“data processing”的搜索结果

Analyze large scientific tables with Vaex, including datasets that exceed available memory. Build calculated columns, filter rows, compute statistics, visualize aggregated data, and convert files while identifying operations that may still require substantial RAM.

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• Computing statistics on tables larger than available memory
• Creating calculated features without storing full columns
• Visualizing large datasets with aggregated grids

Process molecular datasets in Python through a simpler interface to RDKit, a chemistry toolkit. Convert and standardize structures, calculate molecular properties, compare and cluster compounds, generate 3D conformations, and visualize results for drug discovery workflows.

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• Load and standardize molecular datasets
• Calculate molecular properties and similarities
• Cluster compounds and select diverse subsets

Guide cross-platform app development with Avalonia UI and Zafiro using consistent architecture and coding conventions. Keep screen logic separate from UI dependencies, handle errors explicitly, and reuse existing helpers with ReactiveUI and DynamicData.

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• Align design conventions for an Avalonia UI app
• Keep ViewModels independent of Avalonia
• Review error handling with Result types

Export data from Google Sheets as CSV for local backups or further processing. The recipe covers checking spreadsheet details, exporting through Google Drive, and reading a specified sheet range directly in CSV format.

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• Keeping a local CSV backup of spreadsheet data
• Exporting spreadsheet data for further processing
• Reading a selected sheet range as CSV

Build table data processing with TanStack Table v9 while keeping data and column definitions stable. The skill guides type inference, which determines types automatically, row identity, optional features, and checks for missing APIs.

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• Set up the foundation for table data processing
• Troubleshoot column type inference
• Investigate missing APIs or optional features

Prepare an internal Privacy Impact Assessment, a review of how a feature uses personal data. Check whether assessment is needed, examine legal grounds and policy commitments, and document specific risks, required actions, and owners in the team’s format.

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• Reviewing personal data use in a new feature
• Checking whether a privacy assessment is needed
• Comparing processing plans with privacy policy commitments

Learn Apache Beam's core concepts through Java and Python examples. Understand distributed datasets, processing steps, pipeline execution, and time windows, with guidance on aggregation, late data, error handling, and sharing processing steps across languages.

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• Learn how Pipeline, PCollection, and PTransform fit together
• Understand grouping, aggregation, merging, and splitting
• Study time windows, output timing, and late data

Develop and verify changes to Apache Beam's Python SDK. Set up a virtual environment, run tests, build installable packages or containers, and run data processing pipelines with the modified SDK.

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• Set up an Apache Beam Python SDK development environment
• Run unit and integration tests for SDK changes
• Build SDK packages and container images

Process spreadsheets with Python after checking source files, required sheets and columns, and external data access. The workflow includes backup source checks and clear failure reports when the required data cannot be reached.

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• Check required files and columns before processing Excel
• Verify external data access before updating a spreadsheet
• Check backup data when the primary source is unavailable

Validate spreadsheet sources before running Python scripts to transform data, update multiple sheets, or apply formatting. Check required data first, try backup sources when needed, and report errors clearly if processing cannot proceed.

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• Checking required columns before processing Excel files
• Transforming data and saving a new workbook
• Updating formatting across multiple sheets

Retrieve public eQTL Catalogue association data, linking genes and genetic variants, along with study metadata. Filter requests to specific identifiers and receive concise Markdown summaries or raw JSON when machine-readable output is requested.

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• Look up association data for a specific gene
• Filter association results by a genetic variant
• Check study metadata in the public catalogue

Test chatbots and AI agents for injected instructions, data leaks, and unauthorized access across accounts. It emphasizes reproducible evidence, verified external requests, and real data access rather than trusting the model's claims.

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• Test document-based prompt injection
• Verify data leaks through tools or rendered images
• Check data isolation between accounts

Develop and check research code in MATLAB/Octave or Python for signal processing, image analysis, statistics, and simulations. This skill supports reproducible workflows, checks units and assumptions, and prepares figures for papers and presentations.

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• Debugging MATLAB signal-processing scripts
• Analyzing sensor time series in Python
• Checking units and assumptions in optical simulations

Write MATLAB or GNU Octave scripts for matrix calculations, data analysis, signal processing, and scientific plots. The skill also helps explain syntax and functions, convert code between MATLAB and Python, and run scripts when an interpreter is installed.

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• Analyzing experimental CSV data and saving plots
• Solving matrix equations and differential equations
• Analyzing signal frequencies and applying filters

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

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Comprehensive guide for Go database access — parameterized queries, struct scanning, NULLable columns, transactions, isolation levels, SELECT FOR UPDATE, connection pool, batch processing, context propagation, and migration tooling. Use when writing, reviewing, or debugging Golang code that interacts with PostgreSQL, MariaDB, MySQL, or SQLite; for database testing; or for questions about database/sql, sqlx, or pgx. Does NOT generate database schemas or migration SQL.

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An overview of the core data API of ObsPy, a Python framework for processing seismological data. It is useful for parsing common seismological file formats, or manipulating custom data into standard objects for downstream use cases such as ObsPy's signal processing routines or SeisBench's modeling API.

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You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.

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Load this first for any task involving images, pictures, photos, scans, frames, volumes, or visual data — including reading, writing, filtering, enhancing, denoising, sharpening, deblurring, thresholding, segmenting, masking, detecting, finding or counting objects, removing backgrounds, measuring, comparing, registering, stitching, aligning, cropping, resizing, transforming, or analyzing. Use for image I/O (TIFF, PNG, JPEG, RAW, HDR), histograms, edge detection, color processing, morphology, geometric transforms, deep learning for images, quality assessment, and 3-D volume processing. Also triggers when users describe image problems (dark, noisy, blurry, low contrast, uneven lighting, hazy, color cast) or data exploration tasks (folder of images, characterize data, understand format). Handles microscopy (cells, particles) and general scientific imaging. Routes to sibling skills for medical imaging (DICOM, NIfTI), hyperspectral data, and visual inspection.

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Assess obligations under Regulation (EU) 2023/2854 for connected products, related services, statutory B2B data sharing, unfair data-contract terms, exceptional-need B2G requests, data-processing-service switching, international governmental access, interoperability, smart contracts, enforcement, and non-EU representatives. Use for Data Act scope analysis, gap assessments, product and service design, request handling, contract review, cloud exit planning, or cross-regulation mapping.

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Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.

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Use this skill when working with Hugging Face Datasets: loading local or Hub datasets, defining Features schemas, processing/streaming datasets, converting formats, sharing to the Hub, managing cache/offline behavior, or using datasets-cli.

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Assesses the GDPR Article 30(5) exemption for organisations under 250 employees. Covers the three exception conditions that negate the exemption: non-occasional processing, risk to data subject rights, and special category data processing. Activate for Art. 30(5), 250 employee exemption, small business RoPA, SME exemption, occasional processing.

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SaaS vendor data processing inventory management. Covers shadow IT discovery, API-based data flow detection, processing purpose mapping, contract status tracking, and continuous inventory reconciliation for cloud service providers.

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