On Page Seo Tool Market Overview: The On-Page SEO Tool Market Size was valued at 2,370 USD Million in 2024. The On-Page SEO Tool Market is expected to grow from 2,600 USD Million in 2025 to 6.5 USD Billion by 2035. The On-Page SEO Tool Market CAGR (growth rate) is expected to be around 9.6% during the forecast period (2025 - 2035). Key On Page Seo Tool Market Trends Highlighted The Global On-Page SEO Tool Market is experiencing significant shifts driven by the increasing reliance on digital content. Key market drivers include the growing importance of website optimization for businesses aiming to improve their online visibility and organic search rankings. With more companies investing in digital marketing strategies, the demand for effective SEO tools is on the rise. Recent trends indicate a growing preference for tools that integrate artificial intelligence and machine learning, allowing users to analyze data more efficiently and tailor their content strategies to enhance engagement. Opportunities in the market are particularly visible in the rising demand for local SEO tools, as businesses seek to target their local customer base more effectively.Moreover, the advent of mobile optimization and voice search is prompting the development of specialized on-page SEO tools that cater to these trends, thereby creating openings for innovative solutions. The usage of data analytics to track user behavior is also gaining traction, enabling companies to refine their strategies based on real-time metrics. In recent times, there's been a noticeable shift towards the adoption of cloud-based solutions, allowing businesses to access tools easily regardless of location. The global nature of the market fosters a growing ecosystem where companies can collaborate and share best practices across regions.As businesses continue to prioritize search engine optimization, the focus on effective on-page strategies is expected to remain significant, setting the stage for sustained growth and development within the Global On-Page SEO Tool Market. Source: Primary Research, Secondary Research, WGR Database and Analyst Review On Page Seo Tool Market Segment Insights: On Page Seo Tool Market Regional Insights In the Regional segmentation of the Global On-Page SEO Tool Market, North America is the sector with the highest valuation, being valued at 778 USD Million in 2024 and expected to reach 2,125 USD Million by 2035. This region's dominance is attributed to a high concentration of digital marketing investments and strong adoption of technology across various industries, creating a robust demand for On-Page SEO tools. Europe shows a steady expansion, driven by increasing online marketing efforts and technology adoption among businesses, while APAC experiences significant growth, led by a rising number of internet users and growing interest in digital marketing solutions.South America is also witnessing moderate growth as more businesses recognize the importance of online presence, and in MEA, a gradual increase is noted as companies begin to invest more in online strategies. These trends reflect the varying dynamics and opportunities present in different regions of the Global On-Page SEO Tool Market ecosystem. Source: Primary Research, Secondary Research, WGR Database and Analyst Review North America : The North American On-Page SEO tool market is driven by increased digital marketing investments, particularly in the e-commerce and healthcare sectors. The adoption of AI technologies, such as AI-driven content optimization tools, is gaining momentum. Major trends include stricter data privacy policies like the California Consumer Privacy Act, influencing businesses to enhance their online visibility responsibly. Europe : Europe's On-Page SEO tool market is shaped by the growing demand for compliance with GDPR, which has led organizations to focus on transparent SEO practices. The rise in digital channels in retail and finance sectors is notable, alongside increased investments in AI-based optimization tools to drive user engagement and web traffic. Asia : In Asia, the On-Page SEO tool market is rapidly expanding, primarily in regions like Southeast Asia, where internet penetration is increasing. Governments are implementing digital economy initiatives, such as India's Digital India, promoting e-commerce growth. AI and machine learning tools for SEO are becoming integral for businesses looking to enhance their digital footprint. On Page Seo Tool Ma

Comprehensive analysis of how schema markup and meta descriptions work together to improve search visibility, click-through rates, and conversions. This dataset examines the strategic implementation of structured data and optimized meta descriptions to enhance search engine results appearance and user engagement, with focus on local SEO applications and mobile optimization strategies.

