Introduction:The Commission on Cancer (CoC) implemented six operative standards to reduce technical variation in cancer surgery. While designed to guide surgeons in performing key oncologic techniques, these standards also provide a framework for teaching high-quality cancer surgery to trainees. We assessed surgical trainees’ familiarity with the CoC Operative Standards and underlying cancer surgery principles to identify knowledge gaps and opportunities for improved education.Methods:The American College of Surgeons Cancer Surgery Standards Program distributed an anonymous survey to general surgery residents and surgical fellows across the US. Questions addressed the CoC Operative Standards and cancer surgery principles, with scores calculated for correctness. Univariate logistic regression evaluated associations between trainee or program characteristics and knowledge. Trainees were also asked about their role in procedural documentation, given the synoptic operative report requirements of the standards.Results:244 surveys were completed by 205 residents and 39 fellows. Fellows reported greater familiarity with the CoC Operative Standards (68% vs 24%, P<0.001). Only 30% of trainees reported receiving formal curriculum on the standards. Correct response rates were 30% for CoC Operative Standards questions versus 50% for cancer surgery principles, with minimal association between knowledge and trainee/program characteristics. Most residents (71%) reported contributing to operative documentation.Conclusions:Surgical trainees demonstrated limited knowledge of the CoC Operative Standards, highlighting the need for multifaceted educational strategies at the national and institutional levels. Incorporating education about the CoC Operative Standards into surgical training can help emphasize the importance of performing and documenting critical elements of cancer operations.

In recent years, artificial intelligence (AI) has increasingly influenced daily life and scientific research. Traditionally, AI-related courses have targeted computer science majors, while systematic instructional opportunities for early-stage undergraduates from non-computing backgrounds remain limited. To bridge this gap, we developed an AI course that integrates project-based learning with large language models (LLMs). Specifically, we designed four progressive assignments based on our research project (i.e., drug–drug interaction network clustering analysis). The course does not require prior knowledge of pharmacology or programming. Instead, LLMs are used as assistive tools to support programming, data analysis, and result interpretation. Students engage in a complete workflow, including data curation, algorithm implementation, and critical evaluation of results. Preliminary feedback shows that this approach supports the development of problem-solving skills and increases student engagement. This study provides a framework for integrating LLMs into project-based learning. We believe that this teaching proposal will be valuable and inspiring for educators seeking to design or enrich similar courses.

The rapid expansion of global financial markets and the increasing participation of retail investors have created a strong demand for accessible and interpretable stock market analytics tools. Despite the availability of large volumes of financial data, extracting meaningful insights from raw market information remains challenging for students, novice investors, and small-scale market participants due to the complexity, technical requirements, and high costs associated with many professional trading platforms. To address this issue, this paper presents INVESTO, a web-based interactive stock market analytics and visualization platform designed to simplify financial data exploration and support informed investment decision-making. The platform is developed using Python and the Streamlit framework and provides an intuitive interface that enables users to access both historical and real-time stock market data without requiring programming knowledge or advanced financial expertise. INVESTO integrates financial data acquisition through external APIs, preprocessing and normalization of time-series data, and computation of key technical indicators such as Simple Moving Average (SMA), Exponential Moving Average (EMA), and volatility metrics. These analytical outputs are presented through interactive visualizations built using Plotly, allowing users to analyze price movements, detect trends, and compare stock performance dynamically. The system architecture follows a modular three-layer design consisting of the Data Acquisition Layer, Processing Layer, and Presentation Layer, ensuring scalability, maintainability, and efficient data flow. Cloud-based deployment through Streamlit Cloud enables platform accessibility through web browsers while maintaining efficient resource utilization and responsive performance. Performance optimizations such as session-based caching, vectorized computations, and selective rendering improve system responsiveness and reduce computational overhead. Security considerations focus on maintaining data integrity, validating external API responses, and preserving user privacy through a read-only analytical design that avoids storage of sensitive user information. Experimental evaluation and usability observations indicate that the platform improves comprehension of market trends, supports exploratory financial analysis, and enhances financial literacy for students and beginner investors. While INVESTO prioritizes interpretability and visualization rather than predictive modelling, the modular architecture allows future integration of advanced features such as machine learning-based forecasting, sentiment analysis, risk metrics, and portfolio simulation. Overall, the platform demonstrates the effectiveness of combining modern web technologies, cloud deployment, and interactive visualization techniques to bridge the gap between raw financial data and actionable analytical insights in educational financial technology systems.

