science-database.com

What It Is

science-database.com positions itself as “The Knowledge Interface” — a knowledge layer that connects raw scientific research with end-user understanding. It organizes research content from sources such as arXiv, PubMed, Nature, OpenAlex, and Semantic Scholar into structured knowledge with evidence ratings, and provides it in three formats: web pages, API responses, and Markdown files. Its target users include readers, developers, and AI systems.

Core Capabilities

The platform is not a general-purpose chatbot; its core value lies in serving as a data layer for AI applications. Each article can be presented as a semantic web page, including Schema.org JSON-LD, evidence badges, multi-level summaries, and related research. Developers can retrieve structured data via REST and GraphQL APIs, with filters by discipline, evidence level, and study type. AI agents can directly use Markdown, YAML frontmatter, llms.txt indexes, and bulk ZIP downloads to integrate the content into RAG pipelines. Its evidence system assigns A–D grades based on factors such as study type, peer review, and replication status, but the text does not clarify whether the ratings are produced manually, automatically, or through a hybrid process.

Pricing and Integration

The text explicitly mentions that the API is “Rate-limited & free,” indicating that limited free access is available. However, it does not disclose specific request quotas, registration requirements, paid plans, commercial licensing terms, or SLA details. On the integration side, it is fairly developer-friendly: the combination of REST, GraphQL, JSON-LD, Markdown, and llms.txt covers common use cases such as search applications, knowledge base synchronization, and feeding data into LLMs.

Pros and Cons

Its strengths are machine-oriented formatting, clear evidence metadata, and an attempt to fill the gap between Google Scholar’s search-only approach, Science Daily’s lighter coverage, and Wikipedia’s slower update cycle. For AI developers, Markdown and llms.txt are especially valuable. The limitations are also obvious: it does not disclose its privacy policy, copyright licensing, API limits, model sources, or evidence-rating workflow; Chinese-language support is not mentioned; and the example technical topics and number of papers appear limited. Its real-world coverage, update quality, and stability still need to be verified.

Who It’s For and Access from China

It is suitable for teams working on research topic reviews, technology trend tracking, background research for science news, and building trusted scientific knowledge sources for Agent/RAG systems. The text provides no information about access from China, so network connectivity, payment methods, and compliance licensing all need to be tested in practice. Possible alternatives to compare include OpenAlex, Semantic Scholar, PubMed, arXiv, Elicit, and Consensus.