“reproducibility”的搜索结果

Conduct a peer review of research methodology, experimental design, and manuscript quality. Covers methodology evaluation, statistical appropriateness, reproducibility assessment, bias identification, and constructive feedback. Use when reviewing a manuscript, preprint, or internal research report, evaluating a research proposal or study protocol, assessing evidence quality behind a claim, or reviewing a thesis chapter or dissertation section.

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Conduct rigorous cross-study meta-analysis of scRNA-seq data from ENCODE, integrating multiple single-cell transcriptomic datasets for a tissue/cell type. Use when the user wants to answer "what cell types exist in my tissue and what genes define them?" by combining scRNA-seq data across donors, labs, and platforms. Follows the Mawla et al. 2019 framework for assessing cross-study reproducibility, TIN-based quality filtering, and detection-limit-aware interpretation. Handles batch correction (Harmony/Seurat), dropout awareness, cross-contamination artifacts, and platform-specific biases. Use this skill for ANY scRNA-seq integration task, cross-dataset comparison, cell atlas construction, or reproducibility assessment involving ENCODE single-cell data.

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Audit ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science teams.

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Query the Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.

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Query the Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.

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Query the Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.

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阅读学术论文，评估主要论点、证据、研究方法、创新性及其与相关研究的关系，具体指出优点与不足，并整理成包含改进建议的评审。

• 了解论文成果与局限
• 起草同行评审意见
• 检查实验设计与可复现性

Generate images, video, and music through ComfyUI in OpenMontage. Helps adapt community workflows, select the final output, identify missing models, and choose workflows suited to available GPU memory while recording settings for reproducibility.

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• Generating images, video, or music in OpenMontage
• Importing a community ComfyUI workflow
• Choosing workflows for limited GPU memory

Explore gene, drug, disease, and phenotype associations in a downloaded PrimeKG CSV. Retrieve direct relationships, disease summaries, and paths through one intermediate node while preserving relationship types and data provenance for reproducible research and hypothesis generation.

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• Checking associations between genes, drugs, and diseases
• Summarizing genes, drugs, and phenotypes linked to a disease
• Exploring short paths between two entities

Assess Web3 vulnerability findings before submission by checking scope, reproducibility, prerequisites, and attack costs. Organize the root cause, measurable impact, working proof of concept, and proposed fix into an Immunefi report, with examples for comparison.

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• Checking whether a finding is ready for submission
• Drafting an Immunefi vulnerability report
• Reviewing reproduction code and measurable impact

Evaluate security findings before writing a bug bounty report. Check reproducibility, scope, practical impact, duplicates, and behavior across user sessions, then use submission checklists and CVSS 3.1 guidance to prepare evidence and assess severity.

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• Deciding whether a finding is ready to report
• Rechecking access across different user sessions
• Reviewing duplicate or intended behavior

Review vulnerability findings before drafting a bug bounty report. Seven validation questions and submission checklists help assess scope, reproducibility, demonstrated impact, duplicate reports, and whether the claimed severity matches the available evidence.

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• Decide whether a finding is ready to report
• Recheck suspected authentication bypasses
• Match severity claims to demonstrated impact

Audit a research replication package with ten checks covering R syntax, file references, output freshness, dependencies, data sources, and documentation. Receive a pass/fail report with specific issues and suggested fixes for preparing the package for submission.

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• Check a replication package before submission
• Find missing inputs and outdated outputs
• Review dependency and data source documentation

Plan literature searches for six review types, from systematic to rapid reviews. Develop database-specific search queries, selection criteria, citation tracking, and search records, while comparing approaches for coverage, reproducibility, and available time.

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• Choose a review method for a research question
• Build search queries for multiple databases
• Map a research field and identify gaps

Turn a recently solved research problem into searchable Markdown documentation. Capture symptoms, failed attempts, root causes, working code, and prevention checks, then organize the solution by category and link it to related records.

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• Recording a fix for estimation convergence failures
• Documenting causes and fixes for data processing errors
• Preserving solutions to reproducibility problems

Analyze datasets in R or Python, from exploring distributions and missing values to fitting regressions. Produce tables and figures for publication, save scripts and model results, and review the analysis for common problems.

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• Explore distributions, missing values, and relationships
• Run regressions on cross-sectional or panel data
• Export publication-ready regression tables and figures

Carry out research implementation plans from environment setup through code, tests, and pull requests. The workflow checks estimation convergence, standard errors, diagnostics, and reproducibility while tracking completed tasks in the original plan.

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• Implement an estimation method from a research plan
• Build and verify a research data pipeline
• Run robustness checks and document results

Build research workflows that regenerate results from raw data with a single command. Organize project files, configure workflow tools, manage software environments and data versions, and prepare replication packages for journal submission.

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• Setting up folders for a new research project
• Automating analysis and figure generation
• Debugging results that differ across computers

Plan how to introduce AI into economic research, teaching, or policy analysis. Identify suitable tasks, choose tools with privacy and licensing in mind, and design reproducible data workflows with output checks and ongoing model monitoring.

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• Plan AI adoption for an economics research team
• Design data workflows that protect personal information
• Choose AI tools for teaching or policy analysis

Review scientific manuscripts and grant proposals using structured checklists. Assess methods, statistics, reproducibility, ethics, and reporting completeness, then organize findings into a balanced report with major concerns, minor comments, and specific suggestions for revision.

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• Drafting a structured journal peer review
• Reviewing methods in a grant proposal
• Checking a manuscript before revision

Write research/technical reports with strong structure + figure standards. Supports Markdown/HTML/PDF outputs (Quarto optional), executive summary, methods, results, discussion, and reproducibility appendix.

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Write structured reviews of scientific manuscripts and grant proposals. Assess methods, statistics, reproducibility, ethics, and reporting completeness, then organize strengths, major concerns, minor issues, and specific suggestions into constructive feedback.

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• Drafting a journal manuscript review
• Reviewing a research grant proposal
• Checking methods and statistical reporting before revision

Use LaminDB to organize biological datasets, search their metadata, and track how analyses produce results. Standardize annotations with biological vocabularies, validate data structures, and record versions and computational history for reproducible research.

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• Standardizing cell type annotations in single-cell data
• Searching datasets across multiple experiments
• Tracing analysis results back to their inputs and code

Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow. Use after inference, including NV-Generate runs; not for live training tracking, model registration, or clinical use.

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