RNA-seq quantifies the abundance of transcripts within a biological sample and performs differential analysis between different conditions to reveal regulated gene signatures. Three challenges exist: (1) different analytical packages can often report different expression patterns and false-discovery-rates and P-values; (2) the effective use of these analytical packages requires substantial knowledge of programming and bioinformatics; and (3) there are a lack of intuitive methods to prioritize target genes for further investigation. To address these challenges, we developed Confidence, a web-based application to perform simultaneous statistical analysis of RNA-seq count data. Confidence incorporates the Confidence Score (CS), ranging from 1 to 4 to aid in gene prioritization, where 1 represents low confidence and 4 represents high confidence. The Confidence web-based application was designed for rapid and intuitive analysis of standard experimental metadata and gene count inputs providing a web-based, 'wide-net' approach to RNA-seq analysis. Gene scoring allows for unbiased gene selection and identification of novel genes strongly associated with disease/treatment models across multiple species. Pathway analysis has been integrated so that highly confident genes can be placed into biological context. Confidence provides a new strategy for target prioritization in RNA-seq analysis and the generation of publication-quality figures, which we demonstrate here using a published database.

DEqMS is an R package-based statistical tool for differential protein expression analysis in quantitative mass spectrometry-based proteomics. It implements a robust Bayesian method for accurate variance estimation that accounts for the number of mass spectrometry features used for protein quantification (number of peptide precursors or peptide spectrum matches). Originally validated for data-dependent acquisition proteomics, DEqMS now extends to data-independent acquisition workflows, as demonstrated using both spike-in and real-world datasets. Given a peptide- or protein-level quantification table with mass spectrometry feature count as inputs, DEqMS outputs a protein- or gene-level results table containing fold changes and multiple statistics (t-values, P value, among others) adjusted according to mass spectrometry feature count. Here we detail the use of the DEqMS R package. This updated workflow broadens DEqMS's applicability, enabling researchers with basic R programming knowledge to identify proteins with significantly altered abundance between sample groups across diverse quantitative proteomics datasets. DEqMS is available to install at https://bioconductor.org/packages/DEqMS/ .

Strength and conditioning (S&amp;C) is a field within exercise science that advocates evidence-based practice. As empirical knowledge evolves, translation of this knowledge into coaching practice is expected. However, knowledge translation remains challenging across many industries, particularly S&amp;C, due to varying professional standards and limited resources. This study investigated current practices, challenges, and trends among S&amp;C coaches in Southeast Asia (SEA). Seventy-one S&amp;C coaches (87% male) from major sports organisations completed an online survey covering demographics, job satisfaction, motivation, programming knowledge, AI use, training principles, and exercise methods. Frequency analysis and Chi-square tests assessed data distribution and group differences. Most coaches held a bachelor's degree (93%) and S&amp;C certification(s) (73%). Approximately half (49%) were satisfied with their roles, with mixed views on salary (38% satisfied vs. 38% dissatisfied). Passion (89%) was the primary motivator, while limited facilities (72%) and financial incentives (61%) were common challenges. Coaches reported greater proficiency in strength (87%) than conditioning (57%) programming. AI use within programming (e.g., ChatGPT) was limited (8%). Exercise selection was guided by training goals (93%), experience (87%), specificity (85%), and periodisation (80%). Load intensity and progression were primarily prescribed using percent-based (80% and 85%, respectively) and RPE-based (80%, 72%), with lower use of velocity-based methods (48%, 34%). Application of conventional, eccentric-, concentric-, and isometric-emphasis training were widespread (≥85%), with 100% using supersets for strength and hypertrophy development. S&amp;C coaches in SEA demonstrated foundational qualifications, programming skills, and awareness of evidence-based methods, while emphasising athlete-centred approaches and communication. Structured professional development, consideration of AI-based tools, and organisational support are recommended.

