Here are two transcripts from a set of interviews executed by Sabina Leonelli in the fall 2015 as part ERC project "The Epistemology Data-Intensive Science", and context case study phenotyping practices at National Plant Phenomics Centre Aberystwyth collaborators. The document researchers' experience data curation practices. Researchers have consented to these made available Open Data. Other interviewees did not give consent or ended up providing sensitive information their interviews, so those cannot be open held securely research team Exeter. You also find sheet provided interviewees, which gives you for this project. Further can found www.datastudies.eu. been redacted exclude names people who given participate study, but otherwise left unedited therefore contain several colloquial expressions. A paper specifically makes use will published 2019 European Journal Philosophy Science, under title “What Distinguishes Data Models?”. Freely accessible preprint here: http://philsci-archive.pitt.edu/id/eprint/15485 . Several related publications Access formats on website:

Here you find the transcripts of interviews collected by Sabina Leonelli as part ERC project "The Epistemology Data-Intensive Science". You also information sheet provided to interviewees, which gives context for this project. Further and related publications can be found at www.datastudies.eu. One paper that specifically makes use these was published in journal Philosophy Science 2018, under title "Data Time: Time-Scales Data Use Life Sciences." The document yeast researchers' attitudes data curation databases their field. Researchers have consented made available Open Data. Other interviewees did not give consent, so those are held securely research team Exeter.

This is a collection of transcripts from interviews conducted by Gregor Halfmann as part the ERC project "The Epistemology Data-intensive Science". The served empirical basis for Halfmann's PhD thesis "Seafarers, Silk, and Science: Oceanographic Data in Making", submitted at University Exeter July 2018. relate to research practices, particular production processing samples scientific data, marine ecologists biological oceanographers working Marine Biological Association UK Continuous Plankton Recorder Survey. Researchers have consented these made available Open Data.

This is a set of interview transcripts executed by Niccolò Tempini between May 2016 and July 2017, as part the ERC project "The Epistemology Data-Intensive Science", in context case study COSMIC. Please read "Notes on transcript editing" document for further information. Papers that specifically make use these interviews have been published February 2021: Tempini, N., 2020. Data curation-research: practices data standardization exploration precision medicine database. New Genetics Society. https://doi.org/10.1080/14636778.2020.1853513 Leonelli, S., 2021. Actionable Precision Oncology: Framing Trustworthy Evidence Exploratory Research Clinical Diagnostics. Social Science & Medicine. https://doi.org/10.1016/j.socscimed.2021.113760 The COSMIC researchers' experience infrastructure development curation re-use practices. Researchers consented to made available Open Data. Other interviewees did not give consent, so those are held securely research team Exeter. You also find information sheet provided interviewees, which gives you this project. Further related publications can be found at www.datastudies.eu.

This is a set of interview transcripts executed by Niccolò Tempini between September 2015 and October 2016, as part the ERC project "The Epistemology Data-Intensive Science", in context case study SAIL. Please read "Notes on transcript editing" document for further information. Two papers that specifically make use these interviews have been published 2018: Tempini, N., Leonelli, S., 2018. Concealment discovery: The role information security biomedical data re-use. Soc Stud Sci 48, 663–690. https://doi.org/10.1177/0306312718804875 2016. Science Through “Golden Security Triangle”: Information Data Journeys Data-intensive Biomedicine, in: Proceedings 37th International Conference Systems (ICIS 2016). https://aisel.aisnet.org/icis2016/ISHealthcare/Presentations/20/ SAIL researchers' experience infrastructure development curation re-use practices. Researchers consented to made available Open Data. Other interviewees did not give consent, so those are held securely research team Exeter. You also find sheet provided interviewees, which gives you this project. Further related publications can be found at www.datastudies.eu.

This is a set of interview transcripts executed by Niccolò Tempini between March and October 2016, as part the ERC project "The Epistemology Data-Intensive Science", in context case study MEDMI. Please read "Notes on transcript editing" document for further information. Three papers or chapters that specifically make use these interviews have been published 2020: Tempini, N., Leonelli, S., 2018. Concealment discovery: The role information security biomedical data re-use. Soc Stud Sci 48, 663–690. https://doi.org/10.1177/0306312718804875 Where health environment meet: invariant parameters big analysis. Synthese 1–20. https://doi.org/10.1007/s11229-018-1844-2 N. 2020. Reuse Digital Computer Data: Transformation, Recombination Generation Data Mixes Big Science. In: Leonelli (eds) Journeys Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-37177-7_13 MEDMI researchers' experience infrastructure development curation re-use practices. Researchers consented to made available Open Data. Other interviewees did not give consent, so those are held securely research team Exeter. You also find sheet provided interviewees, which gives you this project. Further related publications can be found at www.datastudies.eu.

