Results for “data-structure”

Queries 20+ bioinformatics databases from the command line or Python for gene info, sequences, BLAST/BLAT, protein structures, viral data, and expression metrics.

Extract structured job listing data from Indeed search results, including job titles, companies, salaries, descriptions, benefits, and apply links.

Extracts structured data from Reddit posts and comments via the BrowserAct API for competitor analysis, brand sentiment tracking, and market research.

Performs HLA allele genotyping from WGS/WES VCF data, producing a structured markdown report and machine-readable JSON results.

Generate structured research briefs from RSS feeds and web sources on any topic. Synthesizes multiple sources into actionable analysis with key findings, data points, expert perspectives, and content angles.

Extracts structured product listings from Amazon, including titles, ASINs, prices, ratings, and specifications, using the BrowserAct API.

Project directory organization and script naming conventions for research

Query 20+ bioinformatics databases from the command line or Python for gene information, sequences, protein structures, enrichment analysis, and more.

Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems.

Provides reference documentation and code patterns for Biopython, covering sequence handling, alignments, NCBI database access, BLAST, protein structures, phylogenetics, and other bioinformatics tasks.

Automates question submission to Amazon's Alexa/Rufus AI shopping assistant and collects structured response data, with optional keyword search context for category-specific answers.

Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page.

Extract structured review data from Google Maps search results using the BrowserAct API, enabling local business analysis, reputation monitoring, and competitive benchmarking.

Returns structured prediction-market data for Polymarket, Kalshi, Limitless, Opinion, Predict.Fun, dFlow, and UMA oracle via a local API, covering markets, cross-venue search, leaderboards, smart money, wallet analytics, identity clustering, and resolution status.

Analyzes competitor products by synthesizing data from pricing pages, app store reviews, job postings, SEO signals, and social media into structured competitive intelligence. Produces feature comparison matrices, SWOT analyses, positioning maps, UX audits, pricing breakdowns, action plans, and presentations.

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.