Marketing data and research
Find external data for market research, competitor analysis and content: data tools, public datasets, industry benchmarks, surveys and reports. Compare coverage, access and limitations.
Analyze public attitudes and behaviors when a relevant released survey includes the population and question you need.
Coverage: Varies by individual U.S. and international study · Respondent-level case data for eligible released surveys
Explore self-reported developer tools, work practices and attitudes; compare survey years with consistent definitions where possible.
Coverage: Respondents across countries; inspect each year's methodology and respondent mix · Annual respondent-level and summary files, varying by year
Use OpenAlex to find research trends, institutions, experts and topic-level scholarly signals.
Coverage: Coverage depends on the source's documented geographic and time scope · Provider-described dataset or API records available at the indicated geography and time scope.
Use the API to explore public technical questions and topics in a specific Stack Exchange community.
Coverage: Coverage depends on the source's documented geographic and time scope · Provider-described dataset or API records available at the indicated geography and time scope.
Map emerging AI and data-engineering research, identify institutions and collaborators, and build evidence-backed topic or competitor intelligence. Valuable for research-heavy AI/data founders building discovery, knowledge, evaluation, or scientific-data products.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Map emerging AI and data-engineering research, identify institutions and collaborators, and build evidence-backed topic or competitor intelligence.
Assess R&D ecosystems, technical workforce supply, university and institution markets, regional innovation, and AI/software adoption signals. Strong primary evidence for founders selling to research, engineering, AI, and data-intensive organizations or choosing launch geographies.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Assess R&D ecosystems, technical workforce supply, university and institution markets, regional innovation, and AI/software adoption signals.
Track research themes, institutions, funders, citations/DOIs, and emerging technical domains; build discovery, enrichment, or research-intelligence features. Particularly valuable for developer-tools founders monitoring technical research, data infrastructure trends, and institutional or funder demand.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Track research themes, institutions, funders, citations/DOIs, and emerging technical domains; build discovery, enrichment, or research-intelligence features.
Discover and enrich research datasets, benchmark artifacts, software releases, and papers; build dataset discovery, provenance, citation, or research-intelligence products for technical users. Strong fit for ML/data-product founders who need technical datasets, benchmark provenance, research artifacts, or DOI-linked discovery without depending on a commercial catalog.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Discover and enrich research datasets, benchmark artifacts, software releases, and papers; build dataset discovery, provenance, citation, or research-intelligence products for technical users.
Power DOI resolution, dataset and software discovery, citation/provenance graphs, institutional research analytics, and metadata enrichment for data-product catalogs. Strong fit for developer-tool and data-product founders building research-data catalogs, dataset lineage, DOI-aware integrations, or scholarly usage analytics.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Power DOI resolution, dataset and software discovery, citation/provenance graphs, institutional research analytics, and metadata enrichment for data-product catalogs.
Source reproducible evaluation datasets, benchmark suites, and metadata for testing data tooling, model pipelines, retrieval systems, and developer-oriented ML products. Strong fit for developer-tools founders who need repeatable public benchmarks and structured dataset metadata rather than an undifferentiated dataset dump.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Source reproducible evaluation datasets, benchmark suites, and metadata for testing data tooling, model pipelines, retrieval systems, and developer-oriented ML products.
Source reproducible benchmark datasets, compare task/domain coverage, and identify common ML evaluation patterns for prototyping data products or model tooling. Useful for ML/data-product founders, evaluation-platform builders, educators, and teams needing recognizable public datasets for demos and baseline experiments.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Source reproducible benchmark datasets, compare task/domain coverage, and identify common ML evaluation patterns for prototyping data products or model tooling.
AI investment, workforce, business value, governance and operating-model adoption
Coverage: Global/market-specific editions · annual; executive and industry themes
comparative model adoption/quality signals, preference trends and model-release tracking
Coverage: global participating users and AI models · model, modality, pairwise vote, rating and leaderboard time series
model selection, cost-performance benchmarking, provider adoption comparisons and inference economics
Coverage: global commercial and open model providers · model/provider, intelligence score, token price, speed, latency and context window
Discovering reproducible research data and building scholarly-data products
Coverage: Global; repository and dataset-specific · Repository, dataset, file, version and metadata
Finding reusable research data, validation sets and domain-specific evidence
Coverage: Global; publication/study-specific · Dataset, file, version, citation and research metadata
IoT market sizing, platform adoption and embedded product planning
Coverage: Global IoT and embedded developers · Respondent, technology, use case and industry dimensions
AI capability/training-compute trends, release timelines, organization comparisons and historical adoption analysis
Coverage: global machine-learning models and organizations · model, organization, release date, training compute and model metadata
Go ecosystem demand, tooling and developer-product strategy
Coverage: Global Go developers · Respondent, OS, tools, use cases, sentiment and demographics
Research, workforce and social-science validation datasets
Coverage: Primarily US and international studies · Study/file/variable; study-specific periods and geographies
Open-source workforce, sustainability and ecosystem planning
Coverage: Global open-source contributors · Developer, organization, contribution and project-practice dimensions
agent capability progression, long-task automation analysis and model evaluation
Coverage: frontier AI models evaluated on software/research tasks · model, task, human task duration, success rate and fitted time horizon
Economic research, market modeling, replication and historical benchmarking
Coverage: Primarily U.S. economic, demographic and enterprise data; dataset-specific · Dataset, variable, observation, code and documentation
AI demand, developer learning and technical market signals
Coverage: Global O’Reilly learners · Content views, topics and year-over-year trends
Rust ecosystem product planning and developer workforce research
Coverage: Global Rust community · Respondent and Rust usage, tooling, adoption and challenges
AI adoption, open-source AI ecosystem and founder positioning
Coverage: Global · Organizations, practitioners and open-source/AI ecosystem themes
enterprise GenAI adoption, investment, value realization, governance and barriers
Coverage: Global · quarterly/annual; region, role and adoption-stage cuts
AI adoption, enterprise demand and product-market prioritization
Coverage: Global · Organizations, functions, AI use and value outcomes
Ground AI market research in multi-country enterprise survey evidence and compare adoption barriers.
Coverage: G7 countries and Brazil; survey includes 840 firms in G7 and 167 in Brazil · AI adoption, barriers, cloud use, skills, business functions and policy needs
Research buyer trust and technology expectations for AI-enabled SaaS and customer-facing products.
Coverage: More than 16,000 consumers and business buyers worldwide · AI expectations, customer trust, buying behavior and service preferences