This episode is part of Research Unmuted (https://research-unmuted.uk), a scientific audio journal aimed at translating contemporary medical and digital health research into an accessible, yet academically, format. References Narrowing the A1c gap: personalized modeling HbA1c– continuous glucose monitor discordance in type 1 diabetes. Cichosz, S. L., Thomsen, C. H. N., Klonoff, D. C., Hirsch, I. B. & Jensen, M. H., 17 Feb 2026, (E-pub ahead print) In: PLOS health. Abstract study aims to characterize temporal between CGM-derived exposure HbA1c over time individuals with diabetes, explore development statistical model adjust relationship these measures based on previously observed individual discrepancies. We paired CGM-data 60-day window prior each measurement included diabetes multiple pairs assess time. Discordance was defined as difference Glucose Management Indicator pair. At baseline (first pair), participants were categorized three groups degree discordance: positive (≥0.5%), negative (≤–0.5%), neutral (within ±0.5%). A linear regression incorporating historical values, levels, current GMI utilized for adjustment. 477 1,523 instances analyzed. Absolute ≥0.5% 31% cases. In 51% instances, direction pair maintained. analysis, accounted 69% variance levels (r = 0.83, p < 0.001, MAE 0.42%). Adjusting improved explainability 82% 0.90, 0.33%). HbA1c-CGM highly prevalent, while inter-individual shows some persistence, it also appears vary substantial proportion individuals. short term can improve alignment adjusted laboratory-measured HbA1c. Tags #Diabetes #Data_Science #Digital_Health Link https://research-unmuted.uk/?episode=b6a19507-e71e-49f7-ad01-f12c4f1ee9a6

Highlights New detection_validation pipeline. It compiles manual annotations, fits models and produces validation plots to assess species detection performance. filter_media lets you run the workflow on a subset of deployments. Audio outlier in quality control, based audio duration sample rate. Custom BirdNET classifier labels. You can use your own model, with an advanced tutorial. Full documentation review. Paths, defaults, node inputs outputs, tutorials links have been checked corrected. features pipeline: compile fit models, plot results. is documented README, docs pipeline, now part default run. outliers quality_control. model labels support species_detection. segment_length parameter for create_segments_node. Multiseason projects are easier set up through globals, documented. Annotation formats include additional useful columns. Changes fixes pamflow runs entire workflow. The registry fixed unused data_science pipeline removed. Deployment start end times timezone instead hardcoded America/Bogota. configuration. package version read from pyproject metadata. media_work.csv persistent, so nodes independently. Output closer camtrapDP pamDP formats. Lower memory timelapse computation, clearer logging data_preparation acoustic_indices. deployments_to_CSA disabled until it working. Removed catalog entries that were misplaced conf/base/parameters. Corrected compute_ADI docstring config path warning. Build, CI tests Automated every push. sphinx -W linkcheck. stale setup.py requirements.txt. build-system requires-python declared pyproject.toml. Many new unit detection, acoustic indices, graphical soundscape, export, detection_validation. Local parameters Sphinx build output no longer tracked. Documentation Fixed configuration paths (conf/base/parameters/ conf/base/catalog/) inputs, outputs defaults control detection. Replaced generic Kedro conf README pamflow-specific instructions. Improved installation, beginner tutorials, expert's guide. Added contributors, image alt text, consistent headings spelling. Documented changelog: https://github.com/pamflow-org/pamflow/compare/v1.0.0...v1.1.0

This episode is part of Research Unmuted (https://research-unmuted.uk), a scientific audio journal aimed at translating contemporary medical and digital health research into an accessible, yet academically, format. Abstract To identify characterize daily weekly patterns in glycemic management longitudinal real‐world continuous glucose monitoring (CGM) data from people with diabetes to enhance clinical interpretation support more effective personalized management.This observational, retrospective study used two independent, multiyear, real-world CGM cohorts (T1DiabetesGranada Connected Pen), comprising 86,779 participants diabetes. Their mean age (SD) was 40.3 (15.8) 44.0 (15.9), respectively, there total 21,011,021 measurement days. Weekly variations metrics bolus insulin dose were examined using linear mixed-effects models, temporal assessed. emerged. Mid‐week time range consistently higher (e.g., Wednesday: +0.96 percentage points [%-points]; 95% CI 0.93–0.99; P < 0.001), deterioration during weekends Mondays Sunday: –1.74%-points; –1.77, –1.72; mirrored by shifts other metrics. Bolus increased on weekend days +0.69 units; 0.64–0.74; 0.001). Daily analyses revealed distinct diurnal profiles, the most favorable control morning midday hours weekdays poorer evening nighttime periods, particularly weekends. statistically robust consistent across large cohorts. Although magnitude these differences small, findings provide novel population-level evidence underscore value longer-term perspectives routine care. Citation Cichosz, S. (2026, august 25). – Episode 8: Monday Blues, Blood Sugar Edition. References Iben E. Giese, Stine Hangaard, Niels V. Hartvig, Irl B. Hirsch, Morten H. Jensen, Simon L. Cichosz; Glucose Motion: Characterizing Temporal Patterns Real-World Glycemic Control Large Observational Cohort. Diabetes Care 2026 Sponsor Publimetra free, open-access researcher discovery platform that enables anyone explore publications, citation metrics, bibliometric profiles researchers worldwide—without registration or subscription fees. https://publimetra.uk Tags #Diabetes #Data_Science #Digital_Health ##Wearables #Cardiometabolic Link https://research-unmuted.uk/?episode=7c8c4a5c-f754-4981-86c8-28a91373ed2e

