'datasets' 검색 결과

Query documents and conversations stored in cognee to retrieve answers, supporting passages, or context for an AI model. This skill guides search method selection, source filtering, result interpretation, and troubleshooting empty or unexpected results.

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• Searching selected datasets or conversation sessions
• Retrieving supporting passages or model context
• Choosing a search method for a question

Analyze single-cell data with scvi-tools, using deep learning to combine datasets, correct differences between experiments, and transfer cell labels. Model guides and reusable scripts cover RNA, protein, chromatin accessibility, spatial data, and downstream analysis.

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• Integrate single-cell RNA datasets and correct batch effects
• Analyze paired RNA and protein or RNA and ATAC measurements
• Transfer cell-type labels from reference data

Create realistic sample data for testing, development, and demos. Specify fields, record counts, value ranges, and business rules, then receive CSV, JSON, SQL INSERT statements, or a Python generator script with validation and usage instructions.

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• Creating test data that follows business rules
• Preparing realistic sample data for demos
• Populating a test database

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

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• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Evaluate AI models and agents on Google Cloud using test datasets and scoring criteria. Generate simulated user scenarios, analyze failures, and compare results across changes to guide improvements to prompts and code.

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• Build evaluation datasets from conversation logs
• Generate simulated user scenarios
• Choose scoring criteria for responses and tool use

Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.

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• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

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• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Learn how GPT language models work by preparing text, training a small model, and generating samples. Covers CPU experiments, custom datasets, further training of pretrained GPT-2 models, and reproducing GPT-2 with multiple GPUs.

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• Train a small model on Shakespeare using a CPU
• Prepare custom text for model training
• Further train a pretrained GPT-2 model

Build Apache Spark pipelines for large datasets, including transformations, aggregations, and streaming analysis. Get PySpark or Scala code, partitioning strategies for dividing data across workers, and configuration advice to investigate slow jobs and resource usage.

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• Build a pipeline to transform and aggregate large datasets
• Investigate slow Spark jobs and uneven data distribution
• Tune memory settings and data partitions

Clean, combine, summarize, and reshape tabular data with pandas DataFrames, Python's table-like data structures. Produces transformation code and validation checks, including time series processing and memory-conscious approaches for large datasets.

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• Fill missing values and correct data types
• Combine tables using multiple matching columns
• Create grouped summaries and pivot tables

Adapt large language models to specific tasks through fine-tuning, or additional training. Prepares and validates datasets, configures LoRA or QLoRA to train a small subset of parameters, and supplies training settings, evaluation scripts, and deployment optimization guidance.

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• Preparing and validating JSONL training data
• Choosing LoRA or QLoRA for available GPU memory
• Configuring training and monitoring overfitting

Explore Hugging Face datasets through the Dataset Viewer API. Preview and retrieve rows, search text, filter records, inspect sizes and column statistics, and find Parquet download links for further analysis or data extraction.

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• Preview dataset contents and data splits
• Search text and filter dataset rows
• Inspect dataset sizes, statistics, and Parquet links

Build or review quality control workflows for single-cell and single-nucleus RNA sequencing data. Select filters from observed data, detect doublets, assign cell types, and create UMAP plots that summarize cell relationships, while recording analysis decisions and outputs.

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• Choosing quality filters from dataset distributions
• Detecting doublets and checking ambient RNA
• Assigning cell types using references or marker genes

Query ProteomeXchange through its PROXI API to inspect datasets, libraries, proteins, peptide forms, and spectra. Receive concise summaries focused on a dataset or identifier, and save complete responses when more detail is needed.

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• Inspect a dataset by its identifier
• Check proteins, peptide forms, and spectrum matches
• Retrieve spectra and USI identifier examples

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

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• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Collect customer reviews for a Taobao or Tmall product using its item ID. Extract reviewer names, dates, purchased variants, review text, and photo URLs across review pages to prepare material for feedback analysis or review datasets.

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• Collecting customer reviews for a specific product
• Preparing review data for sentiment analysis
• Checking feedback by purchased product variant

Calculate the count, sum, minimum, maximum, and average of supplied numbers or datasets, then present the results in a clear, structured format. Analysis results can also be saved to a file when requested.

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• Check the count and total of supplied numbers
• Summarize minimum, maximum, and average values
• Get a basic numerical summary of a dataset

Prepare data availability statements for Nature-family submissions by mapping research datasets to access routes, planning repositories, drafting dataset citations, and checking metadata—the descriptive information needed to find and reuse data.

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• Drafting a data availability statement for a manuscript
• Planning repositories and dataset citations
• Clarifying access conditions for restricted data

Prepare large Excel datasets by combining sheets, using Parquet, a format for efficient table storage, and cleaning a selected text column to retain only Chinese characters. Export the cleaned data as an Excel file with a local download link.

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• Removing symbols and numbers from Chinese text columns
• Preparing large Excel datasets for analysis
• Combining sheets and exporting cleaned data

Search GitHub, Stack Overflow, Hacker News, and HuggingFace for code examples, projects, technical answers, discussions, models, datasets, and Spaces. Filter supported searches and receive structured JSON results containing titles, links, and snippets.

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• Find code examples and open-source projects
• Search technical answers and GitHub Issues
• Explore developer news and discussions

Design ClickHouse tables, write analytical queries, and plan data loading using practical SQL and code patterns. Use it to review slow queries, build recurring summaries, and analyze user activity across large datasets.

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• Designing tables for large analytical datasets
• Investigating slow queries
• Building real-time summaries

Set up a research project with separate folders for code, source data, cleaned datasets, results, and documents. Create a research rules file from a template, a project README, and an initial progress log.

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• Starting a new research project
• Standardizing folders across research projects
• Separating source data from cleaned datasets

Find NBER working papers, research papers shared before formal publication, by keyword or research program. Retrieve recent papers, authors, dates, and citation details, and access US business cycle dates and links to economic datasets.

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• Find economics papers by topic
• Check this week's new working papers
• Retrieve authors, DOI, and PDF links

Search Kaggle datasets, competitions, notebooks, and models through its official command-line tool. Download public data into a chosen directory and preserve audit records and file hashes, identifiers used to check downloaded files.

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• Finding Kaggle datasets and inspecting metadata
• Downloading public data into a chosen directory
• Discovering competitions, notebooks, and models