Vzdělávání Expert Claude

Research paper writing - akademický framework

Kompletní proces psaní research paper od výběru tématu přes metodologii až po publikaci.

Prompt text

Délka: Dlouhý
Vytvoř framework pro psaní research paper v oboru [OBOR/DISCIPLÍNA]. Typ práce: [BAKALÁŘSKÁ/DIPLOMOVÁ/DISERTAČNÍ/JOURNAL ARTICLE]. Struktura: 1) **Topic Selection & Research Question** - literature gap analysis (what's been studied? what's missing?), FINER criteria (Feasible, Interesting, Novel, Ethical, Relevant), research question formulation (specific, answerable, significant), hypothesis development (testable prediction, null vs alternative), scope definition (boundaries, what's included/excluded), 2) **Literature Review** - systematic search (databases: Google Scholar, PubMed, IEEE, JSTOR), search strategy (keywords, Boolean operators, filters), citation management (Zotero, Mendeley, EndNote), reading strategy (abstract screening → full text → note-taking), synthesis approach (chronological, thematic, methodological), critical analysis (strengths/weaknesses of existing research), 3) **Research Methodology** - research design (quantitative, qualitative, mixed methods), data collection (surveys, interviews, experiments, observations), sampling strategy (random, stratified, convenience, sample size calculation), ethical considerations (IRB approval, informed consent, confidentiality), validity & reliability (internal, external, construct validity), 4) **Paper Structure (IMRaD)** - Abstract (150-250 words, purpose, methods, results, conclusions), Introduction (background, research gap, research question, significance), Methods (participants, materials, procedures, analysis plan, replicable), Results (findings, statistics, tables/figures, no interpretation), Discussion (interpretation, implications, limitations, future research), Conclusion (summary, contributions, recommendations), 5) **Writing Process** - outlining (detailed structure before writing), first draft (write quickly, don't edit yet, get ideas down), iterative revision (structure → arguments → clarity → grammar), paragraph structure (PEEL: Point, Evidence, Explanation, Link), transitions (logical flow between sections), academic voice (objective, formal, third person), 6) **Data Presentation** - tables (structured data, clear labels, referenced in text), figures (graphs, charts, images, high resolution 300dpi), statistics reporting (APA format: t(28) = 2.45, p < .05), data visualization (choose appropriate chart type, clear legends), caption writing (standalone, descriptive, numbered), 7) **Citation & Referencing** - citation style (APA, MLA, Chicago, Harvard, IEEE), in-text citations (author-date, numbering system), reference list (alphabetical, complete information), avoiding plagiarism (paraphrase, quote with attribution, common knowledge), citation management tools (automatic formatting, sync across documents), 8) **Peer Review Process** - selecting journal (impact factor, scope fit, open access vs traditional), cover letter (why this journal, novelty, significance), submission process (formatting, supplementary materials, declaration), responding to reviewers (point-by-point response, revisions, rebuttal), dealing with rejection (common, learn from feedback, submit elsewhere), 9) **Quality Checklist** - logical flow (each paragraph follows naturally), evidence support (every claim backed by citation or data), clarity (concise, avoid jargon, define technical terms), consistency (terminology, formatting, style throughout), grammar & spelling (proofread, use tools like Grammarly), 10) **Common Pitfalls** - scope too broad (narrow focus is better), weak research question (vague, not testable), insufficient literature review (need comprehensive coverage), methods unclear (can't replicate study), results interpretation in results section (save for discussion), p-hacking (multiple testing without correction), overselling findings (acknowledge limitations). Zahrň writing timeline template a revision checklist.
Interaktivní tvůrce

Příklad použití

Vstup:

Obor: Computer Science - Machine Learning. Typ: Master's thesis. Téma: "Improving accuracy of sentiment analysis using transformer models". Timeline: 6 měsíců.

Výstup:

[18,000+ word research paper framework with topic selection (literature gap: current models struggle with sarcasm and context, research question "Can attention mechanisms in transformers improve sarcasm detection in sentiment analysis compared to LSTM models?", hypothesis "Transformer models will achieve >10% higher F1 score on sarcastic tweets", scope: Twitter data, English language, binary sentiment, exclude multimodal), literature review strategy (searched 150+ papers in Google Scholar + ACL Anthology, keywords "sentiment analysis" AND "transformer" AND "sarcasm", 30 most relevant papers selected, synthesized into themes: LSTM approaches, BERT variants, attention mechanisms, sarcasm detection challenges), methodology designed (quantitative experimental study, dataset: SemEval-2018 Task 3 with 3,834 tweets, train 80% / test 20% split, models compared: LSTM baseline, BERT-base, RoBERTa, custom architecture with multi-head attention, evaluation metrics: F1-score, precision, recall, accuracy, statistical significance via paired t-test), IMRaD structure outline (Abstract 200 words covering all sections, Introduction 4 pages with ML background → sentiment analysis evolution → sarcasm problem → research question, Methods 6 pages detailing dataset preprocessing, model architectures with diagrams, training procedures hyperparameters, Results 5 pages with comparison tables showing RoBERTa achieved 0.847 F1 vs LSTM 0.731 = 15.8% improvement, Discussion 6 pages interpreting why attention helps with context, limitations of dataset size, implications for industry applications), data presentation (Table 1: Model comparison with F1/precision/recall, Figure 1: Training curves showing convergence, Figure 2: Attention heatmap visualization on example sarcastic tweet, Figure 3: Confusion matrices for each model, all figures 300dpi publication-ready), citation management (80 references in ACM format managed in Zotero, in-text as [Author, Year], reference section auto-generated), 6-month timeline (Month 1: Literature review + research question finalization, Month 2: Methodology design + IRB approval, Month 3: Data collection + preprocessing + pilot experiments, Month 4: Main experiments + results analysis, Month 5: Writing first draft all sections, Month 6: Revision + advisor feedback + final submission), quality checklist completed (logical flow verified, 80 citations supporting claims, technical terms defined, consistent "deep learning" vs "DL" usage, Grammarly showed 0 errors, advisor reviewed 3x), journal submission plan (targeting ACL conference, 8-page limit, LaTeX template, supplementary code on GitHub, cover letter emphasizing novel attention mechanism contribution)]

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