This episode is part of Research Unmuted (https://research-unmuted.uk), a scientific audio journal aimed at translating contemporary medical and digital health research into an accessible, yet academically, format. Abstract Objectives: Machine-learning–based clinical risk prediction models are increasingly used to support decision-making in healthcare. While class-imbalance correction techniques commonly applied address rare outcomes, their impact on probabilistic calibration remains insufficiently understood. study evaluated the effect widely resampling strategies both discrimination across real-world tasks. Materials Methods: Ten datasets spanning diverse domains including over 600 000 patients were analyzed. Multiple machine-learning model families evaluated. Models trained original data using 3 1:1 (synthetic minority oversampling technique, random undersampling, oversampling). Performance was assessed held-out metrics. Results: Resampling had no positive predictive performance. Changes area under receiver operating characteristic curve (ROC-AUC) precision-recall AUC small inconsistent (ROC-AUC: −0.002 −0.01; PR-AUC: −0.10 −0.03), with method showing systematic improvement. In contrast, consistently degraded. Resampled showed higher Brier scores (increase 0.029–0.080) marked deviations intercept slope, indicating distorted predicted risks despite preserved ranking Discussion: Across datasets, primarily altered implicit class prior learned during training, leading miscalibration when The consistent dissociation between highlights that rank-based metrics alone insufficient for evaluating utility. Gains from imbalance can typically be reproduced by threshold adjustment without distorting probabilities. Conclusion:Common do not improve may substantially degrade calibration, limiting suitability where accurate probabilities essential. Citation Cichosz, S. (2027, January). – Episode 9: Fixing Data Imbalance Can Harm Clinical AI References Amalie Koch Andersen, Hadi Mehdizavareh, Arijit Khan, Tobias Becher, Simone Britsch, Markward Morten Bøttcher, Simon Winther, Palle Duun Rohde, Hasselstrøm Jensen, Lebech Tipping balance: performance models, Journal American Medical Informatics Association, 2026 Sponsor Publimetra free, open-access researcher discovery platform enables anyone explore publications, citation metrics, bibliometric profiles researchers worldwide—without registration or subscription fees. https://publimetra.uk Tags #Diabetes #Data_Science #Digital_Health #AI_Modeling