The swift progression of Artificial Intelligence (AI) has had a profound impact on software development and programming education, permitting the formation of adept systems that aid programmers in producing effective and flaw-free code. However, there is a lack of personalized mentorship and real-time feedback in existing coding platforms, which are crucial for enhancing problem-solving abilities and interview preparation. This research solves this problem using the AI Driven Programming Mistake Analyser, an AI-based programming analyzer that offers users structured and mentoring support for learning and problem-solving. Rule-based analysis, combined with basic AI techniques to check users’ submitted codes, leads to the system that is developed in Python. It generates the syntax, clarifies logical errors and offers suggestions for further improvements in the overall coding experience. The methodology includes designing a modular architecture for this purpose that receives input code, applies analysis algorithms, and gives meaningful feedback to the users in an interactive manner. The results show that the proposed system is successful in helping the users to identify errors in their codes, programming knowledge, and problem-solving skills. The results indicate that compared to traditional coding tools, the proposed system performs better in helping users to identify errors in their code, assimilate programming concepts, and hone their problem-solving skills. It is to say that the study concludes that incorporating AI-based systems into programming education can greatly improve learning efficiency as well as increase user engagement. It can be further improved by integrating powerful AI models or introducing multi-lingual capabilities to make it more effective in its assistance. Keywords— Artificial Intelligence; Programming Analyzer; Code Analysis; Intelligent Tutoring System; Software Development; Machine Learning

ObjectiveFamilies of preterm infants can experience high stress during neonatal intensive care unit (NICU) hospitalisation, which interferes with the development of parenting confidence and self-efficacy through active engagement with their infants. Parent NICU support programmes provide guidance and education to build this connection. This systematic review examined the effects of parent education programmes on confidence and self-efficacy among parents of preterm infants.DesignSystematic review in PubMed/Medline, PsycINFO, CINAHL, Google Scholar and Web of Science.SettingNICU.ParticipantsParents of preterm infants.InterventionEducation programmes offered to parents in the NICU.Main outcome measuresQuantitative surveys measuring parent confidence or self-efficacy.Results14 studies met inclusion criteria from an initial 220 identified. Five were randomised controlled trials and nine were non-randomised studies. Programmes combining information delivery with opportunities for parent−infant engagement were most effective. Infant access and time to practise skills were key factors in transferring programme knowledge to increase parent confidence and self-efficacy. While 6 of the 14 studies did not show statistically significant differences between groups, all interventions demonstrated improvements in parental confidence or self-efficacy from baseline. Vast variability in duration of intervention, proper descriptions of facilitator trainings, methodological limitations and potential confounding factors reduced the overall quality of evidence to a low level.ConclusionBoth unit-specific programmes developed within individual NICUs and commercially available, copyrighted parent education programmes hold value. They are generally effective in building parent confidence and self-efficacy among parents of preterm infants. Integrating either type of programme into standard NICU care may hold promise for strengthening parental competence and promoting family-centred outcomes. Clarity on content/topics of programmes, duration and level of facilitator trainings can strengthen the process of supporting parents in the NICU, even in low resource settings.PROSPERO registration numberCRD42024524063.

This paper provides a scoping review of gamified learning in virtual reality (VR) from 2010-2025, which combines trends from various disciplines, game types, and learning approaches. This paper differs from previous ones, which mainly focus on bibliometric results, by critically examining 108 publications to determine the dominant types of VR games, their correlation with learning outcomes, and the impact on the learning process. This paper follows the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) protocol. The findings suggest that simulation, role-playing, and problem-solving VR games are the most common, particularly in healthcare and science, technology, engineering, and mathematics (STEM) fields, improving engagement, retention, and skill development. The use of VR games in other fields and the needs of neurodiverse or physically disabled learners have yet to be explored. The review discusses the use of big data and cloud computing for VR deployment and adaption, and the significance of low and no-code technologies for educators developing VR without programming knowledge. Through the synthesis of patterns, research gaps, and cross-disciplinary challenges, this study provides a roadmap for VR-based gamification research, with emphasis on inclusivity, ethical considerations, long-term learning effectiveness beyond novelty effects, and sustainable educational integration.

