we discover that developers in the 2021 Xi'an lockdown increased OSS contributions by 9.0 %, while those in the 2020 Wuhan lockdown reduced their contributions by 10.5 %. A subsequent survey study elucidates this divergence, uncovering an adaptation effect wherein Xi'an developers became more accustomed to the new norm of WFH over time, capitalizing on the flexibility and opportunities of remote work. Moreover, our findings across both lockdowns reveal that the lack of face-to-face (F2F) interactions significantly impeded OSS contributions, whereas the increased available time at home positively influenced them. This finding is especially noteworthy as it challenges the assumption that CMC can effortlessly substitute for F2F interactions without negatively affecting productivity. We further examine the impacts of stay-at-home orders in the United States (US) on OSS contributions and find no significant effects. Collectively, our research offers valuable insights into the multifaceted impacts of lockdown on productivity, shedding light on how individuals adapt to remote work norms during protracted disruptions like a pandemic. These insights provide various stakeholders, including individuals, organizations, and policymakers, with vital knowledge to prepare for future disruptions, foster sustainable resilience, and adeptly navigate the evolving landscape of remote work in a post-pandemic world.

OXREF: Open XR for Education Framework

Extended reality (XR), which encompasses virtual reality (VR), augmented reality (AR), and mixed reality (MR), offers powerful affordances for improving teaching and learning experiences in a post-pandemic world. Increasingly, many governments and institutions around the world are making major investments in XR technologies to prepare education systems for the future. However, many of these investments remain isolated pilot projects which, while they attest to the potential of XR in education, are unlikely to be scaled up due to lack of sustainability and collaboration. Based on literature and empirical evidence, I have identified major barriers to the wider adoption of XR in education, including the lack of (a) open content, tools, and skills; (b) sound pedagogy and instructional design; and (c) scalability and sustainability. As a potential solution, I introduce the Open XR for Education Framework (OXREF), an empirical framework that proposes a holistic solution to XR object creation, implementation, and deployment, while covering pedagogical, technological, and policy perspectives. The contribution of the OXREF is its ability to build fit-for-purpose XR experiences in a scalable, sustainable, and collaborative manner while promoting openness, accessibility, equity, and reuse. The novelty of the proposed framework is its use of open educational resources (OER), open educational practices (OEP), as well as free and open-source software (FOSS) tools and platforms. Its cloud-based infrastructure and open licenses support viable operationalization strategies that can be implemented by educational institutions and governments.

Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future

Renewables are the greener substitute for the conventional polluting sources of generating energy. For their successful integration into the power grid, accurate forecasts are required. In this paper, we report the lessons acquired from our previous works on generating time-series ARIMA-based forecasting models for renewables. To this end, we considered a consistent dataset spanning the last four years. Assuming four different performance metrics for each of the best ARIMA-based models of our previous works, we derived a new optimal model for each month of the year, as well as for the two different methodologies suggested in those works. We then evaluated the performance of those models, by comparing the two methodologies: in doing so, we proposed a hybrid methodology that took the best models out of those two methodologies. We show that our proposed hybrid methodology has improved yearly accuracy of about 89.5% averaged over 12 months of the year. Also, we illustrate in detail for the four years under study and each month of the year the observed percentage of renewables and its corresponding accuracy compared to the generated forecasts. Finally, we give the implementation details of our open-source REN4KAST software platform, which provides several services related to renewables in Germany.

Optimization of Revision Hip Arthroplasty Workflow by Means of Detailed Pre-Surgical Planning Using Computed Tomography Data, Open-Source Software and Three-Dimensional-Printed Models.

In revision hip arthroplasty (RHA), establishing the center of rotation (COR) can be technically challenging due to the acetabular bone destruction that is usually present, particularly in severe cases such as Paprosky type II and III defects. The aim of this study was to demonstrate the use of open-source medical image reconstruction software and low-cost 3D anatomical models in pre-surgical planning of RHA. A total of 10 patients, underwent RHA and were included in the study. Computed tomography (CT) scans were performed for all cases, before surgery and approximately 1 week after the procedure. The reconstruction of CT data, 3D virtual planning of the COR and positioning of acetabular cups, including their inclination and anteversion angles, was carried out using the free open source software platform 3D Slicer. In addition, anatomical models of the pelvis were built on a desktop 3D printer from polylactic acid (PLA). Preoperative and postoperative reconstructed imaging data were compared for each patient, and the position of the acetabular cups as well as the COR were evaluated for each case. Analysis of the pre- and post-op center of rotation position data indicated statistically insignificant differences for the location of the COR on the X-axis (1.5 mm, t = 0.5741, p = 0.5868) with a fairly strong correlation of the results (r = -0.672, p = 0.0982), whilst for the location of the COR in the Y and Z-axes, there was statistical dependence (Y axis, 4.7 mm, t = 3.168 and p = 0.0194; Z axis, 1.9 mm, t = 1.887 and p = 0.1081). A strong correlation for both axes was also observed (Y and Z) (Y-axis, r = 0.9438 and p = 0.0014; Z-axis, r = 0.8829 and p = 0.0084). Analysis of inclination angle values showed a statistically insignificant difference between mean values (3.9 degrees, t = 1.111, p = 0.3092) and a moderate correlation was found between mean values (r = -0.4042, p = 0.3685). Analysis of the anteversion angle showed a statistically insignificant difference between mean values (1.9 degrees, t = 0.8671, p = 0.4192), while a moderate correlation between mean values was found (r = -0.4782, p = 0.2777). Three-dimensional reconstruction software, together with low-cost anatomical models, are very effective tools for pre-surgical planning, which have great potential use in orthopedic surgery, particularly RHA. In up and in- and up and out-type defects, it is essential to establish a new COR and to identify three support points within the revision acetabulum in order to correctly position acetabular cups.