JSON-LD Schema & Meta Tag Extractor

What this dataset contains

This dataset is a static sample of 1 rows produced by the JSON-LD Schema & Meta Tag Extractor Apify actor. It targets the data_extraction niche (categories: SEO_TOOLS, DEVELOPER_TOOLS).

Extract JSON-LD/Schema.org structured data, Meta tags, OpenGraph and Twitter Cards from any URL. Get page title + meta description with a clean JSON output for SEO audits, validation, competitor research and AI datasets. Proxy-ready for large crawls.

From the actor's documentation: # 🧩 JSON-LD Schema & Meta Tag Extractor — Scrape Schema.org, OpenGraph & Meta Tags Extract structured data and SEO metadata from any webpage in seconds. This JSON-LD extractor scrapes JSON-LD / Schema.org markup, standard meta tags (title and description), OpenGraph (OG) tags and Twitter Card tags from a list of URLs and returns a clean, structured JSON dataset. If you need a schema scraper, meta tag checker, OpenGraph scraper or **Twitter c...

Each row captures a single record. The schema is reflected in the 7 field(s) listed below; values follow the structure produced by the actor during normal runs.

Source

This is a static sample. Live, fresh, customizable extractions are available via the Apify actor: logiover/json-ld-schema-meta-tag-extractor

Use the actor for: - Fresh, on-demand extractions - Custom input parameters (filters, regions, queries) - Scheduled, recurring runs - JSON / CSV / Excel export

Fields

• url — Canonical URL.
• pageTitle — Display name or title.
• metaDescription — Long-form textual content.
• jsonLd — Field produced by the actor; see live run for full semantics.
• openGraph — Field produced by the actor; see live run for full semantics.
• twitter — Field produced by the actor; see live run for full semantics.
• scrapeDate — Date/time field (ISO-formatted in most rows).

Sample preview

[
 {
  "url": "https://www.allrecipes.com/recipe/158968/spinach-and-feta-turkey-burgers/",
  "pageTitle": "Spinach and Feta Turkey Burgers Recipe",
  "metaDescription": "These spinach and feta turkey burgers are moist and easy to make in one bowl with simple ingredients, shaped into patties, and cooked on a hot grill.",
  "jsonLd": [
   [
    {
     "@context": "http://schema.org",
     "@type": [
      "Recipe"
     ],
     "headline": "Spinach and Feta Turkey Burgers",
     "datePublished": "2008-01-17T12:22:38-05:00",
     "dateModified": "2025-12-18T20:14:19-05:00",
     "author": [
      {
       "@type": "Person",
       "name": "FoodieGeek"
      }
     ],
     "description": "These spinach and feta turkey burgers are moist and easy to make in one bowl with simple ingredients, shaped into patties, and cooked on a hot grill.",
     "image": {
      "@type": "ImageObject",
      "url": "https://www.allrecipes.com/thmb/cpf6Rics5oHGq1TZ1df5fEaImwM=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/1360550-582be362ee99424bb4f363c2274a9d0d.jpg",
      "height": 960,
      "width": 960
     },
     "video": {
      "@type": "VideoObject",
      "contentUrl": "https://content.jwplatform.com/videos/mfrpy5wt-K3AjnAEN.mp4",
      "description": "Elevate your backyard turkey burgers with this flavor-packed version, brimming with garlic, feta cheese and chopped spinach. Simply fire up your grill, combine all ingredients, and in 20 minutes you’ll have turkey burgers cooked perfectly and ready for your favorite toppings!",
      "duration": "PT49S",
      "name": "Spinach and Feta Turkey Burgers",
      "thumbnailUrl": "https://cdn.jwplayer.com/v2/media/mfrpy5wt/poster.jpg?width=1280",
      "uploadDate": "2021-07-02T09:11:18-04:00"
     },
     "publisher": {
      "@type": "Organization",
      "name": "Allrecipes",
      "url": "https://www.allrecipes.com",
      "logo": {
       "@type": "ImageObject",
       "url": "https://www.allrecipes.com/thmb/Z9lwz1y0B5aX-cemPiTgpn5YB0k=/112x112/filters:no_upscale():max_bytes(150000):strip_icc()/allrecipes_logo_schema-867c69d2999b439a9eba923a445ccfe3.png",
       "width": 112,
       "height": 112
      },
      "brand": "Allrecipes",
      "publishingPrinciples": "https://www.allrecipes.com/about-us-6648102#toc-editorial-guidelines",
      "sameAs": [
       "https://www.facebook.com/allrecipes",
       "https://www.instagram.com/allrecipes/",
       "https://www.pinterest.com/allrecipes/",
       "https://www.tiktok.com/@allrecipes",
       "https://www.youtube.com/user/allrec...