Medicines form a major component of Ghana's healthcare delivery system. However, stockpile of unwanted medicines in many Ghanaian homes and lack of proper disposal system results in indiscriminate disposal of unwanted medicines. Improper disposal affects the environment and economy. The study aims to assess disposal of unwanted medicines within the past 12 months, methods of disposal, and associated factors. A cross-sectional analytic survey involving 426 randomly selected households in the Ga Central municipality was conducted using structured questionnaires. The primary outcome was disposal of expired/unused medicines within the past 12 months measured categorically as yes disposal versus no disposal. The secondary outcome was type of disposal method, measured as home disposal versus organized disposal. Prevalence of expired/unused medicines at home, knowledge, attitude and awareness of disposal programs were assessed. Descriptive, logistic regression and post-estimation analyses were performed using Stata 16 at an alpha of 0.05. The mean age of respondents was 29.5 years (+/-11.9), 226 (53.1%) were females and 37.1% (95% CI: 32.48-41.87) disposed of expired/unused medicines, out of which majority (91.8%; CI: 86.34-95.54) disposed of their expired/unused medicines at home. The prevalence of unwanted medicines at homes was 56.6% (95% CI: 51.7-61.3). Knowledge of disposal practices (14.5%; 95% CI: 11.34-18.27) and awareness of Take-Back-Unwanted-Medicine (9.4%; 95% CI: 6.79-12.57) were low. Attitude (67.1%; 95% CI: 62.5-71.6) towards good disposal was high. Elderly individuals in households (AOR = 0.50; 95% CI: 0.29-0.84), adverse drug reactions (AOR = 2.03; 95% CI: 1.05-3.9), knowledge (AOR = 3.01; 95% CI: 1.53-6.30), and awareness (AOR = 2.91; 95% CI: 1.17-7.24) were associated with expired/unused medicines disposal. Majority disposed of unwanted medicines at home, thus Food and Drugs Authority should create awareness on safe medicines disposal through community outreach, integration into healthcare training programs and scaling up of the TBUM program to other districts.

Designing, developing and applying an instructional framework for a neonatal resuscitation program: Action research.

To understand the operational challenges in the effective implementation of the home-based newborn care (HBNC) program based on qualitative assessment of the stakeholders. Interviews were conducted among various stakeholders [Accredited Social Health Activists(ASHAs), Auxillary Nurse Midwives (ANMs), medical officers, beneficiaries, ASHA coordinators, district program officers, nodal officers] of all the 11 districts of Delhi. Data management was done using NVivo 11 software and analyzed using inductive content analysis. The knowledge of the HBNC program was satisfactory with a lack of specific and regular training on the subject. The lack of trust in quality of the HBNC kit, ineffective referral systems, and lack of on-the-job field monitoring and supervision of ASHAs were the major challenges in the program implementation. Migration of beneficiaries, myths, and cultural practices in the society along with administrative work overload affected the performance of the program. Regular training for ASHAs, quality assurance, and testing of kits are required for better HBNC service delivery. Framework for supportive supervision and procedural support in establishing referral linkages for sick newborns is essential for the effective implementation of the program.

ChatGPT, an AI-driven chatbot capable of generating code and providing timely, personalized feedback, offers new opportunities for supporting programming learning among younger learners. This study employed a quasi-experimental design to examine the effects of Self-Directed Programming (SDP) and ChatGPT-Facilitated Programming (CFP) learning modes on primary learners’ programming knowledge, skills, and attitudes. In addition, learners’ programming behaviors were explored within the CFP learning mode. A total of 71 primary learners were randomly assigned to either the CFP or SDP class. Learners in the SDP class engaged in self-directed programming activities, whereas learners in the CFP class learned programming through a ChatGPT-facilitated learning platform, CodewithAI. A mixed-methods approach was adopted to collect and analyze multidimensional data. The results revealed a significant difference in programming knowledge performance between the CFP and SDP classes. Behavioral analysis indicated that primary learners in the CFP class frequently sought assistance from ChatGPT when encountering programming challenges, particularly by requesting explanations of code and error messages. In addition, primary learners in the CFP class reported relatively high levels of confidence compared with those in the SDP class. Based on these findings, this study offers implications for the design of primary programming instruction and the development of AI-supported instructional tools. By providing fine-grained empirical evidence, this study contributes to the field of the effectiveness of ChatGPT-facilitated programming learning for primary students.