Open source software and global entrepreneurship

This is the first study to consider the relationship between open source software (OSS) and entrepreneurship around the globe. This study measures whether country-level participation on the GitHub OSS platform affects the founding of innovative ventures, and where it does so, for what types of ventures. We estimate these effects using cross-country variation in new venture founding and OSS participation. We propose an approach using instrumental variables, and cannot reject a causal interpretation. The study finds that an increase in GitHub participation in a given country generates an increase in the number of new technology ventures within that country in the subsequent year. The evidence suggests this relationship is complementary to a country's endowments, and does not substitute for them. In addition to this positive change in the rate of entrepreneurship, we also find a change in direction—OSS contributions lead to new ventures that are more mission- and global-oriented and are of a higher quality. Together, the results suggest that OSS can boost entrepreneurial activity, albeit with a human capital prerequisite. Finally, we consider the implications for policies that encourage OSS as a lever for stimulating entrepreneurial growth.

STRategy: A support system for collecting and analyzing next-generation sequencing data of short tandem repeats for forensic science.

Short tandem repeats (STRs) are short repeated sequences commonly found in the human genome and valuable in forensic science, used for human identity and relatedness markers. Next-generation sequencing (NGS) technologies, e.g., ForenSeq Signature Prep, can sequence STRs, inferring length-based alleles and single nucleotide polymorphisms (SNPs) and providing valuable insights into population and sub-population structures. Despite the potential benefits of NGS for STRs, no open-source software platform integrates the collection, management, and analysis of STR data from NGS into one place. Users must use multiple programs to process their STR data and then collect the results into a separate database or a file system folder. Moreover, analyzing repeat structures (STR repeat motifs) may require learning multiple software tools, making the process inefficient and cumbersome. To address this gap, we introduce the STRategy, a standalone web-based application supporting essential STR data management and analysis capabilities. The STRategy allows users to collect their data into its database, automatically calculates forensic parameters, and visualizes the analyzed data in various forms. Users can search the database using different options, such as by profile, loci, and genotypes, with and without a specific test kit. Moreover, users can also find the nucleotide variants of a locus among the samples. We designed the STRategy for internal use in a laboratory or an organization. Hence, our system includes role-based access control that allows users to search for or access specific data based on their responsibilities. The administrator role can customize the system, for example, configure maps according to the samples' geographic data, and manage reference STR repeat motifs. A laboratory or an organization can download and install a copy of STRategy on their local system using Docker, as described in https://github.com/cucpbioinfo/STRategy. In summary, the STRategy is an end-to-end system that provides users with a database to collect the analyzed STR data from NGS, the dynamic analyses of forensic parameters, and the variants of STR patterns according to the newly added samples, which are then explorable via various search options and visualizations. The system is helpful for both forensic investigations and forensic genetics.

CloMet: A Novel Open-Source and Modular Software Platform That Connects Established Metabolomics Repositories and Data Analysis Resources.

The field of metabolomics has witnessed the development of hundreds of computational tools, but only a few have become cornerstones of this field. While MetaboLights and Metabolomics Workbench are two well-established data repositories for metabolomics data sets, Workflows4Metabolomics and MetaboAnalyst are two well-established web-based data analysis platforms for metabolomics. Yet, the raw data stored in the aforementioned repositories lack standardization in terms of the file system format used to store the associated acquisition files. Consequently, it is not straightforward to reuse available data sets as input data in the above-mentioned data analysis resources, especially for non-expert users. This paper presents CloMet, a novel open-source modular software platform that contributes to standardization, reusability, and reproducibility in the metabolomics field. CloMet, which is available through a Docker file, converts raw and NMR-based metabolomics data from MetaboLights and Metabolomics Workbench to a file format that can be used directly either in MetaboAnalyst or in Workflows4Metabolomics. We validated both CloMet and the output data using data sets from these repositories. Overall, CloMet fills the gap between well-established data repositories and web-based statistical platforms and contributes to the consolidation of a data-driven perspective of the metabolomics field by leveraging and connecting existing data and resources.