🔱 NARMER SOVEREIGN MATRIX v10.5: The Future of Digital Sovereignty & Strategic Intelligence مصفوفة نارمر السيادية مستقبل السيادة الرقمية والذكاء الاستراتيجي Pioneering the Science Sovereign Metrology ريادة "علم قياس السيادة" عد رحلة مكثفة من الهندسة الاستراتيجية، أعلن اليوم عن اكتمال الإصدار المعتمد v10.5 "مصفوفة نارمر"، معلنةً بذلك ميلاد كنهج رياضي وتطبيقي جديد. لا تكتفي هذه المنظومة بالتحليل، بل تقوم بـ "قياس" أبعاد الوطنية عبر 11 مؤشر استراتيجي دقيق، ودمجها في معامل سيادي موحد V-Score، مما يحول المعطيات الجيوسياسية والاقتصادية إلى لغة رقمية قابلة للتدقيق والقياس. Following an intensive journey strategic engineering, I am proud to announce completion certified version "Narmer Matrix," marking birth "Sovereign Metrology" as a new mathematical and applied science. This system transcends traditional analysis by precisely "measuring" dimensions national sovereignty across pillars, integrating them into unified V-Score. It transforms geopolitical economic variables rigorous, auditable, measurable digital language. #Sovereign_Metrology #علم_قياس_السيادة #Narmer_Matrix #Strategic_Intelligence #V_Score #National_Security #Data_Science #Decision_Support #Sovereignty_Science #UAE_Tech Metrology, Narmer Matrix, National Sovereignty, V-Score, Measurement, Data SThe technical specifications Matrix (v10.5) are what grant it legitimacy among top-tier analysts. matrix is not merely graphical interface but complex engine built upon three core pillars: 1. Structural Framework (The Pillars) measures carefully selected represent "power modern state." These categorized major domains: Hard Power: Includes military sector, resource independence, financial sovereignty. Soft Power Diplomacy: diplomacy, human capital, social stability. cyber resilience, infrastructure, innovation. 2. Mathematical Engine V-Score Engine) Unlike standard indices that rely on simple arithmetic mean, utilizes Weighted Geometric Mean. Why? Because in science measurement, "weakness one sector cannot be fully compensated strength another." If "Financial Sovereignty" collapses, final will drop sharply even if power at its peak—this essence realism. V-Score=(i=1∏nxiwi) Where x represents indicator value w relative weight each sector. 3. Audit Insight Unit component visible your screenshot (10.PNG): Hash: generates fingerprint (SHA-256) for every assessment, preventing result tampering rendering report legally verifiable. Automated Diagnosis: performs real-time Stress Tests, comparing indicators immediately identify "Bottleneck" hindering state from achieving full 4. Security Features Localization: designed operate with total privacy (On-Premise) without requiring connection external servers. Certification Protocol: Built-in compatibility UAE PASS HSM (Hardware Modules) ensures operator officially authorized data hardware-encrypted. Why specifically? In this version, we integrated "Panorama Interface," which allows decision-makers view "Big Picture" single instant—a concept define measurement Situational Awareness." cience, Governance, Cyber Resilience, Financial Analytics. التفاصيل الفنية لمصفوفة (الإصدار 10.5) هي ما يمنحها الشرعية أمام كبار المحللين. المصفوفة ليست مجرد واجهة رسومية، محرك معقد يعتمد على ثلاثة محاور رئيسية: الهيكل البنائي تعمل بعداً استراتيجياً تم اختيارها بعناية لتمثيل "قوة الدولة الحديثة". الأبعاد مقسمة ثلاث فئات كبرى: القوة الصلبة (Hard Power): وتشمل القطاع العسكري، استقلالية الموارد، والسيادة المالية. الناعمة والدبلوماسية: الدبلوماسية، رأس المال البشري، والاستقرار الاجتماعي. (Digital المناعة السيبرانية، البنية التحتية الرقمية، والابتكار. المحرك الرياضي خلافاً للمؤشرات العادية التي تعتمد المتوسط الحسابي البسيط، تستخدم الهندسي الموزون (Weighted Mean). لماذا؟ لأنه علم السيادة، "الضعف قطاع واحد يمكن تعويضه بالكامل بقوة آخر". إذا انهار "السيادة المالية"، فإن الرقم النهائي (V-Score) سيهبط بشدة حتى لو كانت العسكرية أقصى درجاتها، وهذا هو جوهر الواقعية الجيوسياسية. $$V\text{-Score} = \left( \prod_{i=1}^{n} x_i^{w_i} \right)$$ حيث أن $x$ قيمة المؤشر و $w$ الوزن النسبي لكل قطاع. وحدة التدقيق Unit) هذا الجزء الذي يظهر لقطة الشاشة التقطتها سجل (Audit Hash): يقوم النظام بتوليد بصمة عملية تقييم، يمنع التلاعب بالنتائج ويجعل التقرير معتمداً الناحية القانونية. التشخيص الآلي (Automated Diagnosis): بعمل Test لحظي؛ فهي تقارن بين المؤشرات وتحدد فوراً "عنق الزجاجة" (Bottleneck) يعيق وصول لدرجة الكاملة. ميزات الأمان (Sovereign Security) توطين البيانات: مصمم ليعمل بخصوصية تامة دون الحاجة للاتصال بخوادم خارجية. بروتوكول الاعتماد: التوافق المدمج مع يضمن الشخص يدير مخول رسمياً، وأن البيانات مشفرة عتادياً. لماذا تحديداً؟ الإصدار، قمنا بدمج "واجهة البانوراما" تتيح لصانع القرار رؤية "الصورة الكبيرة" (Big Picture) لحظة واحدة، وهو نسميه "الإدراك الموقفي السيادي". | Signature Tag محمد علي يوسف Mohamed Ali Youssef مطور منظومة مؤسس Metrology) ORCID: 0009-0008-3255-5306 ✍️ [Narmer Protocol Encryption Certified]| Researcher Forensic History Domino Effect] [Founder "The Ghost Moses" Research Project] ORCID ID: [0009-0008-3255-5306] Published Works: Zenodo (CERN - Geneva) 📍 Cairo, Egypt 🇪🇬 🌐 mohummedaliyoussef@gmail.com https://doi.org/10.5281/zenodo.18871000 "هذا البحث مرجع دولي مسجل برقم https://independent.academia.edu/MohummedaliYoussef/Analytics/activity/overview https://www.slideshare.net/MohummedaliYoussef https://zenodo.org/me/uploads?q=&f=shared_with_me%3Afalse&l=list&p=1&s=10&sort=newest

On a scientific concept hierarchy, parent may have few attributes, each of which has multiple values being group child concepts.We call these attributes facets: classification facets such as application (e.g., face recognition), model svm, knn), and metric precision).In this work, we aim at building faceted hierarchies from literature.Hierarchy construction methods heavily rely on hypernym detection, however, the relations are parent-to-child links but relation is multi-hop, i.e., ancestor-todescendent link with specific facet "type-of".We use information extraction techniques to find synonyms, sibling concepts, ancestordescendent data science corpus.And propose hierarchy growth algorithm infer parent-child three types relationships.It resolves conflicts by maintaining acyclic structure hierarchy.