Background/Objectives: Standardized reporting of astigmatism outcomes is essential for comparability, reproducibility, and interpretation of refractive surgery studies. Vectorial analyses based on established metrics are increasingly required by major journals, yet no freely available tool exists for generating publication-ready vector analysis tables with statistical comparisons. This study presents AstigMETRICS, a standalone application for automated calculation, formatting, and statistical comparison of standard vector metrics in refractive surgery. Methods: AstigMETRICS was developed in MATLAB and compiled as a standalone executable requiring no programming knowledge. The software accepts preoperative, intended, and postoperative astigmatism data in spreadsheet format for both refractive and corneal measurements. It calculates seven standard vector metrics following the Alpins method: the target-induced astigmatism (TIA), surgically induced astigmatism (SIA), difference vector (DV), correction index (CI), magnitude of error (ME), angle of error (AE), and index of success (IOS). Statistical comparisons are performed automatically using appropriate parametric or nonparametric tests for paired and unpaired study designs, with p-values and Cohen's d effect sizes reported. Results: AstigMETRICS generates standardized tables reporting the means, standard deviations, and clinically relevant proportions (percentage of eyes with an ME within ±0.50 D or ±1.00 D, and an AE within ±15°). Three simulated datasets were created to validate the software functionality across common surgical scenarios: a contralateral eye laser vision correction, toric phakic IOL implantation, and cataract surgery with toric IOLs. The output tables are displayed in standardized format and saved as high-resolution TIFF images suitable for publication. The software is freely available and a download link is provided in this article. Conclusions: AstigMETRICS enables clinicians and researchers to perform standardized, reproducible astigmatism vector analyses with built-in statistical comparisons. This freely available tool simplifies outcome reporting and improves methodological consistency in refractive surgery research.

(1) Background: Nutrition research in cancer care has largely focused on disease prevention and management, overlooking the importance of food literacy. Culinary cancer care programs may address this gap by facilitating the practical application of nutrition through culinary skills, fostering social connections over nutrient-dense meals, and supporting individuals during periods of physical and social vulnerability. The Not-Just-Supper Club (NJSC) at Gilda's Club Toronto (GT) is a community-based culinary cancer care program delivering evidence-based, plant-forward meals. The objectives of this study were to examine how NJSC supports its members and to provide recommendations to inform future models of culinary cancer care programs. (2) Methods: An explanatory sequential mixed methods design was used. Participants completed a modified food frequency questionnaire (FFQ) assessing major protein food groups since joining NJSC. Semi-structured interviews explored perceived dietary changes, food literacy, and social engagement. Associations between duration of participation and protein food intake were examined using multivariable-adjusted linear regression models. Interview field notes and transcripts were coded in NVivo 12 and thematically analyzed. (3) Results: Among 41 participants, 36 (88%) were female and 17 (41%) were of White ethnicity. A total of 38 (93%) participants reported that NJSC had a positive impact on their lives, and 27 (66%) reported positive changes in eating habits. In multivariable-adjusted analyses, longer participation in NJSC was associated with higher nut consumption (β = 0.49 servings/day per year; 95% CI, 0.02-0.96). Interviews were completed by 40 participants. Seven themes described program support across psychosocial domains (social network; social support; emotional support and mental health; impact on health) and practical nutritional domains (improved food literacy and skills; food decisions; inclusion of plant-based foods). Participants described applying program knowledge at home and perceived improvements in well-being and cancer-related symptoms. (4) Conclusions: NJSC was perceived by members as beneficial across psychosocial and nutritional domains and supported food literacy and plant-forward dietary choices. These findings contribute to our understanding of how culinary cancer care programs can complement existing cancer support services and provide insights for designing future programs for cancer survivors and their support networks.

Over the past two decades, open-source research software such as SPM, AFNI and FSL formed the substrate for advancements in the brain functional magnetic resonance imaging (fMRI) field. The spinal cord fMRI field has matured substantially over the past decade, yet there is limited research software tailored for processing cord fMRI data that has distinct noise sources, unique challenges, niche processing requirements and special needs. Spinal cord fMRI data analysis is a ‘different beast’, involving specialized pre- and post-processing steps due to the cord’s unique anatomy and higher distortions/physiological noise, thus requiring extensive and careful quality assessment. Building upon 10+ years of research and development, we present Neptune – a user-interface-based MATLAB toolbox. With 30,000+ lines of in-house code, it is designed to be easy to use and does not require programming knowledge. Neptune builds on our previously published 15-step pre-processing pipeline (Barry et al., 2016) and presents a 19-step pipeline with new processing steps, and enhancements to existing steps. Neptune has a 4-step post-processing pipeline aimed at fMRI connectivity modeling. It generates extensive and novel quality control visuals to enable a thorough assessment of data quality, and displays them in an elegant webpage format. We demonstrate the utility of Neptune on our 7T data. Certain features of the popular Spinal Cord Toolbox (SCT) are integrated into Neptune, and users can import/export between Neptune and other software such as FSL and SPM. The availability of this open-source, easy-to-use software will benefit the spinal cord fMRI community, and also tip the cost-benefit balance for brain fMRI researchers to invest in learning new software to conduct important neuroscientific and clinical research using spinal cord fMRI.

