'sample' 검색 결과

Plan or run bulk RNA-seq analysis to find genes whose expression differs between conditions. Check sample replication, study design, batch effects, and data quality, then produce comparison tables, diagnostic plots, and ranked gene lists.

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• Comparing gene expression between experimental conditions
• Planning analyses with donor pairing or batch variables
• Checking sample quality with PCA and distance plots

Organize ATAC-seq, ChIP-seq, CUT&RUN, or CUT&Tag inputs and route them to suitable analysis workflows. Covers initial quality checks and preparation for finding peaks, genomic regions with concentrated signal, and comparing them across samples.

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• Choose an analysis workflow for an epigenomics assay
• Check FASTQ inputs and sample-sheet readiness
• Organize control and biological replicate information

Design charts and dashboards that show data distributions, uncertainty, missing values, and sample sizes clearly. Choose visual forms that preserve meaningful variation and explain where averages, aggregation, or interval choices could mislead readers.

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• Choosing a chart to show a data distribution
• Presenting confidence intervals clearly
• Revealing variation hidden by dashboard averages

Batch retouch portrait photos with Adobe tools, correcting alignment and tone and adding optional presets, background blur, or cropping. Review a sample before processing the full set, then receive previews and download links.

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• Batch retouching wedding portraits
• Polishing a folder of event photos
• Applying a consistent look to headshots

Inspect Kiln’s interface in a real browser, capture screenshots, and run or debug end-to-end tests that check complete user flows. A sandbox with sample project data provides populated screens for checking UI changes.

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• Checking the appearance and behavior of a changed screen
• Capturing screenshots of Kiln
• Running or debugging the existing end-to-end test suite

Analyze relationships between people, objects, or papers as networks in Python. Use NetworkX to find shortest paths, identify important nodes and communities, visualize connections, exchange graph data, and generate sample networks for testing.

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• Visualizing social relationships or paper citation networks
• Finding shortest paths through a network
• Identifying important nodes and closely connected groups

Choose statistical tests for research data, check their assumptions, and interpret effect sizes, which describe the magnitude of differences or relationships. This skill also supports sample size planning and produces results in APA reporting style, with figures and tables.

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• Choosing a test for group comparisons or relationships
• Checking normality, variance, and outliers before analysis
• Estimating the sample size needed for a study

Plan quantitative research by comparing experiments and surveys against research goals and resource limits. Get design recommendations, sample size calculations, participant recruitment strategies, and experimental materials, with an assessment of factors that could distort results.

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• Compare experimental and survey designs
• Estimate the number of participants needed
• Plan participant sampling and recruitment

Examine figures, tables, and empirical results before interpreting them. The skill reviews every visible feature, explores possible explanations, checks sample sizes and patterns across estimates, and records unresolved questions in a structured report.

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• Reviewing a figure before publication
• Investigating unexpected spikes or dips
• Checking sample sizes across tables

Develop Bayesian models for quantitative social science, from choosing priors to reporting posterior results. Guide MCMC, a sampling method for estimating distributions, check convergence, and assess sensitivity for hierarchical models and small datasets.

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• Choosing priors for models with small datasets
• Implementing hierarchical models in Stan or PyMC
• Investigating MCMC convergence problems

Create summary tables, treatment and control comparisons, and correlation matrices using Python, R, or Stata. Summarize averages, variation, sample sizes, and missing data to understand a dataset before regression or prepare tables for a research paper.

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• Explore a dataset before regression
• Prepare a paper's summary statistics table
• Compare treatment and control groups

Document datasets so others can understand and reproduce an analysis. Create variable definitions, source records, sample filtering tables, and merge documentation, or assemble a codebook describing how the data was constructed.

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• Prepare dataset documentation for replication materials
• Explain variables and construction steps to co-authors
• Record merge keys and matching rates

Check an academic paper’s tables, numerical claims, and modeling choices against R scripts and outputs. Produce discrepancy reports, a record linking claims to code, and automated tests that rerun the analysis.

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• Checking table values against R outputs
• Tracing numerical claims to supporting results
• Reviewing sample sizes and model specifications

Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.

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• Building an end-to-end empirical analysis workflow in Python
• Replicating an applied economics paper
• Producing regression tables and event-study plots

Test local Tika changes with Jazzer, a tool that mutates inputs to uncover failures. Build parser fuzzers, check existing files, reproduce crashes, add starting samples, and retest fixes for memory exhaustion, hangs, and unexpected exceptions.

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• Fuzzing local Tika parser changes before merging
• Running existing files through a parser once each
• Reproducing and retesting an OSS-Fuzz crash

Analyze traffic from Android apps, browser JavaScript, and desktop clients to prepare requests for replay in Burp. Investigate signatures, encryption, and session dependencies when they block replay, and retain working request samples with supporting observations.

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• Capture Android app traffic during real user flows
• Investigate browser request signatures and tokens
• Analyze desktop client signing logic

Analyze research data with suitable statistical tests, regression, correlation, or Bayesian methods. Check assumptions, estimate effect sizes and uncertainty, plan sample sizes, and prepare figures, tables, and reports following APA academic reporting conventions.

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• Choosing statistical tests for research data
• Checking analysis assumptions and alternative methods
• Estimating sample sizes before collecting data

Analyze biological data in Python, from DNA, RNA, and protein sequences to evolutionary trees and microbial communities. Read and convert files, compare diversity within and between samples, and test differences between groups.

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• Processing DNA, RNA, and protein sequences
• Building and comparing evolutionary trees
• Comparing microbial community diversity

Build and deploy biological data analysis pipelines on Latch using Python, Nextflow, or Snakemake. The skill also guides cloud file management, sample tracking, and CPU, memory, and GPU settings for individual processing tasks.

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• Building and deploying RNA-seq analysis pipelines
• Moving Nextflow or Snakemake pipelines to Latch
• Tracking sample data and results in Registry

Search NCBI GEO for gene expression studies, retrieve experiment details and expression tables, and prepare public datasets for analysis. Includes workflows for data quality checks, comparisons between sample groups, and expression summaries across studies.

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• Finding expression datasets for a disease or organism
• Downloading expression tables and sample details
• Comparing control and treatment groups

Fine-tune NV-Generate-CTMR MR-Brain v1 using supplied brain MRI volumes. Check the input file list, prepare training configurations, run training, and optionally generate sample volumes, producing a saved model and a structured run summary.

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• Check a brain MRI dataset before GPU training
• Fine-tune a model with T1, T2, FLAIR, SWI, or MRA data
• Prepare a training command with explicit data and output paths

Check Jetson video encoding and decoding capabilities by comparing live API reports, verified NVIDIA samples, and official documentation. Distinguish documented product support from observed behavior for specific formats, bit depths, dimensions, and tested operations.

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• Checking support for video formats and bit depths
• Comparing API reports with official product documentation
• Verifying encoding and decoding under specific conditions

Prepare NVIDIA Jetson setup files by checking supported releases and downloading the BSP, sample root filesystem, sources, toolchain, and guides. Files are placed where the following setup skills expect to find them.

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• Preparing files for Jetson Auto Setup
• Gathering artifacts for a supported Jetson Linux release
• Staging downloads before image and source initialization

Grade recorded workflow episodes against a written success criterion using a vision model, which interprets sampled camera images. Summarize pass and fail counts, or optionally extract successful episodes into a separate HDF5 dataset.

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• Label recorded episodes against a written success criterion
• Extract successful episodes into a separate dataset
• Check sampled camera frames before sending images