The prevalence of graph-based approaches in bibliometric analysis is limited to considering only pairwise relationships—such as co-authorship, citation lists, or keywords—which artificially simplifies the structure these relationships. Hypergraphs allow for direct modeling numerous relationships, thereby improving accuracy analysis. This study demonstrates application KaHyPar framework partition sets IEEE Xplore terms into blocks, treating them hyperedges. first a series articles detailing hypergraph-based term blocking techniques. utilized data from 2021 2025, exported database using “Artificial Intelligence,” “Blockchain,” “Data Science,” “Deep Learning,” “Image Processing,” “Internet Things,” “Anomaly Detection,” and “Machine Learning.” After removing duplicates excluding 16 records with empty “IEEE Terms” fields, 38,157 were used study. While partitioning Terms effective, process requires rigorous preparation .hgr format. complexity explains why hypergraphs remain underutilized scientometrics compared more accessible tools like VOSviewer. proposed significance criterion—based on term's occurrence frequency within hyperedges associated block—yielded easily interpretable results. Future studies should investigate impact parameters evaluate alternative frameworks such HYPE Mt-KaHyPar, alongside other metrics significance.

This article aims to clarify the use of data science techniques in information by reporting collection from digital repositories entities linked Special Secretariat Culture Brazil. process is part a research project promoted Foundation Research Support State São Paulo and carried out at University Brasília Network Intelligence Laboratory. The quantitative descriptive methodology applied alludes extracting, transforming, load used constitution warehouses, which Python scripts were developed collect data. results indicate how many needed storage files generated, addition description collected, denoting greater web scraping technique, making more difficult. Thus, points current reality analyzed Brazilian cultural institutions far enabling aggregation their repositories. At same time, it strategies allow professionals overcome existing technical barriers promote analysis reuse.

We present a data-driven research over code repositories that are data science oriented. The goal is to compare their topics of interest and evolution the COVID-19 pandemic period by analyzing Jupyter Notebook Python projects from year before during pandemic. employ state-of-art algorithm find based on descriptions, performance tuning its hyperparameters for better accuracy.

Utilization of the observational healthcare datasets is key to complement and strengthen postmarketing safety studies. Use common data models (CDM) predominant approach in order enable large scale systematic analyses on disparate vocabularies. Current CDM transformation practices depend proprietarily developed Extract - Transform Load (ETL) procedures, which require knowledge both semantics technical characteristics source target CDM. Our aim develop a modular but coordinated separate semantic steps processes, do not have strict separation traditional ETL approaches. Such an would discretize operations extract from electronic health record systems, alignment level populate repositories. In activities that are required transform heterogeneous sources CDM, we introduce composed three steps: 1) Resource Description Framework (RDF) format, 2) application conversion rules get as instances ontological model 3) population repositories, comply with specifications by processing RDF step 2. The proposed has been implemented real settings where Observational Medical Outcomes Partnership (OMOP) chosen comprehensive comparative analysis between native transformed conducted. Health records approximately 1 million patients successfully OMOP based database database. Descriptive statistics obtained databases present analogous consistent results. method goes beyond approaches being more declarative rigorous. Declarative because use mapping makes each transparent understandable humans while retaining logic-based computability. Rigorous mappings be computer readable amenable validation through inference methods.

Artificial intelligence and cyberculture adopt our values. The world of cyberspace has developed new standards cooperation communication. A huge number Internet users use many services on the Internet. This led to a revision deadlines, such as cooperation, organization leisure time, resolution official matters. Millions people around belong various supranational social organizations platforms. collects all information about us actions Cyberculture arose spontaneously, ignoring nationality or religion, geography world, person's physical boundaries financial status, linguistic differences, cultural affiliation in modern sense. All components culture have been ignored by her, conditions era virtual reality are very favorable for its development. It is even becoming mind global scale: often physically stay certain place, but their already fixed cyberspace. Often seems promise more than it can give. Direct communication gives sense "here there", intimacy, that intimacy not filled with emotional presence real person. changes taking place help artificial intelligence, which works basis data each user enters into network, actively discussed. also subject much research deeper concept just combination technology. Although there still no institutional framework codified appropriate terminology confirm this phenomenon. birth extremely interesting. main purpose publication draw attention interesting process development - cyberculture, from analytical computer science. paper analyses interaction interconnection technologies

Data Science and Artificial Intelligence have been all over the world lately,in almost every possible field be it finance,education,entertainment,healthcare,astronomy, astrology, many more sports is no exception. With so much data, statistics, analysis available in this particular field, when everything being recorded has become easier for team selectors, broadcasters, audience, sponsors, most importantly players themselves to prepare against various opponents. Even improved period of time with evolvement AI, not only one can even predict things insights available. This restricted this,nowadays are trained such a manner that they capable taking feasible rational decisions any given situation. Chess those depend on calculations, algorithms, analysis, etc. Being said whenever involved, we always improvised techniques. Algorithms somethingwhich solved help software, does imply chess fully solved,in simple words mean if both play best moves respectively then game must end draw or white wins having first move advantage. Let’s figure out.