Synesthesia is a neurological phenomenon in which healthy individuals experience additional, automatic, and consistent perceptions unrelated to veridical sensory input. For most (but not all) synesthetes, this additional experience is a color: for example, grapheme-color synesthetes experience colors for letters of the alphabet. Measuring these color associations is of central importance to synesthesia research, but there is no standard color picker "tool" that researchers can adapt to use in their own experiments: each researcher must code their own. This is a barrier to entry for synesthesia research, and additionally creates potential methodological confounds because different researchers make color pickers with different properties. SynesthesiaColorPicker is an open-source, mobile-friendly color picker tool that can be integrated with two popular online experiment platforms (Qualtrics and lab.js/Open Lab) without any prior programming knowledge. The templates, underlying JavaScript code, and detailed instructions are available for download on a GitHub repository. Furthermore, a comparison between data collected with SynesthesiaColorPicker and with the Synesthesia Battery shows that two methodological design choices in SynesthesiaColorPicker overcome measurable confounds in existing color picker methodology.

Participation rates in Science, Technology, Engineering and Maths (STEM) are low for people with intellectual disabilities. Electronic making (eMaking) incorporates STEM concepts and involves maker activities that use technologies, enabling skill development, leisure participation and lifelong learning. This study aimed to identify tools, technology and techniques used within, and examine barriers and enablers to, meaningful eMaking experiences for people with intellectual disabilities. Using a qualitative study design, data were collected via interviews and focus groups with three senior disability provider managers, six eMaking program coaches, and four eMaking experts designing curriculum. This data were audio recorded and transcribed verbatim. Program coach written reflections and photographs of eMaking activities were also collected. All data were analysed using reflexive thematic analysis. Twelve themes were identified. Themes related to tools and technology (using a range of equipment and resources; having designated space and structure and program outlines; using task modification to remove barriers and risks); techniques (starting with interests; using principles of active support; and seeking feedback); and potential program barriers and enablers (skills of the coach; support staff training and interest; knowledge and perceptions of the abilities of participants; program knowledge; and logistical aspects of the program). eMaking can be accessible to people with intellectual disabilities and is enabled by customised tools and techniques and a positive attitudinal support environment.

Peptides are versatile molecules with a growing relevance in addressing previously untreatable and complex diseases and targets. Computational methods offer powerful strategies to streamline peptide drug discovery by accelerating design-test cycles and guiding efforts toward promising candidates. The application of such tools requires specialized knowledge in informatics, programming, and statistics, with a growing number of computational tools and frameworks becoming available. In this review, we provide an overview of current computational approaches in peptide research, covering different phases of the computational pipeline, such as representation, similarity assessments, machine/deep learning (ML/DL) models, and peptide design. We further highlight available peptide cheminformatics tools based on their key features to facilitate their integration into peptide drug discovery pipelines.

Processing of electroencephalography (EEG) data requires multiple steps to remove noise and artifacts and select good-quality data. While powerful open-source toolboxes like MNE-Python exist, their command-line nature can pose a barrier for researchers without programming experience. Here, we present EEG-Pype, an open-source (Apache-2.0 licensed) graphical user interface application using MNE-Python functions. EEG-Pype provides an intuitive workflow tailored for preprocessing of resting-state EEG data, including frequency band filtering, independent component analysis and atlas-based beamforming for source-level analysis. The application supports several common raw EEG input file formats and guides users through a comprehensive pipeline focused on manual bad channel and epoch selection. Manual steps are streamlined using MNE-Python's interactive plots, resulting in a user-friendly experience. Configuration saving and loading allows for batch (re)runs, while a separate log is also saved, improving reproducibility and documentation. Output can be saved after filtering in canonical frequency bands, ready for further analysis. EEG-Pype includes a module for calculating quantitative EEG measures on preprocessed data, including spectral, functional connectivity and network analysis metrics. This softwareaims to lower the entry barrier for standardized EEG preprocessing, promoting reproducible research practices among neuroscientists and clinicians without requiring programming knowledge. EEG-Pype can be downloaded from: https://github.com/yorbenlodema/EEG-Pype and is not dependent on a specific operating system.