Historische Cost-per-Click (CPC) Daten für das Keyword 'meta tag tester' über die letzten 12 Monate

Historische Wettbewerbs-Daten (Google Ads) für das Keyword 'meta tag tester' über die letzten 12 Monate

AI Code Optimization for Sustainability: Dataset

Refactoring Python Code for Energy-Efficiency using Qwen3: Dataset based on HumanEval, MBPP, and Mercury

📄 Read the Paper | HuggingFace Mirror | DOI: 10.5281/zenodo.18377893 | About the author

This dataset is a part of a Master thesis research internship investigating the use of LLMs to optimize Python code for energy efficiency. The research was conducted as part of the Greenify My Code (GMC) project at the Netherlands Organisation for Applied Scientific Research (TNO).

The code samples were curated from Mercury paper, dataset, Google MBPP (paper, dataset), and OpenAI HumanEval (paper, dataset) datasets. The 1,763 unique Python code samples analyzed before and after refactoring by Qwen3 models using four inference strategies (prompt and interaction style): cot-code-single, cot-code-plan, cot-suggestions-single, and cot-suggestions-plan. The analysis provides the following information (when possible):

• Functional correctness via provided test suites
• Static code analysis via Radon, eco-code-analyzer (custom fork used) and custom AST metrics
• Runtime profiling in a sandboxed environment using pyRAPL to measure energy consumption (µJ) under synthetic stress load (80% using stress-ng):
  • baseline: 10ms (no code execution)
  • warmup: 10ms (running test suite in a loop)
  • profiling: 1s (running test suite in a loop)

This analysis uses the following versions of Qwen3 models:

• 0.6B-Q8-GGUF
• 1.7B-Q8-GGUF
• 4B-Q8-GGUF
• 8B-Q8-GGUF
• 14B-Q8-GGUF
• 32B-Q8-GGUF

Dataset Structure & Column Descriptions

The dataset is structured hierarchically using dot-notation to separate nodes. The message column provides a status message of the entire process (success means that analysis before, refactoring, and analysis after were all successful)

1. Input Sample Metadata

Information regarding the source code sample.

2. Analysis

These suffixes apply to both the original code (analysis_before) and the refactored code (analysis_after).

Static Analysis

Runtime & Energy Profiling

Measured using pyRAPL. Energy values are in Microjoules (µJ). Metrics starting with ... are the same for baseline, warmup, and profiling

3. Greenify/Refactoring Process

Details on the model, prompts, and the refactoring process. Columns starting with ... are the same for plan and refactor phases, however plan may be empty if a single-phase strategy is used.

Attribution and Licensing

This dataset contains derivative work of the source datasets and is thus licensed under CC BY-NC 4.0.

Complete dataset containing the academic profile, journal ranking, indexing metrics, and publication metadata for ACM Transactions on Architecture and Code Optimization (TACO) Journal (Computer Science) [ISSN: 1544-3566].

SEO-Schwierigkeitsgrad für das Keyword 'meta tag tester' - Bewertung der Ranking-Schwierigkeit

Code for the manuscript Meta-Learning Accelerates Multi-Objective Bayesian Optimization of Chemical Reaction: A Monoacylation Case Study