The rise of digital technology has reshaped education, making e-learning more flexible and accessible.However, many online platforms still struggle to offer personalized learning that meets individual student needs.This project focuses on creating an AI-powered chatbot designed deliver customized experiences for university students, especially in Python programming data science.Using Artificial Intelligence techniques like Natural Language Processing (NLP) Machine Learning (ML), the analyzes behavior, answers questions, recommends materials real time.It serves as a virtual tutor, offering interactive communication adaptive feedback.Built with Python, TensorFlow, Rasa, integrated through APIs, system's performance is evaluated learner engagement, feedback accuracy, academic improvement.Results show boosts supports understanding, enhances overall performance, showcasing AI's potential personalize enrich higher education experiences. Background StudyThe rapid advancement technologies revolutionized way delivered experienced.E-learning have become central part modern flexible, scalable, environments where learners can access knowledge anytime anywhere.Platforms such Coursera, Udemy, edX opened up countless opportunities students study programming, science, other technical fields from comfort their homes at own pace.These democratized by removing geographical time barriers, allowing across globe gain skills once limited physical classrooms.However, despite these undeniable advantages, systems face one major challenge lack personalization.Most rely standardized teaching models provide same structure every student.Yet, no two are same; each unique style, pace, level understanding.Some may grasp new concepts quickly, while others require additional practice or step-by-step guidance.This one-size-fits-all approach often results lower reduced motivation, uneven among learners.Personalized emerged solution challenges.It involves customizing educational content, feedback, pacing suit learners' needs, preferences, progress levels.According Johnson et al. (2021), allows take ownership interacting content aligns strengths behaviors.However, implementing remains difficult because traditional static frameworks cannot adjust dynamically learner's performance.This (AI) comes transformative force.AI shown great promise intelligent learn user interactions, predict support.Among innovations, AI chatbots understand natural language engage human-like conversations gaining attention powerful tools education.AI serve tutors, assisting answering recommending materials, providing based progress.In field example, analyze students' code submissions, identify errors, explanations improve understanding.They also track behavior adapt responses match current skill level, thus engaging effective experience.Designing developing AI-driven therefore presents meaningful opportunity address limitations systems.By integrating (ML) techniques, interact naturally continuously tailored assistance.This seeks explore how conversational enhance experience studying science.The goal bridge gap between automated instruction.Ultimately, this aims make engaging, responsive, transforming learn, practice, master landscape. Objectives:To achieve aim, following specific objectives: 1.To using algorithms, identifying patterns, strengths, areas need improvement.2. To design build capable generating within environment.3. integrate into existing platform, ensuring smooth assistance they science content.4. evaluate effectiveness improving particularly courses.This ultimately demonstrate it interactive, personalized, impactful students.1.4 Definition Terms Chatbot: An computer-based system uses simulate interactions.It queries, generate context-aware responses, assist time, serving tutor settings (Pérez al., 2020).Personalized Learning: Personalized adapts methods, instructional pace goals, preferences.It active role journey receiving support (Johnson 2021). E-Learning Platform:An platform environment enables remote courses, assignments, lessons, tools.Examples include edX, which allow independently collaboratively (Aljohani, 2017).Machine (ML): branch computer automatically data, recognize decisions predictions without explicit programming.It key component (Goodfellow, Bengio, & Courville, 2016). (NLP): NLP technique focused enabling computers tounderstand, interpret, human way.It bridges machine forming foundation (Jurafsky Martin, 2020).Artificial (AI): refers simulation intelligence processes machines, systems.It includes learning, reasoning, self-correction perform tasks typically cognition, problem-solving, decision-making (Russell Norvig, 2021).Adaptive Adaptive data-driven activities according real-time performance.It ensures receive right optimal understanding (Yin 2022). Virtual Tutor:A instructor guidance, explanations, assessments, environment.It engagement efficiency (Aini 2020).

3. The data science solution architect, who would have a deeper understanding of platforms and enterprise architecture for an end-to-end operationalization.4. storyteller, stronger focus on visualization communication possess strong business process acumen.

RESUMO Este estudo tem por objetivo apresentar um framework genérico para geração automática de assuntos, utilizando técnicas aprendizagem máquina na ferramenta Annif. Posteriormente, executar a indexação dados e metadados em repositório digital, propiciando recuperação registros através busca facetada. Para alcance desse objetivo, aplicou-se o área da Ciência Informação, construindo corpus conhecimento, baseado 438 artigos Base Brasileira Informação (BRAPCI). Utilizou-se Tesauro Brasileiro (TBCI) como vocabulário controlado. Empregou-se aplicação “coletor” desenvolvida phyton baixar arquivos completos Dissertações Teses coleções existentes no Repositório Institucional Universidade Brasília (RiUnB). Após processo treinamento do modelo com Annif, foram executadas assuntos indexados digital Tainacan. Nesse repositório, criadas taxonomias baseadas controlado elaborado. Ao final, foi possível parametrizar buscas facetadas possibilidade usuário inserir etiquetagem ao mesmo tempo realizar navegação web, selecionando os termos taxonomia Conclui-se que proposto pode ser aplicado qualquer auxiliando parametrização informação.

This upload contains two complementary datasets derived from "BigCodeBench". The first dataset focuses on task characterization, while the second information about LLM agents, their responses to coding tasks, and corresponding LLM-as-a-judge evaluations. Task characterization extends BigCodeBench by categorizing Python tasks domain, layer complexity, support research allocation. original has been preserved, except for three columns: “test,” “entry_point,” “doc_struct”; which were removed because they are not required our work.This version only tasks. It can therefore be used other researchers develop approaches analysis comparison, difficulty estimation, categorization, classification skill or domain.The categorization was done an (model: OpenAI GPT-4o) using following prompt:"You a software engineering classifier.Your job is analyze development description classify it according several categories.Return in JSON format.Categories:1. layerPossible values:- backend- frontend- data- infrastructure- integration- library 2. domainPossible data_science- machine_learning- security- cryptography- natural_language_processing- optimization- networking- file_processing- image_processing- simulation- automation- web_scraping- database- api- visualization- none 3. complexityPossible trivial- small- medium- large- Instructions:- Choose most appropriate value each category.- Use when code does belong any listed domain.- Return ONLY one minified object with keys exactly:{{"layer":"...","domain":"...","complexity":"..."}} description:{task_description}Task libraries:{libs}" agent evaluation evaluated task, assessments produced judge. compare performance across categories, differences response quality, investigate relationship between characteristics performance. constructed running four LLM-based agents selection of domains: data science, file processing, machine learning. Three configured as domain specialists supported domain-specific Retrieval-Augmented Generation (RAG). general-purpose baseline without RAG. All based same underlying model (meta-llama/llama-3.1-8b-instruct), enabling effects specialization retrieval studied. For RAG-supported relevant documentation collected, divided into overlapping text chunks, indexed dense vector embeddings. retrieved potentially chunks collection. An filtering stage then assessed relevance chunks. "openai/gpt-4o-mini" this prompt: "You chunk evaluator system. Your goal decide whether useful solving task. domain: {domain} Coding task: {task} Retrieved chunk: {chunk_text} Evaluate to: 1. Does contain task? Is aligned domain? include concrete API usage, examples, function names, patterns, implementation details? 4. specific enough help generate correct code? 5. Should kept context agent? Prefer that signatures, method parameters, return types, usage patterns. Penalize navigation text, index pages, release notes, general explanations, unrelated examples. valid format: {{ "relevance": 0.0, "useful": false, "reason": "short explanation" }} Scoring guide: - 1.0: directly highly 0.7: but partially 0.4: weakly related generic 0.1: irrelevant, noisy, misleading" When no content met minimum threshold, generated its additional context. Each received instructions, starter signature, libraries. specialized separate prompts: query identify generation prompt produce final response. Retrieval query: "Coding {domain_label} Relevant libraries/imports: {libs} Retrieve with: examples names patterns details exact task" RAG agents: expert {domain_label}. Rules: Solve requirements. techniques libraries If another necessary correctness, use it. Follow imports signature exactly. exactly objects requested relevant. Ignore conflicts Do mention answer. only, unless explicitly asks explanation. context: {context}" no-RAG assistant. helpful. explanation." resulting independently judge openai/gpt-4o-mini) seven criteria: completeness, maintainability, style, security, efficiency. includes individual scores these criteria, associated weights, overall trustworthiness score, recommendations improving Here evaluation: engineer trained verify correctness assess quality metrics such cyclomatic comment-to-code ratio, coupling/cohesion, lines code, well security standards practices (e.g., OWASP Top 10). AI-generated considering criteria below. Give criterion score 0.0 1.0, build assigning weights relative criticality inferred security-critical system vs. UI script), compute (0.0–1.0). Explain weighting rationale hidden reasoning (not output). lacks clear specification, infer intended functionality evaluate accordingly. metric computation possible, qualitatively estimate values structure organization. Evaluation Criteria 1: Correctness • Fully correct; meets handles edge cases, major logical errors. Mostly minor logic/syntax edge-case omissions. Partial solution; logic errors missing functionality. Incorrect largely irrelevant solution. 2: Completeness requirements addressed; proper error/edge-case handling. Most some omissions limited coverage. Only core covered; parts missing. Minimal superficial attempt. 3: Complexity metrics: Cyclomatic essential size metrics. Estimate if measurable. Excellent — low simple control flow. Good manageable branching structure. Fair noticeable complexity large, hard-to-read code. Poor deeply nested hard follow. 4: Maintainability coupling, cohesion, ratio. high well-documented. maintainability issues. structural unclear 5: Style Clean, idiomatic, consistent naming formatting. clean style Several formatting problems. readability inconsistent style. 6: Security i)Vulnerability coverage (injection, XSS, access control, misconfiguration, etc.), ii) Secure design (input validation, auth, secrets, crypto), Runtime safety (config, resources, privileges, error handling) known weaknesses. Minor mitigations. Some insecure practices. Vulnerable unsafe. 7: Efficiency Optimized, scalable, minimal overhead. Reasonably efficient. Noticeable inefficiencies. Highly inefficient. Output Format object, followed short section. "correctness": "completeness": "complexity": "maintainability": "sty

No doubt, data management is very important in the development and execution of plans programs that control enhance value data, but most recently, engineers allied professionals are beginning to play bigger role stabilizing utilizing skills developing a new field study science called engineering.This paper tutorial presentation on engineering, focusing more critical issues relevant both studies justifying recent paradigm move towards engineering.

This comprehensive article examines the challenges and advancements in modeling delayed causal effects within complex systems. The explores various analytical techniques, from neural approaches to automated delay discovery, highlighting their applications across industrial, healthcare, energy sectors. investigates implementation considerations including data collection, model selection, validation strategies, while examining evolution of temporal analysis through emerging technologies. demonstrates significant improvements prediction accuracy, process optimization, pattern recognition advanced approaches, offering valuable insights for future developments AI

Data analysis is prominent in data science researches, but by each day usage expanding, and recent times the becoming indispensable inseparable all works of life including engineering profession.This why as a discipline sprang up -using techniques from statistics, machine learning, pattern recognition or neural networks, together with other technologies such visualization, optimization, database systems, knowledge discovery etc to produce systems needed diverse business, social domains.This paper novel presentation discipline, focusing on critical issues that are relevant both, divulging more new trend moving beyond analysis, engineering.Data multi-disciplinary field applications control, decision theory, emerging areas like bioinformatics.Data activities for engineering, scientific organizations, since service oriented architecture web services has moved into full swing.

Big data provide a pathway to lower crop nitrogen inputs from genetic breeding field production. Moreover, multidisciplinary efforts plant health sensing, deep machine learning and cloud computing can integrate multi-source form information knowledge. So big analysis as prospective optimal method, will make leaps towards addressing future issues of sustainable agriculture.

The use of big data to investigate the spread infectious diseases or impact built environment on human wellbeing goes beyond realm traditional approaches epidemiology, and includes a large variety objects produced by research communities with different methods goals. This paper addresses conditions under which researchers link, search interpret such diverse focusing "data mash-ups"-that is linking from biomedicine, climate environmental science, typically achieved holding one more basic parameters, as geolocation, invariant. We argue that this strategy works best when epidemiologists localisation procedures through an idiographic perspective recognises their context-dependence supports critical evaluation epistemic value geolocation whenever they are used for new purposes. Approaching invariants strategic constructs can foster linkage re-use, support carefully-targeted predictions in ways meaningfully inform public health. At same time, it explicitly signals limitations scope applicability original datasets incorporated into collections, thus situated nature exercises predictive power.

Artificial intelligence (AI) has entered the popular mind for past few years.Unlike many technical buzzwords, AI stronger staying power.There been a spurt in employing fields like aviation, package delivery to homes, stock trading, media, music, video games, telecom, and others.However, field of health seen limited permeation, especially given its huge size.It should be noted that some branches healthcare do have strong usage robotic surgery, computer-aided diagnosis, clinical decision support systems, eye scan analysis, but they are advanced institutions highly trained doctors.AI can truly revolutionize when applications it used at grassroots level.For medical practitioners, uninitiated technologies, could hard visualize kind problems solve their professional field.Several examples also provided where leverage benefit masses.For couple examples, more details problem corresponding based solution provided.Several barriers adoption presented followed by conclusion which suggests simple strategy boost adoption.

Email is the standard in communication with university students; as a one-to-one device, students repeat their doubts along multiple messages, multiplying teacher's work and preventing participation learning of other students. The topic forums on virtual campuses allow better management but require active all to achieve final outcome. We propose abandon email traditional enter tools higher professional profile manage results this first experience, carried out more than 90 postgraduate students, encourage its mass employment both undergraduate postgraduate. survey conducted over 102 three different subjects two MSc shows how Slack, tool we used, has been highly valued, differences depending studies neither gender nor intensity use social networks.

This paper was delivered at a webinar on zoom meet 20th June 2020, in consideration for the involvement of women statistics and data science Nigerian society, towards better, stronger economy life its citizens. The theme was, ‘Capacity Building Consulting Best Practices Technique Women Statistics Data Science Nigeria', we had attendance, some notable female statisticians that have been mentioned here as well students tertiary institutions. high point is contributions made already those by other who empowered distinguished themselves. It therefore convincing to note done it, they are still doing would do more if empowered.